Burst communication by using self-adaptive buffer allocation with energy-efficient in-body sensor networks

Abstract

The revolution of wireless sensory devices in healthcare services is monitoring of human movements. Such type of motion analysis uses human's inertial information. This study is achieved by using Body Sensor Network (BSN) in which many wireless sensor nodes are placed on different parts of the body. Disease is recognised by shared processing of sensor information from multiple locations of the body. Even though this platform has high potential in healthcare, the commercialisation of these systems is difficult because of power requirements and wearability problems. In this paper, a self-adaptive greedy buffer allocation and scheduling algorithm (SGBAS) is presented for optimisation of communication model for BSN applications which uses self-adaptive buffers for limitation of communication to short bursts, power usage and transmitting sensed data to the backend servers periodically. The optimisation of QoS is achieved by introducing buffers. This project provides a self-adaptive heuristic scheme which is formulated to reduce transmissions among sensor nodes. For early diagnosis, it sends the data at right time for the analysis of healthcare professionals. Several experimental evaluation and simulations is to be done to assess the proposed SGBAS technique for allocation of buffers in real time under various simulation setups. Experiments demonstrate the effectiveness of SGBAS power reduction techniques.