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Multipath data transmission in WSN using exponential cat swarm and fuzzy optimisation

Multipath data transmission in WSN using exponential cat swarm and fuzzy optimisation

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This study proposes a technique for multipath data transmission in Wireless Sensor Network (WSN) by proposing a novel optimisation algorithm, named exponential cat swarm optimisation (ECSO), by integrating the exponential weighted moving average and CSO. Initially, the CH is selected by the penguin fuzzy-based ant colony optimisation (PFuzzyACO) technique, which is the integration of fuzzy, ACO and penguin search optimisation algorithm (PeSOA). After the selection of the optimal CH, the multipath transmission is done by the proposed ECSO algorithm. Here, an optimal path is selected for transmitting the routeing information from source to destination based on various parameters such as trust, energy, distance, delay, traffic density and link lifetime (LLT). Thus, the CHs with maximum trust, energy and LLT, and minimum distance, delay and traffic density are adapted for multipath data transmission using the proposed ECSO algorithm. The proposed ECSO algorithm shows 18.75, 2.99 and 29.87% improvements in terms of number of alive nodes, throughput and network energy, respectively, than the existing PFuzzyACO, which has high performance than the other comparative methods such as artificial bee colony, ACO, fractional artificial bee colony, Low-Energy Adaptive Clustering Hierarchy (LEACH), FuzzyACO and PeSOA.

References

    1. 1)
      • 1. Ramluckun, N., Bassoo, V.: ‘Energy-efficient chain-cluster based intelligent routing technique for wireless sensor networks’, Appl. Comput. Inf., 2018.
    2. 2)
      • 2. Goh, H.G.: ‘Performance study of tree-based routing algorithm for 2D grid, networks’. 2004 Proc. 12th IEEE Int. Conf., Singapore, 2004, vol. 2, pp. 530534.
    3. 3)
      • 3. Cheng, Kiat Tan, Soung-Yue, Liew, Hock Guan, Goh, et al: ‘A fast, adaptive, and energy-efficient multi-path-multi-channel data collection protocol for wireless sensor networks’, In Proceedings of the International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom), Da Nang, Vietnam, 2017.
    4. 4)
      • 4. Kumar, R., Kumar, D., Kumar, D.: ‘Exponential ant colony optimization and fractional artificial bee colony to multi-path data transmission’, IET Communications, 2016, 11, (4), pp. 127.
    5. 5)
      • 5. Chang, C.Y., Chang, H.R.: ‘Energy-aware node placement, topology control and MAC scheduling for wireless sensor networks’, Comput. Netw., 2008, 52, (11), pp. 21892204.
    6. 6)
      • 6. Darabkh, K.A., Al-Rawashdeh, W.S., Hawa, M., et al: ‘MT-CHR: a modified threshold-based cluster head replacement protocol for wireless sensor networks’, Comput. Electr. Eng., 2018, 72, pp. 926938.
    7. 7)
      • 7. Al-Ariki, H.D.E., Swamy, M.N.S.: ‘A survey and analysis of multipath routing protocols in wireless multimedia sensor networks’, Wirel. Netw., 2017, 23, (6), pp. 18231835.
    8. 8)
      • 8. Li, H., Chen, Q., Ran, Y., et al: ‘BIM 2 RT: BWAS-immune mechanism based multipath reliable transmission with fault tolerance in wireless sensor networks’, Swarm EComput., 2017, pp. 112.
    9. 9)
      • 9. Munir, A., Gordon-Ross, A.: ‘Optimization approaches in wireless sensor networks’, Sustain. Wirel. Sens. Netw., 2010, pp. 313338.
    10. 10)
      • 10. Wang, J., Cao, Y., Li, B., et al: ‘Particle swarm optimization based clustering algorithm with mobile sink for WSNs’, Future Gener. Comput. Syst., 2017, 76, pp. 452457.
    11. 11)
      • 11. Bahrami, M., Bozorg-haddad, O., Chu, X.: ‘Cat swarm optimization (CSO) algorithm’, Adv. Opt. Nat.-Inspired Algorithms, 2018, 720, pp. 918.
    12. 12)
      • 12. Deepa, O., Suguna, J.: ‘An optimized QoS-based clustering with multipath routing protocol for wireless sensor networks’, J. King Saud Univ. Comput. Inf. Sci., 2017.
    13. 13)
      • 13. Sahaaya, S.A., Mary, A., Gnanadurai, J.B.: ‘Enhanced zone stable election protocol based on fuzzy logic for cluster head election in wireless sensor networks’, Int. J. Fuzzy Syst., 2017, 19, (3), pp. 799812.
    14. 14)
      • 14. Sarkar, A., Murugan, T.S.: ‘Cluster head selection for energy efficient and delay-less routing in wireless sensor network’, Wirel. Netw., 2019, 25, (1), pp. 303320.
    15. 15)
      • 15. Khan, B.M., Bilal, R., Young, R.: ‘Fuzzy-TOPSIS based cluster head selection in mobile wireless sensor networks’, J. Electr. Syst. Inf. Technol., 2018, 5, (3), pp. 928943.
    16. 16)
      • 16. Jung, K.D., Lee, J.Y., Jeong, H.Y.: ‘Improving adaptive cluster head selection of TEEN protocol using fuzzy logic for WMSN’, Multimedia Tools Appl., 2017, 76, (17), pp. 1817518190.
    17. 17)
      • 17. Singh, R., Verma, A.K.: ‘Energy efficient cross-layer based adaptive threshold routing protocol for WSN’, AEU-Int. J. Electron. Commun., 2017, 72, pp. 166173.
    18. 18)
      • 18. Sajwan, M., Gosain, D., Sharma, A.K.: ‘Hybrid energy-efficient multi-path routing for wireless sensor networks’, Comput. Electr. Eng., 2018, 67, pp. 96113.
    19. 19)
      • 19. Mohanty, P., Kabat, M.R.: ‘Energy efficient reliable multi-path data transmission in WSN for health care application’, Int. J. Wirel. Inf. Netw., 2016, 23, (2), pp. 162172.
    20. 20)
      • 20. Kumar, R., Kumar, D., Kumar, D.: ‘EACO and FABC to multi-path data transmission in wireless sensor networks’, IET Commun., 2016, 11, (4), pp. 522530.
    21. 21)
      • 21. Dhumane, A.V., Prasad, R.S.: ‘Fractional gravitational Grey Wolf optimization to multi-path data transmission in IoT’, Wirel. Pers. Commun., 2018, 102, (1), pp. 411436.
    22. 22)
      • 22. Maimour, M.: ‘Interference-aware multipath routing for WSNs: overview and performance evaluation’, Appl. Comput. Inf., 2018.
    23. 23)
      • 23. Kumar, R., Kumar, D.: ‘Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network’, Wirel. Netw., 2016, 22, (5), pp. 14611474.
    24. 24)
      • 24. Saccucci, M.S., Amin, R.W., Lucas, J.M.: ‘Exponentially weighted moving average control schemes with variable sampling intervals’, Commun. Stat. - Simul. Comput., 1992, 21, (3), pp. 627657.
    25. 25)
      • 25. Wang, B., Chen, X., Chang, W.: ‘A light-weight trust-based QoS routing algorithm for ad hoc networks’, Pervasive Mob. Comput., 2014, 13, pp. 164180.
    26. 26)
      • 26. Karaboga, D., Okdem, S., Ozturk, C.: ‘Cluster based wireless sensor network routing using artificial bee colony algorithm’, Wirel. Netw., 2012, 18, (7), pp. 847860.
    27. 27)
      • 27. Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: ‘Energy-efficient communication protocol for wireless microsensor networks’. Proc. 33rd Annual Hawaii Int. Conf. System Sciences, Maui, HI, USA, 2000, vol. 1, no. 10.
    28. 28)
      • 28. Singh, G., Singh, H.P., Sharma, A.: ‘Cluster head selection using modified ACO’. Proc. Third Int. Conf. Soft Computing for Problem Solving, New Delhi, 2014, vol. 259.
    29. 29)
      • 29. Hindriks, K.V., Hoogendoorn, M., Goebel, R.: ‘Penguins search optimization algorithm (PeSOA) in applied artificial’, 2013.
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