Aggregation Technique Using Dynamic Cross-Propagation Clustering Algorithm in Wireless Body Sensor Networks

Abstract

Wireless body sensor networks (WBSNs) are characterized by a large number of battery-powered wireless sensor nodes, and the most challenging aspects of WBSNs are sensor node energy consumption, delay, and security (communication and data) while maintaining regular wireless sensor network (WSN) capabilities. Data aggregation, as a common procedure in data gathering applications, can waste a lot of energy since sensor nodes may stay in the listen to state even when they are not receiving data during the data collection process. In this research work, introducing the Self-Executing-Dynamic Cross-Propagation Clustering (SE-DCPC) algorithm helps to improve the node energy consumption positively by turning nodes to the accessible state when not in use and waking them up when necessary. The algorithm is energy-based and uses a self-executing-based dynamic cross-propagation clustering system to send/receive scheduling data in the WSN platform. The energy level of the nodes is the most essential component in constructing network communication, in contrast to earlier clustering algorithms. The purpose of this proposed algorithm is to enhance the traditional notion of the clustering algorithm (location-based clustering), leading to the primary goal of enhancing the permanence of the wireless sensor network, which is to conserve network coverage, using self-executing DCPC clustering technology for location and power. The result of performance analysis of the SE-DCPC is achieved by simulation using two different communication processes of clustering and intelligence decision-making methods. The numerical results shows that SE-DCPC can effectively handle and maintain a high rate of network node energy consumption. The simulation result shows that the proposed approaches securely obtain the high throughput and very minimal delay at the client side, compared to existing clustering algorithm approaches. This SE-DCPC algorithm increased by 21.89% the communication medium lifetime and by 37% the energy consumption and reduced by 23.27% the overhead compared with existing clustering algorithms.