Special Issues
Table of Content

From Nodes to Knowledge: Harnessing Wireless Sensor Networks

Submission Deadline: 28 February 2025 View: 475 Submit to Special Issue

Guest Editors

Prof. Dr. Abdel-Hamid Soliman

Email: a.soliman@staffs.ac.uk

Affiliation: School of Digital, Technology, Innovation & Business, Staffordshire University, ST4 2DE, United Kingdom

Homepage: 

Research Interests: digital signal processing, telecommunications, data acquisition systems, wireless sensor networks (WSN), Internet of Things (IoT), Fiber Optics communication and image/video processing 

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Mr. Tamoor Shafique

Email: tamoor.shafique@staffs.ac.uk

Affiliation: School of Digital, Technology, Innovation & Business, Staffordshire University, ST4 2DE, United Kingdom

Homepage:

Research Interests: Resource Constraints in Smart Cities, Hostile Wireless Sensor Networks, Make-up Invariant Face recognition and Standardized Routing Protocols for Internet of Things

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Dr. Anas Amjad

Email: a.amjad@staffs.ac.uk

Affiliation: School of Digital, Technology, Innovation & Business, Staffordshire University, ST4 2DE, United Kingdom

Homepage: 

Research Interests: resource optimization for Internet of Things, data prioritization in smart cities, and quality assessment for future generation of wireless network

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Summary

Wireless Sensor Networks (WSNs) have emerged as a transformative technology, offering unparalleled capabilities in monitoring, data collection, and intelligent decision-making across a wide range of applications. This special issue, titled "From Nodes to Knowledge: Harnessing Wireless Sensor Networks," aims to provide a comprehensive overview of the latest advancements, challenges, and opportunities in this rapidly evolving field.


Introduction:

The proliferation of WSNs has revolutionized the way we interact with our environment, enabling real-time monitoring and control in areas such as environmental monitoring, healthcare, smart cities, industrial automation, and more. The importance of this research area lies in its potential to significantly enhance the efficiency, accuracy, and scalability of various systems through the seamless integration of sensor nodes and sophisticated data processing techniques. As the demand for smarter and more connected systems grows, understanding and leveraging the full potential of WSNs becomes increasingly crucial.


Aim and Scope

This Special Issue aims to explore the challenges and prospects associated with IoT-based WSNs, providing insights into the advancements and considerations required to unlock their full potential. We invite both original research papers and review articles that showcase the significant developments in these fields. Potential areas of interest include, but are not limited to, the following topics:


· Wireless Sensor Networks (WSNs)

· Internet of Things (IoT)

· Smart cities, smart homes, smart buildings, and smart industry

· WSN & IoT applications in:

  - Environmental monitoring

  - Healthcare

  - Agriculture

· Sensor data fusion

· Resource management in WSN & IoT including

  - Cost (of node, energy, development, deployment, maintenance)

  - Energy (reliability, management)

· Wireless technologies for WSNs and IoT, including short- and long-range radio technologies, non-radio-based technologies, etc.

· Real-time communication and monitoring

· WSN & IoT Adaptability (to environment, energy, faults)

· Self-learning (pattern discovery, prediction, auto-configuration)


Keywords

· Wireless Sensor Networks
· Internet of Things
· Communication protocols
· Sensing technologies
· Environmental monitoring
· Information-centric networking
· Sensor systems and applications
· Intelligent systems
· Machine learning
· Smart systems

Published Papers


  • Open Access

    ARTICLE

    A Base Station Deployment Algorithm for Wireless Positioning Considering Dynamic Obstacles

    Aiguo Li, Yunfei Jia
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.059184
    (This article belongs to the Special Issue: From Nodes to Knowledge: Harnessing Wireless Sensor Networks)
    Abstract In the context of security systems, adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel. Most studies focus on optimizing base station deployment under the assumption of static obstacles, aiming to maximize the perception coverage of wireless RF (Radio Frequency) signals and reduce positioning blind spots. However, in practical security systems, obstacles are subject to change, necessitating the consideration of base station deployment in dynamic environments. Nevertheless, research in this area still needs to be conducted. This paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm (DIE-BDA)… More >

  • Open Access

    ARTICLE

    Machine Learning-Based Detection and Selective Mitigation of Denial-of-Service Attacks in Wireless Sensor Networks

    Soyoung Joo, So-Hyun Park, Hye-Yeon Shim, Ye-Sol Oh, Il-Gu Lee
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.058963
    (This article belongs to the Special Issue: From Nodes to Knowledge: Harnessing Wireless Sensor Networks)
    Abstract As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by More >

  • Open Access

    ARTICLE

    Machine Learning for QoS Optimization and Energy-Efficient in Routing Clustering Wireless Sensors

    Rahma Gantassi, Zaki Masood, Yonghoon Choi
    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 327-343, 2025, DOI:10.32604/cmc.2024.058143
    (This article belongs to the Special Issue: From Nodes to Knowledge: Harnessing Wireless Sensor Networks)
    Abstract Wireless sensor network (WSN) technologies have advanced significantly in recent years. Within WSNs, machine learning algorithms are crucial in selecting cluster heads (CHs) based on various quality of service (QoS) metrics. This paper proposes a new clustering routing protocol employing the Traveling Salesman Problem (TSP) to locate the optimal path traversed by the Mobile Data Collector (MDC), in terms of energy and QoS efficiency. To be more specific, to minimize energy consumption in the CH election stage, we have developed the M-T protocol using the K-Means and the grid clustering algorithms. In addition, to improve More >

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