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Special Issue "Intelligent Wireless Technologies for Future Sensor Networks"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editor

Prof. Hyung Seok Kim
Website
Guest Editor
Department of Information and Communication Engineering, Sejong University, Seoul, South Korea
Interests: wireless communication; sensor network; cognitive radio; tactile internet; machine learning and IoT
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Dynamically and efficiently allocating resources to meet the need for diversity in various services gives rise to intelligent wireless technologies. They enable the wireless systems to perceive and estimate the available resources and autonomously adapt to the wireless environment, and to reconfigure itself to maximize resource utilization.

As a promising machine learning, deep learning (DL) is becoming a powerful method to add intelligence to wireless networks. Cognitive technology, covering spectrum sensing, and access approaches can enhance spectrum utilization and reduce energy consumption. The perception capability and reconfigurability are essential elements for the cognitive technology and machine learning techniques provide effectiveness for adaptation in wireless communication.

This Special Issue anticipates state-of-the-art technologies for the cognitive technology and machine learning techniques for the future wireless sensor networks, covering new research results with a wide range of ingredients within the intelligent wireless technologies for future sensor networks.

Potential topics include but are not limited to the following:

  • Ultra-reliable and low-latency sensor networks
  • AI or machine learning-based intelligent sensor networks
  • Applications and protocols using optical wireless communication
  • Energy-efficient system and network design for various applications
  • Multiple access schemes considering energy consumption and delay constraints
  • Cooperative haptic sensor networks for the tactile Internet
  • Sensor network optimization using machine learning and game-theoretic approaches
  • Sensor Networks in 5G/B5G networks (URLLC and mMTC)
  • Cognitive Radio Sensor Networks
  • Security and privacy for sensor networks
  • Sensors and hardware for sensor networks
  • Resource management, edge computing, and network slicing for efficient sensor networks
  • Sensor networks for mission-critical applications
  • Other related issues.

Prof. Hyung Seok Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sensor networks
  • Artificial intelligence
  • Machine learning
  • Cognitive radio
  • Tactile internet
  • mMTC
  • URLLC

Published Papers (6 papers)

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Research

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Open AccessArticle
Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns
Sensors 2020, 20(15), 4158; https://doi.org/10.3390/s20154158 - 26 Jul 2020
Abstract
The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based [...] Read more.
The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms. Full article
(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)
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Open AccessArticle
An Efficient Superframe Structure with Optimal Bandwidth Utilization and Reduced Delay for Internet of Things Based Wireless Sensor Networks
Sensors 2020, 20(7), 1971; https://doi.org/10.3390/s20071971 - 01 Apr 2020
Abstract
Internet of Things (IoT) is a promising technology that uses wireless sensor networks to enable data collection, monitoring, and transmission from the physical devices to the Internet. Due to its potential large scale usage, efficient routing and Medium Access Control (MAC) techniques are [...] Read more.
Internet of Things (IoT) is a promising technology that uses wireless sensor networks to enable data collection, monitoring, and transmission from the physical devices to the Internet. Due to its potential large scale usage, efficient routing and Medium Access Control (MAC) techniques are vital to meet various application requirements. Most of the IoT applications need low data rate and low powered wireless transmissions and IEEE 802.15.4 standard is mostly used in this regard which offers superframe structure at the MAC layer. However, for IoT applications where nodes have adaptive data traffic, the standard has some limitations such as bandwidth wastage and latency. In this paper, a new superframe structure is proposed that is backward compatible with the existing parameters of the standard. The proposed superframe overcomes limitations of the standard by fine-tuning its superframe structure and squeezing the size of its contention-free slots. Thus, the proposed superframe adjusts its duty cycle according to the traffic requirements and accommodates more nodes in a superframe structure. The analytical results show that our proposed superframe structure has almost 50% less delay, accommodate more nodes and has better link utilization in a superframe as compared to the IEEE 802.15.4 standard. Full article
(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)
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Open AccessArticle
Edge4TSC: Binary Distribution Tree-Enabled Time Series Classification in Edge Environment
Sensors 2020, 20(7), 1908; https://doi.org/10.3390/s20071908 - 30 Mar 2020
Abstract
In the past decade, time series data have been generated from various fields at a rapid speed, which offers a huge opportunity for mining valuable knowledge. As a typical task of time series mining, Time Series Classification (TSC) has attracted lots of attention [...] Read more.
In the past decade, time series data have been generated from various fields at a rapid speed, which offers a huge opportunity for mining valuable knowledge. As a typical task of time series mining, Time Series Classification (TSC) has attracted lots of attention from both researchers and domain experts due to its broad applications ranging from human activity recognition to smart city governance. Specifically, there is an increasing requirement for performing classification tasks on diverse types of time series data in a timely manner without costly hand-crafting feature engineering. Therefore, in this paper, we propose a framework named Edge4TSC that allows time series to be processed in the edge environment, so that the classification results can be instantly returned to the end-users. Meanwhile, to get rid of the costly hand-crafting feature engineering process, deep learning techniques are applied for automatic feature extraction, which shows competitive or even superior performance compared to state-of-the-art TSC solutions. However, because time series presents complex patterns, even deep learning models are not capable of achieving satisfactory classification accuracy, which motivated us to explore new time series representation methods to help classifiers further improve the classification accuracy. In the proposed framework Edge4TSC, by building the binary distribution tree, a new time series representation method was designed for addressing the classification accuracy concern in TSC tasks. By conducting comprehensive experiments on six challenging time series datasets in the edge environment, the potential of the proposed framework for its generalization ability and classification accuracy improvement is firmly validated with a number of helpful insights. Full article
(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)
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Open AccessArticle
Traffic Offloading in Multicast Device-to-Device Cellular Networks: A Combinatorial Auction-Based Matching Algorithm
Sensors 2020, 20(4), 1128; https://doi.org/10.3390/s20041128 - 19 Feb 2020
Cited by 1
Abstract
In the last few years, multicast device-to-device (D2D) cellular networks has become a highly attractive area of research. However, a particularly challenging class of issues in this area is data traffic, which increases due to increase in video and audio streaming applications. Therefore, [...] Read more.
In the last few years, multicast device-to-device (D2D) cellular networks has become a highly attractive area of research. However, a particularly challenging class of issues in this area is data traffic, which increases due to increase in video and audio streaming applications. Therefore, there is need for smart spectrum management policies. In this paper, we consider a fractional frequency reuse (FFR) technique which divides the whole spectrum into multiple sections and allows reusing of spectrum resources between the conventional cellular users and multicast D2D users in a non-orthogonal scenario. Since conventional cellular users and multicast D2D users shared same resources simultaneously, they generate severe data traffic and high communication overhead. To overcome these issues, in this paper we propose Lagrange relaxation technique to solve the non-convex problem and combinatorial auction-based matching algorithm to select the most desirable resource reuse partners by fulfilling the quality of service (QoS) requirements for both the conventional cellular users and multicast D2D users. Then, we formulate an optimization problem to maximize the overall system performance with least computational complexity. We demonstrate that our method can exploit a higher data rate, spectrum efficiency, traffic offload rate, coverage probability, and lower computational complexity. Full article
(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)
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Review

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Open AccessReview
Resource Management in Cloud Radio Access Network: Conventional and New Approaches
Sensors 2020, 20(9), 2708; https://doi.org/10.3390/s20092708 - 09 May 2020
Abstract
Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs. C-RAN is a practical solution to the strict energy-constrained wireless sensor nodes, often found in Internet of Things [...] Read more.
Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs. C-RAN is a practical solution to the strict energy-constrained wireless sensor nodes, often found in Internet of Things (IoT) applications. Although this architecture can provide energy efficiency and reduce cost, it is a challenging task in C-RAN to utilize the resources efficiently, considering the dynamic real-time environment. Several research works have proposed different methodologies for effective resource management in C-RAN. This study performs a comprehensive survey on the state-of-the-art resource management techniques that have been proposed recently for this architecture. The resource management techniques are categorized into computational resource management (CRM) and radio resource management (RRM) techniques. Then both of the techniques are further classified and analyzed based on the strategies used in the studies. Remote radio head (RRH) clustering schemes used in CRM techniques are discussed extensively. In this research work, the investigated performance metrics and their validation techniques are critically analyzed. Moreover, other important challenges and open research issues for efficient resource management in C-RAN are highlighted to provide future research direction. Full article
(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)
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Open AccessReview
Advancing the State of the Fog Computing to Enable 5G Network Technologies
Sensors 2020, 20(6), 1754; https://doi.org/10.3390/s20061754 - 21 Mar 2020
Cited by 2
Abstract
Fog Computing (FC) is promising to Internet architecture for the emerging of modern technological approaches such as Fifth Generation (5G) networks and the Internet of Things (IoT). These are the advanced technologies that enable Internet architecture to enhance the data dissemination services based [...] Read more.
Fog Computing (FC) is promising to Internet architecture for the emerging of modern technological approaches such as Fifth Generation (5G) networks and the Internet of Things (IoT). These are the advanced technologies that enable Internet architecture to enhance the data dissemination services based on numerous sensors generating continuous sensory information. It is tough for the current Internet architecture to meet up with the growing demands of the users for such a massive amount of information. Therefore, it needs to adopt modern technologies for efficient data dissemination services across the Internet. Thus, the FC and 5G are updating the data transmission using new technological approaches that are intelligently processing data to provide enhanced communications. This study proposes necessary measures to boost the growth of FC to 5G network usage. It is done by taking an extensive review of how 5G operates as well as studying its taxonomy, the idea of IoT, reviewed projects on IoT applicability, comparison of computing technologies, and the importance of FC. Moreover, it elaborates dynamic issues of computing network technologies, and information is provided on how to remedy these for future recommendations in the field of research and computing network technologies. This paper heavily focuses on the applications of FC as an enabler to the 5G network by identifying the necessary services and network-oriented features that are needed to be used in the place for an improved future enterprise network technology. Full article
(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)
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