Energy-Optimal Control of Multiple Smart Sensors With Faulty External Signal

For multiple smart sensors (MSSs) operating with limited energy, the external signal (ES) often serves an indispensable role in enabling them to report their sensing data, reflecting the physical world with tolerable sensing errors. However, due to interior disturbances lurking in the MSSs or exterior noises originating from practical environments, the ES may become faulty for some smart sensors and subsequently affect the sensor connections. This not only deteriorates sensing errors but also wastes energy. Therefore, addressing the problem of assuring MSSs to report tolerable sensing errors with minimum energy consumption under the faulty ES (FES) emerges as an interesting and challenging issue. To address this problem, this paper first formulates MSSs as a temporal multiple input multiple output (MIMO) system, considering all the ESs and sensing errors as the input and output, respectively. It then reveals a necessary and sufficient condition for MSSs to determine whether or not to update the sensor connections, discloses a criterion guiding MSSs in performing such updates, and finally determines the optimal ES in terms of controlling MSSs to report tolerable sensing errors with the minimum energy consumption under the FES. An algorithm based on our discovered theoretical foundations is further proposed to implement such an optimal ES. The theoretical analysis and experiments against existing methods validate its performance.
Note to Practitioners
—This paper addresses the interesting issue of how to ensure that MSSs report acceptable sensing errors with the minimum energy consumption under the FES. Though the insightful disclosure of criterions for MSSs to judge whether and guide how to update the sensor connections, and the optimal ES corresponding to the minimum energy supporting MSSs to report the acceptable sensing errors under the FES, this paper enables MSSs to perform operations in a high-accuracy, energy-efficiency and FES-tolerance manner, which we believe could help the Internet of Things (IoT) or Cyber-Physical Systems (CPS) that employ MSSs settings to capture the physical world to improve their power usage, data accuracy and fault tolerance. Theoretical analysis and experimental investigations both verify that the proposed solution here outperforms the existing solutions.