MobEyes: Smart Mobs for Urban Monitoring with Vehicular Sensor Networks

Vehicular sensor networks are emerging as a new network paradigm of primary relevance, especially for proactively gathering monitoring information in urban environments (proactive urban monitoring). Each vehicle, which typically has no strict constraints on processing power and storage capabilities, can sense one or more events (e.g., imaging from streets and detecting toxic chemicals), process sensed data (e.g., recognizing license plates) and route messages to other vehicles (e.g., diffusing relevant notification to drivers or police agents). In this novel and challenging mobile environment, sensors can generate a sheer amount of data, and traditional sensor network approaches for data reporting become infeasible. This paper proposes MobEyes, an efficient lightweight support for proactive urban monitoring based on the primary idea of exploiting vehicle mobility to opportunistically diffuse concise summaries describing sensed data. MobEyes harvests these summaries and builds a lowcost distributed index of the autonomous mobile storage of sensed data. The reported experimental/analytic results show that MobEyes-based indexing can achieve reasonable completeness with good scalability and limited overhead in completely decentralized deployment scenarios.