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Energies
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Energy Efficiency in IoT and Wireless Sensor Networks
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Energy Efficiency in IoT and Wireless Sensor Networks

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 2174

Special Issue Editor

Dr. Deepak Mishra

E-Mail Website
Guest Editor
School of Electrical Engineering and Telecommunications, University of New South Wales (UNSW) Sydney, Sydney, NSW 2052, Australia
Interests: communication systems; signal processing; wireless networks

Special Issue Information

Dear Colleagues,

Recent advancements in integrated circuit technology have led to the ubiquitous deployment of new low-cost, tiny sensors and actuators in Internet of Things (IoT) applications like smart homes, smart cities, healthcare, wearables, transportation, security, surveillance, critical infrastructure security, and food safety. To handle the limited onboard energy resources and enable the perpetual network operation of IoT and wireless sensor networks (WSNs), these sensor nodes need to employ energy-efficient communication protocols, and it is necessary to develop optimal energy management policies as well as energy-aware self-organization mechanisms.

On another front, with the growth of these low-cost and high-end wireless devices in the IoT, Quality of Service (QoS)-aware connectivity and energy sustainability have become the two major bottlenecks to be overcome in order to meet the ever-increasing demand for pervasive sustainable computing in advanced wireless sensor networks. As these IoT applications have even stricter QoS requirements, they consume extensive energy, causing very low battery life for high-end mobile devices and eventually contributing to user dissatisfaction. To tackle these problems of pervasive computing and energy sustainability, novel green designs for smart wireless energy harvesting (EH) protocols, both from signal processing and telecommunication networking perspectives, using artificial intelligence tools, are required to allow self-organizability in IoT nodes. Energy-efficient IoT and WSN can reduce the growing environmental footprint of computer networks and information and communications technology (ICT) infrastructure due to their significant energy consumption, heavy burden on electric grids, and greenhouse gas emission. So, there has been an increasing need for energy-efficient cooperative multi-antenna transmission schemes to enhance the achievable joint spatial spectrum and energy efficiency performance. More specifically, noting the stringent EH demands, optimal novel energy cooperation techniques such as energy relaying, energy sharing, energy borrowing, energy scavenging, and, more importantly, energy trading, are being explored. Novel massive array processing techniques for supporting energy beamforming, spatial multiplexing, beam alignment, and selection for hybrid architectures are also gaining interest with the aim of minimizing energy dissipation losses. Further, these energy demands of IoT and WSNs can be reduced through efficient cross-layer protocols exploiting energy-aware routing algorithms and power-saving modes at the sensor nodes, in addition to exploring advanced distributed data storage and dissemination to save energy.

This Special Issue aims to bring together the recent developments and original contributions related to Energy Efficiency in IoT and WSNs.

Dr. Deepak Mishra
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 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. Energies 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 2600 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

  • joint PHY–MAC layer optimization for energy efficiency
  • low-power, distributed data processing on sensors
  • energy harvesting, storage, recycling, and wireless power transfer
  • green energy and power systems for smart grids and smart cities
  • green social networks and societal applications
  • energy efficiency in aerial/UAV communication and cognitive radio networks
  • applications of blockchain in energy management and trading
  • energy-efficient beamforming, modulation, and coding techniques for green IoT and WSNs
  • context awareness and signaling for energy-saving strategies for IoT and WSNs
  • advances in green software, hardware, sensors, and devices for smart cities
  • antenna design and transmission technologies for reducing energy consumption
  • energy-efficient public health solutions based on 5G networks
  • energy-efficient automation and industrial communications
  • techniques for ensuring Quality of Service (QoS) in energy-efficient IoT and WSNs
  • cross-layer design and optimization for green communications and computing
  • economics and pricing for green systems and services
  • energy footprint evaluation in networks and computing architectures
  • experimental test beds and results for green communications and computing in IoT and WSNs
  • field trials and deployment experiences of sustainable IoT and WSNs
  • green communications via backscatter and meta-surfaces
  • green optical communications, switching and networking
  • renewable power at the service of data centers, edge, fog and cloud computing
  • security and privacy in energy-efficient IoT and WSNs
  • standardization, policy and regulation for green IoT and WSNs
  • carbon-neutral or zero-emission base stations, communication devices, and networks

Published Papers (2 papers)

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Research

16 pages, 903 KiB  
Article
Smart Energy Borrowing and Relaying in Wireless-Powered Networks: A Deep Reinforcement Learning Approach
by Abhishek Mondal, Md. Sarfraz Alam, Deepak Mishra and Ganesh Prasad
Energies 2023, 16(21), 7433; https://doi.org/10.3390/en16217433 - 03 Nov 2023
Viewed by 595
Abstract
Wireless energy harvesting (EH) communication has long been considered a sustainable networking solution. However, it has been limited in efficiency, which has been a major obstacle. Recently, strategies such as energy relaying and borrowing have been explored to overcome these difficulties and provide [...] Read more.
Wireless energy harvesting (EH) communication has long been considered a sustainable networking solution. However, it has been limited in efficiency, which has been a major obstacle. Recently, strategies such as energy relaying and borrowing have been explored to overcome these difficulties and provide long-range wireless sensor connectivity. In this article, we examine the reliability of a wireless-powered communication network by maximizing the net bit rate. To accomplish our goal, we focus on enhancing the performance of hybrid access points and information sources by optimizing their transmit power. Additionally, we aim to maximize the use of harvested energy, by using energy-harvesting relays for both information transmission and energy relaying. However, this optimization problem is complex, as it involves non-convex variables and requires combinatorial relay selection indicator optimization for decode and forward (DF) relaying. To simplify this problem, we utilize the Markov decision process and deep reinforcement learning framework based on the deep deterministic policy gradient algorithm. This approach enables us to tackle this non-tractable problem, which conventional convex optimization techniques would have difficulty solving in complex problem environments. The proposed algorithm significantly improved the end-to-end net bit rate of the smart energy borrowing and relaying EH system by 13.22%, 27.57%, and 14.12% compared to the benchmark algorithm based on borrowing energy with an adaptive reward for Quadrature Phase Shift Keying, 8-PSK, and 16-Quadrature amplitude modulation schemes, respectively. Full article
(This article belongs to the Special Issue Energy Efficiency in IoT and Wireless Sensor Networks)
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20 pages, 5588 KiB  
Article
A Long-Distance First Matching Algorithm for Charging Scheduling in Wireless Rechargeable Sensor Networks
by Jing-Jing Chen, Chang Wu Yu and Wen Liu
Energies 2023, 16(18), 6463; https://doi.org/10.3390/en16186463 - 07 Sep 2023
Cited by 1 | Viewed by 739
Abstract
In large wireless rechargeable sensor networks (WRSNs), the limited battery capacity of sensor nodes and finite network lifetime are commonly considered as performance bottlenecks. Previous works have employed wireless mobile vehicles (vehicles) to charge sensor nodes (nodes), but they face limitations in terms [...] Read more.
In large wireless rechargeable sensor networks (WRSNs), the limited battery capacity of sensor nodes and finite network lifetime are commonly considered as performance bottlenecks. Previous works have employed wireless mobile vehicles (vehicles) to charge sensor nodes (nodes), but they face limitations in terms of low speed and offroad terrain. The rapid development of wireless charging drones (drones) brings a new perspective on charging nodes; nevertheless, their use is limited by small capacity batteries and cannot cover large regions alone. Most existing works consider the charging of nodes only with vehicles or drones. However, these solutions may not be robust enough, as some nodes’ energy will have run out before vehicles’ or drones’ arrival. Considering the merits and demerits of vehicles and drones comprehensively, we propose a novel WRSN model whose charging system integrates one vehicle, multiple drones and one base station together. Moreover, a charging strategy named long-distance first matching (LDFM) algorithm to schedule the vehicle and multiple drones collaboratively is proposed. In the proposed scheme, drones that are carried by the vehicle start from the base station. According to distance and deadline of nodes with charging requests, LDFM prioritizes nodes with the longest matching distance for allocation to drones. As a result, the proposed scheme aims to minimize the moving distance of charging scheduling of the WCV on premise of satisfying charging requests with the cooperation of WCVs and drones. Our proposed scheme is thus designed to maximize the efficiency of drone usage and shares the charging burden of the vehicle, which makes WRSNs work well in large and complex terrain regions, such as a hill, natural disaster areas or war zones. Simulation results confirm that our proposed scheme outperforms hybrid scheme in previous work with respect to total number of charging nodes and network energy consumption. Especially with heavy traffic load, the proposed scheme can charge more than 10% additional nodes compared to the hybrid. Moreover, the proposed scheme achieves a reduction of over 50% in the moving distance compared to the hybrid. Full article
(This article belongs to the Special Issue Energy Efficiency in IoT and Wireless Sensor Networks)
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