Skip to main content
SpringerLink
Log in

Designing Human Assisted Wireless Sensor and Robot Networks Using Probabilistic Model Checking

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Wireless sensor networks (WSNs) have a wide variety of applications in environment monitoring (such as air pollution and fire detection), industrial operations (such as machine surveillance), and precision agriculture. It is an arduous task to manage a large WSN as constant monitoring is required to keep it operational. Mobile robots are used to deploy, manage, and perform various application specific tasks in WSNs. However, a fully autonomous robot lacks the ability of proper decision-making in complex situations such as network coverage in disastrous areas. A remote human operator can assist the robot in improved decision-making, specially in odd situations that arise due to either inherent application needs or changes in the environment. In addition to the complexity of WSN managed by a robot, analyzing the effect of human operator in managing WSN poses further challenge. This is due to the fact that the performance of a human operator is also influenced by internal (such as fatigue) as well as external (such as workload conditions) factors. In this paper, we use probabilistic model checking to analyze the performance of robot assisted WSN. This study enables WSN administrators to analyze and plan WSN management before the actual deployment of robot and sensors in the field. Given specific application requirements, we are able to examine key parameters such as size of the network, number of sensors needed to keep the network operational, and time to service the farthest location. With the help of remote human operator, we introduce several degrees of autonomy to the mobile robot managing WSN. Markov decision process is used to capture uncertainties and imperfections in the human−robot interactions. We demonstrate the benefits obtained due to intelligent decision-making by a realistic human operator whose performance is affected by both external and internal factors. We demonstrate the applicability of our approach via detailed case studies in planning and managing WSNs.

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

Similar content being viewed by others

References

  1. Akyildiz, I.F., Melodia, T., Chowdhury, K.R.: A survey on wireless multimedia sensor networks. Comput. Netw. 51(4), 921–960 (2007)

    Article  Google Scholar 

  2. Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. IEEE Commun. Mag. 40(8), 102–114 (2002)

    Article  Google Scholar 

  3. Amato, G., Bacciu, D., Broxvall, M., Chessa, S., Coleman, S., Di Rocco, M., Dragone, M., Gallicchio, C., Gennaro, C., Lozano, H., et al: Robotic ubiquitous cognitive ecology for smart homes. J. Intell. Robot. Syst. 80(1), 57–81 (2015)

    Article  Google Scholar 

  4. Aslanyan, Z., Nielson, F., Parker, D.: Quantitative verification and synthesis of attack-defence scenarios. In: Proc. of Computer Security Foundations Symposium CSF (2016)

  5. Ballarini, P., Miller, A.: Model checking medium access control for sensor networks. In: Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, 2006. ISoLA 2006. pp. 255–262. IEEE (2006)

  6. Barbot, B., Kwiatkowska, M.: On quantitative modelling and verification of DNA walker circuits using stochastic petri nets. In: International Conference on Applications and Theory of Petri Nets and Concurrency, pp. 1–32. Springer (2015)

  7. Basañez, L., Suárez, R.: Teleoperation. In: Springer Handbook of Automation, pp. 449–468. Springer (2009)

  8. Bhatti, S., Xu, J., Memon, M.: Model checking of a target tracking protocol for wireless sensor networks. In: 2010 IEEE 10th International Conference on Computer and Information Technology (CIT), pp. 2867–2872. IEEE (2010)

  9. Bolton, M.L., Bass, E.J., Siminiceanu, R.I.: Using formal verification to evaluate human-automation interaction: a review. IEEE Trans. Syst. Man Cybern. Syst. 43(3), 488–503 (2013)

    Article  Google Scholar 

  10. Cañas, J. J., Antolí, A., Quesada, J.F.: The role of working memory on measuring mental models of physical systems. Psicológica 22(1), 25–42 (2001)

    Google Scholar 

  11. Casini, E., Depree, J., Suri, N., Bradshaw, J.M., Nieten, T.: Enhancing decision-making by leveraging human intervention in large-scale sensor networks. In: 2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision, pp. 200–205. IEEE (2015)

  12. Chessa, S., Gallicchio, C., Guzman, R., Micheli, A.: Robot localization by echo state networks using RSS. In: Recent Advances of Neural Network Models and Applications, pp. 147–154. Springer (2014)

  13. Crow, J., Javaux, D., Rushby, J.: Models and mechanized methods that integrate human factors into automation design. In: International Conference on Human-Computer Interaction in Aeronautics: HCI-Aero, pp. 163–168. Tolosa Press, Toulouse (2000)

  14. Curiac, D.I.: Towards wireless sensor, actuator and robot networks: Conceptual framework, challenges and perspectives. J. Netw. Comput. Appl. 63, 14–23 (2016)

    Article  Google Scholar 

  15. Deshpande, N., Grant, E., Henderson, T.C.: Target localization and autonomous navigation using wireless sensor networks - a pseudogradient algorithm approach. IEEE Syst. J. 8(1), 93–103 (2014)

    Article  Google Scholar 

  16. Dunbabin, M., Corke, P., Vasilescu, I., Rus, D.: Data muling over underwater wireless sensor networks using an autonomous underwater vehicle. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, pp. 2091–2098. IEEE (2006)

  17. von Essen, C., Jobstmann, B., Parker, D., Varshneya, R.: Synthesizing efficient systems in probabilistic environments. Acta Informatica, 1–33 (2015)

  18. Falcon, R., Nayak, A., Stojmenovic, I.: Robot-Assisted Wireless Sensor Networks: Recent Applications and Future Challenges. Mobile Ad Hoc Networking: Cutting Edge Directions, 2nd edn., pp. 737–768 (2013)

  19. Feng, L., Wiltsche, C., Humphrey, L., Topcu, U.: Synthesis of human-in-the-loop control protocols for autonomous systems. IEEE Trans. Autom. Sci. Eng. 13(2), 450–462 (2016)

    Article  Google Scholar 

  20. Guerrero, E., Xiong, H., Gao, Q., Cova, G., Ricardo, R., Estévez, J.: Adal: A distributed range-free localization algorithm based on a mobile beacon for wireless sensor networks. In: 2009 International Conference on Ultra Modern Telecommunications & Workshops, pp. 1–7. IEEE (2009)

  21. Hasbullah, H., et al.: Impact of gaussian deployment strategies on the performance of wireless sensor network. In: 2012 International Conference on Computer & Information Science (ICCIS), vol. 2, pp. 771–776. IEEE (2012)

  22. Heidemann, J., Li, Y., Syed, A., Wills, J., Ye, W.: Underwater sensor networking: Research challenges and potential applications. In: Proceedings of the Technical Report ISI-TR-2005-603 USC/Information Sciences Institute (2005)

  23. Ho, D.T., Grøtli, E.I., Sujit, P., Johansen, T.A., Sousa, J.B.: Optimization of wireless sensor network and uav data acquisition. J. Intell. Robot. Syst. 78(1), 159 (2015)

    Article  Google Scholar 

  24. Jawhar, I., Mohamed, N., Al-Jaroodi, J., Zhang, S.: A framework for using unmanned aerial vehicles for data collection in linear wireless sensor networks. J. Intell. Robot. Syst. 74(1–2), 437 (2014)

    Article  Google Scholar 

  25. Jiang, J.R., Lai, Y.L., Deng, F.C.: Mobile robot coordination and navigation with directional antennas in positionless wireless sensor networks. Int. J. Ad Hoc Ubiquit. Comput. 7(4), 272–280 (2011)

    Article  Google Scholar 

  26. Johnson, B., Kress-Gazit, H.: Probabilistic guarantees for high-level robot behavior in the presence of sensor error. Auton. Robot. 33(3), 309–321 (2012)

    Article  Google Scholar 

  27. Katsikiotis, C., Zorbas, D., Chatzimisios, P.: Connectivity restoration and amelioration in wireless ad-hoc networks: A practical solution. In: International Conference on Ad Hoc Networks, pp. 255–264. Springer (2014)

  28. Khalid, O., Sualeh, M.: Comparative study on mobile wireless sensor network testbeds. Int. J. Comput. Theory Eng. 5(2), 204 (2013)

    Article  Google Scholar 

  29. Khan, A.W., Abdullah, A.H., Razzaque, M.A., Bangash, J.I.: Vgdra: A virtual grid-based dynamic routes adjustment scheme for mobile sink-based wireless sensor networks. IEEE Sensors J. 15(1), 526–534 (2015)

    Article  Google Scholar 

  30. Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: Verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) Proc. 23rd International Conference on Computer Aided Verification (CAV’11), LNCS, vol. 6806, pp. 585–591. Springer (2011)

  31. Kwon, Y., Agha, G.: Scalable modeling and performance evaluation of wireless sensor networks. In: Proceedings of the 12th IEEE Real-Time and Embedded Technology and Applications Symposium, 2006. pp. 49–58. IEEE (2006)

  32. Li, M., Liu, Y.: Underground structure monitoring with wireless sensor networks. In: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, pp. 69–78. ACM (2007)

  33. Li, X., Lille, I., Falcon, R., Nayak, A., Stojmenovic, I.: Servicing wireless sensor networks by mobile robots. IEEE Commun. Mag. 50(7), 147–154 (2012)

    Article  Google Scholar 

  34. Liang, W., Xu, W., Ren, X., Jia, X., Lin, X.: Maintaining large-scale rechargeable sensor networks perpetually via multiple mobile charging vehicles. ACM Trans. Sensor Netw. (TOSN) 12(2), 14 (2016)

    Google Scholar 

  35. Martinez, R.F.: Towards fault reactiveness in wireless sensor networks with mobile carrier robots. Université d’Ottawa/University of Ottawa, Ph.D. thesis (2012)

    Google Scholar 

  36. Martini, S., Di Baccio, D., Romero, F.A., Jiménez, A. V., Pallottino, L., Dini, G., Ollero, A.: Distributed motion misbehavior detection in teams of heterogeneous aerial robots. Robot. Auton. Syst. 74, 30–39 (2015)

    Article  Google Scholar 

  37. Mu, C., Dittrich, P., Parker, D., Rowe, J.E.: Formal quantitative analysis of reaction networks using chemical organisation theory. In: International Conference on Computational Methods in Systems Biology, pp. 232–251. Springer (2016)

  38. Rahman, A.U., Alharby, A., Hasbullah, H., Almuzaini, K.: Corona based deployment strategies in wireless sensor network: A survey. J. Netw. Comput. Appl. 64, 176–193 (2016)

    Article  Google Scholar 

  39. Soeanu, A., Debbabi, M., Alhadidi, D., Makkawi, M., Allouche, M., Bélanger, M., Léchevin, N.: Transportation risk analysis using probabilistic model checking. Expert Syst. Appl. 42(9), 4410–4421 (2015)

    Article  Google Scholar 

  40. Soysal, O., Demirbas, M.: Data spider: a resilient mobile basestation protocol for efficient data collection in wireless sensor networks. In; International Conference on Distributed Computing in Sensor Systems, pp. 393–408. Springer (2010)

  41. Tekdas, O., Isler, V., Lim, J.H., Terzis, A.: Using mobile robots to harvest data from sensor fields. IEEE Wirel. Commun. 16(1), 22 (2009)

    Article  Google Scholar 

  42. Tuna, G., Gungor, V.C., Gulez, K.: An autonomous wireless sensor network deployment system using mobile robots for human existence detection in case of disasters. Ad Hoc Netw. 13, 54–68 (2014)

    Article  Google Scholar 

  43. Wichmann, A., Okkalioglu, B.D., Korkmaz, T.: The integration of mobile (tele) robotics and wireless sensor networks: a survey. Comput. Commun. 51, 21–35 (2014)

    Article  Google Scholar 

  44. Zhang, K., Collins, E.G. Jr, Barbu, A.: An efficient stochastic clustering auction for heterogeneous robotic collaborative teams. J. Intell. Robot. Syst. 72(3-4), 541 (2013)

    Article  Google Scholar 

  45. Zhang, K., Collins, E.G. Jr, Shi, D.: Centralized and distributed task allocation in multi-robot teams via a stochastic clustering auction. ACM Trans. Autonom. Adapt. Syst. (TAAS) 7(2), 21 (2012)

    Google Scholar 

  46. Zhang, K., Collins, E.G. Jr, Shi, D., Liu, X., Chuy, O. Jr: A stochastic clustering auction (sca) for centralized and distributed task allocation in multi-agent teams. In: Distributed Autonomous Robotic Systems 8, pp. 345–354. Springer (2009)

  47. Zhang, K., Li, X.: Human-robot team coordination that considers human fatigue. Int. J. Adv. Robot. Syst. 11(6), 91 (2014)

    Article  Google Scholar 

Download references

Acknowledgements

Authors would like to thank Prince Mohammad Bin Fahd University (PMU) for providing computing facilities.

Author information

Authors and Affiliations

  1. Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia

    Shahabuddin Muhammad, Nazeeruddin Mohammad, Abul Bashar & Majid Ali Khan

Authors
  1. Shahabuddin Muhammad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nazeeruddin Mohammad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abul Bashar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Majid Ali Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shahabuddin Muhammad.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Muhammad, S., Mohammad, N., Bashar, A. et al. Designing Human Assisted Wireless Sensor and Robot Networks Using Probabilistic Model Checking. J Intell Robot Syst 94, 687–709 (2019). https://doi.org/10.1007/s10846-018-0901-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-018-0901-x

Keywords

Search

Navigation