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Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks

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Abstract

Internet of Things (IoT) based applications are being evolved in multiple fields to provide enhanced service to the world. IoT is a recent computing concept interconnecting the wired and wireless networks through the internet. Most mobile devices function only in an ad-hoc manner. Infrastructureless networks are called ad-hoc networks. IoT is an effective technology to utilize in Cognitive Radio Mobile Ad-hoc Network (CRMANET) instantaneously. The protocols that are developed for common ad-hoc networks will never suit for IoT-based-CRMANET because the delay they face is inversely proportional with real-time applications. Hence, there exists a need for designing and developing a better routing protocol that suits IoT-based ad-hoc networks. Multi adaptive route indicates the optimum cum efficient path which is selected when the priority of the node gets changed or failed, it may be due to problems that arise in nodes or network components. Multi-adaptive routes make sure the connectivity of the network and its operations before sending the data packet. This paper focuses on developing a Multi-Adaptive Routing Protocol (MARP) inspired by natural characteristics of fish for IoT-based ad-hoc networks to minimize the delay and the energy consumption to extend a network lifetime. NS3 simulation results indicate that MARP gives its best performance than other routing protocols in terms of Throughput, Packet Delivery Ratio, Packet Drop Ratio, Delay and Energy Consumption.

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This research work utilizes no data or dataset. This research work utilizes the randomly generated data by the simulator as input.

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References

  1. Singh, V. K., Mukhopadhyay, S., Xhafa, F., & Sharma, A. (2020). A budget feasible peer graded mechanism for iot-based crowdsourcing. Journal of Ambient Intelligence and Humanized Computing, 11(4), 1531–1551. https://doi.org/10.1007/s12652-019-01219-z.

    Article  Google Scholar 

  2. Yohan, A., & Lo, N. W. (2020). FOTB: A secure blockchain-based firmware update framework for IoT environment. International Journal of Information Security 19, 257–278. Springer. https://doi.org/10.1007/s10207-019-00467-6.

  3. Mukherjee, A., Deb, P., De, D., & Buyya, R. (2019). IoT-F2N: An energy-efficient architectural model for IoT using Femtolet-based fog network. Journal of Supercomputing, 75(11), 7125–7146. https://doi.org/10.1007/s11227-019-02928-0.

    Article  Google Scholar 

  4. Hwang, J., Aziz, A., Sung, N., Ahmad, A., Le Gall, F., & Song, J. (2020). AUTOCON-IoT: Automated and Scalable Online Conformance Testing for IoT Applications. IEEE Access, 8, 43111–43121. https://doi.org/10.1109/ACCESS.2020.2976718.

    Article  Google Scholar 

  5. Chowdhury, A., & Raut, S. (2019). Scheduling Correlated IoT Application Requests Within IoT Eco-System: An Incremental Cloud Oriented Approach. Wireless Personal Communications, 108(2), 1275–1310. https://doi.org/10.1007/s11277-019-06469-w.

    Article  Google Scholar 

  6. Neshenko, N., Bou-Harb, E., Crichigno, J., Kaddoum, G., & Ghani, N. (2019). Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations. IEEE Communications Surveys and Tutorials, 21(3), 2702–2733. https://doi.org/10.1109/COMST.2019.2910750.

    Article  Google Scholar 

  7. Condry, M. W., & Nelson, C. B. (2016). Using Smart Edge IoT Devices for Safer, Rapid Response with Industry IoT Control Operations. Proceedings of the IEEE, 104(5), 938–946. https://doi.org/10.1109/JPROC.2015.2513672.

    Article  Google Scholar 

  8. Ramkumar, J., & Vadivel, R. (2020). Improved wolf prey inspired protocol for routing in cognitive radio ad hoc networks. International Journal of Computer Networks and Applications, 7(5), 126–136. https://doi.org/10.22247/ijcna/2020/202977.

  9. Vivekanand, C. V., & Bagan, K. B. (2020). Secure Distance Based Improved Leach Routing to Prevent Puea in Cognitive Radio Network. Wireless Personal Communications, 113(4), 1823–1837. https://doi.org/10.1007/s11277-020-07294-2.

    Article  Google Scholar 

  10. Singh, K., & Verma, A. K. (2020). TBCS: A Trust Based Clustering Scheme for Secure Communication in Flying Ad-Hoc Networks. Wireless Personal Communications, 114(4), 3173–3196. https://doi.org/10.1007/s11277-020-07523-8.

    Article  Google Scholar 

  11. Ramkumar, J., & Vadivel, R. (2020). Intelligent fish swarm inspired protocol (IFSIP) for dynamic ideal routing in cognitive radio ad-hoc networks. International Journal of Computing and Digital Systems, 10, 2–11. https://journal.uob.edu.bh/handle/123456789/3961?show=full.

  12. Li, C., & Dai, H. (2014). Throughput Scaling of Primary and Secondary Ad Hoc Networks With Same-Order Dimensions. IEEE Transactions on Vehicular Technology, 63(8), 3957–3966. https://doi.org/10.1109/TVT.2014.2310424.

    Article  Google Scholar 

  13. Wang, C., Tang, S., Li, X., & Jiang, C. (2012). Multicast Capacity Scaling Laws for Multihop Cognitive Networks. IEEE Transactions on Mobile Computing, 11(11), 1627–1639. https://doi.org/10.1109/TMC.2011.212.

    Article  Google Scholar 

  14. Musavi, M., Yau, K.-L. A., Syed, A. R., Mohamad, H., & Ramli, N. (2018). Route selection over clustered cognitive radio networks: An experimental evaluation. Computer Communications, 129, 138–151. https://doi.org/10.1016/j.comcom.2018.07.035.

  15. Vadivel, R., & Ramkumar, J. (2019). QoS-Enabled improved cuckoo search-inspired protocol (ICSIP) for IoT-based healthcare applications, 109–121. https://doi.org/10.4018/978-1-7998-1090-2.ch006.

  16. Dakulagi, V., & Alagirisamy, M. (2020). Adaptive Beamformers for High-Speed Mobile Communication. Wireless Personal Communications, 113(4), 1691–1707. https://doi.org/10.1007/s11277-020-07287-1.

    Article  Google Scholar 

  17. To, M. A. (2016). A Proactive Approach for Strip Interoperability in Wireless Ad hoc Routing Protocols. IEEE Latin America Transactions, 14(6), 2543–2549. https://doi.org/10.1109/TLA.2016.7555216.

    Article  Google Scholar 

  18. Ochola, E. O., Mejaele, L. F., Eloff, M. M., & Poll, J. A. van der. (2017). Manet reactive routing protocols node mobility variation effect in analysing the impact of black hole attack. SAIEE Africa Research Journal, 108(2), 80–92. https://doi.org/10.23919/SAIEE.2017.8531629.

  19. Ramkumar, J., & Vadivel, R. (2020). Bee inspired secured protocol for routing in cognitive radio ad hoc networks. Indian Journal of Science and Technology, 13(30), 3059–3069. https://doi.org/10.17485/IJST/v13i30.1152.

  20. Singh, K., & Moh, S. (2016). Routing protocols in cognitive radio ad hoc networks: A comprehensive review. Journal of Network and Computer Applications, 72, 28–37. https://doi.org/10.1016/j.jnca.2016.07.006.

  21. Ramkumar, J., & Vadivel, R. (2017). CSIP- cuckoo search inspired protocol for routing in cognitive radio ad hoc networks. In Advances in Intelligent Systems and Computing 556, 145–153. Springer. https://doi.org/10.1007/978-981-10-3874-7_14.

  22. Zarca, A. M., Bernabe, J. B., Skarmeta, A., & Alcaraz Calero, J. M. (2020). Virtual IoT HoneyNets to mitigate cyberattacks in SDN/NFV-Enabled IoT networks. IEEE Journal on Selected Areas in Communications, 38(6), 1262–1277. https://doi.org/10.1109/JSAC.2020.2986621.

    Article  Google Scholar 

  23. Frustaci, M., Pace, P., Aloi, G., & Fortino, G. (2018). Evaluating critical security issues of the IoT world: Present and future challenges. IEEE Internet of Things Journal, 5(4), 2483–2495. https://doi.org/10.1109/JIOT.2017.2767291.

    Article  Google Scholar 

  24. Said, O., Al-Makhadmeh, Z., & Tolba, A. (2020). EMS: An Energy Management Scheme for Green IoT Environments. IEEE Access, 8, 44983–44998. https://doi.org/10.1109/ACCESS.2020.2976641.

    Article  Google Scholar 

  25. Airehrour, D., Gutierrez, J. A., & Ray, S. K. (2019). SecTrust-RPL: A secure trust-aware RPL routing protocol for Internet of Things. Future Generation Computer Systems, 93, 860–876. https://doi.org/10.1016/j.future.2018.03.021.

    Article  Google Scholar 

  26. Jin, Y., Gormus, S., Kulkarni, P., & Sooriyabandara, M. (2016). Content centric routing in IoT networks and its integration in RPL. Computer Communications, 89–90, 87–104. https://doi.org/10.1016/j.comcom.2016.03.005.

    Article  Google Scholar 

  27. Li, J., Silva, B. N., Diyan, M., Cao, Z., & Han, K. (2018). A clustering based routing algorithm in IoT aware wireless mesh networks. Sustainable Cities and Society, 40, 657–666. https://doi.org/10.1016/j.scs.2018.02.017.

    Article  Google Scholar 

  28. Pan, M. S., & Yang, S. W. (2017). A lightweight and distributed geographic multicast routing protocol for IoT applications. Computer Networks, 112, 95–107. https://doi.org/10.1016/j.comnet.2016.11.006.

    Article  Google Scholar 

  29. Vashishth, V., Chhabra, A., & Sharma, D. K. (2019). GMMR: A Gaussian mixture model based unsupervised machine learning approach for optimal routing in opportunistic IoT networks. Computer Communications, 134, 138–148. https://doi.org/10.1016/j.comcom.2018.12.001.

    Article  Google Scholar 

  30. Dhurandher, S. K., Borah, S. J., Woungang, I., Bansal, A., & Gupta, A. (2018). A location Prediction-based routing scheme for opportunistic networks in an IoT scenario. Journal of Parallel and Distributed Computing, 118, 369–378. https://doi.org/10.1016/j.jpdc.2017.08.008.

    Article  Google Scholar 

  31. Anamalamudi, S., Sangi, A. R., Alkatheiri, M., & Ahmed, A. M. (2018). AODV routing protocol for Cognitive radio access based Internet of Things (IoT). Future Generation Computer Systems, 83, 228–238. https://doi.org/10.1016/j.future.2017.12.060.

  32. Al-Turjman, F. (2019). Cognitive routing protocol for disaster-inspired Internet of Things. Future Generation Computer Systems, 92, 1103–1115. https://doi.org/10.1016/j.future.2017.03.014.

    Article  Google Scholar 

  33. Chemodanov, D., Esposito, F., Sukhov, A., Calyam, P., Trinh, H., & Oraibi, Z. (2019). AGRA: AI-augmented geographic routing approach for IoT-based incident-supporting applications. Future Generation Computer Systems, 92, 1051–1065. https://doi.org/10.1016/j.future.2017.08.009.

    Article  Google Scholar 

  34. AlZubi, A. A., Al-Maitah, M., & Alarifi, A. (2019). A best-fit routing algorithm for non-redundant communication in large-scale IoT based network. Computer Networks, 152, 106–113. https://doi.org/10.1016/j.comnet.2019.01.030.

    Article  Google Scholar 

  35. Borah, S. J., Dhurandher, S. K., Woungang, I., & Kumar, V. (2017). A game theoretic context-based routing protocol for opportunistic networks in an IoT scenario. Computer Networks, 129, 572–584. https://doi.org/10.1016/j.comnet.2017.07.005.

    Article  Google Scholar 

  36. Sadek, R. A. (2018). Hybrid energy aware clustered protocol for IoT heterogeneous network. Future Computing and Informatics Journal, 3(2), 166–177. https://doi.org/10.1016/j.fcij.2018.02.003.

    Article  MathSciNet  Google Scholar 

  37. Qiu, T., Lv, Y., Xia, F., Chen, N., Wan, J., & Tolba, A. (2016). ERGID: An efficient routing protocol for emergency response Internet of Things. Journal of Network and Computer Applications, 72, 104–112. https://doi.org/10.1016/j.jnca.2016.06.009.

    Article  Google Scholar 

  38. Debroy, S., Samanta, P., Bashir, A., & Chatterjee, M. (2019). SpEED-IoT: Spectrum aware energy efficient routing for device-to-device IoT communication. Future Generation Computer Systems, 93, 833–848. https://doi.org/10.1016/j.future.2018.01.002.

    Article  Google Scholar 

  39. Cacciapuoti, A. S., Caleffi, M., & Paura, L. (2012). Reactive routing for mobile cognitive radio ad hoc networks. Ad Hoc Networks, 10(5), 803–815. https://doi.org/10.1016/j.adhoc.2011.04.004.

  40. Liu, L., Ma, Z., & Meng, W. (2019). Detection of multiple-mix-attack malicious nodes using perceptron-based trust in IoT networks. Future Generation Computer Systems, 101, 865–879. https://doi.org/10.1016/j.future.2019.07.021.

    Article  Google Scholar 

  41. Gill, S. S., Garraghan, P., & Buyya, R. (2019, August 1). ROUTER: Fog enabled cloud based intelligent resource management approach for smart home IoT devices. Journal of Systems and Software. Elsevier Inc. https://doi.org/10.1016/j.jss.2019.04.058.

  42. Meddeb, M., Dhraief, A., Belghith, A., Monteil, T., Drira, K., & Gannouni, S. (2018). AFIRM: Adaptive forwarding based link recovery for mobility support in NDN/IoT networks. Future Generation Computer Systems, 87, 351–363. https://doi.org/10.1016/j.future.2018.04.087.

    Article  Google Scholar 

  43. Shah, S. B., Chen, Z., Yin, F., Khan, I. U., & Ahmad, N. (2018). Energy and interoperable aware routing for throughput optimization in clustered IoT-wireless sensor networks. Future Generation Computer Systems, 81, 372–381. https://doi.org/10.1016/j.future.2017.09.043.

    Article  Google Scholar 

  44. Safaei, B., Mohammad Salehi, A. A., Hosseini Monazzah, A. M., & Ejlali, A. (2019). Effects of RPL objective functions on the primitive characteristics of mobile and static IoT infrastructures. Microprocessors and Microsystems, 69, 79–91. https://doi.org/10.1016/j.micpro.2019.05.010.

    Article  Google Scholar 

  45. Elappila, M., Chinara, S., & Parhi, D. R. (2018). Survivable Path Routing in WSN for IoT applications. Pervasive and Mobile Computing, 43, 49–63. https://doi.org/10.1016/j.pmcj.2017.11.004.

    Article  Google Scholar 

  46. Wang, H., Han, G., Zhou, L., Ansere, J. A., & Zhang, W. (2019). A source location privacy protection scheme based on ring-loop routing for the IoT. Computer Networks, 148, 142–150. https://doi.org/10.1016/j.comnet.2018.11.005.

    Article  Google Scholar 

  47. Jin, X., Zhang, R., Sun, J., & Zhang, Y. (2014). TIGHT: A geographic routing protocol for cognitive radio mobile Ad Hoc networks. IEEE Transactions on Wireless Communications, 13(8), 4670–4681. https://doi.org/10.1109/TWC.2014.2320950.

    Article  Google Scholar 

  48. Ramkumar, J., & Vadivel, R. (2018). Improved frog leap inspired protocol (IFLIP) – for routing in cognitive radio ad hoc networks (CRAHN). World Journal of Engineering, 15(2). https://doi.org/10.1108/WJE-08-2017-0260.

  49. Shao, B., & Leeson, M. S. (2019). PaFiR: Particle Filter Routing – A predictive relaying scheme for UAV-assisted IoT communications in future innovated networks. Internet of Things. https://doi.org/10.1016/j.iot.2019.100077.

    Article  Google Scholar 

  50. Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S., & Kannan, A. (2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer Networks, 151, 211–223. https://doi.org/10.1016/j.comnet.2019.01.024.

    Article  Google Scholar 

  51. Kabilan, K., Bhalaji, N., Selvaraj, C., Kumaar, B., & M., & P T R, K. . (2018). Performance analysis of IoT protocol under different mobility models. Computers and Electrical Engineering, 72, 154–168. https://doi.org/10.1016/j.compeleceng.2018.09.007.

    Article  Google Scholar 

  52. Ramkumar, J., & Vadivel, R. (2019). Performance modeling of bio-inspired routing protocols in cognitive radio ad hoc network to reduce end-to-end delay. International Journal of Intelligent Engineering and Systems, 12(1), 221–231. https://doi.org/10.22266/ijies2019.0228.22.

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Ramkumar, J., Vadivel, R. Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks. Wireless Pers Commun 120, 887–909 (2021). https://doi.org/10.1007/s11277-021-08495-z

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