iSense: Energy-aware crowd-sensing framework

Recently, crowd-sensing has rapidly been evolved thanks to the technological advancement in personal mobile devices. This emerging technology opens the door for numerous applications to collect sensory data from the crowd. To provide people with a motive for participating in data acquisition, the crowd-sensing systems have to sidestep burdening the resources allocated to the mobile devices, i.e. computing power and energy budget. In this paper, we propose iSense, a novel framework for reducing the energy costs of participating in crowd-sensing. We mainly target the superfluous energy overhead on the mobile devices to sense and report their position information to the back-end servers. To relieve such an overhead, iSense entirely offloads the localization burden to the crowd-sensing servers. In this manner, iSense enables the utilization of advanced localization approaches thanks to the high resources of the crowd-sensing servers. To this end, iSense opportunistically exploits the “already-existent” network signaling exchanged frequently between the mobile devices and the WiFi networks or the cellular networks. To collect the localization data, we implement a lightweight data collection algorithm on a set of off-the-shelves access points. As a case study, we implement a two-step localization method, including a coarse- and a fine-grained localization. In this regard, compressed sensing is employed to estimate the fine-grained solution. To assess the effectiveness of iSense, we implemented a testbed to evaluate the energy consumption and the localization accuracy with different mobility and usage patterns. The results show that using iSense, compared to some baseline methods, we can identify up to 95% savings in the consumed energy.