Skip to main content

Advertisement

SpringerLink
Log in

Efficient Data Reporting in Intelligent Transportation Systems

  • Published:
Networks and Spatial Economics Aims and scope Submit manuscript

Abstract

Recently, intelligent transportation systems (ITSs) have emerged. These systems can improve traditional transportation systems and provide traffic information to travelers. In the area of transportation, wireless sensor networks (WSNs) can replace the existing wired sensors and expensive traffic monitoring systems to mitigate the time and costs of installing such systems. However, accurate and on-time traffic information delivery is a major challenge, considering the energy constraints of sensor nodes. In this paper, we propose a two-tier architecture that includes a network of mobile objects (vehicles) in the upper layer and a hierarchical WSN in the bottom layer. Using this approach, a portion of loads on the low-power static sensor nodes can be transferred to mobile objects, such as powerful mobile devices. Moreover, to provide accurate and timely traffic information, a QoS-aware link cost function has been proposed and used for data transmission between the static sensor nodes. In addition, due to the mobility of the objects and the probability of losing packets in the mobile object tier, a reliable data forwarding mechanism has been proposed for this tier. In this mechanism, data packets are forwarded to the neighbors, which enhance the probability of the packets’ being received. The performance evaluation results indicate the effectiveness of the proposed architecture and data reporting mechanism for use in ITS applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Akyildiz IF, Kasimoglu IH (2004) Wireless sensor and actor networks: research challenges. Ad Hoc Netw 2(4):351–367

    Article  Google Scholar 

  • Anisi MH, Abdullah AH, Razak SA, Ngadi MA (2012) Overview of data routing approaches for wireless sensor networks. Sensors 12(4):3964–3996

    Article  Google Scholar 

  • Anisi MH, Abdullah AH, Razak SA (2013) Energy-efficient and reliable data delivery in wireless sensor networks. Wirel Netw 19(4):495–505

    Article  Google Scholar 

  • Bettstetter C, Resta G, Santi P (2003) The node distribution of the random waypoint mobility model for wireless ad hoc networks. Mobile Comput IEEE Trans 2(3):257–269

    Article  Google Scholar 

  • Bhattacharyya D, Kim TH, Pal SA (2010) A comparative study of wireless sensor networks and their routing protocols. Sensors 10:10506–10523

    Article  Google Scholar 

  • Camp T, Boleng J, Davies V (2002) A survey of mobility models for ad hoc network research. Wirel Commun Mob Comput 2(5):483–502

    Article  Google Scholar 

  • Chang BR, Tsai HF, Young CP (2010) Intelligent data fusion system for predicting vehicle collision warning using vision/GPS sensing. Expert Syst Appl 37(3):2439–2450

    Article  Google Scholar 

  • Chen D, Varshney PK (2006) On demand geographic forwarding for data delivery in wireless sensor networks. Comput Commun 30(14–15):2954–2967

    Google Scholar 

  • Chen S, Chen F, Liu J, Wu J, Bienkiewicz B (2010) Mobile mapping technology of wind velocity data along highway for traffic safety evaluation. Trans Res C Emerg Technol 18(4):507–518

    Article  Google Scholar 

  • Chen YB, Lam WHK, Sumalee A, Li Q, Shao H, Fang Z (2013) Finding reliable shortest paths in road networks under uncertainty. Netw Spat Econ 13(2):123–148

    Article  Google Scholar 

  • Choe HJ, Ghosh P, Das SK (2010) QoS-aware data reporting control in cluster-based wireless sensor networks. Comput Commun 33:1244–1254

    Article  Google Scholar 

  • Coleri S, Cheung SY, Varaiya P (2004) Sensor networks for monitoring traffic. In: Proc. of Allerton Conf

  • Cordeiro CDM, Agrawal D (2006) Ad-hoc & sensor networks: theory and application. World scientific Publisher, USA

    Book  Google Scholar 

  • Crainic TG, Gendreau M, Potvin J-Y (2009) Intelligent freight-transportation systems: assessment and the contribution of operations research. Trans Res C Emerg Technol 17(6):541–557

    Article  Google Scholar 

  • Faouzi N-EE, Leung H, Kurian A (2011) Data fusion in intelligent transportation systems: progress and challenges – A survey. Inf Fusion 12(1):4–10

    Article  Google Scholar 

  • Güner AR, Murat A, Chinnam RB (2012) Dynamic routing under recurrent and non-recurrent congestion using real-time ITS information. Comput Oper Res 39(2):358–373

    Article  Google Scholar 

  • Heinzelman WB, Chandrakasan AP, Balakrishnan H (2000) Energy efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Hawaaian Interantional Conference on System Science, Hawaii

  • Hickman JS, Hanowski RJ (2011) Use of a video monitoring approach to reduce at-risk driving behaviors in commercial vehicle operations. Transport Res F: Traffic Psychol Behav 14(3):189–198

    Article  Google Scholar 

  • Huang JH, Wang LC, Chang CJ (2008) QoS provisioning in a scalable wireless mesh network for intelligent transportation systems. IEEE Trans Veh Technol 57(5):3121–3135

    Article  Google Scholar 

  • Jin W-L (2014) Advances in dynamic traffic assgmnt: TAC. Netw Spat Econ. doi:10.1007/s11067-014-9250-x

    Google Scholar 

  • Karpinski M, Senart A, Cahill V (2006) Sensor networks for smart roads. In: Proc. of IEEE PerCom Workshops, Pisa, Italy

  • Kottapalli V, Kiremidjian A, Lynch J, Carryer E, Kenny T, Law K, Lei Y (2003) Two-tiered wireless sensor network architecture for structural health monitoring, in: The 10th Annual International Symposium on Smart Structures and Materials, San Diego, USA, March 2003

  • Laborczi P, Mezny B, Török A, Ruzsa Z (2010) Query-based information gathering in intelligent transportation systems. Electron Notes Discrete Math 36:1201–1208

    Article  Google Scholar 

  • Lee S, Ghosh DYA (2008) Intelligent parking lot application using wireless sensor networks. In: Proc. of CTS, Irvine, CA, USA

  • Lee S, Younis M (2010) Recovery from multiple simultaneous failures in wireless sensor networks using minimum Steiner tree. J Parallel Distrib Comput 70(5):525–536

    Article  Google Scholar 

  • Lee J, Chung W, Kim E (2013) A new kernelized approach to wireless sensor network localization. Inf Sci 243:20–38

    Article  Google Scholar 

  • Li LI, Liu YA, Tang BH (2007) SNMS: an intelligent transportation system network architecture based on WSN and P2P network. J China Univ Posts Telecommun 14(1):65–70

    Article  Google Scholar 

  • Lin X, Tampere CMJ, Immers B (2014) The cost of environmental constraints in traffic networks: assessing the loss of optimality. Netw Spat Econ. doi:10.1007/s11067-014-9228-8

    Google Scholar 

  • Madden S, Franklin MJ, Hellerstein JM, Hong W (2002) TAG: a tiny aggregation service for ad-hoc sensor networks. In: ACM SIGOPS Operating Systems Review, Boston, MA, USA, Dec 2002

  • Miranda-Moreno LF, Fu L (2006) A comparative study of alternative model structures and criteria for ranking locations for safety improvements. Netw Spat Econ 6(2):97–110

    Article  Google Scholar 

  • Misra S, Thomasinous PD (2010) A simple, least-time, and energy-efficient routing protocol with one-level data aggregation for wireless sensor networks. J Syst Softw 83(5):852–860

    Article  Google Scholar 

  • Misra S, Tiwari V, Obaidat MS (2009) LACAS: learning automata-based congestion avoidance scheme for healthcare wireless sensor networks. IEEE J Sel Areas Commun 27(4):789–817

    Article  Google Scholar 

  • Munaka T, Yamamoto T, Watanabe T (2005) A reliable advanced-join system for data multicasting in ITS networks. IEEE Trans Intell Transp Syst 6(4):424–438

    Article  Google Scholar 

  • Qin X, Khan AM (2012) Control strategies of traffic signal timing transition for emergency vehicle pre-emption. Trans Res C Emerg Technol 25:1–17

    Article  Google Scholar 

  • Rupi F, Bernardi S, Rossi G, Danesi A (2014) The evaluation of road network vulnerability in mountainous areas: a case study. Netw Spat Econ. doi:10.1007/s11067-014-9260-8

    Google Scholar 

  • Simroth A, Zahle H (2011) Travel time prediction using floating car data applied to logistics planning. IEEE Trans Intell Transp Syst 12(1):243–253

    Article  Google Scholar 

  • Sobeih A, Hou J, Kung L-C, Li N, Zhang H, Chen W-P, Tyan H-Y, Lim H (2006) J-Sim: a simulation and emulation environment for wireless sensor networks. IEEE Wirel Commun 13(4):104–119

    Article  Google Scholar 

  • Soro S, Heinzelman WB (2005) Prolonging the lifetime of wireless sensor networks via unequal clustering. In: Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS’05), 260–264, Denver, Colorado

  • Stojmenovic I, Lin X (2001) Loop-free hybrid single-path/flooding routing algorithms with guaranteed delivery for wireless networks. IEEE Trans Parallel Distrib Syst 12(10):1023–1032

    Article  Google Scholar 

  • Tacconi D, Miorandi D, Carreras L, Chiti F, Fantacci R (2010) Using wireless sensor networks to support intelligent transportation systems. Ad Hoc Netw 8(5):462–473

    Article  Google Scholar 

  • Tang V, Yuan Z, Jiannong C (2006) An intelligent car park management system based on wireless sensor networks. In: Proc. of ISWPC, Phuket, Thailand

  • Tonguz O, Wisitpongphan N, Bai F, Mudalige P, Sadekar V (2007) Broadcasting in VANET. In: Proceedings of 2007 mobile networking for vehicular environments 7–12, Anchorage, Alaska

  • Xiangning F, Yulin S (2007) Improvement on LEACH protocol of wireless sensor network. In: Proceedings of international conference on sensor technologies and applications (SENSORCOMM 2007), 260–264, Valencia, Spain

  • Xie F, Levinson D (2009) Modeling the growth of transportation networks: a comprehensive review. Netw Spat Econ 9(3):291–307

    Article  Google Scholar 

  • Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330

    Article  Google Scholar 

  • Zhang J, Wang FY, Wang K, Lin WH, Xu X, Chen C (2011) Data-driven intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 12(4):1624–1639

    Article  Google Scholar 

  • Zhao F, Guibas L (2004) Wireless sensor networks: an information processing approach. Elsevier/Morgan-Kaufmann, Boston

    Google Scholar 

  • Zografos KG, Androutsopoulos KN, Spitadakis V (2009) Design and assessment of an online passenger information system for integrated multimodal trip planning. IEEE Trans Intell Transp Syst 10(2):311–323

    Article  Google Scholar 

Download references

Acknowledgement

The authors thank University of Malaya for the financial support (UMRG grant RG325-15AFR) and facilities to carry out the work.

Author information

Authors and Affiliations

  1. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Mohammad Hossein Anisi

  2. Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Abdul Hanan Abdullah

Authors
  1. Mohammad Hossein Anisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdul Hanan Abdullah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Hossein Anisi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anisi, M.H., Abdullah, A.H. Efficient Data Reporting in Intelligent Transportation Systems. Netw Spat Econ 16, 623–642 (2016). https://doi.org/10.1007/s11067-015-9291-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11067-015-9291-9

Keywords

Search

Navigation