Skip to main content

Advertisement

SpringerLink
Log in

Route selection for mobile sensor nodes on public transport networks

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

The sensing range of a sensor is spatially limited. Thus, achieving a good coverage of a large area of interest requires installation of a huge number of sensors which is cost and labor intensive. For example, monitoring air pollution in a city needs a high density of measurement stations installed throughout the area of interest. As alternative, we install a smaller number of mobile sensing nodes on top of public transport vehicles that regularly traverse the city. In this paper, we consider the problem of selecting a subnetwork of a city’s public transport network to achieve a good coverage of the area of interest. In general case, public transport vehicles are not assigned to fix lines but rather to depots where they are parked overnight. We introduce an algorithm that selects the installation locations, i.e., number of vehicles within each host depot, such that sensing coverage is maximized. Since we are working with low-cost sensors, which exhibit failures and drift over time, vehicles selected for sensor installation have to be in each other’s vicinity from time to time to allow comparing sensor readings. We refer to such meeting points as checkpoints. Our algorithm optimizes sensing coverage while providing a sufficient number of checkpoint locations. We evaluate our algorithm based on the tram network of Zurich and show how an accurate selection of vehicles for installing measurement stations affects the overall system quality. We show that our algorithm outperforms random search, simulated annealing, and the greedy approach.

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
Fig. 12

Similar content being viewed by others

Notes

  1. MiCS-OZ-47 from http://www.e2v.com, CO-AF from http://www.alphasense.com, DiSCmini from http://www.matter-aerosol.ch.

  2. The lines on the figure are re-numbered to have a sequential ordering of lines without gaps.

  3. http://www.bafu.admin.ch/luft/luftbelastung.

  4. http://www.ostluft.ch.

  5. We denote with \({\bf s}_{\bf f}\) a selector vector whose fth element is 1 and all other elements are 0, i.e., \(s_3^T = (0, 0, 1, 0, \ldots, 0). \)

  6. http://www.openstreetmap.org.

  7. http://www.zvv.ch.

  8. Pollution data and real-time tram locations are online at http://data.opensense.ethz.ch/position.html.

References

  • Aberer K, Sathe S, Chakraborty D, Martinoli A, Barrenetxea G, Faltings B, Thiele L (2010) Opensense: open community driven sensing of environment. In: Proceedings of the 1st International Workshop on GeoStreaming (IWGS), ACM, pp 39–42

  • Alon N (1995) A note on network reliability. In: Discrete probability and algorithms. Springer, Berlin, pp 72:11–14

  • Back T, Fogel DB, Michalewicz Z (1997) Handbook of evolutionary computation, 1st edn. IOP Publishing Ltd, Bristol

  • Basagni S, Carosi A, Petrioli C, Phillips CA (2011) Coordinated and controlled mobility of multiple sinks for maximizing the lifetime of wireless sensor networks. Wirel Netw 17:759–778

    Article  Google Scholar 

  • Boscolo A, Mangiavacchi C (1998) Pollution dynamic monitoring system for city air quality control. In: Proceedings of the international conference on instrumentation and measurement technology (I2MTC), IEEE, vol 1, pp 202–205

  • Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. In: Proceedings of the 7th international conference on world wide web (WWW), Elsevier, Amsterdam, pp 107–117

  • Buchli B, Yuecel M, Lim R, Gsell T, Beutel J (2011) Feature-rich platform for WSN design space exploration. In: Proceedings of the 10th international conference on information processing in sensor networks (IPSN), ACM/IEEE, pp 115–116

  • Carullo A, Corbellini S, Grassini S (2007) A remotely controlled calibrator for chemical pollutant measuring-units. IEEE Trans Instrum Measur 56:1212–1218

    Article  Google Scholar 

  • Choi S, Kim N, Cha H, Ha R (2009) Micro sensor node for air pollutant monitoring: hardware and software issues. MDPI Sensors 9:7970–7987

    Article  Google Scholar 

  • Cortes J, Martinez S, Karatas T, Bullo F (2004) Coverage control for mobile sensing networks. IEEE Trans Robot Autom 20:243–255

    Article  Google Scholar 

  • De Vito S, Massera E, Piga M, Martinotto L, Di Francia G (2008) On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sens Actuators B: Chem 129:750–757

    Article  Google Scholar 

  • Dutta P, Aoki P, Kumar N, Mainwaring A, Myers C, Willett W, Woodruff A (2009) Common sense: participatory urban sensing using a network of handheld air quality monitors. In: Proceedings of the 7th international conference on embedded networked sensor systems (SenSys), ACM, pp 349–350

  • Fierz M, Houle C, Steigmeier P, Burtscher H (2011) Design, calibration, and field performance of a miniature diffusion size classifier. Aerosol Sci Technol 45:1–10

    Article  Google Scholar 

  • Ganguli A, Susca S, Martinez S, Bullo F, Cortes J (2005) On collective motion in sensor networks: sample problems and distributed algorithms. In: Proceedings of the 44th international conference on decision and control (CDC), IEEE, pp 4239–4244

  • Gil-Castiñeira F, González-Castaño F, Duro R, Lopez-Peña F (2008) Urban pollution monitoring through opportunistic mobile sensor networks based on public transport. In: Proceedings of the international conference on computational intelligence for measurement systems and applications (CIMSA), IEEE, pp 70–74

  • Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading

  • Gu Y, Bozdag D, Ekici E (2006) Mobile element based differentiated message delivery in wireless sensor networks. In: Proceedings of the international symposium on world of wireless, mobile and multimedia networks (WOWMOM), IEEE, pp 83–92

  • Harik G (1995) Finding multimodal solutions using restricted tournament selection. In: Proceedings of the 6th international conference on genetic algorithms (ICGA), Morgan Kaufmann, Los Altos, pp 24–31

  • Hasenfratz D, Saukh O, Sturzenegger S, Thiele L (2012a) Participatory air pollution monitoring using smartphones. In: Proceedings of the 2nd international workshop on mobile sensing, ACM, New york

  • Hasenfratz D, Saukh O, Thiele L (2012b) On-the-fly calibration of low-cost gas sensors. Proceedings of the 9th European Conference on Wireless Sensor Networks (EWSN) pp 228–244

  • Honicky R, Brewer E, Paulos E, White R (2008) N-smarts: networked suite of mobile atmospheric real-time sensors. In: Proceedings of the 2nd international workshop on networked systems for developing regions (NSDR), ACM, 25–30

  • Hussein I, Stipanovic D (2006) Effective coverage control using dynamic sensor networks. In: Proceedings of the 45th international conference decision and control (DAC), IEEE, pp 2747–2752

  • Jea D, Somasundara A, Srivastava M (2005) Multiple controlled mobile elements (data mules) for data collection in sensor networks. In: Proceedings of the 1st international conference on distributed computing in sensor systems (DCOSS), Springer LNCS, pp 244–257

  • Kamionka M, Breuil P, Pijolat C (2006) Calibration of a multivariate gas sensing device for atmospheric pollution measurement. Sens Actuators B: Chem 118:323–327

    Article  Google Scholar 

  • Karger DR (1995) A randomized fully polynomial time approximation scheme for the all terminal network reliability problem. In: Proceedings of the 27th annual symposium on theory of computing (STOC), ACM, pp 11–17

  • Krause A (2008) Optimizing sensing: theory and applications. PhD thesis, Carnegie Mellon University

  • Krause A, Guestrin C (2009) Optimizing sensing: from water to the web. IEEE Comput Archit Lett 42:38–45

    Article  Google Scholar 

  • Krause A, Rajagopal R, Gupta A, Guestrin C (2009) Simultaneous placement and scheduling of sensors. In: Proceedings of the 8th international conference on information processing in sensor networks (IPSN), ACM/IEEE, pp 181–192

  • Pyrga E, Schulz F, Wagner D, Zaroliagis C (2008) Efficient models for timetable information in public transportation systems. Exp Algorithm (JEA) 12:2–4

    MathSciNet  Google Scholar 

  • Saukh O, Hasenfratz D, Noori A, Ulrich T, Thiele L (2012) Route selection for mobile sensors with checkpointing constraints. In: Proceedings of the 8th international workshop on sensor networks and systems for pervasive computing (PerSeNS), IEEE, pp 2–4

  • Skiścim C, Golden B (1983) Optimization by simulated annealing: A preliminary computational study for the tsp. In: Proceedings of the 15th conference on winter simulation, IEEE, pp 523–535

  • So A, Ye Y (2005) On solving coverage problems in a wireless sensor network using voronoi diagrams. Internet Netw Econ 3828:584–593

    Google Scholar 

  • Somasundara A, Kansal A, Jea D, Estrin D, Srivastava M (2006) Controllably mobile infrastructure for low energy embedded networks. IEEE Trans Mobile Comput 5:958–973

    Article  Google Scholar 

  • Turgut D, Bölöni L (2011) Heuristic approaches for transmission scheduling in sensor networks with multiple mobile sinks. Comput J 54:332–344

    Article  Google Scholar 

  • Vardoulakis S, Fisher B, Pericleous K, Gonzalez-Flesca N (2003) Modelling air quality in street canyons: a review. Atmos Environ 37:155–182

    Google Scholar 

  • Völgyesi P, Nadas A, Koutsoukos X, Lédeczi Á (2008) Air quality monitoring with sensormap. In: Proceedings of the 8th international conference on information processing in sensor networks (IPSN), ACM/IEEE, pp 529–530

  • Wang Y, Hussein I (2010) Awareness coverage control over large-scale domains with intermittent communications. IEEE Trans Autom Control 55:1850–1859

    Article  MathSciNet  Google Scholar 

  • Xing G, Wang T, Jia W, Li M (2008) Rendezvous design algorithms for wireless sensor networks with a mobile base station. In: Proceedings of the 9th international symposium on mobile ad hoc networking and computing (MobiHoc), ACM, pp 231–240

  • Yamazoe N, Miura N (1995) Development of gas sensors for environmental protection. IEEE Trans Compon Packag Manuf Technol 18:252–256

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank the anonymous reviewers for their valuable comments and suggestions. Further, we thank Christoph Walser for his technical support and VBZ for their support and collaboration. This work was funded by NanoTera.ch with Swiss Confederation financing.

Author information

Authors and Affiliations

  1. Computer Engineering and Networks Laboratory, ETH Zurich, Zurich, Switzerland

    Olga Saukh, David Hasenfratz & Lothar Thiele

Authors
  1. Olga Saukh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Hasenfratz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lothar Thiele
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Hasenfratz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Saukh, O., Hasenfratz, D. & Thiele, L. Route selection for mobile sensor nodes on public transport networks. J Ambient Intell Human Comput 5, 307–321 (2014). https://doi.org/10.1007/s12652-012-0170-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-012-0170-7

Keywords

Search

Navigation