Ad hoc networks formed by traveling vehicles are envisaged to become a common platform that will support a wide variety of applications, ranging from road safety to advertising and entertainment. The multitude of vehicular applications calls for routing schemes that satisfy user-defined delay requirements while at the same time maintaining a low level of channel utilization to allow their coexistence. This paper focuses on the development of carry-and-forward schemes that attempt to deliver data from vehicles to fixed infrastructure nodes in an urban setting. The proposed algorithms leverage local or global knowledge of traffic statistics to carefully alternate between the Data Muling and Multihop Forwarding strategies, in order to minimize communication overhead while adhering to delay constraints imposed by the application. We provide an extensive evaluation of our schemes using realistic vehicular traces on a real city map.
Road congestion and traffic-related pollution have a large negative social and economic impact on several economies worldwide. We believe that investment in the monitoring, distribution, and processing of traffic information should enable better strategic planning and encourage better use of public transport, both of which would help cut pollution and congestion. This paper investigates the problem of efficiently collecting and disseminating traffic information in an urban setting. We formulate the traffic data acquisition problem and explore solutions in the mobile sensor network domain while considering realistic application requirements. By leveraging existing infrastructure such as traveling vehicles in the city, we propose traffic data dissemination schemes that operate on both the routing and the application layer; our schemes are frugal in the use of the wireless medium, rendering our system interoperable with the proliferation of competing applications. We introduce the following two routing algorithms for vehicular networks that aim at minimizing communication and, at the same time, adhering to a delay threshold set by the application: 1) delay-bounded greedy forwarding and 2) delay-bounded minimum-cost forwarding. We propose a framework that jointly optimizes the two key processes associated with monitoring traffic, i.e., data acquisition and data delivery, and provide a thorough experimental evaluation based on realistic vehicular traces on a real city map.
Wireless sensor networks present significant opportunities for fine-grained and continuous monitoring of road traffic, enabling careful city planning, automated road maintenance and accident detection. Users are typically willing to tolerate a small error in car-flow data, in order to reduce the cost of data propagation from the sensor nodes to the gateway nodes, to which users are connected. In this paper, we first examine the relative performance of Fourier-and Wavelet-based algorithms for compressing traffic data locally at the sensor nodes. Using real traffic information from the city of Cambridge (UK), we then demonstrate that car-flow data collected across geographically dispersed sensor nodes exhibit strong spatial and temporal correlations. We then combine lossy Fourier-compression with correlation-based compression to achieve further communication savings within a user-specified error threshold. For a tolerated error of 5-15 cars per 5 min, it is shown that exploitation of temporal correlations yields 14-30% savings relative to Fourier compression alone, whilst use of spatial correlations results in 10-35% savings.Acknowledgments-Our work was supported by the Engineering and Physical Sciences Research Council (EPSRC) under the TIME-EACM award EP/C547640/1. Our traffic dataset was provided by SCOOT.
Road congestion and traffic-related pollution have a large, negative social and economic impact, and we believe many of these problems can be reduced through investment in monitoring, distribution and processing of traffic information. This paper outlines how our on-going work on the TIME project (Transport Information Monitoring Environment) provides a solution, using traffic sensor systems and the design and development of an open and decentralised software framework. We also discuss how we address the privacy and security implications of the increased use of sensors and data processing.
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