Sensor nodes in close proximity to the sink experience heavy traffic than the nodes which are far from the sink. Since, the continuous monitoring and reporting of events throughout the network involves the nodes closer to the sink to be part of the relay, the transmission load is hardly even throughout the network. The uneven energy consumption in the network eventually results nodes near the sink to die out quickly, creating energy holes in the network. This work proposes a compressive in-network data processing scheme, to resolve the energy hole problem and to improve the network lifetime. The sensed data is transmitted to the sink through a three-level clustering scheme. A few designated nodes in the first and second level apply compressed sensing and obtain weighted samples of the received data. A lossless compression is applied at the nodes in the third level before it is finally transmitted to the sink. Results show the proposed scheme to be is 43.75% transmission efficient and is able to resolve the energy hole problem efficiently by distributing the load evenly over the network.
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Singh, V.K., Kumar, M. A Compressed Sensing Approach to Resolve The Energy Hole Problem in Large Scale WSNs. Wireless Pers Commun 99, 185–201 (2018). https://doi.org/10.1007/s11277-017-5047-9
- In-network inference
- Compressed data gathering
- Traffic load balancing
- Uniform energy consumption
- Wireless sensor networks