Energy-Efficient Protocols in Wireless Sensor Networks
Summary
The severe energy constraints of battery-powered sensor nodes have catalysed the design of protocols that minimise power consumption while maintaining robust network performance. Energy-efficient routing schemes seek to distribute communication load evenly, reduce redundant transmissions and adapt dynamically to environmental changes. Key strategies include clustering, whereby selected nodes aggregate local data to curtail long-range transmissions; hierarchical routing that balances energy expenditure across multiple tiers; and duty-cycling protocols that switch radios on and off to conserve power. Recent innovations incorporate mobile sinks to alleviate energy hot-spots, data fusion at cluster heads to compress traffic, and heuristic or machine-learning-based approaches to optimise cluster formation and route selection. These advances have been demonstrated in applications such as environmental monitoring, industrial automation and smart-city deployment, yielding appreciable gains in network lifetime, throughput and latency. The increasing global emphasis on sustainable sensing has also driven integration with energy-harvesting modules, edge-computing architectures and Internet of Things frameworks to enhance autonomy and resilience.
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Energy-Efficient Protocols in Wireless Sensor Networks publication trend
The graph below shows the total number of articles in energy-efficient protocols in wireless sensor networks across all publications each year (not limited to Nature Index journals).
Technical terms
Clustering routing protocol: A method that organises sensor nodes into clusters with elected leaders for local aggregation and forwarding.
Mobile sink: A moving data collector that visits nodes to distribute communication energy more uniformly across the network.
Hot-spot problem: The phenomenon whereby nodes closest to the sink exhaust their energy more rapidly due to disproportionate forwarding demands.
Data fusion: The process of combining and filtering data at intermediate nodes to eliminate redundancy and reduce transmission volume.
Particle swarm optimisation: A bio-inspired heuristic algorithm that simulates social behaviour to select optimal cluster heads and routes.
References
- Applications of Wireless Sensor Networks: An Up-to-Date Survey. Applied System Innovation (2020).
- An Enhanced PEGASIS Algorithm with Mobile Sink Support for Wireless Sensor Networks. Wireless Communications and Mobile Computing (2018).
- Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review. Sensors (2022).
- An Improved Routing Schema with Special Clustering Using PSO Algorithm for Heterogeneous Wireless Sensor Network. Sensors (2019).
- An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks. International Journal of Distributed Sensor Networks (2019).
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