Stopping-Time Control of Multiple Smart Sensors Based on Tradeoffs Between Sensing Accuracy and Energy Consumption While Maintaining Energy Consumption Balance

This paper considers the stopping-time control of multiple smart sensors which independently sample the physical parameter of interests and request the desired data from the common server with probabilities. More samples may lead to higher sensing accuracy but cost more energy. More requests may facilitate the smart sensors to obtain the desired data with higher probability while yet cost more energy. Meanwhile, the heterogeneities of smart sensors issuing the requests may lead to early energy exhaustion for some smart sensors. Therefore, how to make an optimal tradeoff between sensing accuracy and energy consumption while keeping the energy consumption balance arises as an interesting problem. To address this issue, this paper first formulates the stopping-time control policy of an individual smart sensor as a partially observable Markov decision process (POMDP), then coordinates the stopping-time control policies of multiple smart sensors within a noncooperative game (NCG), and finally proposes a noncooperative PODMP game-based algorithm to make the above tradeoff. Theoretical analysis and field experiments jointly document the performance. Note to Practitioners—This paper addresses the interesting tradeoff between sensing accuracy and energy cost while balancing the energy consumption in a sensor network that operates in the environments where the available energy is limited and the early energy exhaustion for some of the smart sensors should be avoided. It helps to improve the operation efficiency of the sensor network with multiple smart sensors and a shared server setting in the area of Internet of Things (IoT) or Cyber-Physical Systems (CPS) to minimize the estimation error, while keeping the accumulative energy cost within a range and balancing the energy consumption. Field experimental investigations show that the solution proposed here outperforms existing solutions.