Abstract
In this paper we have presented an algorithm to construct a rooted tree with base station as root connecting all the sensor nodes. The tree is constructed with the aim of maximizing the network life-time. It is assumed that all the nodes have same initial energy, but they can adjust their transmission range and thus the amount of energy needed for transmission may vary. The cost of a node is the amount of energy spent by it in each data gathering round. While determining the lifetime of a node, the energy lost in the process of constructing the tree is also considered. Thus the lifetime of a node is its residual energy (initial energy minus energy spent in exchanging messages for construction) divided by its cost. The lifetime of the network is the minimum of the lifetimes of all the nodes in the sensor network. It is also assumed the sensed data is aggregated so that nodes send a fixed sized message to its parent in each data gathering round. The algorithm works in two phases: In the first phase, an initial tree is constructed where the path from a sensor node to the base station consists of least possible number of hops. In the second phase (called fine-tuning) nodes may change their parents if that lead to a reduction in maximum cost of the three nodes involved (the node, its present parent, its future parent). The algorithms for both the phases (initial tree construction and fine-tuning) are distributed where nodes take decision Bbased on the status of its neighbors. Experimental results show that fine-tuning leads to considerable improvement in life-time of the network. The lifetime computed is significantly higher than those obtained by other well-known algorithms for data gathering.
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Dutta, A., Thakkar, K., Khatua, S., Das, R.K. (2013). Power Efficient Data Gathering for Sensor Network. In: Hota, C., Srimani, P.K. (eds) Distributed Computing and Internet Technology. ICDCIT 2013. Lecture Notes in Computer Science, vol 7753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36071-8_18
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DOI: https://doi.org/10.1007/978-3-642-36071-8_18
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