Dissemination and Harvesting of Urban Data Using Vehicular Sensing Platforms

摘要

Recent advances in vehicular communications make it possible to realize vehicular sensor networks, i.e., collaborative environments where mobile vehicles that are equipped with sensors of different nature (from toxic detectors to still/video cameras) interwork 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 allow 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, unlike 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 already-known solutions for sensor network data reporting inapplicable. This paper describes MobEyes, which is an effective middleware that was specifically designed for proactive urban monitoring and 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 demonstrated their effectiveness in terms of indexing completeness, harvesting time, and overhead. In particular, this paper includes 1) analytic models for MobEyes protocol performance and their consistency with simulation-based results, 2) evaluation of performance as a function of vehicle mobility, 3) effects of concurrent exploitation of multiple harvesting agents with single/multihop communications, 4) evaluation of network overhead and overall system stability, and 5) performance validation of MobEyes in a challenging urban tracking application where the po-n-nlice reconstruct the movements of a suspicious driver, e.g., by specifying the license number of a car.