Self-Adaptive Greedy Buffer Allocation and Scheduling (Sgbas) for Energy-Efficient Body Sensor Networks

Abstract

In order to minimize energy usage and reduce end-to-end delay in Body Sensor Networks (BSNs), this study introduces a method called Self-Adaptive Greedy Buffer Allocation and Scheduling (SGBAS). SGBAS reduces the number of transmissions and node energy consumption by adaptively resizing buffers and scheduling packet transmissions to minimize redundant data forwarding. We test the suggested approach in NS2 with different sensor fluxes and deployment densities. With up to 39% less energy consumption and major reductions in latency, the simulation findings show that standard Greedy Buffer Allocation (GBA) is significantly outperformed. The results show that SGBAS is a good way to extend the life of BSNs without lowering their data delivery performance.