Adaptive Traffic Light Control with Statistical Multiplexing Technique and Particle Swarm Optimization in Smart Cities

Author(s):  
Brabim Manandhar ◽  
Basanta Joshi
Author(s):  
Dinesh Kumar Prajapat ◽  
Rahul Chopra

In an IOT based smart city various component like smart water supply management, traffic light control, street lightning system and many more systems are make a city to a smart city. In a smart city allthe necessary facilities such as transportation, water, energy and security etc. related issue can be solving in easy way and the community and people provide a healthy and safety environment. The world is moving forward at a fast with increasing technology in recent time. Thus, a lot of safety issues in all parts of world. So, in this paper mainly focus to provide a well deserved life of all the persons living in the cities. Generally the smart cities definition depends on geographical, environmental andeconomical.


2018 ◽  
Vol 51 (4) ◽  
pp. 382-387 ◽  
Author(s):  
Cosmin Copot ◽  
Thoa Mac Thi ◽  
Clara Ionescu

2020 ◽  
Vol 39 (4) ◽  
pp. 4959-4969
Author(s):  
Weiqiang Wang

In smart city wireless network infrastructure, network node deployment directly affects network service quality. This problem can be attributed to deploying a suitable ordinary AP node as a wireless terminal access node on a given geometric plane, and deploying a special node as a gateway to aggregate. Traffic from ordinary nodes is to the wired network. In this paper, Pareto multi-objective optimization strategy is introduced into the wireless sensor network node security deployment, and an improved multi-objective particle swarm coverage algorithm based on secure connection is designed. Firstly, based on the mathematical model of Pareto multi-objective optimization, the multi-target node security deployment model is established, and the security connectivity and node network coverage are taken as the objective functions, and the problems of wireless sensor network security and network coverage quality are considered. The multi-objective particle swarm optimization algorithm is improved by adaptively adjusting the inertia weight and particle velocity update. At the same time, the elite archive strategy is used to dynamically maintain the optimal solution set. The high-frequency simulation software Matlab and simulation platform data interaction are used to realize the automatic modeling, simulation analysis, parameter prediction and iterative optimization of wireless network node deployment in smart city based on adaptive particle swarm optimization. Under the premise of meeting the performance requirements of wireless network nodes in smart cities, the experimental results show that although the proposed algorithm could not achieve the accuracy of using only particle swarm optimization algorithm to optimize the parameters of wireless network nodes in smart cities, the algorithm is completed. The antenna parameter optimization process takes less time and the optimization efficiency is higher.


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 58427-58438 ◽  
Author(s):  
Majid Abdullateef Abdullah ◽  
Tawfik Al-Hadhrami ◽  
Chee Wei Tan ◽  
Abdul Halim Yatim

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