International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 12, No. 1, 8 Feb. 2022
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Achieving Performability and Reliability of Data Storage in the Internet of Things
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Author(s)
Index Terms
IoT, Distributed Storage, Reliability, Energy Efficiency, PSO Algorithm, K-means, C4.5 Tree
Abstract
Internet of things (IoT) includes a lot of key technologies; In this emerging field, wireless sensors have a key role to play in sensing and collecting measures on the surrounding environment. In the deployment of large-scale observation systems in remote areas, when there is not a permanent connection with the Internet, the network requires distributed storage techniques for increasing the amount of data storage which decreases the probability of data loss. Unlike conventional networked data storage, distributed storage is constrained by the limited resources of the sensors. In this research, we present a distributed data storage method with the combined K-means and PSO clustering mechanism organized with the binary decision tree C4.5 in the IoT area with considering efficiency and reliability approach. This scheme can provide reliability in responding to inquiries while minimizing the use of energy and computational resources. Simulation results and evaluations show that the proposed approach, due to the distributed data storage with minimal repeat publishing according to the decision tree structure, increases the reliability and availability, reduces the communication costs, and improves the Energy consumption, saving memory consumption without registering the same event and compared to other methods performed in this area have good results.
Cite This Paper
Negar Taheri, Shahram Jamali, Mohammad Esmaeili, " Achieving Performability and Reliability of Data Storage in the Internet of Things ", International Journal of Engineering and Manufacturing (IJEM), Vol.12, No.1, pp. 12-28, 2022. DOI: 10.5815/ijem.2022.01.02
Reference
[1] Taheri, Negar, AND Jamali, Shahram. "Distributed Data Storage in the IoT: A Performance and Reliability Approach" Networking and Communication Engineering, 2020.
[2] Esmaeili, Mohammad, AND Jamali, Shahram, "A Survey: Optimization of Energy Consumption by using the Genetic Algorithm in WSN based Internet of Things", CiiT International Journal of Wireless Communication, 2016.
[3] L. Atzori, A. Iera, G. Morabito. “The Internet of Things: A survey”. Journal of Computer Network, 2010.
[4] Pietro Gonizzi, Gianluigi Ferrari, Vincent Gay, Jérémie Leguay. ‘‘Data Dissemination Scheme for Distributed Storage for IoT Observation Systems at Large Scale”. Journal of Information Fusion, 2013.
[5] Neenu M. Nair, J. Sebastian Terence. ‘‘Survey on Distributed Data Storage Schemes in Wireless Sensor Networks”. Indian Journal of Computer Science and Engineering (IJCSE), 2014.
[6] Nukarapu Dharma, Tang Bin, Wang Liqiang, Lu Shiyong, “Data Replication in Data Intensive Scientific Applications with Performance Guarantee”, IEEE Transactions on Parallel and Distributed Systems”, 2011.
[7] B. Meroufel, G. Belalem ‘‘Managing Data Replication and Placement based on Availability”, 2013.
[8] K. PIoTrowski, P. Langendoerfer, S. Peter. ‘‘tinyDSM: A Highly Reliable Cooperative Data Storage for Wireless Sensor Networks”, 2009.
[9] A. Omotayo, M. Hammad, K. Barker, ‘‘a cost model for storing and retrieving data in wireless sensor networks)”, 2007.
[10] Jamali S, Taheri N, Esmaeili M. A hybrid method for energy efficient data storage in the internet of things.J Commun Technol ElectronComput Sci. 2021;26:1-5.
[11] B. Zerhari, A. Lahcen, S.Mouline, ‘‘Big Data Clustering: Algorithms and Challenges” Conference Paper, 2015.
[12] Jana Neumann, Christoph Reinke, Nils Hoeller, Volker Linnemann. ‘‘Adaptive Quality-Aware Replication in Wireless Sensor Networks. In International Workshop on Wireless Ad Hoc, Mesh and Sensor Networks”, 2009.
[13] Ángel Cuevas Rumín, Manuel Urueña Pascual, Ricardo Romeral Ortega, David Larrabeiti López. ‘‘Data Centric Storage Technologies: Analysis and Enhancement. Sensors”, 2010.
[14] A. Awad, R. Germany, F. Dressler. ‘‘Data-Centric Cooperative Storage in Wireless Sensor Network. 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL)”, 2009.
[15] M. Albano, S. Chessa, F. Nidito, S. Pelagatti, ‘‘Dealing with nonuniformity in data centric storage for wireless sensor networks”, IEEE Transactions on Parallel and Distributed Systems, 2011.
[16] H. Shen, L. Zhao, Z. Li, ‘‘A distributed spatial–temporal similarity data storage scheme in wireless sensor networks, IEEE Transactions on Mobile Computing”, 2011.
[17] A. Awad, R. Germany, F. Dressler. ‘‘Data-Centric Cooperative Storage in Wireless Sensor Network. 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL)”, 2009.
[18] A. Omotayo, M. Hammad, K. Barker, ‘‘a cost model for storing and retrieving data in wireless sensor networks, in: IEEE 23rd International Conference on Data Engineering Workshop (ICDE)”, 2007.
[19] M. Takahashi, B. Tang, N. Jaggi, ‘‘Energy-efficient data preservation in intermittently connected sensor networks, in: IEEE 30th Conference on Computer Communications Workshops”, 2011.
[20] L. Luo, C. Huang, T. Abdelzaher, J. Stankovic, Envirostore: ‘‘a cooperative storage system for disconnected operation in sensor networks, in: 26th IEEE International Conference on Computer Communications”,2007.
[21] Y.-C. Tseng, F.-J. Wu, W.-T. Lai, ‘‘Opportunistic data collection for disconnected wireless sensor networks by mobile mules, Ad Hoc Networks, 2013.
[22] G. Maia, D.L. Guidoni, A.C. Viana, A.L. Aquino, R.A. Mini, A.A. Loureiro. ‘‘A Distributed Data Storage Protocol for Heterogeneous Wireless Sensor Networks with Mobile Sinks. Journal of Ad Hoc Networks”, 2013.
[23] K. Piotrowski, P. Langendoerfer, S. Peter. ‘‘tinyDSM: A Highly Reliable Cooperative Data Storage for Wireless Sensor Networks. International Symposium on Collaborative Technologies and Systems”, 2009.
[24] Raghavendra V. Kulkarni, Senior Member, ‘‘Particle Swarm Optimization in Wireless Sensor Networks:” A Brief Survey, 2008.
[25] Youguo Li, Haiyan Wu, ‘‘A Clustering Method Based on K-Means Algorithm”, 2012.
[26] M. Esmaeili, S. Jamali, and H. S. Fard, "Energy-Aware Clustering in the Internet of Things by Using the Genetic Algorithm," Journal of Telecommunication, Electronic and Computer Engineering (JTEC), vol. 12, no. 2, pp. 29-37, 2020.
[27] V. Kumar, S. B. Dhok, R. Tripathi, and S. Tiwari, ‘‘A review study on analytical estimation of optimal”.2008
[28] S. Ruggieri,Dipartimento di information, Universita di Pisa. ‘‘Efficient C4.5”,2010.
[29] C. Viana, T. Herault, T. Largillier, S. Peyronnet, F. Zaı¨di, Supple: a flexible probabilistic data dissemination protocol for wireless sensor networks, in: 13th ACM International Conference on Modeling, analysis, and simulation of wireless and mobile systems,2010.
[30] Amin Rezaeipanah, Hamed Nazari, MohammadJavad Abdollahi, " Reducing Energy Consumption in Wireless Sensor Networks Using a Routing Protocol Based on Multi-level Clustering and Genetic Algorithm ", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.10, No.3, pp. 1-16, 2020.DOI: 10.5815/ijwmt.2020.03.01
[31] Atul Kumar Pandey, Nisha Gupta, "An Energy Efficient Clustering-based Load Adaptive MAC (CLA-MAC) Protocol for Wireless Sensor Networks in IoT", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.9, No.5, pp. 38-55, 2019.DOI: 10.5815/ijwmt.2019.05.04
[32] Md. Imran Hossain, M. Mahbubur Rahman, Tapan Kumar Godder, Mst. Titasa Khatun,"Improving Energy Efficient Clustering Method for Wireless Sensor Network", International Journal of Information Technology and Computer Science(IJITCS), vol.5, no.9, pp.73-79, 2013. DOI: 10.5815/ijitcs.2013.09.07