Currently, significant amounts of IoT data are being collected as big data for deep neural network learning algorithms that are used to extract meaningful information from big data and applied to various areas. However, a neural network’s over-fitting problem and rising computational costs associated with high levels of time complexity are obstacles to deep learning computations. Because of these problems, response delays are prevalent in big data learning processes and data extraction processes that use deep neural networks, which exponentially increase the cost of data extraction. Therefore, the amount of computation must be controlled so that data processing models can be used practically, and various dedicated devices can be used to process big data, including methods such as distributed processing methods. In general, a P2P method is a typical distributed processing method. P2P is based on traffic distribution and cooperation, and edge computing is a method that increases efficiency by locally processing large volumes of data produced and only transmitting essential information. If P2P method-based edge computing techniques are used, an effective parallel processing model can be constructed, reducing the computing requirements of a central server and the load of a network. In this paper, we propose an edge computing health model using P2P-based deep neural networks. The proposed method is used to process health big data in edge computing. To construct the model, multiple edge nodes are required, and the edge node modules are deployed at locations where health data are produced and directly connected to the deep neural network model. At the edge nodes, a modularized deep neural network is constructed, thereby implementing a parallel big data processing system. In addition, a single server is required for gathering the results of a neural network model, and because the server only collects evaluation results and provides them to the users, the response time delay is improved. In this paper, to evaluate an improved response time, a regular server model and an edge computing health model are constructed separately, and positive numerical values are extracted from the experiments. The extraction results confirm that a combination of parallel processing models and deep neural network techniques can be used to distribute and process computing operations of big data size, ensuring an effective system for reducing response time delay.
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Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805
Hsieh HC, Lee CS, Chen JL (2018) Mobile edge computing platform with container-based virtualization technology for IoT applications. Wirel Pers Commun 102(1):527–542
Corcoran P, Datta SK (2016) Mobile-edge computing and the internet of things for consumers: extending cloud computing and services to the edge of the network. IEEE Consumer Electronics Magazine 5(4):73–74
Chaib A, Boussebough I, Chaoui A (2018) Adaptive service composition in an ambient environment with a multi-agent system. J Ambient Intell Humaniz Comput 9(2):367–380
Chen T, Tsai HR (2018) Application of industrial engineering concepts and techniques to ambient intelligence: a case study. J Ambient Intell Humaniz Comput 9(2):215–223
Orciuoli F, Parente M (2017) An ontology-driven context-aware recommender system for indoor shopping based on cellular automata. J Ambient Intell Humaniz Comput 8(6):937–955
Jung H, Chung K (2016) Life style improvement mobile service for high risk chronic disease based on PHR platform. Clust Comput 19(2):967–977
Jung H, Yoo H, Chung K (2016) Associative context mining for ontology-driven hidden knowledge discovery. Clust Comput 19(4):2261–2271
Kim JC, Chung K (2017) Emerging risk forecast system using associative index mining analysis. Clust Comput 20(1):547–558
Yao J, Warren S (2005) Applying the ISO/IEEE 11073 standards to wearable home health monitoring systems. J Clin Monit Comput 19(6):427–436
Jung EY, Kim JH, Chung K, Park DK (2013) Home health gateway based healthcare services through U-health platform. Wirel Pers Commun 73(2):207–218
Nasrollahi A, Deng W, Ma Z, Rizzo P (2018) Multimodal structural health monitoring based on active and passive sensing. Struct Health Monit 17(2):395–409
Dastjerdi AV, Buyya R (2016) Fog computing: helping the internet of things realize its potential. Computer. 49(8):112–116
Chung K, Yoo H, Choe DE (2018) Ambient context-based modeling for health risk assessment using deep neural network. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-1033-7
Kim JC, Chung K (2018) Neural-network based adaptive context prediction model for ambient intelligence. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0972-3
Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 50:159–175
Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270(2):654–669
Hsieh TJ, Hsiao HF, Yeh WC (2011) Forecasting stock markets using wavelet transforms and recurrent neural networks: an integrated system based on artificial bee colony algorithm. Appl Soft Comput 11(2):2510–2525
Kim JC, Chung K (2018) Mining health-risk factors using PHR similarity in a hybrid P2P network. Peer-to-Peer Networking and Applications 11(6):1278–1287
Chung K, Kim JC, Park RC (2016) Knowledge-based health service considering user convenience using hybrid Wi-fi P2P. Inf Technol Manag 17(1):67–80
Yoo H, Chung K (2018) Mining-based Lifecare recommendation using peer-to-peer dataset and adaptive decision feedback. Peer-to-Peer Networking and Applications. 11(6):1309–1320
Chung K, Na Y, Lee JH (2013) Interactive design recommendation using sensor based smart wear and weather WebBot. Wirel Pers Commun 73(2):243–256
Chung K, Yoo H, Choe DE, Jung H (2019) Blockchain network based topic mining process for cognitive manufacturing. Wirel Pers Commun 105(2):583–597
Chung K, Yoo H, Choe DE, Jung H (2018) Blockchain network based topic mining process for cognitive manufacturing. Wirel Pers Commun 105:583–597. https://doi.org/10.1007/s11277-018-5979-8
Kim JC, Chung K (2019) Mining based time-series sleeping pattern analysis for life big-data. Wirel Pers Commun 105(2):475–489
Yoo H, Chung K (2018) Heart Rate Variability based Stress Index Service Model using Bio-Sensor. Clust Comput 21(1):1139–1149
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2018-0-01405) supervised by the IITP(Institute for Information & communications Technology Promotion).
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This article is part of the Topical Collection: Special Issue on P2P Computing for Intelligence of Things
Guest Editors: Sunmoon Jo, Jieun Lee, Jungsoo Han, and Supratip Ghose
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Chung, K., Yoo, H. Edge computing health model using P2P-based deep neural networks. Peer-to-Peer Netw. Appl. 13, 694–703 (2020). https://doi.org/10.1007/s12083-019-00738-y
- Edge computing
- Data mining
- Deep neural network
- Hybrid P2P networking