IESDCC-KM: an improved energy-saving distributed cluster–chain K-communication scheme for smart sensor networks

Owing to the inexpensive, adaptable, and scalable features of WSNs (wireless sensor networks), the network is viewed as a vital technology to support distinct smart applications. The primary issue is to strengthen the lifespan of the network, as the node devices have bounded lifecycles owing to notable power constraints. Thus, to sustain the lifespan of WSNs, it is crucial to reinforce the energy administration at the node devices, as they are routinely deployed in secluded areas. Several energy management strategies have recently been used in this context. Among these alternatives, cluster-chain hybrid networks have demonstrated the ability to significantly reduce the node energy usage. However, the chain formation techniques often result in increased latency in large-scale scenarios. Similarly, it has been proven that coding techniques preserves the network reliability and energy efficiency. While utilising the benefits of these coding schemes it is necessary to carefully secure the topology, link quality, and coding vectors. In this paper, an improved energy-saving distributed cluster–chain K-communication mechanism for smart sensor networks is proposed. In the proposed solution named IESDCC-KM, a dual K-means technique is adapted to form unequal clusters. IESDCC-KM implements a competing and ideal weight function to select the cluster heads and establishes perpendicular chain trees among heads based on their distances and a threshold value. It establishes gradient-based dis-joint multiple routes from the source to the destination and implements discrete wavelet transform to compress the accumulated inter-cluster data. At the intermediate nodes on the path along the source and destination, the packets from the different link nodes are encoded utilizing linear network coding. MATLAB 2018b experimental analysis demonstrates the proposed IESDCC-KM improves the reception ratio by 0.20 to 0.03 at 0.99 to 0.96 precision rate. Furthermore, it showed a 28.57% throughput increase with a 1% reduction in delay and a 2.86% boost in energy-saving for 4000 rounds.