Dhruv Sharma, Kayiram Kavitha, R Gururaj
The lifetime of a sensor network depends on the judicious utilization of the resource-constrained nodes. Practices like data aggregation, sleep scheduling play a major role in conserving the node’s energy. But in most cases, we observe a disparity in energy consumption rates among different sensors. This disparity results from higher utilization of a small set of deployed sensors in the field leaving these sensors drained out of power. To overcome this problem, it is often required to deploy redundant sensors to act as replacements for a faulty node. Secondly, the sensor network technology, being an application oriented technology, experiences variance in network parameters from application to application because of the various dynamics in nature. It is not often viable to go for a theoretically determined sensor distribution technique. Thus, it is often required to place sensors by studying the geographical constraints. These have proven to be highly valuable in designing energy efficient routing schemes anThe lifetime of a sensor network depends on the judicious utilization of the resource-constrained nodes. Practices like data aggregation, sleep scheduling play a major role in conserving the node’s energy. But in most cases, we observe a disparity in energy consumption rates among different sensors. This disparity results from higher utilization of a small set of deployed sensors in the field leaving these sensors drained out of power. To overcome this problem, it is often required to deploy redundant sensors to act as replacements for a faulty node. Secondly, the sensor network technology, being an application oriented technology, experiences variance in network parameters from application to application because of the various dynamics in nature. It is not often viable to go for a theoretically determined sensor distribution technique. Thus, it is often required to place sensors by studying the geographical constraints. These have proven to be highly valuable in designing energy efficient routing schemes and network topologies for sensor networks. In this paper we propose a scheme to decide how the distribution of available redundant sensor nodes should take place around sensor nodes. The scheme gives the flexibility to determine sensor positions based on application and geographical constraints. We propose to use the probability estimates of the utilization of a sensor in a given deployment to achieve desired network lifetimes. We also show how in some cases we can leverage the relative position from source(s) and sink be used for the same.d network topologies for sensor networks. In this paper we propose a scheme to decide how the distribution of available redundant sensor nodes should take place around sensor nodes. The scheme gives the flexibility to determine sensor positions based on application and geographical constraints. We propose to use the probability estimates of the utilization of a sensor in a given deployment to achieve desired network lifetimes. We also show how in some cases we can leverage the relative position from source(s) and sink be used for the same.
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