Abstract
The substantial growth of Internet of Things (IoT) over the past few decades has drawn the attention of academia and research. The growing technology is faced with challenges like limited bandwidth and power. The low power and lossy IoT networks mainly depend on wireless protocols for communication and connectivity. Wireless spectrum, whether licensed or unlicensed, is limited and already crowded. To address the issue of limited wireless spectrum, integration of wired and wireless techniques has been proposed by researchers. However, state-of-art solutions mainly focus on partially wired and wireless networks. Seamless integration of these techniques needs to be explored at front end due to voluminous IoT traffic. The paper presents an intelligent gateway to support hybridization of wired and wireless networks at front end. The proposed gateway utilizes machine learning algorithm for classifying the incoming user tasks and selecting a particular link (wired or wireless). k-Nearest Neighbor (kNN), Decision tree (DT), Support Vector Machine (SVM) and Naive Bayes (NB) have been compared for performance using MATLABR2015a. Furthermore, performance analysis of the proposed architecture has been done for reliability, Bit Error Rate (BER), effective throughput and cost. The results prove the efficacy of hybrid architecture as compared to completely wired and wireless solutions.
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References
Laghari AA, Wu K, Laghari RA, et al. A review and state of art of internet of things (IoT). Arch Comput Methods Eng. 2022;29:1395–413. https://doi.org/10.1007/S11831-021-09622-6.
Khan AA, Laghari AA, Shaikh AA, et al. Innovation in multimedia using IoT systems. In: Multimedia computing systems and virtual reality. Boca Raton: CRS Press; 2022. p. 171–87.
Van DP, Rimal BP, Chen J, et al. Power-saving methods for internet of things over converged fiber-wireless access networks. IEEE Commun Mag. 2016;54:166–75. https://doi.org/10.1109/MCOM.2016.1500635CM.
Peng M, Wang C, Lau V, Poor HV. Fronthaul-constrained cloud radio access networks: insights and challenges. IEEE Wirel Commun. 2015;22:152–60. https://doi.org/10.1109/MWC.2015.7096298.
Wang P, Marshell A, Noordin KA, et al (2010) Hybrid network combining PLC and IEEE802.16 for hospital environment. In: IEEE ISPLC 2010 - International Symposium on Power Line Communications and its Applications. pp 267–272
Mckeown A, Rashvand H, Wilcox T, Thomas P (2015) Priority SDN Controlled Integrated Wireless and Powerline Wired for Smart-Home Internet of Things. In: 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom). IEEE, pp 1825–1830
Khan MA, Khan A, Khan MN, Anwar S (2014) A novel learning method to classify data streams in the internet of things. In: National Software Engineering Conference, NSEC 2014. Institute of Electrical and Electronics Engineers Inc., pp 61–66
Ma X, Wu YJ, Wang Y, et al. Mining smart card data for transit riders’ travel patterns. Transp Res Part C Emerg Technol. 2013;36:1–12. https://doi.org/10.1016/J.TRC.2013.07.010.
Han W, Gu Y, Zhang Y, Zheng L (2014) Data driven quantitative trust model for the Internet of Agricultural Things. In: 2014 International Conference on the Internet of Things, IOT 2014. Institute of Electrical and Electronics Engineers Inc., pp 31–36
Shukla M, Kosta YP, Chauhan P (2016) Analysis and evaluation of outlier detection algorithms in data streams. In: IEEE International Conference on Computer Communication and Control, IC4 2015. Institute of Electrical and Electronics Engineers Inc.
Shilton A, Rajasegarar S, Leckie C, Palaniswami M (2015) DP1SVM: A dynamic planar one-class support vector machine for Internet of Things environment. In: 2015 International Conference on Recent Advances in Internet of Things, RIoT 2015. Institute of Electrical and Electronics Engineers Inc.
Waqas M, Kumar K, Laghari AA, et al. Botnet attack detection in Internet of Things devices over cloud environment via machine learning. Concurr Comput Pract Exp. 2022. https://doi.org/10.1002/CPE.6662.
Vijay R, Prabhakar T V., Hegde V, et al (2019) A Heterogeneous PLC with BLE Mesh network for Reliable and Real-time Smart Cargo Monitoring. In: Proceedings of the 2019 IEEE International Symposium on Power Line Communications and its Applications, ISPLC 2019. Institute of Electrical and Electronics Engineers Inc., pp 72–77
Mohamed N, Jawhar I, Al-Jaroodi J, Zhang L. Sensor network architectures for monitoring underwater pipelines. Sensors. 2011;11:10738–64. https://doi.org/10.3390/s111110738.
Qian Y, Yan J, Guan H, et al. Design of hybrid wireless and power line sensor networks with dual-interface relay in IoT. IEEE Internet Things J. 2019;6:239–49. https://doi.org/10.1109/JIOT.2017.2725451.
Lucas-Estañ M, Sepulcre M, Raptis T, et al. Emerging trends in hybrid wireless communication and data management for the industry 4.0. Electronics. 2018;7:400. https://doi.org/10.3390/electronics7120400.
Van Pham D, Rimal BP, Maier M, Valcarenghi L. Design, analysis, and hardware emulation of a novel energy conservation scheme for sensor enhanced FiWi networks (ECO-SFiWi). IEEE J Sel Areas Commun. 2016;34:1645–62. https://doi.org/10.1109/JSAC.2016.2545380.
Ghazisaidi N, Maier M. Fiber-wireless (FiWi) access networks: Challenges and opportunities. IEEE Netw. 2011;25:36–42. https://doi.org/10.1109/MNET.2011.5687951.
Liu J, Guo H, Nishiyama H, et al. New perspectives on future smart FiWi networks: scalability, reliability, and energy efficiency. IEEE Commun Surv Tutor. 2016;18:1045–72. https://doi.org/10.1109/COMST.2015.2500960.
Gowda AS, Dhaini AR, Kazovsky LG, et al. Towards green optical/wireless in-building networks: radio-over-fiber. J Light Technol. 2014;32:3545–56. https://doi.org/10.1109/JLT.2014.2315960.
Oliveira RS, Oliveira RS, Frances CRL, et al (2014) Analysis of the cost-effective digital radio over fiber system in the NG-PON2 context. In: 2014 16th International Telecommunications Network Strategy and Planning Symposium (Networks). IEEE, pp 1–6
Jawhar I, Mohamed N, Agrawal DP. Linear wireless sensor networks: classification and applications. J Netw Comput Appl. 2011;34:1671–82. https://doi.org/10.1016/J.JNCA.2011.05.006.
Bharti S, Pattanaik KK. Task requirement aware pre-processing and scheduling for IoT sensory environments. Ad Hoc Netw. 2016. https://doi.org/10.1016/j.adhoc.2016.07.005.
Goodfellow I, Bengio Y, Courville A. Deep learning. MIT Press; 2016.
Mahdavinejad MS, Rezvan M, Barekatain M, et al. Machine learning for internet of things data analysis: a survey. Digit Commun Netw. 2018;4:161–75. https://doi.org/10.1016/J.DCAN.2017.10.002.
Alam F, Mehmood R, Katib I, Albeshri A. Analysis of eight data mining algorithms for smarter internet of things (IoT). Proced Comput Sci. 2016;98:437–42. https://doi.org/10.1016/J.PROCS.2016.09.068.
Li W, Yi P, Wu Y, et al. A new intrusion detection system based on KNN classification algorithm in wireless sensor network. J Electr Comput Eng. 2014;2014:1–8. https://doi.org/10.1155/2014/240217.
Li D, Zhang B, Yao Z, Li C (2014) A feature scaling based k-nearest neighbor algorithm for indoor positioning system. In: 2014 IEEE Global Communications Conference. IEEE, pp 436–441
Kolomvatsos K, Anagnostopoulos C. Multi-criteria optimal task allocation at the edge. Futur Gener Comput Syst. 2019;93:358–72. https://doi.org/10.1016/J.FUTURE.2018.10.051.
Quinlan JR. Decision Trees and Decisionmaking. IEEE Trans Syst Man Cybern. 1990;20:339–46. https://doi.org/10.1109/21.52545.
Yazici M, Basurra S, Gaber M. Edge machine learning: enabling smart internet of things applications. Big Data Cogn Comput. 2018;2:26. https://doi.org/10.3390/bdcc2030026.
Chettri R, Pradhan S, Chettri L. Internet of things: comparative study on classification algorithms (k-NN, Naive Bayes and case based reasoning). Int J Comput Appl. 2015;130:975–8887.
Berrar D Bayes’. 2019 Theorem and Naive Bayes Classifier Call for Papers for Machine Learning journal: Machine Learning for Soccer View project Bayes Theorem and Naive Bayes Classifier. Pp. 403-412
Sonego P, Kocsor A, Pongor S. ROC analysis: applications to the classification of biological sequences and 3D structures. Brief Bioinform. 2008;9:198–209. https://doi.org/10.1093/bib/bbm064.
Fabbri F, Buratti C (2011) Throughput Analysis of Wireless Sensor Networks via Evaluation of Connectivity and MAC Performance. In: Emerging Communications for Wireless Sensor Networks. InTech. https://doi.org/10.5772/10514.
Dongxu Shen, Zhengang Pan, Kai-Kit Wong, Li VOK Effective throughput: a unified benchmark for pilot-aided OFDM/SDMA wireless communication systems. In: IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428). IEEE, pp 1603–1613
Jagannatham AK. Advanced 3G and 4G Wireless Communication. http://www.nitttrc.edu.in/nptel/courses/video/117104099/lec10.pdf.
Mahloo M, Monti P, Chen J, Wosinska L (2014) Cost modeling of backhaul for mobile networks. In: 2014 IEEE International Conference on Communications Workshops (ICC). IEEE, pp 397–402
Maleki S, Sawhney R, Farvaresh H, Sepehri MM. Energy efficient hybrid wired-cum-wireless sensor network design. J Clean Prod. 2014;85:408–18. https://doi.org/10.1016/J.JCLEPRO.2014.04.038.
Sensor Networking Cost Model - Advantech B+B SmartWorx. http://advantech-bb.com/sensor-networking-cost-model/. Accessed 9 Sep 2018
Kulkarni P, Farnham T. Smart city wireless connectivity considerations and cost analysis: lessons learnt from smart water case studies. IEEE Access. 2016;4:660–72. https://doi.org/10.1109/ACCESS.2016.2525041.
Sherazi HHR, Piro G, Grieco LA, Boggia G. When renewable energy meets LoRa: a feasibility analysis on cable-less deployments. IEEE Internet Things J. 2018. https://doi.org/10.1109/JIOT.2018.2839359.
Alex O (2009) Voice and Data Communication over Power Lines. The University of Nairobi. https://eie.uonbi.ac.ke/sites/default/files/cae/engineering/eie/VOICE AND DATA COMMUNICATION OVER POWER LINES-PRESENTATION.pdf.
Olde Keizer MCA, Teunter RH, Veldman J, Babai MZ. Condition-based maintenance for systems with economic dependence and load sharing. Int J Prod Econ. 2018;195:319–27. https://doi.org/10.1016/J.IJPE.2017.10.030.
(2014) RF Sensor Node Development Platform for 6LoWPAN and 2.4 GHz Applications. https://www.ti.com/lit/ug/tidu240/tidu240.pdf.
Lumpkins W. Home automation: insteon (X10 meets powerline) [product reviews]. IEEE Consum Electron Mag. 2015;4:140–4. https://doi.org/10.1109/MCE.2015.2463451.
(2001) KNX System Specifications 3 Architecture 1 1 1 KNX Standard System Specifications Architecture
Dell Edge Gateways for IoT | Dell United States. https://www.dell.com/en-us/work/shop/cty/sf/edge-gateway. Accessed 6 Aug 2018
Acknowledgements
We want to thank the Department of Science and Technology (DST), Govt. of India, for supporting the project grant no. SR/WOS-A/ET-8/2018(G)&(C) and Gautam Buddha University to provide facilities and support for completing this research work.
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Gupta, N., Sharma, V. Context Aware Hybrid Network Architecture for Iot with Machine Learning Based Intelligent Gateway. SN COMPUT. SCI. 4, 297 (2023). https://doi.org/10.1007/s42979-023-01736-x
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DOI: https://doi.org/10.1007/s42979-023-01736-x