Data Flow and Distributed Deep Neural Network based low latency IoT-Edge computation model for big data environment

The trillion-fold increase in computing power brings the accessibility of deep learning to everyone. Deep learning offers precise information almost all the time when compared to other learning algorithms. On the other hand, the popularity of Internet of Things (IoT) has increased in various areas s...

Full description

Saved in:

Bibliographic Details
Published in:	Engineering applications of artificial intelligence Vol. 94; p. 103785
Main Authors:	Veeramanikandan, Sankaranarayanan, Suresh, Rodrigues, Joel J.P.C., Sugumaran, Vijayan, Kozlov, Sergei
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.09.2020
Subjects:	Big data Data flow processing Data in motion Deep Neural Networking Edge computing Fog computing Internet of Things Latency Fog computing Deep Neural Networking Big data Data in motion Internet of Things Data flow processing Edge computing Latency
ISSN:	0952-1976, 1873-6769
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	The trillion-fold increase in computing power brings the accessibility of deep learning to everyone. Deep learning offers precise information almost all the time when compared to other learning algorithms. On the other hand, the popularity of Internet of Things (IoT) has increased in various areas such as Smart City, Oil Mining, and Transportation. Edge/Fog computing environment helps to handle significant challenges faced by the IoT, viz. latency, bandwidth consumption, and everlasting network connectivity. For analytics in Edge computing, which is distributed in nature, the trend is more towards distributed machine learning. This research work is focused on the integration of data flow and distributed deep learning in the IoT-Edge environment to bring down the latency and increase accuracy starting from the data generation phase. To this end, a novel Data Flow and Distributed Deep Neural Network (DF-DDNN) based IoT-Edge model for big data environment has been proposed. Our proposed method has resulted in latency reduction of up to 33% when compared to the existing traditional IoT-Cloud model. •Data Flow and Distributed Deep Learning based Edge computation model for Big Data.•Data Flow based data gathering at the Edge with reduced bandwidth.•Intelligent decisions at various levels of Edge in a distributed manner.•Aggregation schemes for improving the overall performance of the system.•AI-assisted Edge services for resource-constrained heterogeneous devices.
AbstractList	The trillion-fold increase in computing power brings the accessibility of deep learning to everyone. Deep learning offers precise information almost all the time when compared to other learning algorithms. On the other hand, the popularity of Internet of Things (IoT) has increased in various areas such as Smart City, Oil Mining, and Transportation. Edge/Fog computing environment helps to handle significant challenges faced by the IoT, viz. latency, bandwidth consumption, and everlasting network connectivity. For analytics in Edge computing, which is distributed in nature, the trend is more towards distributed machine learning. This research work is focused on the integration of data flow and distributed deep learning in the IoT-Edge environment to bring down the latency and increase accuracy starting from the data generation phase. To this end, a novel Data Flow and Distributed Deep Neural Network (DF-DDNN) based IoT-Edge model for big data environment has been proposed. Our proposed method has resulted in latency reduction of up to 33% when compared to the existing traditional IoT-Cloud model. •Data Flow and Distributed Deep Learning based Edge computation model for Big Data.•Data Flow based data gathering at the Edge with reduced bandwidth.•Intelligent decisions at various levels of Edge in a distributed manner.•Aggregation schemes for improving the overall performance of the system.•AI-assisted Edge services for resource-constrained heterogeneous devices.
ArticleNumber	103785
Author	Sugumaran, Vijayan Rodrigues, Joel J.P.C. Veeramanikandan Sankaranarayanan, Suresh Kozlov, Sergei
Author_xml	– sequence: 1 surname: Veeramanikandan fullname: Veeramanikandan organization: Aegis School of Data Science, Bangalore, India – sequence: 2 givenname: Suresh surname: Sankaranarayanan fullname: Sankaranarayanan, Suresh email: pessuresh@hotmail.com organization: SRM Institute of Science and Technology, Chennai, India – sequence: 3 givenname: Joel J.P.C. surname: Rodrigues fullname: Rodrigues, Joel J.P.C. organization: Federal University of Piauí (UFPI), Teresina - PI, Brazil – sequence: 4 givenname: Vijayan surname: Sugumaran fullname: Sugumaran, Vijayan organization: Oakland University, USA – sequence: 5 givenname: Sergei surname: Kozlov fullname: Kozlov, Sergei organization: ITMO University, St. Petersburg, Russia
BookMark	eNqFkE1OwzAQhS0EEi1wBeQLpNiJ8yexANEWkBAsYG9N7EnlktqR7bbq7UlU2LDpakZv5r3RfFNybp1FQm45m3HGi7v1DO0K-h7MLGXpKGZllZ-RCa_KLCnKoj4nE1bnacLrsrgk0xDWjLGsEsWEhDlEoMvO7SlYTecmRG-abcShR-zpO249dEOJe-e_aQNhmIzbHUS06kBf3Vey0Cukym36bYRonKUbp7GjrfO0MSuqxxNod8Y7u0Ebr8lFC13Am996RT6Xi6-nl-Tt4_n16fEtURlPY9IialarkpV13ehK5KUQbS4AVA65rtqCV00BIhUg-CCkSjHN0zZVWYUiy65IcUxV3oXgsZW9NxvwB8mZHMHJtfwDJ0dw8ghuMN7_Mypz_Ct6MN1p-8PRjsNrO4NeBmUGVKiNRxWlduZUxA9xR5Iy
CitedBy_id	crossref_primary_10_3233_JHS_230165 crossref_primary_10_1002_spe_3033 crossref_primary_10_1109_COMST_2021_3094993 crossref_primary_10_4018_IJIIT_344024 crossref_primary_10_1108_MD_07_2021_0954 crossref_primary_10_1109_ACCESS_2021_3131396 crossref_primary_10_1557_s43577_023_00613_5 crossref_primary_10_1007_s11227_022_04357_y crossref_primary_10_3233_IDT_230240 crossref_primary_10_1515_ijeeps_2023_0154 crossref_primary_10_1155_2021_6805104 crossref_primary_10_1007_s40747_021_00505_8 crossref_primary_10_1016_j_iswa_2023_200231 crossref_primary_10_1016_j_engappai_2023_107149 crossref_primary_10_1016_j_procs_2022_12_277 crossref_primary_10_1109_ACCESS_2025_3586800 crossref_primary_10_3390_mca29040049 crossref_primary_10_4018_IJIIT_357696 crossref_primary_10_1007_s11276_022_03177_5 crossref_primary_10_3390_fire6070271 crossref_primary_10_1145_3629979 crossref_primary_10_3389_fnbot_2023_1132679 crossref_primary_10_2478_amns_2024_2978 crossref_primary_10_1007_s00500_022_07107_7 crossref_primary_10_3390_fi17030118 crossref_primary_10_1007_s11277_021_08699_3
Cites_doi	10.4018/IJIIT.2020040104 10.1109/CVPR.2016.90 10.1109/WICSA.2016.49 10.1145/2967977 10.1109/ICDE.2016.7498421 10.1109/ICDCS.2017.226 10.1109/ICNP.2017.8117585 10.1016/S0169-7439(97)00061-0 10.1162/neco.2006.18.7.1527 10.1016/j.future.2018.09.005 10.1162/neco.1997.9.8.1735 10.1016/j.ins.2019.05.058 10.1109/WF-IoT.2015.7389116 10.1109/PIMRC.2017.8292681 10.1109/BigData.2017.8258272 10.1109/BigDataCongress.2016.40 10.1016/j.future.2018.07.050 10.1109/COMST.2018.2844341 10.1109/COGSIMA.2015.7108192 10.1016/j.future.2018.05.008 10.1109/ICCCN.2019.8846941 10.4018/IJIIT.2020040103 10.1109/MNET.2018.1700202 10.1109/ICECDS.2017.8389592 10.1016/j.neunet.2014.09.003 10.1109/IIKI.2014.18
ContentType	Journal Article
Copyright	2020 Elsevier Ltd
Copyright_xml	– notice: 2020 Elsevier Ltd
DBID	AAYXX CITATION
DOI	10.1016/j.engappai.2020.103785
DatabaseName	CrossRef
DatabaseTitle	CrossRef
DatabaseTitleList
DeliveryMethod	fulltext_linktorsrc
Discipline	Applied Sciences Computer Science
EISSN	1873-6769
ExternalDocumentID	10_1016_j_engappai_2020_103785 S0952197620301780
GroupedDBID	--K --M .DC .~1 0R~ 1B1 1~. 1~5 29G 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABMAC ABXDB ABYKQ ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SET SEW SPC SPCBC SST SSV SSZ T5K TN5 UHS WUQ ZMT ~G- 9DU AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD
ID	FETCH-LOGICAL-c312t-feed09c70799bd845744f54aac5a5d8f618b6a424a41a5d2cc0d12f2c38e433
ISICitedReferencesCount	28
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000595977800004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0952-1976
IngestDate	Tue Nov 18 20:59:35 EST 2025 Sat Nov 29 07:06:52 EST 2025 Fri Feb 23 02:46:53 EST 2024
IsPeerReviewed	true
IsScholarly	true
Keywords	Fog computing Deep Neural Networking Big data Data in motion Internet of Things Data flow processing Edge computing Latency
Language	English
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c312t-feed09c70799bd845744f54aac5a5d8f618b6a424a41a5d2cc0d12f2c38e433
ParticipantIDs	crossref_primary_10_1016_j_engappai_2020_103785 crossref_citationtrail_10_1016_j_engappai_2020_103785 elsevier_sciencedirect_doi_10_1016_j_engappai_2020_103785
PublicationCentury	2000
PublicationDate	September 2020 2020-09-00
PublicationDateYYYYMMDD	2020-09-01
PublicationDate_xml	– month: 09 year: 2020 text: September 2020
PublicationDecade	2020
PublicationTitle	Engineering applications of artificial intelligence
PublicationYear	2020
Publisher	Elsevier Ltd
Publisher_xml	– name: Elsevier Ltd
References	Pei Yun, Meng Chou, Giancarlo (b22) 2018; 88 MD Golam, Rabiul (b18) 2019; 90 Hochreiter, Schmidhuber (b9) 1997; 9 Kevin (b14) 2016; 59 Preden, J., Kaugerand, J., Suurjaak, E., Astapov, S., Motus, L., Pahtma, R., 2015. Data to decision: pushing situational information needs to the Edge of the network. In: 2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision, Orlando, FL, pp. 158–164. Roberto (b25) 2019; 91 Jazib, Carlos, Jeff, Monique (b12) 2019 Seifermann, S., 2016. Architectural data flow analysis. In: 2016 13th Working IEEE/IFIP Conference on Software Architecture, WICSA, Venice, pp. 270–271. Papageorgiou, A., Poormohammady, E., Cheng, B., 2016. Edge-computing-aware deployment of stream processing tasks based on topology-external information: Model, algorithms, and a storm-based prototype. In: 2016 IEEE International Congress on Big Data, BigData Congress, San Francisco, CA, pp. 259–266. White (b34) 2015 Calo, S.B., Touna, M., Verma, D.C., Cullen, A., 2017. Edge computing architecture for applying AI to IoT. In: IEEE International Conference on Big Data. pp. 3012–3016. Schmidhuber (b27) 2015; 61 He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778. Mikolov, Joulin, Chopra, Mathieu, Ranzato (b19) 2014 El-Alami, El Alaoui, En-Nahnahi (b4) 2020; 16 Cheng, B., Papageorgiou, A., Cirillo, F., Kovacs, E., 2015. GeeLytics: Geo-distributed Edge analytics for large scale IoT systems based on dynamic topology. In: 2015 IEEE 2nd World Forum on Internet of Things, WF-IoT, Milan, pp. 565–570. Glorot, X., Bordes, A., Bengio, Y., 2011. Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. pp. 315–323. Huang, Y., Ma, X., Fan, X., Liu, J., Gong, W., 2017. When deep learning meets Edge computing. In: 2017 IEEE 25th International Conference on Network Protocols, ICNP, Toronto, pp. 1–2. . Sutskever, I., Martens, J., Dahl, G., Hinton, G., 2013. On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning. pp. 1139–1147. Svozil, Kvasnicka, Pospichal (b30) 1997; 39 Mohammadi, Al-Fuqaha, Sorour, Guizani (b20) 2018; 20 Li, Ota, Dong (b17) 2018; 32 Kourtellis, N., De Francisci Morales, G., Bonchi, F., 2016. Scalable online betweenness centrality in evolving graphs. In: 2016 IEEE 32nd International Conference on Data Engineering, ICDE, Helsinki, pp. 1580–1581. Volk, Staegemann, Jamous, Pohl, Turowski (b33) 2020; 16 Hinton, Osindero, Teh (b7) 2006; 18 Casadei (b2) 2019; 498 Veeramanikandan, M., Sankaranarayanan, S., 2017. Publish/subscribe broker based architecture for fog computing. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS, Chennai, pp. 1024–1026. Hinton, Srivastava, Krizhevsky, Sutskever, Salakhutdinov (b8) 2012 Savaglio, C., Gerace, P., Di Fatta, G., Fortino, G., 2019. Data mining at the IoT edge. In: 2019 28th International Conference on Computer Communication and Networks, ICCCN, Valencia, Spain, pp. 1–6. Teerapittayanon, S., McDanel, B., Kung, H.T., 2017. Distributed deep neural networks over the cloud, the edge and end devices. In: 2017 IEEE 37th International Conference on Distributed Computing Systems, ICDCS, Atlanta, GA, pp. 328–339. Kevin (b13) 2009 Kos, A., Tomazic, S., Salom, J., Trifunovic, N., Valero, M., Milutinovic, V., 2014. Big data processing: Data flow vs control flow (New benchmarking methodology). In: 2014 International Conference on Identification, Information and Knowledge in the Internet of Things, Beijing, pp. 56–59. Ridhawi, N., Mostafa, Y., Kotb, M., Aloqaily, M., Abualhaol, I., 2017. Data caching and selection in 5G networks using F2F communication. In: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC, Montreal, QC, pp. 1–6. Jain (b11) 2017 10.1016/j.engappai.2020.103785_b1 MD Golam (10.1016/j.engappai.2020.103785_b18) 2019; 90 Hochreiter (10.1016/j.engappai.2020.103785_b9) 1997; 9 El-Alami (10.1016/j.engappai.2020.103785_b4) 2020; 16 Li (10.1016/j.engappai.2020.103785_b17) 2018; 32 Kevin (10.1016/j.engappai.2020.103785_b13) 2009 Svozil (10.1016/j.engappai.2020.103785_b30) 1997; 39 10.1016/j.engappai.2020.103785_b16 White (10.1016/j.engappai.2020.103785_b34) 2015 10.1016/j.engappai.2020.103785_b15 10.1016/j.engappai.2020.103785_b10 10.1016/j.engappai.2020.103785_b32 10.1016/j.engappai.2020.103785_b31 Schmidhuber (10.1016/j.engappai.2020.103785_b27) 2015; 61 Kevin (10.1016/j.engappai.2020.103785_b14) 2016; 59 Hinton (10.1016/j.engappai.2020.103785_b7) 2006; 18 Pei Yun (10.1016/j.engappai.2020.103785_b22) 2018; 88 Roberto (10.1016/j.engappai.2020.103785_b25) 2019; 91 Hinton (10.1016/j.engappai.2020.103785_b8) 2012 Volk (10.1016/j.engappai.2020.103785_b33) 2020; 16 Jazib (10.1016/j.engappai.2020.103785_b12) 2019 Mohammadi (10.1016/j.engappai.2020.103785_b20) 2018; 20 10.1016/j.engappai.2020.103785_b29 10.1016/j.engappai.2020.103785_b28 10.1016/j.engappai.2020.103785_b24 10.1016/j.engappai.2020.103785_b6 Jain (10.1016/j.engappai.2020.103785_b11) 2017 10.1016/j.engappai.2020.103785_b26 10.1016/j.engappai.2020.103785_b21 10.1016/j.engappai.2020.103785_b5 Mikolov (10.1016/j.engappai.2020.103785_b19) 2014 10.1016/j.engappai.2020.103785_b23 Casadei (10.1016/j.engappai.2020.103785_b2) 2019; 498 10.1016/j.engappai.2020.103785_b3
References_xml	– volume: 91 start-page: 252 year: 2019 end-page: 262 ident: b25 article-title: Modelling and simulation of opportunistic iot services with aggregate computing publication-title: Future Gener. Comput. Syst. – reference: Huang, Y., Ma, X., Fan, X., Liu, J., Gong, W., 2017. When deep learning meets Edge computing. In: 2017 IEEE 25th International Conference on Network Protocols, ICNP, Toronto, pp. 1–2. – volume: 18 start-page: 1527 year: 2006 end-page: 1554 ident: b7 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. – reference: Kos, A., Tomazic, S., Salom, J., Trifunovic, N., Valero, M., Milutinovic, V., 2014. Big data processing: Data flow vs control flow (New benchmarking methodology). In: 2014 International Conference on Identification, Information and Knowledge in the Internet of Things, Beijing, pp. 56–59. – volume: 88 start-page: 16 year: 2018 end-page: 27 ident: b22 article-title: Security and trust issues in fog computing - A survey publication-title: Future Gener. Comput. Syst. – volume: 59 start-page: 17 year: 2016 end-page: 19 ident: b14 article-title: The Edge of the uncanny publication-title: Commun. ACM – reference: Seifermann, S., 2016. Architectural data flow analysis. In: 2016 13th Working IEEE/IFIP Conference on Software Architecture, WICSA, Venice, pp. 270–271. – reference: He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778. – volume: 9 start-page: 1735 year: 1997 end-page: 1780 ident: b9 article-title: Long short-term memory publication-title: Neural Comput. – year: 2012 ident: b8 article-title: Improving neural networks by preventing co-adaptation of feature detectors – year: 2017 ident: b11 article-title: Mastering Apache Storm – volume: 32 start-page: 96 year: 2018 end-page: 101 ident: b17 article-title: Learning IoT in Edge: Deep learning for the Internet of Things with Edge computing publication-title: IEEE Netw. – volume: 20 start-page: 2923 year: 2018 end-page: 2960 ident: b20 article-title: Deep learning for IoT big data and streaming analytics: A survey publication-title: IEEE Commun. Surv. Tutor. – year: 2014 ident: b19 article-title: Learning longer memory in recurrent neural networks – year: 2019 ident: b12 article-title: Securing the internet of things: A proposed framework – volume: 16 start-page: 49 year: 2020 end-page: 73 ident: b33 article-title: Providing clarity on big data technologies: The BDTOnto ontology publication-title: Int. J. Intell. Inf. Technol. – volume: 61 start-page: 85 year: 2015 end-page: 117 ident: b27 article-title: Deep learning in neural networks: An overview publication-title: Neural Netw. – reference: Savaglio, C., Gerace, P., Di Fatta, G., Fortino, G., 2019. Data mining at the IoT edge. In: 2019 28th International Conference on Computer Communication and Networks, ICCCN, Valencia, Spain, pp. 1–6. – volume: 39 start-page: 43 year: 1997 end-page: 62 ident: b30 article-title: Introduction to multilayer feed-forward neural networks publication-title: Chemometr. Intell. Lab. Syst. – start-page: 4986 year: 2009 ident: b13 article-title: That internet of things thing publication-title: RFID J. – volume: 90 start-page: 149 year: 2019 end-page: 157 ident: b18 article-title: Autonomic computational offloading in mobile edge for iot applications publication-title: Future Generation Computer systems – year: 2015 ident: b34 article-title: Hadoop: The Definitive Guide – reference: Glorot, X., Bordes, A., Bengio, Y., 2011. Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. pp. 315–323. – volume: 16 start-page: 74 year: 2020 end-page: 86 ident: b4 article-title: Deep neural models and retrofitting for arabic text categorization publication-title: Int. J. Intell. Inf. Technol. – reference: . – reference: Ridhawi, N., Mostafa, Y., Kotb, M., Aloqaily, M., Abualhaol, I., 2017. Data caching and selection in 5G networks using F2F communication. In: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC, Montreal, QC, pp. 1–6. – reference: Calo, S.B., Touna, M., Verma, D.C., Cullen, A., 2017. Edge computing architecture for applying AI to IoT. In: IEEE International Conference on Big Data. pp. 3012–3016. – volume: 498 start-page: 154 year: 2019 end-page: 169 ident: b2 article-title: A development approach for collective opportunistic Edge-of-Things services publication-title: Inform. Sci. – reference: Teerapittayanon, S., McDanel, B., Kung, H.T., 2017. Distributed deep neural networks over the cloud, the edge and end devices. In: 2017 IEEE 37th International Conference on Distributed Computing Systems, ICDCS, Atlanta, GA, pp. 328–339. – reference: Papageorgiou, A., Poormohammady, E., Cheng, B., 2016. Edge-computing-aware deployment of stream processing tasks based on topology-external information: Model, algorithms, and a storm-based prototype. In: 2016 IEEE International Congress on Big Data, BigData Congress, San Francisco, CA, pp. 259–266. – reference: Preden, J., Kaugerand, J., Suurjaak, E., Astapov, S., Motus, L., Pahtma, R., 2015. Data to decision: pushing situational information needs to the Edge of the network. In: 2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision, Orlando, FL, pp. 158–164. – reference: Cheng, B., Papageorgiou, A., Cirillo, F., Kovacs, E., 2015. GeeLytics: Geo-distributed Edge analytics for large scale IoT systems based on dynamic topology. In: 2015 IEEE 2nd World Forum on Internet of Things, WF-IoT, Milan, pp. 565–570. – reference: Kourtellis, N., De Francisci Morales, G., Bonchi, F., 2016. Scalable online betweenness centrality in evolving graphs. In: 2016 IEEE 32nd International Conference on Data Engineering, ICDE, Helsinki, pp. 1580–1581. – reference: Sutskever, I., Martens, J., Dahl, G., Hinton, G., 2013. On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning. pp. 1139–1147. – reference: Veeramanikandan, M., Sankaranarayanan, S., 2017. Publish/subscribe broker based architecture for fog computing. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS, Chennai, pp. 1024–1026. – volume: 16 start-page: 74 issue: 2 year: 2020 ident: 10.1016/j.engappai.2020.103785_b4 article-title: Deep neural models and retrofitting for arabic text categorization publication-title: Int. J. Intell. Inf. Technol. doi: 10.4018/IJIIT.2020040104 – ident: 10.1016/j.engappai.2020.103785_b6 doi: 10.1109/CVPR.2016.90 – ident: 10.1016/j.engappai.2020.103785_b28 doi: 10.1109/WICSA.2016.49 – volume: 59 start-page: 17 issue: 9 year: 2016 ident: 10.1016/j.engappai.2020.103785_b14 article-title: The Edge of the uncanny publication-title: Commun. ACM doi: 10.1145/2967977 – ident: 10.1016/j.engappai.2020.103785_b16 doi: 10.1109/ICDE.2016.7498421 – ident: 10.1016/j.engappai.2020.103785_b31 doi: 10.1109/ICDCS.2017.226 – year: 2017 ident: 10.1016/j.engappai.2020.103785_b11 – ident: 10.1016/j.engappai.2020.103785_b10 doi: 10.1109/ICNP.2017.8117585 – volume: 39 start-page: 43 issue: 1 year: 1997 ident: 10.1016/j.engappai.2020.103785_b30 article-title: Introduction to multilayer feed-forward neural networks publication-title: Chemometr. Intell. Lab. Syst. doi: 10.1016/S0169-7439(97)00061-0 – volume: 18 start-page: 1527 issue: 7 year: 2006 ident: 10.1016/j.engappai.2020.103785_b7 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. doi: 10.1162/neco.2006.18.7.1527 – volume: 91 start-page: 252 year: 2019 ident: 10.1016/j.engappai.2020.103785_b25 article-title: Modelling and simulation of opportunistic iot services with aggregate computing publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2018.09.005 – volume: 9 start-page: 1735 issue: 8 year: 1997 ident: 10.1016/j.engappai.2020.103785_b9 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – volume: 498 start-page: 154 year: 2019 ident: 10.1016/j.engappai.2020.103785_b2 article-title: A development approach for collective opportunistic Edge-of-Things services publication-title: Inform. Sci. doi: 10.1016/j.ins.2019.05.058 – year: 2015 ident: 10.1016/j.engappai.2020.103785_b34 – ident: 10.1016/j.engappai.2020.103785_b5 – year: 2014 ident: 10.1016/j.engappai.2020.103785_b19 – start-page: 4986 year: 2009 ident: 10.1016/j.engappai.2020.103785_b13 article-title: That internet of things thing publication-title: RFID J. – ident: 10.1016/j.engappai.2020.103785_b3 doi: 10.1109/WF-IoT.2015.7389116 – ident: 10.1016/j.engappai.2020.103785_b24 doi: 10.1109/PIMRC.2017.8292681 – ident: 10.1016/j.engappai.2020.103785_b29 – year: 2019 ident: 10.1016/j.engappai.2020.103785_b12 – ident: 10.1016/j.engappai.2020.103785_b1 doi: 10.1109/BigData.2017.8258272 – ident: 10.1016/j.engappai.2020.103785_b21 doi: 10.1109/BigDataCongress.2016.40 – volume: 90 start-page: 149 year: 2019 ident: 10.1016/j.engappai.2020.103785_b18 article-title: Autonomic computational offloading in mobile edge for iot applications publication-title: Future Generation Computer systems doi: 10.1016/j.future.2018.07.050 – volume: 20 start-page: 2923 issue: 4 year: 2018 ident: 10.1016/j.engappai.2020.103785_b20 article-title: Deep learning for IoT big data and streaming analytics: A survey publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2018.2844341 – ident: 10.1016/j.engappai.2020.103785_b23 doi: 10.1109/COGSIMA.2015.7108192 – volume: 88 start-page: 16 year: 2018 ident: 10.1016/j.engappai.2020.103785_b22 article-title: Security and trust issues in fog computing - A survey publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2018.05.008 – year: 2012 ident: 10.1016/j.engappai.2020.103785_b8 – ident: 10.1016/j.engappai.2020.103785_b26 doi: 10.1109/ICCCN.2019.8846941 – volume: 16 start-page: 49 issue: 2 year: 2020 ident: 10.1016/j.engappai.2020.103785_b33 article-title: Providing clarity on big data technologies: The BDTOnto ontology publication-title: Int. J. Intell. Inf. Technol. doi: 10.4018/IJIIT.2020040103 – volume: 32 start-page: 96 issue: 1 year: 2018 ident: 10.1016/j.engappai.2020.103785_b17 article-title: Learning IoT in Edge: Deep learning for the Internet of Things with Edge computing publication-title: IEEE Netw. doi: 10.1109/MNET.2018.1700202 – ident: 10.1016/j.engappai.2020.103785_b32 doi: 10.1109/ICECDS.2017.8389592 – volume: 61 start-page: 85 year: 2015 ident: 10.1016/j.engappai.2020.103785_b27 article-title: Deep learning in neural networks: An overview publication-title: Neural Netw. doi: 10.1016/j.neunet.2014.09.003 – ident: 10.1016/j.engappai.2020.103785_b15 doi: 10.1109/IIKI.2014.18
SSID	ssj0003846
Score	2.4163997
Snippet	The trillion-fold increase in computing power brings the accessibility of deep learning to everyone. Deep learning offers precise information almost all the...
SourceID	crossref elsevier
SourceType	Enrichment Source Index Database Publisher
StartPage	103785
SubjectTerms	Big data Data flow processing Data in motion Deep Neural Networking Edge computing Fog computing Internet of Things Latency
Title	Data Flow and Distributed Deep Neural Network based low latency IoT-Edge computation model for big data environment
URI	https://dx.doi.org/10.1016/j.engappai.2020.103785
Volume	94
WOSCitedRecordID	wos000595977800004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-6769 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0003846 issn: 0952-1976 databaseCode: AIEXJ dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV07b9swECZcp0OXvoukL3DoJki1JMqWxiBx0GQwgtoIvAkURRp2DNmQ5SD9i_1VvRNJSWmDphk6WDAI3elxn3jH4z0I-TLIYMZLfOUq0Ecuy6RyeSICN0f1oBQLlRJ1s4nRZBLP58llr_fT5sLcrEdFEd_eJtv_KmoYA2Fj6uwjxN0whQH4D0KHI4gdjv8k-FNecedsvdGhvqdYGBd7WkmMOJZbB6txgFgmOvzbQS2WO3j2mld1Gub5ZuaOMYlL1A0fNEDqhjk6tHO5cDCstJsid8e93xY4dLq743XAQVlHJtV9QjqlQK3Qr4CMYz2Oa3RvNKid8uKag0qF3w9u2ilP96XcNZ7s75u8XC720uxpwJ1eeJfeidfueC0wklyTXi1XyKfr7oC1rY3nav2WgesnummMncIT1pmDMfNRtwH6Qz1oT8XKk8UC3gBfengFryW4W4_7Nz3ZRC_awLhVavmkyCfVfJ6Qg2AUJXGfHByfj-cXjV0QxjptzD5AJ1_9_ju631TqmD-zl-S5WbfQY423V6Qni9fkhVnDUKMhdjBk24TYsTdkh4ikiEgKcqUdRFJEJNWIpAaRtEYkxbMNIqlFJO0gktaIpIBICoikiEjaQeRbMj0bz06-uabXhytCP6hcBbbaIBFYrzHJ8phFI8ZUxDgXEY_yWA39OBtyFjDOfBgIhBjkfqACEcaSheE70i82hTwk1I-5j7yYhJW-GIhsyPycZWCIxTwTih2RyL7UVJgy-NiNZZ3-XaxH5GtDt9WFYB6kSKzMUmPOajM1BTg-QPv-0Vf7QJ61n8tH0q_KvfxEnoqbarkrPxss_gL6_swg
linkProvider	Elsevier
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Data+Flow+and+Distributed+Deep+Neural+Network+based+low+latency+IoT-Edge+computation+model+for+big+data+environment&rft.jtitle=Engineering+applications+of+artificial+intelligence&rft.au=Veeramanikandan&rft.au=Sankaranarayanan%2C+Suresh&rft.au=Rodrigues%2C+Joel+J.P.C.&rft.au=Sugumaran%2C+Vijayan&rft.date=2020-09-01&rft.issn=0952-1976&rft.volume=94&rft.spage=103785&rft_id=info:doi/10.1016%2Fj.engappai.2020.103785&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_engappai_2020_103785
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0952-1976&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0952-1976&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0952-1976&client=summon