Sensor control and scheduling strategies for sensor networks

We investigate sensor control and scheduling strategies to most effectively use the limited resources of an ad hoc network or closed-loop sensor network. In this context, we examine the following three problems. Where to focus sensing? Certain types of sensors, such as cameras or radars, are unable to simultaneously collect high fidelity data from all environmental locations, and thus require some sort of sensing strategy. Considering a meteorological radar network, we show that the main benefits of optimizing sensing over expected future states of the environment are when there are multiple small phenomena in the environment. Considering multiple users, we show that the problem of call admission control (i.e., deciding which sensing requests to satisfy) in the context of a virtualized private sensor network can be solved in polynomial time when sensor requests are divisible or fixed in time. When sensor requests are indivisible but may be shifted in time, we show that the call admission control problem is NP-complete. How to make sensing robust to delayed and dropped packets? In a closed-loop sensor network, data collected by the sensors determines each sensor\u27s future data collection strategy. Network delays, however, constrain the quantity of data received by the time a control decision must be made, and consequently affect the quality of the computed sensor control. We investigate the value of separate handling of sensor control and data traffic, during times of congestion, in a closed-loop sensor network. Grounding our analysis in a meteorological radar network, we show that prioritizing sensor control traffic decreases the round-trip control-loop delay, and thus increases the quantity and quality of the collected data and improves application performance. How to make routing robust to network changes? In wireless sensor and mobile ad-hoc networks, variable link characteristics and node mobility give rise to changing network conditions. We propose a routing algorithm that selects a type of routing subgraph (a braid) that is robust to changes in the network topology. We analytically characterize the reliability of a class of braids and their optimality properties, and give counter-examples to other conjectured optimality properties in a well-structured (grid) network. Comparing with dynamic source routing, we show that braid routing can significantly decrease control overhead while only minimally degrading the number of packets delivered, with gains dependent on node density