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Multi-objective approach to improve network lifetime and congestion control routing for wireless sensor networks

Prakash Suman P., Kavitha D., Reddy P. Chenna

Computer Assisted Methods in Engineering and Science

2022

Vol. 29, no. 1-2 spec.

49--69

The wireless sensor networks (WSNs) and their extensive characteristics and applicabilityto a wide range of applications attract researchers attention. WSN is an emerging technology where the sensor nodes are its major elements used to monitor and control physicaland environmental systems. Clustering in wireless sensor networks groups all the nodesin a region, uses a single node as a cluster head, and communicates with the sink. However, the resource-constrained nodes’ lifetime reduces in the communication process. Toimprove the network lifetime, an efficient cluster head selection process is widely adopted.Similarly, identifying energy-efficient routing reduces the node energy requirements andenhances the network lifetime. Considering these two characteristics as objective, thisresearch work proposes a fuzzy neural network-based clustering with dolphin swarm optimization routing and congestion control (FNDSCC), where an energy-efficient cluster headselection using a deep fuzzy neural network (DFNN) model and an energy-aware optimalrouting using an improved dolphin swarm optimization (DSO) enhance the network life-time by reducing the energy consumption of the nodes. Moreover, novel rate adjustmenttechniques to overcome the congestion inside the network are introduced. Proposed modelperformance is experimentally verified and compared with conventional methods such asgenetic based efficient clustering (GEC), hybrid particle swarm optimization (HPSO), andartificial bee colony (ABC) optimization and rate-controlled reliable transport (RCRT)protocol in terms of latency, reliability, packet delivery ratio, network lifetime and ef-ficiency. The results demonstrate that the proposed multi-objective approach performsbetter than conventional models.