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Achieving Realistic Sensing Coverage in Wireless Sensor Networks

Despite the well-known fact that sensing patterns in reality are highly irregular, researchers continue to develop protocols with simplifying assumptions about the sensing. For example, a circular 0/1 sensing model [2] is widely used in most existing simulators and analysis. While this model provides high-level guidelines, it could cause wrong estimation of system performance in the real world. Our answer to this issue is a sensing area modeling technique, which obtains the coverage of sensor nodes through event training. The main idea is using discrete controlled events to identify the sensing coverage based on event detection results by individual sensor nodes. A key architectural advantage of this approach is a lightweight design in sensor node with minimal overhead. Besides communication, each sensor node only needs to support a simple detection function (with optional time synchronization requirement). We evaluate our model using a physical experiment on a testbed consisting of 40 MicaZ motes. Our evaluation results shows we can identify the sensing area with low error rate with simple training method.