Advertisement

Fog-based energy-efficient routing protocol for wireless sensor networks

  • Elham Mirzavand Borujeni
  • Dadmehr Rahbari
  • Mohsen Nickray
Article

Abstract

By exploiting the benefits of wireless sensor networks (WSNs), the Internet of Things (IoT) has caused many advances in the modern world. Since WSNs have limitations in energy usage, it is critical to save live nodes. Fog computing is a good solution to reduce the limitations of WSNs with its ability to meet the requirements of the IoT applications. Fog computing brings computing and storage resources closer to end users. P-SEP uses fog-based architecture to decrease energy consumption and increase network lifetime. To do so, in this paper, we introduce a new method based on P-SEP which uses FECR and FEAR algorithms in implementation. These algorithms improve the performance of fog-supported WSNs and prolong the lifetime of networks. The performance of the proposed approach is evaluated in comparison with P-SEP. The results of the simulation show that the average amount of energy usage in FECR protocol has been reduced by 9% and by 8% in FEAR. The number of live nodes saved in the network increased by 74% in FECR and 83% in FEAR in comparison with P-SEP protocol.

Keywords

Wireless sensor network Fog computing Lifetime Energy efficiency 

References

  1. 1.
    Dastjerdi AV, Buyya R (2016) Fog computing: helping the internet of things realize its potential. Computer 49(8):112–116CrossRefGoogle Scholar
  2. 2.
    Mahmud R, Kotagiri R, Buyya R (2018) Fog computing: a taxonomy, survey and future directions. In: Internet of everything. Springer, pp 103–130Google Scholar
  3. 3.
    Ivanov S, Balasubramaniam S, Botvich D, Akan OB (2016) Gravity gradient routing for information delivery in fog wireless sensor networks. Ad Hoc Netw 46:61–74CrossRefGoogle Scholar
  4. 4.
    Dastjerdi AV, Gupta H, Calheiros RN, Ghosh SK, Buyya R (2016) Fog computing: principles, architectures, and applications. In: Internet of Things. Elsevier, pp 61–75Google Scholar
  5. 5.
    Aazam M, St-Hilaire M, Lung C-H, Lambadaris I, Huh E-N (2018) Iot resource estimation challenges and modeling in fog. In: Fog Computing in the Internet of Things. Springer, pp 17–31Google Scholar
  6. 6.
    Firdhous M, Ghazali O, Hassan S (2014) Fog computing: will it be the future of cloud computing. In: The 3rd International Conference on Informatics and Applications (ICIA2014), pp 8–15Google Scholar
  7. 7.
    Yi S, Hao Z, Qin Z, Li Q (2015) Fog computing: platform and applications. In 3rd IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb), pp 73–78Google Scholar
  8. 8.
    Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (iot): a vision, architectural elements, and future directions. Fut Gener Comput Syst 29(7):1645–1660CrossRefGoogle Scholar
  9. 9.
    Xia H, Zhang R-H, Yu J, Pan Z-K (2016) Energy-efficient routing algorithm based on unequal clustering and connected graph in wireless sensor networks. Int J Wirel Inf Netw 23(2):141–150CrossRefGoogle Scholar
  10. 10.
    Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd IEEE Annual Hawaii International Conference on System Sciences, p 10Google Scholar
  11. 11.
    Singh D, Panda CK (2015) Performance analysis of modified stable election protocol in heterogeneous WSN. In: IEEE International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), pp 1–5Google Scholar
  12. 12.
    Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670CrossRefGoogle Scholar
  13. 13.
    Smaragdakis G, Matta I, Bestavros A (2004) Sep: a stable election protocol for clustered heterogeneous wireless sensor networks. Technical report, Boston University Computer Science DepartmentGoogle Scholar
  14. 14.
    Razaque A, Mudigulam S, Gavini K, Amsaad F, Abdulgader M, Krishna GS (2016) H-leach: hybrid-low energy adaptive clustering hierarchy for wireless sensor networks. In: IEEE Long Island Systems, Applications and Technology Conference (LISAT), pp 1–4Google Scholar
  15. 15.
    Naranjo PGV, Shojafar M, Mostafaei H, Pooranian Z, Baccarelli E (2017) P-sep: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks. J Supercomput 73(2):733–755CrossRefGoogle Scholar
  16. 16.
    Heinzelman WB (2000) Application-specific protocol architectures for wireless networks. PhD thesis, Massachusetts Institute of TechnologyGoogle Scholar
  17. 17.
    Liu Y, Gao J, Jia Y, Zhu L (2008) A cluster maintenance algorithm based on leach-dchs protocol. In: IEEE International Conference on Networking, Architecture, and Storage, NAS’08. pp 165–166Google Scholar
  18. 18.
    Lindsey S, Raghavendra CS (2002) Pegasis: power-efficient gathering in sensor information systems. IEEE Aerosp Conf Proc 3:3–3Google Scholar
  19. 19.
    Malluh AA, Elleithy KM, Qawaqneh Z, Mstafa RJ, Alanazi A (2014) Em-sep: an efficient modified stable election protocol. In: Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1), 2014. IEEE, pp 1–7Google Scholar
  20. 20.
    Wang J, Yang X, Ma T, Wu M, Kim J-U (2012) An energy-efficient competitive clustering algorithm for wireless sensor networks using mobile sink. Int J Grid Distrib Comput 5(4):79–92Google Scholar
  21. 21.
    Karaboga D, Okdem S, Ozturk C (2012) Cluster based wireless sensor network routing using artificial bee colony algorithm. Wirel Netw 18(7):847–860CrossRefGoogle Scholar
  22. 22.
    Baccarelli E, Cordeschi N, Polli V (2013) Optimal self-adaptive qos resource management in interference-affected multicast wireless networks. IEEE/ACM Trans Netw (TON) 21(6):1750–1759CrossRefGoogle Scholar
  23. 23.
    Petrioli C, Nati M, Casari P, Zorzi M, Basagni S (2014) Alba-r: Load-balancing geographic routing around connectivity holes in wireless sensor networks. IEEE Trans Parall Distrib Syst 25(3):529–539CrossRefGoogle Scholar
  24. 24.
    Tanwar S, Kumar N, Niu J-W (2014) Eemhr: Energy-efficient multilevel heterogeneous routing protocol for wireless sensor networks. Int J Commun Syst 27(9):1289–1318CrossRefGoogle Scholar
  25. 25.
    Jiang D, Xu Z, Li W, Chen Z (2017) Topology control-based collaborative multicast routing algorithm with minimum energy consumption. Int J Commun Syst 30(1):e2905Google Scholar
  26. 26.
    Orojloo H, Haghighat AT (2016) A tabu search based routing algorithm for wireless sensor networks. Wirel Netw 22(5):1711–1724CrossRefGoogle Scholar
  27. 27.
    Chen D-R (2016) An energy-efficient qos routing for wireless sensor networks using self-stabilizing algorithm. Ad Hoc Netw 37:240–255CrossRefGoogle Scholar
  28. 28.
    Kar P, Misra S (2017) Detouring dynamic routing holes in stationary wireless sensor networks in the presence of temporarily misbehaving nodes. Int J Commun Syst 30(4):e3009Google Scholar
  29. 29.
    Sun X, Ansari N (2016) Edgeiot: mobile edge computing for the internet of things. IEEE Commun Mag 54(12):22–29CrossRefGoogle Scholar
  30. 30.
    Tomovic S, Yoshigoe K, Maljevic I, Radusinovic I (2017) Software-defined fog network architecture for IOT. Wirel Pers Commun 92(1):181–196CrossRefGoogle Scholar
  31. 31.
    Rahat AA, Everson RM, Fieldsend JE (2016) Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks. Ad Hoc Netw. 52:130–145CrossRefGoogle Scholar
  32. 32.
    Xie G, Ota K, Dong M, Pan F, Liu A (2017) Energy-efficient routing for mobile data collectors in wireless sensor networks with obstacles. Peer-to-Peer Netw Appl 10(3):472–483CrossRefGoogle Scholar
  33. 33.
    Aslam M, Munir EU, Rafique MM, Hu X (2016) Adaptive energy-efficient clustering path planning routing protocols for heterogeneous wireless sensor networks. Sust Comput: Inf Syst 12:57–71Google Scholar
  34. 34.
    Alam S, De D (2017) Cloud smoke sensing using iarp, ierp and zrp routing protocols for wireless senor network. CSI Trans ICT 5(1):119–124CrossRefGoogle Scholar
  35. 35.
    Moreno-Vozmediano R, Montero RS, Huedo E, Llorente IM (2017) Cross-site virtual network in cloud and fog computing. IEEE Cloud Comput 4(2):46–53CrossRefGoogle Scholar
  36. 36.
    Wang J, Cao J, Ji S, Park JH (2017) Energy-efficient cluster-based dynamic routes adjustment approach for wireless sensor networks with mobile sinks. J Supercomput 73(7):3277–3290CrossRefGoogle Scholar
  37. 37.
    Liu X (2017) Routing protocols based on ant colony optimization in wireless sensor networks: a survey. IEEE AccessGoogle Scholar
  38. 38.
    Malik SK, Dave M, Dhurandher SK, Woungang I, Barolli L (2017) An ant-based qos-aware routing protocol for heterogeneous wireless sensor networks. Soft Comput 21(21):6225–6236CrossRefGoogle Scholar
  39. 39.
    Sharma S, Kushwah RS (2017) ACO based wireless sensor network routing for energy saving. In: IEEE International Conference on Inventive Communication and Computational Technologies (ICICCT), pp 150–154Google Scholar
  40. 40.
    Kannan M, Chinnappan S, Krishnamoorthy C (2017) Ant star fuzzy routing for industrial wireless sensor network. In: Third IEEE International Conference on Sensing, Signal Processing and Security (ICSSS), pp 444–446Google Scholar
  41. 41.
    Chen H, Lv Z, Tang R, Tao Y (2017) Clustering energy-efficient transmission protocol for wireless sensor networks based on ant colony path optimization. In: IEEE International Conference on Computer, Information and Telecommunication Systems (CITS), pp 15–19Google Scholar
  42. 42.
    Enxing Z, Ranran L (2017) Routing technology in wireless sensor network based on ant colony optimization algorithm. Wirel Pers Commun 95(3):1911–1925CrossRefGoogle Scholar
  43. 43.
    Sun Y, Dong W, Chen Y (2017) An improved routing algorithm based on ant colony optimization in wireless sensor networks. IEEE Commun Lett 21(6):1317–1320CrossRefGoogle Scholar
  44. 44.
    Yang J, Shi X, Marchese M, Liang Y (2008) An ant colony optimization method for generalized TSP problem. Progr Nat Sci 18(11):1417–1422MathSciNetCrossRefGoogle Scholar
  45. 45.
    Dorigo M, Birattari M (2011) Ant colony optimization. In: Encyclopedia of machine learning. Springer, pp 36–39Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Elham Mirzavand Borujeni
    • 1
  • Dadmehr Rahbari
    • 1
  • Mohsen Nickray
    • 1
  1. 1.Department of Computer Engineering and Information TechnologyUniversity of QomQomIran

Personalised recommendations