Optimizing Energy-Latency Trade-Off in Sensor Networks with Controlled Mobility

We consider the problem of planning path and speed of a "data mule" in a sensor network. This problem is encountered in various situations, such as modeling the motion of a data-collecting UAV (unmanned aerial vehicle) in a field of sensors for structural health monitoring. Our specific context here is use of a data mule as an alternative or supplement to multihop forwarding in a sensor network. While a data mule can reduce the energy consumption at each sensor node, it increases the latency from the time the data is generated at a node to the time the base station receives it. In this paper, we introduce the "data mule scheduling" or DMS framework that enables data mule motion planning to minimize the data delivery latency. The DMS framework is general; it can express many previously proposed problem formulations and problem settings related to data mules. We design algorithms for DMS and extend to the more general case of combined data mule and multihop forwarding to enable a flexible trade-off between energy consumption and data delivery latency. Using DMS, we can calculate the optimal way for node-to-node forwarding and data mule motion plan. Our implementation and simulation results using ns2 show nearly monotonic decrease of data delivery latency when each node can use more energy, thus vastly increasing the flexibility in the energy-latency trade-off for sensor network communications.