Classifier place in edge computing for internet of things


Internet of Things Cloud Computing is more and more substituted with Edge Computing. Such substitution solves problems of costly data, crowdedness and effectiveness of datacenters. This paper reviews and compares essential features of Cloud and Edge Computing technologies, revealing their structural relationship. Review of technologies applied in Edge computing in terms of technical equipment, methods and software used, revealed demand of classifier incorporation. To highlight classifiers ad-vantages in Edge Computing, application fields were investigated, therefore currently existing solutions, with classifiers used were named. After determination of classification methods and most popular classifiers employed in Edge Computing it is observed that self-organized classifiers are insufficiently analyzed and requires additional research. Finally, based on existing solutions three categories – software, hardware and mixed type of possible classifier implementations in Edge Computing are presented.

Article in Lithuanian.

Klasifikatoriaus vieta daiktų interneto kraštų kompiuterijoje


Tradicinė daiktų interneto debesų kompiuterija yra palengva keičiama kraštų kompiuterijos technologija. Kraštų kompiuterijos santvarka sprendžia brangių duomenų, perpildytų duomenų centrų ir jų efektyvumo problemas. Šiame straipsnyje apžvelgiamos ir palyginamos debesų ir kraštų kompiuterijos esminės savybės, atskleidžiami jų tarpusavio sąryšiai struktūriniu aspektu. Apžvelgus kraštų kompiuterijoje taikomas technologijas, techninės įrangos, metodų ir programinių priemonių kontekste išaiškėjo poreikis integruoti klasifikatorių. Siekiant pabrėžti klasifikatoriaus kraštų kompiuterijoje privalumus, ištirtos jų taikymo sritys, įvardyti esami sprendimai ir juose taikomi klasifikatoriai. Išsiaiškinus kraštų kompiuterijoje taikomus klasifikavimo metodus ir populiariausius klasifikatorių tipus nustatyta, kad kraštų kompiuterijoje nepakankamai išnagrinėtas saviorganizuojančių klasifikatorių taikymas, egzistuoja poreikis atlikti papildomus mokslinius tyrimus. Galiausiai apžvelgti galimi įgyvendinimo būdai remiantis esamais sprendimais, suskirsčius būdus į tris kategorijas – programinį, aparatinį ir mišrų.

Reikšminiai žodžiai: kraštų kompiuterija, daiktų internetas, saviorganizuojantis klasifikatorius.

Keyword : edge computing, internet of things, self-organized classifier

How to Cite
Skirelis, J. (2018). Classifier place in edge computing for internet of things. Mokslas – Lietuvos Ateitis / Science – Future of Lithuania, 10.
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Oct 9, 2018
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Ai, Y., Peng, M., & Zhang, K. (2017). Edge cloud computing technologies for internet of things: a primer. Digital Communications and Networks, 4(2), 77-86.

Al-Sayed, M. M., Khattab, S., & Omara, F. A. (2016). Prediction mechanisms for monitoring state of cloud resources using Markov chain model. Journal of Parallel and Distributed Computing, 96, 163-171.

Alippi, C., Fantacci, R., Marabissi, D., & Roveri, M. (2016). A Cloud to the ground: the new frontier of intelligent and autonomous networks of things, IEEE Communications Magazine, 54(12), 140-20.

Alrawais, A., Alhothaily, A., Hu, C., & Cheng, X. (2017). Fog computing for the internet of things: security and privacy issues. IEEE Internet Computing, 21(2), 34-42.

Barcelo, M., Correa, A., Llorca, J., Tulino, A. M., Vicario, J. L., & Morell, A. (2016). IoT-Cloud service optimization in next generation smart environments. IEEE Journal on Selected Areas in Communications, 34(12), 4077-4090.

Chen, X., Shi, Q., Yang, L., & Xu, J. (2018). ThriftyEdge: resource-efficient edge computing for intelligent IoT applications. IEEE Network, 32(1).

Danner, J., Wills, L., Ruiz, E. M., & Lerner, L. W. (2016). Rapid precedent-aware pedestrian and car classification on constrained IoT platforms. Proceedings of the 14th ACM/IEEE Symposium on Embedded Systems for Real-Time Multimedia – ESTIMedia’16. Pittsburgh, Pennsylvania, JAV.

Diallo, L., Hassan, A., Hashim, A., Eyiomika, M. J., Babiker, S., & Elagib, O. (2017). the rise of internet of things and Big Data on the Cloud: challenges and future trends. International Journal of Future Generation Communication and Networking, 10(3), 49-56.

Din, S., Paul, A., Ahmad, A., Gupta, B., & Rho, S. (2018). Service orchestration of optimizing continuous features in industrial surveillance using Big Data based fog-enabled internet of things. IEEE Access, 6.

El-Sayed, H., Sankar, S., Prasad, M., Puthal, D., Gupta, A., Mohanty, M., & Lin, C. T. (2017). Edge of things: the big picture on the integration of edge, IoT and the Cloud in a distributed computing environment, IEEE Access, 6.

Farris, I., Militano, L., Nitti, M., Atzori, L., & Iera, A. (2016). Federated edge-assisted mobile clouds for service provisioning in heterogeneous IoT environments. IEEE World Forum on Internet of Things, WF-IoT 2015 – Proceedings (pp. 591-596).

Georgakopoulos, D., Jayaraman, P. P., Fazia, M., Villari, M., & Ranjan, R. (2016). Internet of Things and Edge Cloud Computing Roadmap for Manufacturing. IEEE Cloud Computing, 3(4), 66-73.

Hammadi-Mesmoudi, F., & Korczak, J. J. (1995). An unsupervised neural network classifier and its application in remote sensing, Proceedings of the International Conference on Image Processing, (410), 4-6.

Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P.-L., Iorkyase, E., Tachtatzis, C., & Atkinson, R. (2016). Threat analysis of IoT networks using artificial neural network intrusion detection system. 2016 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). Hammamet, Tunisas.

Holden, A. J., et al. (2006). Reducing the dimensionality of data with neural networks. Science, 313(July), 504-507.

Intharawijitr, K., Member, S., Iida, K., & Member, S. (2017). Simulation study of low latency network architecture using mobile edge computing. IEICE TRANSACTIONS on Information and Systems, (5), 963-972.

Jridi, M., Chapel, T., Dorez, V., Le Bougeant, G., & Le Botlan, A. (2018). SoC-based edge computing gateway in the context of the internet of multimedia things: experimental platform. Journal of Low Power Electronics and Applications, 8(1).

Ke, Q., Zhang, J., Song, H., & Wan, Y. (2018). Big data analytics enabled by feature extraction based on partial independence. Neurocomputing, 288, 3-10.

Kubota, M., Fukuta, S., Yoshihide, N., & Kenichi, A. (2016). Dynamic resource controller technology to accelerate processing and utilization of IoT data. FUJITSU Scientific & Technical Journal, 52(4), 41-51.

Li, G., Song, J., Wu, J., & Wang, J. (2018). Method of resource estimation based on QoS in edge computing. Wireless Communications and Mobile Computing, 2018, Article ID 73 089 13.

Li, H., Ota, K., & Dong, M. (2018). Learning IoT in edge: deep learning for the internet of things with edge computing, IEEE Network, 32(1).

Mao, Y., Zhang, J., Song, S. H., & Letaief, K. B. (2017). Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems. IEEE Transactions on Wireless Communications, 16(9).

Pasca, T. V., Dama, S., Sathya, V., & Kuchi, K. (2017). A feasible cellular internet of things. IEEE Consumer Electronics Magazine, 6(1), 66-72.

Sabella, D., Vaillant, A., Kuure, P., Rauschenbach, U., & Giust, F. (2016). Mobile-edge computing architecture: the role of MEC in the internet of things. IEEE Consumer Electronics Magazine, 5(4), 84-91.

Satya, M. S. (2016). Edge computing: vision and challenges. IEEE Internet of Things Journal, 3(5), 637-646.

Sheng, Y., Wang, J., & Zhao, Z. (2016). A communication-efficient model of sparse neural network for distributed intelligence. Proceedings – IEEE INFOCOM 2016, Septe(2), 515-520.

Skirelis, J., & Navakauskas, D. (2017). Edge computing in IoT: preliminary results on modeling and performance analysis. 2017 5th IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) (pp. 1-4). Ryga, Latvija.

Taleb, T., Dutta, S., Ksentini, A., Iqbal, M., & Flinck, H. (2017). Mobile edge computing potential in making cities smarter. IEEE Communications Magazine, 55(3), 38-43.

Tran, T. X., Hajisami, A., Pandey, P., & Pompili, D. (2017). Collaborative mobile edge computing in 5G networks: new paradigms, scenarios, and challenges. IEEE Communications Magazine, 55(April), 54-61.

Vahid Dastjerdi, A., & Buyya, R. (2016). Fog computing: helping the internet of things realize. IEEE Computer Society, 49(8), 112-116.

Wadood, A., Ali, Z., Ghouzali, S., ALfawaz, B., Muhammad, G., & Hossain M. S. (2017). Biometric Security Through Visual Encryption for Fog Edge Computing, IEEE Access, 5, 5531-5538.

Weisong, S., & Dustda,r S. (2016). The promise of edge computing. IEEE Computer Society, 59(5), 78-81.

Zalieckaitė, L. ir Žilinskas, R. (2015). Daiktų interneto technologijos taikymo versle nauda ir rizika. Informacijos Mokslai, 72, 102-117.

Zhang, J., Li, Q., Wang, X., Feng, B., & Guo, D. (2017). Towards fast and lightweight spam account detection in mobile social networks through fog computing, Peer-to-Peer Networking and Applications, 11(4), 778-792.