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Topical Advisory Panel Members’ Collection Series: Protocol and Optimization of Sensor Networks

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

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 6413

Special Issue Editors

Dr. Zhongjiang Yan

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
Interests: wireless network protocols; multiple access control; wireless resource allocation; network topology
Prof. Dr. Ki-Il Kim

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
Interests: wireless sensor networks; wireless body area networks; real-time communications; protocol engineering; QoS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, we have seen a growing interest in the potential use of sensor networks, which offer new opportunities through the implementation of a sensor network protocol in many different applications. Various optimization methods, e.g., convex optimization, machine learning (ML) and artificial intelligence (AI), have been explored to determine how sensor networks work efficiently in different scenarios, e.g., machine type communications (MTCs) and unmanned aerial vehicles (UAVs).

This Special Issue, therefore, aims to collect together original research and review articles on the recent advances, technologies, solutions, applications and new challenges in the field of sensor network protocol and optimization.

Potential topics include, but are not limited to:

  • Multiple access control (MAC) protocols and optimizations;
  • Topology control and routing protocols and optimizations;
  • ML/AI-based resource allocation algorithms;
  • Quality-of-service (QoS) guarantee algorithms;
  • Hybrid Wi-Fi, Bluetooth, and Zigbee networks;
  • Car-to-car and infrastructure-to-car protocols and optimizations;
  • Outdoor applications and localization/positioning;
  • Multiuser networking and advance modulation techniques;
  • Underwater sensor network protocols and optimizations;
  • Sensor networks in medical and manufacturing industries.

Dr. Zhongjiang Yan
Prof. Dr. Ki-Il Kim
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. 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 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.

Dr. Zhongjiang Yan
Prof. Dr. Ki-Il Kim
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. 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 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

  • sensor networks
  • protocols
  • optimization

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Published Papers (2 papers)

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Research

23 pages, 1553 KiB  
Article
Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks
by Cormac D. Fay, Brian Corcoran and Dermot Diamond
Sensors 2024, 24(1), 162; https://doi.org/10.3390/s24010162 - 27 Dec 2023
Viewed by 2311
Abstract
This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This [...] Read more.
This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This showcases their efficiency in processing sensor data and reducing power consumption without the need for extensive training sets. Two case studies focusing on landfill CO2 emissions and home energy usage demonstrate the feasibility and effectiveness of this approach. The findings highlight significant power savings achieved by minimizing data transmission during non-event periods (94.8–99.8%), in addition to presenting a sustainable alternative to traditional resource-intensive AI/ML platforms that comparatively draw and produce 20,000 times the amount of power and carbon emissions, respectively. Full article
(This article belongs to the Special Issue Topical Advisory Panel Members’ Collection Series: Protocol and Optimization of Sensor Networks)
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22 pages, 900 KiB  
Article
An Energy-Efficient Routing Protocol with Reinforcement Learning in Software-Defined Wireless Sensor Networks
by Daniel Godfrey, BeomKyu Suh, Byung Hyun Lim, Kyu-Chul Lee and Ki-Il Kim
Sensors 2023, 23(20), 8435; https://doi.org/10.3390/s23208435 - 13 Oct 2023
Cited by 8 | Viewed by 3420
Abstract
The enormous increase in heterogeneous wireless devices operating in real-time applications for Internet of Things (IoT) applications presents new challenges, including heterogeneity, reliability, and scalability. To address these issues effectively, a novel architecture has been introduced, combining Software-Defined Wireless Sensor Networks (SDWSN) with [...] Read more.
The enormous increase in heterogeneous wireless devices operating in real-time applications for Internet of Things (IoT) applications presents new challenges, including heterogeneity, reliability, and scalability. To address these issues effectively, a novel architecture has been introduced, combining Software-Defined Wireless Sensor Networks (SDWSN) with the IoT, known as the SDWSN-IoT. However, wireless IoT devices deployed in such systems face limitations in the energy supply, unpredicted network changes, and the quality of service requirements. Such challenges necessitate the careful design of the underlying routing protocol, as failure to address them often results in constantly disconnected networks with poor network performance. In this paper, we present an intelligent, energy-efficient multi-objective routing protocol based on the Reinforcement Learning (RL) algorithm with Dynamic Objective Selection (DOS-RL). The primary goal of applying the proposed DOS-RL routing scheme is to optimize energy consumption in IoT networks, a paramount concern given the limited energy reserves of wireless IoT devices and the adaptability to network changes to facilitate a seamless adaption to sudden network changes, mitigating disruptions and optimizing the overall network performance. The algorithm considers correlated objectives with informative-shaped rewards to accelerate the learning process. Through the diverse simulations, we demonstrated improved energy efficiency and fast adaptation to unexpected network changes by enhancing the packet delivery ratio and reducing data delivery latency when compared to traditional routing protocols such as the Open Shortest Path First (OSPF) and the multi-objective Q-routing for Software-Defined Networks (SDN-Q). Full article
(This article belongs to the Special Issue Topical Advisory Panel Members’ Collection Series: Protocol and Optimization of Sensor Networks)
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