#network lifetime

Clustering is an effective approach for organizing a network into a connected hierarchy, load balancing, and prolonging the network lifetime. This paper proposes an energy-aware distributed dynamic clustering protocol (PFC) which applies three techniques: 1) non-probabilistic Cluster Head (CH) elections.2) on demand clustering. The remaining energy of the nodes is the primary parameter for electing tentative C's via a non-probabilistic fashion.Anon-probabilistic Selection is implemented by introducing a delay inversely proportional to the residual energy of each node. Therefore, tentative CHs are selected based on their remaining energy. Besides, in ECPF, CH elections are performed sporadically (in contrast to performing it every round). Simulation results demonstrate that our approach performs better than well known protocols (LEACH, HEED, and CHEF) in terms of extending network lifetime and saving energy. 

Recent years have witnessed an increasing interest in Wireless Sensor Networks (WSNs) for various applications such as environmental monitoring and military field surveillance. WSN have a number of sensor nodes that communicate wirelessly and it deployed to gather data for various environments. But it has issue with the energy efficiency of sensor nodes and network lifetime along with packet scheduling. The target coverage problem is another problem hence the overall network performance is reduced significantly. In this research, new Markov Chain Monte Carlo (MCMC) is introduced which solves the energy efficiency of sensor nodes in HWSN. At initially graph model is modeled to represent distributed and heterogeneous (HWSNs) with each vertex representing the assignment of a sensor nodes in a subset. Modified Bat Optimization (MBAT) is proposed to maximize the number of Disjoint Connected Covers (DCC) and K Coverage (KC) known as MBAT-MDCCKC. Based on echolocation capability from the MBAT, the bat seeks an optimal path on the construction routing for packet transmission that maximizes the MDCCKC. MBAT bats thus focus on finding one more connected covers and avoids creating subsets particularly. It designed to increase the search efficiency and hence energy efficiency is improved prominently. The proposed MBAT-MDCCKC approach has been applied to a variety of HWSNs. The results show that the MBAT-MDCCKC approach is efficient and successful in finding optimal results for maximizing the lifetime of HWSNs. Experimental results show that, proposed MBAT-MDCCKC approach performs better than, TFMGA, Bacteria Foraging Optimization (BFO) based approach, Ant Colony Optimization (ACO) method, and the performance of the MBAT-MDCCKC approach is closer to the energy conserving strategy.