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BICOT: Bulut Tabanlı IoT Sistemleri Kümeleme için Büyük Veri Analizi Yaklaşımı

Year 2021, Issue: 26 - Ejosat Special Issue 2021 (HORA), 395 - 400, 31.07.2021
https://doi.org/10.31590/ejosat.960360

Abstract

Nesnelerin İnterneti (Internet of Things: IoT) milyarlarca cihazın İnternet üzerinden bağlanmasını öngörmektedir. Bu büyük miktardaki cihazların ürettiği veriler katlanarak büyümektedir, bu nedenle bu büyük veriyi geleneksel yöntemlerle analiz etmek mümkün olmamaktadır. Güncel bulut bilişim ve sanallaştırma teknolojileri, IoT verilerini işleyerek ve depolayarak bu sorunlarla başa çıkmaktadır. Kablosuz sensör ağlar (KSA’lar), ortamdan veri toplamayı sağlayan IoT sistemlerinin büyük veri kaynaklarıdır. KSA'lar, habitat izleme, askeri gözetim ve akıllı tarım gibi çeşitli uygulamalarda kullanılmaktadır. Çıkış düğümüne veri iletimi, KSA'lar için temel gereksinimlerden biridir. Kümeleme; verimli veri iletimi, zaman senkronizasyonu, yük dengeleme ve güvenlik servisleri için kullanılan temel bir tekniktir. Bu makalede IoT sistemleri için uyarlanmış KSA'lar için BICOT diye adlandırdığımız bir kümeleme çerçevesi önermekteyiz. BICOT, büyük ölçekli düğüm konumu, iletim alanı ve düğüm enerji verilerini girdi olarak almakta ve kümeleme bilgisini çıktı olarak üretmektedir. İlk algoritmamız (BICOT-CDS), bağlı hakim küme (connected dominating set: CDS) yapısına dayanmakta ve küme sayısını azaltmayı amaçlamaktadır. İkinci algoritmamız, küme başları olarak yüksek enerjiye sahip düğümleri seçmeyi hedefleyen ağırlıklı bağlı hakim küme (weighted connected dominating set: WCDS) yaklaşımı kullanmaktadır. Bu algoritmaları ns2 simülatör ortamında uygulamakta ve küme sayısı ve küme başı değerlerinin toplam ağırlığını ölçmekteyiz. Algoritmalar, düğüm sayılarına ve ortalama düğüm derecelerine göre test ortamında benzetimleri yapılmaktadır. Kapsamlı simülasyon ölçümlerinden, BICOT-CDS tarafından üretilen küme sayılarının rakiplerinin ürettiği küme sayılarından çok daha iyi olduğunu ve ağ boyutu arttıkça önerilen algoritmanın daha iyi performans gösterdiğini elde etmekteyiz. BICOT-WCDS algoritması tarafından üretilen hakim düğümlerin maliyeti, rakiplerinin ürettiğinden önemli ölçüde daha düşüktür. Bu bulgular bize önerdiğimiz algoritmaların bulut tabanlı IoT sistemleri için uygun büyük veri analizi yaklaşımları olduğunu göstermektedir.

References

  • Bao, L. and Garcia-Luna-Aceves, J. J. (2003) Topology management in ad hoc networks. Proc. of the 4th ACM Int. Symp. on Mobile Ad Hoc Networking & Computing, pp. 129-140, ACM Press, New York.
  • Chatterjee, M., Das, S. K., and Turgut, D. (2001) WCA: weighted clustering algorithm for mobile ad hoc networks. Journal of Cluster Computing (Special Issue on Mobile Ad hoc Networks), 5, 193-204.
  • Chvatal, V. (1979) A greedy heuristic for the set-covering problem, Mathematics of Operations Research. INFORMS, 4(3), 233-235.
  • Guha, S. and Khuller, S. (1998) Approximation algorithms for connected dominating sets. Algorithmica, 20, 374-387.
  • Harb, H., Makhoul, A., Idrees, A., Zahwe and O. and Taam, M.. (2017) Wireless Sensor Networks: A Big Data Source in Internet of Things. International Journal of Sensors, Wireless Communications and Control.
  • Kim, B.-.S, Kim, K.-I., Shah, B., Chow, F. and Kim, K. H. (2019) Wireless Sensor Networks for Big Data Systems, Sensors 19, no. 7, 1565.
  • Klein, P. and Ravi, R. (1995) A nearly best-possible approximation algorithm for node-weighted steiner trees. J. Algorithms, 19(1), 104-105.
  • Liu, X., Zhu, R., Anjum, A., Wang, J., Zhang, H. and Ma, M. (2020) Intelligent data fusion algorithm based on hybrid delay-aware adaptive clustering in wireless sensor networks, Future Generation Computer Systems, vol.104, pp. 1-14.
  • Lotfinezhad, M. and Liang, B. (2005) Energy efficient clustering in sensor networks with mobile agents. Proc. of the IEEE Wireless Communications and Networking Conf., New Orleans, USA, 13-17 March, pp. 1872-1877. IEEE, Washington.
  • Palaniswami, M., Rao, A. S., Kumar, D., Rathore, P. and Rajasegarar, S., (2020) The Role of Visual Assessment of Clusters for Big Data Analysis: From Real-World Internet of Things, IEEE Systems, Man, and Cybernetics Magazine, vol. 6, no. 4, pp. 45-53.
  • Tripathi, A. K., Sharma, K., Bala, M., Kumar, A., Menon, V. G. and Bashir, A. K. (2021) A Parallel Military-Dog-Based Algorithm for Clustering Big Data in Cognitive Industrial Internet of Things, IEEE Transactions on Industrial Informatics, vol. 17, no. 3, pp. 2134-2142.
  • Wang, Q., Guo, S., Hu, J. and Yang, Y., (2018) Spectral partitioning and fuzzy C-means based clustering algorithm for big data wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 54.
  • Wang, Y., Wang, W., and Li, X.-Y. (2006) Efficient distributed low-cost backbone formation for wireless networks. IEEE Trans. on Parallel and Dist. Syst., 17(7), 681-693.
  • Wu, J. and Li, H. (1999) On calculating connected dominating set for efficient routing in ad hoc wireless networks. Proc. of the 3rd Int. Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications, Seattle, Washington, United States, pp. 7-14. ACM, New York.
  • Vaiyapuri, T., Parvathy, V.S., Manikandan, Krishnaraj, V. N., Gupta, D. and Shankar, K. (2021) A Novel Hybrid Optimization for Cluster‐Based Routing Protocol in Information-Centric Wireless Sensor Networks for IoT Based Mobile Edge Computing. Wireless Personal Communications, https://doi.org/10.1007/s11277-021-08088 -w.
  • VINT project. (2021) Network Simulator version 2 (NS-2). Technical Report, available from: http://nsnam.sourceforge. net/wiki/ index.php/Main_Page.

BICOT: Big Data Analysis Approach for Clustering Cloud based IoT Systems

Year 2021, Issue: 26 - Ejosat Special Issue 2021 (HORA), 395 - 400, 31.07.2021
https://doi.org/10.31590/ejosat.960360

Abstract

Internet of Things (IoT) envisions the connection of billions of devices over the Internet. The data produced by these huge amount of devices grow exponentially, so analyzing this big data with traditional methods is not viable. Recent cloud computing and virtualization technologies cope with these issues by processing and storing IoT data. Wireless sensor networks (WSNs) are big data sources of IoT systems which provides data collection from the environment. WSNs are used in various applications such as habitat monitoring, military surveillance and smart agriculture. Data transmission to the sink node is one of the essential requirements for WSNs. Clustering is a fundamental technique that is used for efficient data transmission, time synchronizaion, load balancing and security services. In this paper, we propose a clustering framework that we call BICOT for WSNs tailored for IoT systems. BICOT inputs large scale node position, transmission range and node energy data and outputs clustering information. Our first algorithm (BICOT-CDS) is based on connected dominating set (CDS) structure and aims to reduce the cluster count. Our second algorithm uses a weighted CDS (WCDS) approach that targets to select nodes with high energy as cluster heads. We implement these algorithms in ns2 simulator environment and measure cluster count and total weight of cluster head values. The algorithms are tested against node counts and average node degrees. From extensive simulation measurements, we obtain that the cluster count generated by BICOT-CDS is far more better than its counterparts and as the network size increases the proposed algorithm performs better. The cost of dominators produced by the BICOT-WCDS algorithm is significantly lower than its competitors. These findings show us that our proposed algorithms are favorable big data analysis approaches for cloud based IoT systems.

References

  • Bao, L. and Garcia-Luna-Aceves, J. J. (2003) Topology management in ad hoc networks. Proc. of the 4th ACM Int. Symp. on Mobile Ad Hoc Networking & Computing, pp. 129-140, ACM Press, New York.
  • Chatterjee, M., Das, S. K., and Turgut, D. (2001) WCA: weighted clustering algorithm for mobile ad hoc networks. Journal of Cluster Computing (Special Issue on Mobile Ad hoc Networks), 5, 193-204.
  • Chvatal, V. (1979) A greedy heuristic for the set-covering problem, Mathematics of Operations Research. INFORMS, 4(3), 233-235.
  • Guha, S. and Khuller, S. (1998) Approximation algorithms for connected dominating sets. Algorithmica, 20, 374-387.
  • Harb, H., Makhoul, A., Idrees, A., Zahwe and O. and Taam, M.. (2017) Wireless Sensor Networks: A Big Data Source in Internet of Things. International Journal of Sensors, Wireless Communications and Control.
  • Kim, B.-.S, Kim, K.-I., Shah, B., Chow, F. and Kim, K. H. (2019) Wireless Sensor Networks for Big Data Systems, Sensors 19, no. 7, 1565.
  • Klein, P. and Ravi, R. (1995) A nearly best-possible approximation algorithm for node-weighted steiner trees. J. Algorithms, 19(1), 104-105.
  • Liu, X., Zhu, R., Anjum, A., Wang, J., Zhang, H. and Ma, M. (2020) Intelligent data fusion algorithm based on hybrid delay-aware adaptive clustering in wireless sensor networks, Future Generation Computer Systems, vol.104, pp. 1-14.
  • Lotfinezhad, M. and Liang, B. (2005) Energy efficient clustering in sensor networks with mobile agents. Proc. of the IEEE Wireless Communications and Networking Conf., New Orleans, USA, 13-17 March, pp. 1872-1877. IEEE, Washington.
  • Palaniswami, M., Rao, A. S., Kumar, D., Rathore, P. and Rajasegarar, S., (2020) The Role of Visual Assessment of Clusters for Big Data Analysis: From Real-World Internet of Things, IEEE Systems, Man, and Cybernetics Magazine, vol. 6, no. 4, pp. 45-53.
  • Tripathi, A. K., Sharma, K., Bala, M., Kumar, A., Menon, V. G. and Bashir, A. K. (2021) A Parallel Military-Dog-Based Algorithm for Clustering Big Data in Cognitive Industrial Internet of Things, IEEE Transactions on Industrial Informatics, vol. 17, no. 3, pp. 2134-2142.
  • Wang, Q., Guo, S., Hu, J. and Yang, Y., (2018) Spectral partitioning and fuzzy C-means based clustering algorithm for big data wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 54.
  • Wang, Y., Wang, W., and Li, X.-Y. (2006) Efficient distributed low-cost backbone formation for wireless networks. IEEE Trans. on Parallel and Dist. Syst., 17(7), 681-693.
  • Wu, J. and Li, H. (1999) On calculating connected dominating set for efficient routing in ad hoc wireless networks. Proc. of the 3rd Int. Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications, Seattle, Washington, United States, pp. 7-14. ACM, New York.
  • Vaiyapuri, T., Parvathy, V.S., Manikandan, Krishnaraj, V. N., Gupta, D. and Shankar, K. (2021) A Novel Hybrid Optimization for Cluster‐Based Routing Protocol in Information-Centric Wireless Sensor Networks for IoT Based Mobile Edge Computing. Wireless Personal Communications, https://doi.org/10.1007/s11277-021-08088 -w.
  • VINT project. (2021) Network Simulator version 2 (NS-2). Technical Report, available from: http://nsnam.sourceforge. net/wiki/ index.php/Main_Page.
There are 16 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Zuleyha Akusta Dagdevıren 0000-0001-9365-326X

Orhan Dağdeviren 0000-0001-8789-5086

Publication Date July 31, 2021
Published in Issue Year 2021 Issue: 26 - Ejosat Special Issue 2021 (HORA)

Cite

APA Akusta Dagdevıren, Z., & Dağdeviren, O. (2021). BICOT: Big Data Analysis Approach for Clustering Cloud based IoT Systems. Avrupa Bilim Ve Teknoloji Dergisi(26), 395-400. https://doi.org/10.31590/ejosat.960360