Road Network Sensor Deployment Optimization Method for Road User Perception

Author(s):  
Xueyang Chang ◽  
Qing Xu ◽  
Keqiang Li
2011 ◽  
Author(s):  
Sergio D. Cabrera ◽  
Veenarai Moram ◽  
Jose Gerardo Rosiles

2013 ◽  
Vol 779-780 ◽  
pp. 544-549
Author(s):  
Xiao Nian Sun ◽  
Shuo Chen ◽  
Yuan Yuan Mai ◽  
Xian Guang Wang

This paper conducts an in-depth research on applications reliability in highway network planning and optimization of construction sequence. It introduces the concept of reliability to the layout of the importance of the road network node and establishes a fairly comprehensive theoretical method of highway network planning based on the reliability from the aspects of the road network layout, evaluation, and construction sequence arrangements. Case study illustrates the proposed layout optimization method has achieved satisfactory results in the road network reliability analysis, displaying strong practical promotional value.


Author(s):  
Aparna Pradeep Laturkar ◽  
Sridharan Bhavani ◽  
DeepaliParag Adhyapak

<span>Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling &amp; data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. PSO is a multidimensional optimization method inspired from the social behavior of birds called flocking. Basic version of PSO has the drawback of sometimes getting trapped in local optima as particles learn from each other and past solutions. This issue is solved by discrete version of PSO known as Modified Discrete Binary PSO (MDBPSO) as it uses probabilistic approach. This paper discusses performance analysis of random; grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization), Force Based VFCPSO and Combination of Grid &amp; Force Based sensor deployment algorithms based on interval and packet size. </span><span>From the results of Combination of Grid &amp; Force Based sensor deployment algorithm, it can be concluded that its performance is best for all parameters as compared to rest of the three methods when interval and packet size is varied.</span>


Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6573
Author(s):  
Hai-Peng Li ◽  
Da-Zheng Feng ◽  
Shao-Feng Chen ◽  
Ya-Peng Zhou

To construct circular barrier coverage (CBC) with multistatic radars, a deployment optimization method based on equipartition strategy is proposed in this paper. In the method, the whole circular area is divided into several sub-circles with equal width, and each sub-circle is blanketed by a sub-CBC that is built based on the multistatic radar deployment patterns. To determine the optimal deployment patterns for each sub-CBC, the optimization conditions are firstly studied. Then, to optimize the deployment of the whole circular area, a model based on minimum deployment cost is proposed, and the proposed model is divided into two sub-models to solve the optimization issue. In the inner model, it is assumed that the width of a sub-circle is given. Based on the optimization conditions of the deployment pattern, integer linear programming (ILP) and exhaustive method (EM) are jointly adopted to determine the types and numbers of deployment patterns. Moreover, a modified formula is introduced to calculate the maximum valid number of receivers in a pattern, thus narrowing the search scope of the EM. In the outer model, the width of a sub-circle is assumed to be a variable, and the EM is adopted to determine the minimum total deployment cost and the optimal deployment patterns on each sub-circle. Moreover, the improved formula is exploited to determine the range of width for a sub-circle barrier and reduce the search scope of the EM. Finally, simulations are conducted in different conditions to verify the effectiveness of the proposed method. The simulation results indicate that the proposed method can spend less deployment cost and deploy fewer transmitters than the state-of-the-artwork.


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