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Special Issue "Energy Harvesting for IoT Networks"

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

Deadline for manuscript submissions: closed (20 November 2021).

Special Issue Editor

Dr. Dimitrios Zorbas
E-Mail Website
Guest Editor
1. Tyndall National Institute, University College Cork, T12R5CP Cork, Ireland
2. School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan
Interests: Internet of Things; low power networks; energy harvesting

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) networks consist of hundreds of tiny wireless devices, with sensing capabilities that are usually powered by batteries. These devices not only have limited power resources, but very often they are deployed in inaccessible places, making their battery replacement a difficult and costly task. To alleviate the energy demands of IoT devices, the use of energy harvesting techniques has recently gained a lot of ground. Though tiny sometimes, the amount of power produced by ambient and natural resources is enough to extend the battery lifetime and, thus, reduce the operating costs.

Despite the recent big steps to develop efficient energy harvesting solutions, a number of challenges still exist related to the miniaturisation of harvesters, their effectiveness on a broad range of applications, the optimisation of costs, and the long-term evaluation of the solutions.

This Special Issue aims to report topics on recent advances on energy harvesting to support wireless IoT networks. We are seeking both innovative works in unexplored and/or emerging topics on energy harvesting fundamentals, design, evaluation, and experimentation. We invite submissions on a wide range of topics related to IoT networks, including, but not limited to:

- Energy harvesting techniques for network longevity

- Smart sensing and energy trade-offs

- Network design with harvesting properties

- Energy-autonomous IoT networks

- Performance evaluation and experimentation

- Adaptive MAC layer protocols for energy harvesting

- Energy harvesting models based on machine learning

Dr. Dimitrios Zorbas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at 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 2200 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.


  • energy harvesting internet of things sensing networks

Published Papers (1 paper)

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Smart Energy Harvesting for Internet of Things Networks
Sensors 2021, 21(8), 2755; - 13 Apr 2021
Viewed by 681
In this article, we address the problem of prolonging the battery life of Internet of Things (IoT) nodes by introducing a smart energy harvesting framework for IoT networks supported by femtocell access points (FAPs) based on the principles of Contract Theory and Reinforcement [...] Read more.
In this article, we address the problem of prolonging the battery life of Internet of Things (IoT) nodes by introducing a smart energy harvesting framework for IoT networks supported by femtocell access points (FAPs) based on the principles of Contract Theory and Reinforcement Learning. Initially, the IoT nodes’ social and physical characteristics are identified and captured through the concept of IoT node types. Then, Contract Theory is adopted to capture the interactions among the FAPs, who provide personalized rewards, i.e., charging power, to the IoT nodes to incentivize them to invest their effort, i.e., transmission power, to report their data to the FAPs. The IoT nodes’ and FAPs’ contract-theoretic utility functions are formulated, following the network economic concept of the involved entities’ personalized profit. A contract-theoretic optimization problem is introduced to determine the optimal personalized contracts among each IoT node connected to a FAP, i.e., a pair of transmission and charging power, aiming to jointly guarantee the optimal satisfaction of all the involved entities in the examined IoT system. An artificial intelligent framework based on reinforcement learning is introduced to support the IoT nodes’ autonomous association to the most beneficial FAP in terms of long-term gained rewards. Finally, a detailed simulation and comparative results are presented to show the pure operation performance of the proposed framework, as well as its drawbacks and benefits, compared to other approaches. Our findings show that the personalized contracts offered to the IoT nodes outperform by a factor of four compared to an agnostic type approach in terms of the achieved IoT system’s social welfare. Full article
(This article belongs to the Special Issue Energy Harvesting for IoT Networks)
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