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Machine Learning for Wireless Sensor Networks and Systems

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 1819

Special Issue Editor


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Guest Editor
Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
Interests: smart wireless systems; mobile and edge computing; software-defined networks; network security and privacy; Internet-of-Things and smart city systems; vehicular networks; intelligent transportation systems; location determination systems
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Special Issue Information

Dear Colleagues,

Wireless sensors are quickly becoming a critical enabler for highly intelligent data-rich applications, and they are the driving force behind smart-computing domains such as smart homes, connected health, connected vehicles, automated enterprise workflows, smart cities, and smart grids. With the present extreme miniaturization of many sensor system components, sensors are becoming increasingly sophisticated and intelligent, allowing innovative mobile and sensor systems, applications, and services. In order to deploy and operate networks of wireless sensors efficiently in real dynamic environments, a variety of technical challenges must be addressed.

Artificial Intelligence (AI) and Machine Learning (ML) technologies have seen tremendous success in a variety of application sectors recently. The emergence of improved design, combined with increased complexity in wireless sensor networks and protocols, has fueled the need for improved network autonomy in agile infrastructures, which can be combined with AI/ML techniques to execute efficient, self-adaptive, rapid, and collaborative networks. The future role of wireless sensor networks, systems, and applications is becoming limitless by integrating advances in wireless sensor networks and edge systems with advances in machine learning (ML) and artificial intelligence (AI), and it is expected to revolutionize the world's future within the next few years.

This special issue seeks papers on the use of AI/ML approaches in novel wireless sensing technologies, novel sensor network and system architectures, and wireless sensor applications and services. This special journal issue will collect and present contributions from prominent specialists in this field of research.

Dr. Tamer M. Nadeem
Guest Editor

Manuscript Submission Information

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Published Papers (1 paper)

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Research

24 pages, 5100 KiB  
Article
Cognitive Radio with Machine Learning to Increase Spectral Efficiency in Indoor Applications on the 2.5 GHz Band
by Marilson Duarte Soares, Diego Passos and Pedro Vladimir Gonzalez Castellanos
Sensors 2023, 23(10), 4914; https://0-doi-org.brum.beds.ac.uk/10.3390/s23104914 - 19 May 2023
Cited by 1 | Viewed by 1505
Abstract
Due to the propagation characteristics in the 2.5 GHz band, the signal is significantly degraded by building entry loss (BEL), making coverage in indoor environments in some cases non-existent. Signal degradation inside buildings is a challenge for planning engineers, but it can be [...] Read more.
Due to the propagation characteristics in the 2.5 GHz band, the signal is significantly degraded by building entry loss (BEL), making coverage in indoor environments in some cases non-existent. Signal degradation inside buildings is a challenge for planning engineers, but it can be seen as a spectrum usage opportunity for a cognitive radio communication system. This work presents a methodology based on statistical modeling of data collected by a spectrum analyzer and the application of machine learning (ML) to leverage the use of those opportunities by autonomous and decentralized cognitive radios (CRs), independent of any mobile operator or external database. The proposed design targets using as few narrowband spectrum sensors as possible in order to reduce the cost of the CRs and sensing time, as well as improving energy efficiency. Those characteristics make our design especially interesting for internet of things (IoT) applications or low-cost sensor networks that may use idle mobile spectrum with high reliability and good recall. Full article
(This article belongs to the Special Issue Machine Learning for Wireless Sensor Networks and Systems)
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