On data gathering protocols for in-body biomedical sensor networks

This paper investigates the effectiveness of data gathering protocols for in-body biomedical sensor networks. We studied the performance of representatives from each of three major protocol categories: (1) low energy adaptive clustering hierarchy (LEACH), a cluster-based protocol, (2) power efficient gathering for sensory information systems (PEGASIS), a chain-based protocol, and (3) hybrid indirect transmissions (HIT), a hybrid of chains and clusters. First, the ability of each protocol to perform in-network source separation was judged. We consider a human-machine interaction application in which implanted bio-sensors communicate motor unit actions of human muscles to a remote computer. Motor unit action potentials (MUAPs) were modeled, and source separation and recovery at each sensor was simulated. We compare the performance of HIT and LEACH in terms of signal distortion ratios and the energy costs of fusion and communication. Second, we report on the efficiency of each protocol for in-body data collection, using Gupta et al´s propagation loss model for biomedical applications (PMBA) - an accurate model of power loss due to signal absorption by the human body. We investigate the effectiveness of HIT, LEACH, and PEGASIS under this model, and compare their performance in terms of energy efficiency and network lifetime