Journal ArticleDOI
Cluster Based Data Aggregation Scheme for Latency and Packet Loss Reduction in WSN
TLDR
Simulation results show that the proposed cluster based Data Aggregation Scheme for Latency and Packet Loss Reduction in WSN reduces the latency and overhead and increases the packet delivery ratio and residual energy.About:
This article is published in Computer Communications.The article was published on 2020-01-01. It has received 59 citations till now. The article focuses on the topics: Packet loss & Network packet.read more
Citations
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Journal ArticleDOI
SHSDA: secure hybrid structure data aggregation method in wireless sensor networks
Maryam Naghibi,Hamid Barati +1 more
TL;DR: In this paper, a secure data aggregation method based on a combination of star and tree structures is suggested, where the network is geographically divided into four equal parts, and a stable star structure is formed in each part.
Journal ArticleDOI
A Systematic Review of Quality of Service in Wireless Sensor Networks using Machine Learning: Recent Trend and Future Vision
TL;DR: A statistical analysis of the past ten years ranging from 2011 to 2020 on various ML techniques used for the QoS parameters in the light of Machine Learning (ML) techniques is presented.
Posted ContentDOI
Energy-Efficient Cluster-Based Routing Protocol for WSN Based on Hybrid BSO–TLBO Optimization Model
TL;DR: A novel energy-efficient method which utilizes the Brainstorm algorithm in order to adopt the ideal CH to reduce energy-draining and can be used to attain an improved throughput, network lifetime, and to reduce the energy consumption by nodes and CH.
Journal ArticleDOI
A survey of low-latency transmission strategies in software defined networking
TL;DR: relevant technologies and research findings in terms of traffic management, congestion control, load balancing, and flow table management in SDN are reviewed and analyzed to provide interested researchers with insights to promote their ongoing research in low-latency communications.
Journal ArticleDOI
MWCSGA-Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network.
TL;DR: In this article, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA), which mainly consists of six sections: system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster and intra cluster communication.
References
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Journal ArticleDOI
Energy Efficient and Balanced Cluster-Based Data Aggregation Algorithm for Wireless Sensor Networks
TL;DR: This paper proposes an energy efficient and balanced cluster-based data aggregation algorithm (EEBCDA) that can remarkably enhance energy efficiency, balance energy dissipation and prolong network lifetime in clusterbased WSNs.
Journal ArticleDOI
Distributed Low-Latency Data Aggregation Scheduling in Wireless Sensor Networks
TL;DR: The algorithm proposed in this article (Distributed algorithm for Integrated tree Construction and data Aggregation (DICA) intertwines the tree formation and node scheduling to reduce the time latency and show that DICA outperforms competing schemes.
Journal ArticleDOI
Sparsest Random Scheduling for Compressive Data Gathering in Wireless Sensor Networks
TL;DR: A sparsest random scheduling for compressive data gathering scheme, which decreases each measurement transmission cost from O(N) to O(log(N)) without increasing the number of CS measurements as well, and can recover the real sensory data accurately.
Journal ArticleDOI
Bandwidth efficient cluster-based data aggregation for Wireless Sensor Network
TL;DR: The proposed Bandwidth Efficient Cluster-based Data Aggregation (BECDA) algorithm presents the solution for the effective data gathering with in-network aggregation and shows significant improvement in PDR and throughput as compared to the state-of-the-art solutions.
Proceedings ArticleDOI
A Hybrid Approach for Clustering-Based Data Aggregation in Wireless Sensor Networks
TL;DR: This work proposes a hybrid clustering based data aggregation scheme that can adaptively choose a suitable clustering technique depending on the status of the network, increasing the data aggregation efficiency as well as energy consumption and successful data transmission ratio.
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