Scalable Delay-Sensitive Polling of Sensors

Abstract

In a sensor-rich Internet of Things environment, we may be unable to gather all data at a processing centre at the rate at which the data is generated. The rate of data collection from a sensor may be limited by available bandwidth/cost (or energy considerations), especially if one were to use cellular networks for such systems. In this context, we present a mechanism for determining which sensors to gather data from at each polling epoch. Our sensor polling mechanism prioritizes sensors using information about the data generation rate, the expected value of the data as well as its time sensitivity. Our problem formulation and its solution relate to the restless bandit model for sequential decision making. Whereas existing methods for the restless bandit model are not directly applicable because the state space is continuous and not discrete, we prove that similar techniques can be used because of particular characteristics of the underlying problem. We then show that our approach can be very effective even when not optimal through an extensive quantitative study where event arrivals follow a hyper-exponential distribution.

Keywords

• Internet of Things
• data freshness
• stochastic scheduling
• soft real-time systems
• dynamic programming
• reinforcement learning