Extended Comb Needle Model for Energy Efficient Data Aggregation in Random Wireless Sensor Networks

Background/Objectives: Energy conservation in Wireless Sensor Network is essential to enhance its life. A sensor node consumes more energy for communication than performing data gathering or data processing. Data aggregation minimizes the data size for communication. Methods/Statistical Analysis: The Comb Needle model is available in literature to perform data aggregation for grid networks (regular deployment). Extended the Basic Comb Needle Model in randomly deployed sensor networks. The simple random network with Comb Needle Model is compared with simple random network without Comb Needle Model. The theoretical analysis and simulation study shows that Extended Comb Needle Model performs better data aggregation. Findings: When we apply the Proposed Model in random network, the communication cost, overhead, and energy consumption are significantly reduced. The simulation results for the proposed Extended Comb Needle Model prove that the energy consumption and overall communication costs are substantially minimized. The simulation comparison is done for simple random network with and without Comb Needle Model in terms of communication cost, energy consumption, delay, packet loss, packet delivery ratio, and throughput. We found that the communication cost is decreased from 82% to 58%. the average energy consumption is decreased from 80% to 40%. Delay is decreased from 76% to 20%. Packet loss in decreased from 67% to 12%. Packet Delivery Ratio is increased from 82% to 87%. And throughput is increased from 70% to 90%. Application/Improvements: Proposed Model optimizes WSN performance in terms of better packet delivery ratio, improved throughput, minimized energy consumption and reduced delay. Simulation results as well as theoretical analysis affirm the same.