Maximizing Information in Unreliable Sensor Networks Under Deadline and Energy Constraints

We study the problem of maximizing the information in a wireless sensor network with unreliable links. We consider a sensor network with a tree topology, where the root corresponds to the sink, and the rest of the network detects an event and transmits data to the sink. We formulate a combinatorial optimization problem that maximizes the information that reaches the sink under deadline, energy, and interference constraints. This framework allows using a variety of error recovery schemes to tackle link unreliability. We show that this optimization problem is NP-hard in the strong sense when the input is the maximum node degree of the tree. We then propose a dynamic programming framework for solving the problem exactly, which involves solving a special case of the job interval selection problem (JISP) at each node. Our solution has a polynomial time complexity when the maximum node degree is $O(\log N)$ in a tree with $N$ nodes. For trees with higher node degrees, we further develop a suboptimal solution, which has low complexity and allows distributed implementation. We investigate tree structures for which this solution is optimal to the original problem. The efficiency of the suboptimal solution is further demonstrated through numerical results on general trees.