Yazar "Khalilpour Akram V." seçeneğine göre listele

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    A distributed depth first search based algorithm for edge connectivity estimation
    (Institute of Electrical and Electronics Engineers Inc., 2020) Ugurlu O.; Khalilpour Akram V.; Tursel Eliiyi D.
    The edge connectivity of a network is the minimum number of edges whose removal disconnect the network. The edge connectivity determines the minimum number of edge-disjoint paths between all nodes. Hence finding the edge connectivity can reveal useful information about reliability, alternative paths and bottlenecks. In this paper, we propose a cost-effective distributed algorithm that finds a lower bound for the edge connectivity of a network via finding at most c depth-first-search trees, where c is the edge connectivity. The proposed algorithm is asynchronous and does not need any synchronization between the nodes. In the proposed algorithm, the root node starts a distributed depth-first-search algorithm, and the nodes select next node in the tree based on their available edges to maximize the total number of established trees. The simulation results show that the proposed algorithm finds the edge connectivity with an average of 48% accuracy ratio. © 2020 IFIP.
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    Improving the Deployment of WSNs by Localized Detection of Covered Redundant Nodes in Industry 4.0 Applications
    (MDPI, 2022) Aljubori M.H.H.; Khalilpour Akram V.; Challenger M.
    Wireless sensor networks can be used as cost-effective monitoring and automation platforms in smart manufacturing and Industry 4.0. Maximizing the covered area and increasing the network lifetime are two challenging tasks in wireless sensor networks. A feasible solution for maximizing the coverage area and network lifetime is detecting and relocating the covered redundant nodes. A covered redundant node is a node whose covered area is also covered by the other active nodes in the network. After identifying the covered redundant nodes, putting them in sleep mode can increase the network lifetime. In addition, moving the detected redundant nodes to the uncovered locations can improve the overall covered area by the sensor nodes. However, finding the redundant nodes is an NP-complete problem. In this paper, we propose a localized distributed algorithm for identifying the redundant nodes based on the 2-hop local neighborhood information of the nodes. The proposed algorithm uses the existing connections between the neighbors of each sensor node to decide the redundancy of the node. The algorithm is localized and does not need the entire topology of the network or the coordinates of the nodes. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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    KEIP: a distributed k-connectivity estimation algorithm based on independent paths for wireless sensor networks
    (Springer New York LLC, 2018) Dagdeviren O.; Khalilpour Akram V.
    Maintaining the connectivity between all nodes is a challenging task in wireless sensor networks (WSNs) because failure of some nodes may divide the network to disconnected parts. A network is k-connected if we need to remove at least k nodes to disconnect it. There are at least k disjoint paths between each pair of nodes in a k-connected network which preserves the connectivity of the network after removing k-1 arbitrary nodes. Therefore, with high k values the network may tolerate more failures without losing its connectivity. Finding the k value of a given network can provide useful information about the robustness of the connectivity. The existing distributed k-connectivity estimation (detection) algorithms use local neighborhood information to find an approximated value for k. In this paper, we propose a new energy efficient distributed algorithm which finds the k value of the given WSN with more accuracy by detecting the minimum number of disjoint paths between the sink and all other nodes. We extend the definition of disjoint paths to independent paths, which are the disjoint paths with shared nodes, and use this concept to find the k value. The comprehensive simulation and testbed results show that the proposed algorithm is faster and has at least 20% higher correct detection ratio, lower mean square error ratio and also lower energy consumption than the existing algorithms. © 2018 Springer Science+Business Media, LLC, part of Springer Nature