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High Capacity Trucks Serving as Mobile Depots for Waste Collection in IoT-Enabled Smart Cities

  • Theodoros AnagnostopoulosEmail author
  • Arkady Zaslavsky
  • Stefanos Georgiou
  • Sergey Khoruzhnikov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9247)

Abstract

Internet of Things (IoT) enables Smart Cities with novel services. Waste collection in Smart Cities becomes a dynamic process with the proliferation of sensors and actuators embedded on real waste bins. Heterogeneous fleets of trucks are used for efficient waste collection exploiting the diverse road network. In this paper we propose a novel approach by incorporating Low Capacity Trucks (LCTs) and High Capacity Trucks (HCTs). However, HCTs are serving as Mobile Depots (MDs) which are cost efficient and decongest traffic in Smart Cities. A detailed system overview illustrates the architecture of the proposed approach. We also propose novel algorithms which support dynamic waste collection with MDs. Scheduling and routing are transformed to dynamic models. Specifically, we propose a novel scheduling algorithm while we customize an existing routing algorithm. The models where experimentally evaluated with real and synthetic data from the city of St. Petersburg, Russia. The results were promising and proved that the incorporation of MDs is efficient for waste collection in IoT-enabled Smart Cities. Finally, we perform an economic analysis in order to define the economic impact of the proposed solution to the municipality budget for an ownership cost for a period of five years; in which the proposed solution proved to be cost efficient.

Keywords

Smart cities Internet of things Waste collection Dynamic models Mobile depots 

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References

  1. 1.
    Fazio, M., Paone, M., Puliafito, A., Villari, M.: Heterogeneous sensors become homogeneous things in smart cities. In: 6th IEEE International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), Palermo, Italy, pp. 775–780, July 2012Google Scholar
  2. 2.
    Balakrishna, C.: Enabling technologies for smart city services and applications. In: 6th IEEE International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST), Paris, France, pp. 223–227, September 2012Google Scholar
  3. 3.
    Sanchez Lopez, T., Ranasinghe, D.C., Harrison, M., Mcfarlane, D.: Adding sense to the Internet of Things. Personal and Ubiquitous Computing 16(3), 291–308 (2012)zbMATHCrossRefGoogle Scholar
  4. 4.
    Jara, A.J., Lopez, P., Fernandez, D., Castillo, J.F., Zamora, M.A., Skarmeta, A.F.: Mobile digcovery: discovering and interacting with the world through the Internet of Things. Personal and Ubiquitous Computing 18(2), 323–338 (2014)CrossRefGoogle Scholar
  5. 5.
    Suakanto, S., Supangkat, S.H., Suhardi, Saragih, R.: Smart city dashboard for integrating various data of sensor networks. In: IEEE International Conference on ICT for Smart Society (ICISS), Jakarta, Indonesia, pp. 1–5, June 2013Google Scholar
  6. 6.
    Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L.: Measuring and managing the smartness of cities: a framework for classifying performance indicators. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), Manchester, UK, pp. 1288–1293, October 2013Google Scholar
  7. 7.
    Priano, F.H., Guerra, C.F.: A framework for measuring smart cities. In: The Proceedings of the 15th Annual ACM International Conference on Digital Government Research, dg.o 2014, Aguascalientes, Mexico, pp. 44–54, June 2014Google Scholar
  8. 8.
    Nam, T., Pardo, T.A.: Smart city as urban innovation: focusing on management, policy, and context. In: The Proceedings of the 5th ACM International Conference on Theory and Practice of Electronic Governance, ICEGOV 2011, Tallinn, Estonia, pp. 185–194, September 2011Google Scholar
  9. 9.
    Nam, T., Pardo, T.A.: Conceptualizing smart city with dimensions of technology, people and institutions. In: the Proceedings of the 12th Annual ACM International Digital Government Research Conference: Digital Government Innovation in Challenging Times, dg.o 2011, College Park, MD, USA, pp. 282–291, June 2012Google Scholar
  10. 10.
    Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanovic, N., Meijers, E.: Smart Cities: Ranking of European medium-sized cities, Centre of Regional Science (SRF). Vienna University of Technology, Vienna (2007). http://www.smart-cities.eu (Accessed on March 18, 2015)Google Scholar
  11. 11.
    Samaras, C., Vakali, A., Giatsoglou, M., Chatzakou, D., Angelis, L.: Requirements and architecture design principles for a smart city experiment with sensor and social networks integration. In: The Proceedings of the 17th Panhellenic Convference on Informatics, Thessaloniki, Greece, PCI 2013, pp. 327–334, September 2013Google Scholar
  12. 12.
    Milić, P., Jovanović, M.: The Advanced System for Dynamic Vehicle Routing in the Process of Waste Collection. Facta Universitatis, Series: Mechanical Engineering 9(1), 127–136 (2011)Google Scholar
  13. 13.
    Minh, T.T., Van Hoai, T., Nguyet, T.T.N.: A memetic algorithm for waste collection vehicle routing problem with time windows and conflicts. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part I. LNCS, vol. 7971, pp. 485–499. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Li, J.Q., Borenstein, D., Mirchandani, P.B.: Truck Scheduling for Solid Waste Collection in the City of Porto Alegre, Brazil. Omega 36, 1133–1149 (2008)CrossRefGoogle Scholar
  15. 15.
    Ramos, P.T.R., Gomes, M.I., Povoa, A.P.B.: Assessing and Improving Management Practices when Planning Packaging Waste Collection Systems. Resources Conservation and Recycling 85, 116–129 (2014)CrossRefGoogle Scholar
  16. 16.
    Stellingwerff, A.: Dynamic Waste Collection: Assessing the Usage of Dynamic Routing Methodologies. Master Thesis, Industrial Engineering & Management, University of Twente, Twente Milieu (2011)Google Scholar
  17. 17.
    Nadizadeha, A., Nasaba, H.H.: Solving the Dynamic Capacitated Location-Routing Problem with Fuzzy Demands by Hybrid Heuristic Algorithm. European Journal of Operational Research (2014) (in press available online, Elsevier)Google Scholar
  18. 18.
    Buhrkal, K., Larsen, A., Ropke, S.: The Waste Collection Vehicle Routing Problem with Time Windows in a City Logistics Context. Procedia Social and Behavioral Sciences 39, 241–254 (2012)CrossRefGoogle Scholar
  19. 19.
    Juyoung, W., Byung-In, K., Seongbae, K.: The rollon–rolloff waste collection vehicle routing problem with time windows. European Journal of Operational Research 224(3), 466–476 (2013)zbMATHCrossRefGoogle Scholar
  20. 20.
    Mes, M.: Using simulation to assess the opportunities of dynamic waste collection. In: Bangsow, S. (ed.) Use Cases of Discrete Event Simulation. Non-series, vol. 109, pp. 277–307. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    Bing, X.: Vehicle routing for the eco-efficient collection of household plastic waste. Waste Management 34(4), 719–729 (2014)CrossRefGoogle Scholar
  22. 22.
    Hemmelmayr, V., Doerner, K.F., Hartl, R.F., Rath, S.: A heuristic solution method for node routing based solid waste collection problems. Journal of Heuristics 19(2), 129–156 (2013)CrossRefGoogle Scholar
  23. 23.
    Johansson, O.M.: The effect of dynamic scheduling and routing in a solid waste management system. Waste Management 26, 875–885 (2006)CrossRefGoogle Scholar
  24. 24.
    Nuortio, T., Kytojoki, J., Niska, H., Braysy, O.: Improved route planning and scheduling of waste collection and transport. Expert Systems with Applications 30, 223–232 (2006)CrossRefGoogle Scholar
  25. 25.
    Von Poser, I., Awad, A.R.: Optimal Routing for Solid Waste Collection in Cities by using Real Genetic Algorithm. Information and Communication Technologies, ICTTA 1, 221–226 (2006)Google Scholar
  26. 26.
    Mes, M., Schutten, M., Rivera, A.P.: Inventory routing for dynamic waste collection. Beta conference, WP No. 431, Eindhoven, Netherlands (2013)Google Scholar
  27. 27.
    Reed, M., Yiannakou, A., Evering, R.: An ant colony algorithm for the multi-compartment vehicle routing problem. Applied Soft Computing 15, 169–176 (2014)CrossRefGoogle Scholar
  28. 28.
    Zsigraiova, Z., Semiao, V., Beijoco, F.: Operation Costs and Pollutant Emissions Reduction by Definition of new Collection Scheduling and Optimization of MSW Collection Routes using GIS. The Case Study of Barreiro, Portugal. Waste Management 33, 793–806 (2013)CrossRefGoogle Scholar
  29. 29.
    Anagnostopoulos, T.V., Zaslavsky, A.: Effective waste collection with shortest path semi-static and dynamic routing. In: Balandin, S., Andreev, S., Koucheryavy, Y. (eds.) NEW2AN/ruSMART 2014. LNCS, vol. 8638, pp. 95–105. Springer, Heidelberg (2014)Google Scholar
  30. 30.
    Anagnostopoulos, T., Zaslavsky, A., Medvedev, A.: Robust waste collection exploiting cost efficiency of IoT potentiality in smart cities. In: IEEE 1st International Conference on Recent Advances in Internet of Things (RIoT) (Accepted on February 4, 2015)Google Scholar
  31. 31.
    Avella, P., Boccia, M., Sforza, A.: Resource Constraint Shortest Path Problems in Path Planning for Fleet Management. Journal of Mathematical Modeling and Algorithms 3, 1–17 (2004)zbMATHMathSciNetCrossRefGoogle Scholar
  32. 32.
    Real allocation distribution of bins within the city of Saint Petersburg. http://wikimapia.org/ (Accessed on: March 27, 2015)

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Theodoros Anagnostopoulos
    • 1
    • 3
    Email author
  • Arkady Zaslavsky
    • 1
    • 2
  • Stefanos Georgiou
    • 1
  • Sergey Khoruzhnikov
    • 1
  1. 1.Department of Infocommunication TechnologiesITMO UniversitySt. PetersburgRussia
  2. 2.CSIRO Computational InformaticsCSIROClayton SouthAustralia
  3. 3.Community Imaging GroupUniversity of OuluOuluFinland

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