Abstract
Autonomous Vehicles (AVs), drones and robots will revolutionize our way of travelling and understanding urban space. In order to operate, all of these devices are expected to collect and analyze a lot of sensitive data about our daily activities. However, current operational models for these devices have extensively relied on centralized models of managing these data. The security of these models unveiled significant issues. This paper proposes BASIC, the Blockchained Agent-based Simulator for Cities. This tool aims to verify the feasibility of the use of blockchain in simulated urban scenarios by considering the communication between agents through smart contracts. In order to test the proposed tool, we implemented a car-sharing model within the city of Cambridge (Massachusetts, USA). In this research, the relevant literature was explored, new methods were developed and different solutions were designed and tested. Finally, conclusions about the feasibility of the combination between blockchain technology and agent-based simulations were drawn.
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http://www.focas.eu/manifesto/ - FoCAS Manifesto: A roadmap to the future of Collective Adaptive Systems.
References
Żak, J., Hadas, Y., Rossi, R. (eds.): Advanced Concepts, Methodologies and Technologies for Transportation and Logistics. AISC, vol. 572. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-57105-8
Johansson, C., et al.: Impacts on air pollution and health by changing commuting from car to bicycle. Sci. Total Environ. 584–585, 55–63 (2017)
Fiedler, D., Certický, M., Alonso-Mora, J., Cáp, M.: The impact of ridesharing in mobility-on-demand systems: simulation case study in Prague. CoRR, abs/1807.03352 (2018)
Schrank, D., Eisele, B., Lomax, T., Bak, J.: Urban mobility scorecard. Technical report, Texas A&M Transportation Institute (2015)
Seidler, A., et al.: Association between aircraft, road and railway traffic noise and depression in a large case-control study based on secondary data. Environ. Res. 152, 263–271 (2017)
Alonso, L., et al.: CityScope: a data-driven interactive simulation tool for urban design. Use case volpe. In: Morales, A.J., Gershenson, C., Braha, D., Minai, A.A., Bar-Yam, Y. (eds.) ICCS 2018. SPC, pp. 253–261. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96661-8_27
Chen, X., Zheng, H., Wang, Z., Chen, X.: Exploring impacts of on-demand ridesplitting on mobility via real-world ridesourcing data and questionnaires. Transportation, August 2018
Nijland, H., van Meerkerk, J.: Mobility and environmental impacts of car sharing in the Netherlands. Environ. Innov. Societal Transit. 23, 84–91 (2017)
Giesel, F., Nobis, C.: The impact of carsharing on car ownership in German cities. Transp. Res. Procedia 19, 215–224 (2016)
Fagnant, D.J., Kockelman, K.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. Part A: Policy Pract. 77, 167–181 (2015)
BBC New: Who is responsible for a driverless car accident? BBC News Online (2015). http://www.bbc.com/news/technology-34475031
Millard-Ball, A.: Pedestrians, autonomous vehicles, and cities. J. Plann. Educ. Res. 38(1), 6–12 (2018)
Haboucha, C.J., Ishaq, R., Shiftan, Y.: User preferences regarding autonomous vehicles. Transp. Res. Part C: Emerg. Technol. 78, 37–49 (2017)
Serra, M.: An exploratory paper of the privacy paradox in the age of big data and emerging technologies. Master’s thesis, KTH, School of Electrical Engineering and Computer Science (EECS) (2018)
Zyskind, G., Nathan, O., Pentland, A.: Decentralizing privacy: using blockchain to protect personal data. In: 2015 IEEE Symposium on Security and Privacy Workshops, SPW 2015, San Jose, CA, USA, 21–22 May 2015, pp. 180–184 (2015)
Oyola, J.O., Hoffman, W., Schwab, K., Marcus, A., Luzi, M.: Personal data: the emergence of a new asset class. In: An Initiative of the World Economic Forum (2011)
Uber’s big data platform: 100+ petabytes with minute latency (2019). https://eng.uber.com/uber-big-data-platform/
Former employees say Lyft staffers spied on passengers (2019). https://techcrunch.com/2018/01/25/lyft-god-view/
Fan, L., Ramon Gil-Garcia, J., Werthmuller, D., Brian Burke, G., Hong, X.: Investigating blockchain as a data management tool for IoT devices in smart city initiatives. In: Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, DG.O 2018, pp. 100:1–100:2. ACM, New York (2018)
Michelin, R.A., et al.: SpeedyChain: a framework for decoupling data from blockchain for smart cities. In: Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2018, New York City, NY, USA, 5–7 November 2018, pp. 145–154 (2018)
Castelló Ferrer, E., Rudovic, O., Hardjono, T., Pentland, A.: RoboChain: a secure data-sharing framework for human-robot interaction. CoRR, abs/1802.04480 (2018)
Strobel, V., Ferrer, E.C., Dorigo, M.: Managing byzantine robots via blockchain technology in a swarm robotics collective decision making scenario. In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden, 10–15 July 2018, pp. 541–549 (2018)
Alphand, O., et al.: IoTChain: a blockchain security architecture for the Internet of Things. In: WCNC, pp. 1–6. IEEE (2018)
Alowayed, Y., Canini, M., Marcos, P., Chiesa, M., Barcellos, M.P.: Picking a partner: a fair blockchain based scoring protocol for autonomous systems. In: Proceedings of the Applied Networking Research Workshop, ANRW 2018, Montreal, QC, Canada, 16 July 2018, pp. 33–39 (2018)
Singh, M., Kim, S.: Branch based blockchain technology in intelligent vehicle. Comput. Netw. 145, 219–231 (2018)
Grignard, A., Alonso, L., Taillandier, P., Gaudou, B., Nguyen-Huu, T., Gruel, W., Larson, K.: The impact of new mobility modes on a city: a generic approach using ABM. In: Morales, A.J., Gershenson, C., Braha, D., Minai, A.A., Bar-Yam, Y. (eds.) ICCS 2018. SPC, pp. 272–280. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96661-8_29
Alfeo, A.L., et al.: Urban swarms: a new approach for autonomous waste management. CoRR, abs/1810.07910 (2018)
Grignard, A., Taillandier, P., Gaudou, B., Vo, D.A., Huynh, N.Q., Drogoul, A.: GAMA 1.6: advancing the art of complex agent-based modeling and simulation. In: Boella, G., Elkind, E., Savarimuthu, B.T.R., Dignum, F., Purvis, M.K. (eds.) PRIMA 2013. LNCS (LNAI), vol. 8291, pp. 117–131. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-44927-7_9
Castelló Ferrer, E.: The blockchain: a new framework for robotic swarm systems. CoRR, abs/1608.00695 (2016)
Bucchiarone, A., De Sanctis, M., Marconi, A., Martinelli, A.: DeMOCAS: domain objects for service-based collective adaptive systems. In: Drira, K., et al. (eds.) ICSOC 2016. LNCS, vol. 10380, pp. 174–178. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68136-8_19
Acknowledgments
This project has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No. 751615.
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Marrocco, L. et al. (2019). BASIC: Towards a Blockchained Agent-Based SImulator for Cities. In: Lin, D., Ishida, T., Zambonelli, F., Noda, I. (eds) Massively Multi-Agent Systems II. MMAS 2018. Lecture Notes in Computer Science(), vol 11422. Springer, Cham. https://doi.org/10.1007/978-3-030-20937-7_10
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