Sensor networks have many potential applications in biology, physics, medicine, and the military. One major challenge in sensor networks is to maximize network life under the constraint of extremely limited power supply. This is especially true for biomedical applications, which require large numbers of nodes that may be implanted in a subject: frequent battery changes are impractical. This paper addresses the challenge by proposing a new data gathering protocol, and analyzing a potential application in biomedical sensing technology. The protocol, named Hybrid Indirect Transmission (HIT), is based on an architecture consisting of one or more clusters that cooperatively compute multiple, multi-hop indirect transmission routes. In order to minimize both energy consumption and network delay, parallel transmissions with a collision avoidance guarantee are used throughout the network; adjacent clusters do not prevent this mechanism from working. To accomplish this, each sensor independently computes a medium access controlling TDMA schedule.
The computation within each sensor is intelligent yet simple; it requires O(n) space and O(n×logn) time complexities, and O(1) setup messages prior to the computation,where n is the number of sensors in the network. HIT does not require sensor nodes with CDMA capability; nor does it rely on the remote base station to compute a data gathering schedule. HIT was simulated along with LEACH (Low Energy Adaptive Clustering Hierarch), PEGASIS (Power Efficient Gathering for Sensor Information System), and Direct Transmission; a comparison of their performance in terms of energy efficiency, delay, and network lifetime is provided. Results show that HIT greatly reduces both energy consumption and network delay; it also maintains longer network life compared to these three existing protocols. We believe that this work is significant to the advancement of energy-efficient micro sensor networks; the proposed protocol is promising and would contribute to the use of wireless micro sensor networks in future biomedical technologies.
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