A Location-Based Clustering Topology Control Algorithm in WSN

Aiming at the existing problems in Leach algorithm,which has short network survival time and high energy consumption,a new location-based clustering topology control algorithm is proposed.Based on Leach algorithm,improvements have been done.Firstly,when selecting cluster head,node degree,remaining energy,and the number of being cluster head,these three elements are taken into consideration.Secondly,by running the minimum spanning tree algorithm,the tree routing is constructed.Finally,selecting the next hop between clusters is done by MTE algorithm.Simulation results show that the presented control algorithm has not only a better adaptability in the large-scale networks,but also a bigger improvement in terms of some indicators of performance such as network lifetime and network energy consumption.

Genetic Algorithm Routing Protocol for Mobile Ad Hoc Network

Mobile ad hoc network(MANET)is a dynamically reconfigurable wireless network with time-variable infrastructure.Given that nodes are highly mobile,MANET’s topology often changes.These changes increase the difficulty in finding the routes that the packets use when they are routed.This study proposes an algorithm called genetic algorithm-based location-aided routing(GALAR)to enhance the MANET routing protocol efficiency.The GALAR algorithm maintains an adaptive update of the node location information by adding the transmitting node location information to the routing packet and selecting the transmitting node to carry the packets to their destination.The GALAR was constructed based on a genetic optimization scheme that considers all contributing factors in the delivery behavior using criterion function optimization.Simulation results showed that the GALAR algorithm can make the probability of packet delivery ratio more than 99%with less network overhead.Moreover,GALAR was compared to other algorithms in terms of different network evaluation measures.The GALAR algorithm significantly outperformed the other algorithms that were used in the study.

Monocular Distance Estimated Based on PTZ Camera

This paper introduces an intelligent computational approach for extracting salient objects fromimages and estimatingtheir distance information with PTZ (Pan-Tilt-Zoom) cameras. PTZ cameras have found wide applications innumerous public places, serving various purposes such as public securitymanagement, natural disastermonitoring,and crisis alarms, particularly with the rapid development of Artificial Intelligence and global infrastructuralprojects. In this paper, we combine Gauss optical principles with the PTZ camera’s capabilities of horizontal andpitch rotation, as well as optical zoom, to estimate the distance of the object.We present a novel monocular objectdistance estimation model based on the Focal Length-Target Pixel Size (FLTPS) relationship, achieving an accuracyrate of over 95% for objects within a 5 km range. The salient object extraction is achieved through a simplifiedconvolution kernel and the utilization of the object’s RGB features, which offer significantly faster computingspeeds compared to Convolutional Neural Networks (CNNs). Additionally, we introduce the dark channel beforethe fog removal algorithm, resulting in a 20 dB increase in image definition, which significantly benefits distanceestimation. Our system offers the advantages of stability and low device load, making it an asset for public securityaffairs and providing a reference point for future developments in surveillance hardware.