Next Article in Journal
Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System
Next Article in Special Issue
AI-Driven Aeronautical Ad Hoc Networks for 6G Wireless: Challenges, Opportunities, and the Road Ahead
Previous Article in Journal
Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Future Wireless Communication Technology towards 6G IoT: An Application-Based Analysis of IoT in Real-Time Location Monitoring of Employees Inside Underground Mines by Using BLE

by
Sushant Kumar Pattnaik
1,
Soumya Ranjan Samal
2,3,*,
Shuvabrata Bandopadhaya
4,
Kaliprasanna Swain
5,
Subhashree Choudhury
6,
Jitendra Kumar Das
1,
Albena Mihovska
7 and
Vladimir Poulkov
2
1
School of Electronics Engineering, KIIT University, Bhubaneswar 751024, India
2
Faculty of Telecommunications, Technical University of Sofia, 1756 Sofia, Bulgaria
3
Department of Electronics & Communication Engineering, Silicon Institute of Technology, Bhubaneswar 751024, India
4
School of Physical Sciences, Banasthali Vidyapith University, Rajasthan 304022, India
5
Department of Electronics & Communication Engineering, Gandhi Institute for Technological Advancements, Bhubaneswar 752054, India
6
Department of Electrical and Electronics Engineering, Siksha ‘O’ Anusandhan, Bhubaneswar 751030, India
7
Department of Business Development & Technologies, Aarhus University, 8000 Aarhus, Denmark
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(9), 3438; https://doi.org/10.3390/s22093438
Submission received: 26 March 2022 / Revised: 27 April 2022 / Accepted: 27 April 2022 / Published: 30 April 2022
(This article belongs to the Special Issue 6G Wireless Communication and Its Applications)

Abstract

:
In recent years, the IoT has emerged as the most promising technology in the key evolution of industry 4.0/industry 5.0, smart home automation (SHA), smart cities, energy savings and many other areas of wireless communication. There is a massively growing number of static and mobile IoT devices with a diversified range of speed and bandwidth, along with a growing demand for high data rates, which makes the network denser and more complicated. In this context, the next-generation communication technology, i.e., sixth generation (6G), is trying to build up the base to meet the imperative need of future network deployment. This article adopts the vision for 6G IoT systems and proposes an IoT-based real-time location monitoring system using Bluetooth Low Energy (BLE) for underground communication applications. An application-based analysis of industrial positioning systems is also presented.

1. Introduction

In recent years, wireless technology has been one of the fastest-growing technologies in the area of communication. Today, wireless technology is becoming one of the largest carriers of digital data around the globe. According to the Cisco Visual Networking Index (VNI) Global Mobile Data Traffic for 2016 to 2022, worldwide mobile data traffic increased about 10-fold over these 6 years, reaching 77 exabytes (approx.) per month by 2022 (Figure 1a [1]). According to [1], the device mix is becoming smarter (advanced computing and multimedia competencies with at least 3G connectivity) with an increasing number of smart devices with high computing capabilities and better network connectivity, which creates a growing demand for smarter and more intelligent networks. The share of smart devices and connections as a percentage of the total will increase from 46 percent in 2016 to 85 percent by 2022, a more than two-fold increase during the figure time frame Figure 1b [1]. It is expected that 75 billion devices will be connected by the end of 2025 [2]. Service providers around the globe are busy rolling out 5G networks to meet the growing demand of the end consumer for greater bandwidth, higher safety and quicker connectivity on the move. Many vendors have additionally begun area trials for 6G and are getting closer to rolling out 5G deployments in the direction of the end of the forecast length.
Moreover, the heterogeneous nature of the next-generation communication networks in terms of the application, communication technology used and involvement of diversified devices brings a large variety of requirements and expectations. Today’s world is focusing more on the IoT due to its wide range of applications from human-centric to industry 4.0/industry 5.0. Nevertheless, device-to-device (D2D), machine-to-machine (M2M) and vehicle-to-vehicle (V2V)/V2X communication technologies constitute the real applications showing the widespread advantages of the IoT [3,4,5,6,7,8,9]. Furthermore, reliable data transmission with low latency is another key challenge for successful IoT applications [10]. The emergence of the Internet of Everything (IoE), which offers remarkable solutions for massive data transmission to the edge network, and the integration of Industrial Control Systems (ICSs) with the IoE recast it as the Industrial Internet of Everything (IIoE) [5]. Again, with the evolution of different emerging technologies such as artificial intelligence (AI), machine learning (ML), cloud computing, cognitive computing, edge computing, fog computing, blockchain technology, etc., various challenges are being addressed in different IoT industrial applications. Such complex IoT networks provide substantial technological prospects that facilitate the realization of good quality of service (QoS) and quality of experience (QoE). For example, the Internet of SpaceThings (IoST) for high speed, reduced latency and umbrella Internet coverage; the Social Internet of Things (SIoT) for an interface between human and social networks; the Internet of NanoThings (IoNT) for telemedicine; and the Internet of UnderwaterThings (IoUT) for improving ocean water quality, cyclonic/tsunami disaster management, etc. [11].
In view of this, the IoE introduces essential protection challenges due to the wide variety of functionality and demanding situations. There is always a dependency of the IoT on cellular networks since long-term evolution (LTE) was introduced, which is enhanced as 5G/6G in some specific scenarios. The demand for high throughput, high energy efficiency and better connectivity with reduced latency time can be attained beyond 5G/6G networks [12]. The 6G system will offer a better enrollment of the IoT devices as the 5G IoT has provided a solid foundation. The future 6G network is envisioned to be service-oriented, where software-defined networks (SDN) and network function virtualization (NFV) will play a vital role in the end-to-end architecture [13]. These technologies are capable of providing better coverage with high throughput, improved spectrum efficiency, greater bandwidth and ultra-low latency. The 6G IoT system is sustainable for high-accuracy localization and sensing, which are necessary for most of the envisioned highly computationally intensive applications.

Related Work and Key Contributions

A growing number of research works focus on current advances in wireless and IoT technology, including in-depth analysis of the advanced technology concepts, methodology and techniques.
Specifically, [14] provides a comprehensive survey on key enabling technology for 6G, where the emphasis is on a discussion of the operation of the individual technology with useful statistics for industries and academic researchers on the potential for investigating new research directions. The authors of [15] discussed the requirements of 6G and recent research trends to enable 6G capabilities and design dimensions by employing disruptive technologies such as artificial intelligence (AI) and driving the emergence of new use cases and applications manifested by stringent performance requirements. A review of 6G in terms of use cases, technical requirements, usage and key performance indicators (KPI) is presented in [16]. Here, the authors presented a preliminary definition roadmap, specifications, standardization and regulation for 6G. A survey on wireless evolution toward 6G networks is presented in [17], discussing the capabilities of network slicing technology with AI to enable a multitude of services with different quality of service (QoS) requirements for 6G networks. A comprehensive survey on the existing trends, applications, network structure and technologies of 6G is presented in [18], with a focus on industrial markets and use cases of 6G that take advantage of a better on-device processing and sensing, high data rates, ultra-low latencies and advanced AI. In [19], the authors presented an overview of 6G describing the complete evolution path from 1G networks to date and focusing on several key technologies such as terahertz communications, optical wireless communications (OWC) and quantum communications for improving the data rates.
A comprehensive survey on the convergence of the IoT and 6G is presented in [20,21] with a focus on edge intelligence, reconfigurable intelligent surfaces, space–air–ground–underwater communications, terahertz communications, massive ultra-reliable and low-latency communications and blockchain as the technologies that empower future IoT networks. A comprehensive study on 6G-enabled massive IoT is presented in [22], where ML and blockchain technologies are discussed as the primary security and privacy enablers. In [23], the potential of the IoT and 6G for various use cases in healthcare, smart grid, transport and Industry 4.0 have been elaborated jointly with the challenges during their practical implementations. Several shortcomings of 5G and features of 6G related to social, economic, technological and operational aspects such as the weakness of short packet and sensing-based URLLC, which may limit the dependability of low-latency services with high data rates or the lack of support of advanced IoT technologies are discussed in [24]. Current research activities, therefore, should focus on innovative techniques such as advanced time-stamp stream filtering combined with intelligent network slicing to support multi-party (source) data stream synchronization in very low latency environments coupled with distributed control (at the edge).
In [25], the author mainly focuses on the integration of blockchain technology into 6G, the IoTand IIoT networks. Blockchain technology has a strong potential to fulfill the requirements for massive 6G-based IoT for the integrity of personal data protection, data privacy and security and scalability. Furthermore, a sustainable ecosystem-focused business model, driven by blockchain-empowered 6G networks is thoroughly analyzed to deal with the cutting-edge worldwide economic disaster. Envisioning the green 6G–IoT network, a novel joint design technique using intelligent reflective surface (IRS) and ambient backscatter communication (ABC) is proposed in [26]. This method is primarily based on the joint design of an iterative beamforming vector, an IRS phase shift and reflection coefficients to decrease the AP’s transmit power without affecting the QoS. The author in [27] addressed the three fundamental components, i.e., artificial intelligence (AI), mobile ultra-high speed and the (IoT) for the future 6G network. The authors focused on the recent approaches, research issues and key challenges of IoT network topology and terahertz (Tz) frequency. A comprehensive survey of existing 6G and IoT-related works is summarized in Table 1.
The contributions to this paper can be outlined as follows:
  • We present the vision of the IoT with the technologies impacting it with their key features
  • We review several applications and challenges of the IoT in different domains.
  • We present different connectivity standards of the IoT and a rigorous review of these technological standards
  • We present a comparative analysis between 5G and 6G.
  • We present the vision and key features of 6G with its different aspects.
  • We present a brief review of several challenges of 6G.
  • We propose a BLE-based real-time location monitoring system by using the IoT
The remainder of this article is organized as follows. Section 2 presents the vision, applications and challenges of the IoT, including the connectivity standards and a comparative analysis of their capabilities. In Section 3, a comparative analysis of 5G and 6G with the vision key features and the challenges of 6G is presented. Section 4 proposes and discusses a BLE-based real-time location monitoring system by using the IoT. Finally, we draw conclusions in Section 5. Related abbreviations are listed in the Appendix. A schematic representation of the structure of the paper is shown in Figure 2.

2. Visions, Applications and Challenges of the IoT

In the last few decades, the IoT has become the most promising and thriving area of research in academia and industry. The IoT extends the existence of communication by converging clients, businesses and industries by connecting intelligent things with each other through the cloud. These smart connections encompass different network applications, communication technologies and smart devices along with physical and virtual things. The IoT paradigm is a transformation from a centralized computer-based network to a completely distributed network of smart devices. To take the potential benefits of the IoT and to compete globally, the IoT European Research Cluster (IERC) has focused mainly on establishing a cooperation platform between companies and organizations for developing more research activities on the IoT at the European level. The primary objective of IERC is to facilitate making the research activities more ambitious and neoteric. The International Telecom Union (ITU) was the first international agency to produce a report on the IoT in 2005 [28]. Thereafter a new standard of the IoT was approved by the ITU in 2012 [29]. However, the term IoT was first used by the Massachusetts Institute of Technology’s (MIT’s) Kevin Ashton in 1999 [30].

2.1. Vison of the IoT

The vision of the IoT has different perspectives based on the data generated by the connected objects and the technology used. During the early stages of IoT implementation, the vision was to identify the physical objects by using radio frequency identification (RFID) tags. However, due to recent technological advances, the vision of the IoT has been reformed by encapsulating varying technologies and smart sensors. The IoT leads the way in unfolding the new generations of different compelling applications and services in the field of Industrial IoT (IIoT), Industry 4.0 and Society 5.0. Figure 3, illustrates the key technologies that impact the IoT.

2.2. Applications of the IoT

IoT applications in various sectors have been assessed based on their impacts on society and the economy along with their technology readiness level (TRL). The applications of the IoT are diversified based on their use in different fields such as intelligent homes, healthcare, agriculture, transportation, the environment, education, retail and logistics, industries and many more [31,32,33,34]. Consequently, the IoT has also had an impact during the pandemic era of COVID-19 in many aspects, e.g., contact tracing, virus detection by temperature scanning, remote health monitoring, quarantine e-tracking, virus spread control, etc., and also in tackling the post-COVID-19 situation [35,36,37,38]. AI-integrated IoT technology for the early detection of COVID-19 is discussed in [37]. This research mainly focuses on analyzing the extracted features of cough, shortness of breath and speech difficulties by using long short-term memory (LSTM) with recurrent neural network (RNN). In [38], an IoT-based real-time learning system is developed to control the spread of COVID-19 infection in the context of smart healthcare for residents. The system is used to monitor and analyze user activities and environmental parameters which helps predict critical cases, so alerts can be sent to the caretakers. A few applications of the IoT are briefly presented in Table 2.

2.3. The IoT Challenges

With an increase in the number of smart devices and real-time applications, the complexity of IoT networks has increased in terms of their densities and architecture. These complexities scale down the performance competencies of the current IoT network. There are several IoT challenges, namely, universal standardization, connectivity, cloud computing, energy efficiency, IoT protocol and architecture in addition to security and privacy. The IoT is still in its developing stage; so many more challenges have to be addressed with the revolution of technologies in the future research domains of the IoT. A few challenges of the IoT are briefly presented in Table 3.

2.4. IoT Connectivity Standards

As per the IoT analytics report, there are mainly 21 IoT connectivity standards that can be broadly classified in two ways: as cellular IoT and non-cellular IoT connectivity standards. The cellular IoT standards are operated at a licensed spectrum, whereas the non-cellular IoT is operated at a non-licensed spectrum. Different IoT connectivity standards are depicted in Figure 4 [135] [Source: IoT Analytics Report 2021]. A comparative analysis of different IoT connectivity standards is presented in Table 4.

3. Vision, Key Features and Challenges of 6G

With the standardization of 5G about to complete and its commenced global deployment, several latent limitations to meet the necessary requirements of IoT systems still remain. These impediments mainly relate to the high computation, security, wireless brain-computer interface (WBCI) intelligent communication in terms of more autonomous human-to-machine (H2M) communication, holographic communication (augmented reality/virtual reality) and AI. These data-hungry applications require more spectrum bandwidth (e.g., mm-wave) and high spectral efficiency which can be realized at the sub-terahertz (sub-THz) and THz bands [154]. Furthermore, due to the incorporation of a wide variety of mobile applications, there are some more challenges (beyond uRLLC, coverage, localization, privacy, power consumption, better quality-of-service, etc.) that need to be addressed in the future B5G wireless communication standards. In this context, the 6G is attracting more researchers from academia and industries towards itself. A comparative analysis between 5G and 6G is presented in Table 5.

3.1. Vision and Key Features of 6G

Despite the dramatic revolution of IoT–5G application in today’s wireless networks, 6G is anticipated to excel 5G in many ways, not only in daily life, but also in Society 5.0. Even though 6G is not a talking point of global harmony so far, some additional features with more potential and capabilities are being discussed. In this section, a comprehensive vision of a 6G network is presented from multiple perspectives as shown in Figure 5.

3.1.1. Intelligent Network

As 6G is envisioned as a fully automated and smart network, the incorporation of AI, MLand quantum machine learning (QML) makes the future wireless networks more intelligent and predictive by limiting human efforts [176,187]. AI and ML are the transforming technologies and data analytics tools in the modern era of wireless communication that bring new research challenges in the field of 6G IoT [186]. By using big data and ML, a more precise performance prediction model can be implemented in a 6G IoT network to make smart decisions for security, optimization, resource allocation, network management, self-organization, etc., [155,156,157,158,159,160,161,162,163,164,165,188]. Due to the high veracity/volume data and complex 6G IoT network structure, it is necessary to instigate more futuristic learning/training frameworks for high-dimension neural networks (HDNN) [165].

3.1.2. Decentralized Network

Due to the emergence of multi-access edge computing (MEC) in the 5G network, there are several limitations in the centralized network, e.g., privacy, security, trust, incompatibility of the existing protocol to the dynamic connectivity and distributed and ubiquitous computing [166]. Thus, it is necessary to prepare a blueprint of decentralized architecture to support such a dynamic and autonomous network. In this regard, blockchain is a promising technology for the future 6G network and is capable of dealing with these challenges. Blockchain technology can provide a decentralized network management framework that can be used for resource management, data sharing/storage, spectrum sharing and other challenges [172,173,174,175,189].

3.1.3. Green Network

The 6G network is expected to meet the essential requirements for energy-efficient wireless communication globally. The green 6G network enables minimum energy utilization and helps achieve a peak data rate (THz) during signal transmission. A significant improvement in the energy efficiency of a network can be greatly experienced by incorporating different energy-harvesting techniques [154,190]. This also helps facilitate green communication by reducing CO2 emission. In addition, several communication techniques, e.g., D2D communication, massive multiuser multiple-input-multiple-output (MIMO), heterogeneous network (HetNet), green IoT, non-orthogonal multiple access, energy-harvesting communications, etc., may be adopted to facilitate green communication for future wireless networks [191,192,193].

3.1.4. Superfast Network

With reference to the data analysis shown in Figure 1, the ever-increasing demand for high data rate and seamless connectivity to such ultra-dense networks can be provided by integrating terahertz (THz) (ranges from 0.1–10 THz) communication in 6G networks [168,177,178]. A vast amount of unused radio spectra which can be efficiently used to increase network capacity is available in the THz band. THz is additionally reasonable for high data rate transmission and short-range communication by empowering the ultra-high bandwidth and uLLC paradigms. An extensive review of THz communication with its future scope and challenges is presented in [194].

3.1.5. Human-Centric

It is believed that human-centric communication is a key feature of the 6G network. With the help of this technology, sharing and/or accessing different physical features can be possible by humans. To accelerate human-centric communication rather than technology/machine-centric communication, the principal means of human perception must be incorporated into the communication system module [195]. A human-centric communication framework needs two fundamental aspects—technology and user experience (UE). The latter includes human behavior as well as psychological and socioeconomic contexts and needs to be considered during the modeling and analysis of the communication system [183,184,195].
In 2016, Society 5.0 was initiated by the Japanese cabinet in its Fifth Science and Technology with a vision to build a “Super Smart Society” [196]. Later, the vision was revised and presented by the Keidanren Business Federation with the prime focus of delivering sustainable development goals (SDGs) through the creation of Society 5.0 [183,184,197]. Society 5.0 is designed to solve different social issues by taking advantage of technological advancements. Considering different aspects of economic growth, social and environmental conditions, 17 primary objectives and 167 goals are listed in the Agenda 2030 by the United Nations to address several global challenges [198,199].

3.2. Challenges of 6G

Even though several advanced features have been added to 6G networks to enhance the performance matrices in comparison with 5G networks, there are still some key challenges that must be addressed further. These challenges are broadly classified into two categories: (i) technological challenges that include high throughput, EE, connectivity flexibility, more intelligent optimization techniques, etc., and (ii) non-technological challenges including industry barriers, spectrum allocation, regulatory policies and standardization, etc. [200]. A few key challenges of the future 6G networks are summarized in Figure 6.
In addition, due to the integration of the IoE, terrestrial and non-terrestrial communication networks in 6G, their different heterogeneous highlights must be considered to productively coordinate them. Heterogeneity is likewise present in the protocol that those communication networks will comply with. Thus, 6G is taking on the massive task of integrating a number of heterogeneous aspects [203]. Furthermore, due to the inclusion of mm-Wave and THz communication, 6G networks are facing several more open challenges, e.g., more sensitive low-power transmitter, new model architecture, advanced propagation techniques for better coverage and directional communication. The networks must also deal with system noise, channel fading and fluctuations [169,203,204,205]. Several more challenges such as computational and processing resources due to the application of AI [206], a few ML application-related challenges [207], training issues and interoperability challenges [208], challenges in estimating the channel information by using reconfigurable intelligent surfaces (RIS) [209,210] and computational and trade-off challenges due to the application of artificial neural networks (ANN) in the IoT [211] have been recognized for the future 6G networks.

4. An IoT-Based Real-Time Location Monitoring System by Using BLE

Mining is one of the most speculative businesses around the globe. Most of the mines all over the world are lagging in different safety measures causing many casualties and deaths. The basic causes of death in underground mines are gas accidents, rock falling, ventilator accidents, fire, explosions, etc. Considering the safety issues of the employees/workers inside the mines, real-time location tracing of those employees becomes a major concern. Effective underground communication is necessary to collect more information about the mines or workers. However, there are various constraints while collecting the real-time data inside the mines such as restricted transmitting power, large attenuation of the transmitted signal from the rock wall and low penetration of the electromagnetic signal. In this regard, it is always beneficial to take the potential advantages of low-power and short-range communication technologies such as, RFID, Zigbee, Bluetooth, Bluetooth low energy (BLE), etc.
In this section, a scenario for a Bluetooth low energy (BLE) beacon-based real-time location monitoring of employees/workers by using the IoT is presented. A BLE beacon and microcontroller are used to design this asset-tracking product and have been implemented in the IoT here by connecting this device to the cloud.

4.1. State-of-Art

Underground communication inside mines is a major factor for the safety and security concerns of the mineworkers. The advent of IoT technology and its usefulness can be beneficial for the mining industry. It is believed that a robust communication infrastructure using IoT technology inside the mines may enhance the safety of the workers and is also capable of providing real-time information resulting in quick action to avoid lethal situations. Several researchers have proposed various frameworks and ideas for efficient communication inside the mines based on IoT technology, which includes low-power and short-range communication.
The authors of [212,213] proposed a wireless sensor network (WSN)-based monitoring system for underground mines. In this proposed technique, various sensors are placed at different locations to collect activities and positions of the employees, and the collected data are transferred to the end user or the central server via BS. Nageswari et al. [214], proposed an IoT-based smart mine monitoring system that uses radio frequency (RF) technology for communication purposes inside the mines. With this proposed technique the real-time location and real-time sensing of the dynamically varying environment can be achieved by using RF technology and WSN network, respectively. The major drawback of this proposed model is that large-signal transmission loss occurs through the walls of underground mines. An IoT-based mine safety system using WSN was proposed in [215,216]. In these proposed techniques, the authors used a Zigbee module for information collection from the cloud and measured the surrounding parameters of underground mines with the help of various sensors. A mine safety system using WSN was proposed in [217], where the authors constructed a prototype by using Zigbee and WSN to monitor safety issues and to measure the ambient properties, e.g., temperature, humidity, airflow, etc., inside the underground mines. A Zigbee compliant RFID-based safety system for underground coal mines was proposed in [218], where a unified wireless mesh-network infrastructure was used to monitor and locate the workers and measure the different environmental parameters inside the coal mines. Similarly, an IoT-based system for underground coal mines that uses a microcontroller, a node MCU and various sensors to measure the environmental conditions and safety measures of workers was proposed in [219]. A LoRaWAN-based coal safety and health monitoring system was proposed in [220]. In this proposed methodology, LoRaWAN uses low-power RF with a wide communication range and IoT technology for monitoring the workers’ health and observing the status of the circumstances in the coal mines.
There are several existing technologies used for communication purposes in underground mines. The most common approaches are RFID, Zigbee, Bluetooth, GPS, etc. Table 6 presents a comparative analysis of some existing technologies in terms of their pros and cons [214,215,216,217,218,219,220,221].

4.2. Proposed System Architecture and Workflow

To overcome these issues, our proposed technique uses BLE, which is a low-power and low-cost technology. This proposed methodology reduces the deployment cost and complexities by using the BSs of the existing cellular network infrastructure for the communication process. The system architecture of BLE-based real-time location monitoring in mines by using the IoT is shown in Figure 7. In this scenario, two base stations (BS) are deployed to provide necessary services (uplink/downlink) to the BLE devices through the central office server as shown in Figure 7a, and the complete workflow is shown in Figure 7b.
Figure 7a shows the coverage area of BLE and cell towers based on their transmitted power. The blue and green colored portion shows the energy region of BLE devices and cell towers, respectively. As can be seen, cell tower 0 transmits more power compared to cell tower 1. All the BLE devices are wearable or are attached to the employee working inside the mines. In this proposed method, beacons are considered because they can be easily identified by single board computers (SBC) as shown in Figure 8. Different beacons are accessed by the nearest SBC based on their coverage area. The blue-colored region indicates the transmitted energy by the beacon signal as shown in Figure 8a. The system contains beacons that are small and inexpensive, which emit signals in the same fashion as BLE. The used beacons have a short-range and can triangulate position in the same way that a phone uses cell towers with an assisted global positioning system (AGPS). These transmitters are deployed at known points inside the mines, and they permit the device to obtain area fixes. This data can be utilized to make new client encounters, for example, turn-by-divert headings for indoor situating from gateways/applications that read the guide signals.
The scenario presented in Figure 8a,b shows the position of the SBC (fixed position) and a random distribution of BLE devices, as the position of BLE device (wearable) depends on the position of the employee working inside the mine. The BLE receivers/gateway receives the universally unique identifier (UUID) transmitted by the beacons in a repetitive manner as shown in Figure 8a,b. These signals can be utilized to differentiate between subgroups and individual ones in the subgroups. It is modified to check the accessible BLE signals “on the air” and the received signals contain the accompanying snippets of data in a bundle size of 60 bits, with 10 bits specifying major and minor values. The received signal strength indicator (RSSI) values can be utilized to decide the distance of the receiver to every one of the reference points. As those region statistics are stored inside the database, navigation of the receiver also can be tracked, and alerts can be generated if certain rules are violated. All the beacon data are stored in a local server through the gateway and then transferred to the central office server through cell towers as shown in Figure 8a,b. The central office server is continuously updated based on the real-time information sent by the BLE beacon through the gateway. This information can be used to find the real-time location of the employees/workers inside the mines.

4.3. Simulation Result and Discussion

The simulation result in Figure 9 shows the discovery time of the BLE devices. It can be seen that the visibility time of the BLE device is constant, and the delay time is also very small. Hence, it helps to find the real-time location of the employees/workers inside the mines within a short time. Due to the small visibility time, the rescue process can be improved for the employees/workers (real-time locations) inside the underground mines during any hazardous situation.

5. Conclusions

This paper summarizes and relates the future direction of IoT applications to current 6G trends, development sand challenges. The study looked at the vision and different technologies impacting the IoT as outcomes of international research. The paper considered the applications in various sectors and provided a summary of the different IoT technologies. The various IoT connectivity standards and a few challenges remaining open for IoT integration with cellular systems were outlined. The IoT is a basic building block for next-generation industrial standard 4.0/5.0 smart applications in home, city, agriculture, healthcare and many more uses, but this requires a major upgrade of the physical and network layers of upcoming cellular wireless networks. In this paper, a brief comparison between 5G and 6G was presented in terms of the technical features. The vision and key features of 6G along with the implementation challenges were discussed. This paper also includes a case study related to the real-time application of the IoT to locate the employees in underground mines using BLE technology. The system architecture and workflow for the given application were presented. This article might assist the researcher apprehend various challenges with their applications of the IoT and 6G to the real world.

Author Contributions

S.K.P.: concept and setup preparation, design of system model; S.R.S.: concept, methodology creation, model selection, analysis and simulations supervision, text editing; S.B.: text and plot preparation, design of system model supervision, simulations and review; K.S. and S.C.: setup preparation, design of system model supervision, data preparation and text editing; J.K.D. and A.M.: methodology validation, model validation, setup preparation and data preparation, V.P.: overview of model validation, final model preparation, data preparation supervision, text editing and review. All authors have read and agreed to the published version of the manuscript.

Funding

The APC was funded by the Ministry of Education and Science of Bulgaria fund research project HOLOTWIN (D01-285/06.10.2020).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgments

The authors acknowledged support from the project HOLOTWIN (D01-285/06.10.2020) financed by the Ministry of Education and Science of Bulgaria.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

3GPP3rd Generation Partnership Project
ABCAmbient Backscatter Communication
AGPSAssisted Global Positioning System
AIArtificial Intelligence
ANNArtificial Neural Networks
ARAugmented Reality
BSBase Stations
BLEBluetooth Low Energy
CapEXCapital Expenditure
CDMACode-Division Multiple Access
CRCCyclic Redundancy Check
D2DDevice-to-Device
EEEnergy efficient
eMBBEnhanced Mobile Broadband
FBMCFilter-bank Multicarrier
GFDMGeneralized Frequency-Division Multiplexing
GPSGlobal Positioning System
GSMGlobal System for Mobile Communication
H2MHuman-to-Machine
HCS Human-Centric Service
HDNNHigh Dimension Neural Networks
HetNetHeterogeneous Network
ICSIndustrial Control System
IERCIoT European Research Cluster
IIoTIndustrial Internet of Things
IoEInternet of Everything
IoNTInternet of NanoThings
IoSTInternet of SpaceThings
IoTInternet of Things
IoUTInternet of UnderwaterThings
IRSReflective Surface
ITUInternational Telecom Union
KPIKey Performance Indicator
LoRaWANLong Range Wide Area Network
LPWALow-Power Wide-Area
LPWANLow-Power Wide-Area Networks
LSTMLong Short-Term Memory
LTELong Term Evolution
M2MMachine-to-Machine
MACMessage Authentication Code
mbRLLCMobile broadband RLLC
MECMobile Edge Computing
MIMOMultiple-Input-Multiple-Output
MITMassachute Institute of Technology
MLMachine Learning
mMTCMassive Machine Type Communication
MPSMultipurpose 3CLS and energy services
MTCMachine-type Communicaiton
muRLLCMassive uRLLC
NFCNear Field Communication
NOMANon-Orthogonal Multiple Access
OAMOrbital Angular Momentum
OMAOrthogonal Multiple Access
OFDMOrthogonal Frequency-Division Multiplexing
OpEXOperational Expenditure
OWCOptical Wireless Communications
QMLQuantum Machine Learning
QoEQuality of Experience
QoSQuality of Service
RADARRadio Detection And Ranging
SBCSingle Board Computer
RFRadio Frequency
RFIDRadio Frequency Identification
RISReconfigurable Intelligent Surfaces
RNNRecurrent Neural Network
RSSIreceived signal strength indicator
RTLSReal-Time Location monitoring System
SDGsSustainable Development Goals
SIoTSocial Internet of Things
SONSelf-Organizing Network
SWIPTSimultaneous Wireless and Information Power Transfer
TCPTransmission Control Protocol
THzTerahertz
TRLTechnology Readiness Level
UDPUser Datagram Protocol
uRLLCUltra-Reliable Low Latency Communication
UUIDUniversally Unique Identifier
V2VVehicle-to-Vehicle
VRVirtual Reality
VNIVisual Networking Index
VLCVisible Light Communication
WBCIWireless Brain-Computer Interface
WLANWireless Local Area Network
WMMIWireless Mind-Machine Interface
WNANWireless Neighborhood Area Network
WPANWireless Personal Area Network
WSNWireless Sensor Network

References

  1. Cisco Visual Networking Index Forecast Projects 13-Fold Growth in Global Mobile Internet Data Traffic from 2012–2017. Available online: https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11-520862.html/ (accessed on 12 March 2021).
  2. Statistica. Internet of Things (Iot) Connected Devices Installed Base Worldwide From 2015 to 2025 (In Billions). Available online: https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/ (accessed on 18 March 2021).
  3. Pawar, P.; Trivedi, A. Device-to-Device Communication Based IoT System: Benefits and Challenges. IETE Tech. Rev. 2019, 36, 362–374. [Google Scholar] [CrossRef]
  4. Rahmani, A.M.; Bayramov, S.; Kalejahi, B.K. Internet of Things Applications: Opportunities and Threats. Wirel. Pers. Commun. 2021, 122, 451–476. [Google Scholar] [CrossRef] [PubMed]
  5. Padhi, P.; Charrua-Santos, F. 6G Enabled Industrial Internet of Everything: Towards a Theoretical Framework. Appl. Syst. Innov. 2021, 4, 11. [Google Scholar] [CrossRef]
  6. Amodu, O.A.; Othman, M. Machine-to-Machine Communication: An Overview of Opportunities. Comput. Netw. 2018, 145, 255–276. [Google Scholar] [CrossRef]
  7. El Zorkany, M.; Yasser, A.; Galal, A.I. Vehicle To Vehicle “V2V” Communication: Scope, Importance, Challenges, Research Directions and Future. Open Transp. J. 2020, 14, 86–98. [Google Scholar] [CrossRef]
  8. Iqbal, S.; Zafar, N.A.; Ali, T.; Alkhammash, E.H. Efficient IoT-Based Formal Model for Vehicle-Life Interaction in VANETs Using VDM-SL. Energies 2022, 15, 1013. [Google Scholar] [CrossRef]
  9. Zhang, H.; Lu, X. Vehicle communication network in intelligent transportation system based on Internet of Things. Comput. Commun. 2020, 160, 799–806. [Google Scholar] [CrossRef]
  10. Khan, M.Z.; Alhazmi, O.H.; Javed, M.A.; Ghandorh, H.; Aloufi, K.S. Reliable Internet of Things: Challenges and Future Trends. Electronics 2021, 10, 2377. [Google Scholar] [CrossRef]
  11. Ali, O.; Ishak, M.K.; Bhatti, M.K.L. Emerging IoT domains, current standings and open research challenges: A review. PeerJ Comput. Sci. 2021, 7, e659. [Google Scholar] [CrossRef]
  12. Faizan, Q. Enhancing QOS Performance of the 5G Network by Characterizing Mm-Wave Channel and Optimizing Interference Cancellation Scheme/Faizan Qamar. Ph.D. Thesis, University of Malaya, Kuala Lumpur, Malaysia, 2019. [Google Scholar]
  13. Marsch, P.; Bulakci, Ö.; Queseth, O.; Boldi, M. E2E Architecture. In 5G System Design: Architectural and Functional Considerations and Long Term Research, 1st ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2018; pp. 81–115. [Google Scholar]
  14. Alsabah, M.; Naser, M.A.; Mahmmod, B.M.; Abdulhussain, S.H.; Eissa, M.R.; Al-Baidhani, A.; Noordin, N.K.; Sait, S.M.; Al-Utaibi, K.A.; Hashim, F. 6G Wireless Communications Networks: A Comprehensive Survey. IEEE Access 2021, 9, 148191–148243. [Google Scholar] [CrossRef]
  15. Shahraki, A.; Abbasi, M.; Piran, M.J.; Taherkordi, A. A comprehensive survey on 6G networks: Applications, core services, nabling technologies, and future challenges. arXiv 2021, arXiv:2101.12475. [Google Scholar]
  16. Jiang, W.; Han, B.; Habibi, M.A.; Schotten, H.D. The Road Towards 6G: A Comprehensive Survey. IEEE Open J. Commun. Soc. 2021, 2, 334–366. [Google Scholar] [CrossRef]
  17. Nasir, N.M.; Hassan, S.; Zaini, K.M. Evolution Towards 6G Intelligent Wireless Networks: The Motivations and Challenges on the Enabling Technologies. In Proceedings of the 2021 IEEE 19th Student Conference on Research and Development (SCOReD), Kota Kinabalu, Malaysia, 23–25 November 2021; pp. 305–310. [Google Scholar]
  18. Abdel Hakeem, S.A.; Hussein, H.H.; Kim, H. Vision and research directions of 6G technologies and applications. J. King Saud Univ. Comput. Inf. Sci. 2022. [Google Scholar] [CrossRef]
  19. Qadir, Z.; Munawar, H.S.; Saeed, N.; Le, K. Towards 6G Internet of Things: Recent Advances, Use Cases, and Open Challenges. 2021. Available online: https://arxiv.org/pdf/2111.06596v1.pdf (accessed on 19 April 2022).
  20. Nguyen, D.C.; Ding, M.; Pathirana, P.N.; Seneviratne, A.; Li, J.; Niyato, D.; Dobre, O.; Poor, H.V. 6G Internet of Things: A Comprehensive Survey. IEEE Internet Things J. 2021, 9, 359–383. [Google Scholar] [CrossRef]
  21. Kim, J.H. 6G and Internet of Things: A survey. J. Manag. Anal. 2021, 8, 316–332. [Google Scholar] [CrossRef]
  22. Guo, F.; Yu, F.R.; Zhang, H.; Li, X.; Ji, H.; Leung, V.C.M. Enabling Massive IoT Toward 6G: A Comprehensive Survey. IEEE Internet Things J. 2021, 8, 11891–11915. [Google Scholar] [CrossRef]
  23. Barakat, B.; Taha, A.; Samson, R.; Steponenaite, A.; Ansari, S.; Langdon, P.; Wassell, I.; Abbasi, Q.; Imran, M.; Keates, S. 6G Opportunities Arising from Internet of Things Use Cases: A Review Paper. Future Internet 2021, 13, 159. [Google Scholar] [CrossRef]
  24. Mahdi, M.N.; Ahmad, A.R.; Qassim, Q.S.; Natiq, H.; Subhi, M.A.; Mahmoud, M. From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey. Appl. Sci. 2021, 11, 8117. [Google Scholar] [CrossRef]
  25. Jahid, A.; Alsharif, M.H.; Hall, T.J. The Convergence of Blockchain, IoT and 6G: Potential, Opportunities, Challenges and Research Roadmap. arXiv 2021, arXiv:2109.03184. [Google Scholar] [CrossRef]
  26. Liu, Q.; Sun, S.; Wang, H.; Zhang, S. 6G Green IoT Network: Joint Design of Intelligent Reflective Surface and Ambient Backscatter Communication. Wirel. Commun. Mob. Comput. 2021, 2021, 9912265. [Google Scholar] [CrossRef]
  27. Ndiaye, M.; Saley, A.M.; Niane, K.; Raimy, A. Future 6G communication networks: Typical IoT network topology and Terahertz frequency challenges and research issues. In Proceedings of the 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Meknes, Morocco, 3–4 March 2022; pp. 1–5. [Google Scholar]
  28. The Internet of Things. 2005. Available online: http://www.itu.int/osg/spu/publications/internetofthings/ (accessed on 15 March 2021).
  29. ITU, Global Standards for the Internet of Things. ed: ITU, 2012. Available online: https://www.itu.int/en/ITU-T/gsi/iot/Pages/default.aspx#:~:text=The%20Internet%20of%20Things%20(IoT,interoperable%20information%20and%20communication%20technologies (accessed on 15 March 2021).
  30. Ashton, K. That “Internet of Things” thing. RfiD J. 2009, 22, 97–114. [Google Scholar]
  31. Arshdeep, B.; Madisetti, V. Internet of Things: A Hands-On Approach; Vijay Madisetti: Atlanta, GA, USA, 2014. [Google Scholar]
  32. Kumar, S.; Tiwari, P.; Zymbler, M. Internet of Things is a revolutionary approach for future technology enhancement: A review. J. Big Data 2019, 6, 111. [Google Scholar] [CrossRef] [Green Version]
  33. Seth, I.; Panda, S.N.; Guleria, K. IoT based Smart Applications and Recent Research Trends. In Proceedings of the 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India, 7–9 October 2021; pp. 407–412. [Google Scholar] [CrossRef]
  34. Hassan, R.; Qamar, F.; Hasan, M.K.; Aman, A.H.M.; Ahmed, A.S. Internet of Things and Its Applications: A Comprehensive Survey. Symmetry 2020, 12, 1674. [Google Scholar] [CrossRef]
  35. Yousif, M.; Hewage, C.; Nawaf, L. IoT Technologies during and Beyond COVID-19: A Comprehensive Review. Future Internet 2021, 13, 105. [Google Scholar] [CrossRef]
  36. Mondal, S.; Mitra, P. The Role of Emerging Technologies to Fight Against COVID-19 Pandemic: An Exploratory Review. Trans. Indian Natl. Acad. Eng. 2022, 7, 157–174. [Google Scholar] [CrossRef]
  37. Kollu, P.K.; Kumar, K.; Kshirsagar, P.R.; Islam, S.; Naveed, Q.N.; Hussain, M.R.; Sundramurthy, V.P. Development of Advanced Artificial Intelligence and IoT Automation in the Crisis of COVID-19 Detection. J. Health Eng. 2022, 2022, 1987917. [Google Scholar] [CrossRef] [PubMed]
  38. Erişen, S.; Pham, D.T. IoT-Based Real-Time updating multi-layered learning system applied for a special care context during COVID-19. Cogent Eng. 2022, 9. [Google Scholar] [CrossRef]
  39. Sovacool, B.K.; Rio, D.F.D. Smart home technologies in Europe: A critical review of concepts, benefits, risks and policies. Renew. Sustain. Energy Rev. 2020, 120, 109663. [Google Scholar] [CrossRef]
  40. Nauman, A.; Qadri, Y.A.; Amjad, M.; Bin Zikria, Y.; Afzal, M.K.; Kim, S.W. Multimedia Internet of Things: A Comprehensive Survey. IEEE Access 2020, 8, 8202–8250. [Google Scholar] [CrossRef]
  41. Zia, T.; Liu, P.; Han, W. Application-Specific Digital Forensics Investigative Model in Internet of Things (IoT). In Proceedings of the 12th International Conference on Availability, Reliability and Security, Reggio Calabria, Italy, 29 August 2017; pp. 1–7. [Google Scholar]
  42. Zeng, X.; Garg, S.K.; Strazdins, P.; Jayaraman, P.P.; Georgakopoulos, D.; Ranjan, R. IOTSim: A simulator for analysing IoT applications. J. Syst. Arch. 2017, 72, 93–107. [Google Scholar] [CrossRef]
  43. Stolojescu-Crisan, C.; Crisan, C.; Butunoi, B.-P. An IoT-Based Smart Home Automation System. Sensors 2021, 21, 3784. [Google Scholar] [CrossRef]
  44. Yuen, M.C.; Chu, S.Y.; Hong Chu, W.; Shuen Cheng, H.; Lam Ng, H.; Pang Yuen, S. A low-cost IoT smart home system. Int. J. Eng. Technol. 2018, 7, 3143–3147. [Google Scholar]
  45. Taiwo, O.; Ezugwu, A.E. Internet of Things-Based Intelligent Smart Home Control System. Secur. Commun. Networks 2021, 2021, 9928254. [Google Scholar] [CrossRef]
  46. Lee, C.; Wang, C.; Kim, E.; Helal, S. Blueprint Flow: A Declarative Service Composition Framework for Cloud Applications. IEEE Access 2017, 5, 17634–17643. [Google Scholar] [CrossRef]
  47. Lin, Y.B.; Lin, Y.W.; Hsiao, C.Y.; Wang, S.Y. Location-based IoT applications on campus: The IoT talk approach. Pervasive Mob. Comput. 2017, 40, 660–673. [Google Scholar] [CrossRef]
  48. Sun, X.; Ansari, N. Dynamic Resource Caching in the IoT Application Layer for Smart Cities. IEEE Internet Things J. 2017, 5, 606–613. [Google Scholar] [CrossRef]
  49. Sun, X.; Ansari, N. Traffic Load Balancing Among Brokers at the IoT Application Layer. IEEE Trans. Netw. Serv. Manag. 2018, 15, 489–502. [Google Scholar] [CrossRef]
  50. Bellini, P.; Nesi, P.; Pantaleo, G. IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies. Appl. Sci. 2022, 12, 1607. [Google Scholar] [CrossRef]
  51. Syed, A.; Sierra-Sosa, D.; Kumar, A.; Elmaghraby, A. IoT in Smart Cities: A Survey of Technologies, Practices and Challenges. Smart Cities 2021, 4, 429–475. [Google Scholar] [CrossRef]
  52. Wang, Z. Research on Smart City Environment Design and Planning Based on Internet of Things. J. Sensors 2022, 2022, 2348573. [Google Scholar] [CrossRef]
  53. Humayun, M.; Alsaqer, M.S.; Jhanjhi, N. Energy Optimization for Smart Cities Using IoT. Appl. Artif. Intell. 2022, 1–17. [Google Scholar] [CrossRef]
  54. Kim, S.; Kim, S. User preference for an IoT healthcare application for lifestyle disease management. Telecommun. Policy 2018, 42, 304–314. [Google Scholar] [CrossRef]
  55. Yang, X.; Wang, X.; Li, X.; Gu, D.; Liang, C.; Li, K.; Zhang, G.; Zhong, J. Exploring emerging IoT technologies in smart health research: A knowledge graph analysis. BMC Med. Inform. Decis. Mak. 2020, 20, 260. [Google Scholar] [CrossRef] [PubMed]
  56. Nayak, S.; Patgiri, R. 6G Communication Technology: A Vision on Intelligent Healthcare. arXiv 2020, arXiv:2005.07532. [Google Scholar]
  57. Yaacoub, E.; Abualsaud, K.; Khattab, T.; Chehab, A. Secure Transmission of IoT mHealth Patient Monitoring Data from Remote Areas Using DTN. IEEE Netw. 2020, 34, 226–231. [Google Scholar] [CrossRef]
  58. Xie, C.; Yang, P.; Yang, Y. Open Knowledge Accessing Method in IoT-Based Hospital Information System for Medical Record Enrichment. IEEE Access 2018, 6, 15202–15211. [Google Scholar] [CrossRef]
  59. Islam, M.S.; Islam, M.T.; Almutairi, A.F.; Beng, G.K.; Misran, N.; Amin, N. Monitoring of the Human Body Signal through the Internet of Things (IoT) Based LoRa Wireless Network System. Appl. Sci. 2019, 9, 1884. [Google Scholar] [CrossRef] [Green Version]
  60. Lu, Z.-X.; Qian, P.; Bi, D.; Ye, Z.-W.; He, X.; Zhao, Y.-H.; Su, L.; Li, S.-L.; Zhu, Z.-L. Application of AI and IoT in Clinical Medicine: Summary and Challenges. Curr. Med Sci. 2021, 41, 1134–1150. [Google Scholar] [CrossRef]
  61. Chen, W.; Hao, X.; Lu, J.; Yan, K.; Liu, J.; He, C.; Xu, X. Research and Design of Distributed IoT Water Environment Monitoring System Based on LoRa. Wirel. Commun. Mob. Comput. 2021, 2021, 9403963. [Google Scholar] [CrossRef]
  62. Li, H.; Wang, H.; Yin, W.; Li, Y.; Qian, Y.; Hu, F. Development of a Remote Monitoring System for Henhouse Environment Based on IoT Technology. Future Internet 2015, 7, 329–341. [Google Scholar] [CrossRef] [Green Version]
  63. Kim, N.-S.; Lee, K.; Ryu, J.-H. Study on IoT based wild vegetation community ecological monitoring system. In Proceedings of the 2015 Seventh International Conference on Ubiquitous and Future Networks, Sapporo, Japan, 7–10 July 2015; pp. 311–316. [Google Scholar]
  64. Nordin, R.; Mohamad, H.; Behjati, M.; Kelechi, A.H.; Ramli, N.; Ishizu, K.; Kojima, F.; Ismail, M.; Idris, M. The world-first deployment of narrowband IoT for rural hydrological monitoring in UNESCO biosphere environment. In Proceedings of the 2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), Putrajaya, Malaysia, 28–30 November 2017; pp. 1–5. [Google Scholar]
  65. Zhang, Y.; Xiong, Z.; Niyato, D.; Wang, P.; Han, Z. Information Trading in Internet of Things for Smart Cities: A Market-Oriented Analysis. IEEE Netw. 2020, 34, 122–129. [Google Scholar] [CrossRef]
  66. Sukmaningsih, D.W.; Suparta, W.; Trisetyarso, A.; Abbas, B.S.; Kang, C.H. Proposing Smart Disaster Management in Urban Area. In Proceedings of the Studies in Computational Intelligence, Yogyakarta, Indonesia, 8–11 April 2019; pp. 3–16. [Google Scholar]
  67. Suparta, W.; Alhasa, K.M.; Singh, M.S.J. Preliminary Development of Greenhouse Gases System Data Logger Using Microcontroller Netduino. Adv. Sci. Lett. 2017, 23, 1398–1402. [Google Scholar] [CrossRef]
  68. Sahota, H.; Kumar, R.; Kamal, A.; Huang, J. An energy-efficient wireless sensor network for precision agriculture. In Proceedings of the IEEE symposium on Computers and Communications, Riccione, Italy, 22–25 June 2010; pp. 347–350. [Google Scholar]
  69. Jawad, H.M.; Nordin, R.; Gharghan, S.K.; Jawad, A.M.; Ismail, M. Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review. Sensors 2017, 17, 1781. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Quy, V.K.; Van Hau, N.; Van Anh, D.; Quy, N.M.; Ban, N.T.; Lanza, S.; Randazzo, G.; Muzirafuti, A. IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. Appl. Sci. 2022, 12, 3396. [Google Scholar] [CrossRef]
  71. Mohamed, E.S.; Belal, A.; Abd-Elmabod, S.K.; El-Shirbeny, M.A.; Gad, A.; Zahran, M.B. Smart farming for improving agricultural management. Egypt. J. Remote Sens. Space Sci. 2021, 24, 971–981. [Google Scholar] [CrossRef]
  72. Vijaya Saraswathi, R.; Sridharani, J.; Saranya Chowdary, P.; Nikhil, K.; Sri Harshitha, M.; Mahanth Sai, K. Smart Farming: The IoT based Future Agriculture. In Proceedings of the 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 20–22 January 2022; pp. 150–155. [Google Scholar]
  73. Xu, J.; Gu, B.; Tian, G. Review of agricultural IoT technology. Artif. Intell. Agric. 2022, 6, 10–22. [Google Scholar] [CrossRef]
  74. Swamidason, I.T.J.; Pandiyarajan, S.; Velswamy, K.; Jancy, P.L. Futuristic IoT based Smart Precision Agriculture: Brief Analysis. J. Mob. Multimedia 2022, 18, 935–956. [Google Scholar] [CrossRef]
  75. Zheng, G.; Zang, X.; Xu, N.; Wei, H.; Yu, Z.; Gayah, V.; Xu, K.; Li, Z. Diagnosing reinforcement learning for traffic signal control. arXiv 2019, arXiv:1905.04716. [Google Scholar]
  76. Zhang, T.; Zhu, Q. Distributed Privacy-Preserving Collaborative Intrusion Detection Systems for VANETs. IEEE Trans. Signal Inf. Process. Over Netw. 2018, 4, 148–161. [Google Scholar] [CrossRef]
  77. Elliott, D.; Keen, W.; Miao, L. Recent advances in connected and automated vehicles. J. Traffic Transp. Eng. 2019, 6, 109–131. [Google Scholar] [CrossRef]
  78. Mustakim, H.U. 5G Vehicular Network for Smart Vehicles in Smart City: A Review. J. Comput. Electron. Telecommun. 2020, 1, 12–16. [Google Scholar] [CrossRef]
  79. Dogra, A.K.; Kaur, J. Moving towards smart transportation with machine learning and Internet of Things (IoT): A review. J. Smart Environ. Green Comput. 2022, 2, 3–18. [Google Scholar] [CrossRef]
  80. Temglit, N.; Chibani, A.; Djouani, K.; Nacer, M.A. A Distributed Agent-Based Approach for Optimal QoS Selection in Web of Object Choreography. IEEE Syst. J. 2017, 12, 1655–1666. [Google Scholar] [CrossRef]
  81. Cao, B.; Liu, J.; Wen, Y.; Li, H.; Xiao, Q.; Chen, J. QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications. J. Parallel Distrib. Comput. 2019, 132, 177–189. [Google Scholar] [CrossRef]
  82. Cuomo, S.; Di Somma, V.; Sica, F. An application of the one-factor HullWhite model in an IoT financial scenario. Sustain. Cities Soc. 2018, 38, 18–20. [Google Scholar] [CrossRef]
  83. Song, Y.; Yu, F.R.; Zhou, L.; Yang, X.; He, Z. Applications of the Internet of Things (IoT) in Smart Logistics: A Comprehensive Survey. IEEE Internet Things J. 2021, 8, 4250–4274. [Google Scholar] [CrossRef]
  84. Sharma, V.; Gandhi, M.K. Internet of Things (IoT) on E-commerce Logistics: A Review. J. Phys. Conf. Ser. 2021, 1964, 62113. [Google Scholar] [CrossRef]
  85. Rejeb, A.; Simske, S.; Rejeb, K.; Treiblmaier, H.; Zailani, S. Internet of Things research in supply chain management and logistics: A bibliometric analysis. Internet Things 2020, 12, 100318. [Google Scholar] [CrossRef]
  86. Sekaran, R.; Patan, R.; Raveendran, A.; Al-Turjman, F.; Ramachandran, M.; Mostarda, L. Survival Study on Blockchain Based 6G-Enabled Mobile Edge Computation for IoT Automation. IEEE Access 2020, 8, 143453–143463. [Google Scholar] [CrossRef]
  87. Li, L.; Li, S.; Zhao, S. QoS-Aware Scheduling of Services-Oriented Internet of Things. IEEE Trans. Ind. Inform. 2014, 10, 1497–1505. [Google Scholar] [CrossRef]
  88. Venticinque, S.; Amato, A. A methodology for deployment of IoT application in fog. J. Ambient Intell. Humaniz. Comput. 2018, 10, 1955–1976. [Google Scholar] [CrossRef]
  89. Luvisotto, M.; Tramarin, F.; Vangelista, L.; Vitturi, S. On the Use of LoRaWAN for Indoor Industrial IoT Applications. Wirel. Commun. Mob. Comput. 2018, 2018, 3982646. [Google Scholar] [CrossRef] [Green Version]
  90. Mazzei, D.; Baldi, G.; Fantoni, G.; Montelisciani, G.; Pitasi, A.; Ricci, L.; Rizzello, L. A Blockchain Tokenizer for Industrial IOT trustless applications. Future Gener. Comput. Syst. 2020, 105, 432–445. [Google Scholar] [CrossRef]
  91. Jadala, V.C.; Pasupuletti, S.K.; Raju, S.H.; Kavitha, S.; Bhaba, C.H.S.; Sreedhar, B. Need of Intenet of Things, Industrial IoT, Industry 4.0 and Integration of Cloud for Industrial Revolution. In Proceedings of the 2021 Innovations in Power and Advanced Computing Technologies (i-PACT), Kuala Lumpur, Malaysia, 27–29 November 2021; pp. 1–5. [Google Scholar]
  92. Kalsoom, T.; Ahmed, S.; Rafi-Ul-Shan, P.M.; Azmat, M.; Akhtar, P.; Pervez, Z.; Imran, M.A.; Ur-Rehman, M. Impact of IoT on Manufacturing Industry 4.0: A New Triangular Systematic Review. Sustainability 2021, 13, 12506. [Google Scholar] [CrossRef]
  93. Suhonen, J. Designs for the Quality of Service Support in Low-Energy Wireless Sensor Network Protocols. Ph.D. Thesis, Tampere University of Technology, Tampere, Finland, 2012. [Google Scholar]
  94. Kwon, H.; Park, J.; Kang, N. Challenges in Deploying CoAP Over DTLS in Resource Constrained Environments. In Information Security Applications. WISA 2015. Lecture Notes in Computer Science; Kim, H., Choi, D., Eds.; Springer: Cham, Germany, 2016; Volume 9503, pp. 269–280. [Google Scholar]
  95. Shafique, K.; Khawaja, B.A.; Sabir, F.; Qazi, S.; Mustaqim, M. Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios. IEEE Access 2020, 8, 23022–23040. [Google Scholar] [CrossRef]
  96. Chen, S.; Xu, H.; Liu, D.; Hu, B.; Wang, H. A Vision of IoT: Applications, Challenges, and Opportunities with China Perspective. IEEE Internet Things J. 2014, 1, 349–359. [Google Scholar] [CrossRef]
  97. Donta, P.K.; Srirama, S.N.; Amgoth, T.; Annavarapu, C.S.R. Survey on recent advances in IoT application layer protocols and machine learning scope for research directions. Digit. Commun. Networks Sci. Direct 2021. [Google Scholar] [CrossRef]
  98. Pereira, F.; Correia, R.; Pinho, P.; Lopes, S.I.; Carvalho, N.B. Challenges in Resource-Constrained IoT Devices: Energy and Communication as Critical Success Factors for Future IoT Deployment. Sensors 2020, 20, 6420. [Google Scholar] [CrossRef]
  99. Atzori, L.; Iera, A.; Morabito, G. The Internet of Things: A survey. Comput. Netw. ISDN Syst. 2010, 54, 2787–2805. [Google Scholar] [CrossRef]
  100. Giuliano, R.; Mazzenga, F.; Neri, A.; Vegni, A.M. Security Access Protocols in IoT Capillary Networks. IEEE Internet Things J. 2017, 4, 645–657. [Google Scholar] [CrossRef]
  101. European Commission. Expert Group on the Internet of Things (IoT-EG). Available online: http://ec.europa.eu/information_society/newsroom/cf/dae/document.cfm?doc_id=1752JeCyd173g&sig2=a3cHVzht3OtpsHdevmA87w (accessed on 17 March 2017).
  102. Raza, S.; Duquennoy, S.; Höglund, J.; Roedig, U.; Voigt, T. Secure communication for the Internet of Things-a comparison of link-layer security and IPsec for 6LoWPAN. Secur. Commun. Networks 2012, 7, 2654–2668. [Google Scholar] [CrossRef]
  103. Lee, C.; Zappaterra, L.; Choi, K.; Choi, H.-A. Securing smart home: Technologies, security challenges, and security requirements. In Proceedings of the Workshop on Security and Privacy in Machine-to-Machine Communications (M2MSec’14), San Francisco, CA, USA, 29 October 2014; pp. 67–72. [Google Scholar]
  104. Mehmood, Y.; Ahmad, F.; Yaqoob, I.; Adnane, A.; Imran, M.; Guizani, S. Internet-of-Things-Based Smart Cities: Recent Advances and Challenges. IEEE Commun. Mag. 2017, 55, 16–24. [Google Scholar] [CrossRef]
  105. Sadeeq, M.M.; Abdulkareem, N.M.; Zeebaree, S.R.M.; Ahmed, D.M.; Sami, A.S.; Zebari, R.R. IoT and Cloud Computing Issues, Challenges and Opportunities: A Review. Qubahan Acad. J. 2021, 1, 1–7. [Google Scholar] [CrossRef]
  106. Yao, X.; Farha, F.; Li, R.; Psychoula, I.; Chen, L.; Ning, H. Security and privacy issues of physical objects in the IoT: Challenges and opportunities. Digit. Commun. Networks 2021, 7, 373–384. [Google Scholar] [CrossRef]
  107. HaddadPajouh, H.; Dehghantanha, A.; Parizi, R.M.; Aledhari, M.; Karimipour, H. A survey on internet of things security: Requirements, challenges, and solutions. Internet Things 2021, 14, 100129. [Google Scholar] [CrossRef]
  108. Gupta, K.; Shukla, S. Internet of Things: Security challenges for next generation networks. In Proceedings of the 2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH), Greater Noida, India, 3–5 February 2016; pp. 315–318. [Google Scholar]
  109. Granjal, J.; Monteiro, E.; Silva, J.S. Security for the Internet of Things: A Survey of Existing Protocols and Open Research Issues. IEEE Commun. Surv. Tutor. 2015, 17, 1294–1312. [Google Scholar] [CrossRef]
  110. Garcia-Morchon, O.; Rietman, R.; Sharma, S.; Tolhuizen, L.; Torre-Arce, J. A Comprehensive and Lightweight Security Architecture to Secure the IoT Throughout the Lifecycle of a Device Based on HIMMO. In Algorithms for Sensor Systems, Proceedings of the 11th International Symposium on Algorithms and Experiments for Wireless Sensor Networks (ALGOSENSORS), Patras, Greece, 17–18 September 2015; Bose, P., Gasieniec, L., Römer, K., Wattenhofer, R., Eds.; Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2015; pp. 112–128. [Google Scholar]
  111. Hummen, R.; Hiller, J.; Wirtz, H.; Henze, M.; Shafagh, H.; Wehrle, K. 6LoWPAN fragmentation attacks and mitigation mechanisms. In Proceedings of the sixth ACM conference on Security and privacy in wireless and mobile networks—WiSec ’13, Budapest, Hungary, 17–19 April 2013; pp. 55–66. [Google Scholar]
  112. Ni, J.; Lin, X.; Zhang, K.; Shen, X. Privacy-Preserving Real-Time Navigation System Using Vehicular Crowdsourcing. In Proceedings of the 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Montreal, QC, Canada, 18–21 September 2016; pp. 1–5. [Google Scholar]
  113. Zhang, J.; Wang, B.; Xhafa, F.; Wang, X.A.; Li, C. Energy-efficient secure outsourcing decryption of attribute based encryption for mobile device in cloud computation. J. Ambient Intell. Humaniz. Comput. 2017, 10, 429–438. [Google Scholar] [CrossRef] [Green Version]
  114. Hamad, S.A.; Zhang, W.E.; Sheng, Q.Z.; Nepal, S. IoT Device Identification via Network-Flow Based Fingerprinting and Learning. In Proceedings of the 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications(TrustCom), Rotorua, New Zealand, 5–8 August 2019; pp. 103–111. [Google Scholar]
  115. Hamad, S.A.; Sheng, Q.Z.; Zhang, W.E.; Nepal, S. Realizing an Internet of Secure Things: A Survey on Issues and Enabling Technologies. IEEE Commun. Surv. Tutor. 2020, 22, 1372–1391. [Google Scholar] [CrossRef]
  116. Yang, L.; Humayed, A.; Li, F. A multi-cloud based privacy-preserving data publishing scheme for the internet of things. In Proceedings of the 32nd Annual Conference on Computer Security Applications, Los Angeles, CA, USA, 5–8 December 2016; Schwab, S., Robertson, W.K., Balzarotti, D., Eds.; ACM: Los Angeles, CA, USA, 2016; pp. 30–39. [Google Scholar]
  117. Sengupta, J.; Ruj, S.; Das Bit, S. A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT. J. Netw. Comput. Appl. 2020, 149, 102481. [Google Scholar] [CrossRef]
  118. Abdullah, P.Y.; Zeebaree, S.R.M.; Jacksi, K.; Zeabri, R.R. An hrm system for small and medium enterprises (sme)s based on cloud computing technology. Int. J. Res.Granthaalayah 2020, 8, 56–64. [Google Scholar] [CrossRef]
  119. Thakkar, A.; Lohiya, R. A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges. Arch. Comput. Methods Eng. 2021, 28, 3211–3243. [Google Scholar] [CrossRef]
  120. Gubbi, J.; Buyya, R.; Marusic, S.; Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Gener. Comput. Syst. 2013, 29, 1645–1660. [Google Scholar] [CrossRef] [Green Version]
  121. Arridha, R.; Sukaridhoto, S.; Pramadihanto, D.; Funabiki, N. Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system. Int. J. Space-Based Situated Comput. 2017, 7, 82. [Google Scholar] [CrossRef] [Green Version]
  122. Centenaro, M.; Costa, C.E.; Granelli, F.; Sacchi, C.; Vangelista, L. A Survey on Technologies, Standards and Open Challenges in Satellite IoT. IEEE Commun. Surv. Tutor. 2021, 23, 1693–1720. [Google Scholar] [CrossRef]
  123. Ghorpade, S.; Zennaro, M.; Chaudhari, B. Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues. Future Internet 2021, 13, 210. [Google Scholar] [CrossRef]
  124. Li, S.; Da Xu, L.; Zhao, S. 5G Internet of Things: A survey. J. Ind. Inf. Integr. 2018, 10, 1–9. [Google Scholar] [CrossRef]
  125. Palattella, M.R.; Dohler, M.; Grieco, L.A.; Rizzo, G.; Torsner, J.; Engel, T.; Ladid, L. Internet of Things in the 5G Era: Enablers, Architecture, and Business Models. IEEE J. Sel. Areas Commun. 2016, 34, 510–527. [Google Scholar] [CrossRef] [Green Version]
  126. Sanislav, T.; Mois, G.D.; Zeadally, S.; Folea, S.C. Energy Harvesting Techniques for Internet of Things (IoT). IEEE Access 2021, 9, 39530–39549. [Google Scholar] [CrossRef]
  127. Farhan, L.; Hameed, R.S.; Ahmed, A.S.; Fadel, A.H.; Gheth, W.; Alzubaidi, L.; Fadhel, M.A.; Al-Amidie, M. Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey. Network 2021, 1, 279–314. [Google Scholar] [CrossRef]
  128. Shafique, K.; Khawaja, B.A.; Khurram, M.D.; Sibtain, S.M.; Siddiqui, Y.; Mustaqim, M.; Chattha, H.T.; Yang, X. Energy Harvesting Using a Low-Cost Rectenna for Internet of Things (IoT) Applications. IEEE Access 2018, 6, 30932–30941. [Google Scholar] [CrossRef]
  129. Awais, Q.; Jin, Y.; Chattha, H.T.; Jamil, M.; Qiang, H.; Khawaja, B.A. A compact rectenna system with high conversion effciency for wireless energy harvesting. IEEE Access 2018, 6, 35857–35866. [Google Scholar] [CrossRef]
  130. Pang, B.-M.; Shi, H.-S.; Li, Y.-X. An energy-effcient MAC protocol for wireless sensor network. In Future Wireless Networks and Information Systems; Zhang, Y., Ed.; Springer: Berlin, Germany, 2012; Volume 143, pp. 163–170. [Google Scholar]
  131. Rani, S.; Ahmed, S.H.; Talwar, R.; Malhotra, J.; Song, H. IoMT: A Reliable Cross Layer Protocol for Internet of Multimedia Things. IEEE Internet Things J. 2017, 4, 832–839. [Google Scholar] [CrossRef]
  132. Benhamaid, S.; Bouabdallah, A.; Lakhlef, H. Recent advances in energy management for Green-IoT: An up-to-date and comprehensive survey. J. Netw. Comput. Appl. 2021, 198, 103257. [Google Scholar] [CrossRef]
  133. Guo, J.; Wang, Z.; Shi, X.; Yang, X.; Yu, P.; Feng, L.; Li, W. A Deep Reinforcement Learning based Mechanism for Cell Outage Compensation in Massive IoT Environments. In Proceedings of the 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), Tangier, Morocco, 24–28 June 2019; pp. 284–289. [Google Scholar]
  134. Sobin, C.C. A Survey on Architecture, Protocols and Challenges in IoT. Wirel. Pers. Commun. 2020, 112, 1383–1429. [Google Scholar] [CrossRef]
  135. IoT Analytics Report. Available online: https://iot-analytics.com/rise-of-iot-semiconductor/ (accessed on 4 January 2022).
  136. Oliveira, L.; Rodrigues, J.J.P.C.; Kozlov, S.A.; Rabêlo, R.A.L.; de Albuquerque, V.H.C. MAC Layer Protocols for Internet of Things: A Survey. Future Internet 2019, 11, 16. [Google Scholar] [CrossRef] [Green Version]
  137. Minihold, R. Near Field Communication (NFC) Technology and Measurements, White Paper; Rohde & Schwarz: Munich, Germany, 2011. [Google Scholar]
  138. Mendes, T.; Godina, R.; Rodrigues, E.M.G.; Matias, J.C.O.; Catalão, J.P.S. Smart Home Communication Technologies and Applications: Wireless Protocol Assessment for Home Area Network Resources. Energies 2015, 8, 7279–7311. [Google Scholar] [CrossRef] [Green Version]
  139. Vermesan, O.; Friess, P. Internet of Things—From Research and Innovation to Market Deployment; River Publishers: Aalborg, Denmark, 2014. [Google Scholar]
  140. Horyachyy, O. Comparison of Wireless Communication Technologies used in a Smart Home: Analysis of wireless sensor node based on Arduino in home automation scenario. Master’s Thesis, Blekinge Institute of Technology, Karlskrona, Sweden, 2017. [Google Scholar]
  141. Danbatta, S.J.; Varol, A. Comparison of Zigbee, Z-Wave, Wi-Fi, and Bluetooth Wireless Technologies Used in Home Automation. In Proceedings of the 2019 7th International Symposium on Digital Forensics and Security (ISDFS), Barcelos, Portugal, 10–12 June 2019; pp. 1–5. [Google Scholar]
  142. Chen, S.; Liu, B.; Chen, X.; Zhang, Y.; Huang, G. Framework for Adaptive Computation Offloading in IoT Applications. In Proceedings of the 9th Asia-Pacific Symposium on Internetware, Shanghai, China, 23 September 2017; pp. 1–6. [Google Scholar]
  143. Ertürk, M.A.; Aydın, M.A.; Büyükakkaşlar, M.T.; Evirgen, H. A Survey on LoRaWAN Architecture, Protocol and Technologies. Future Internet 2019, 11, 216. [Google Scholar] [CrossRef] [Green Version]
  144. Mekki, K.; Bajic, E.; Chaxel, F.; Meyer, F. A comparative study of LPWAN technologies for large-scale IoT deployment. ICT Express 2019, 5, 1–7. [Google Scholar] [CrossRef]
  145. Nolan, K.E.; Guibene, W.; Kelly, M.Y. An evaluation of low power wide area network technologies for the Internet of Things. In Proceedings of the 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, Cyprus, 5–9 September 2016; pp. 439–444. [Google Scholar]
  146. Finnegan, J.; Brown, S. A Comparative Survey of LPWA Networking. arXiv 2018, arXiv:abs/1802.04222. [Google Scholar]
  147. Raza, U.; Kulkarni, P.; Sooriyabandara, M. Low Power Wide Area Networks: An Overview. IEEE Commun. Surv. Tutor. 2017, 19, 855–873. [Google Scholar] [CrossRef] [Green Version]
  148. Ismail, D.; Rahman, M.; Saifullah, A. Low-power wide-area networks: Opportunities, challenges, and directions. In Proceedings of the Workshops ICDCN, Varanasi, India, 4–7 January 2018; pp. 1–6. [Google Scholar]
  149. Qadir, Q.M.; Rashid, T.A.; Al-Salihi, N.K.; Ismael, B.; Kist, A.A.; Zhang, Z. Low Power Wide Area Networks: A Survey of Enabling Technologies, Applications and Interoperability Needs. IEEE Access 2018, 6, 77454–77473. [Google Scholar] [CrossRef]
  150. LoRaWAN and Cellular IoT (NB-IoT, LTE-M)-How do they Complement Each Other? Actility SA: Lannion, France, 2018.
  151. Chaudhari, B.S.; Zennaro, M.; Borkar, S. LPWAN Technologies: Emerging Application Characteristics, Requirements, and Design Considerations. Future Internet 2020, 12, 46. [Google Scholar] [CrossRef] [Green Version]
  152. SigFox. SigFox Technology Overview. Available online: https://www.sigfox.com/en/sigfox-iot-technologyoverview (accessed on 2 April 2021).
  153. SigFox. Sigfox Technical Overview. Available online: https://www.disk91.com/wp-content/uploads/2017/05/4967675830228422064.pdf (accessed on 2 April 2021).
  154. Alsharif, M.H.; Kelechi, A.H.; Albreem, M.A.; Chaudhry, S.A.; Zia, M.S.; Kim, S. Sixth Generation (6G) Wireless Networks: Vision, Research Activities, Challenges and Potential Solutions. Symmetry 2020, 12, 676. [Google Scholar] [CrossRef]
  155. Chen, N.; Okada, M. Toward 6G Internet of Things and the Convergence With RoF System. IEEE Internet Things J. 2021, 8, 8719–8733. [Google Scholar] [CrossRef]
  156. Akyildiz, I.F.; Kak, A.; Nie, S. 6G and Beyond: The Future of Wireless Communications Systems. IEEE Access 2020, 8, 133995–134030. [Google Scholar] [CrossRef]
  157. Technology Digest on the Topic “Evolution of Mobile Communications”; Part 2, Issue; Telecom Regulatory Authority of India: New Delhi, India, 2018.
  158. Michailow, N.; Matthe, M.; Gaspar, I.S.; Caldevilla, A.N.; Mendes, L.; Festag, A.; Fettweis, G. Generalized Frequency Division Multiplexing for 5th Generation Cellular Networks. IEEE Trans. Commun. 2014, 62, 3045–3061. [Google Scholar] [CrossRef]
  159. Bedoui, A.; Et-Tolba, M. A comparative analysis of filter bank multicarrier (FBMC) as 5G multiplexing technique. In Proceedings of the 2017 International Conference on Wireless Networks and Mobile Communications (WINCOM), Rabat, Morocco, 1–4 November 2017; pp. 1–7. [Google Scholar]
  160. Farhang, M.; Bizaki, H.K. Adaptive time-frequency multiplexing for 5G applications. AEU-Int. J. Electron. Commun. 2020, 117, 153089. [Google Scholar] [CrossRef]
  161. Baghani, M.; Parsaeefard, S.; Derakhshani, M.; Saad, W. Dynamic Non-Orthogonal Multiple Access and Orthogonal Multiple Access in 5G Wireless Networks. IEEE Trans. Commun. 2019, 67, 6360–6373. [Google Scholar] [CrossRef] [Green Version]
  162. Cheng, W.; Zhang, W.; Jing, H.; Gao, S.; Zhang, H. Orbital Angular Momentum for Wireless Communications. IEEE Wirel. Commun. 2019, 26, 100–107. [Google Scholar] [CrossRef] [Green Version]
  163. Akay, E.; Sengul, E.; Ayanoglu, E. Achieving full spatial multiplexing and full diversity in wireless communications. IEEE Wirel. Commun. Netw. Conf. WCNC 2006 2006, 4, 2046–2050. [Google Scholar] [CrossRef]
  164. Zhao, Y.; Zhai, W.; Zhao, J.; Zhang, T.; Sun, S.; Niyato, D.; Lam, K. A Comprehensive Survey of 6G Wireless Communications. arXiv 2020, arXiv:2101.03889. [Google Scholar]
  165. Chen, Y.; Liu, W.; Niu, Z.; Feng, Z.; Hu, Q.; Jiang, T. Pervasive intelligent endogenous 6G wireless systems: Prospects, theories and key technologies. Digit. Commun. Networks 2020, 6, 312–320. [Google Scholar] [CrossRef]
  166. Qiao, X.; Huang, Y.; Dustdar, S.; Chen, J. 6G Vision: An AI-Driven Decentralized Network and Service Architecture. IEEE Internet Comput. 2020, 24, 33–40. [Google Scholar] [CrossRef]
  167. Van Huynh, N.; Hoang, D.T.; Lu, X.; Niyato, D.; Wang, P.; Kim, D.I. Ambient Backscatter Communications: A Contemporary Survey. IEEE Commun. Surv. Tutor. 2018, 20, 2889–2922. [Google Scholar] [CrossRef] [Green Version]
  168. Strinati, E.C.; Barbarossa, S.; Gonzalez-Jimenez, J.L.; Kténas, D.; Cassiau, N.; Maret, L.; Dehos, C. 6G: The Next Frontier: From Holographic Messaging to Artificial Intelligence Using Subterahertz and Visible Light Communication. IEEE Veh. Technol. Mag. 2019, 14, 42–58. [Google Scholar] [CrossRef]
  169. Imoize, A.; Adedeji, O.; Tandiya, N.; Shetty, S. 6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap. Sensors 2021, 21, 1709. [Google Scholar] [CrossRef] [PubMed]
  170. Fager, C.; Member, S.; Eriksson, T.; Member, S.; Fellow, H.Z.; Dielacher, F.; Member, S.; Studer, C.; Member, S. Implementation Challenges and Opportunities in Beyond-5G and 6G Communication. IEEE J. Microw. 2021, 1, 86–100. [Google Scholar]
  171. Zhou, Z.; Gong, J.; He, Y.; Zhang, Y. Software Defined Machine-to-Machine Communication for Smart Energy Management. IEEE Commun. Mag. 2017, 55, 52–60. [Google Scholar] [CrossRef]
  172. Maksymyuk, T.; Gazda, J.; Volosin, M.; Bugar, G.; Horvath, D.; Klymash, M.; Dohler, M. Blockchain-Empowered Framework for Decentralized Network Management in 6G. IEEE Commun. Mag. 2020, 58, 86–92. [Google Scholar] [CrossRef]
  173. Hewa, T.; Gur, G.; Kalla, A.; Ylianttila, M.; Bracken, A.; Liyanage, M. The Role of Blockchain in 6G: Challenges, Opportunities and Research Directions. In Proceedings of the 2020 2nd 6G Wireless Summit (6G SUMMIT), Levi, Finland, 17–20 March 2020; pp. 1–5. [Google Scholar] [CrossRef]
  174. Xu, H.; Klaine, P.V.; Onireti, O.; Cao, B.; Imran, M.; Zhang, L. Blockchain-enabled resource management and sharing for 6G communications. Digit. Commun. Networks 2020, 6, 261–269. [Google Scholar] [CrossRef]
  175. Khan, A.H.; Hassan, N.U.; Yuen, C.; Zhao, J.; Niyato, D.; Zhang, Y.; Poor, H.V. Blockchain and 6G: The Future of Secure and Ubiquitous Communication. IEEE Wirel. Commun. 2021, 1–8. [Google Scholar] [CrossRef]
  176. Nawaz, S.J.; Sharma, S.K.; Wyne, S.; Patwary, M.N. Asaduzzaman Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future. IEEE Access 2019, 7, 46317–46350. [Google Scholar] [CrossRef]
  177. Chen, Z.; Ma, X.; Zhang, B.; Zhang, Y.; Niu, Z.; Kuang, N.; Chen, W.; Li, L.; Li, S. A survey on terahertz communications. China Comm. 2019, 16, 1–35. [Google Scholar] [CrossRef]
  178. Elayan, H.; Amin, O.; Shubair, R.M.; Alouini, M.-S. Terahertz communication: The opportunities of wireless technology beyond 5G. In Proceedings of the International Conference on Advanced Communication Technologies and Networking, Marrakech, Morocco, 2–4 April 2018; pp. 1–5. [Google Scholar]
  179. Rappaport, T.S.; Xing, Y.; Kanhere, O.; Ju, S.; Madanayake, A.; Mandal, S.; Alkhateeb, A.; Trichopoulos, G.C. Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond. IEEE Access 2019, 7, 78729–78757. [Google Scholar] [CrossRef]
  180. Akyildiz, I.F.; Han, C.; Nie, S. Combating the Distance Problem in the Millimeter Wave and Terahertz Frequency Bands. IEEE Commun. Mag. 2018, 56, 102–108. [Google Scholar] [CrossRef] [Green Version]
  181. Huang, T.; Yang, W.; Wu, J.; Ma, J.; Zhang, X.; Zhang, D. A Survey on Green 6G Network: Architecture and Technologies. IEEE Access 2019, 7, 175758–175768. [Google Scholar] [CrossRef]
  182. Dang, S.; Amin, O.; Shihada, B.; Alouini, M. From a Human-Centric Perspective: What Might 6G Be? arXiv 2019, arXiv:abs/1906.00741. [Google Scholar]
  183. Prasad, R. Human bond communication. Wirel. Pers. Commun. 2016, 87, 619–627. [Google Scholar] [CrossRef]
  184. Shiroishi, Y.; Uchiyama, K.; Suzuki, N. Society 5.0: For Human Security and Well-Being. Computer 2018, 51, 91–95. [Google Scholar] [CrossRef]
  185. Rojas, C.N.; Peñafiel, G.A.; Buitrago, D.L.; Romero, C.T. Society 5.0: A Japanese Concept for a Superintelligent Society. Sustainability 2021, 13, 6567. [Google Scholar] [CrossRef]
  186. Alsharif, M.H.; Kelechi, A.H.; Yahya, K.; Chaudhry, S.A. Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends. Symmetry 2020, 12, 88. [Google Scholar] [CrossRef] [Green Version]
  187. Letaief, K.B.; Chen, W.; Shi, Y.; Zhang, J.; Zhang, Y.-J.A. The Roadmap to 6G: AI Empowered Wireless Networks. IEEE Commun. Mag. 2019, 57, 84–90. [Google Scholar] [CrossRef] [Green Version]
  188. Albreem, M.A.; Alsharif, M.H.; Kim, S. A Low Complexity Near-Optimal Iterative Linear Detector for Massive MIMO in Realistic Radio Channels of 5G Communication Systems. Entropy 2020, 22, 388. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  189. Guimarães, D.; Pereira, E.; Alberti, A.; Moreira, J. Design Guidelines for Database-Driven Internet of Things-Enabled Dynamic Spectrum Access. Sensors 2021, 21, 3194. [Google Scholar] [CrossRef] [PubMed]
  190. Samdanis, K.; Rost, P.; Maeder, A.; Meo, M.; Verikoukis, C. Green Communications: Principles, Concepts and Practice; Wiley Telecom: Hoboken, NJ, USA, 2015; ISBN 978-1-118-75926-4. [Google Scholar]
  191. Malik, N.A.; Ur-Rehman, M. Green Communications: Techniques and Challenges. EAI Endorsed Trans. Energy Web 2017, 4, 153162. [Google Scholar] [CrossRef] [Green Version]
  192. Jamil, S.; Fawad; Abbas, M.S.; Umair, M.; Hussain, Y. A Review of Techniques and Challenges in Green Communication. In Proceedings of the 2020 International Conference on Information Science and Communication Technology (ICISCT), Tashkent, Uzbekistan, 4–6 November 2020; pp. 1–6. [Google Scholar]
  193. Suraweera, H.A.; Yang, J.; Zappone, A.; Thompson, J. Green Communications for Energy-Efficient Wireless Systems and Networks; Institution of Engineering and Technology: London, UK, 2020; ISBN 9781839530685. [Google Scholar] [CrossRef]
  194. Song, H.-J.; Nagatsuma, T. Present and Future of Terahertz Communications. IEEE Trans. Terahertz Sci. Technol. 2011, 1, 256–263. [Google Scholar] [CrossRef]
  195. Luo, K.; Dang, S.; Shihada, B.; Alouini, M.-S. Prospect Theory for Human-Centric Communications. Front. Commun. Networks 2021, 2, 634950. [Google Scholar] [CrossRef]
  196. The 5th Science and Technology Basic Plan. Government of Japan, 22 January 2016. Available online: http://www8.cao.go.jp/cstp/english/basic/5thbasicplan.pdf (accessed on 18 October 2021).
  197. Gladden, M.E. Who Will Be the Members of Society 5.0? Towards an Anthropology of Technologically Posthumanized Future Societies. Soc. Sci. 2019, 8, 148. [Google Scholar] [CrossRef] [Green Version]
  198. From Industry 4.0 to Society 5.0: The Big Societal Transformation Plan of Japan, 2016. Available online: https://www.i-scoop.eu/industry-4-0/society-5-0/ (accessed on 18 October 2021).
  199. A Holistic Approach to Creating Smart Societies. Available online: https://www.itu.int/dms_pub/itu-d/oth/07/17/D07170000020001PDFE.pdf (accessed on 12 October 2021).
  200. Lu, Y.; Zheng, X. 6G: A survey on technologies, scenarios, challenges, and the related issues. J. Ind. Inf. Integr. 2020, 19, 100158. [Google Scholar] [CrossRef]
  201. Tataria, H.; Shafi, M.; Molisch, A.F.; Dohler, M.; Sjoland, H.; Tufvesson, F. 6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities. Proc. IEEE 2021, 109, 1166–1199. [Google Scholar] [CrossRef]
  202. Akhtar, M.W.; Hassan, S.A.; Ghaffar, R.; Jung, H.; Garg, S.; Hossain, M.S. The shift to 6G communications: Vision and requirements. Hum. Cent. Comput. Inf. Sci. 2020, 10, 53. [Google Scholar] [CrossRef]
  203. Nayak, S.; Patgiri, R. 6G Communication: Envisioning the Key Issues and Challenges. EAI Endorsed Trans. Internet Things 2021, 6, 166959. [Google Scholar] [CrossRef]
  204. Xing, Y.; Rappaport, T.S. Propagation Measurement System and Approach at 140 GHz-Moving to 6G and Above 100 GHz. In Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 9–13 December 2018; pp. 1–6. [Google Scholar]
  205. Yan, L.; Han, C.; Yuan, J. Hybrid Precoding for 6G Terahertz Communications: Performance Evaluation and Open Problems. In Proceedings of the 2020 2nd 6G Wireless Summit (6G SUMMIT), Levi, Finland, 17–20 March 2020; pp. 1–5. [Google Scholar]
  206. Ahmad, I.; Shahabuddin, S.; Kumar, T.; Harjula, E.; Meisel, M.; Juntti, M.; Sauter, T.; Ylianttila, M. Challenges of AI in Wireless Networks for IoT. IEEE Ind. Electron. Mag. 2021, 15, 16–29. [Google Scholar] [CrossRef]
  207. Kato, N.; Mao, B.; Tang, F.; Kawamoto, Y.; Liu, J. Ten Challenges in Advancing Machine Learning Technologies toward 6G. IEEE Wirel. Commun. 2020, 27, 96–103. [Google Scholar] [CrossRef]
  208. Shafin, R.; Liu, L.; Chandrasekhar, V.; Chen, H.; Reed, J.; Zhang, J.C. Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G. IEEE Wirel. Commun. 2020, 27, 212–217. [Google Scholar] [CrossRef] [Green Version]
  209. Wu, Q.; Zhang, R. Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming. IEEE Trans. Wirel. Commun. 2019, 18, 5394–5409. [Google Scholar] [CrossRef] [Green Version]
  210. Jung, M.; Saad, W.; Jang, Y.; Kong, G.; Choi, S. Performance Analysis of Large Intelligent Surfaces (LISs): Asymptotic Data Rate and Channel Hardening Effects. IEEE Trans. Wirel. Commun. 2020, 19, 2052–2065. [Google Scholar] [CrossRef] [Green Version]
  211. Chen, M.; Challita, U.; Saad, W.; Yin, C.; Debbah, M. Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial. IEEE Commun. Surv. Tutor. 2019, 21, 3039–3071. [Google Scholar] [CrossRef] [Green Version]
  212. Zhu, Y.; You, G. Monitoring System for Coal Mine Safety Based on Wireless Sensor Network. In Proceedings of the 2019 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC), Taiyuan, China, 18–21 July 2019; pp. 1–2. [Google Scholar]
  213. Mangulkar, P.; Shrawankar, U. Monitoring and Safety System for Underground Coal Mines. In Proceedings of the 1st IEEE International Conference on Power Energy, Environment & Intelligent Control (PEEIC2018), Greater Noida, India, 13–14 April 2018. [Google Scholar]
  214. Nageswari, C.S.; Sangeetha, C.G.; Yogambigai, V.B. IoT based Smart Mine Monitoring System. Int. J. Electron. Electr. Comput. Syst. 2018, 7, 690–695. [Google Scholar]
  215. Roopashree; Srujana; Chaithanya. IoT based mine safety system using wireless sensor network. Int. J. Adv. Res. Innov. Ideas Educ. 2017, 2, 58–62. [Google Scholar]
  216. Ansari, A.H.; Shaikh, K.; Kadu, P.; Rishikesh, N. IOT Based Coal Mine Safety Monitoring and Alerting System. Int. J. Sci. Res. Sci. Eng. Technol. 2021, 8, 404–410. [Google Scholar] [CrossRef]
  217. Henriques, V.; Malekian, R. Mine Safety System Using Wireless Sensor Network. IEEE Access 2016, 4, 3511–3521. [Google Scholar] [CrossRef]
  218. Bandyopadhyay, L.K.; Chaulya, S.K.; Mishra, P.K.; Choure, A. Wireless Information and Safety System for Underground Mines; Central Institute of Mining and Fuel Research: Dhanbad, India, 2009. [Google Scholar]
  219. Kumar, B.V.; Jayasree, M.B.; Kiruthika, M.D. Iot based Underground Coalmine Safety System. J. Physics Conf. Ser. 2021, 1717, 12030. [Google Scholar] [CrossRef]
  220. Porselvi, T.; Sai Ganesh, C.; Janaki, B.; Priyadarshini, K.; Shajitha, S.B. IoT Based Coal Mine Safety and Health Monitoring System using LoRaWAN. In Proceedings of the 2021 3rd International Conference on Signal Processing and Communication (ICPSC), Coimbatore, India, 13–14 May 2021; pp. 49–53. [Google Scholar]
  221. Bandyopadhyay, L.K.; Chaulya, S.K.; Mishra, P.K. Wireless Communication in Underground Mines; Springer: Boston, MA, USA, 2010; ISBN 978-0-387-98165-9. [Google Scholar] [CrossRef]
Figure 1. Cisco Annual Report from 2016 to 2022 [2]: (a) Cisco Visual Networking Index Global Mobile Data Traffic from 2016 to 2022; (b) Global Growth of Smart Mobile Devices and Connections Excluding Low-Power Wide-Area (LPWA).
Figure 1. Cisco Annual Report from 2016 to 2022 [2]: (a) Cisco Visual Networking Index Global Mobile Data Traffic from 2016 to 2022; (b) Global Growth of Smart Mobile Devices and Connections Excluding Low-Power Wide-Area (LPWA).
Sensors 22 03438 g001
Figure 2. Structure of the Paper.
Figure 2. Structure of the Paper.
Sensors 22 03438 g002
Figure 3. Technologies impacting the IoT.
Figure 3. Technologies impacting the IoT.
Sensors 22 03438 g003
Figure 4. IoT Connectivity Standards.
Figure 4. IoT Connectivity Standards.
Sensors 22 03438 g004
Figure 5. Vision and key features of 6G [22,26,154,156,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186].
Figure 5. Vision and key features of 6G [22,26,154,156,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186].
Sensors 22 03438 g005
Figure 6. Challenges of 6G [5,154,156,200,201,202,203].
Figure 6. Challenges of 6G [5,154,156,200,201,202,203].
Sensors 22 03438 g006
Figure 7. Proposed system architecture and workflow. (a) Proposed System Model; (b) Complete Workflow Process.
Figure 7. Proposed system architecture and workflow. (a) Proposed System Model; (b) Complete Workflow Process.
Sensors 22 03438 g007aSensors 22 03438 g007b
Figure 8. Position of SBC and random distribution of BLE devices inside the mine area (a,b).
Figure 8. Position of SBC and random distribution of BLE devices inside the mine area (a,b).
Sensors 22 03438 g008
Figure 9. Discovery time of BLE device.
Figure 9. Discovery time of BLE device.
Sensors 22 03438 g009
Table 1. A Comprehensive Survey of existing 6G and IoT related works.
Table 1. A Comprehensive Survey of existing 6G and IoT related works.
ReferencesAuthorsYearResearch TopicObjectives/Key Contributions
[14]Alsabah et al.2021Concept on 6G NetworkA comprehensive review fn 6G-enabling technologies with a short discussion on their principle of operations, applications, current researchand challenges.
[15]Shahraki et al.2021Enabling technologies and future challenges for 6GA brief discussion on the enabling technologies, requirementsand trends of 6G with a focus on challenges and recent research activities, including tactile Internet and terahertz communication.
[16]Jiang et al.2021Roadmap definition and Key Performance Indicators of 6GA comprehensive survey on 6G use cases, architecture, key drivers, enabling technologies, etc.
[17]Nasir, et al.2021Evolution of intelligent 6G network
  • A review on the evolution of wireless technology toward 6G, focusing on the key driving forces behind the shift.
  • A short discussion on network slicing technology with AI to facilitate multimode services with varying QoS.
[18]Hakeem et al.20226G applications and future researchA brief discussion on trends, regulations, industrial marketsand analysis of 6G requirements in terms of network architecture and hardware–software design.
[19]Qadir et al. 6G-IoT conceptA brief survey on 6G networks, research activities, key enabling technologiesand case studies with the main focus given to the discussion of terahertz communication and visible light communication.
[20]Nguyen et al.20226G-enabled IoT networks
  • A holistic review of the convergence of 6G and IoT networks with a brief discussion on the key enabling technologies for the IoT including terahertz communication, reconfigurable intelligent surfaces and blockchain.
  • A few research challenges and applications of the IoT are also discussed in depth.
[21]J. H. Kim 2021Recent trends in 6G related to IoT technologyA short discussion on key drivers, enabling technologiesand current research trends of 6G with a brief introduction about viable applications of 6G to the IoT.
[22]Guo et al.20216G-enabled massive IoT
  • A survey on the key drivers and requirements for IoT-enabled applications with several constraints of 5G are also highlighted.
  • A case study on fully autonomous driving is presented to manifest the support of 6G to massive IoT.
  • A few key technologies such as ML and blockchain technologies are also discussed.
[23]Barakat et al.2021Opportunities of 6G in IoT technology perspectiveA comprehensive review of the IoT use cases based on its wide variety of implementations.
[24]Mahdi et al.2021Road map from 5G to 6GA holistic review of 5G and 6G technologies in terms of energy, he IoTand ML.
[25]Jahid et al.2021Integration of blockchain technology with 6G and Ithe IoTA comprehensive survey on integrity, privacyand security issues, with the mitigation techniques encountered in blockchain-integrated 6G cellular networks.
[26]Liu et al.20216G green IoT networkA novel method of minimizing the access point’s transmitting power is introduced by implementing the ABC and IRS technique jointly.
[27]Ndiaye et al.2022IoT network topology and 6G communication technology
  • A brief discussion on the fundamental components of a 6G network.
  • A short overview of key challenges and research issues of IoT network topology and terahertz frequency
Table 2. Applications of the IoT.
Table 2. Applications of the IoT.
Focused AreaApplicationsReferences
Intelligent Home
  • Facilitating comfortable lifestyle
  • Helps in reducing the carbon footprint of energy consumption
  • Intrusion detection
  • QoS-based services
  • Design of sensitive home automation system
  • Indoor monitoring
[39,40,41,42,43,44,45,46]
Smart Cities
  • Analyze and predict the performance of applications used in scalable platforms
  • Location finding along with the updated location configuration features
  • Smart energy
  • Smart mobility and traffic management
  • Digital forensics
  • Smart governance
  • Smart healthcare
  • Smart education
[41,43,47,48,49,50,51,52,53]
Medical and Health Care
  • Health and fitness monitoring
  • Remote medical diagnostics
  • Wearable electronics gadgets
  • Patient monitoring
  • Disease management system to improve reliability
  • Mobile medical home monitoring system to improve the rapidity of factor measurements
  • Human factor evaluation in information exchange in the healthcare environment
  • Integration of AI in clinical medicine
[35,36,50,51,54,55,56,57,58,59,60]
Environment
  • Ecological habitat monitoring
  • Weather monitoring
  • CO2 Emission monitoring
  • Collection of recyclable materials
  • Smart disaster management system
  • The revival of a rural hydrological/water monitoring system
  • Smart environment
  • Water environment monitoring
[50,51,61,62,63,64,65,66,67]
Agriculture
  • Automated irrigation control
  • Green house control
  • Precision agriculture field operation and evaluation
  • Smart farming
  • Aquaponics farming
  • Smart precision farming
  • Livestock farming
  • Smart decision-making system for real-time analysis
  • Integration of AI in monitoring and management
[62,64,67,68,69,70,71,72,73,74]
Transport
  • Optimal route finding
  • Smart traffic
  • Vehicular speed monitoring
  • Toll fee collection
  • Information about busy traffic
  • Smart parking
  • Surveillance monitoring
  • Automated/Driverless vehicle
  • ML-enabled smart transport
[48,49,75,76,77,78,79]
Retail and Logistics
  • Smart payments through near field communication (NFC) and Bluetooth
  • Stock management
  • Shipment monitoring
  • Cargo handling and tracking
  • Remote vehicle diagnostics
  • Supply chain management
[77,78,80,81,82,83,84,85]
Industry
  • Machine diagnosis and prognosis
  • Indoor air quality monitoring
  • Manufacturing automation
  • Industrial blockchain technology
  • IIoT for low-power wide-area networks (LPWANs)
  • Smart factories
[33,86,87,88,89,90,91,92]
Table 3. Challenges of the IoT.
Table 3. Challenges of the IoT.
Focused AreaChallengesReferences
Constrained Resources
  • Limited manufacturing techniques for small size and low-cost device resources
  • Spectrum resources scarcity for IoT enabling technologies
  • Smart antenna
[93,94,95,96,97,98]
Scalability, Reliability and Interoperability
  • Self-addressing, discovering and classification
  • Host identification and address mapping
  • Interoperability and availability
  • Lack of efficient and reliable communication by using TCP (transmission control protocol)/UDP (user datagram protocol) protocol
  • Unreliable packet delivery
  • Lack of interoperability between different protocols
[96,99,100,101,102,103,104,105]
Privacy and Security
  • Integrity, validation, authentication and trust
  • Data and physical device security
  • Confidentiality
  • Cyclic redundancy check (CRC)
  • Message authentication code (MAC)
  • Limitations of symmetric cryptography and public–key cryptography operation
  • Different IoT threats such as fragmentation attack
  • Poor encryption
[35,36,96,101,104,106,107,108,109,110,111,112,113,114,115,116,117,118,119]
Big Data and Cloud Computing
  • Lack of computational resources
  • Low data storage
  • Loss of data packets
  • Optimization of multi-objective functions
  • Edge computing
  • Liability sensitization toward redundant tasks
  • Centralized data acquisition system
  • Must support domain-specific programming
[104,105,107,108,120,121]
Universal
Standardization
  • For technology and other regulatory
  • For communication among heterogeneous devices
  • Protocol standardization
  • Spectrum harmonization
[95,96,120,122]
Connectivity
  • Supportiveness of tactile Internet and multimedia communications
  • High data rate applications, e.g., AR and VR
  • Reduced latency for real-time applications
  • Fast and précised localization determination
  • Good QoS
  • Signaling overhead on edge devices
  • Seamless connectivity
  • Internetworking
  • Wide range of connectivity
  • Gossip-based algorithm for better connectivity for poor communication network
[95,104,117,120,123,124,125]
Energy Efficiency
  • Energy harvesting
  • Energy efficient (EE) LPWANs
  • Self-sustainability of machines due to limited energy
  • Power losses and energy conversions
  • EE MAC and cross-layer protocols
  • Technologies for green IoT
  • Intelligent energy management
  • Energy saving solutions for network softwarization
[95,96,107,126,127,128,129,130,131,132]
IoT Architecture and Protocol
  • Autonomous and incremental computation framework/architecture
  • Flexible and open architecture for heterogeneous devices
  • More intelligent self-organizing network (SON)
  • Efficient management of radio resources, service provisions, orchestration, etc.
  • Integration with AI
  • Traditional business model
  • Mobility management
  • Simple, light efficient security protocol
  • Efficient risk management
  • Efficient radio access protocol
  • Efficient tracking and protection management in cloud environment
[95,96,104,105,107,122,133,134]
Table 4. Comparison of different IoT Connectivity Standards.
Table 4. Comparison of different IoT Connectivity Standards.
StandardsRange of
Communication
Max. Data RateFrequency Spectrum UsedPower
Consumption
StandardizationModulationMultiplexing/
MAC Scheme
Security
Algorithm
NFC0.1 m [136]106–848 Kbps [136]13.56 MHz
[34,136]
Low (<40 mA) [136]ISO/IEC 14443, 18092 JIS X6319-4 [136]ASK, BPSK [136]TDMA [137]Encryption Cryptographic, Secure Channel, Key Agreements [136]
Bluetooth0–10 m [138]24 Mbps [138]2.4 Ghz [138]10 mw [12], 2.5–100 mW [139]IEEE
802.15.1 [140]
GFSK, DQPSK, 8DPSK [138,140]TDD [138], FHSS [140]
E0, E1, E21,
E22, E3,
56–128 bit [140]
BLE50 m [89], 70 m [136]1 Mbps [136,140]2.4 Ghz [140]Low
(<12.5 mA) [140]
IEEE
802.15.1 [140]
GFSK, FHSS Star [136]FHSS [140]AES-128 [140]
ANT<30 m [140]1 Mbps [140]2.4 Ghz [140]Low (<16 mA) [140]Proprietary [140]GFSK [140]TDMA [140]AES-128,
64 bit [140]
Zigbee10–300 m [138]20–250 Kbps [138]ISM Bands 2.4 GHz/915 MHz (USA)/868 MHz (EU) [138]Medium (1 mw-100 mw) [141]IEEE
802.15.4 [140]
BPSK (868–915 MHz) O-QPSK (2.4 GHz)
[138,140]
DSSS [89],
CSMA/CA TDMA + CSMA/CA [138]
AES-128 [138,140]
Zwave100 m [136],
0–30 m [138]
9–100 Kbps [136], 40 kbps [138]2.4 GHz 908.4 MHz (USA) 868.4 MHz (EU) [138]Medium (1 mW) [141]Proprietary [140], ITU G.9959 [142]FSK, GFSK [136,137,140]FHSS [89],
CSMA/CA [138]
AES-128 [138,140]
WiFi10–100 m [138]65 Mbps [138]ISM Bands
2.4–5 Ghz [138]
Low to Medium (32–200 mW) [138,139]IEEE 802.11 [143]BPSK, QPSK, COFDM, CCK, M-QAM [138]CSMA/CA + PCF [138]CCMP 128 [138]
LoRaWAN5–20 km [144]50 kbps [144]Unlicensed ISM bands (868 MHz in Europe, 915
MHz in North America and 433 MHz in Asia) [144]
Low
(10.5–28 mA) [145]
LoRa Alliance [143]LoRa CSS [143,146,147,148]Pure—ALOHA [146,147,149]AES-128 encryption [146,147]
NB-IoT1–10 km [144]204.7–234.8 Kbps [136], 200 kbps [144]Licensed LTE frequency
Bands [136,144]
Low (46 mA) [150]3GPP [136,143]QPSK [143], BPSK [147], GFSK, BPSK [136]OFDMA for downlink and SC-FDMA for uplink [151]3GPP 128–256 bit [136,144,146]
Sigfox10–40 km [136,144]100–600 bps [136], 100 bps [144]Unlicensed ISM bands (868 MHz in Europe, 915
MHz in North America and 433 MHz in Asia) [136,144]
Low (10–50 mA) [145]Sigfox [143]BPSK [92], DBPSK for Uplink and Gaussian frequency shift keying (GFSK) for downlink [136,147,148]R-FDMA [152,153]AES-128 encryption [147,148]
Table 5. A comparative analysis between 5G and 6G.
Table 5. A comparative analysis between 5G and 6G.
ParametersTechnological Standards
5G6G
Frequency BandSub 6 GHz, 30–300 GHz [155]Sub 6 GHz, 30–300 GHz, 0.3–3 THz [155]
Average Data Rate100 Mbps [155]1 Gbps [155]
Latency1 ms [155]<1 ms [155]
Mobility≥500 kmph [155,156]≥1000 kmph [155,156]
Maximum Channel Bandwidth1 GHz [156]100 GHz [156]
Connection Density 10 6   devices / km 2 [156] 10 7   devices / km 2   [156]
Reliability (Packet Error Rate) 10 5 [156] 10 9 [156]
Area Traffic Capacity 10   Mbps / m 2 [155,156] 10   Gbps / m 2 [155,156]
Service TypeseMBB, mMTC, uRLLC [155]mbRLLC, muRLLC, HCS, MPS [155]
MultiplexingCDMA [157,158], OFDM, GFDM [158], FBMC [159], Adaptive
Time–Frequency Multiplexing [160]
Smart OFDMA + Index Modulation, OMA [161], NOMA [161], OAM [162], Spatial Multiplexing [163]
Power ConsumptionLow to MediumUltra-low [164]
Downlink Spectral Efficiency30 bps/Hz [165]100 bps/Hz [165]
Energy Efficiency Gains in Comparison With 4G10× [165]1000× [165]
Network ArchitectureCentralized [155]Decentralized [155,166]
Table 6. A comparative analysis of some existing technologies.
Table 6. A comparative analysis of some existing technologies.
TechnologyAdvantagesDisadvantages
GPSLarge coverage areaInefficient for underground mines
GSMLarge coverage areaCommunication delay exists
RFIDNon line-of-sight
Communication, High Penetration, Compact Size
High maintenance of RFID tags, Low Security
RF TECHNOLOGYNon line-of-sight
Communication
High penetration loss/ Signal attenuation is very high
RADARAccurate and High PenetrationHigh CapEx and OpEx
ZIGBEELow Power Consumption, Low Latency Time, CheapLow Penetration, Poor non-interference
BLUETOOTHLow Power Consumption, Low Latency TimeHigh CapEx and OpEx, Small coverage area
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Pattnaik, S.K.; Samal, S.R.; Bandopadhaya, S.; Swain, K.; Choudhury, S.; Das, J.K.; Mihovska, A.; Poulkov, V. Future Wireless Communication Technology towards 6G IoT: An Application-Based Analysis of IoT in Real-Time Location Monitoring of Employees Inside Underground Mines by Using BLE. Sensors 2022, 22, 3438. https://doi.org/10.3390/s22093438

AMA Style

Pattnaik SK, Samal SR, Bandopadhaya S, Swain K, Choudhury S, Das JK, Mihovska A, Poulkov V. Future Wireless Communication Technology towards 6G IoT: An Application-Based Analysis of IoT in Real-Time Location Monitoring of Employees Inside Underground Mines by Using BLE. Sensors. 2022; 22(9):3438. https://doi.org/10.3390/s22093438

Chicago/Turabian Style

Pattnaik, Sushant Kumar, Soumya Ranjan Samal, Shuvabrata Bandopadhaya, Kaliprasanna Swain, Subhashree Choudhury, Jitendra Kumar Das, Albena Mihovska, and Vladimir Poulkov. 2022. "Future Wireless Communication Technology towards 6G IoT: An Application-Based Analysis of IoT in Real-Time Location Monitoring of Employees Inside Underground Mines by Using BLE" Sensors 22, no. 9: 3438. https://doi.org/10.3390/s22093438

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop