Research Article

An improved Whale Optimization Approach for Effective Data Transmission for IoT Communication

Abidullha Adel
Nanjing University of Information Science and Technology, China
* Corresponding author
Md Sohel Rana
Nanjing University of Information Science and Technology, China
Nuruzzam Rana
Daffodil International University, Bangladesh
Md Alamin Hosan
Daffodil International University, Bangladesh
Mohammad Akbar Shapoor
Kunduz University, Afghanistan
DOI icon 10.24018/ejece.2021.5.6.375

Read Counter
463

Downloads
327

Citations

Share

Article Sidebar

Submitted 2021-11-15
Published 2021-12-16

Read counter = 463 times

Table of Contents

Article Main Content

Internet of Things (IoT) offers interconnection among several wireless communication devices for the provision of device accessibility and in-built capacity. IoT provides device interaction and provision of advantages capability for networking and socialization with consideration of intermediate devices. Through innovation in technology IoT devices convert cyber environments with hyper-connectivity. IoT communication contains several smart devices such as body sensors, smartphones, tags, electronic gadgets, and so on. IoT communication is involved in the provision of heterogeneous connectivity among devices for the provision of interface and connectivity for enhancing service quality. The data sending among IoT devices is affected by several threats that have an impact on the network’s performance. To overcome the limitation related to IoT communication, it is necessary to develop an appropriate technique for enhancing IoT network communication performance. In this research developed a multi-channel routing approach is adopted in IoT communication. The developed approach utilizes a meta-heuristics approach with probability-based characteristics. For the meta-heuristics approach this research utilizes whale optimization technique combined with probability characteristics for improving the IoT communication performance of the network. The proposed approach utilizes initially constructs the IoT communication path for information sharing and gathering. This path information is identified through the objective function of a meta-heuristic approach. Based on the objective function hoping between the devices is minimized through which data are transmitted in the network. Simulation is performed as a unique proposed approach with a coverage area of 100 meters. For identification of the optimal path in the network, WOA identifies the path of communication through probability function. Comparative analysis of research exhibited that WOA provides significant performance with the identification of optimal value at the range of 1.0746e-78. Further, the proposed probability-based WOA approach significantly improves the performance of the IoT network.

Keywords: The Internet of Things (IoT), Meta-heuristics, Whale Optimization, Multi-path, Optimal path

References

  1. Z. Sheng, S. Yang, Y. Yu, A. V. Vasilakos, J. A. McCann, and K. K. Leung. A survey on the IETF protocol suite for the internet of things: Standards, challenges, and opportunities. IEEE wireless communications, vol. 20, no. 6, pp. 91-98, 2013.
     Google Scholar
  2. F. Rusek et al. Scaling up MIMO: Opportunities and challenges with very large arrays. IEEE signal processing magazine, vol. 30, no. 1, pp. 40-60, 2012.
     Google Scholar
  3. O. Raeesi, J. Pirskanen, A. Hazmi, T. Levanen, and M. Valkama, Performance evaluation of IEEE 802.11 ah and its restricted access window mechanism.In 2014 IEEE international conference on communications workshops (ICC), 2014: IEEE, pp. 460-466.
     Google Scholar
  4. S. Debroy, S. Bhattacharjee, and M. Chatterjee. Spectrum map and its application in resource management in cognitive radio networks. IEEE Transactions on Cognitive Communications and Networking, vol. 1, no. 4, pp. 406-419, 2015.
     Google Scholar
  5. A. A. AlZubi, M. Al-Maitah, and A. Alarifi. A best-fit routing algorithm for non-redundant communication in large-scale IoT based network. Computer Networks, vol. 152, pp. 106-113, 2019.
     Google Scholar
  6. B. B. Olyaei, J. Pirskanen, O. Raeesi, A. Hazmi, and M. Valkama. Performance comparison between slotted IEEE 802.15. 4 and IEEE 802.1 lah in IoT based applications.In 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 2013: IEEE, pp. 332-337.
     Google Scholar
  7. H. Bogucka, P. Kryszkiewicz, and A. Kliks. Dynamic spectrum aggregation for future 5G communications. IEEE Communications Magazine, vol. 53, no. 5, pp. 35-43, 2015.
     Google Scholar
  8. K. Mekki, E. Bajic, F. Chaxel, and F. Meyer. A comparative study of LPWAN technologies for large-scale IoT deployment. ICT express, vol. 5, no. 1, pp. 1-7, 2019.
     Google Scholar
  9. M. A. da Cruz, J. J. Rodrigues, A. K. Sangaiah, J. Al-Muhtadi, and V. Korotaev. Performance evaluation of IoT middle ware. Journal of Network and Computer Applications, vol. 109, pp. 53-65, 2018.
     Google Scholar
  10. J. Li, B. N. Silva, M. Diyan, Z. Cao, and K. Han. A clustering based routing algorithm in IoT aware Wireless Mesh Networks. Sustainable cities and society, vol. 40, pp. 657-666, 2018.
     Google Scholar
  11. D. Chemodanov, F. Esposito, A. Sukhov, P. Calyam, H. Trinh, and Z. Oraibi. AGRA: AI-augmented geographic routing approach for IoT-based incident-supporting applications. Future Generation Computer Systems, vol. 92, pp. 1051-1065, 2019.
     Google Scholar
  12. Z. Han, Y. Li, and J. Li. A novel routing algorithm for IoT cloud based on hash offset tree. Future Generation Computer Systems, vol. 86, pp. 456-463, 2018.
     Google Scholar
  13. L. Calderoni, A. Magnani, and D. Maio. IoT Manager: An open-source IoT framework for smart cities. Journal of Systems Architecture, vol. 98, pp. 413-423, 2019.
     Google Scholar
  14. S. Jeon and I. Jung. Experimental evaluation of improved IoT middleware for flexible performance and efficient connectivity. Ad Hoc Networks, vol. 70, pp. 61-72, 2018.
     Google Scholar
  15. S. Mukherjee and G. Biswas. Networking for IoT and applications using existing communication technology. Egyptian Informatics Journal, vol. 19, no. 2, pp. 107-127, 2018.
     Google Scholar
  16. V. Casola, A. De Benedictis, M. Rak, and U. Villano. Toward the automation of threat modeling and risk assessment in IoT systems. Internet of Things, vol. 7, p. 100056, 2019.
     Google Scholar
  17. M. S. Pour, E. Bou-Harb, K. Varma, N. Neshenko, D. A. Pados, and K.-K. R. Choo. Comprehending the IoT cyber threat landscape: A data dimensionality reduction technique to infer and characterize Internet-scale IoT probing campaigns/ Digital Investigation, vol. 28, pp. S40-S49, 2019.
     Google Scholar
  18. M. Hasan, M. M. Islam, M. I. I. Zarif, and M. Hashem. Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things, vol. 7, p. 100059, 2019.
     Google Scholar
  19. P. Valerio. Can Sub-1GHz WiFi Solve The IoT Connectivity Issues. The New Global Enterprise, 2014.
     Google Scholar
  20. B. B. Olyaei, J. Pirskanen, O. Raeesi, A. Hazmi, and M. Valkama. Performance comparison between slotted IEEE 802.15. 4 and IEEE 802.1 lah in IoT based applications. In 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 2013: IEEE, pp. 332-337.
     Google Scholar
  21. L. Monostori et al. Cyber-physical systems in manufacturing. Cirp Annals, vol. 65, no. 2, pp. 621-641, 2016.
     Google Scholar
  22. T. Rault, A. Bouabdallah, and Y. Challal. Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, vol. 67, pp. 104-122, 2014.
     Google Scholar
  23. K. Mekki, E. Bajic, F. Chaxel, and F. Meyer. A comparative study of LPWAN technologies for large-scale IoT deployment. ICT express, vol. 5, no. 1, pp. 1-7, 2019.
     Google Scholar
  24. M.-S. Pan and S.-W. Yang. A lightweight and distributed geographic multicast routing protocol for IoT applications. Computer Networks, vol. 112, pp. 95-107, 2017.
     Google Scholar
  25. J. Huang, Q. Duan, Y. Zhao, Z. Zheng, and W. Wang. Multicast routing for multimedia communications in the Internet of Things. IEEE Internet of Things Journal, vol. 4, no. 1, pp. 215-224, 2016.
     Google Scholar
  26. M. Z. Hasan and F. Al-Turjman. Optimizing multipath routing with guaranteed fault tolerance in Internet of Things. IEEE Sensors Journal, vol. 17, no. 19, pp. 6463-6473, 2017.
     Google Scholar
  27. D. Ros?rio, Z. Zhao, A. Santos, T. Braun, and E. Cerqueira. A beaconless opportunistic routing based on a cross-layer approach for efficient video dissemination in mobile multimedia IoT applications. Computer communications, vol. 45, pp. 21-31, 2014.
     Google Scholar
  28. M. Ishino, Y. Koizumi, and T. Hasegawa. A routing-based mobility management scheme for IoT devices in wireless mobile networks. IEICE Transactions on Communications, vol. 98, no. 12, pp. 2376-2381, 2015.
     Google Scholar
  29. A. A. Khan, M. H. Rehmani, and M. Reisslein. Requirements, design challenges, and review of routing and MAC protocols for CR-based smart grid systems. IEEE Communications Magazine, vol. 55, no. 5, pp. 206-215, 2017.
     Google Scholar
  30. J. Zhu, Y. Song, D. Jiang, and H. Song. Multi-armed bandit channel access scheme with cognitive radio technology in wireless sensor networks for the internet of things. IEEE access, vol. 4, pp. 4609-4617, 2016.
     Google Scholar
  31. F. Al-Turjman. Cognitive routing protocol for disaster-inspired internet of things. Future Generation Computer Systems, vol. 92, pp. 1103-1115, 2019.
     Google Scholar
  32. H. Bogucka, P. Kryszkiewicz, and A. Kliks.Dynamic spectrum aggregation for future 5G communications. IEEE Communications Magazine, vol. 53, no. 5, pp. 35-43, 2015.
     Google Scholar
  33. Y. Takizawa, Y. Takashima, and N. Adachi. Self-Organizing Localization for Wireless Sensor Networks Based on Neighbor Topology. In Proc. IARIA UBICOMM 2013, 2013, pp. 102-108.
     Google Scholar
  34. S. Huaizhou, R. V. Prasad, E. Onur, and I. Niemegeers. Fairness in wireless networks: Issues, measures and challenges. IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 5-24, 2013.
     Google Scholar
  35. W. Rehan, S. Fischer, M. Rehan, and M. H. Rehmani. A comprehensive survey on multichannel routing in wireless sensor networks. Journal of Network and Computer Applications, vol. 95, pp. 1-25, 2017.
     Google Scholar
  36. O. Iova, P. Picco, T. Istomin, and C. Kiraly. Rpl: The routing standard for the internet of things... or is it? IEEE Communications Magazine, vol. 54, no. 12, pp. 16-22, 2016.
     Google Scholar
  37. B. P. Santos, O. Goussevskaia, L. F. Vieira, M. A. Vieira, and A. A. Loureiro. Mobile Matrix: Routing under mobility in IoT, IoMT, and Social IoT. d Hoc Networks, vol. 78, pp. 84-98, 2018.
     Google Scholar
  38. B. Afzal, M. Umair, G. A. Shah, and E. Ahmed. Enabling IoT platforms for social IoT applications: vision, feature mapping, and challenges. Future Generation Computer Systems, vol. 92, pp. 718-731, 2019.
     Google Scholar
  39. L. Atzori, A. Iera, G. Morabito, and M. Nitti. The social internet of things (siot)?when social networks meet the internet of things: Concept, architecture and network characterization. Computer networks, vol. 56, no. 16, pp. 3594-3608, 2012.
     Google Scholar
  40. O. Gnawali, R. Fonseca, K. Jamieson, M. Kazandjieva, D. Moss, and P. Levis. CTP: An efficient, robust, and reliable collection tree protocol for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), vol. 10, no. 1, pp. 1-49, 2013.
     Google Scholar
  41. B. Peres et al. Matrix: Multihop address allocation and dynamic any-to-any routing for 6LoWPAN. Computer Networks, vol. 140, pp. 28-40, 2018.
     Google Scholar
  42. D. Johnson, C. Perkins, and J. Arkko. Mobility support in IPv6. ed: RFC 3775, june, 2004.
     Google Scholar
  43. M. D. Esfahani, A. A. Rahman, and N. H. Zakaria. Green IT/IS adoption as corporate ecological responsiveness: an academic literature review. Journal of Soft Computing and Decision Support Systems, vol. 2, no. 1, pp. 35-43, 2015.
     Google Scholar
  44. M.-C. Chuang and J.-F. Lee. FH-PMIPv6: A fast handoff scheme in Proxy Mobile IPv6 networks. In 2011International Conference on Consumer Electronics, Communications and Networks (CECNet), 2011: IEEE, pp. 1297-1300.
     Google Scholar
  45. A. J. Jabir, S. Shamala, and Z. Zuriati. A New Strategy for Signaling Overhead Reduction in the Proxy Mobile IPv6 Protocol. American Journal of Applied Sciences 9 (4): 535-541, 2012
     Google Scholar
  46. S. H. Chae, W. Choi, J. H. Lee, and T. Q. Quek. Enhanced secrecy in stochastic wireless networks: Artificial noise with secrecy protected zone. IEEE Transactions on Information Forensics and Security, vol. 9, no. 10, pp. 1617-1628, 2014.
     Google Scholar
  47. P. N. Mahalle, B. Anggorojati, N. R. Prasad, and R. Prasad. Identity authentication and capability based access control (iacac) for the internet of things. Journal of Cyber Security and Mobility, vol. 1, no. 4, pp. 309-348, 2013.
     Google Scholar
  48. M. Henze, B. Wolters, R. Matzutt, T. Zimmermann, and K. Wehrle. Distributed configuration, authorization and management in the cloud-based internet of things. in 2017 IEEE Trustcom/BigDataSE/ICESS, 2017: IEEE, pp. 185-192.
     Google Scholar
  49. T. Pecorella, L. Brilli, and L. Mucchi. The role of physical layer security in IoT: A novel perspective. Information, vol. 7, no. 3, p. 49, 2016.
     Google Scholar
  50. H. Yu, M. Kaminsky, P. B. Gibbons, and A. Flaxman. Sybilguard: defending against sybil attacks via social networks. In Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications, 2006, pp. 267-278.
     Google Scholar
  51. J. Mattila. The blockchain phenomenon?the disruptive potential of distributed consensus architectures. ETLA working papers, 2016.
     Google Scholar
  52. J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami. Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, vol. 29, no. 7, pp. 1645-1660, 2013.
     Google Scholar
  53. N. Michael and A. Tang. Halo: Hop-by-hop adaptive link-state optimal routing. EEE/ACM Transactions on Networking, vol. 23, no. 6, pp. 1862-1875, 2014.
     Google Scholar
  54. F. J. Rodriguez, S. Fernandez, I. Sanz, M. Moranchel, and E. J. Bueno. Distributed approach for smart grids reconfiguration based on the OSPF routing protocol. IEEE Transactions on Industrial Informatics, vol. 12, no. 2, pp. 864-871, 2015.
     Google Scholar
  55. S. Debroy, S. De, and M. Chatterjee. Contention based multichannel MAC protocol for distributed cognitive radio networks. IEEE Transactions on Mobile Computing, vol. 13, no. 12, pp. 2749-2762, 2014.
     Google Scholar
  56. S. Bhattarai, J.-M. J. Park, B. Gao, K. Bian, and W. Lehr. An overview of dynamic spectrum sharing: Ongoing initiatives, challenges, and a roadmap for future research. IEEE Transactions on Cognitive Communications and Networking, vol. 2, no. 2, pp. 110-128, 2016.
     Google Scholar
  57. I. Yaqoob et al. Internet of things architecture: Recent advances, taxonomy, requirements, and open challenges. IEEE wireless communications, vol. 24, no. 3, pp. 10-16, 2017.
     Google Scholar
  58. M. Brachmann, O. Garcia-Mochon, S.-L.Keoh, and S. S. Kumar. Security considerations around end-to-end security in the IP-based Internet of things. in Workshop on Smart Object Security, in conjunction with IETF83, Paris, France, March 23, 2012, 2012.
     Google Scholar
  59. J. M. Mohammed, B.-L. Ong, R. B. Ahmad, and M. Hakawati. Internet of things (IoT) mobility support based on distributed sensor proxy mipv6. Journal of Theoretical & Applied Information Technology, vol. 95, no. 17, 2017.
     Google Scholar
  60. P. Kamalinejad, C. Mahapatra, Z. Sheng, S. Mirabbasi, V. C. Leung, and Y. L. Guan. Wireless energy harvesting for the Internet of Things. IEEE Communications Magazine, vol. 53, no. 6, pp. 102-108, 2015.
     Google Scholar