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Wireless Networks
Published in: Wireless Networks 3/2021

15-02-2021

A parallel compact cat swarm optimization and its application in DV-Hop node localization for wireless sensor network

Authors: Jianpo Li, Min Gao, Jeng-Shyang Pan, Shu-Chuan Chu

Published in: Wireless Networks | Issue 3/2021

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Abstract

Cat swarm optimization (CSO) has been applied to a variety of fields because of the better capacity of searching for optimum and higher robustness. However, the poor convergency and larger memory consumption are still core defects, which restricts the efficiency of optimization to a larger extent. A new heuristic algorithm named Parallel Compact Cat Swarm Optimization (PCCSO) with three separate communication strategies and the concept of the compact are presented in this article. The advantage of PCCSO is not only reflected in enhancing the ability of local search, but also in saving the computer memory. The experimental results on CEC2013 benchmark functions demonstrate that the PCCSO is always superior to PSO, CSO, and improved CSO in getting convergent. Then, the PCCSO is applied to DV-Hop to effectively improve the localization accuracy of unknown nodes while also saving WSN memory. The experimental results based on PCCSO from the different number of sensor nodes also illustrate that the PCCSO-DV-Hop shows a lower localization error compared to other optimization algorithms based on DV-Hop.
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Literature
1.
Wang, H., Zhang, G., Mingjie, E., & Sun, N. (2011). A novel intrusion detection method based on improved SVM by combining PCA and PSO. Wuhan University Journal of Natural Sciences, 16(5), 409.CrossRef
2.
Qin, S., Sun, C., Zhang, G., He, X., & Tan, Y. (2020). A modified particle swarm optimization based on decomposition with different ideal points for many-objective optimization problems. Complex & Intelligent Systems, 6, 263274.CrossRef
3.
Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm Intelligence, 1(1), 33–57.CrossRef
4.
Wang, H., Liang, M., Sun, C., Zhang, G., & Xie, L. (2020). Multiple-strategy learning particle swarm optimization for large-scale optimization problems. Complex & Intelligent Systems, 1–16.
5.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.CrossRef
6.
Pan, J. S., Hu, P., & Chu, S. C. (2019). Novel parallel heterogeneous meta-heuristic and its communication strategies for the prediction of wind power. Processes, 7(11), 845.CrossRef
7.
Hu, P., Pan, J., & Chu, S. (2020). Improved binary grey wolf optimizer and its application for feature selection. Knowledge Based Systems, 105746.
8.
Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: A new metaheuristic optimization algorithm. Computers & Structures, 139, 98–112.CrossRef
9.
Du, Z. G., Pan, J. S., Chu, S. C., Luo, H. J., & Hu, P. (2020). Quasi-affine transformation evolutionary algorithm with communication schemes for application of RSSI in wireless sensor networks. IEEE Access, 8, 8583–8594.CrossRef
10.
Pan, J. S., Kong, L., Sung, T. W., Tsai, P. W., & Snášel, V. (2018). A clustering scheme for wireless sensor networks based on genetic algorithm and dominating set. Journal of Internet Technology, 19(4), 1111–1118.
11.
Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: A new meta-heuristic. In Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406) (Vol. 2, pp. 1470–1477). IEEE.
12.
Chu, S. C., Tsai, P. W., & Pan, J. S. (2006). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854–858). Springer.
13.
Neri, F., Mininno, E., & Iacca, G. (2013). Compact particle swarm optimization. Information Sciences, 239, 96–121.MathSciNetCrossRef
14.
Harik, G. R., Lobo, F. G., & Goldberg, D. E. (1999). The compact genetic algorithm. IEEE Transactions on Evolutionary Computation, 3(4), 287–297.CrossRef
15.
Mininno, E., Neri, F., Cupertino, F., & Naso, D. (2010). Compact differential evolution. IEEE Transactions on Evolutionary Computation, 15(1), 32–54.CrossRef
16.
Tian, A. Q., Chu, S. C., Pan, J. S., Cui, H., & Zheng, W. M. (2020). A compact pigeon-inspired optimization for maximum short-term generation mode in cascade hydroelectric power station. Sustainability, 12(3), 767.CrossRef
17.
Pan, J. S., Dao, T. K., et al. (2019). A novel improved bat algorithm based on hybrid parallel and compact for balancing an energy consumption problem. Information, 10(6), 194.CrossRef
18.
Pan, J. S., Dao, T. K., et al. (2019). A compact bat algorithm for unequal clustering in wireless sensor networks. Applied Sciences, 9(10), 1973.CrossRef
19.
Pan, J. S., Song, P. C., Chu, S. C., & Peng, Y. J. (2020). Improved compact cuckoo search algorithm applied to location of drone logistics hub. Mathematics, 8(3), 333.CrossRef
20.
Zhao, M. (2018). A novel compact cat swarm optimization based on differential method. Enterprise Information Systems, 14, 1–25.
21.
Xue, X., & Pan, J. S. (2018). A compact co-evolutionary algorithm for sensor ontology meta-matching. Knowledge and Information Systems, 56(2), 335–353.CrossRef
22.
Chu, S. C., Xue, X., Pan, J. S., & Wu, X. (2020). Optimizing ontology alignment in vector space. Journal of Internet Technology, 21(1), 15–22.
23.
Song, P. C., Pan, J. S., & Chu, S. C. (2020). A parallel compact cuckoo search algorithm for three-dimensional path planning. Applied Soft Computing, 106443.
24.
Pan, J. S., Kong, L., Sung, T. W., Tsai, P. W., & Snášel, V. (2018). \(\alpha \)-Fraction first strategy for hierarchical model in wireless sensor networks. Journal of Internet Technology, 19(6), 1717–1726.
25.
Chen, C. H., Lee, C. A., & Lo, C. C. (2016). Vehicle localization and velocity estimation based on mobile phone sensing. IEEE Access, 4, 803–817.CrossRef
26.
Wang, J., Gao, Y., Wang, K., Sangaiah, A. K., & Lim, S. J. (2019). An affinity propagation-based self-adaptive clustering method for wireless sensor networks. Sensors, 19(11), 2579.CrossRef
27.
Halder, S., & Ghosal, A. (2016). A survey on mobile anchor assisted localization techniques in wireless sensor networks. Wireless Networks, 22(7), 2317–2336.CrossRef
28.
Kulkarni, V. R., Desai, V., & Kulkarni, R. V. (2019). A comparative investigation of deterministic and metaheuristic algorithms for node localization in wireless sensor networks. Wireless Networks, 25(5), 2789–2803.CrossRef
29.
Zaruba, G. V., Huber, M., Kamangar, F. A., & Chlamtac, I. (2007). Indoor location tracking using RSSI readings from a single Wi-Fi access point. Wireless Networks, 13(2), 221–235.CrossRef
30.
GhasemAghaei, R., Rahman, M., Gueaieb, W. & El Saddik, A. (2007). Ant colony-based reinforcement learning algorithm for routing in wireless sensor networks. 2007 IEEE instrumentation & measurement technology conference IMTC 2007 (pp. 1–6). IEEE.
31.
Shi, H. Y., Wang, W. L., Kwok, N. M., & Chen, S. Y. (2012). Game theory for wireless sensor networks: A survey. Sensors, 12(7), 9055–9097.CrossRef
32.
Sikeridis, D., Tsiropoulou, E. E., Devetsikiotis, M., & Papavassiliou, S. (2018). Wireless powered Public Safety IoT: A UAV-assisted adaptive-learning approach towards energy efficiency. Journal of Network and Computer Applications, 123, 69–79.CrossRef
33.
Fragkos, G., Apostolopoulos, P. A., & Tsiropoulou, E. E. (2018). ESCAPE: Evacuation strategy through clustering and autonomous operation in public safety systems. Future Internet, 11(1), 20.CrossRef
34.
Huang, X. L., Ma, X., & Hu, F. (2018). Machine learning and intelligent communications. Mobile Networks and Applications, 23(1), 68–70.CrossRef
35.
Kong, L., Chen, C. M., Shih, H. C., Lin, C. W., & Pan, J. S. (2014). An energy-aware routing protocol using cat swarm optimization for wireless sensor networks. Lecture Notes in Electrical Engineering, 260, 311–318.CrossRef
36.
Temel, S., Unaldi, N., & Kaynak, O. (2013). On deployment of wireless sensors on 3-D terrains to maximize sensing coverage by utilizing cat swarm optimization with wavelet transform. IEEE Transactions on Systems Man & Cybernetics Systems, 44(1), 111–120.CrossRef
37.
Kasana, R., & Kumar, S. (2017). A geographic routing algorithm based on cat swarm optimization for vehicular ad-hoc networks (pp. 86–90).
38.
Kong, L., Pan, J. S., Tsai, P. W., Vaclav, S., & Ho, J. H. (2015). A balanced power consumption algorithm based on enhanced parallel cat swarm optimization for wireless sensor network. International Journal of Distributed Sensor Networks, 11(3), 729680.CrossRef
39.
Kanwar, V., & Kumar, A. (2020). DV-Hop localization methods for displaced sensor nodes in wireless. Wireless Networks, 1–12.
40.
Gumaida, B. F., & Luo, J. (2019). Novel localization algorithm for wireless sensor network based on intelligent water drops. Wireless Networks, 25(2), 597–609.CrossRef
41.
Tsai, P. W., Pan, J. S., Chen, S. M., & Liao, B. Y. (2012). Enhanced parallel cat swarm optimization based on the Taguchi method. Expert Systems with Applications, 39(7), 6309–6319.CrossRef
42.
Chang, J. F., Chu, S. C., Roddick, J. F., & Pan, J. S. (2005). A parallel particle swarm optimization algorithm with communication strategies. Journal of Information ENCE & Engineering, 21(4), 809–818.
43.
Liang, J., Qu, B., Suganthan, P., & Hernández-Díaz, A. G. (2013). Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report (Vol. 201212, No. 34, pp. 281–295).
44.
Chen, X., & Zhang, B. (2012). Improved DV-Hop node localization algorithm in wireless sensor networks. International Journal of Distributed Sensor Networks, 2012(6), 1018–1020.
Metadata
Title
A parallel compact cat swarm optimization and its application in DV-Hop node localization for wireless sensor network
Authors
Jianpo Li
Min Gao
Jeng-Shyang Pan
Shu-Chuan Chu
Publication date
15-02-2021
Publisher
Springer US
Published in
Wireless Networks / Issue 3/2021
Print ISSN: 1022-0038
Electronic ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-021-02563-9

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