{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T06:32:22Z","timestamp":1764570742514,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,19]],"date-time":"2018-04-19T00:00:00Z","timestamp":1524096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aiming at the problem of network congestion caused by the large number of data transmissions in wireless routing nodes of wireless sensor network (WSN), this paper puts forward an algorithm based on standard particle swarm\u2013neural PID congestion control (PNPID). Firstly, PID control theory was applied to the queue management of wireless sensor nodes. Then, the self-learning and self-organizing ability of neurons was used to achieve online adjustment of weights to adjust the proportion, integral and differential parameters of the PID controller. Finally, the standard particle swarm optimization to neural PID (NPID) algorithm of initial values of proportion, integral and differential parameters and neuron learning rates were used for online optimization. This paper describes experiments and simulations which show that the PNPID algorithm effectively stabilized queue length near the expected value. At the same time, network performance, such as throughput and packet loss rate, was greatly improved, which alleviated network congestion and improved network QoS.<\/jats:p>","DOI":"10.3390\/s18041265","type":"journal-article","created":{"date-parts":[[2018,4,20]],"date-time":"2018-04-20T04:24:21Z","timestamp":1524198261000},"page":"1265","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID"],"prefix":"10.3390","volume":"18","author":[{"given":"Xiaoping","family":"Yang","sequence":"first","affiliation":[{"name":"School of Communication Engineering, Jilin University, Changchun 130025, China"}]},{"given":"Xueying","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Communication Engineering, Jilin University, Changchun 130025, China"}]},{"given":"Riting","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Communication Engineering, Jilin University, Changchun 130025, China"}]},{"given":"Zhihong","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Communication Engineering, Jilin University, Changchun 130025, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4957","DOI":"10.1007\/s11227-017-2067-x","article-title":"Barrier coverage of WSNs with the imperialist competitive algorithm","volume":"73","author":"Mostafaei","year":"2017","journal-title":"J. Supercomput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s11227-016-1785-9","article-title":"P-SEP: A prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks","volume":"73","author":"Shojafar","year":"2017","journal-title":"J. Supercomput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.jnca.2015.03.002","article-title":"Congestion control mechanisms in wireless sensor networks: A survey","volume":"52","author":"Ghaffari","year":"2015","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.3724\/SP.J.1016.2011.02197","article-title":"\u03b5-Approximation and Weighted Fairness Guaranteed Congestion Control Algorithm for Wireless Sensor Networks","volume":"34","author":"Li","year":"2011","journal-title":"Chin. J. Comput."},{"key":"ref_5","first-page":"762","article-title":"Congestion Control Based on Reliable Transmission in Wireless Sensor Networks","volume":"9","author":"Sun","year":"2014","journal-title":"J. Netw."},{"key":"ref_6","unstructured":"Gu, D., and Zhang, W. (2009, January 15\u201318). Design of an H\u221e based PI controller for AQM routers supporting TCP flows. Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 28th Chinese Control Conference, Shanghai, China."},{"key":"ref_7","first-page":"13","article-title":"Congestion Control Strategy Based on RED Algorithm in Wireless Sensor Network","volume":"3","author":"Li","year":"2012","journal-title":"Comput. Simul."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, S.J., Wang, P.P., and He, J.H. (2011, January 19\u201322). Simulation Analysis of Congestion Control in WSN Based on AQM. Proceedings of the 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), Jilin, China.","DOI":"10.1109\/MEC.2011.6025434"},{"key":"ref_9","first-page":"760","article-title":"Congestion Avoidance Scheme in Wireless Sensor Network","volume":"34","author":"Zhao","year":"2013","journal-title":"J. Chin. Comput. Syst."},{"key":"ref_10","first-page":"3656","article-title":"Design of Wireless Sensor Network Congestion Control Algorithm Based on Active PI Model and Improved Quantum Particle Swarm Optimizing Algorithm","volume":"22","author":"Li","year":"2014","journal-title":"Comput. Meas. Control"},{"key":"ref_11","first-page":"1244","article-title":"A congestion Control Algorithm with Fairness for Wireless Sensor Networks","volume":"6","author":"Gan","year":"2015","journal-title":"J. Chin. Comput. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s00521-016-2341-5","article-title":"Dynamic behavioral assessment model based on Hebb learning rule","volume":"28","author":"Yin","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_13","unstructured":"Sun, P.L., and Niu, L.G. (2008, January 2\u20134). Adaptive congestion control algorithm based on predictive control. Proceedings of the IEEE Conference Publications of the 2008 Chinese Control and Decision Conference (CCDC), Yantai, China."},{"key":"ref_14","first-page":"475","article-title":"Performance evaluation of fuzzy and BPN based congestion controller in WSN","volume":"7","author":"Chakravarthi","year":"2015","journal-title":"Int. J. Eng. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/s11227-013-1054-0","article-title":"An efficient routing algorithm to preserve k-coverage in wireless sensor networks","volume":"68","author":"Ahmadi","year":"2014","journal-title":"J. Supercomput."},{"key":"ref_16","first-page":"2763","article-title":"Research on fault diagnosis method of wireless sensor node on module level","volume":"34","author":"Li","year":"2013","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_17","first-page":"32","article-title":"Congestion Algorithm of Wireless Sensor Network Based on RBF Predicted Neural Network Controller","volume":"31","author":"Tang","year":"2010","journal-title":"Mini-Micro Syst."},{"key":"ref_18","unstructured":"Kennedy, J., and Eberhart, R.C. (December, January 27). Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia."},{"key":"ref_19","first-page":"2395","article-title":"Application of improved particle-swarm-optimization in stabilized platform based on multiple reference frame model","volume":"44","author":"Fan","year":"2015","journal-title":"Infrared Laser Eng."},{"key":"ref_20","first-page":"149","article-title":"WSN node fault diagnosis based on particle swarm optimization and Gauss distribution","volume":"33","author":"Yu","year":"2013","journal-title":"J. Vib. Meas. Diagn."},{"key":"ref_21","first-page":"470","article-title":"Distributed neural network learning algorithm based on Hebb rule","volume":"30","author":"Tian","year":"2007","journal-title":"Chin. J. Comput."},{"key":"ref_22","first-page":"69","article-title":"Research and simulation of single neuron PID controller","volume":"39","author":"Zhang","year":"2009","journal-title":"Mech. Eng. Autom."},{"key":"ref_23","first-page":"68","article-title":"The static robust and optimization in router level of Internet","volume":"9","author":"Fu","year":"2012","journal-title":"Complex Syst. Complex. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/TEVC.2010.2052054","article-title":"Orthogonal Learning Particle Swarm Optimization","volume":"15","author":"Zhan","year":"2011","journal-title":"IEEE Trans. Evolut. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1049\/el:20060961","article-title":"Stable parameters for PI-control AQM scheme","volume":"42","author":"Li","year":"2006","journal-title":"Electron. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4543","DOI":"10.1002\/dac.2634","article-title":"A compensated PID active queue management controller using an improved queue dynamic model","volume":"27","author":"Kahe","year":"2014","journal-title":"Inter. J. Commun. 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