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. initially graph model is modelled to represent distributed and heterogeneous (HWSNs) vertex representing the assignment of a 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 opt path on the construction routing for packet transmission that maximizes the MDCCKC. bats thus focus on finding one more connected covers and avoids creating subsets particularly. 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 @ IJTSRD |