Design Of Low-Power-Consumption Body Area Networks Based On Distributed Compressed Sensing Transmission

There has been considerable recent interest in Body Area Networks (BAN), dueto its potential for applications. Body Area Networks is a kind of emerging technologywith great practical applicability, including health care, life and entertainment,military communications, emergency and disaster relief, and aerospace etc.An overview of BAN is presented at first. The main technology challenges ofBAN are discussed, including low power consumption design, the establishment ofbiology channel model, communication protocol and the security of data transmission.The advances of these problems are introduced, in which low power consumptiondesign is the most critical. Because in considerable cases, such as health care,emergency and disaster relief, it is necessary for BAN to keep working for a long timewithout energy supplement. Hence, this paper focuses on low power consumptiondesign in BAN.Most energy of BAN is used for data transmission among sensor nodes. The datatransmission in BAN is decomposed into several point-to-point transmissions.Compressed sensing, a novel source encoding method, is introduced to reduce thetransmission data, and lower the entire power consumption substantially. Signal iscompressed at transmitter through compressed sensing, then the receiver nodesreconstruct the signal through corresponding reconstruction algorithm, which isequivalent to solve a optimization problem. An improved orthogonal matching pursuitalgorithm is proposed. Simulation results show that power consumption is greatlysaved through compressed sensing.Then, using the correlation among sensor nodes, distributed compressed sensingis presented to further reduce overall power consumption. Several different jointsparse signal models, namely JSM-1, JSM-2and JSM-3, are introduced. ForJSM-1signal, the reconstruction of several signals is converted to the reconstruction ofan equivalent signal through a linear programming problem with low complexity,using the feature that JSM-1signal consists of a sum of two components: a commoncomponent that is present in all of the signals and an innovations component that is unique to each signal. Simulation results show that, compared with individualreconstruction, joint reconstruction can further reduce the overall power consumptionand the performance advantages is impacted by the proportion of common component.A Multi-Subspace Pursuit algorithm is proposed for JSM-2signal, compared with theprevious One Step Greedy Algorithm and Simultaneous Orthogonal Matching Pursuitalgorithm, the proposed algorithm guarantees performance with lower complexity.JSM-3signals is uncompressed due to its lack of sparsity. However, using the featurethat common elements exist, the signals can be reconstructed through iterativeestimation of common and innovation elements.