Maximizing data preservation time in intermittently connected sensor networks

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Hou, Xiang
Tang, Bin

In intermittently connected sensor networks, wherein sensor nodes do have connected paths to the base station periodically, preserving generated data inside the network is a new and challenging problem. We propose to preserve data items by distributing them from storage-depleted data generating nodes to sensor nodes with available storage space and high battery energy, under the constraints that each node has limited storage capacity and battery power. The goal is to maximize the minimum remaining energy among the nodes storing data items, in order to preserve them for maximum amount of time until next uploading opportunity arises. We refer to this problem as storage-depletion induced data preservation problem (SDP). First, we give feasibility condition of this issue by proposing and applying a Modified Edmonds-Karp Algorithm (MEA) on an appropriately transformed flow network. We then show that when feasible solutions exist, finding the optimal solution is NP-hard. Moreover, we develop a sufficient condition to solve SDP optimally. Finally, we design a distributed algorithm with less time complexity then compare it with flow based algorithm then show via simulations that distributed algorithm performs close to optimal solution.

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Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science