Optimized fuzzy clustering in wireless sensor networks using improved squirrel search algorithm

Wireless sensor networks (WSNs) are important tools to monitor various events happening in specific environments. In WSNs, regarding the network size and number of sensor nodes, several mobile sink nodes can be used to collect the monitored data. In such cases, having a clustered WSN helps to get the sensed data more efficiently to the sink nodes. Besides, clustering is one of the wellknown methods for mitigating the energy consumption of WSNs. This paper presents an efficient distributed multi-level clustering scheme using Sugeno-based Fuzzy Logic Controller (FLC) to organize the WSN into several multi-level clusters. These cluster heads (CHs) collect their members' data and forward them to one of the sinks. Besides, CHs cooperate in multi-hop routing of the data packets to the destination sinks. Also, an improved SSA algorithm is provided to optimize the clustering FLC. By optimizing the FLC its rule-base can be minimized and its effectiveness in the clustering process can be improved. Extensive simulations are conducted in the OMNET++ simulator to evaluate the proposed scheme regarding various metrics such FND (First Node Dead), HND (Half Nodes Dead), LND (Last Node Dead), the average energy of the WSN's nodes, data packet loss, and retransmission ratio. The achieved results exhibit that our presented approach outperforms several well-known clustering and multi-hop routing approaches. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Wireless sensor networks are important for monitoring events in specific environments. Clustering can improve data transmission efficiency and reduce energy consumption. This paper proposes an efficient distributed multi-level clustering scheme using a fuzzy logic controller, and improves its effectiveness through optimization. Experimental results show that this approach outperforms other methods.