Energy-Efficient Wireless Sensor Networks for IoT Applications

Dear Colleagues,

This Special Issue addresses the growing demand for energy-efficient solutions in wireless sensor networks (WSNs) deployed for Internet of Things (IoT) applications. With the proliferation of IoT devices and the increasing demand for data-driven insights, optimizing energy consumption in WSNs has become paramount. WSNs consist of numerous low-power sensors that collect and transmit data wirelessly, often powered by batteries with limited energy capacity. Efficient utilization of energy resources is crucial to prolong the network lifetime, reduce maintenance costs, and enable ubiquitous connectivity for IoT applications.

The aim of this Special Issue aligns closely with the journal's scope, which encompasses research on wireless communication systems, sensor networks, and IoT technologies. By focusing on energy-efficient solutions for WSNs, the Special Issue seeks to advance the understanding and development of strategies that minimize energy consumption while ensuring reliable communication and data delivery in IoT deployments. The research presented in this Special Issue contributes to enhancing wireless sensor networks' sustainability, scalability, and resilience in diverse application domains.

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

• Energy-aware routing protocols: Explore novel routing algorithms and protocols designed to optimize energy consumption in WSNs by dynamically adapting network topology, balancing traffic load, and considering node energy levels.
• Power management techniques: Investigate strategies for efficient power management at the sensor node, communication module, and network infrastructure levels, including sleep-wake scheduling, duty cycling, adaptive transmission power control, and energy harvesting mechanisms.
• Communication protocols for energy efficiency: Discuss advanced communication protocols tailored for WSNs, focusing on reducing energy consumption during data transmission, improving signal robustness, and enhancing network scalability through techniques such as data aggregation, compression, and error control coding.
• Energy-efficient data processing and decision-making algorithms: Present algorithms for optimizing data processing and analysis in WSNs, leveraging data fusion, machine learning, and distributed computing techniques to minimize computational complexity and energy consumption while maintaining high accuracy in data analysis.

I look forward to receiving your contributions.

Dr. Hosam El-Ocla
Guest Editor

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