Sensorem - an efficient mobile platform for wireless sensor network visualisation

Abstract

We design and implement an Android application, Sensorem, for efficient retrieval and visualization of wireless sensor network (WSN) data. In light of data distribution and visualization being important developmental keystones of smart cities, we seek to enhance sensor data accessibility by developing a user-friendly mobile application (Sensorem) for meaningful visualization of sensor data, targeted at maintenance personnel. Sensor selection can be made with reference to geographical location through an embedded Google Map fragment, as well as a sensor list with sensors in order of WSN node ID. Sensor data is presented through interactive graphs, and graph overlaying functions are offered for easy comparison of data trends. We also employ a Bayesian prefetch algorithm and caching mechanisms to minimize sensor data access latency, such that the app system is able to cope with network and back-end bottlenecks.

Efficient data retrieval for large-scale smart city applications through applied Bayesian inference

“…We incorporate the proposed algorithm into Sensorem, a mobile visualization platform for wireless sensor networks that we have developed [9]. We evaluate the optimized Bayesian prefetching algorithm against the Naïve and Random prefetch baselines, using both simulated and actual data usage patterns on Sensorem.…”