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Fast multicriteria network selection scheme using hybrid of cat swarm optimization and TOPSIS algorithm for optimal handover in VANET

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TRANSACTIONS ON EMERGING TELECOMMUNIcatIONS TECHNOLOGIES 2023年第5期34卷 e4743-e4743页

作者： Jahangeer, Naskath Natl Engn Coll Dept Comp Sci & Engn Kovilpatti India

Future generation vehicular networks will grant the technique for efficient data dissemination over dense heterogeneous radio technologies by considering existing communication technology to attain higher spectral competence for successful data rates. Due to higher mobility schemes, a vehicle frequently switches among different heterogeneous networks, which leads to unnecessary handovers. In this scenario, an amateurish handover algorithm delivers data with heavy packet loss, caused by frequent and ping-pong handover. Hence, this article presents the model for. A network simulator is used to prove the performance of the algorithm. The performance of the algorithm is analyzed in terms of vehicle density, velocity, throughput, packet loss, and jitter.

关键词： cat optimization algorithm connectivity fuzzy ranking TOPSIS algorithm vehicular adhoc network vertical handover

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A Fault-Tolerant Clustering Approach for Target Tracking in Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNIcatIONS 2024年第4期137卷 2303-2322页

作者： Tabatabaei, Shayesteh Higher Educ Complex Saravan Dept Comp Engn Saravan Iran

Target tracking is a crucial application in wireless sensor networks. Current algorithms for target tracking primarily involve node scheduling based on trajectory prediction. However, when the target is lost due to prediction errors, a target recovery mechanism initiates a search operation, potentially activating numerous nodes and leading to increased energy consumption. Furthermore, the recovery process may result in data loss. To address these challenges, we propose a fault-tolerant clustering approach using the cat optimization algorithm to minimize the probability of target loss. To assess the effectiveness of our approach, simulations were conducted in OPNET using the NODIC, DCRRP, BFOABMS, and AFSRP protocols. The results illustrate that our method excels over existing approaches across various metrics. Specifically, compared to the well-known NODIC method, our approach reduces end-to-end delay by 84.93%, media access delay by 15.08%, increases throughput rate by 3.84%, lowers energy consumption by 4.49%, improves signal-to-noise ratio by 9.99%, and enhances delivery rate of data to the sink by 1.02%. Additionally, compared to the widely recognized DCRRP method, our method improves media access delay by 2.90%, throughput rate by 2.02%, reduces energy consumption by 0.30%, enhances signal-to-noise ratio by 7.36%, and improves the delivery rate of data to the sink by 0.41%. Moreover, our proposed method decreases the end-to-end delay by 10.28% compared to DCRRP. Also, the superior performance of the proposed method in terms of end-to-end delay is 1.52%, media access delay by 8.73%, throughput rate by 1.97%, energy consumption by 0.33%, signal-to-noise ratio by 9.25%, and delivery rate of successfully sending data to the sink is 0.76% higher than the well-known AFSRP ***, compared to the widely recognized BFOABMS method, our method improves media access delay by 9.56% and enhances the delivery rate of data to the sink by 0.70%. However, in our pr

关键词： cat optimization algorithm Fault tolerant Energy consumption Wireless sensor networks Target tracking

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Geospatial analysis of terrain through optimized feature extraction and regression model with preserved convex region

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MULTIMEDIA TOOLS AND APPLIcatIONS 2018年第24期77卷 31855-31873页

作者： Prabhakaran, N. Ramakrishnan, S. S. Shanker, N. R. Vel Tech High Tech Dr Rangarajan Dr Sakunthala En Madras 600062 Tamil Nadu India Anna Univ Inst Remote Sensing Madras 600025 Tamil Nadu India IAF Aalim Muhammed Salegh Coll Engn Madras 600055 Tamil Nadu India

In this paper, cat optimization algorithm for feature extraction in satellite image has been proposed. In cat optimization, cost function computes the pixel in the satellite image to preserve the boundary shape and avoid non-convex part of the contour of the image. However, the existing feature extraction optimization algorithm measures the distinct data framework and thematic information to insight land cover such as waterbody, urban and vegetation. The land cover is obtained from different optimized feature extraction algorithms never provide proper boundary shape and land feature. Furthermore, the proposed cat optimized algorithm distinguishes the inner, outer and extended boundary along with the land cover. The cat-optimised algorithm for low and high-resolution satellite image shows the better result of 85%, with the preserved convex region when compared with the existing feature extraction algorithm such as fuzzy and Particle Swarm optimization (PSO).

关键词： cat optimization algorithm Particle swarm optimization Fuzzy shape model Boundary region Regression

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