Energy-Efficient Data Storage Solutions under Sink Failures

In challenging environment, sensory data must be stored inside the network in case of sink failures. Since all sensor nodes have limited storage capacity and energy, not all the data can be stored. In order to preserve more data, we need to redistribute partial data items from storage-depleted source nodes to sensor nodes with available storage space and residual energy. In this paper, we study the data redistribution and retrieval with priority problem (DRRP) and try to preserve data that maximizing the total preserved data priorities, while minimizing data redistribution and retrieval costs. By utilizing graph transformation, we convert this problem into a minimum cost maximum weighted flow problem. In order to find the optimal solution, we propose an improved linear programming algorithm named ( energy-efficient data preservation with priority). Through extensive simulations, we show that the proposed algorithm outperforms the other two traditional algorithms in terms of energy consumption.