LIPS: Link prediction as a service for adaptive data aggregation in wireless sensor networks

A central component of the design of wireless sensor networks is reliable and efficient transmission of data from source to destination. However, this remains a challenging problem because of the dynamic nature of wireless links, such as interference, diffusion, and path fading. When the link quality worsens, packets will get lost even with retransmissions and acknowledgments when internal queues become full. For example, in a well-known study to monitor volcano behavior, the measured data yield of nodes ranges from 20% to 80%. To address this challenge brought by unreliable links, in this paper, we propose the idea of LIPS, or Link Prediction as a Service. Specifically, we argue that it is beneficial for the applications to be designed as adaptive from the start, by taking into account the future link quality estimates based on past measurements. In particular, we present a novel state-space based approach for link quality prediction, and demonstrate that it is possible to integrate this model into higher layer data aggregation protocols to improve their performance.