Using the Cloud to Improve Sensor Availability and Reliability in Remote Monitoring

Although there have been significant advancements in low-power remote sensors in recent years, the challenge of sensor availability and data reliability in remote monitoring applications still persists. The fault and failure of sensors will affect the reliability of the monitored data and subsequently the adverse effect will inevitably propagate itself to the data analytics stage. There are many existing solutions focusing on improving sensor nodes to enhance data reliability and couple it with various energy harvesting techniques to prolong the availability of sensor nodes. This paper presents a complementary solution to these existing solutions by analyzing the correlation between data from different sensor nodes using cloud computing resources. The discovered relationship between the sensor nodes can then be used to improve data reliability and availability of sensor nodes. Performance evaluations using real data sets show that there are indeed relationships between the collected data, and through these discovered relationships the fault detection and fault masking methods outperform conventional approaches such as autoregressive-integrated moving average. In addition, this paper also proposes an approach to extend operation of sensor nodes duration through the discovered relationships, with experiments showing promising results.