An Optimized Energy Efficient Approach for Realistic Physical Layer Data Transmission in Wireless Sensor Network

A typical wireless network consists of closely located static nodes with one stationary base station. The base station maintains the different node activity with more energy consumption. Existing Network coding-based cooperative ARQ scheme perform the multi-hop transmission among a set of relay nodes. However, the impact of realistic physical layer is not considered for data transmission. In addition, Polynomial time K-approximation algorithm MAP (Multi-constrained AnyPath) supports multipath path routing in wireless network. K-approximation algorithm exploits the many single paths with less costly system but data transmission is not carried out with the higher throughput level. To perfectly carry out the realistic physical layer data transmission, Energy Effective Cumulative Curvature (EECC) approach is developed in this paper. The network to perform the multi-hop transmission with higher throughput level. The dynamic programming significantly performs EECC method attains the optimal data transmission result by forwarding the data packets with changeable target constraints in the queue. Proposed EECC uses the heuristic method to handle the arbitrary rate of packet arrival to the queue list and also achieve the minimal energy consumption on the large scale wireless network. The EECC method uses the discrete-time stochastic dynamic programming in wireless the multi-hop transmission at the discrete time interval to improve the data transmission rate. Heuristic method in EECC method obtains the best energy approximation result on discrete-time stochastic function and effective data transmitted based on the queue length. Experimental evaluation of EECC method is done with the performance metrics such as throughput level, energy consumption rate, delay time, data transmission rate. Experimental analysis shows that the EECC method is able to improve the throughput level by 34 % and reduces the energy consumption by 26 % when compared to the state-of-the-art works.