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Wireless Personal Communications

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28-08-2021

GAPSO-SVM: An IDSS-based Energy-Aware Clustering Routing Algorithm for IoT Perception Layer

Authors: Mozhdeh Norouzi Shad, Mohsen Maadani, Meisam Nesari Moghadam

Published in: Wireless Personal Communications | Issue 3/2022

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Abstract

With the emergence of Internet of Things (IoT) having large scale and generating huge amount of data, Intelligent Decision Support Systems (IDSSs) have attracted a lot of attention for provisioning the required Quality of Service. IoT perception layer is responsible for data dissemination of the “Things”, and energy efficient clustering protocols play an important role in providing them with long-time battery operation. Clustering routing protocols are among the most efficient methods in large scale IoT networks and using location-based decision support can highly simplify the routing problem. Existing literature either assume that the nodes’ location is known, or rely on the expensive and energy consuming GPS modules which are not practical in most IoT use cases. Developing a low-cost and low-energy localization solution is an ongoing challenge. In this paper, an IDSS based clustering routing protocol, named GAPSO-SVM, is proposed for the IoT perception layer utilizing a Support Vector Machine (SVM) based algorithm to estimate the nodes’ locations, and a hybrid Genetic Algorithm-Particle Swarm Optimization (GAPSO) based mechanism for clustering optimization. Simulation results show that, although the exact location of the nodes is not available, compared with recent similar works the convergence rate and network lifetime is enhanced by up to 80% and 11%, respectively.

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Rahimi, M., Songhorabadi, M., & Haghi Kashani, M. (2020). Fog-based smart homes: A systematic review. Journal of Network and Computer Applications, 153, 102531. https://doi.org/10.1016/j.jnca.2020.102531CrossRef

Haghi Kashani, M., Rahmani, A. M., & Jafari Navimipour, N. (2020). Quality of service-aware approaches in fog computing. International Journal of Communication Systems, 33, e4340.CrossRef

Bazzaz Abkenar, S., Haghi Kashani, M., Mahdipour, E., & Jameii, S. M. (2020). Big data analytics meets social media: A systematic review of techniques, open issues, and future directions. Telematics and Informatics, 57, 101517–110555. https://doi.org/10.1016/j.tele.2020.101517CrossRef

Li, J., Liu, Y., Xie, J., Li, M., Sun, M., Liu, Z., et al. (2019). A remote monitoring and diagnosis method based on four-layer IoT frame perception. IEEE Access, 7, 144324–144338.CrossRef

Karimi, Y., Haghi Kashani, M., Akbari, M., & Mahdipour, E. (2021). Leveraging big data in smart cities: A systematic review. Concurrency and Computation: Practice and Experience, Early Access. https://doi.org/10.1002/cpe.6379.CrossRef

Haghi Kashani, M., Madanipour, M., Nikravan, M., Asghari, P., & Mahdipour, E. (2021). A systematic review of IoT in healthcare: Applications, techniques, and trends. Journal of Network and Computer Applications, Early Access. https://doi.org/10.1016/j.jnca.2021.103164.CrossRef

Fathi, M., Haghi Kashani, M., Jameii, S. M., & Mahdipour, E. (2021). Big data analytics in weather forecasting: A systematic review. Archives of Computational Methods in Engineering, Early Access. https://doi.org/10.1007/s11831-021-09616-4.CrossRef

Bellavista, P., Cardone, G., Corradi, A., & Foschini, L. (2013). Convergence of MANET and WSN in IoT urban scenarios. IEEE Sensors Journal, 13(10), 3558–3567.CrossRef

Souri, A., Hussien, A., Hoseyninezhad, M., & Norouzi, M. (2019). A systematic review of IoT communication strategies for an efficient smart environment. Transactions on Emerging Telecommunications Technologies, Early Access. https://doi.org/10.1002/ett.3736.CrossRef

10.

Maadani, M., Motamedi, S. A., & Safdarkhani, H. (2011). Delay-reliability trade-off in MIMO-enabled IEEE 802.11-based wireless sensor and actuator networks. Procedia Computer Science, 5, 945–950.CrossRef

11.

Zarei, M., Rahmani, A. M., & Farazkish, R. (2011). CCTF: Congestion control protocol based on trustworthiness of nodes in wireless sensor networks using fuzzy logic. International Journal of Ad Hoc and Ubiquitous Computing, 8(1–2), 54–63.CrossRef

12.

Maadani, M., & Motamedi, S. A. (2011). EDCA delay analysis of spatial diversity in IEEE 802.11-based real-time wireless sensor and actuator networks. In 8th International Symposium on Wireless Communication Systems, 675–679. https://doi.org/10.1109/ISWCS.2011.6125438.

13.

Nikravan, M., Jameii, S. M., & Kashani, M. H. (2011). An intelligent energy efficient QoS-routing scheme for WSN. International Journal of Advanced Engineering Sciences and Technologies, 8(1), 121–124.

14.

Maadani, M., Motamedi, S. A., & Safdarkhani, H. (2011). An adaptive rate and coding scheme for MIMO-enabled IEEE 802.11-based Soft-Real-Time wireless sensor and actuator networks. In 3rd International Conference on Computer Research and Development, 439–443. https://doi.org/10.1109/ICCRD.2011.5764053.

15.

Bahaghighat, M., & Motamedi, S. A. (2016). It-mac: Enhanced mac layer for image transmission over cognitive radio sensor networks. International Journal of Computer Science and Information Security, 14(12), 234.

16.

Maadani, M., Motamedi, S. A., & Soltani, M. (2012). EDCA delay analysis of spatial multiplexing in IEEE802. 11-based wireless sensor and actuator networks. International Journal of Information and Electronics Engineering, 2(3), 318–322.

17.

Kaur, T., & Kumar, D. (2020). A survey on QoS mechanisms in WSN for computational intelligence based routing protocols. Wireless Networks, 26(4), 2465–2486.CrossRef

18.

Darabkh, K. A., & Al-Jdayeh, L. (2019). AEA-FCP: An adaptive energy-aware fixed clustering protocol for data dissemination in wireless sensor networks. Personal and Ubiquitous Computing, 23(5–6), 819–837.CrossRef

19.

Zarei, M., & Rahmani, A. M. (2017). Analysis of vehicular mobility in a dynamic free-flow highway. Vehicular Communications, 7, 51–57.CrossRef

20.

Mohammadi, J., & Akbari, R. (2010). Vehicle speed estimation based on the image motion blur using radon transform. In 2nd International Conference on Signal Processing Systems, V1-243-V1-247. https://doi.org/10.1109/ICSPS.2010.5555577.

21.

Zarei, M., & Rahmani, A. M. (2016). Renewal process of information propagation in delay tolerant VANETs. Wireless Personal Communications, 89(4), 1045–1063.CrossRef

22.

Bahaghighat, M., & Motamedi, S. A. (2017). Psnr enhancement in image streaming over cognitive radio sensor networks. Etri Journal, 39(5), 683–694.CrossRef

23.

Zarei, M., Rahmani, A. M., & Samimi, H. (2017). Connectivity analysis for dynamic movement of vehicular ad hoc networks. Wireless Networks, 23(3), 843–858.CrossRef

24.

Esmaeili Kelishomi, A., Garmabaki, A., Bahaghighat, M., & Dong, J. (2019). Mobile user indoor-outdoor detection through physical daily activities. Sensors, 19(3), 1–29. https://doi.org/10.3390/s19030511.CrossRef

25.

Zarei, M., Rahmani, A. M., Farazkish, R., & Zahirnia, S. (2010). FCCTF: Fairness congestion control for a distrustful wireless sensor network using fuzzy logic. In 10th International Conference on Hybrid Intelligent Systems, 1–6. https://doi.org/10.1109/HIS.2010.5601071.

26.

Bahaghighat, M., Motamedi, S. A., & Xin, Q. (2019). Image transmission over cognitive radio networks for smart grid applications. Applied Sciences, 9(24), 5498.CrossRef

27.

Zarei, M. (2020). Traffic-centric mesoscopic analysis of connectivity in VANETs. The Computer Journal, 63(2), 203–219.CrossRef

28.

Mohajerani, A., & Gharavian, D. (2016). An ant colony optimization based routing algorithm for extending network lifetime in wireless sensor networks. Wireless Networks, 22(8), 2637–2647.CrossRef

29.

Liu, X., & Liu, Q. (2018). A virtual uneven grid-based routing protocol for mobile sink-based WSNs in a smart home system. Personal and Ubiquitous Computing, 22(1), 111–120.CrossRef

30.

Orojloo, H., & Haghighat, A. T. (2016). A Tabu search based routing algorithm for wireless sensor networks. Wireless Networks, 22(5), 1711–1724.CrossRef

31.

Azharuddin, M., & Jana, P. K. (2017). PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks. Soft Computing, 21(22), 6825–6839.CrossRef

32.

Gupta, G. P., & Jha, S. (2018). Integrated clustering and routing protocol for wireless sensor networks using Cuckoo and Harmony Search based metaheuristic techniques. Engineering Applications of Artificial Intelligence, 68, 101–109.CrossRef

33.

Souidi, M., Habbani, A., Berradi, H., & El Mahdi, F. (2019). Geographic forwarding rules to reduce broadcast redundancy in mobile ad hoc wireless networks. Personal and Ubiquitous Computing, 23(5–6), 765–775.CrossRef

34.

Wang, S., Yu, J., Atiquzzaman, M., Chen, H., & Ni, L. (2018). CRPD: A novel clustering routing protocol for dynamic wireless sensor networks. Personal and Ubiquitous Computing, 22(3), 545–559.CrossRef

35.

Khabiri, M., & Ghaffari, A. (2018). Energy-aware clustering-based routing in wireless sensor networks using cuckoo optimization algorithm. Wireless Personal Communications, 98(3), 2473–2495.CrossRef

36.

Chen, Y.-N., Lyu, N.-Q., Song, G.-H., Yang, B.-W., & Jiang, X.-H. (2020). A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc networks. Frontiers of Information Technology and Electronic Engineering, 21(9), 1308–1320.CrossRef

37.

Safara, F., Souri, A., Baker, T., Al Ridhawi, I., & Aloqaily, M. (2020). PriNergy: A priority-based energy-efficient routing method for IoT systems. The Journal of Supercomputing, 76, 8609–8626. https://doi.org/10.1007/s11277-020-03147-8.CrossRef

38.

Pandiyaraju, V., Logambigai, R., Ganapathy, S., & Kannan, A. (2020). An energy efficient routing algorithm for WSNs using intelligent fuzzy rules in precision agriculture. Wireless Personal Communications, 112, 243–259. https://doi.org/10.1007/s11277-020-07024-8CrossRef

39.

Hashemi, S., & Zarei, M. (2021). Internet of things backdoors: Resource management issues, security challenges, and detection methods. Transactions on Emerging Telecommunications Technologies, 32(2), e4142.CrossRef

40.

Vaiyapuri, T., Parvathy, V. S., Manikandan, V., Krishnaraj, N., Gupta, D., & Shankar, K. (2021). A novel hybrid optimization for cluster‐based routing protocol in information-centric wireless sensor networks for IoT based mobile edge computing. Wireless Personal Communications, Early Access. https://doi.org/10.1007/s11277-021-08088-w.CrossRef

41.

Haseeb, K., Bakar, K. A., Ahmed, A., Darwish, T., & Ahmed, I. (2017). WECRR: Weighted energy-efficient clustering with robust routing for wireless sensor networks. Wireless Personal Communications, 97(1), 695–721.CrossRef

42.

Wang, Z.-X., Zhang, M., Gao, X., Wang, W., & Li, X. (2019). A clustering WSN routing protocol based on node energy and multipath. Cluster Computing, 22(3), 5811–5823.CrossRef

43.

Pal, R., Yadav, S., & Karnwal, R. (2020). EEWC: Energy-efficient weighted clustering method based on genetic algorithm for HWSNs. Complex & Intelligent Systems, 6(2), 391–400. https://doi.org/10.1007/s40747-020-00137-4.CrossRef

44.

Tran, D. A., & Nguyen, T. (2008). Localization in wireless sensor networks based on support vector machines. IEEE Transactions on Parallel and Distributed Systems, 19(7), 981–994.CrossRef

45.

Song, L., Zhao, L., & Ye, J. (2019). DV-hop node location algorithm based on GSO in wireless sensor networks. Journal of Sensors, 2019, 1–9. https://doi.org/10.1155/2019/2986954.CrossRef

46.

Sharma, D., Gaur, P., & Mittal, A. (2014). Comparative analysis of hybrid GAPSO optimization technique with GA and PSO methods for cost optimization of an off-grid hybrid energy system. Energy Technology and Policy, 1(1), 106–114.CrossRef

47.

Keshanchi, B., Souri, A., & Navimipour, N. J. (2017). An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: Formal verification, simulation, and statistical testing. Journal of Systems and Software, 124, 1–21.CrossRef

48.

Caputo, D., Grimaccia, F., Mussetta, M., & Zich, R. E. (2010). Genetical swarm optimization of multihop routes in wireless sensor networks. Applied Computational Intelligence and Soft Computing, 2010, 1–4. https://doi.org/10.1155/2010/523943.CrossRef

49.

Gandelli, A., Grimaccia, F., Mussetta, M., Pirinoli, P., & Zich, R. E. (2006). Genetical swarm optimization: An evolutionary algorithm for antenna design. Automatika: časopis za automatiku, mjerenje, elektroniku računarstvo i komunikacije, 47(3–4), 105–112.

50.

Kuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.CrossRef

51.

Wang, J., Cheng, Z., Ersoy, O. K., Zhang, P., & Dai, W. (2019). Multi-offspring genetic algorithm with two-point crossover and the relationship between number of offsprings and computational speed. Journal of Computers, 30(5), 111–127.

52.

Gupta, S. K., & Jana, P. K. (2015). Energy efficient clustering and routing algorithms for wireless sensor networks: GA based approach. Wireless Personal Communications, 83(3), 2403–2423.CrossRef

Title: GAPSO-SVM: An IDSS-based Energy-Aware Clustering Routing Algorithm for IoT Perception Layer
Authors: Mozhdeh Norouzi Shad
Mohsen Maadani
Meisam Nesari Moghadam
Publication date: 28-08-2021
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 3/2022
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-021-09051-5

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