Applications of Wireless Sensor Networks and Internet of Things—2nd Edition

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 1684

Special Issue Editors


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Guest Editor
Department of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, Greece
Interests: embedded systems; wireless sensor networks with IoT applications in smart grids; smart cities; internet of energy; LPWAN technologies; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, Greece
Interests: wireless sensors networks; embedded systems; wearables; sensors; renewable resources systems; automotive; autonomous vehicles design; internet of things; data acquisition; cyber–physical systems; autonomous vehicles; hardware design; smart agriculture; energy management
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
microSENSES Laboratory, Department of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, Greece
Interests: algorithmic study of wireless sensor networks in terms of routing protocols; energy efficiency; congestion avoidance; coverage maximization; multiobjective optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rise of the Internet of Things (IoT) has been rapid. Indeed, it is estimated that by 2030 over 125 billion devices will be included within the IoT sphere. IoT applications are becoming increasingly popular in the context of a variety of residential, industrial, and commercial sectors. Application areas/verticals vary between sensor networks for smart city infrastructure, smart home or building management, precision agriculture and forest protection and monitoring, private and industrial transportation, the Internet of Energy, etc. In this context, low-power, wide-area networks (LPWAN) technologies have to offer numerous solutions, while 5G mobile communications enable a massive IoT area and promise a very high density for a wide variety of application scenarios. However, the requirements for massive IoT applications with cellular IoT technologies in licensed spectrum bands, such as LTE IoT extensions NB -IoT and eMTC, have shown that the maximum scalability is limited. As an alternative to licensed technologies, LPWANs operating in unlicensed spectrum bands enable simple, low-cost network operation independently of commercial network operators. With this approach, the various IoT service providers are responsible for planning and operating the network with an unknown number of LPWAN network operators and subscribers in the unlicensed spectrum bands, resulting in questions about the performance of these deployments.

This Special Issue intends to bring together contributions from researchers in various scientific areas in order to define the importance of various critical components in the design and deployment of IoT applications based on wireless sensor networking technologies. We welcome original research papers, reviews, successful case studies, and high-quality and novel opinion papers on “Applications of Wireless Sensor Networks and Internet of Things.”

Prof. Dr. Panagiotis Papageorgas
Dr. Dimitrios Piromalis
Prof. Dr. Dionisis Kandris
Guest Editors

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Keywords

  • IoT
  • wireless sensor networks
  • LPWAN
  • NBIoT
  • sigfox
  • LoRaWAN
  • wireless node
  • smart cities
  • smart home
  • precision agriculture
  • WSNs in industry
  • WSNs in healthcare
  • environmental monitoring

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Published Papers (1 paper)

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Research

14 pages, 1933 KiB  
Article
Deep Reinforcement Learning for UAV-Based SDWSN Data Collection
by Pejman A. Karegar, Duaa Zuhair Al-Hamid and Peter Han Joo Chong
Future Internet 2024, 16(11), 398; https://doi.org/10.3390/fi16110398 - 30 Oct 2024
Cited by 1 | Viewed by 1248
Abstract
Recent advancements in Unmanned Aerial Vehicle (UAV) technology have made them effective platforms for data capture in applications like environmental monitoring. UAVs, acting as mobile data ferries, can significantly improve ground network performance by involving ground network representatives in data collection. These representatives [...] Read more.
Recent advancements in Unmanned Aerial Vehicle (UAV) technology have made them effective platforms for data capture in applications like environmental monitoring. UAVs, acting as mobile data ferries, can significantly improve ground network performance by involving ground network representatives in data collection. These representatives communicate opportunistically with accessible UAVs. Emerging technologies such as Software Defined Wireless Sensor Networks (SDWSN), wherein the role/function of sensor nodes is defined via software, can offer a flexible operation for UAV data-gathering approaches. In this paper, we introduce the “UAV Fuzzy Travel Path”, a novel approach that utilizes Deep Reinforcement Learning (DRL) algorithms, which is a subfield of machine learning, for optimal UAV trajectory planning. The approach also involves the integration between UAV and SDWSN wherein nodes acting as gateways (GWs) receive data from the flexibly formulated group members via software definition. A UAV is then dispatched to capture data from GWs along a planned trajectory within a fuzzy span. Our dual objectives are to minimize the total energy consumption of the UAV system during each data collection round and to enhance the communication bit rate on the UAV-Ground connectivity. We formulate this problem as a constrained combinatorial optimization problem, jointly planning the UAV path with improved communication performance. To tackle the NP-hard nature of this problem, we propose a novel DRL technique based on Deep Q-Learning. By learning from UAV path policy experiences, our approach efficiently reduces energy consumption while maximizing packet delivery. Full article
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