This research aims at using vehicular ad-hoc networks as infra-structure for an urban cyber-physical system in order to gather data about a city. In this scenario, all nodes are data sources and there is a gateway as ultimate destination for all packets. Because of the volatility of the network connections and uncertainty of actual node placement, we argue that a broadcast-based protocol is the most adequate solution, despite the high overhead. The Urban Data Collector (UDC) protocol has been proposed which uses a distributed election of the forwarding node among the nodes receiving the packet: nodes that are nearer to the gateway have shorter timers and a higher forwarding probabilities. The performance of the UDC protocol has been evaluated with different suppression levels in terms of the amount of collected data from each road segment using NS-3, and our results show that UDC can achieve significantly higher sensing accuracy than to other broadcast-based protocols.
Recent advances in wireless inter-vehicle communication systems enable the establishment of vehicular ad-hoc networks (VANET) and create significant opportunities for the deployment of a wide variety of applications and services to vehicles. In this work, we investigate the problem of developing services that can provide car drivers with time-sensitive information about traffic conditions and roadside facilities. We introduce the vehicular information transfer protocol (VITP), a location- aware, application-layer, communication protocol designed to support a distributed service infrastructure over vehicular ad- hoc networks. We describe the key design concepts of the VITP protocol and infrastructure. We provide an extensive simulation study of VITP performance on large-scale vehicular networks under realistic highway and city traffic conditions. Our results demonstrate the viability and effectiveness of VITP in providing location-aware services over VANETs. CarTel is a mobile sensor computing system designed to collect, process, deliver, and visualize data from sensors located on mobile units such as automobiles. A CarTel node is a mobile embedded computer coupled to a set of sensors. Each node gathers and processes sensor readings locally before delivering them to a central portal, where the data is stored in a database for further analysis and visualization. In the automotive context, a variety of on-board and external sensors collect data as users drive.CarTel provides a simple query-oriented programming interface, handles large amounts of heterogeneous data from sensors, and handles intermittent and variable network connectivity. CarTel nodes rely primarily on opportunistic wireless (e.g., Wi-Fi, Bluetooth) connectivity to the Internet, or to "data mules" such as other CarTel nodes, mobile phone flash memories, or USB keys-to communicate with the portal. CarTel applications run on the portal, using a delay-tolerant continuous query processor, ICEDB, to specify how the mobile nodes should summarize, filter, and dynamically prioritize data. The portal and the mobile nodes use a delay-tolerant network stack, CafNet, to communicat.CarTel has been deployed on six cars, running on a small scale in Boston and Seattle for over a year. It has been used to analyze commute times, analyze metropolitan Wi-Fi deployments, and for automotive diagnostics. 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 police reconstruct the movements of a suspicious driver, e.g., by specifying the license number of a car.