Journal of Intelligent Manufacturing

, Volume 30, Issue 5, pp 2129–2155 | Cite as

Impacts of wireless sensor networks strategies and topologies on prognostics and health management

  • Ahmad FarhatEmail author
  • Christophe Guyeux
  • Abdallah Makhoul
  • Ali Jaber
  • Rami Tawil
  • Abbas Hijazi


In this article, we used wireless sensor network (WSN) techniques for monitoring an area under consideration, in order to diagnose its state in real time. What differentiates this type of network from the traditional computer ones is that it is composed by a large number of sensor nodes having very limited and almost nonrenewable energy. A key issue in designing such networks is energy conservation because once a sensor depletes its resources, it will be dropped from the network. This will lead to coverage hole and incomplete data arriving to the sink. Therefore, preserving the energy held by the nodes so that the network keeps running for as long as possible is a very important concern. If we achieve to improve the network lifetime and Quality of Service (QoS). Diagnosing the state of area will be more accurate for a longer time. One of the most important elements to achieve a QoS in WSN is the network coverage which is usually interpreted as how well the network can observe a given area. Obviously, if the coverage decreases over time, the diagnosis quality decreases accordingly. Various coverage strategies are thus proposed by the WSN community, in order to guarantee a certain coverage rate as long as possible, to reach a certain QoS that in turn will impact the diagnosis and prognostic quality. Various other strategies are in common use in WSN like data aggregation and scheduling, to preserve a QoS in wireless sensor networks, as long as possible. We argue that such strategies are not neutral if this network is used for prognostic and health management. Some politics may have a positive impact while other ones may blur the sensed data, like data aggregation or redundancy suppression, leading to erroneous diagnostics and/or prognostics. In this work, we will show and measure the impact of each WSN strategy on the resulting estimation of diagnostics. We emphasized several issues and studied various parameters related to these strategies that have a very important impact on the network, and therefore on data diagnostics over time. To reach this goal, to evaluate both prognostic and health management with the WSN strategies, we have used six diagnostic algorithms.


Wireless sensor networks Coverage Scheduling mechanisms Topology Prognostic and health management Diagnostics Machine learning algorithms 



This paper is partially funded by the Labex ACTION program (ANR-11-LABX-01-01 contract) and by the Interreg RESponSE project.


  1. Adlakha, S., & Srivastava, M. (2003). Critical density thresholds for coverage in wireless sensor networks. In Wireless communications and networking, 2003. WCNC 2003. 2003 IEEE (Vol. 3, pp. 1615–1620). IEEE.Google Scholar
  2. Ahmed, M. H., Alam, S. W., Qureshi, N., & Baig, I. (2011). Security for wsn based on elliptic curve cryptography. In International conference on computer networks and information technology (ICCNIT) (pp. 75–79). IEEE.Google Scholar
  3. Akan, Ö. B., & Akyildiz, I. F. (2005). Event-to-sink reliable transport in wireless sensor networks. IEEE/ACM Transactions on Networking (TON), 13(5), 1003–1016.Google Scholar
  4. Akyildiz, I. F., Melodia, T., & Chowdhury, K. R. (2007). A survey on wireless multimedia sensor networks. Computer Networks, 51(4), 921–960.Google Scholar
  5. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.Google Scholar
  6. Ammari, H. M. (2016). A unified framework for k-coverage and data collection in heterogeneous wireless sensor networks. Journal of Parallel and Distributed Computing, 89, 37–49.Google Scholar
  7. Bae, S., Cha, H., & Suh, Y. (2014). Study on condition based maintenance using on-line monitoring and prognostics suitable to a research reactor. In European conference of the prognostics and health management society.Google Scholar
  8. Bahi, J., Elghazel, W., Guyeux, C., Haddad, M., Hakem, M., Medjaher, K., et al. (2016). Resiliency in distributed sensor networks for prognostics and health management of the monitoring targets. The Computer Journal, 59(2), 275–284.Google Scholar
  9. Bahi, J. M., Guyeux, C., Makhoul, A., & Pham, C. (2012). Low-cost monitoring and intruders detection using wireless video sensor networks. International Journal of Distributed Sensor Networks, 8(5), 929542.Google Scholar
  10. Bahi, J. M., Makhoul, A., & Mostefaoui, A. (2008). Localization and coverage for high density sensor networks. Computer Communications, 31(4), 770–781.Google Scholar
  11. Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123–140.Google Scholar
  12. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.Google Scholar
  13. Bühlmann, P., & Hothorn, T. (2007). Boosting algorithms: Regularization, prediction and model fitting. Statistical Science, 22(4), 477–505.Google Scholar
  14. Cardei, M., & Wu, J. (2004). Coverage in wireless sensor networks. Handbook of Sensor Networks, 21, 201–202.Google Scholar
  15. Carman, D. W., Kruus, P. S., & Matt, B. J. (2000). Constraints and approaches for distributed sensor network security (final). DARPA Project report, (Cryptographic Technologies Group, Trusted Information System, NAI Labs), Vol. 1, no. 1.Google Scholar
  16. Chang, R.-S., & Wang, S.-H. (2010). Deployment strategies for wireless sensor networks. In Handbook of research on developments and trends in wireless sensor networks: From principle to practice (pp. 20–37). IGI Global.Google Scholar
  17. Choi, J., Hahn, J., & Ha, R. (2009). Short paper. Journal of Information Science and Engineering, 25, 273–287.Google Scholar
  18. Elghazel, W., Bahi, J., Farhat, A., Guyeux, C., Hakem, M., Medjaher, K., & Zerhouni, N. (2015a) Random forests for industrial device functioning diagnostics using wireless sensor networks. In IEEE Aerospace conference (pp. 1–9). Big Sky, Montana, USA.Google Scholar
  19. Elghazel, W., Bahi, J., Guyeux, C., Hakem, M., Medjaher, K., & Zerhouni, N. (2015b). Dependability of wireless sensor networks for industrial prognostics and health management. Computers in Industry, 68, 1–15.Google Scholar
  20. Elghazel, W., Bahi, J., Guyeux, C., Hakem, M., Medjaher, K., & Zerhouni, N. (2015c). Prognostics and health management based on dependable wireless sensor networks. In SENSORNETS 2015, 4th international conference on sensor networks (pp. 1–15), Angers, France.Google Scholar
  21. Elghazel, W., Medjaher, K., Guyeux, C., Hakem, M., Zerhouni, N., & Bahi, J. (2014). Dependable wireless sensor networks for prognostics and health management: A survey. In Annual conference of the prognostics and health management society, PHM’14 (Vol. 68, pp. 1–15). Fort Worth, Texas, USA.Google Scholar
  22. Elghazel, W., Medjaher, K., Zerhouni, N., Bahi, J., Farhat, A., Guyeux, C., & Hakem, M. (2015d). Random forests for industrial device functioning diagnostics using wireless sensor networks. In Aerospace conference, 2015 IEEE (pp. 1–9). IEEE.Google Scholar
  23. Estrin, D., Govindan, R., Heidemann, J., & Kumar, S. (1999). Next century challenges: Scalable coordination in sensor networks. In Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking (pp. 263–270). ACM.Google Scholar
  24. Fan, G. J., & Jin, S. Y. (2010). Coverage problem in wireless sensor network: A survey. JNW, 5(9), 1033–1040.Google Scholar
  25. Feng, J., Koushanfar, F., & Potkonjak, M. (2002). System-architectures for sensor networks issues, alternatives, and directions. In Computer design: VLSI in computers and processors, 2002. Proceedings. 2002 IEEE international conference on (pp. 226–231). IEEE.Google Scholar
  26. Galar, D., Kumar, U., Lee, J., & Zhao, W. (2012). Remaining useful life estimation using time trajectory tracking and support vector machines. Journal of Physics: Conference Series 364, 012063. IOP Publishing.Google Scholar
  27. Hareb, H., Makhoul, A., & Couturier, R. (2015). An enhanced K-means and ANOVA-based clustering approach for similarity aggregation in underwater wireless sensor networks. IEEE Sensors Journal, 15(10), 5483–5493.Google Scholar
  28. Hareb, H., Makhoul, A., Tawil, R., & Jaber, A. (2014). Energy-efficient data aggregation and transfer in periodic sensor networks. IET Wireless Sensor Systems, 4(4), 149–158.Google Scholar
  29. He, T. C., Cao, W. M., & Xie, W. X. (2009). Coverage analyses of plane target in sensor networks based on clifford algebra. Acta Electronica Sinica, 37(8), 1681–1685.Google Scholar
  30. Hefeeda, M., & Ahmadi, H. (2010). Energy-efficient protocol for deterministic and probabilistic coverage in sensor networks. IEEE Transactions on Parallel and Distributed Systems, 21(5), 579–593.Google Scholar
  31. Heng, A., Zhang, S., Tan, A. C. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724–739.Google Scholar
  32. Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844.Google Scholar
  33. ISO Condition Monitoring. (2004). Diagnostics of machines-prognostics part 1: General guidelines. ISO13381-1: (e). vol. ISO/IEC Directives Part 2, IO f. S (p. 14).Google Scholar
  34. Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.Google Scholar
  35. Kantaros, Y., & Zavlanos, M. M. (2011). Lifetime and coverage guarantees through distributed coordinate-free sensor activation. IEEE/ACM Transactions on Networking, 19(2), 470–483.Google Scholar
  36. Kantaros, Y., & Zavlanos, M. M. (2016). Distributed communication-aware coverage control by mobile sensor networks. Automatica, 63, 209–220.Google Scholar
  37. Krishnamachari, L., Estrin, D., & Wicker, S. (2002). The impact of data aggregation in wireless sensor networks. In 22nd international conference on distributed computing systems workshops, 2002. Proceedings (pp. 575–578). IEEE.Google Scholar
  38. Kwon, S., Ko, J. H., Kim, J., & Kim, C. (2011). Dinamic timeout for data aggregation in wireless sensor netwoks. Computer Networks, 55, 650–664.Google Scholar
  39. Li, L., Zhang, B., & Zheng, J. (2013). A study on one-dimensional k-coverage problem in wireless sensor networks. Wireless Communications and Mobile Computing, 13(1), 1–11.Google Scholar
  40. Li, M., Li, Z., & Vasilakos, A. V. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.Google Scholar
  41. Li, M., & Yang, B. (2006). A survey on topology issues in wireless sensor network. In ICWN (p. 503).Google Scholar
  42. Li, Z., & Gong, G. (2008). Survey on security in wireless sensor. Journal of the Korea Institute of Information Security and Cryptology, 18(6B), 233–248.Google Scholar
  43. Liang, J., Liu, M., & Kui, X. (2014). A survey of coverage problems in wireless sensor networks. Sensors & Transducers, 163(1), 1726–5479.Google Scholar
  44. Maheswari, K. M. U., Devi, S. K., & Govindarajan, S. (2011). Data aggregation in wireless sensor networks. Wireless. Communication, 3(6), 476–480.Google Scholar
  45. Makhoul, A., Harb, H., & Laiymani, D. (2015a). Residual energy-based adaptive data collection approach for periodic sensor networks. Ad Hoc Networks, 35, 149–160.Google Scholar
  46. Makhoul, A., Laiymani, D., Hareb, H., & Bahi, J. (2015b). An adaptive scheme for data collection and aggregation in periodic sensor networks. International Journal of Sensor Networks, 18(1/2), 62–74.Google Scholar
  47. McRoberts, R. E. (2012). Estimating forest attribute parameters for small areas using nearest neighbors techniques. Forest Ecology and Management, 272, 3–12.Google Scholar
  48. Ng, S. S. Y., Xing, Y., & Tsui, K. L. (2014). A naive bayes model for robust remaining useful life prediction of lithium-ion battery. Applied Energy, 118, 114–123.Google Scholar
  49. Niu, G., & Yang, B.-S. (2010). Intelligent condition monitoring and prognostics system based on data-fusion strategy. Expert Systems with Applications, 37(12), 8831–8840.Google Scholar
  50. Pathan, A.-S. K., Lee, H.-W., & Hong, C. S. (2006). Security in wireless sensor networks: Issues and challenges. In The 8th international conference advanced communication technology, 2006. ICACT 2006 (Vol. 2, pp. 6). IEEE.Google Scholar
  51. Patil, A. K., & Patil, A. J. (2013). Issues of connectivity and coverage in wireless sensor networks. International Journal of Electrical and Electronics Engineering Research (IJEEER), 1(3), 249–258.Google Scholar
  52. Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: a review. The International Journal of Advanced Manufacturing Technology, 50(1–4), 297–313.Google Scholar
  53. Pham, C., Makhoul, A., & Saadi, R. (2011). Risk-based adaptive scheduling in randomly deployed video sensor networks for critical surveillance applications. Journal of Network and Computer Applications, 34(2), 783–795.Google Scholar
  54. Raja, K., Daskalopoulos, I., Diall, H., Hailes, S., Torfs, T., Van Hoof, C., & Roussos, G. (2006). Sensor cubes: A modular, ultra-compact, power-aware platform for sensor networks. In International Conference on Information Processing in Sensor Networks (IPSN SPOTS). Citeseer.Google Scholar
  55. Russell, S., & Norvig, P. (2003). Artificial intelligence: A modern approach (Vol. 22, pp. 25–26). London: Pearson education Inc.Google Scholar
  56. Saxena, A., & Goebel, K. (2008). Phm08 challenge data set. NASA Ames Prognostics Data Repository. CA: NASA Ames Research Center, Moffett Field.
  57. Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), 1803–1836.Google Scholar
  58. Silva, I., Guedes, L. A., Portugal, P., & Vasques, F. (2012). Reliability and availability evaluation of wireless sensor networks for industrial applications. Sensors, 12(1), 806–838.Google Scholar
  59. Sugumaran, V., Muralidharan, V., & Ramachandran, K. I. (2007). Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mechanical Systems and Signal Processing, 21(2), 930–942.Google Scholar
  60. Sun, B., Kang, R., & Jin-song, X. I. E. (2007). Research and application of the prognostic and health management system. Systems Engineering and Electronics, 10, 041.Google Scholar
  61. Taherkordi, A., Taleghan, M. A., & Sharifi, M. (2006). Dependability considerations in wireless sensor networks applications. Journal of Networks, 1(6), 28–35.Google Scholar
  62. Tian, D., & Georganas, N. D. (2003). A node scheduling scheme for energy conservation in large wireless sensor networks. Wireless Communications and Mobile Computing, 3(2), 271–290.Google Scholar
  63. Tian, D., & Georganas, N. D. (2004). Location and calculation-free node-scheduling schemes in large wireless sensor networks. Ad Hoc Networks, 2(1), 65–85.Google Scholar
  64. Tian, D., & Georganas, N. D. (2005). Connectivity maintenance and coverage preservation in wireless sensor networks ad hoc networks journal. Ad Hoc Networks Journal. Citeseer.Google Scholar
  65. Tian, J., Liang, X., & Wang, G. (2016). Deployment and reallocation in mobile survivability-heterogeneous wireless sensor networks for barrier coverage. Ad Hoc Networks, 36, 321–331.Google Scholar
  66. Tobon-Mejia, D. A., Medjaher, K., & Zerhouni, N. (2012a). CNC machine tool’s wear diagnostic and prognostic by using dynamic Bayesian networks. Mechanical Systems and Signal Processing, 28, 167–182.Google Scholar
  67. Tobon-Mejia, D. A., Medjaher, K., Zerhouni, N., & Tripot, G. (2012b). A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models. IEEE Transactions on Reliability, 61(2), 491–503.Google Scholar
  68. Torkestani, J. A. (2013). An adaptive energy-efficient area coverage algorithm for wireless sensor networks. Ad Hoc Networks, 11(6), 1655–1666.Google Scholar
  69. Walters, J. P., Liang, Z., Shi, W., & Chaudhary, V. (2007). Wireless sensor network security: A survey. Security in Distributed, Grid, Mobile, and Pervasive Computing, 1, 367.Google Scholar
  70. Wang, L., & Xiao, Y. (2006). A survey of energy-efficient scheduling mechanisms in sensor networks. Mobile Networks and Applications, 11(5), 723–740.Google Scholar
  71. Wang, X., Xing, G., Zhang, Y., Lu, C., Pless, R., & Gill, C. (2003). Integrated coverage and connectivity configuration in wireless sensor networks. In Proceedings of the 1st international conference on Embedded networked sensor systems (pp. 28–39). ACM.Google Scholar
  72. Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficient mac protocol for wireless sensor networks. In INFOCOM 2002. Twenty-first annual joint conference of the IEEE computer and communications societies. Proceedings. IEEE (Vol. 3, pp. 1567–1576). IEEE.Google Scholar
  73. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.Google Scholar
  74. Yuan, F., Zhan, Y., & Wang, Y. (2014). Data density correlation degree clustering method for data aggregation in wsn. IEEE Sensors Journal, 14(4), 1089–1098.Google Scholar
  75. Zhang, H. (2004). The optimality of naive bayes. AA, 1(2), 3.Google Scholar
  76. Zhu, C., Zheng, C., Shu, L., & Han, G. (2012). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35(2), 619–632.Google Scholar
  77. Zio, E., & Di Maio, F. (2010). A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system. Reliability Engineering & System Safety, 95(1), 49–57.Google Scholar
  78. Zorbas, D., Glynos, D., Kotzanikolaou, P., & Douligeris, C. (2007). B \(\{\)GOP\(\}\): An adaptive algorithm for coverage problems in wireless sensor networks. In 13th European wireless conference, EW.Google Scholar

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Authors and Affiliations

  1. 1.FEMTO-ST Institute, UMR 6174 CNRSUniversité de Bourgogne Franche-ComtéBesanconFrance
  2. 2.Department of Computer ScienceLebanese UniversityBeirutLebanon

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