Autonomous Agents and Multi-Agent Systems

, Volume 31, Issue 1, pp 1–35 | Cite as

Mobile crowdsensing with mobile agents

  • Teemu Leppänen
  • José Álvarez Lacasia
  • Yoshito Tobe
  • Kaoru Sezaki
  • Jukka Riekki
Article

Abstract

We introduce mobile agents for mobile crowdsensing. Crowdsensing campaigns are designed through different roles that are implemented as mobile agents. The role-based tasks of mobile agents include collecting data, analyzing data and sharing data in the campaign. Mobile agents execute and control the campaign autonomously as a multi-agent system and migrate in the opportunistic network of participants’ devices. Mobile agents take into account the available resources in the devices and match participants’ privacy requirements to the campaign requirements. Sharing of task results in real-time facilitates cooperation towards the campaign goal while maintaining a selected global measure, such as energy efficiency. We discuss current challenges in crowdsensing and propose mobile agent based solutions for campaign execution and monitoring, addressing data collection and participant-related issues. We present a software framework for mobile agents-based crowdsensing that is seamlessly integrated into the Web. A set of simulations are conducted to compare mobile agent-based campaigns with existing crowdsensing approaches. We implemented and evaluated a small-scale real-world mobile agent based campaign for pedestrian flock detection. The simulation and evaluation results show that mobile agent based campaigns produce comparable results with less energy consumption when the number of agents is relatively small and enables in-network data processing with sharing of data and task results with insignificant overhead.

Keywords

Distributed computing Multi-agent systems Mobile computing  Mobile agents Mobile crowdsensing 

References

  1. 1.
    Abdelzaher, T., Anokwa, Y., Boda, P., Burke, J., Estrin, D., Guibas, L., et al. (2007). Mobiscopes for human spaces. IEEE Pervasive Computing, 6, 20–29.CrossRefGoogle Scholar
  2. 2.
    Álvarez Lacasia, J., Leppänen, T., Iwai, M., Kobayashi, H., & Sezaki, K. (2013). A method for grouping smartphone users based on wi-fi signal strength. In Forum on Information Technology, vol. J-032. IPSJ.Google Scholar
  3. 3.
    Bellifemine, F., Poggi, A., & Rimassa, G. (2001). Developing multi-agent systems with a FIPA-compliant agent framework. Software - Practice and Experience, 31(2), 103–128.CrossRefMATHGoogle Scholar
  4. 4.
    Burke, J., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., & Srivastava, M. (2006). Participatory sensing. In 4th ACM Conference on Embedded Networked Sensor Systems , 1st Workshop on World-Sensor-Web: Mobile Device Centric Sensory Networks and Applications. ACM.Google Scholar
  5. 5.
    Campbell, A., Eisenman, S., Lane, N., Miluzzo, E., Peterson, R., Lu, H., et al. (2008). The rise of people-centric sensing. IEEE Internet Computing, 12(4), 12–21.CrossRefGoogle Scholar
  6. 6.
    Campbell, A., & Wu, A. (2011). Multi-agent role allocation: issues, approaches, and multiple perspectives. Autonomous Agents and Multi-Agent Systems, 22(2), 317–355.CrossRefGoogle Scholar
  7. 7.
    Cardone, G., Cirri, A., Corradi, A., & Foschini, L. (2014). The participact mobile crowd sensing living lab: The testbed for smart cities. IEEE Communications Magazine, 52(10), 78–85.CrossRefGoogle Scholar
  8. 8.
    Cardone, G., Foschini, L., Bellavista, P., Corradi, A., Borcea, C., Talasila, M., et al. (2013). Fostering participaction in smart cities: a geo-social crowdsensing platform. IEEE Communications Magazine, 51(6), 112–119.CrossRefGoogle Scholar
  9. 9.
    Carreras, I., Miorandi, D., Tamilin, A., Ssebaggala, E., & Conci, N. (2013). Matador: Mobile task detector for context-aware crowd-sensing campaigns. In International Conference on Pervasive Computing and Communications Workshops (PerCom2013 Workshops), (pp. 212–217). IEEE.Google Scholar
  10. 10.
    Chen, M., Kwon, T., Yuan, Y., & Leung, V. (2006). Mobile agent based wireless sensor networks. Journal of computers, 1(1), 14–21.CrossRefGoogle Scholar
  11. 11.
    Chon, Y., Lane, N., Kim, Y., Zhao, F., & Cha, H. (2013). Understanding the coverage and scalability of place-centric crowdsensing. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing - UbiComp ’13, (pp. 3–12).Google Scholar
  12. 12.
    Christin, D., Reinhardt, A., Kanhere, S., & Hollick, M. (2011). A survey on privacy in mobile participatory sensing applications. Journal of Systems and Software, 84, 1928–1946.CrossRefGoogle Scholar
  13. 13.
    Conti, M., & Giordano, S. (2014). Mobile ad hoc networking: Milestones, challenges, and new research directions. IEEE Communications Magazine, 52, 85–96.CrossRefGoogle Scholar
  14. 14.
    Cornelius, C., Kapadia, A., Kotz, D., Peebles, D., Shin, M., & Triandopoulos, N. (2008). Anonysense: privacy-aware people-centric sensing. In Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services, MobiSys ’08, (pp. 211–224).Google Scholar
  15. 15.
    Das, T., Mohan, P., Padmanabhan, V., Ramjee, R., & Sharma, A. (2010). Prism: Platform for remote sensing using smartphones. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, MobiSys ’10, (pp. 63–76).Google Scholar
  16. 16.
    Dikaiakos, M., Kyriakou, M., & Samaras, G. (2001). Performance evaluation of mobile-agent middleware: A hierarchical approach. In G. Picco (ed.) Mobile Agents, Lecture Notes in Computer Science, vol. 2240, (pp. 244–259). Springer.Google Scholar
  17. 17.
    Eberle, J., Yan, Z., & Aberer, K. (2013). Energy-efficient opportunistic collaborative sensing. 10th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems pp. 374–378.Google Scholar
  18. 18.
    Estrin, D. (2010). Participatory sensing: Applications and architecture. IEEE Internet Computing, 14, 12–14.CrossRefGoogle Scholar
  19. 19.
    Ganti, R., Ye, F., & Lei, H. (2011). Mobile crowdsensing: current state and future challenges. IEEE Communications Magazine, 49(11), 32–39.CrossRefGoogle Scholar
  20. 20.
    Guo, B., Yu, Z., Zhou, X., & Zhang, D. (2014). From participatory sensing to mobile crowd sensing. In IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), (pp. 593–598).Google Scholar
  21. 21.
    Hachem, S., Pathak, A., & Issarny, V. (2014). Service-oriented middleware for large-scale mobile participatory sensing. Pervasive and Mobile Computing, 10, 66–82.CrossRefGoogle Scholar
  22. 22.
    Higuchi, T., Yamaguchi, H., Higashino, T., & Takai, M. (2014). A neighbor collaboration mechanism for mobile crowd sensing in opportunistic networks. In IEEE International Conference on Communications (ICC2014), (pp. 42–47).Google Scholar
  23. 23.
    Hu, X., Li, X., Ngai, E., Leung, V., & Kruchten, P. (2014). Multidimensional context-aware social network architecture for mobile crowdsensing. IEEE Communications Magazine, 52(6), 78–87.CrossRefGoogle Scholar
  24. 24.
    Hu, X., Liu, Q., Zhu, C., Leung, V., Chu, T., & Chan, H. (2013). A mobile crowdsensing system enhanced by cloud-based social networking services. In Proceedings of the First International Workshop on Middleware for Cloud-enabled Sensing, MCS ’13, (pp. 1–6). ACM.Google Scholar
  25. 25.
    Jaimes, L., Vergara-Laurens, I., & Labrador, M. (2012). A location-based incentive mechanism for participatory sensing systems with budget constraints. In IEEE International Conference on Pervasive Computing and Communications, (pp. 103–108).Google Scholar
  26. 26.
    Kanjo, E., Bacon, J., Roberts, D., & Landshoff, P. (2009). Mobsens: Making smart phones smarter. IEEE Pervasive Computing, 8(4), 50–57.CrossRefGoogle Scholar
  27. 27.
    Khan, W., Xiang, Y., Aalsalem, M., & Arshad, Q. (2013). Mobile phone sensing systems: A survey. IEEE Communications Surveys & Tutorials, 15(1), 402–427.CrossRefGoogle Scholar
  28. 28.
    Koutsopoulos, I. (2013). Optimal incentive-driven design of participatory sensing systems. In 32nd IEEE International Conference on Computer Communications, (pp. 1402–1410).Google Scholar
  29. 29.
    Lane, N. (2012). Community-aware smartphone sensing systems. IEEE Internet Computing, 16(3), 60–64.CrossRefGoogle Scholar
  30. 30.
    Lane, N., Eisenman, S., Musolesi, M., Miluzzo, E., & Campbell, A. (2008). Urban sensing systems: opportunistic or participatory? In Proceedings of the 9th workshop on Mobile computing systems and applications, (pp. 11–16). ACM.Google Scholar
  31. 31.
    Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150.CrossRefGoogle Scholar
  32. 32.
    Lange, D., & Oshima, M. (1999). Seven good reasons for mobile agents. Communications of the ACM, 42, 88–89.CrossRefGoogle Scholar
  33. 33.
    Leppänen, T., Álvarez Lacasia, J., Ramalingam, A., Liu, M., Harjula, E., Närhi, P., Ylioja, J., Riekki, J., Sezaki, K., & Tobe, Y., et al. (2013). Interoperable mobile agents in heterogeneous wireless sensor networks. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys’13). ACM.Google Scholar
  34. 34.
    Leppänen, T., Liu, M., Harjula, E., Ramalingam, A., Ylioja, J., Närhi, P., Riekki, J., & Ojala, T. (2013). Mobile agents for integration of internet of things and wireless sensor networks. In International Conference on Systems, Man, and Cybernetics (SMC), (pp. 14–21). IEEE.Google Scholar
  35. 35.
    Liu, M., Leppänen, T., Harjula, E., Ou, Z., Ramalingam, A., Ylianttila, M., & Ojala, T. (2013). Distributed resource directory architecture in machine-to-machine communications. In 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), (pp. 319–324). IEEE.Google Scholar
  36. 36.
    Ma, H., Zhao, D., & Yuan, P. (2014). Opportunities in mobile crowd sensing. IEEE Communications Magazine, 52(8), 29–35.CrossRefGoogle Scholar
  37. 37.
    Miluzzo, E., Cornelius, C., Ramaswamy, A., Choudhury, T., Liu, Z., & Campbell, A. (2010). Darwin phones: the evolution of sensing and inference on mobile phones. In Proceedings of the 8th international conference on Mobile systems, applications, and services, MobiSys ’10, (pp. 5–20). ACM.Google Scholar
  38. 38.
    Miluzzo, E., Lane, N., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S., Zheng, X., & Campbell, A. (2008). Sensing meets mobile social networks: The design, implementation and evaluation of the cenceme application. In Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, SenSys ’08, (pp. 337–350). ACM.Google Scholar
  39. 39.
    Niwa, J., Okada, K., Okuda, T., & Yamaguchi, S. (2013). Mpsdatastore: A sensor data repository system for mobile participatory sensing. In Proceedings of the Second ACM SIGCOMM Workshop on Mobile Cloud Computing, MCC ’13, (pp. 3–8). ACM.Google Scholar
  40. 40.
    Pournajaf, L., Xiong, L., & Sunderam, V. (2014). Dynamic Data Driven Crowd Sensing Task Assignment. Procedia Computer Science, 29, 1314–1323.CrossRefGoogle Scholar
  41. 41.
    Ra, M.R., Liu, B., La Porta, T., & Govindan, R. (2012). Medusa: A programming framework for crowd-sensing applications. In Proceedings of the 10th international conference on Mobile systems, applications, and services, MobiSys ’12, (pp. 337–350). ACM.Google Scholar
  42. 42.
    Ravindranath, L., Thiagarajan, A., Balakrishnan, H., & Madden, S. (2012). Code in the air: simplifying sensing and coordination tasks on smartphones. In Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications, HotMobile ’12. ACM.Google Scholar
  43. 43.
    Reddy, S., Estrin, D., & Srivastava, M. (2010). Recruitment framework for participatory sensing data collections. In P. Floren, A. Krger, M. Spasojevic (eds.) Pervasive Computing, Lecture Notes in Computer Science, vol. 6030, (pp. 138–155). Springer.Google Scholar
  44. 44.
    Reddy, S., Samanta, V., Burke, J., Estrin, D., Hansen, M., & Srivastava, M. (2009). Mobisense - mobile network services for coordinated participatory sensing. In International Symposium on Autonomous Decentralized Systems, ISADS ’09, (pp. 1–6). IEEE.Google Scholar
  45. 45.
    Riahi, M., Papaioannou, T., Trummer, I., & Aberer, K. (2013). Utility-driven data acquisition in participatory sensing. In Proceedings of the 16th International Conference on Extending Database Technology, EDBT ’13, (pp. 251–262).Google Scholar
  46. 46.
    Rice, A., & Hay, S. (2010). Measuring mobile phone energy consumption for 802.11 wireless networking. Pervasive and Mobile Computing, Special Issue PerCom 2010 6(6), pp. 593 – 606.Google Scholar
  47. 47.
    Shilton, K. (2009). Four billion little brothers?: privacy, mobile phones, and ubiquitous data collection. Communications of the ACM, 7, 40–47.Google Scholar
  48. 48.
    Sun, Y., & Nakata, K. (2010). An agent-based architecture for participatory sensing platform. In 4th International Universal Communication Symposium (IUCS), (pp. 392–400). IEEE.Google Scholar
  49. 49.
    Tsujimori, T., Thepvilojanapong, N., Ohta, Y., Zhao, Y., & Tobe, Y. (2014). History-based incentive for crowd sensing. In Proceedings of the International Workshop on Web Intelligence and Smart Sensing, IWWISS ’14, (pp. 1–6). ACM.Google Scholar
  50. 50.
    Tuncay, G., Benincasa, G., & Helmy, A. (2013). Participant recruitment and data collection framework for opportunistic sensing: A comparative analysis. In Proceedings of the 8th ACM MobiCom Workshop on Challenged Networks, CHANTS ’13, (pp. 25–30). ACM.Google Scholar
  51. 51.
    Wang, L., & Manner, J. (2010). Energy consumption analysis of wlan, 2g and 3g interfaces. In Proceedings of the 2010 IEEE/ACM Int’L Conference on Green Computing and Communications & Int’L Conference on Cyber, Physical and Social Computing, GREENCOM-CPSCOM ’10, (pp. 300–307). IEEE Computer Society.Google Scholar
  52. 52.
    Xiao, Y., Simoens, P., Pillai, P., Ha, K., & Satyanarayanan, M. (2013). Lowering the barriers to large-scale mobile crowdsensing. In Proceedings of the 14th Workshop on Mobile Computing Systems and Applications, HotMobile ’13, (pp. 9:1–9:6). ACM.Google Scholar
  53. 53.
    Yan, T., Marzilli, M., Holmes, R., Ganesan, D., & Corner, M. (2009). mcrowd: A platform for mobile crowdsourcing. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, (pp. 347–348). ACM, New York, NY, USA.Google Scholar
  54. 54.
    Ye, F., Ganti, R., Dimaghani, R., Grueneberg, K., & Calo, S. (2012). Meca: Mobile edge capture and analysis middleware for social sensing applications. In Proceedings of the 21st International Conference Companion on World Wide Web, WWW ’12 Companion, (pp. 699–702). ACM, New York, NY, USA.Google Scholar
  55. 55.
    Zhao, D., Ma, H., & Liu, L. (2014). Energy-efficient opportunistic coverage for people-centric urban sensing. Wireless Networks, 20(6), 1461–1476.CrossRefGoogle Scholar
  56. 56.
    Zhao, Q., Zhu, Y., Zhu, H., Cao, J., Xue, G., & Li, B. (2014). Fair energy-efficient sensing task allocation in participatory sensing with smartphones. In INFOCOM2014 Proceedings, (pp. 1366–1374). IEEE.Google Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Teemu Leppänen
    • 1
  • José Álvarez Lacasia
    • 2
  • Yoshito Tobe
    • 3
  • Kaoru Sezaki
    • 2
  • Jukka Riekki
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of OuluOuluFinland
  2. 2.Institute of Industrial ScienceUniversity of TokyoTokyoJapan
  3. 3.RealWorld Communication LaboratoryAoyama Gakuin UniversityTokyoJapan

Personalised recommendations