Coverage and distinguishability requirements for Traffic Flow Monitoring Systems

Traffic flow monitoring systems aim to measure and monitor vehicle trajectories in smart cities. Their critical applications include vehicle theft prevention, vehicle localization, and traffic congestion solution. This paper studies an RoadSide Unit (RSU) placement problem in traffic flow monitoring systems. Given some traffic flows on streets, the objective is to place a minimum number of RSUs to cover and distinguish all traffic flows. A traffic flow is covered and distinguishable, if the set of its passing RSUs is non-empty and unique among all traffic flows. The RSU placement problem is NP-hard, monotonic, and non-submodular. It is a non-trivial extension of the traditional set cover problem that is submodular. We show that, to cover and distinguish an arbitrary pair of traffic flows (f and f'), two RSUs should be placed on streets from two different subsets of f\f', f'\f, and f ∩ f'. Three bounded RSU placement algorithms are proposed. Their approximation ratios are n ln n(n-1)/2 , n+1/2 ln 3n(n-1)/2, and ln n(n+1)/2, respectively. Here, ri is the number of given traffic flows. Extensive real data-driven experiments demonstrate the efficiency and effectiveness of the proposed algorithms.

[1]  Mehul Motani,et al.  A Robust Indoor Pedestrian Tracking System with Sparse Infrastructure Support , 2013, IEEE Transactions on Mobile Computing.

[2]  Girma Tewolde,et al.  Design and implementation of vehicle tracking system using GPS/GSM/GPRS technology and smartphone application , 2014, 2014 IEEE World Forum on Internet of Things (WF-IoT).

[3]  Junaid Malik,et al.  Real-time Vehicle Tracking System Using GPS & GSM , 2013 .

[4]  Mark Braverman,et al.  Information Equals Amortized Communication , 2011, IEEE Transactions on Information Theory.

[5]  Weili Wu,et al.  Energy-efficient target coverage in wireless sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[6]  Mohan M. Trivedi,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Integrated Lane and Vehicle Detection, Localization, , 2022 .

[7]  Mani B. Srivastava,et al.  AnonyCast: privacy-preserving location distribution for anonymous crowd tracking systems , 2015, UbiComp.

[8]  James Biagioni,et al.  Cooperative transit tracking using smart-phones , 2010, SenSys '10.

[9]  Giorgio Gambosi,et al.  Complexity and approximation: combinatorial optimization problems and their approximability properties , 1999 .

[10]  Yih-Chun Hu,et al.  Design and evaluation of a metropolitan area multitier wireless ad hoc network architecture , 2003, 2003 Proceedings Fifth IEEE Workshop on Mobile Computing Systems and Applications.

[11]  Jie Wu,et al.  Optimizing Roadside Advertisement Dissemination in Vehicular Cyber-Physical Systems , 2015, 2015 IEEE 35th International Conference on Distributed Computing Systems.

[12]  Lokukaluge P. Perera,et al.  Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction , 2012, IEEE Transactions on Intelligent Transportation Systems.

[13]  Li Peng,et al.  A Randomized Algorithm for Roadside Units Placement in Vehicular Ad Hoc Network , 2013, 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks.

[14]  Helmut Hlavacs,et al.  Cellular data meet vehicular traffic theory: location area updates and cell transitions for travel time estimation , 2012, UbiComp '12.

[15]  Bhaskaran Raman,et al.  Kyun queue: a sensor network system to monitor road traffic queues , 2012, SenSys '12.

[16]  Lisa Hellerstein,et al.  Approximation Algorithms for Stochastic Boolean Function Evaluation and Stochastic Submodular Set Cover , 2013, SODA.

[17]  Kate Ching-Ju Lin,et al.  Maximizing Submodular Set Function With Connectivity Constraint: Theory and Application to Networks , 2013, IEEE/ACM Transactions on Networking.

[18]  Susana Sargento,et al.  Deploying Roadside Units in Sparse Vehicular Networks: What Really Works and What Does Not , 2014, IEEE Transactions on Vehicular Technology.

[19]  Mohan M. Trivedi,et al.  Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis , 2013, IEEE Transactions on Intelligent Transportation Systems.

[20]  Yizhou Wang,et al.  VeTrack: Real Time Vehicle Tracking in Uninstrumented Indoor Environments , 2015, SenSys.

[21]  Kyomin Jung,et al.  Transitive-Closure Spanners , 2008, SIAM J. Comput..

[22]  Prabal Dutta,et al.  AutoWitness: locating and tracking stolen property while tolerating GPS and radio outages , 2010, SenSys '10.

[23]  Wenyuan Xu,et al.  Security and Privacy Vulnerabilities of In-Car Wireless Networks: A Tire Pressure Monitoring System Case Study , 2010, USENIX Security Symposium.

[24]  Guangjie Han,et al.  A survey on coverage and connectivity issues in wireless sensor networks , 2012, J. Netw. Comput. Appl..