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Special Issue "Intelligent Energy Management for Wireless Sensor Networks and Internet of Things"

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

Deadline for manuscript submissions: closed (31 July 2020).

Special Issue Editors

Prof. Dr. Mario Collotta
Website
Guest Editor
Dr. Renato Ferrero
Website
Guest Editor
Dipartimento di Automatica e Informatica, Politecnico di Torino, 10129 Torino, Italy
Interests: ubiquitous computing; wireless sensor networks; RFID systems
Special Issues and Collections in MDPI journals
Dr. Carles Gomez
Website
Guest Editor
Associate Professor, Department of Network Engineering, Universitat Politecnica de Catalunya, 08860 Castelldefels, Spain
Interests: low-power wireless technologies; IoT; WSNs; BLE; LPWAN; 6LoWPAN; 6Lo; IP-based protocols for constrained-node networks
Special Issues and Collections in MDPI journals

Special Issue Information

Dear colleagues,

Today, the goal of satisfying energy requirements is crucial in a number of trends, such as the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). The design and implementation of energy-aware approaches is a challenging open issue in many applications.

The integration of new technologies and methods into smart and expert systems is highly beneficial for limiting the power consumption of devices. In this way, it would be possible to develop applications that rapidly adapt and respond both to changes in markets’ demands for high-quality products and to the satisfaction of user experience. In fact, smart energy-aware systems lie at the core of Smart Cities, Industry 4.0, medical applications, Body Area Networks, and Smart Homes, with many other possibilities.

A variety of recent advanced technologies and approaches play important roles in intelligent energy management for WSNs and IoT by exploiting innovative technologies and solutions and/or optimization methods. They allow higher levels of adaptiveness and flexibility in energy-aware systems.

This Special Issue solicits high-quality unpublished work on recent advances in energy-aware smart systems in WSN and IoT scenarios. The main topics of interest include, but are not limited to, the following:

  • Green communication architectures and technologies;
  • Low-power wireless technologies: IEEE 802.15.4, Bluetooth Low Energy, LoRa/LoRaWAN, Sigfox, NB-IoT, etc.;
  • 5G and Beyond 5G low-power solutions for IoT;
  • Low-power IP-based solutions for IoT: 6LoWPAN/6Lo, 6TiSCH, IETF LPWAN, RPL, CoAP, HTTP, etc.;
  • Energy harvesting;
  • Ultralow power management;
  • Machine learning solutions in IoT devices;
  • Energy-aware routing protocols for WSNs;
  • Sustainable design and solutions for green automation systems;
  • System-on-chip vs. network-on-chip architectures in green automation systems;
  • Energy-efficient communications and management in medical automation applications;
  • Big data and data management in energy-aware scenarios;
  • Security and cyber-security vs. energy consumption requirements;
  • Real low-power IoT applications and implementations.

Dr. Davide Brunelli
Prof. Dr. Mario Collotta
Dr. Renato Ferrero
Dr. Carles Gomez
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 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.

Published Papers (6 papers)

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Research

Open AccessArticle
A Heuristic Angular Clustering Framework for Secured Statistical Data Aggregation in Sensor Networks
Sensors 2020, 20(17), 4937; https://doi.org/10.3390/s20174937 - 31 Aug 2020
Abstract
Clustering in wireless sensor networks plays a vital role in solving energy and scalability issues. Although multiple deployment structures and cluster shapes have been implemented, they sometimes fail to produce the expected outcomes owing to different geographical area shapes. This paper proposes a [...] Read more.
Clustering in wireless sensor networks plays a vital role in solving energy and scalability issues. Although multiple deployment structures and cluster shapes have been implemented, they sometimes fail to produce the expected outcomes owing to different geographical area shapes. This paper proposes a clustering algorithm with a complex deployment structure called radial-shaped clustering (RSC). The deployment structure is divided into multiple virtual concentric rings, and each ring is further divided into sectors called clusters. The node closest to the midpoint of each sector is selected as the cluster head. Each sector’s data are aggregated and forwarded to the sink node through angular inclination routing. We experimented and compared the proposed RSC performance against that of the existing fan-shaped clustering algorithm. Experimental results reveal that RSC outperforms the existing algorithm in scalability and network lifetime for large-scale sensor deployments. Full article
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Open AccessArticle
Paving the Way to Eco-Friendly IoT Antennas: Tencel-Based Ultra-Thin Compact Monopole and Its Applications to ZigBee
Sensors 2020, 20(13), 3658; https://doi.org/10.3390/s20133658 - 30 Jun 2020
Abstract
An ultrathin, compact ecofriendly antenna suitable for IoT applications around 2.45 GHz is achieved as a result of exploring the use of Tencel fabric for the antenna’s design. The botanical ecofriendly Tencel is electromagnetically characterized, in terms of relative dielectric permittivity and loss [...] Read more.
An ultrathin, compact ecofriendly antenna suitable for IoT applications around 2.45 GHz is achieved as a result of exploring the use of Tencel fabric for the antenna’s design. The botanical ecofriendly Tencel is electromagnetically characterized, in terms of relative dielectric permittivity and loss tangent, in the target IoT frequency band. To explore the suitability of the Tencel, a comparison is conducted with conventionally used RO3003, with similar relative dielectric permittivity, regarding the antenna dimensions and performance. In addition, the antenna robustness under bent conditions is also analyzed by measurement. To assess the relevance of this contribution, the ultrathin ecofriendly Tencel-based antenna is compared with recently published antennas for IoT in the same band and also, with commercial half-wave dipole by performing a range test on a ZigBee-based IoT testbed. Full article
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Open AccessArticle
Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT)
Sensors 2020, 20(12), 3521; https://doi.org/10.3390/s20123521 - 22 Jun 2020
Abstract
The central intelligence offered by Software Defined Networking (SDN) promise the smart and reliable reconfiguration which enables the scalability of dynamic enterprise networks. The decoupled forwarding plane and control plane of SDN infrastructure is a key feature that supports the SDN controller to [...] Read more.
The central intelligence offered by Software Defined Networking (SDN) promise the smart and reliable reconfiguration which enables the scalability of dynamic enterprise networks. The decoupled forwarding plane and control plane of SDN infrastructure is a key feature that supports the SDN controller to extract the physical network topology information at runtime to formulate network reconfigurations. This SDN-based network reconfiguration enables application-aware routing capability for Internet of Thing (IoT). However, these IoT enabled SDN-based routing protocols face some performance limitations in iterative reconfiguration process due to complete centralized path selection mechanism To this end, in this paper, we propose SDN-Based Application-aware Distributed adaptive Flow Iterative Reconfiguring (SADFIR) routing protocol. The proposed routing protocol enables the distributed SDN iterative solver controller to maintain the load-balancing between flow reconfiguration and flow allocation cost. In particular, the proposed routing protocol of SADFIR implements multiple SDN controllers that collaborate with network devices at forwarding plane to develop appropriate clustering strategy for routing the sensed information. This distributed SDN controllers are assisted to clustering topology that successfully map the residual network resources and also enable unique multi-hop application-aware data transmission. In addition, the proposed SADFIR monitor the iterative reconfiguration settings according to the network traffic of heterogeneity-aware network devices. The simulation experiments are conducted in comparison with the state-of-the-art routing protocols which demonstrates that SADFIR is heterogeneity-aware which is able to adopt the different scales of network with maximum network lifetime. Full article
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Open AccessArticle
Efficient Resource Allocation for Backhaul-Aware Unmanned Air Vehicles-to-Everything (U2X)
Sensors 2020, 20(10), 2994; https://doi.org/10.3390/s20102994 - 25 May 2020
Cited by 1
Abstract
Unmanned aerial vehicles (UAVs) allow better coverage, enhanced connectivity, and elongated lifetime when used in telecommunications. However, these features are predominately affected by the policies used for sharing resources amongst the involved nodes. Moreover, the architecture and deployment strategies also have a considerable [...] Read more.
Unmanned aerial vehicles (UAVs) allow better coverage, enhanced connectivity, and elongated lifetime when used in telecommunications. However, these features are predominately affected by the policies used for sharing resources amongst the involved nodes. Moreover, the architecture and deployment strategies also have a considerable impact on their functionality. Recently, many researchers have suggested using layer-based UAV deployment, which allows better communications between the entities. Regardless of these solutions, there are a limited number of studies which focus on connecting layered-UAVs to everything (U2X). In particular, none of them have actually addressed the aspect of resource allocation. This paper considers the issue of resource allocation and helps decide the optimal number of transfers amongst the UAVs, which can conserve the maximum amount of energy while increasing the overall probability of resource allocation. The proposed approach relies on mutual-agreement based reward theory, which considers Minkowski distance as a decisive metric and helps attain efficient resource allocation for backhaul-aware U2X. The effectiveness of the proposed solution is demonstrated using Monte-Carlo simulations. Full article
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Open AccessArticle
Energy-Efficient Industrial Internet of Things Software-Defined Network by Means of the Peano Fractal
Sensors 2020, 20(10), 2855; https://doi.org/10.3390/s20102855 - 18 May 2020
Cited by 3
Abstract
The Industrial Internet of Things (IIoT) network generates great economic benefits in processes, system installation, maintenance, reliability, scalability, and interoperability. Wireless sensor networks (WSNs) allow the IIoT network to collect, process, and share data of different parameters among Industrial IoT sense Node (IISN). [...] Read more.
The Industrial Internet of Things (IIoT) network generates great economic benefits in processes, system installation, maintenance, reliability, scalability, and interoperability. Wireless sensor networks (WSNs) allow the IIoT network to collect, process, and share data of different parameters among Industrial IoT sense Node (IISN). ESP8266 are IISNs connected to the Internet by means of a hub to share their information. In this article, a light-diffusion algorithm in WSN to connect all the IISNs is designed, based on the Peano fractal and swarm intelligence, i.e., without using a hub, simply sharing parameters with two adjacent IINSs, assuming that any IISN knows the parameters of the rest of these devices, even if they are not adjacent. We simulated the performance of our algorithm and compared it with other state-of-the-art protocols, finding that our proposal generates a longer lifetime of the IIoT network when few IISNs were connected. Thus, there is a saving-energy of approximately 5% but with 64 nodes there is a saving of more than 20%, because the IIoT network can grow in a 3 n way and the proposed topology does not impact in a linear way but log 3 , which balances energy consumption throughout the IIoT network. Full article
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Open AccessArticle
An Online Charging Scheme for Wireless Rechargeable Sensor Networks Based on a Radical Basis Function
Sensors 2020, 20(1), 205; https://doi.org/10.3390/s20010205 - 30 Dec 2019
Cited by 1
Abstract
The node energy consumption rate is not dynamically estimated in the online charging schemes of most wireless rechargeable sensor networks, and the charging response of the charging-needed node is fairly poor, which results in nodes easily generating energy holes. Aiming at this problem, [...] Read more.
The node energy consumption rate is not dynamically estimated in the online charging schemes of most wireless rechargeable sensor networks, and the charging response of the charging-needed node is fairly poor, which results in nodes easily generating energy holes. Aiming at this problem, an energy hole avoidance online charging scheme (EHAOCS) based on a radical basis function (RBF) neural network, named RBF-EHAOCS, is proposed. The scheme uses the RBF neural network to predict the dynamic energy consumption rate during the charging process, estimates the optimal threshold value of the node charging request on this basis, and then determines the next charging node per the selected conditions: the minimum energy hole rate and the shortest charging latency time. The simulation results show that the proposed method has a lower node energy hole rate and smaller charging node charging latency than two other existing online charging schemes. Full article
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