Abstract
Data aggregation and dissemination in cloud-based internet of things (IoT) are main issues because of interoperability problems in communication. In an IoT environment, data handling and offloading are constant processes that avoid communication failure and increase service utilization levels. This paper introduces a machine learning (ML)-assisted data aggregation and offloading (ML-DAO) system to improve the reliability of cloud–IoT communication. The method introduced helps reduce the response time and routing cost errors in data aggregation and improve the data service rate. The data handling rate is also enhanced using the IoT assisted by fog elements that maximize edge-level communication. Cloud–IoT communication quality is measured on the basis of time and service attributes; ML techniques are designed to enhance the level’s precision while aggregating the data. To achieve optimum communication quality, the proposed ML-DAO operates on certain measurable functional metrics. The performance of the system is assessed using the following metrics: route cost error, processing time, aggregation delay, service utilization rate, failure probability, and response time. Experimental results prove the consistency of the proposed scheme, as the metrics are optimized with lesser unallocated data chunks.
Similar content being viewed by others
References
Alavi AH, Jiao P, Buttlar WG, Lajnef N (2018) Internet of Things-enabled smart cities: State-of-the-art and future trends. Measurement 129:589–606
AlFarraj O, Tolba AAlZubi,A (2019) Optimized feature selection algorithm based on fireflies with gravitational ant colony algorithm for big data predictive analytics. Neural Comput Appl 31(5):1391–1403
Sicari S, Rizzardi A, Miorandi D, Coen-Porisini A (2018) A risk assessment methodology for the internet of things. Comput Commun 129:67–79
Fouad H, Mahmoud NM, El Issawi MS, Al-Feel H (2020) Distributed and scalable computing framework for improving request processing of wearable IoT assisted medical sensors on pervasive computing system. Comput Commun 151:257–265
Haw R, Alarm M, Hong C (2014) A context-aware content delivery framework for QoS in mobile cloud. Proc. IEEE NOMS, pp 1–6
Al-Makhadmeh Z, Tolba A (2020) SRAF: Scalable Resource Allocation Framework using machine learning in user-centric internet of things. Peer-to-peer networking and applications. https://doi.org/10.1007/s12083-020-00924-3
Sheron PF, Sridhar KP, Baskar S, Shakeel PM (2019) A decentralized scalable security framework for end-to‐end authentication of future IoT communication. Transactions on Emerging Telecommunications Technologies, e3815. https://doi.org/10.1002/ett.3815
Mubarakali A, Durai AD, Alshehri M, AlFarraj O, Ramakrishnan J, Mavaluru D (2020) Fog-based delay-sensitive data transmission algorithm for data forwarding and storage in cloud environment for multimedia applications. Big Data. https://doi.org/10.1089/big.2020.0090
Baskar S, Periyanayagi S, Shakeel PM, Dhulipala VS (2019) An energy persistent range-dependent regulated transmission communication model for vehicular network applications. Comput Netw 152:144–153. https://doi.org/10.1016/j.comnet.2019.01.027
Said O, Al-Makhadmeh Z, Tolba A (2020) EMS: An energy management scheme for green IoT environments. IEEE Access 8:44983–44998
Oteafy SMA, Hassanein HS (2018) IoT in the fog: A roadmap for data-centric IoT development. IEEE Commun Mag 56(3):157–163
Wang J, Tang Y, He S, Zhao C, Sharma PK, Alfarraj O, Tolba A (2020) LogEvent2vec: logEvent-to-vector based anomaly detection for large-scale logs in internet of things. Sensors 20(9):2451
Naha RK, Garg S, Georgakopoulos D, Jayaraman PP, Gao L, Xiang Y, Ranjan R (2018) Fog computing: Survey of trends, architectures, requirements, and research directions. IEEE Access 6:47980–48009
Alsiddiky A, Awwad W, Fouad H, Hassanein AS, Soliman AM (2020) Priority-based data transmission using selective decision modes in wearable sensor based healthcare applications. Comput Commun 160:43–51
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):96–101
Rahim A, Ma K, Zhao W, Tolba A, Al-Makhadmeh Z, Xia F (2018) Cooperative data forwarding based on crowdsourcing in vehicular social networks. Pervasive Mob Comput 51:43–55
Ji H, Alfarraj O, Tolba A (2020) Artificial intelligence-empowered edge of vehicles: architecture, enabling technologies, and applications. IEEE Access 8:61020–61034
Tolba A, Al-Makhadmeh Z (2020) A recursive learning technique for improving information processing through message classification in IoT–cloud storage. Comput Commun 150:719–728
Kato N et al (2017) The deep learning vision for heterogeneous network traffic control: Proposal, challenges, and future perspective. IEEE Wirel Commun 24(3):146–53. https://doi.org/10.1109/MWC.2016.1600317WC
AlFarraj O, Tolba A, Alkhalaf S, AlZubi A (2019) Neighbor predictive adaptive handoff algorithm for improving mobility management in VANETs. Comput Netw 151:224–231
Kayes ASM, Rahayu W, Dillon T (2018) Critical situation management utilizing IoT-based data resources through dynamic contextual role modeling and activation. Computing
Kim J, Jeon Y, Kim H (2016) The intelligent IoT common service platform architecture and service implementation. J Supercomput 74(9):4242–4260
Ullah F, Wang J, Farhan M, Jabbar S, Naseer MK, Asif M (2018) LSA based smart assessment methodology for SDN infrastructure in IoT environment. Int J Parallel Program
Puschmann D, Barnaghi P, Tafazolli R (2016) Adaptive clustering for dynamic IoT data streams. IEEE Internet Things J :1–1
Xiao W, Bao W, Zhu X, Liu L (2017) Cost-aware big data processing across geo-distributed datacenters. IEEE Trans Parallel Distrib Syst 28(11):3114–3127
Lu Z, Wang N, Wu J, Qiu M (2018) IoTDeM: An IoT Big Data-oriented MapReduce performance prediction extended model in multiple edge clouds. J Parallel Distrib Comput 118:316–327
Bu F (2018) An efficient fuzzy c-means approach based on canonical polyadic decomposition for clustering big data in IoT. Future Gener Comput Syst 88:675–682
Cheng B, Solmaz G, Cirillo F, Kovacs E, Terasawa K, Kitazawa A (2018) FogFlow: Easy programming of IoT services over cloud and edges for smart cities. IEEE Internet Things J 5(2):696–707
Yacchirema DC, Sarabia-Jacome D, Palau CE, Esteve M (2018) A smart system for sleep monitoring by integrating IoT with big data analytics. IEEE Access 6:35988–36001
Zhao W, Liu J, Guo H, Hara T (2018) Edge-node-assisted transmitting for the cloud-centric internet of things. IEEE Netw 32(3):101–107
He J, Wei J, Chen K, Tang Z, Zhou Y, Zhang Y (2018) Multitier fog computing with large-scale IoT data analytics for smart cities. IEEE Internet Things J 5(2):677–686
Gupta H, Dastjerdi AV, Ghosh SK, Buyya R (2017) iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things. Edge and Fog computing environments. Softw Pract Experience 47(9):1275–1296
Acknowledgements
This work is funded by the Researchers Supporting Project No. (RSP-2020/102) King Saud University, Riyadh, Saudi Arabia.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the Topical Collection: Special Issue on Network In Box, Architecture, Networking and Applications
Guest Editor: Ching-Hsien Hsu
Rights and permissions
About this article
Cite this article
Alfarraj, O. A machine learning-assisted data aggregation and offloading system for cloud–IoT communication. Peer-to-Peer Netw. Appl. 14, 2554–2564 (2021). https://doi.org/10.1007/s12083-020-01014-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12083-020-01014-0