PFARS : Enhancing throughput and lifetime of heterogeneous WSNs through power-aware fusion, aggregation, and routing scheme
- Khan, Rahim, Zakarya, Muhammad, Tan, Zhiyuan, Usman, Muhammad, Jan, Mian, Khan, Mukhtaj
- Authors: Khan, Rahim , Zakarya, Muhammad , Tan, Zhiyuan , Usman, Muhammad , Jan, Mian , Khan, Mukhtaj
- Date: 2019
- Type: Text , Journal article
- Relation: International Journal of Communication Systems Vol. 32, no. 18 (Dec 2019), p. 21
- Full Text:
- Reviewed:
- Description: Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy-efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application-specific or too complex that make their implementation unrealistic, specifically, in a resource-constrained environment. In this paper, we propose a novel node-level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in-network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real-time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.
- Authors: Khan, Rahim , Zakarya, Muhammad , Tan, Zhiyuan , Usman, Muhammad , Jan, Mian , Khan, Mukhtaj
- Date: 2019
- Type: Text , Journal article
- Relation: International Journal of Communication Systems Vol. 32, no. 18 (Dec 2019), p. 21
- Full Text:
- Reviewed:
- Description: Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy-efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application-specific or too complex that make their implementation unrealistic, specifically, in a resource-constrained environment. In this paper, we propose a novel node-level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in-network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real-time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.
Random walks : a review of algorithms and applications
- Xia, Feng, Liu, Jiaying, Nie, Hansong, Fu, Yonghao, Wan, Liangtian, Kong, Xiangjie
- Authors: Xia, Feng , Liu, Jiaying , Nie, Hansong , Fu, Yonghao , Wan, Liangtian , Kong, Xiangjie
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 4, no. 2 (2020), p. 95-107
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- Reviewed:
- Description: A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science. Furthermore, in quantum mechanics, quantum walks can be regarded as quantum analogues of classical random walks. Classical random walks and quantum walks can be used to calculate the proximity between nodes and extract the topology in the network. Various random walk related models can be applied in different fields, which is of great significance to downstream tasks such as link prediction, recommendation, computer vision, semi-supervised learning, and network embedding. In this article, we aim to provide a comprehensive review of classical random walks and quantum walks. We first review the knowledge of classical random walks and quantum walks, including basic concepts and some typical algorithms. We also compare the algorithms based on quantum walks and classical random walks from the perspective of time complexity. Then we introduce their applications in the field of computer science. Finally we discuss the open issues from the perspectives of efficiency, main-memory volume, and computing time of existing algorithms. This study aims to contribute to this growing area of research by exploring random walks and quantum walks together. © 2017 IEEE.
- Authors: Xia, Feng , Liu, Jiaying , Nie, Hansong , Fu, Yonghao , Wan, Liangtian , Kong, Xiangjie
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 4, no. 2 (2020), p. 95-107
- Full Text:
- Reviewed:
- Description: A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science. Furthermore, in quantum mechanics, quantum walks can be regarded as quantum analogues of classical random walks. Classical random walks and quantum walks can be used to calculate the proximity between nodes and extract the topology in the network. Various random walk related models can be applied in different fields, which is of great significance to downstream tasks such as link prediction, recommendation, computer vision, semi-supervised learning, and network embedding. In this article, we aim to provide a comprehensive review of classical random walks and quantum walks. We first review the knowledge of classical random walks and quantum walks, including basic concepts and some typical algorithms. We also compare the algorithms based on quantum walks and classical random walks from the perspective of time complexity. Then we introduce their applications in the field of computer science. Finally we discuss the open issues from the perspectives of efficiency, main-memory volume, and computing time of existing algorithms. This study aims to contribute to this growing area of research by exploring random walks and quantum walks together. © 2017 IEEE.
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