Utility-based sensor selection

Sensor networks consist of many small sensing devices that monitor an environment and communicate using wireless links. The lifetime of these networks is severely curtailed by the limited battery power of the sensors. One line of research in sensor network lifetime management has examined sensor selection techniques, in which applications judiciously choose which sensors' data should be retrieved and are worth the expended energy. In the past, many ad-hoc approaches for sensor selection have been proposed. In this paper, we argue that sensor selection should be based upon a tradeoff between application-perceived benefit and energy consumption of the selected sensor set. We propose a framework wherein the application can specify the utility of measuring data (nearly) concurrently at each set of sensors. The goal is then to select a sequence of sets to measure whose total utility is maximized, while not exceeding the available energy. Alternatively, we may look for the most cost-effective sensor set, maximizing the product of utility and system lifetime. This approach is very generic, and permits us to model many applications of sensor networks. We proceed to study two important classes of utility functions: submodular and supermodular functions. We show that the optimum solution for submodular functions can be found in polynomial time, while optimizing the cost-effectiveness of supermodular functions is NP-hard. For a practically important subclass of supermodular functions, we present an LP-based solution if nodes can send for different amounts of time, and show that we can achieve an O(log n) approximation ratio if each node has to send for the same amount of time. Finally, we study scenarios in which the quality of measurements is naturally expressed in terms of distances from targets. We show that the utility-based approach is analogous to a penalty-based approach in those scenarios, and present preliminary results on some practically important special cases