Mobeyes: smart mobs for urban monitoring with a vehicular sensor network

TLDR

Vehicular sensor networks are emerging as a new paradigm for urban monitoring, with vehicles offering ample processing power and storage, capable of sensing, processing, and routing data, yet the sheer volume of data they generate makes traditional sensor‑network reporting approaches unfeasible. The article proposes MobEyes to enable proactive urban monitoring by exploiting vehicle mobility to opportunistically diffuse summaries of sensed data. MobEyes is a lightweight, efficient system that opportunistically diffuses these summaries across vehicles, building a low‑cost distributed index. Experimental results demonstrate that MobEyes harvests summaries and constructs a distributed index with reasonable completeness, good scalability, and limited overhead.

Abstract

Vehicular sensor networks are emerging as a new network paradigm of primary relevance, especially for proactively gathering monitoring information in urban environments. Vehicles typically have no strict constraints on processing power and storage capabilities. They can sense events (e.g., imaging from streets), 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 unfeasible. This article proposes MobEyes, an efficient lightweight support for proactive urban monitoring based on the primary idea of exploiting vehicle mobility to opportunistically diffuse summaries about sensed data. The reported experimental/analytic results show that MobEyes can harvest summaries and build a low-cost distributed index with reasonable completeness, good scalability, and limited overhead