Using the internet of things paradigm for monitoring applications requires sufficient coverage of a monitored region to collect accurate data and observe events. The problem of a coverage hole arises when a sensor fails (loses connectivity with its neighbouring sensors) for reasons such as energy exhaustion or physical damage due to deployment in harsh environments. It is vital to detect and repair coverage holes as soon as they appear because they can have debilitating effects on network coverage and connectivity if left unattended. The authors propose a novel approach that employs a fuzzy inference system to select a neighbouring sensor that moves to recover the lost area. All neighbouring sensors communicate with each other to estimate the size and location of the coverage hole. Each sensor considers its own energy level, its distance from the approximated hole, and its redundancy ratio to evaluate its eligibility. The most eligible sensor is then selected to patch the coverage hole. Through extensive simulations, they analysed the performance of the proposed approach and compared it with baseline approaches in terms of coverage recovery effectiveness. The results show that the proposed approach is much more effective in repairing the holes as compared to the baseline approaches.

References

1. 1)
  - 20. Baccar, N., Jridi, M., Bouallegue, R.: ‘Neuro-fuzzy localization in wireless sensor networks’. Int. Symp. on Signal, Image, Video and Communications (ISIVC), Tunis, Tunisia, 2016, pp. 35–40.
2. 2)
  - 16. Aliouane, L., Benchaiba, M.: ‘Hach: healing algorithm of coverage hole in a wireless sensor network’. 2014 Eighth Int. Conf. on Next Generation Mobile Apps, Services and Technologies (NGMAST), Oxford, UK, 2014, pp. 215–220.
3. 3)
  - 23. Maali, Y., Rafiei, A., Abolhasan, M., et al: ‘A fuzzy logic node relocation model in WSNs’. 2012 6th Int. Conf. on Signal Processing and Communication Systems (ICSPCS), Gold Coast, Australia, 2012, pp. 1–6.
4. 4)
  - 26. Mondal, S., Dutta, P., Ghosh, S., et al: ‘Energy efficient rough fuzzy set based clustering and cluster head selection for WSN’. 2016 2nd Int. Conf. on Next Generation Computing Technologies (NGCT), Dehradun, India, 2016, pp. 439–444.
5. 5)
  - 29. Htun, A.M., Maw, M.S., Sasase, I.: ‘Reduced complexity on mobile sensor deployment and coverage hole healing by using adaptive threshold distance in hybrid wireless sensor networks’. 2014 IEEE 25th Annual Int. Symp. on Personal, Indoor, and Mobile Radio Communication (PIMRC), Washington, DC., USA, 2014, pp. 1547–1552.
6. 6)
  - 9. Younis, M., Senturk, I.F., Akkaya, K., et al: ‘Topology management techniques for tolerating node failures in wireless sensor networks: a survey’, Comput. Netw., 2014, 58, pp. 254–283.
7. 7)
  - 30. Cheng, C.T., Chi, K.T., Lau, F.C.: ‘A delay-aware data collection network structure for wireless sensor networks’, IEEE Sens. J., 2011, 11, (3), pp. 699–710.
8. 8)
  - 22. Collotta, M., Pau, G., Maniscalco, V.: ‘A fuzzy logic approach by using particle swarm optimization for effective energy management in IWSNs’, IEEE Trans. Ind. Electron., 2017, 64, (12), pp. 9496–9506.
9. 9)
  - 6. Gubbi, J., Buyya, R., Marusic, S., et al: ‘Internet of things (IoT): a vision, architectural elements, and future directions’, Future Gener. Comput. Syst., 2013, 29, (7), pp. 1645–1660.
10. 10)
  - 7. Bonomi, F., Milito, R., Zhu, J., et al: ‘Fog computing and its role in the internet of things’. Proc. first Edition of the MCC Workshop on Mobile Cloud Computing, Helsinki, Finland, 2012, pp. 13–16.
11. 11)
  - 8. Ning, H., Liu, H., Ma, J., et al: ‘Cybermatics: cyber–physical–social–thinking hyperspace based science and technology’, Future Gener. Comput. Syst., 2016, 56, pp. 504–522.
12. 12)
  - 27. Bhajantri, L.B., Sutagundar, A.V.: ‘Fuzzy logic based cluster head selection and data processing in distributed sensor networks’. Int. Conf. on Computing, Analytics and Security Trends (CAST), Pune, India, 2016, pp. 282–288.
13. 13)
  - 21. Lim, C.G., Kim, Y.M.: ‘An improved location estimation with modular neural fuzzy approach using received signal strength in wireless sensor networks’. Proc. 2017 Int. Conf. on Machine Learning and Soft Computing, Ho Chi Minh City, Vietnam, 2017, pp. 154–160.
14. 14)
  - 5. Zanella, A., Bui, N., Castellani, A., et al: ‘Internet of things for smart cities’, IEEE Internet Things J., 2014, 1, (1), pp. 22–32.
15. 15)
  - 1. Miorandi, D., Sicari, S., De Pellegrini, F., et al: ‘Internet of things: vision, applications and research challenges’, Ad Hoc Netw., 2012, 10, (7), pp. 1497–1516.
16. 16)
  - 32. ‘Intersection of three circles’. Available at http://www.ambrsoft.com/TrigoCalc/Circles3/Intersection.htm.
17. 17)
  - 14. Yi, L., Deng, X., Zou, Z., et al: ‘Confident information coverage hole detection in sensor networks for uranium tailing monitoring’, J. Parallel Distrib. Comput., 2018, 118, (1), pp. 57–66.
18. 18)
  - 33. Kwon, S.M., Kim, J.S.: ‘Coverage ratio in the wireless sensor networks using Monte Carlo simulation’. Fourth Int. Conf. on Networked Computing and Advanced Information Management, 2008. NCM'08, vol. 1, Gyeongju, South Korea, 2008, pp. 235–238.
19. 19)
  - 12. Kumar Sahoo, P., Chiang, M.-J., Wu, S.-L.: ‘An efficient distributed coverage hole detection protocol for wireless sensor networks’, Sensors, 2016, 16, (3), p. 386.
20. 20)
  - 19. Sharma, G., Kumar, A.: ‘Fuzzy logic based 3d localization in wireless sensor networks using invasive weed and bacterial foraging optimization’, Telecommun. Syst., 2017, 67, (2), pp. 149–162.
21. 21)
  - 4. Bellavista, P., Cardone, G., Corradi, A., et al: ‘Convergence of MANET and WSN in IoT urban scenarios’, IEEE Sens. J., 2013, 13, (10), pp. 3558–3567.
22. 22)
  - 3. Al-Fuqaha, A., Guizani, M., Mohammadi, M., et al: ‘Internet of things: a survey on enabling technologies, protocols, and applications’, IEEE Commun. Surv. Tutorials, 2015, 17, (4), pp. 2347–2376.
23. 23)
  - 25. Nayak, P., Bhavani, B.: ‘Energy efficient clustering algorithm for multi hop wireless sensor network using type-2 fuzzy logic’, IEEE Sens. J., 2017, 17, (14), pp. 4492–4499.
24. 24)
  - 2. Atzori, L., Iera, A., Morabito, G.: ‘The internet of things: a survey’, Comput. Netw., 2010, 54, (15), pp. 2787–2805.
25. 25)
  - 24. Wu, X., Cho, J., d'Auriol, B.J., et al: ‘Optimal deployment of mobile sensor networks and its maintenance strategy’. Int. Conf. on Grid and Pervasive Computing, Paris, France, 2007, pp. 112–123.
26. 26)
  - 28. Manoj, B., Sekhar, A., Murthy, C.S.R.: ‘On the use of limited autonomous mobility for dynamic coverage maintenance in sensor networks’, Comput. Netw., 2007, 51, (8), pp. 2126–2143.
27. 27)
  - 18. Chenji, H., Stoleru, R.: ‘Toward accurate mobile sensor network localization in noisy environments’, IEEE Trans. Mob. Comput., 2013, 12, (6), pp. 1094–1106.
28. 28)
  - 11. Fan, X., Zhang, Z., Lin, X., et al: ‘Coverage hole elimination based on sensor intelligent redeployment in WSN’. 2014 IEEE 4th Annual Int. Conf. on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Hong Kong, China, 2014, pp. 336–339.
29. 29)
  - 31. Weisstein, E.W.: ‘Circle-circle intersection’. Available at http://mathworld.wolfram.com/Circle-CircleIntersection.html.
30. 30)
  - 10. Zhao, L., Liu, W., Lei, H., et al: ‘Detecting boundary nodes and coverage holes in wireless sensor networks,’ Mobile Inf. Syst., 2016, 2016, p. 8310296. Available at http://dx.doi.org/10.1155/2016/8310296.
31. 31)
  - 13. Amgoth, T., Jana, P.K.: ‘Coverage hole detection and restoration algorithm for wireless sensor networks’, Peer-to-Peer Netw. Appl., 2017, 10, (1), pp. 66–78.
32. 32)
  - 17. Izadi, D., Abawajy, J., Ghanavati, S.: ‘An alternative node deployment scheme for WSNs’, IEEE Sens. J., 2015, 15, (2), pp. 667–675.
33. 33)
  - 15. Sahoo, P.K., Liao, W.-C.: ‘Hora: a distributed coverage hole repair algorithm for wireless sensor networks’, IEEE Trans. Mob. Comput., 2015, 14, (7), pp. 1397–1410.

Fuzzy logic approach to repair coverage holes in internet of things monitoring applications

References

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