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Planning Robust Sensor Relocation Trajectories for a Mobile Robot with Evolutionary Multi-objective Optimization

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Computational Intelligence in Wireless Sensor Networks

Part of the book series: Studies in Computational Intelligence ((SCI,volume 676))

Abstract

Wireless sensor networks provide a method for monitoring a region of interest. Incorporating a mobile robot within the sensor network allows various types of functionality to be added. One example of this is the replacement of risky and/or damaged sensors with other functional, passive ones. Using a specially designed risk management framework (RMF), we can proactively detect sensors that are at a high risk for failure and replace them before any network coverage is lost. The problem of optimizing the robot trajectory while picking up passive sensors and dropping them at the locations of the damaged sensors in the field has been studied as the “Robot-Assisted Sensor Relocation” (RASR) problem. One shortcoming of existing RASR methods is that the chosen robot trajectory is the one with the shortest length; however, no regards as to the durability of the passive sensors in the relocation chain are taken into consideration. We propose a more robust manner to come up with these trajectories by taking into account the current energy levels of the participating passive sensors as well as the ideal locations for their deployment. We resort to multi-objective optimization (MOO) to handle the tradeoffs among the different decision objectives that are part of this new formulation, named here as “Reliable Robot-Assisted Sensor Relocation”. We outline the RRASR problem as well as the RMF used for detecting risky sensors in the wireless sensor network before the calculation of the sensor relocation trajectory takes place. We also evaluate the performance of six state-of-the-art evolutionary multi-objective optimization (EMOO) algorithms with sensor networks of varying sizes, inflicted damage levels, and passive sensor densities. The empirical results confirm the feasibility of utilizing EMOO approaches to suggest multiple sensor relocation trajectories to the network manager.

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Notes

  1. 1.

    http://moeaframework.org/.

References

  1. Yick, J., Mukherjee, B., Ghosal, D.: Wireless sensor network survey. Comput. Netw. 52(12), 2292–2330 (2008)

    Article  Google Scholar 

  2. Falcon, R.: Towards Fault Reactiveness in Wireless Sensor Networks with Mobile Carrier Robots. PhD thesis, University of Ottawa, Ottawa, ON, Canada (2012)

    Google Scholar 

  3. Falcon, R., Li, X., Nayak, A., Stojmenovic, I.: The one-commodity traveling salesman problem with selective pickup and delivery: an Ant Colony approach. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain, pp. 4326–4333 (2010)

    Google Scholar 

  4. Desjardins, B., Falcon, R., Abielmona, R., Petriu, E.: A multi-objective optimization approach to reliable robot-assisted sensor relocation. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 956–964. IEEE (2015)

    Google Scholar 

  5. Falcon, R., Nayak, A., Abielmona, R.: An evolving risk management framework for wireless sensor networks. In: 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) pp. 1–6. IEEE (2011)

    Google Scholar 

  6. Bringmann, K., Friedrich, T., Neumann, F., Wagner, M.: Approximation-guided evolutionary multi-objective optimization. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, p. 1198 (2011)

    Google Scholar 

  7. Wagner, M., Neumann, F.: A fast approximation-guided evolutionary multi-objective algorithm. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, pp. 687–694. ACM (2013)

    Google Scholar 

  8. Lian-Ming, M., Xi-Li, D.: A novel ant colony system for solving the one-commodity traveling salesman problem with selective pickup and delivery. In: 2012 Eighth International Conference on Natural Computation (ICNC), pp. 1096–1101. IEEE (2012)

    Google Scholar 

  9. Falcon, R., Li, X., Nayak, A., Stojmenovic, I.: A Harmony-seeking firefly swarm to the periodic replacement of damaged sensors by a team of mobile robots. In: 2012 IEEE International Conference on Communications (ICC), (Ottawa, Canada), pp. 6436–6440 (2012)

    Google Scholar 

  10. Magklara, K., Zorbas, D., Razafindralambo, T.: Node discovery and replacement using mobile robot. In: Ad Hoc Networks, pp. 59–71. Springer, Berlin (2013)

    Google Scholar 

  11. Wang, Y., Barnawi, A., De Mello, R.F., Stojmenovic, I.: Localized ant colony of robots for redeployment in wireless sensor networks. Multi-Valued Logic Soft Comput. 23, 35–51 (2014)

    Google Scholar 

  12. Fletcher, G., Li, X., Nayak, A., Stojmenovic, I.: Randomized robot-assisted relocation of sensors for coverage repair in wireless sensor networks. In: 2010 IEEE 72nd Vehicular Technology Conference Fall (VTC 2010-Fall), pp. 1–5. IEEE (2010)

    Google Scholar 

  13. Miao, Y., Yu-Ping, W.: Coverage repair strategies for wireless sensor networks using mobile actor based on evolutionary computing. Bull. Electr. Eng. Inform. 3(3), 213–222 (2014)

    Google Scholar 

  14. Li, H., Barnawi, A., Stojmenovic, I., Wang, C.: Market-based sensor relocation by robot team in wireless sensor networks. Ad Hoc Sens. Wirel. Netw. 22, 259–280 (2014)

    Google Scholar 

  15. Liao, X.-L., Ting, C.-K.: An evolutionary approach for the selective pickup and delivery problem. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)

    Google Scholar 

  16. Huang, Y.-H., Ting, C.-K.: Genetic algorithm with path relinking for the multi-vehicle selective pickup and delivery problem. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 1818–1825 (2011)

    Google Scholar 

  17. Liao, X.-L., Ting, C.-K.: Evolutionary algorithms using adaptive mutation for the selective pickup and delivery problem. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2012)

    Google Scholar 

  18. Bruck, B.P., dos Santos, A.G., Arroyo, J.E.C.: Hybrid metaheuristic for the single vehicle routing problem with deliveries and selective pickups. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2012)

    Google Scholar 

  19. Bruck, B.P., Santos, A., Arroyo, J.: An evolutionary algorithm and a variable neighborhood descent algorithm for the single vehicle problem with deliveries and selective pickups. In: Proceedings of the 2012 CLAIO/SBPO, Rio de Janeiro, Brazil (2012)

    Google Scholar 

  20. Ting, C.-K., Liao, X.-L.: The selective pickup and delivery problem: formulation and a memetic algorithm. Int. J. Prod. Econ. 141(1), 199–211 (2013)

    Article  Google Scholar 

  21. Bruck, B.P., dos Santos, A.: Hybrid approach for the multiple vehicle routing problem with deliveries and selective pickups. In: 2012 12th International Conference on Hybrid Intelligent Systems (HIS), pp. 265–270 (2012)

    Google Scholar 

  22. Falcon, R., Abielmona, R.: A response-aware risk management framework for search-and-rescue operations. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2012)

    Google Scholar 

  23. McCausland, J., Di Nardo, G., Falcon, R., Abielmona, R., Groza, V., Petriu, E.: A proactive risk-aware robotic sensor network for critical infrastructure protection. In: 2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 132–137. IEEE (2013)

    Google Scholar 

  24. McCausland, J., Abielmona, R., Falcon, R., Cretu, A.-M., Petriu, E.: Auction-based node selection of optimal and concurrent responses for a risk-aware robotic sensor network. In: 2013 IEEE International Symposium on Robotic and Sensors Environments (ROSE), pp. 136–141. IEEE (2013)

    Google Scholar 

  25. Falcon, R., Abielmona, R., Billings, S., Plachkov, A., Abbass, H.: Risk management with hard-soft data fusion in maritime domain awareness. In: 2014 Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), pp. 1–8. IEEE (2014)

    Google Scholar 

  26. Falcon, R., Abielmona, R., Billings, S.: Risk-driven intent assessment and response generation in maritime surveillance operations. In: 2015 IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), pp. 151–157. IEEE (2015)

    Google Scholar 

  27. Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man–Mach. Stud. 7(1), 1–13 (1975)

    Article  MATH  Google Scholar 

  28. Chen, J., Kher, S., Somani, A.: Distributed fault detection of wireless sensor networks. In: Proceedings of the 2006 Workshop on Dependability Issues in Wireless Ad Hoc Networks and Sensor Networks, pp. 65–72. ACM (2006)

    Google Scholar 

  29. Golumbic, M.C.: Algorithmic Graph Theory and Perfect Graphs, 2 edn, p. 2. Elsevier, Amsterdam (2004)

    Google Scholar 

  30. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)

    Article  Google Scholar 

  31. Goldberg, D.E., Lingle, R.: Alleles, loci, and the traveling salesman problem. In: Proceedings of an International Conference on Genetic Algorithms and Their Applications, vol. 154. Lawrence Erlbaum, Hillsdale (1985)

    Google Scholar 

  32. Onat, F.A., Stojmenovic, I., Yanikomeroglu, H.: Generating random graphs for the simulation of wireless ad hoc, actuator, sensor, and internet networks. Pervasive Mob. Comput. 4(5), 597–615 (2008)

    Article  Google Scholar 

  33. Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18, 577–601 (2014)

    Article  Google Scholar 

  34. Zitzler, E., Laumanns, M., Thiele, L., Zitzler, E., Zitzler, E., Thiele, L., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Eurogen, vol. 3242, pp. 95–100 (2001)

    Google Scholar 

  35. Corne, D.W., Jerram, N.R., Knowles, J.D., Oates, M.J., et al.: PESA-II: region-based selection in evolutionary multiobjective optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2001, pp. 283–290. Morgan Kaufmann, Los Altos (2001)

    Google Scholar 

  36. Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining convergence and diversity in evolutionary multiobjective optimization. Evol. Comput. 10(3), 263–282 (2002)

    Article  Google Scholar 

  37. Coello, C.C., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-objective Problems. Springer, Berlin (2007)

    MATH  Google Scholar 

  38. Sierra, M.R., Coello, C.A.C.: Improving pso-based multi-objective optimization using crowding, mutation and-dominance. In: Evolutionary Multi-criterion Optimization, pp. 505–519. Springer, Berlin (2005)

    Google Scholar 

  39. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S.: Global Sensitivity Analysis: The Primer. Wiley, New York (2008)

    MATH  Google Scholar 

  40. Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Research and Engineering, Larus Technologies Corporation, 170 Laurier Ave West - Suite 310, Ottawa, ON, K1P 5V5, Canada

    Rafael Falcon & Rami Abielmona

  2. School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward Ave, Ottawa, ON, K1N 6N5, Canada

    Benjamin Desjardins & Emil Petriu

Authors
  1. Benjamin Desjardins
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  2. Rafael Falcon
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  3. Rami Abielmona
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  4. Emil Petriu
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Corresponding author

Correspondence to Rafael Falcon .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

  2. Research & Engineering Division,Larus Technologies 170 Laurier Ave West - Suite 310,, Larus Technologies and School of Electrical Engineering and Computer Science, University of Ottawa 800 King Edward Ave, Ottawa, Ontario, Canada K1N 6N5, Ottawa, Ontario, Canada

    Rafael Falcon

  3. Department of Computer Science and Electronics, Kyushu Institute of Technology Graduate School of Creative Informatics, Fukuoka, Japan

    Mario Koeppen

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Desjardins, B., Falcon, R., Abielmona, R., Petriu, E. (2017). Planning Robust Sensor Relocation Trajectories for a Mobile Robot with Evolutionary Multi-objective Optimization. In: Abraham, A., Falcon, R., Koeppen, M. (eds) Computational Intelligence in Wireless Sensor Networks. Studies in Computational Intelligence, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-319-47715-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-47715-2_8

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  • Publisher Name: Springer, Cham

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