Compressive sensing based data collection in VANETs

A Vehicular Sensor Network (VSN) is a sensing platform composed of smart onboard sensor nodes (vehicles) and roadside units, in which vehicles continuously collect sensor data from the road network to enable a range of real-time dataintensive applications, such as traffic pattern/congestion analysis, road surface diagnosis, and urban pollution monitoring. However, due to lossy links, limited bandwidth and highly dynamic network topology, it is very challenging to efficiently collect the data generated by vehicles on the road, especially under dense traffic situations.In this paper, we propose to deploy mobile agents for collecting sensor readings from a given road segment of interest. The mobile agent migrates among vehicles within the segment via wireless broadcast and uses local on-board computational resources to process and collect data as required. Since the wireless links are generally lossy, a broadcast may not reach all the vehicles within the segment; thus, to improve the reliability of the scheme, we further propose a termination decision algorithm based on recursive Bayesian estimation by which the agent decides whether all vehicles within the segment have been visited. Extensive simulation results show that the proposed agent-based data collection scheme achieves close to 100% data collection coverage under a wide range of vehicular traffic densities, while retaining a small communication overhead.

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“…from an in-built GPS receiver device. This is a common assumption in the related literature [21], [22].…”

Section: A the Scenariomentioning

confidence: 74%

An Agent Based Data Collection Scheme for Vehicular Sensor Networks

Huang

Libman

Geers

2015

2015 24th International Conference on Computer Communication and Networks (ICCCN)

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“…The aforementioned CS/MC based methods can be used to gather the traffic data efficiently with sparsifying sensor (or vehicle in [22]) data, but they are still unable to estimate the data in empty columns of the traffic matrix for urban traffic estimation. In one of our previous works in [10], the traffic estimation problem is also formulated into a MC problem by taking advantage of the low-rank feature, which represents sparsity in singular values.…”

Section: A Traffic Monitoring With Vsnmentioning

confidence: 99%

Effective Urban Traffic Monitoring by Vehicular Sensor Networks

Du¹,

Chen²,

Yang³

et al. 2015

IEEE Trans. Veh. Technol.

146

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Abstract-Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks (VSNs). Probe Vehicles (PVs), such as taxis and buses, and Floating Cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to traffic monitoring center (TMC) for traffic estimation. In TMC, sensing reports are aggregated to form traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads for all the time, TMC needs to estimate the un-sampled data in traffic matrix. As this matrix can be approximated to be of low-rank, Matrix Completion (MC) is an effective method to estimate the un-sampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e. average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real dataset analysis, the estimation error reduces from 35% to about 10%, compared with random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated.

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“…Liu et al in [16] proposed Compressive Sensing Based Data Collection (CS-DC) as a scalable and reliable data collection scheme for VANETs. In CS-DC, the distance and mobility based clustering protocol is proposed to achieve stable clusters, which collects local data reliably and achieves data spatial relevance.…”

Section: Related Workmentioning

confidence: 99%

Development and Testing of a 3G/LTE Adaptive Data Collection System in Vehicular Networks

Drira

Ahn

Rakha

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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Vehicular ad hoc networks (VANETs) are a special case of mobile ad hoc networks (MANETs). The distinctive characteristics of VANETs include high-speed vehicular nodes and significant variability in node density. Collecting data from VANETs is important in monitoring, controlling, and managing road traffic. However, efficient collection of the needed data is challenging because vehicles are continuously moving and generating a significant amount of events and data. The focus of this paper is on vehicle data collection using 3G/LTE. Initially, a comparison of proactive and reactive data collection schemes is conducted using simulation. The results show that proactive schemes produce the lowest delay and bandwidth usage but the highest loss ratio. To efficiently use the available bandwidth, an adaptive data collection scheme is developed and described. This adaptive data collection scheme is based on a proactive scheme using variable polling periods depending on the vehicle positions in the network and travel time to provide accurate traffic and travel time information to the Traffic Management Center (TMC). Simulation results, using taxi traces in Qatar, show that the proposed algorithm consumes an acceptable amount of megabytes (≈31 MB) per month when the basic polling interval is set to 10 s. Furthermore, traffic simulation results demonstrate that the proposed scheme has a minimum impact on delay and travel time estimates (relative error less than 2.5%), but can produce significant degradations in fuel consumption and emission estimates if computations are made at the TMC (relative error greater than 28% and 65%, respectively). These errors can be eliminated if computations are done on the vehicle.Index Terms-Mobile communication, data collection, automotive applications, intelligent transportation systems, wireless networks, traffic information, data aggregation, travel time estimation, vehicle emission estimates. 1524-9050

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Compressive sensing based data collection in VANETs

Cited by 33 publications

References 19 publications

An Agent Based Data Collection Scheme for Vehicular Sensor Networks

An Agent Based Data Collection Scheme for Vehicular Sensor Networks

Effective Urban Traffic Monitoring by Vehicular Sensor Networks

Development and Testing of a 3G/LTE Adaptive Data Collection System in Vehicular Networks

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