The transmission of energy is monitored in the smart grid through deploying sensors in all the components, including the overhead transmission lines. There are many poles/towers supporting a long overhead transmission line. Naturally, sensors are deployed on the location close to the poles/towers on each span. Due to the limited transmission range of the wireless transceiver module of a sensor, researchers generally assume that data generated by a sensor have to be delivered to the substation through a set of sensors in-between. This results in a linear network model. In this paper, we first analyze the performance of this model in handling the traffics extracted from an existing testbed. We realize that the linear network model may not be sufficient to support future smart grid applications which may have diversified requirements on data delivery. We then study a new network model in which sensor/relay nodes can also communicate with other nodes using a wide area network such as the cellular network. In this new model, the network formed can be reconfigured based on the application requirements to deliver information to the substations efficiently and effectively.

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Many sensor network applications require the tracking and the surveillance of target objects. However, in current research, many studies have assumed that a target object can be sufficiently monitored by a single sensor. This assumption is invalid in some situations, especially, when the target object is so large that a single sensor can only monitor a certain portion of it. In this case, several sensors are required to ensure a 360 coverage of the target. To minimize the amount of energy required to cover the target, the minimum set of sensors should be identified. Centralized algorithms are not suitable for sensor applications. In this paper, we describe our novel distributed algorithm for finding the minimum cover. Our algorithm requires fewer messages than earlier mechanisms and we provide a formal proof of correctness and time of convergence. We further demonstrate our performance improvement through extensive simulations.

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