Aims and scope

International Journal of Distributed Sensor Networks focuses on applied research and applications of sensor networks. A large number of important applications depend on sensor networks interfacing with the real world. These applications include medical, healthcare, military, manufacturing, transportation, safety and environmental planning systems. Many have been difficult to realize because of problems involved with inputting data from sensors directly into automated systems.

This journal also acts as a medium for exchanging the latest ideas and breakthroughs about impacts of sensor networks research. More importantly the goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in medical, manufacturing, engineering and environmental systems.

The SAGE Wiley Partnership

This journal is published by Wiley as part of a publishing collaboration with SAGE Publishing. It is a fully open access journal. Wiley manages the editorial and production workflow for each of the journals within the partnership, with SAGE maintaining ownership of the titles.

Special Issue on IoT-Based Distributed Sensor Networks for Smart Cities
Submission Date: 2025-01-31

Description

The rapid urbanization witnessed globally necessitates the transformation of cities into smart cities to enhance the quality of life for their residents. The integration of Internet of Things (IoT) with distributed sensor networks stands at the forefront of this transformation, offering innovative solutions to a myriad of urban challenges. This Special Issue focuses on the critical role IoT-based distributed sensor networks play in smart cities, addressing key areas such as smart infrastructure, traffic management, waste management, energy optimization, public safety, and the essential role of cloud, fog, and edge computing. Smart infrastructure, powered by IoT sensors, enables cities to manage resources efficiently, monitor structural health, and predict maintenance needs, thereby reducing costs and improving safety. Traffic management systems utilizing IoT technology can optimize traffic flow, reduce congestion, and enhance public transportation systems, leading to more sustainable urban mobility. In waste management, IoT sensors provide real-time data on waste levels, optimizing collection routes and reducing environmental impact.

Energy optimization through IoT-based sensor networks is crucial for achieving energy efficiency and sustainability in smart cities. These systems monitor and control energy consumption, integrate renewable energy sources, and enhance overall energy management. Public safety is another critical aspect where IoT sensors and networks provide real-time monitoring, early warning systems, and efficient emergency response, ensuring a safer urban environment. The integration of cloud, fog, and edge computing into IoT-based distributed sensor networks is paramount for continuous data gathering and real-time processing. Cloud computing provides centralized data storage and powerful analytics, while fog and edge computing enables localized processing closer to the data source, reducing latency and improving response times. These computing paradigms ensure efficient data management, scalability, and resilience in smart city applications.

This Special Issue also delves into the technical challenges associated with IoT-based distributed sensor networks, including data fusion, interoperability, security, and the integration of cloud, fog, and edge computing. By addressing these challenges, we can harness the full potential of IoT technologies to create resilient, sustainable, and intelligent urban environments. The contributions to this issue aim to provide a comprehensive overview of the current advancements, innovative solutions, and future directions in the field of IoT-based distributed sensor networks for smart cities.

Potential topics include but are not limited to the following:

    IoT-enabled structural health monitoring
    Intelligent traffic flow optimization with IoT sensors
    IoT-based waste level monitoring and collection route optimization
    Energy management and optimization in smart buildings
    Edge computing for rapid processing of public safety data
    Real-time data processing and analysis for smart city applications
    Interoperability solutions for legacy systems and new IoT technologies
    Cybersecurity strategies for protecting smart city infrastructures

Special Issue on Intelligent Sensor Networks with Machine Learning and AI
Submission Date: 2025-01-31

Description

This Special Issue aims to explore the transformative impact of machine learning (ML) and artificial intelligence (AI) on the design, implementation, and optimization of sensor networks. As sensor networks become increasingly integral to a wide range of applications—from environmental monitoring and smart cities to healthcare and industrial automation—leveraging AI and ML techniques can significantly enhance their performance, efficiency, and adaptability. This special issue seeks contributions that demonstrate how ML and AI can be applied to address key challenges in sensor networks, such as data fusion, anomaly detection, energy management, and network security.

The scope of this Special Issue encompasses a broad array of topics that illustrate the symbiotic relationship between sensor networks and intelligent systems. We invite original research and review articles that cover advances in AI-driven sensor data analytics, real-time decision making, and predictive maintenance. Studies that highlight the development of novel ML algorithms tailored for resource-constrained sensor nodes, as well as those that explore the integration of AI at the edge and fog computing levels, are particularly encouraged. Furthermore, articles investigating the use of deep learning, reinforcement learning, and other advanced AI methodologies to improve sensor network scalability and robustness are of great interest.

In addition, this Special Issue welcomes interdisciplinary research that bridge the gap between theory and practical applications. Contributions that demonstrate real-world deployments of AI-enhanced sensor networks, showcase the benefits of intelligent sensor data processing in various domains, or propose new frameworks for AI integration in distributed sensor environments are highly desirable. By bringing together insights from academia and industry, this Special Issue aims to foster collaboration, stimulate innovation, and pave the way for the next generation of intelligent sensor networks that are more efficient, resilient, and capable of addressing complex challenges in diverse application areas.

Potential topics include but are not limited to the following:

    AI-Based anomaly detection and fault diagnosis in sensor networks
    Energy-efficient AI techniques for prolonging sensor network lifespan
    Edge and fog computing for real-time AI processing in sensor networks
    AI-driven predictive maintenance and health monitoring systems
    Secure and privacy-preserving AI solutions for sensor networks

Special Issue on Multi-UAV-Assisted Wireless Sensor Networks
Submission Date: 2025-02-07

Description

Wireless Sensor Networks (WSNs) are the basis of Internet of Things (IoT), and therefore are playing a vital role in our society. WSNs are, however, facing challenges such as: Quality of service, energy efficiency, network throughput, security issues, etc. This Special Issue aims at exploring how multiple UAVs can be leveraged to solve (some of the) challenges of WSNs, their potential applications, and future directions.

Due to their mobility and flexibility, UAVs are already used in WSN, especially in remote areas, and in delay-tolerant applications. This is because they can avoid long-range transmissions and the need to relay data. Furthermore, latency can often be decreased due to higher line-of-sight (LoS) opportunities. However, many of these current solutions use only one UAV. In this regard, levering multiple UAVs at the same time can be beneficial for further enhancing UAV-assisted Wireless Sensor Networks.

By utilizing a swarm of UAVs (or at least multiple-UAVs centrally managed), it is possible to distribute the workload more evenly, reducing the energy consumption and extending the lifespan of individual sensors. Multiple UAVs can coordinate to provide more robust coverage, dynamically adjusting their positions to ensure optimal communication paths and minimize data loss. This coordination also enhances fault tolerance, as the network can quickly adapt to the failure or absence of any single UAV. Additionally, multiple UAVs can execute concurrent data collection tasks, significantly increasing network throughput and reducing latency. This is particularly beneficial in time-sensitive applications, where rapid data aggregation and processing are crucial. Furthermore, using multiple UAVs can improve security by enabling more frequent and varied data relay patterns, making it harder for potential attackers to predict and intercept data transmissions. Overall, the deployment of multiple UAVs offers a scalable and resilient solution to many of the challenges faced by WSNs. This Special Issue invites original research articles, review papers, and case studies that investigate UAV-Assisted sensor networks.

Potential topics include but are not limited to the following:

    Optimizing Energy Efficiency in UAV-Assisted WSNs
    Quality of Service (QoS) Enhancement Using Multi-UAV Systems
    Network Throughput Improvement with Coordinated Multi-UAV Operations
    Security Solutions in Multi-UAV-Assisted WSNs
    Scalability and Reliability of Multi-UAV Systems in WSNs
    Applications of Multi-UAV WSNs in Remote or Disaster-Prone Areas
    Machine Learning and AI (Artificial Intelligence) Techniques for Multi-UAV WSN Optimization
    Collaborative Control and Path Planning