Dissemination and Harvesting of Urban Data using Vehicular Sensing Platforms

Recent advances in vehicular communications make it possible to realize vehicular sensor networks, i.e., collaborative environments where mobile vehicles equipped with sensors of different nature (from toxic detectors to still/video cameras) inter-work to implement monitoring applications. In particular, there is an increasing interest in proactive urban monitoring where vehicles continuously sense events from urban streets, autonomously process sensed data, e.g., recognizing license plates, and possibly route messages to vehicles in their vicinity to achieve a common goal, e.g., to permit police agents to track the movements of specified cars. This challenging environment requires novel solutions, with respect to those of more traditional wireless sensor nodes. In fact, different from conventional sensor nodes, vehicles exhibit constrained mobility, have no strict limits on processing power and storage capabilities, and host sensors that may generate sheer amounts of data, thus making inapplicable already known solutions for sensor network data reporting. The paper describes MobEyes, an effective middleware specifically designed for proactive urban monitoring, that exploits node mobility to opportunistically diffuse sensed data summaries among neighbor vehicles and to create a low-cost index to query monitoring data. We have thoroughly validated the original MobEyes protocols and have demonstrated their effectiveness in terms of indexing completeness, harvesting time, and overhead. In particular, the paper includes i) analytic models for MobEyes protocol performance and their consistency with simulation-based results, ii) evaluation of performance as a function of vehicle mobility, iii) effects of concurrent exploitation of multiple harvesting agents with single/multi-hop communications, iv) evaluation of network overhead and overall system stability, and v) MobEyes validation in a challenging urban tracking application where the police reconstructs the movements of a suspicious driver, say, by specifying the car license number.