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Special Issues
Artificial Intelligence of Things and Next Generation Networking
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Special Issue "Artificial Intelligence of Things and Next Generation Networking"

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Big Data, Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 5219

Special Issue Editors

Dr. Jawad Rasheed

E-Mail Website
Guest Editor
Department of Software Engineering, Nişantasi University, Istanbul, Turkey
Interests: artificial intelligence; image processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Adnan M. Abu-Mahfouz

E-Mail Website
Guest Editor
Council for Scientific and Industrial Research, Pretoria 0184, South Africa
Interests: wireless sensor and actuator networks; low-power wide-area networks; software-defined wireless sensor networks; cognitive radio; network security; network management; sensor/actuator node development
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Asadullah Shaikh

E-Mail Website
Guest Editor
College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Interests: model verification; Service Oriented Architecture (SOA); Model Driven Development (MDD)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Amir Masoud Rahmani

E-Mail Website
Guest Editor
Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
Interests: internet of things, wireless networks; wearable computing; fog/cloud computing; big data
Special Issues, Collections and Topics in MDPI journals
Dr. Gerhard P. Hancke
E-Mail Website
Guest Editor
Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
Interests: information and system security; Industrial Internet-of-Things

Special Issue Information

Dear Colleagues,

Intelligent computing (IC) is based on the synergistic combination of advances in the Internet of Things (IoT), Sensor-based technologies, Big Data analytics, Mobile and Cloud Computing, Artificial Intelligence (AI), and Smart Cyber-Physical Systems. In recent years, AI and IoT are both accomplished enormous technological advances, thus giving rise to a phenomenon referreto d as Artificial Intelligence of Things (AIoT). Such as Federated Learning (FL), a newly refined AI branch, developed upon distributed data environment to train and test smart devices, and empowers edge layer devices to collectively train and modernize the mutual learning model with the unlocked potential of securing sensitive data and heterogeneity.

In today’s need of the hour, AI-enabled IoT technology has certain enviable dominance in terms ofenergy-efficient efficient information processing, and shows great promises of extremely enhanced data security and protection when enabled in Next Generation Networking (NGN) including beyond 5G and 6G. With the further advancement in AIoT, the research spotlight is moving towards the development of novel applications and intelligent networking services by effectively integrating and unifying various technologies. Despite various unquestionable benefits, the recommended solutions are infeasible and impractical for large-scale advanced systems and sophisticated applications and are often exposed to data breaches and cyberattacks. Thus, there are enormous amounts of communication and computing challenges in designing robust and scalable systems that can administer massive volumes of generated data.

By equipping smart communication and computing processing, IC in an NGN environment brings the opportunity to utilize exponentially growing data and handle unresolved technical shortcomings of intelligent networking applications. Therefore, synergizing AIoT with NGN will bring innovation and transform intelligent networking services and applications in various domains. It will revolutionize industry 4.0, smart healthcare, smart banking and shopping, social computing, energy trading, and cyber-physical smart sensory systems. The integration and unification of these technologies are relatively under-studied areas of research; therefore, this Special Issue aims to collect recent advances and rapidly evolving state-of-the-art approaches to AIoT-based applications focused on Sensor and Actuator Networks. The objective is to broader the understanding of several aspects of IC in NGN.

Topics for this issue include, but are not constrained to the following:

  • AI in large-scale IoT
  • AI in autonomous vehicular networks
  • Integration of AI and IoT in Beyond 5G and 6G network architectures
  • Unmanned Aerial Vehicle (UAV) aided AIoT
  • AIoT with lightweight computation
  • AIoT-based service and applications for vehicular clouds
  • AI for future internet architectures
  • Security and privacy issues in AIoT and NGN
  • Scalable AIoT for intelligent networking services
  • Applications of AIoT in large-scale intelligent networking services
  • AI for emerging networks and NGN
  • AI for IoT healthcare systems
  • Attacks and Defenses for machine learning in IoT and NGN
  • Privacy-preserving AI framework in IoT and NGN

Dr. Jawad Rasheed
Prof. Dr. Adnan M. Abu-Mahfouz
Prof. Dr. Asadullah Shaikh
Prof. Amir Masoud Rahmani
Dr. Gerhard P. Hancke
Guest Editors

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 submissions that pass pre-check are 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. Journal of Sensor and Actuator Networks 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 1600 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

  • artificial intelligence
  • Internet of Things
  • beyond 5G networks
  • autonomous vehicles
  • next generation networks

Published Papers (3 papers)

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Article
Machine-Learning-Based Ground-Level Mobile Network Coverage Prediction Using UAV Measurements
by
Naser Tarhuni
,
Ibtihal Al Saadi
,
Hafiz M. Asif
,
Mostefa Mesbah
,
Omer Eldirdiry
and
Abdulnasir Hossen
J. Sens. Actuator Netw. 2023, 12(3), 44; https://doi.org/10.3390/jsan12030044 - 26 May 2023
Viewed by 864
Abstract
Future mobile network operators and telecommunications authorities aim to provide reliable network coverage. Signal strength, normally assessed using standard drive tests over targeted areas, is an important factor strongly linked to user satisfaction. Drive tests are, however, time-consuming, expensive, and can be dangerous [...] Read more.
Future mobile network operators and telecommunications authorities aim to provide reliable network coverage. Signal strength, normally assessed using standard drive tests over targeted areas, is an important factor strongly linked to user satisfaction. Drive tests are, however, time-consuming, expensive, and can be dangerous in hard-to-reach areas. An alternative safe method involves using drones or unmanned aerial vehicles (UAVs). The objective of this study was to use a drone to measure signal strength at discrete points a few meters above the ground and an artificial neural network (ANN) for processing the measured data and predicting signal strength at ground level. The drone was equipped with low-cost data logging equipment. The ANN was also used to classify specific ground locations in terms of signal coverage into poor, fair, good, and excellent. The data used in training and testing the ANN were collected by a measurement unit attached to a drone in different areas of Sultan Qaboos University campus in Muscat, Oman. A total of 12 locations with different topologies were scanned. The proposed method achieved an accuracy of 97% in predicting the ground level coverage based on measurements taken at higher altitudes. In addition, the performance of the ANN in predicting signal strength at ground level was evaluated using several test scenarios, achieving less than 3% mean square error (MSE). Additionally, data taken at different angles with respect to the vertical were also tested, and the prediction MSE was found to be less than approximately 3% for an angle of 68 degrees. Additionally, outdoor measurements were used to predict indoor coverage with an MSE of less than approximately 6%. Furthermore, in an attempt to find a globally accurate ANN module for the targeted area, all zones’ measurements were cross-tested on ANN modules trained for different zones. It was evaluated that, within the tested scenarios, an MSE of less than approximately 10% can be achieved with an ANN module trained on data from only one zone. Full article
(This article belongs to the Special Issue Artificial Intelligence of Things and Next Generation Networking)
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Article
Enabling LPWANs for Coexistence and Diverse IoT Applications in Smart Cities Using Lightweight Heterogenous Multihomed Network Model
by
Emmanuel Utochukwu Ogbodo
,
Adnan M. Abu-Mahfouz
and
Anish M. Kurien
J. Sens. Actuator Netw. 2022, 11(4), 87; https://doi.org/10.3390/jsan11040087 - 19 Dec 2022
Cited by 3 | Viewed by 1756
Abstract
Smart cities have been envisioned to provide smartness in managing internet of things (IoT) application domains, such as transport and mobility, health care, natural resources, electricity and energy, homes and buildings, commerce and retail, society and workplace, industry, agriculture, and the environment. The [...] Read more.
Smart cities have been envisioned to provide smartness in managing internet of things (IoT) application domains, such as transport and mobility, health care, natural resources, electricity and energy, homes and buildings, commerce and retail, society and workplace, industry, agriculture, and the environment. The growth trajectory in usage of these IoT domains has led to a heterogeneous dense network in a smart city environment. The heterogeneous dense network in smart cities has led to challenges, such as difficulties in the management of LPWAN coexistence, interference, spectrum insufficiency, QoS, and scalability issues. The existing LPWAN technologies cannot support the heterogeneous dense network challenges in smart cities. Further, it cannot support diverse IoT, including medium- to high-bandwidth applications, due to the power, complexity, and resource constraints of the LPWAN devices. Hence, this paper addresses high data rate IoT applications and heterogeneous dense networks. This paper proposes a lightweight heterogenous multihomed network (LHM-N) model for diverse smart city applications that will address dense heterogeneity network challenges in a smart city. The work aims to advocate and integrate a manageable license-free LPWAN that will coexist with 5G private and public cellular networks in the LHM-N model. This will help to provide a cost-effective solution model in a heterogeneous dense smart city environment. Further, a secured lightweight energy-efficient packet-size forwarding engine (PSFE) algorithm is presented using the discrete event simulation (DES) methodological approach in MATLAB for complexity evaluation. In addition, a 5G reduced capability (RedCap) IoT device is integrated into the (LHM-N) model to support smart city. Finally, the results show that the LHM-N model outperforms the conventional quadrature amplitude modulation (QAM) protocol scheme in terms of error rate, latency, and data throughput with reduced energy costs for medium- to high-bandwidth industrial IoT applications. This validates the suitability of the LHM-N model for high data rate IoT applications. Full article
(This article belongs to the Special Issue Artificial Intelligence of Things and Next Generation Networking)
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Article
A Fuzzy-Logic Based Adaptive Data Rate Scheme for Energy-Efficient LoRaWAN Communication
by
Rachel Kufakunesu
,
Gerhard Hancke
and
Adnan Abu-Mahfouz
J. Sens. Actuator Netw. 2022, 11(4), 65; https://doi.org/10.3390/jsan11040065 - 11 Oct 2022
Cited by 3 | Viewed by 1515
Abstract
Long Range Wide Area Network (LoRaWAN) technology is rapidly expanding as a technology with long distance connectivity, low power consumption, low data rates and a large number of end devices (EDs) that connect to the Internet of Things (IoT) network. Due to the [...] Read more.
Long Range Wide Area Network (LoRaWAN) technology is rapidly expanding as a technology with long distance connectivity, low power consumption, low data rates and a large number of end devices (EDs) that connect to the Internet of Things (IoT) network. Due to the heterogeneity of several applications with varying Quality of Service (QoS) requirements, energy is expended as the EDs communicate with applications. The LoRaWAN Adaptive Data Rate (ADR) manages the resource allocation to optimize energy efficiency. The performance of the ADR algorithm gradually deteriorates in dense networks and efforts have been made in various studies to improve the algorithm’s performance. In this paper, we propose a fuzzy-logic based adaptive data rate (FL-ADR) scheme for energy efficient LoRaWAN communication. The scheme is implemented on the network server (NS), which receives sensor data from the EDs via the gateway (GW) node and computes network parameters (such as the spreading factor and transmission power) to optimize the energy consumption of the EDs in the network. The performance of the algorithm is evaluated in ns-3 using a multi-gateway LoRa network with EDs sending data packets at various intervals. Our simulation results are analyzed and compared to the traditional ADR and the ns-3 ADR. The proposed FL-ADR outperforms the traditional ADR algorithm and the ns-3 ADR minimizing the interference rate and energy consumption. Full article
(This article belongs to the Special Issue Artificial Intelligence of Things and Next Generation Networking)
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