EELCR: energy efficient lifetime aware cluster based routing technique for wireless sensor networks using optimal clustering and compression

Wireless sensor networks (WSNs) offer a multitude of advantages and find applications across various domains, garnering substantial research interest. However, a notable drawback in these networks is the energy consumption, which can be mitigated through compression techniques. Additionally, the limited lifespan of sensor batteries remains a concern. Even when incorporating renewable energy sources, ensuring energy efficiency in WSNs is imperative. One prevailing issue is the disregard for spatial data correlation in existing data clustering methods within WSNs. Addressing these challenges necessitates effective modeling and the acquisition of event source locations in the proposed scheme. In this work, we propose an energy-efficient lifetime-aware cluster based routing (EELCR) for WSN. In EELCR technique, modified giant trevally optimization (MGTO) algorithm is introduced for efficient balanced clustering which minimizes energy consumption. An optimal squirrel search (OSS) algorithm is used to selects the best optimal node, named as cluster head (CH) for prolonging the lifetime in the sensor networks. Each CH nodes compress clustering data using optimal selective Huffman compression to achieve maximum compression ratio which overcomes inefficiency of area overhead problem in existing Huffman compression. Furthermore, we develop a hybrid deep learning technique which combines deep neural network (DNN) with Granular neural network (GNN) (named as DGNN) to find optimal way for data broadcast from CH to base station (BS). Finally, we assess the efficacy of the proposed EELCR approach through various simulation scenarios, demonstrating its effectiveness concerning Quality of Service (QoS) parameters. The outcomes reveal a notable enhancement in our coding scheme, with an average compression rate improvement of 9.346% when compared to state-of-the-art coding techniques. Furthermore, our proposed EELCR technique significantly outperforms existing routing methods, exhibiting an average network lifetime improvement of 51.88% in node density considerations and 52.625% in simulation rounds, respectively.