Emerging Theory and Applications in Wireless Sensor Networks and Edge Computing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 April 2024 | Viewed by 1346

Special Issue Editors

Beijing Key Laboratory of Space-Ground Interconnection and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: edge computing and edge intelligence; industrial internet of things; ultra-dense networks including massive MIMO
Director, Innovation Centre for Information Engineering, Western University, London, ON N6A 5B9, Canada
Interests: intelligent wireless communications; wireless IoT communications
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: edge/cloud computing; internet of things; internet of intelligence; satellite networks

Special Issue Information

Dear Colleagues,

Wireless sensor networks (WSNs) have gained great popularity for offering lightweight solutions to Internet of things (IoT) applications such as agriculture, industry, healthcare monitoring, surveillance, traffic control, etc., proliferating the real-time big data exploration. Through interaction between the physical world, various sensors and actuators, WSNs permit the closed cycle of data collection, aggregation, processing, and control, which thereby ensures low-cost automatic operation and long-term reliability. Recently, high-precision, low-cost CPU and semiconductor design has enabled “sensor” as smarter nodes, where many advanced operations such as recognition, multi-modal sensing, processing and even matrix computational abilities are capable on-site, boosting the future extreme sensible word in 6G. However, the implementation of WSNs in practical scenarios is challenging due to the contradiction of continuous sensing and gathering and the limited resources of SNs, such as computing power, energy, and throughput, which may result in latency, data inconsistency and redundancy, high computation overhead, and energy consumption. Edge computing is a potential solution to satisfy computing-intensive and latency-critical applications with resource-limited SNs by performing data processing on computing nodes closer to the data source. When WSNs meet edge computing, due to (1) geographical dispersion, (2) ad hoc deployment and (3) rudimentary support systems compared to cloud data centers, energy efficiency, reliability, optimized resource management, reasonable task scheduling, etc. are critical issues.

This Special Issue, "Emerging Theory and Applications in Wireless Sensor Networks and Edge Computing", is particularly interested in finding, defining, quantifying and solving the new issues, theory and applications that come out from the combination of WSNs and edge computing, including new edge–WSN frameworks, system deployments, task offloading, trust mechanisms, resource sharing, performance modeling, prototypes, system experiences, edge–WSN applications, use cases in IoT/blockchain/digital twins, etc.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Wireless sensor networks;
  • IoT sensing systems;
  • Edge computing and edge intelligence;
  • Novel edge computing paradigms for WSNs;
  • Edge-assisted mobile and sensing systems;
  • System architecture for edge–WSNs;
  • Wireless target sensing;
  • Edge offloading in WSNs;
  • Energy-efficient edge–WSNs;
  • Reliable and sustainable edge provisioning for WSNs;
  • Optimized data collection in edge–WSNs;
  • Trust mechanism in edge–WSNs;
  • Machine learning in edge–WSNs;
  • Performance modeling of edge–WSN systems;
  • 5G/6G edge computing frameworks/systems/prototypes;
  • Edge–WSNs use cases and application systems;
  • Smart healthcare;
  • Smart buildings/cities;
  • Edge-blockchain/digital twins.

Prof. Dr. Yinglei Teng
Prof. Dr. Xianbin Wang
Dr. Qinqin Tang
Guest Editors

Manuscript Submission Information

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Keywords

  • edge computing
  • wireless sensor networks
  • internet of things
  • edge offloading
  • energy-efficiency
  • machine learning
  • edge provisioning
  • data collection
  • target sensing
  • trust mechanism
  • machine learning
  • performance modeling
  • frameworks/systems/prototypes
  • use cases 
  • applications

Published Papers (1 paper)

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Research

17 pages, 479 KiB  
Article
Optimization of Quality of AI Service in 6G Native AI Wireless Networks
by Tianjiao Chen, Juan Deng, Qinqin Tang and Guangyi Liu
Electronics 2023, 12(15), 3306; https://doi.org/10.3390/electronics12153306 - 01 Aug 2023
Cited by 1 | Viewed by 887
Abstract
To comply with the trend of ubiquitous intelligence in 6G, native AI wireless networks are proposed to orchestrate and control communication, computing, data, and AI model resources according to network status, and efficiently provide users with quality-guaranteed AI services. In addition to the [...] Read more.
To comply with the trend of ubiquitous intelligence in 6G, native AI wireless networks are proposed to orchestrate and control communication, computing, data, and AI model resources according to network status, and efficiently provide users with quality-guaranteed AI services. In addition to the quality of communication services, the quality of AI services (QoAISs) includes multiple dimensions, such as AI model accuracy, overhead, and data privacy. This paper proposes a QoAIS optimization method for AI training services in 6G native AI wireless networks. To improve the accuracy and reduce the delay of AI services, we formulate an integer programming problem to obtain proper task scheduling and resource allocation decisions. To quickly obtain decisions that meet the requirements of each dimension of QoAIS, we further transform the single-objective optimization problem into a multi-objective format to facilitate the QoAIS configuration of network protocols. Considering the computational complexity, we propose G-TSRA and NSG-TSRA heuristic algorithms to solve the proposed problems. Finally, the feasibility and performance of QoAIS optimization are verified by simulation. Full article
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