Burst communication by means of buffer allocation in body sensor networks: Exploiting signal processing to reduce the number of transmissions

Monitoring human movements using wireless sensory devices promises to revolutionize the delivery of healthcare services. Such platforms use inertial information of their subjects for motion analysis. Potentially, each action or disease can be discovered by collaborative processing of sensor data from multiple locations on the body. This functionality is provided by a Body Sensor Network (BSN), which consists of several wireless sensor nodes positioned on different parts of the body. In spite of the revolutionary potential of this platform, power requirements and wearability have limited the commercialization of these systems. In this paper, we present an energy-efficient communication model for BSN applications which uses buffers to limit communication to short bursts, decreasing power usage and simplifying the communication. We formulate an optimization problem to reduce transmissions among sensor nodes and present an ILP-based solution and a fast greedy heuristic algorithm. We show that despite the decreased transmission efficiency, our greedy algorithm can be adopted for fast allocation of buffers in real-time. We experimentally compare the performance of both of the proposed approaches to the performance of an unbuffered system. Our results demonstrate that ILP and greedy solutions can reduce the amount of transmissions by an average factor of 70 and 41, respectively.