Energy-Efficient Control of Multiple Smart Sensors With Uncertain Controllability

For multiple smart sensors (MSSs) operating with limited energy, a smart sensor is said to be controllable if there exists proper external signal (ES) enabling it to report the sensing data, reflecting the physical world with tolerable sensing errors. In order to improve energy-efficiency while meeting the required sensing accuracy, the controllable smart sensors are expected to be activated while the uncontrollable ones are not. However, due to the exterior noises or interior interferences (ENII) of MSSs, whether a smart sensor is or not controllable is often uncertain, which may wrongly activate uncontrollable sensors or deactivate controllable ones, not only wasting energy but also deteriorating sensing errors. Therefore, addressing the problem of assuring MSSs with uncertain controllability to report tolerable sensing errors at the minimum cost of energy emerges as an interesting and challenging issue. To address this problem, this paper first formulates MSSs as the probabilistic multiple input multiple output (PMIMO) system, with all the ESs and sensing errors as the input and output, respectively. It then discloses the optimal activation probability for each smart sensor and reveals the optimal ES corresponding to the minimum energy for MSSs experiencing uncertain controllability to report the desired sensing errors. An algorithm is proposed based on our discovered results to implement such ES. The theoretical analysis and field experiments verify its performance.
Note to Practitioners
—This paper addresses the challenging issue of how to guarantee MSSs experiencing uncertain controllability to report tolerable sensing errors at the minimum cost of energy. Though the insightful discovery of the optimal probability that each smart sensor is activated, and further the optimal ES corresponding to the minimum energy for enabling MSSs experiencing uncertain controllability to report the desired sensing errors, this paper facilitates MSSs with uncertain controllability to operate in an energy-efficient as well as high-accurate manner, which we believe could improve the power usage and strengthen the sensing capacity of the Internet of Things (IoT) or Cyber-Physical Systems (CPS) that depend on MSSs settings to sense the physical world. Theoretical analysis and experiments both verify that the proposed solution outperforms existing ones.