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A Novel Data Gathering Framework for Resource-constrained Underwater Sensor Networks
Stella Kafetzoglou, Maria Alexandropoulou and Symeon Papavassiliou

In this paper a novel data gathering framework for resource-constrained sensor networks is introduced and evaluated. The proposed framework is ideal for application in underwater sensor networking environments, which on top of the conventional sensor technology limitations, present additional constraints such as limited bandwidth and large propagation delays. The newly introduced framework consists mainly of two phases. Initially, due to the lack of infrastructure, a simple bridging technique is used to create a multi-hop tree rooted at the collection center. The corresponding tree is constructed by a simple leveling algorithm and is used to forward packets in the network. In the second phase, due to the fact that the environment under consideration is severely resource constrained, a distributed and probabilistic method is applied by the various sensor nodes to perform data aggregation based on their position (level) on the data gathering tree. The adopted aggregation approach aims at utilizing the available limited resources efficiently and effectively reducing significantly the network traffic, and as a result shortening the delays at the intermediate nodes and reducing the corresponding collisions and energy wastage in data transmission. The performance gains that can be achieved by the proposed data aggregation framework are evaluated via modeling and simulation, under different aggregation scenarios and traffic loads.

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