Quality-of-information modeling and adapting for delay-sensitive sensor network applications

Acceptable Quality-of-Information (QoI) is essential for sensor network applications such as infrastructure health monitoring because it directly impacts public safety. However, it is a challenging problem to attain good QoI of sensor applications due to unpredictable environment noise, unreliable network communication, and varying requirements for wide variety of sensor applications. We believe the first step to addressing this challenge is to develop an application-independent QoI model. In this paper, we present a fundamental quality-of-information model based on signal-to-noise ratio. Our model addresses information quality by considering sensor measurement quality, network quality and sensor application requirements by end users. Furthermore, we develop a quality-aware scheduling framework which exploits an analytical queue model to calculate and adapt sensor node sampling rate and base station scheduling priority in order to optimize overall quality. Our results show that sensor measurement quality in terms of sampling rate, network quality in terms of loss rate and delay, all play significant roles in impacting overall quality. A QoI-aware scheduler thus is an effective approach to quantify information quality and adapt for unpredictable sensor networks.