An efficient clustering and path planning strategy for data collection in sensor networks based on space-filling curves

In this paper, we consider a scenario where a mobile robot is tasked with periodically collecting data from a fixed wireless sensor network. Our goal is to minimize the total energy cost of the operation, including the communication cost from the sensors to the robot and the motion cost of the robot. We propose a strategy that properly combines the ideas of clustering and using a mobile robot for data collection. Our approach is based on using space-filling curves, which results in a computationally-efficient algorithm. It can furthermore handle realistic communication environments by utilizing probabilistic channel predictors that go beyond disk models.We mathematically characterize an upper bound for the performance of our proposed algorithm, which shows how the energy saving is related to the total number of generated bits in the network, and the communication and motion parameters. Finally, we verify the effectiveness of our proposed framework in a simulation environment, where a considerable reduction in energy consumption is achieved as compared to the case of no clustering.