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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2021

31.08.2020

A Time Granular Analysis of Software Defined Wireless Mesh Based IoT (SDWM-IoT) Network Traffic Using Supervised Learning

verfasst von: Rohit Kumar, U. Venkanna, Vivek Tiwari

Erschienen in: Wireless Personal Communications | Ausgabe 3/2021

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Abstract

The ceaseless increase in the number of the wireless Internet of Things (IoT) devices has resulted in the need of different traffic engineering techniques to manage the massive network traffic. Wireless Mesh Networks (WMNs) are an important constituent part of the wireless IoT networks, and are helpful to route the IoT networks’ traffic over long distances. The WMN devices are powerful in comparison to the IoT sensor devices, and are suitable to run the traffic engineering algorithms. To further improve the performance of the WMNs, Software Defined Networking can be used. Its unique features like global visibility, agility, etc., guarantee the optimal network management. As granularity plays an important role in data analysis and none of the existing works has discussed a time granularity based network data analysis, this work tries to offer a time granular analysis of Software Defined Wireless Mesh based IoT (SDWM-IoT) network’s traffic using supervised learning approaches. A time granular analysis helps to explore the functional traits of the data at the Coarse, Medium, and Fine granularity levels. This assists in divulging and understanding the hidden characteristics and behaviour of the SDWM-IoT network’s data based on varying time granularity, respectively. Some well known supervised learning algorithms are used to offer an in-depth analysis of the traffic, and to draw the relevant conclusions. Different variants of Decision Tree, Support Vector Machine and K-Nearest Neighbour (KNN) are used to analyze the traffic and achieve a reliable accuracy rate of more than 90%. Among all the variants, fine-KNN produces the best accuracy for most of the traffic classes with a rate of more than 98%. In addition to this, a tenfold cross-validation technique is also used to prevent the the chances of over-fitting.
Vorheriger Artikel Performance Assessment of Hybrid Optical Amplifier for Higher Transmission Efficiency with SD-WDM System
Nächster Artikel Investigation on Multi-user Scheduling Algorithms Over Non-identical Channel Distribution and LTE Networks

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Literatur
1.
Tomovic, S., Yoshigoe, K., Maljevic, I., & Radusinovic, I. (2017). Software-defined fog network architecture for IoT. Wireless Personal Communications, 92(1), 181–196.CrossRef
2.
Gardašević, G., Veletić, M., Maletić, N., Vasiljević, D., Radusinović, I., Tomović, S., et al. (2017). The IoT architectural framework, design issues and application domains. Wireless Personal Communications, 92(1), 127–148.CrossRef
3.
Akyildiz, I. F., Lee, A., Wang, P., Luo, M., & Chou, W. (2016). Research challenges for traffic engineering in software defined networks. IEEE Network, 30(3), 52–58.CrossRef
4.
Goudos, S. K., Dallas, P. I., Chatziefthymiou, S., & Kyriazakos, S. (2017). A survey of IoT key enabling and future technologies: 5G, mobile IoT, sematic web and applications. Wireless Personal Communications, 97(2), 1645–1675.CrossRef
5.
Centenaro, M., Vangelista, L., Zanella, A., & Zorzi, M. (2016). Long-range communications in unlicensed bands: The rising stars in the IoT and smart city scenarios. IEEE Wireless Communications, 23(5), 60–67.CrossRef
6.
Sahni, Y., Cao, J., Zhang, S., & Yang, L. (2017). Edge mesh: A new paradigm to enable distributed intelligence in Internet of Things. IEEE Access, 5, 16441–16458.CrossRef
7.
Conti, M., Boldrini, C., Kanhere, S. S., Mingozzi, E., Pagani, E., Ruiz, P. M., et al. (2015). From MANET to people-centric networking: Milestones and open research challenges. Computer Communications, 71, 1–21.CrossRef
8.
Ojo, M., Adami, D., & Giordano, S. (2016, December). A SDN-IoT architecture with NFV implementation. In 2016 IEEE Globecom Workshops (GC Wkshps) (pp. 1–6). IEEE.
9.
Rademacher, M., Jonas, K., Siebertz, F., Rzyska, A., Schlebusch, M., & Kessel, M. (2017). Software-defined wireless mesh networking: Current status and challenges. The Computer Journal, 60(10), 1520–1535.MathSciNetCrossRef
10.
Jayakumar, H., Raha, A., Kim, Y., Sutar, S., Lee, W. S., & Raghunathan, V. (2016, January). Energy-efficient system design for IoT devices. In 2016 21st Asia and South Pacific design automation conference (ASP-DAC) (pp. 298–301). IEEE.
11.
Aljawarneh, S., Radhakrishna, V., Kumar, P. V., & Janaki, V. (2016, September). A similarity measure for temporal pattern discovery in time series data generated by IoT. In 2016 International conference on engineering and MIS (ICEMIS) (pp. 1–4). IEEE.
12.
Thupae, R., Isong, B., Gasela, N., & Abu-Mahfouz, A. M. (2018, October). Machine learning techniques for traffic identification and classifiacation in SDWSN: A survey. In IECON 2018-44th annual conference of the IEEE industrial electronics society (pp. 4645–4650). IEEE.
13.
Wang, W., Zhu, M., Zeng, X., Ye, X., & Sheng, Y. (2017, January). Malware traffic classification using convolutional neural network for representation learning. In 2017 International conference on information networking (ICOIN) (pp. 712–717). IEEE.
14.
Ducange, P., Mannarà, G., Marcelloni, F., Pecori, R., & Vecchio, M. (2017, July). A novel approach for internet traffic classification based on multi-objective evolutionary fuzzy classifiers. In 2017 IEEE international conference on fuzzy systems (FUZZ-IEEE) (pp. 1–6). IEEE.
15.
Mir, F. G., Brunner, M., Winter, R., & Kutscher, D. (2016). U.S. Patent No. 9,231,876. Washington, DC: U.S. Patent and Trademark Office.
16.
Vlăduţu, A., Comăneci, D., & Dobre, C. (2017). Internet traffic classification based on flows’ statistical properties with machine learning. International Journal of Network Management, 27(3), e1929.CrossRef
17.
Ding, T., AlEroud, A., & Karabatis, G. (2015, May). Multi-granular aggregation of network flows for security analysis. In 2015 IEEE international conference on intelligence and security informatics (ISI) (pp. 173–175). IEEE.
18.
Bartos, K. & Michal S. (2017). Identifying threats based on hierarchical classification. U.S. Patent 9,800,597, issued October 24, 2017.
19.
Dong, Y. N., Zhao, J. J., & Jin, J. (2017). Novel feature selection and classification of Internet video traffic based on a hierarchical scheme. Computer Networks, 119, 102–111.CrossRef
20.
21.
Elzain, H., & Wu, Y. (2019). Software defined wireless mesh network flat distribution control plane. Future Internet, 11(8), 166.CrossRef
22.
Elzain, H., & Yang, W. (2018). QoS-aware topology discovery in decentralized software defined wireless mesh network (D-SDWMN) architecture. In Proceedings of the 2018 2nd international conference on computer science and artificial intelligence (pp. 158–162). ACM
23.
Jiménez, A., Botero, J. F., & Urrea, J. P. (2018). Admission control implementation for QoS performance evaluation over SDWN. In 2018 IEEE Colombian conference on communications and computing (COLCOM) pp. 1–6. IEEE
24.
Yu, C., Yang, Z., Chen, X., & Yang, J. (2018). Scalable video transmission in software defined wireless mesh network. In 2018 4th IEEE conference on network softwarization and workshops (NetSoft) (pp. 456–461). IEEE.
25.
Orrego, J. F. G., & Duque, J. P. U. (2017). Throughput and delay evaluation framework integrating SDN and IEEE 802.11 s WMN. In 2017 IEEE 9th Latin-American conference on communications (LATINCOM) (pp. 1–6). IEEE
26.
Mamidi, A. V., Babu, S., & Manoj, B. S. (2015). Dynamic multi-hop switch handoffs in software defined wireless mesh networks. In 2015 IEEE international conference on advanced networks and telecommuncations systems (ANTS) (pp. 1–6). IEEE
27.
Sriramulu, R. K. (2018). Constructing dynamic ad-hoc emergency networks using software-defined wireless mesh networks.
28.
Pinyoanuntapong, P. (2017). Software defined wireless mesh networks: from theory to practice. PhD diss. Wichita State University.
29.
Brini, O., Deslandes, D., & Nabki, F. (2019). A system-level methodology for the design of reliable low-power wireless sensor networks. Sensors, 19(8), 1800.CrossRef
30.
Charan, P., Usmani, T., Paulus, R., & Saeed, S. H. (2017). Cooperative caching in IEEE802. 15.4 based WSNs. International Journal of Applied Engineering Research, 12(21), 11409–11416.
31.
Saboor, A., Mustafa, A., Ahmad, R., Khan, M. A., Haris, M., & Hameed, R. (2019, March). Evolution of wireless standards for health monitoring. In 2019 9th annual information technology, electromechanical engineering and microelectronics conference (IEMECON) (pp. 268–272). IEEE.
32.
Kulper, R. R., Parikh, V., Moloney, D., Jonathan, P. A. N. G., Toll, D., Mulligan, J., & Szela, M. (2018). U.S. Patent Application No. 15/483,145.
33.
Kobo, H. I., Abu-Mahfouz, A. M., & Hancke, G. P. (2017). A survey on software-defined wireless sensor networks: Challenges and design requirements. IEEE Access, 5, 1872–1899.CrossRef
34.
Kamaruddin, A. F. (2017). Experimentation on dynamic congestion control in software defined networking (SDN) and network function virtualisation (NFV). Doctoral dissertation, Brunel University London.
35.
ElDefrawy, K., & Kaczmarek, T. (2016, June). Byzantine fault tolerant software-defined networking (SDN) controllers. In 2016 IEEE 40th annual computer software and applications conference (COMPSAC) (Vol. 2, pp. 208–213). IEEE.
36.
Haque, I. T., & Abu-Ghazaleh, N. (2016). Wireless software defined networking: A survey and taxonomy. IEEE Communications Surveys and Tutorials, 18(4), 2713–2737.CrossRef
37.
Dang, H. T. (2019). Consensus protocols exploiting network programmability. PhD diss., Università della Svizzera Italiana
38.
Noura, M., Atiquzzaman, M., & Gaedke, M. (2019). Interoperability in internet of things: Taxonomies and open challenges. Mobile Networks and Applications, 24(3), 796–809.CrossRef
39.
40.
Guleria, P., Thakur, N., & Sood, M. (2014, December). Predicting student performance using decision tree classifiers and information gain. In 2014 International conference on parallel, distributed and grid computing (pp. 126–129). IEEE.
41.
Bao, K., Matyjas, J. D., Hu, F., & Kumar, S. (2018). Intelligent software-defined mesh networks with link-failure adaptive traffic balancing. IEEE Transactions on Cognitive Communications and Networking, 4(2), 266–276.CrossRef
42.
Pillai, I., Fumera, G., & Roli, F. (2017). Designing multi-label classifiers that maximize F measures: State of the art. Pattern Recognition, 61, 394–404.CrossRef
43.
von Sperling, T. L., de Caldas Filho, F. L., de Sousa, R. T., e Martins, L. M., & Rocha, R. L. (2017, November). Tracking intruders in IoT networks by means of DNS traffic analysis. In 2017 Workshop on communication networks and power systems (WCNPS) (pp. 1–4). IEEE.
44.
Hoang, D. H., & Nguyen, H. D. (2018, February). A PCA-based method for IoT network traffic anomaly detection. In 2018 20th International conference on advanced communication technology (ICACT) (pp. 381–386). IEEE.
45.
Leite, J. R., Ursini, E. L., & Martins, P. S. (2018, July). Performance analysis of IoT networks with mobility via modeling and simulation. In 2018 International symposium on performance evaluation of computer and telecommunication systems (SPECTS) (pp. 1–13). IEEE.
46.
Bai, L., Yao, L., Kanhere, S. S., Wang, X., & Yang, Z. (2018, October). Automatic device classification from network traffic streams of Internet of Things. In 2018 IEEE 43rd conference on local computer networks (LCN) (pp. 1–9). IEEE.
47.
Msadek, N., Soua, R., & Engel, T. (2019, April). IoT device fingerprinting: machine learning based encrypted traffic analysis. In 2019 IEEE wireless communications and networking conference (WCNC) (pp. 1–8). IEEE.
48.
Sanabria-Russo, L., Pubill, D., Serra, J., & Verikoukis, C. (2019). IoT data analytics as a network edge service. In IEEE INFOCOM 2019—IEEE conference on computer communications workshops (INFOCOM WKSHPS), Paris, France (pp. 969–970).
49.
Hoang, D. H., & Nguyen, H. D. (2019, February). Detecting anomalous network traffic in IoT networks. In 2019 21st international conference on advanced communication technology (ICACT) (pp. 1143–1152). IEEE.
Metadaten
Titel
A Time Granular Analysis of Software Defined Wireless Mesh Based IoT (SDWM-IoT) Network Traffic Using Supervised Learning
verfasst von
Rohit Kumar
U. Venkanna
Vivek Tiwari
Publikationsdatum
31.08.2020
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2021
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-07781-6

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