Communication Algorithm for Statistic Monitoring in People-Centric Sensing Networks

Mobile phones have great potential to act as global mobile sensing devices due to already being equipped with sensors, e.g., sound, image and acceleration sensors. People as individuals or special interest groups can apply the new sensing devices to form sensing networks called people-centric sensing networks that sense what we are doing and support our daily activities. Most of the sensing applications depend on the ability to monitor statistics including max, average, ranking, rather than raw sensor readings. Such an integration of sensing-capable mobile phones into the networking infrastructure shifts the network's main utility from data communication to information filtering. Thus, data reduction is one of the major networking challenges for people-centric sensing networks. In this paper, we propose a communication algorithm for statistic monitoring in people-centric sensing networks. The main feature of the proposed algorithm is the ability to reduce communication traffic by eliminating redundant sensing data transmission without increasing the error of the statistics during monitoring. We show how the proposed algorithm is applied to a ranking monitoring application. We also include a simulation study demonstrating the advantages of the proposed algorithm. Furthermore, we show a prototype system where the proposed algorithm is implemented into a mobile phone.