An Efficient Data Collection Model Using Phantom Partitioning to Reconnect Traffic Partitioned Node in Wireless Sensor Network Structure

Abstract

Abstract Although traffic partitioned node on wireless sensor network has been under way for many years and many energy optimization model have been investigated, it is still unclear whether integer linear programming problem is consistently effective on overlapping time over multiple mobile elements. With the objective of measuring the energy consumption on partitioning multi-hop wireless communications, a Traffic Reconnect Set-up Partitioning (TRSP) method is proposed in this paper. TRSP finds the affected location on varying range of mobile elements and reconstruct the network structure accordingly. A framework for traffic reconnect set-up partitioning based on inter partition gaps using the phantom partitioning concept is designed that identifies the network partitioning for most practical conditions using the NS2 simulator. With this, the TRSP with phantom partitioning balances the constraints with double cut method. The double cut based partitioning leads to safe route path by minimizing the energy consumption of sensor nodes in sensor network structure. The reestablishment of connectivity using the TRSP method provides improved data collection using the centroid mean point collection. The centroid mean point collection works with reconnect partition free sensor network system for varying range of mobile elements by achieving higher performance rate. The most affected location (i.e.,) partitioned nodes is identified and connectivity is reestablished during data aggregation. Simulation results demonstrate that the proposed TRSP method achieves better performance than the state-of-the-art methods in terms of energy consumption, data collection efficiency, and bandwidth rate.