Collaborative Scheduling in Dynamic Environments Using Error Inference

TLDR: The main objective of this work is to develop a generic scheduling mechanism for collaborative sensors to achieve the error-bounded scheduling control in monitoring applications and show that the approach is effective and efficient in tracking the dramatic temperature shift in dynamic environments.

Abstract: Due to the limited power constraint in sensors, dynamic scheduling with data quality management is strongly preferred in the practical deployment of long-term wireless sensor network applications. We could reduce energy consumption by turning off (i.e., duty cycling) sensor, however, at the cost of low-sensing fidelity due to sensing gaps introduced. Typical techniques treat data quality management as an isolated process for individual nodes. And existing techniques have investigated how to collaboratively reduce the sensing gap in space and time domain; however, none of them provides a rigorous approach to confine sensing error is within desirable bound when seeking to optimize the tradeoff between energy consumption and accuracy of predictions. In this paper, we propose and evaluate a scheduling algorithm based on error inference between collaborative sensor pairs, called CIES. Within a node, we use a sensing probability bound to control tolerable sensing error. Within a neighborhood, nodes can trigger additional sensing activities of other nodes when inferred sensing error has aggregately exceeded the tolerance. The main objective of this work is to develop a generic scheduling mechanism for collaborative sensors to achieve the error-bounded scheduling control in monitoring applications. We conducted simulations to investigate system performance using historical soil temperature data in Wisconsin-Minnesota area. The simulation results demonstrate that the system error is confined within the specified error tolerance bounds and that a maximum of 60 percent of the energy savings can be achieved, when the CIES is compared to several fixed probability sensing schemes such as eSense. And further simulation results show the CIES scheme can achieve an improved performance when comparing the metric of a prediction error with baseline schemes. We further validated the simulation and algorithms by constructing a lab test bench to emulate actual environment monitoring applications. The results show that our approach is effective and efficient in tracking the dramatic temperature shift in dynamic environments.