Data collection, storage, and retrieval with an underwater sensor network
Proceedings of the 3rd international conference on Embedded networked sensor systems
On the relationship between capacity and distance in an underwater acoustic communication channel
ACM SIGMOBILE Mobile Computing and Communications Review
A Study of k-Coverage and Measures of Connectivity in 3D Wireless Sensor Networks
IEEE Transactions on Computers
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This paper studies the problem of distributed connectivity assessment for a network of underwater sensors in a data aggregation mission. Motivated by a sufficient condition for asymptotic almost sure consensus in a network defined over a random digraph, vertex connectivity of the expected communication graph is used as a measure for the connectivity of the underwater sensor network. A distributed update scheme is proposed in which the sensors update their perception of the expected communication graph. The expected communication graph can be characterized by its associated probability matrix. A learning algorithm is employed by each sensor to update its belief on the probabilities using the broadcast messages it receives. Each sensor uses a polynomial-time algorithm to estimate the degree of vertex connectivity of the expected graph based on its perception of the network graph. The proposed algorithms can also handle changes in the topology of the network such as node addition, node deletion, and time-varying probabilities. The performance of the proposed algorithms is validated in simulation.