A Parallelly Implemented Hybrid Multi-Objective Efficient Persuasion of Coverage and Redundancy Programming Model for Internet of Things in 5G Networks using Hadoop
B. Ravi Chandra
B. Ravi Chandra
Department of Electronics & Communication Engineering, Lovely professional university, Jalandhar, Phagwara, Punjab 144001 and G.Pullaiah College of Engineering and Technology, Kurnool, 518452, Andhra Pradesh, India.
In 5G networks, the demand for IoT devices is increasing due to their applications. With the development
and widespread adoption of 5G networks, the Internet of Things (IoT) coverage issue will collide with the issue of
enormous nodes. In this paper, a parallell y implemented Hybridised Mayfly and Rat Swarm Optimizer algorithm
utilising Hadoop is proposed for optimising the IoT coverage and node redundancy in IoT with massive nodes, which
automatically lengthens the IoT's lifecycle. Initially, parallel operation d ivides the IoT coverage problem involving
massive nodes into numerous smaller problems in order to reduce the problem's scope, which are then solved using
parallel Hadoop. Using the flight behaviour and mating process of mayflies, we optimise the coverage problem here.
Rats' pursuing and attacking behaviours are employed to optimise the redundancy problem. Then, select the non critical
nodes from the critical nodes in an optimal manner. Lastly, parallel operation effectively resolves the IoT's coverage
issu e through massive nodes by strategically extending the IoT's lifespan. Using the NS2 application, the proposed
method is simulated. Computation Time, Energy efficiency, Lifespan, Lifetime, and Remaining Nodes are analysed as
performance metrics. The propos ed MOP Hyb MFRS IoT 5GN method achieves lower computation times of 98.38%,
92.34%, and 97.45%, higher lifetime of 89.34%, 83.12%, and 88.96%, and lower remaining time as 91.25%, 79.90%,
and 92.88% compared with existing methods such as parallel genetic alg orithm spread the lifespan of internet of things
on 5G networks (MPGA IoT 5GN)
Keywords
Mayfly a nd Rat Swarm Optimiz ation Algorithm, 5G Networks, Hadoop, Multi Objective Programming, IoT Coverage, Node Redundancy
K. Zhan, “Sports and health big data system based on 5G network and Internet of Things system,” Microprocessors and Microsystems, vol. 80, p. 103363, Feb. 2021, doi: 10.1016/j.micpro.2020.103363.
N. Wang, P. Wang, A. Alipour-Fanid, L. Jiao, and K. Zeng, “Physical-Layer Security of 5G Wireless Networks for IoT: Challenges and Opportunities,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8169–8181, Oct. 2019, doi: 10.1109/jiot.2019.2927379.
F. Al-Turjman and J. P. Lemayian, “Intelligence, security, and vehicular sensor networks in internet of things (IoT)-enabled smart-cities: An overview,” Computers & Electrical Engineering, vol. 87, p. 106776, Oct. 2020, doi: 10.1016/j.compeleceng.2020.106776.
J. Liang, W. Liu, N. N. Xiong, A. Liu, and S. Zhang, “An Intelligent and Trust UAV-Assisted Code Dissemination 5G System for Industrial Internet-of-Things,” IEEE Transactions on Industrial Informatics, vol. 18, no. 4, pp. 2877–2889, Apr. 2022, doi: 10.1109/tii.2021.3110734.
S. H. Alsamhi et al., “Green internet of things using UAVs in B5G networks: A review of applications and strategies,” Ad Hoc Networks, vol. 117, p. 102505, Jun. 2021, doi: 10.1016/j.adhoc.2021.102505.
J. Li et al., “Battery-Friendly Relay Selection Scheme for Prolonging the Lifetimes of Sensor Nodes in the Internet of Things,” IEEE Access, vol. 7, pp. 33180–33201, 2019, doi: 10.1109/access.2019.2904079.
G. Chen and M. Rinaldi, “Aluminum Nitride Combined Overtone Resonators for the 5G High Frequency Bands,” Journal of Microelectromechanical Systems, vol. 29, no. 2, pp. 148–159, Apr. 2020, doi: 10.1109/jmems.2020.2975557.
H. Abukwaik, A. Gogolev, C. Groß, and M. Aleksy, “OPC UA Realization for simplified commissioning of adaptive sensing applications for the 5G IIoT,” Internet of Things, vol. 11, p. 100221, Sep. 2020, doi: 10.1016/j.iot.2020.100221.
Q. Wu, C.-M. Wu, and W. Luo, “Distributed mobility management with ID/locator split network-based for future 5G networks,” Telecommunication Systems, vol. 71, no. 3, pp. 459–474, Oct. 2018, doi: 10.1007/s11235-018-0518-1.
C. Liao and L. Nong, “WITHDRAWN: Smart City Sports Tourism Integration Based on 5G Network and Internet of Things,” Microprocessors and Microsystems, p. 103971, Jan. 2021, doi: 10.1016/j.micpro.2021.103971.
Y. Ding, M. Jin, S. Li, and D. Feng, “Smart logistics based on the internet of things technology: an overview,” International Journal of Logistics Research and Applications, vol. 24, no. 4, pp. 323–345, Apr. 2020, doi: 10.1080/13675567.2020.1757053.
M. Zhou, H. Chen, L. Shu, and Y. Liu, “UAV-Assisted Sleep Scheduling Algorithm for Energy-Efficient Data Collection in Agricultural Internet of Things,” IEEE Internet of Things Journal, vol. 9, no. 13, pp. 11043–11056, Jul. 2022, doi: 10.1109/jiot.2021.3125971.
M. B. Dowlatshahi, M. Kuchaki Rafsanjani, and B. B. Gupta, “An energy aware grouping memetic algorithm to schedule the sensing activity in WSNs-based IoT for smart cities,” Applied Soft Computing, vol. 108, p. 107473, Sep. 2021, doi: 10.1016/j.asoc.2021.107473.
H. Sharma, A. Haque, and Z. A. Jaffery, “Maximization of wireless sensor network lifetime using solar energy harvesting for smart agriculture monitoring,” Ad Hoc Networks, vol. 94, p. 101966, Nov. 2019, doi: 10.1016/j.adhoc.2019.101966.
S. Deese et al., “Long-Term Monitoring of Smart City Assets via Internet of Things and Low-Power Wide-Area Networks,” IEEE Internet of Things Journal, vol. 8, no. 1, pp. 222–231, Jan. 2021, doi: 10.1109/jiot.2020.3005830.
O. Cetinkaya and G. V. Merrett, “Efficient Deployment of UAV-powered Sensors for Optimal Coverage and Connectivity,” 2020 IEEE Wireless Communications and Networking Conference (WCNC), May 2020, doi: 10.1109/wcnc45663.2020.9120738.
K. Wang, L. Wang, M. S. Obaidat, C. Lin, and M. Alam, “Extending Network Lifetime for Wireless Rechargeable Sensor Network Systems Through Partial Charge,” IEEE Systems Journal, vol. 15, no. 1, pp. 1307–1317, Mar. 2021, doi: 10.1109/jsyst.2020.2968628.
Foroughi Nematollahi, A. Rahiminejad, and B. Vahidi, “A novel multi-objective optimization algorithm based on Lightning Attachment Procedure Optimization algorithm,” Applied Soft Computing, vol. 75, pp. 404–427, Feb. 2019, doi: 10.1016/j.asoc.2018.11.032.
K. Zervoudakis and S. Tsafarakis, “A mayfly optimization algorithm,” Computers & Industrial Engineering, vol. 145, p. 106559, Jul. 2020, doi: 10.1016/j.cie.2020.106559.
G. Dhiman, M. Garg, A. Nagar, V. Kumar, and M. Dehghani, “A novel algorithm for global optimization: Rat Swarm Optimizer,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 8, pp. 8457–8482, Oct. 2020, doi: 10.1007/s12652-020-02580-0.
Y. Zhang, W. Yu, X. Chen, and J. Jiang, “Parallel Genetic Algorithm to Extend the Lifespan of Internet of Things in 5G Networks,” IEEE Access, vol. 8, pp. 149630–149642, 2020, doi: 10.1109/access.2020.3005986.
P. Yan, S. Choudhury, F. Al-Turjman, and I. Al-Oqily, “An energy-efficient topology control algorithm for optimizing the lifetime of wireless ad-hoc IoT networks in 5G and B5G,” Computer Communications, vol. 159, pp. 83–96, Jun. 2020, doi: 10.1016/j.comcom.2020.05.010.
S. Halder, A. Ghosal, and M. Conti, “LiMCA: an optimal clustering algorithm for lifetime maximization of internet of things,” Wireless Networks, vol. 25, no. 8, pp. 4459–4477, May 2018, doi: 10.1007/s11276-018-1741-0.
C. Jothikumar, K. Ramana, V. D. Chakravarthy, S. Singh, and I.-H. Ra, “An Efficient Routing Approach to Maximize the Lifetime of IoT-Based Wireless Sensor Networks in 5G and Beyond,” Mobile Information Systems, vol. 2021, pp. 1–11, Jul. 2021, doi: 10.1155/2021/9160516.
K. A. Darabkh, W. K. Kassab, and A. F. Khalifeh, “LiM-AHP-G-C: Life Time Maximizing based on Analytical Hierarchal Process and Genetic Clustering protocol for the Internet of Things environment,” Computer Networks, vol. 176, p. 107257, Jul. 2020, doi: 10.1016/j.comnet.2020.107257.
H. Yang, W.-D. Zhong, C. Chen, and A. Alphones, “Integration of Visible Light Communication and Positioning within 5G Networks for Internet of Things,” IEEE Network, vol. 34, no. 5, pp. 134–140, Sep. 2020, doi: 10.1109/mnet.011.1900567.
Shafiei et al., “A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing,” Mathematical Problems in Engineering, vol. 2021, pp. 1–14, Oct. 2021, doi: 10.1155/2021/9194578.
Shafiei et al., “A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing,” Mathematical Problems in Engineering, vol. 2021, pp. 1–14, Oct. 2021, doi: 10.1155/2021/9194578.
Yi Zou and K. Chakrabarty, “A Distributed Coverage- and Connectivity-Centric Technique for Selecting Active Nodes in Wireless Sensor Networks,” IEEE Transactions on Computers, vol. 54, no. 8, pp. 978–991, Aug. 2005, doi: 10.1109/tc.2005.123
R. Chandra, K. Kumar, A. Roy, S. Qamar, M. I. Rahman, and A. G. F. Saif, “Genetic Algorithm For Higher Ensured Lifespan Of Internet Of Things In 5g Network,” Computers and Electrical Engineering, vol. 106, p. 108563, Mar. 2023, doi: 10.1016/j.compeleceng.2022.108563.
Acknowledgements
Author(s) thanks to G.Pullaiah College of Engineering and Technology for research lab and equipment support.
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B. Ravi Chandra
B. Ravi Chandra
Department of Electronics & Communication Engineering, Lovely professional university, Jalandhar, Phagwara, Punjab 144001 and G.Pullaiah College of Engineering and Technology, Kurnool, 518452, Andhra Pradesh, India.
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Cite this article
B. Ravi Chandra and Krishan Kumar, “A Parallelly Implemented Hybrid Multi-Objective Efficient Persuasion of Coverage and Redundancy Programming Model for Internet of Things in 5G Networks using Hadoop, Journal of Machine and Computing, vol.3, no.3, pp. 264-281, July 2023. doi: 10.53759/7669/jmc202303024.
