Random Access in the IoT: An Adaptive Sampling and Transmission Strategy

Monitoring applications are gaining a lot of momentum in the Internet of Things (IoT), bolstered by the availability of cheap and easy-to-install sensors. Often, random access schemes are preferred to coordinated ones because they are more flexible and have no synchronization overhead. The possibility of collisions with other packets and the limited energy availability of the battery-powered nodes demand strategies to reduce the number of transmissions. This, however, is counterbalanced by the need to accurately monitor the process of interest, which requires a sufficient amount of sensed data. In this study, we propose a novel compression and transmission strategy with the objective of prolonging sensors' lifetime while guaranteeing a desired reconstruction accuracy of the tracked data. We consider the effect of both sensing and transmissions on the energy consumption, and adapt the sampling and transmission rates based on the target estimation accuracy and on the probability of packet losses caused by the interference from other sensors.