A Hybrid Approach based on PSO and Boosting Technique for Data Modeling in Sensor Networks
محورهای موضوعی : Machine learning
hadi shakibian
1
,
Jalaledin Nasiri
2
1 - Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
2 - Department of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
کلید واژه: Wireless sensor network, Distributed optimization, Particle swarm optimization, Regression, Boosting,
چکیده مقاله :
An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from consideration, a common regression technique could be employed after transmitting all the network data from the sensor nodes to the fusion center. However, it is not practical nor efferent. To overcome this issue, several distributed methods have been proposed in WSNs where the regression problem has been formulated as an optimization based data modeling problem. Although they are more energy efficient than the centralized method, the latency and prediction accuracy needs to be improved even further. In this paper, a new approach is proposed based on the particle swarm optimization (PSO) algorithm. Assuming a clustered network, firstly, the PSO algorithm is employed asynchronously to learn the network model of each cluster. In this step, every cluster model is learnt based on the size and data pattern of the cluster. Afterwards, the boosting technique is applied to achieve a better accuracy. The experimental results show that the proposed asynchronous distributed PSO brings up to 48% reduction in energy consumption. Moreover, the boosted model improves the prediction accuracy about 9% on the average.
An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from consideration, a common regression technique could be employed after transmitting all the network data from the sensor nodes to the fusion center. However, it is not practical nor efferent. To overcome this issue, several distributed methods have been proposed in WSNs where the regression problem has been formulated as an optimization based data modeling problem. Although they are more energy efficient than the centralized method, the latency and prediction accuracy needs to be improved even further. In this paper, a new approach is proposed based on the particle swarm optimization (PSO) algorithm. Assuming a clustered network, firstly, the PSO algorithm is employed asynchronously to learn the network model of each cluster. In this step, every cluster model is learnt based on the size and data pattern of the cluster. Afterwards, the boosting technique is applied to achieve a better accuracy. The experimental results show that the proposed asynchronous distributed PSO brings up to 48% reduction in energy consumption. Moreover, the boosted model improves the prediction accuracy about 9% on the average.
[1] Sharma, Himanshu, Ahteshamul Haque, and Frede Blaabjerg. "Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey." Electronics 10.9 (2021): 1012.
[2] Liu, Longgeng, et al. "An algorithm based on logistic regression with data fusion in wireless sensor networks." EURASIP Journal on Wireless Communications and Networking 2017.1 (2017): 1-9.
[3] Deng, Yulong, et al. "Temporal and spatial nearest neighbor values based missing data imputation in wireless sensor networks." Sensors 21.5 (2021): 1782.
[4] Zuhairy, Ruwaida M., and Mohammed GH Al Zamil. "Energy-efficient load balancing in wireless sensor network: An application of multinomial regression analysis." International Journal of Distributed Sensor Networks 14.3 (2018): 1550147718764641.
[5] Kumar, D. Praveen, Tarachand Amgoth, and Chandra Sekhara Rao Annavarapu. "Machine learning algorithms for wireless sensor networks: A survey." Information Fusion 49 (2019): 1-25.
[6] Ghate, Vasundhara V., and Vaidehi Vijayakumar. "Machine learning for data aggregation in WSN: A survey." International Journal of Pure and Applied Mathematics 118.24 (2018): 1-12.
[7] Ren, Xiaoxing, et al. "Distributed Subgradient Algorithm for Multi-Agent Optimization With Dynamic Stepsize." IEEE/CAA Journal of Automatica Sinica 8.8 (2021): 1451-1464.
[8] Doan, Thinh T., Siva Theja Maguluri, and Justin Romberg. "Convergence rates of distributed gradient methods under random quantization: A stochastic approximation approach." IEEE Transactions on Automatic Control (2020).
[9] Zhang, Peng, and Gejun Bao. "An incremental subgradient method on Riemannian manifolds." Journal of Optimization Theory and Applications 176.3 (2018): 711-727.
[10] Berahas, Albert S., Charikleia Iakovidou, and Ermin Wei. "Nested distributed gradient methods with adaptive quantized communication." 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019.
[11] Xu, Xiangxiang, and Shao-Lun Huang. "An information theoretic framework for distributed learning algorithms." 2021 IEEE International Symposium on Information Theory (ISIT). IEEE, 2021.
[12] Perumal, T. Sudarson Rama, V. Muthumanikandan, and S. Mohanalakshmi. "Energy Efficiency Optimization in Clustered Wireless Sensor Networks via Machine Learning Algorithms." Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems. CRC Press, 2021. 59-77.
[13] Kumar, D. Praveen, Tarachand Amgoth, and Chandra Sekhara Rao Annavarapu. "Machine learning algorithms for wireless sensor networks: A survey." Information Fusion 49 (2019): 1-25.
[14] Mohanty, Lipika, et al. "Machine Learning-Based Wireless Sensor Networks." Machine Learning: Theoretical Foundations and Practical Applications. Springer, Singapore, 2021. 109-122.
[15] Pundir, Meena, and Jasminder Kaur Sandhu. "A systematic review of Quality of Service in Wireless Sensor Networks using Machine Learning: Recent trend and future vision." Journal of Network and Computer Applications (2021): 103084.
[16] Antonian, Edward and Peters, Gareth and Peters, Gareth and Chantler, Michael John and Yan, Hongxuan, GLS Kernel Regression for Network-Structured Data (August 9, 2021). Available at SSRN: https://ssrn.com/abstract=3901694.
[17] Liu, Longgeng, et al. "An algorithm based on logistic regression with data fusion in wireless sensor networks." EURASIP Journal on Wireless Communications and Networking 2017.1 (2017): 1-9.
[18] Wang, Heyu, Lei Xia, and Chunguang Li. "Distributed online quantile regression over networks with quantized communication." Signal Processing 157 (2019): 141-150.54532343WE332 .
[19] Wang, Heyu, and Chunguang Li. "Distributed quantile regression over sensor networks." IEEE Transactions on Signal and Information Processing over Networks 4.2 (2017): 338-348.
[20] Danaee, Alireza, Rodrigo C. de Lamare, and Vitor H. Nascimento. "Energy-Efficient Distributed Recursive Least Squares Learning with Coarsely Quantized Signals." 2020 54th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2020.
[21] Danaee, Alireza, Rodrigo C. de Lamare, and Vitor H. Nascimento. "Energy-efficient distributed learning with coarsely quantized signals." IEEE Signal Processing Letters 28 (2021): 329-333.
[22] Hellkvist, Martin, Ayça Özçelikkale, and Anders Ahlén. "Generalization error for linear regression under distributed learning." 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2020.
[23] Shuman, David I., et al. "Distributed signal processing via Chebyshev polynomial approximation." IEEE Transactions on Signal and Information Processing over Networks 4.4 (2018): 736-751.
[24] M. Rabbat and R. Nowak, "Distributed Optimization in Sensor Networks," in Proceedings of the 3rd international symposium on Information processing in sensor networks, Berkeley, California, USA, (2004), pp. 20-27.
[25] Bertsekas, Dimitri P., "Incremental gradient, subgradient, and proximal methods for convex optimization: a survey," Optimization for Machine Learning, No. 85, pp. 1-38, 2011.
[26] M. Rabbat, and Nowak, R. "Quantized Incremental Algorithms for Distributed Optimization," IEEE Journal on Selected Areas in Communications, 23 (4) (2006), pp. 798-808.
[27] S.H. Son, M. Chiang, S. R. Kulkarni, and S. C. Schwartz, "The Value of Clustering in Distributed Estimation for Sensor Networks," in proceedings of International Conference on Wireless Networks, Communications and Mobile Computing, Maui, Hawaii, 2 (2005), pp. 969-974.
[28] P.J. Marandi, N.M. Charkari, "Boosted Incremental Nelder-Mead Simplex Algorithm: Distributed Regression in Wireless Sensor Networks," IFIP Joint Conference on Mobile and Wireless Communications Networks, France, (2008), pp. 199-212.
[29] P.J. Marandi, M. Mansourizadeh, N. M. Charkari, "The Effect of Resampling on Incremental Nelder-Mead Simplex Algorithm: Distributed Regression over Wireless Sensor Network," in Proceedings of the Third International Conference on Wireless Algorithms, Systems, and Applications, LNCS, 5258 (2008), Dallas, Texas, pp. 420-431.
[30] H. Shakibian and N. Moghadam Charkari, "D-PSO for Distributed Regression over Wireless Sensor Networks," Iranian Journal of Electrical and Computer Engineering, Vol. 11, No. 1, pp. 43-50, 2012.
[31] Shakibian, Hadi, and Nasrollah Moghadam Charkari. "In-cluster vector evaluated particle swarm optimization for distributed regression in WSNs." Journal of network and computer applications 42 (2014): 80-91.
[32] Zhao, Jijun, Hao Liu, Zhihua Li, and Wei Li., "Periodic Data Prediction Algorithm in Wireless Sensor Networks," In Advances in Wireless Sensor Networks, pp. 695-701, 2013. [33] Cheng, Long, et al. "An Indoor Localization Algorithm based on Modified Joint Probabilistic Data Association for Wireless Sensor Network." IEEE Transactions on Industrial Informatics (2020).
[34] Shahbazian, Reza, and Seyed Ali Ghorashi. "Distributed cooperative target detection and localization in decentralized wireless sensor networks." The Journal of Supercomputing 73.4 (2017): 1715-1732.
[35] Zandhessami, Hessam, Mahmood Alborzi, and Mohammadsadegh Khayyatian. "Energy Efficient Routing-Based Clustering Protocol Using Computational Intelligence Algorithms in Sensor-Based IoT." Journal of Information Systems and Telecommunication (JIST) 1.33 (2021): 55.
[36] Daanoune, Ikram, Baghdad Abdennaceur, and Abdelhakim Ballouk. "A comprehensive survey on LEACH-based clustering routing protocols in Wireless Sensor Networks." Ad Hoc Networks (2021): 102409.
[37] Qi, Hong, et al. "Inversion of particle size distribution by spectral extinction technique using the attractive and repulsive particle swarm optimization algorithm." Thermal Science 19.6 (2015): 2151-2160.
[38] Mo, Simin, Jianchao Zeng, and Weibin Xu. "Attractive and repulsive fully informed particle swarm optimization based on the modified fitness model." Soft Computing 20.3 (2016): 863-884.
[39] Ursem, Rasmus K. "Diversity-guided evolutionary algorithms." International Conference on Parallel Problem Solving from Nature. Springer, Berlin, Heidelberg, 2002. [40] A.P. Engelbrecht, Computational Intelligence: An introduction, 2ed., Wiley, 2007.
[41] Madden, S., 2003. Intel Berkeley research lab data. USA: Intel Corporation, 2004 [2004-06-08]. http://berkeley, intel-research, net/labdata, html.
[42] C. Guestrin, P. Bodi, R. Thibau, M. Paskin, and S. Madde, "Distributed Regression: An Efficient Framework for Modeling Sensor Network data," in Proceedings of third international symposium on Information processing in sensor networks, Berkeley, California, USA, (2004), pp. 1-10.
[43] Y. Shi, R.C. Eberhart, "Empirical study of particle swarm optimization," in Proceedings of the IEEE International Congress on Evolutionary Computation, 3 (1999), pp. 101-106.
[44] Xu, Zhaoyi, Yanjie Guo, and Joseph Homer Saleh. "Multi-objective optimization for sensor placement: An integrated combinatorial approach with reduced order model and Gaussian process." Measurement 187 (2022): 110370.
[45] Premkumar, M., and T. V. P. Sundararajan. "DLDM: Deep learning-based defense mechanism for denial of service attacks in wireless sensor networks." Microprocessors and Microsystems 79 (2020): 103278.
[46] Mohanty, Sachi Nandan, et al. "Deep learning with LSTM based distributed data mining model for energy efficient wireless sensor networks." Physical Communication 40 (2020): 101097.
