Volume 4, Issue 3, May 2019, Page: 82-90
Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network
Sorush Niknamian, Department of Military Medicine, Liberty University, Lynchburg, USA
Received: Mar. 13, 2019;       Accepted: Apr. 15, 2019;       Published: Jun. 26, 2019
DOI: 10.11648/j.ajtte.20190403.12      View  187      Downloads  39
Wireless sensor network refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.
Genetic Algorithm, Wireless Sensor Network Abnormal Vehicle Load Data, Recognition Method
To cite this article
Sorush Niknamian, Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network, American Journal of Traffic and Transportation Engineering. Vol. 4, No. 3, 2019, pp. 82-90. doi: 10.11648/j.ajtte.20190403.12
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Xu Y, Xu N, Zhuang Z, et al. (2017). An Algorithm of Abnormal Data Detection for Internet of Vehicles Based on Crowdsensing. Hunan Daxue Xuebao/journal of Hunan University Natural Sciences, 44 (8): 145-151.
Kailkhura, B., Han, Y. S., Brahma, S., et al. (2015). Distributed Bayesian Detection in the Presence of Byzantine Data. IEEE Transactions on Signal Processing, 63 (19), 5250-5263. DOI: 10.1109/TSP.2015.2450191.
Gao, Y.-H., Zhang, L.-T., Liang, J., et al. (2016). Abnormal noise analysis for commercial vehicle cab based on psychoacoustics. Journal of Jilin University, 46 (01): 43-49.
Kumarage, H., Khalil, I., and Tari, Z. (2015). Granular Evaluation of Anomalies in Wireless Sensor Networks Using Dynamic Data Partitioning with an Entropy Criteria. IEEE Transactions on Computers, 64 (9), 2573-2585. DOI: 10.1109/TC.2014.2366755.
Mehrjoo S, Khunjush F. (2018). Accurate compressive data gathering in wireless sensor networks using weighted spatio-temporal compressive sensing. Telecommunication Systems, 68 (12): 1-10.
Mu, L. W., Peng, X. B., and Huang, L. (2015). Abnormal Data Detection Algorithm in Heterogeneous Complex Information Network. Computer Science, 42 (11), 34-137. DOI: 10.11896/j.issn.1002-137X.2015.11.028.
Xin H, Liu X. (2017). Energy-Balanced Transmission with Accurate Distances for Strip-based Wireless Sensor Networks. IEEE Access, PP. (99): 1-1.
Rathore, M. M., Ahmad, A., and Paul, A. (2016). Real time intrusion detection system for ultra-high-speed big data environments. Journal of Supercomputing, 72 (9), 3489-3510. DOI: https://doi.org/10.1007/s11227-015-1615-5.
Bui T O, Xu P, Phan N Q, et al. (2016). An Accurate and Energy-Efficient Localization Algorithm for Wireless Sensor Networks// IEEE, Vehicular Technology Conference. IEEE, 1-5.
Zhang, J., Li, H., Gao, Q., et al. (2015). Detecting anomalies from big network traffic data using an adaptive detection approach. Information Sciences, 318 (C), 91-110. DOI: org/10.1016/j.ins.2014.07.044.
Zhao, Q. Q. (2017). Under the Environment of Dynamic Data Network Duplicate Data Simulation Test Method. Computer Simulation, 34 (6), 445-448. DOI: 10.3969/j.issn.1006-9348.2017.06.097.
Liu D, Cao Z, Zhang Y, et al. (2017). Achieving Accurate and Real-Time Link Estimation for Low Power Wireless Sensor Networks. IEEE/ACM Transactions on Networking, PP. (99): 1-14.
Dayal, A. (2018). Improving adaptive frameless rendering. International Journal of Computers and Applications, 40 (2): 110-120.
Qiu, K. and Wang, J. R. (2018). A Fractional Integral Identity and its Application to Fractional Hermite-Hadamard Type Inequalities. Journal of Interdisciplinary Mathematics, 21 (1): 1-16.
Chen, D. Z. (2018). Performance Evaluation Model for Government-Supportive Urbanization. Journal of Discrete Mathematical Sciences and Cryptography, 21 (4): 859-868.
Rintala, J. and Kolari, M. (2017). Better Hygiene in Food Packaging Board at Reduced Risk of Rejected Tonnage and Machine Corrosion. Paper Asia, 33 (5): 20-23.
Khorramnejad, K.; Ferdouse, L.; Guan, L., and Anpalagan, A. (2018). Performance of integrated workload scheduling and pre-fetching in multimedia mobile cloud computing. Journal of Cloud Computing, 7 (1).
R, A. K., Dr. K, B. (2017). Pi and sliding mode speed control of permanent magnet synchronous motor fed from three phase four switch vsi. Journal of Mechanical Engineering Research and Developments, 40 (4): 716-725.
Nie, Z. Q. (2018). Discounted cash flow (dcf) model detection based on goodwill impairment test. Journal of Discrete Mathematical Sciences and Cryptography, 21 (4): 959-968.
Browse journals by subject