Maximizing aggregated revenue in sensor networks under deadline constraints

We study the problem of maximizing the aggregated revenue in sensor networks with deadline constraints. Our model is that of a sensor network that is arranged in the form of a tree topology, where the root corresponds to the sink node, and the rest of the network detects an event and transmits data to the sink over one or more hops. We assume a time-slotted synchronized system and a node-exclusive (also called a primary) interference model. We formulate this problem as an integer optimization problem and show that the optimal solution involves solving a Bipartite Maximum Weighted Matching problem at each hop. We propose a polynomial time algorithm based on dynamic programming that uses only local information at each hop to obtain the optimal solution. Thus, we answer the question of when a node should stop waiting to aggregate data from its predecessors and start transmitting in order to maximize revenue within a deadline imposed by the sink. Further, we show that our optimization framework is general enough that it can be extended to a number of interesting cases such as incorporating sleep-wake scheduling, minimizing aggregate sensing error, etc.