Yazar "Akram, Vahid Khalilpour" seçeneğine göre listele

Listeleniyor 1 - 11 / 11
  • Küçük Resim
    Öğe
    An asynchronous distributed algorithm for minimum s - t cut detection in wireless multi-hop networks
    (Elsevier, 2020) Akram, Vahid Khalilpour
    Stable communication is a vital and challenging requirement in wireless multi-hop networks because nodes failure may disconnect other active nodes. Minimum s-t cuts can help to measure the reliability of a network regarding the nodes or links failure. A minimum s-t cut is the smallest subset of links that their failure disconnects all communication paths between nodes s and t. Minimum s-t cuts can also reveal the bottlenecks, bridges, and clusters in the network. This paper proposes an asynchronous distributed algorithm for finding minimum s-t cuts in wireless multi-hop networks. The proposed algorithm iteratively finds link-disjoint paths from s to t and changes the direction of the selected edges to ignore them in the next iteration. After finding all paths, the undiscovered nodes in the last search determine the minimum s-t cut. The comprehensive simulation results on different networks with up to 500 nodes show that the proposed algorithm finds minimum s-t cuts in reasonable time with up to 64% and 39% lower energy consumption than the existing central and distributed algorithms, respectively. (C) 2020 Elsevier B.V. All rights reserved.
  • Küçük Resim
    Öğe
    Detecting the Most Vital Articulation Points in Wireless Multi-Hop Networks
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Akram, Vahid Khalilpour; Ugurlu, Onur
    An articulation point is a node whose removal partitions the network into disconnected segments. The articulation points may affect the reliability and efficiency of wireless multi hop networks from different aspects. Although all articulation points destroy the connectivity of the network, their negative impact on the network is not equal. Removing some articulation points may disconnect a large subset of nodes or generate a large number of partitions, while removing some other articulation points may only disconnect a few nodes. In this paper, we present two novel problems for identifying the most vital articulation points that significantly impact the network. The first problem is finding the p most important articulation points that minimize the largest connected component in the remaining network. The second problem is finding the p most important articulation points whose removal maximizes the number of partitions in the network. We prove that both problems are NP-Hard and propose a distributed algorithm to identify the vital articulation points in both problems. The proposed algorithm establishes a distributed depth-first search tree to identify the articulation points, assigns a score to each articulation point, and selects the prominent articulation points based on their scores. We compare the proposed algorithm with a brute force-based exact algorithm. The simulation result shows that after removing the detected prominent articulation points by the proposed algorithm, the maximum difference between the largest partition size and the number of partitions with the optimal solutions are less than 27.6% and 28.2%, respectively, while the sent bytes of the proposed algorithm can be 89.9% lower.
  • Küçük Resim
    Öğe
    A distributed depth first search based algorithm for edge connectivity estimation
    (IEEE, 2020) Uğurlu, Onur; Akram, Vahid Khalilpour; Eliiyi, Deniz Türsel
    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.
  • Küçük Resim Yok
    Öğe
    A Greedy Algorithm for Minimum Cut into Bounded Sets Problem
    (IEEE, 2021) Ugurlu, Onur; Akram, Vahid Khalilpour; Eliiyi, Deniz Tursel
    Finding critical links and weak points is an important task in almost all types of networks. Minimum cuts provide useful information about the critical links. However, finding a minimum cut of a network may provide insufficient or misleading information on critical links since the number of disconnected nodes in the residual network is not taken into account in this problem. In this work, we study the minimum cut into bounded sets problem, which limits the number of nodes in portioned sets. Finding the minimum cut into bounded sets can provide useful information on important critical links in a different network, whose failure has a hard and unacceptable effect. The minimum cut into bounded sets problem is an open NP-Complete problem. We propose a greedy algorithm for this problem with O(c x n(2)) time complexity and present computational results on random networks. To the best of our knowledge, the proposed algorithm is the first heuristic for the minimum cut into bounded sets problem.
  • Küçük Resim
    Öğe
    Holistic analysis of reliability and lifetime tradeoff in sensor networks
    (IEEE, 2020) Çobanlar, Muhammed; Tavli, Bülent; Akram, Vahid Khalilpour; Dağdeviren, Orhan
    Network lifetime has been the most commonly employed metric for characterization of Wireless Sensor Networks (WSNs) in the literature. Network reliability is also an important aspect of WSNs, especially, deployed for critical missions. However, there is a tradeoff between maximizing the network lifetime and reliability. In this study, we investigate the impact of increasing network reliability in terms of k-connectivity and network lifetime through a mathematical programming framework. We explored a large parameter space to quantitatively characterize this tradeoff. Our results reveal that increasing k leads to significant decrease of network lifetime due to the necessity to utilize energy inefficient routing paths.
  • Küçük Resim
    Öğe
    Kapalı ortamlarda kişi tespitinde makine öğrenmesi algoritmalarının karşılaştırmalı başarım analizi
    (2021) Taşer, Pelin Yıldırım; Akram, Vahid Khalilpour
    Günümüzde, iç mekan konumlandırma ve kişi takip sistemlerinin uygulama alanları her geçen gün artış göstermektedir. Özellikle, hasta, personel, cihaz ve müşteri takip sistemleri ile akıllı binalar ve kalabalık tahminleme gibi birçok alanda, kişilerin konumlarının veya mekan içerisinde bulunma durumlarının doğru tespiti büyük önem taşımaktadır. İç mekan konumlandırma sistemlerinde genellikle, hedef mobil varlığın üzerine periyodik olarak radyo sinyali gönderen küçük bir cihaz yerleştirilir ve bu cihazdan elde edilen sinyaller ile varlığın konumu belirlenir. Fakat bazı ortamlarda, üzerinde herhangi bir sinyal göndericisi taşımayan varlıkların konumlarının tespit edilmesine ihtiyaç duyulmaktadır. Dolayısıyla, mobil cihaz kullanılmayan radyo tabanlı takip sistemlerinde, radyo sinyallerinde meydana gelen dalgalanmalar analiz edilerek, ortamdaki hareketlilik tahmin edilmeye çalışılır. Bu sistemlerde, ortamın çeşitli noktalarına periyodik olarak radyo sinyalleri gönderen ve diğer cihazların gönderdiği sinyalleri alabilen cihazlar yerleştirilir. Ortamda bulunan herhangi bir nesnenin hareket etmesi durumunda, sinyal gücündeki dalgalanmalar analiz edilerek, ortamdaki hareketlilik ve yoğunluk tahmin edilebilir. Ancak bazı durumlarda, radyo sinyallerinde hareketten kaynaklanmayan, geçici ama nispeten şiddetli dalgalanmalar yaşanabilmektedir. Bu tür dalgalanmalar, yanlış tespitlere sebep olarak, sistemin hassasiyetini ve doğruluğunu düşürmektedir. Makine öğrenmesi tekniklerinin, veriler arasındaki gizli örüntü ve karmaşık ilişkileri ortaya çıkarmadaki başarıları sayesinde, makine öğrenmesi tekniklerine dayalı kişi tespit sistemleri, geleneksel yöntemlere göre doğruluğu daha yüksek tahminleme becerisi sunmaktadır. Dolayısıyla, bu çalışmada, kapalı ortamda kişi tespiti için makine öğrenmesi algoritmalarından yararlanılmıştır. Deneysel çalışmalar kapsamında, 10 farklı geleneksel (Naive Bayes, Çok Katmanlı Algılayıcı (MLP), Destek Vektör Makineleri (SVM) ve K-en Yakın Komşuluk (K-NN)) ve karar ağacı tabanlı (C4.5, Random Forest, Random Tree, REPTree, Decision Stump ve HoeffdingTree) sınıflandırma algoritmaları, kapalı ortamdaki 3 farklı telsiz duyarga düğümünden elde edilen ve 23585 kayıttan oluşan veri seti üzerinde ayrı ayrı uygulanmış, doğruluk oranı ve model oluşturma süresi performanslarına göre karşılaştırılmıştır. Elde edilen sonuçlar incelendiğinde, çalışmada uygulanan tüm algoritmaların kapalı alandaki kişi tespitinde %80’in üzerinde başarı performansı sunduğu ve en başarılı algoritmanın %99.68 doğruluk oranı ile Random Forest olduğu gözlemlenmiştir. Ayrıca, geleneksel ve karar ağacı tabanlı sınıflandırma algoritmaları sağlamış oldukları ortalama doğruluk oranlarına göre kıyaslandığında ise karar ağacı tabanlı algoritmaların %95.78 ile daha yüksek tahminleme becerisi sunduğu görülmektedir.
  • Küçük Resim
    Öğe
    A localized distributed algorithm for vertex cover problem
    (Elsevier, 2022) Akram, Vahid Khalilpour; Ugurlu, Onur
    Finding the minimum vertex cover of a given graph is a well-known NP-Hard problem that has many applications in various fields. In this paper, we propose a distributed localized algorithm for detecting vertex cover using 2-hop local neighborhood information in the distributed systems. We propose a scoring based policy and add the 1-hop neighbors of nodes with the highest score among their 2-hop neighbors to the vertex cover. The score of nodes is calculated by dividing the number of uncovered edges in their local subgraph by the number of their 1-hop neighbors. In this way, the score of each node determines the average coverage ratio by each neighbor of that node. The time and bit complexities of the proposed algorithm are O(n/Delta) and O(n(2) x log n) respectively, where Delta is the maximum node degree and n is the number of nodes. The comprehensive simulation results showed that the proposed algorithm could find up to 11% smaller solutions than the existing distributed algorithms with less than 1% difference of optimum solutions in most of the evaluated graphs.
  • Küçük Resim
    Öğe
    Localized identification of malicious nodes in wireless sensor networks
    (IEEE, 2020) Akram, Vahid Khalilpour; Erten, Yusuf Murat
    Securing a wireless sensor network against the various attacks with minimal energy consumption is a challenging task because the nodes usually have limited energy source, are distributed in harsh environments and communicate over standard radio channels. Adding malicious nodes to an existing wireless sensor network is one of the common types of attacks. The malicious nodes can reduce the reliability of a network by flooding heavy traffic, sending fake data or counterfeiting the paths. This study proposes a new localized algorithm for identifying the malicious nodes in WSNs where the existing verified nodes detect the malicious nodes using neighborhood information and signature of messages. The testbed experiments and simulation results show that the proposed algorithm is a feasible and efficient approach for detecting the malicious nodes.
  • Küçük Resim
    Öğe
    Parallel identification of central nodes in wireless multi-hop networks
    (IEEE, 2020) Eliiyi, Deniz Türsel; Arslan, Hilal; Akram, Vahid Khalilpour; Uğurlu, Onur
    A wireless multi-hop network is a collection of nodes that communicate by message passing over multiple links. Sending a message to a remote node can consume some energy from all intermediary nodes. In a network, the nodes with minimum distance to all other nodes are called Jordan central nodes. Selecting the central nodes as sink or base station can considerably reduce the overall energy consumption and increase the network life time. This paper proposes a new parallel algorithm to find all central nodes of a network by finding BFS trees of a subset of nodes. The roots of the trees with smallest height are selected as Jordan central nodes. After finding each tree the algorithm eliminates some nodes from the search space. Available processors construct the BFS tree for different nodes in parallel and eliminate a group of unvisited nodes after creating each tree. The implementation results of the algorithm using different number of processors on topologies with up to 250 nodes showed that the proposed algorithm can find all central nodes by examining less than 20% of nodes in less than 0.034 seconds.
  • Küçük Resim
    Öğe
    A progressive search algorithm for the minimum hitting set problem
    (2021) Arslan, Hilal; Uğurlu, Onur; Akram, Vahid Khalilpour; Eliiyi, Deniz Türsel
    Given a finite universe and a collection of the subsets of the universe, the minimum hitting set of thecollection is the smallest subset of the universe that has non-empty intersection with each set in thecollection. Finding the minimum hitting set is an NP-Hard problem that has many real worldapplications. In this study, we propose a progressive search-based approach to find the minimumhitting set of a given collection. The algorithm starts searching for the hitting sets of size 1 andincrease the expected size of the minimum hitting set by a factor of d. After each unsuccessful search,it increases the expected size by d and generate the candidate sets with the expected size. After eachsuccessful search, the algorithm takes the average of last unsuccessful and successful searches andcontinue the searching with the new expected size. The algorithm terminates when the detectedupper bound coincides with the detected lower bound. The effect of different values for d on theperformance of the algorithm has been experimented on various data sets. Experimental resultsreveal that the proposed method effectively computes the minimum hitting set on real-world dataand random dataset.
  • Küçük Resim
    Öğe
    Telsiz Duyarga Ağlarda Bizans Saldırılarının Topluluk Öğrenme-tabanlı Tespiti
    (2020) Akram, Vahid Khalilpour; Taşer, Pelin Yıldırım
    Telsiz duyarga ağlar (TDA)’da düğümler arasında güvenilir iletişimin sağlanması ve doğru verilerintoplanması birçok açıdan hayati önem taşımaktadır. TDA’ların merkezi iletişim altyapısıolmadığından dolayı, bu ağlar çeşitli saldırılara maruz kalabilmektedirler. TDA’larda yaygın saldırıtürlerinden birisi olan Bizans saldırısında, saldırgan ağ alanına yeni bir düğüm ekleyip sahte verilerüreterek ağın güvenilirliğini düşürebilmektedir. Bu çalışma, TDA’da Bizans saldırılarının tespitineyönelik iki yeni topluluk tabanlı yaklaşım önermektedir. Önerilen bu yaklaşımlar, 3 farklı gelenekselsınıflandırma algoritmasının (Naive Bayes, karar ağacı (C4.5) ve k-en yakın komşuluk (İng. k-NN))voting ve stacking yönetimleri ile bir araya getirilmesinden meydana gelmektedir. Ayrıca, deneyselçalışmalar kapsamında, önerilen iki yeni yaklaşımın yanı sıra, mevcut topluluk öğrenmesiyaklaşımları (C4.5 tabanlı Bagging (Bagging(C4.5)) ve Boosting (AdaBoost)) ile gelenekselalgoritmalar (Naive Bayes, C4.5 ve k-NN) da, 66 IRIS düğümünden (60 normal, 6 saldırgan) oluşanörnek ağ üzerinde uygulanmıştır. Her bir algoritmadan elde edilen sınıflandırma sonuçları, doğrulukoranı ve f-ölçüm değerlerine göre karşılaştırılmıştır. Test yatağından elde edilen sonuçlargöstermektedir ki, topluluk tabanlı yöntemler, TDA’da Bizans saldırılarının tespitinde %98.48doğruluk oranına ulaşırken, geleneksel (tek bir sınıflandırma modeli kullanan) yöntemler %96.97 ilesınırlı kalmaktadır. Çok sayıda düğüm içeren daha büyük ağlarda, bu oranların arasındaki farkartabilir.