{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T09:28:18Z","timestamp":1770974898091,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T00:00:00Z","timestamp":1640563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The Internet of connected vehicles (IoCV) has made people more comfortable and safer while driving vehicles. This technology has made it possible to reduce road casualties; however, increased traffic and uncertainties in environments seem to be limitations to improving the safety of environments. In this paper, driver behavior is analyzed to provide personalized assistance and to alert surrounding vehicles in case of emergencies. The processes involved in this research are as follows. (i) Initially, the vehicles in an environment are clustered to reduce the complexity in analyzing a large number of vehicles. Multi-criterion-based hierarchical correlation clustering (MCB-HCC) is performed to dynamically cluster vehicles. Vehicular motion is detected by edge-assisted road side units (E-RSUs) by using an attention-based residual neural network (AttResNet). (ii) Driver behavior is analyzed based on the physiological parameters of drivers, vehicle on-board parameters, and environmental parameters, and driver behavior is classified into different classes by implementing a refined asynchronous advantage actor critic (RA3C) algorithm for assistance generation. (iii) If the driver\u2019s current state is found to be an emergency state, an alert message is disseminated to the surrounding vehicles in that area and to the neighboring areas based on traffic flow by using jelly fish search optimization (JSO). If a neighboring area does not have a fog node, a virtual fog node is deployed by executing a constraint-based quantum entropy function to disseminate alert messages at ultra-low latency. (iv) Personalized assistance is provided to the driver based on behavior analysis to assist the driver by using a multi-attribute utility model, thereby preventing road accidents. The proposed driver behavior analysis and personalized assistance model are experimented on with the Network Simulator 3.26 tool, and performance was evaluated in terms of prediction error, number of alerts, number of risk maneuvers, accuracy, latency, energy consumption, false alarm rate, safety score, and alert-message dissemination efficiency.<\/jats:p>","DOI":"10.3390\/fi14010012","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T01:00:54Z","timestamp":1640566854000},"page":"12","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Three Layered Architecture for Driver Behavior Analysis and Personalized Assistance with Alert Message Dissemination in 5G Envisioned Fog-IoCV"],"prefix":"10.3390","volume":"14","author":[{"given":"Mazen","family":"Alowish","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Kobe University, Kobe 657-8501, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8970-9408","authenticated-orcid":false,"given":"Yoshiaki","family":"Shiraishi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Kobe University, Kobe 657-8501, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masami","family":"Mohri","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, Gifu University, Gifu 501-1193, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masakatu","family":"Morii","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Kobe University, Kobe 657-8501, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JIOT.2020.2969693","article-title":"Parallel Internet of Vehicles: ACP-Based System Architecture and Behavioral Modeling","volume":"7","author":"Wang","year":"2020","journal-title":"IEEE Int. Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"163085","DOI":"10.1109\/ACCESS.2019.2952049","article-title":"Constrained Optimization and Distributed Model Predictive Control-Based Merging Strategies for Adjacent Connected Autonomous Vehicle Platoons","volume":"7","author":"Min","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","first-page":"100516","article-title":"Understanding ride-sourcing drivers\u2019 behaviour and preferences: Insights from focus groups analysis","volume":"37","author":"Ashkrof","year":"2020","journal-title":"Res. Transp. Bus. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/TBIOM.2019.2962132","article-title":"Evaluation and Visualization of Driver Inattention Rating From Facial Features","volume":"2","author":"Dua","year":"2020","journal-title":"IEEE Trans. Biom. Behav. Identit Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"19638","DOI":"10.1109\/ACCESS.2020.2965940","article-title":"Analysis of Road-User Interaction by Extraction of Driver Behavior Features Using Deep Learning","volume":"8","author":"Bichicchi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3882","DOI":"10.1109\/JIOT.2020.2966506","article-title":"Smoothing Traffic Flow via Control of Autonomous Vehicles","volume":"7","author":"Zheng","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4278","DOI":"10.1109\/JIOT.2019.2956241","article-title":"A Blockchain-SDN-Enabled Internet of Vehicles Environment for Fog Computing and 5G Networks","volume":"7","author":"Gao","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104586","DOI":"10.1016\/j.ssci.2019.104586","article-title":"Sensitivity analysis of driver\u2019s behavior and psychophysical conditions","volume":"125","author":"Herrera","year":"2020","journal-title":"Saf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/MITS.2019.2903539","article-title":"Rough Set Based Method for Vehicle Collision Risk Assessment Through Inferring Driver\u2019s Braking Actions in Near-Crash Situations","volume":"11","author":"Peng","year":"2019","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105008","DOI":"10.1109\/ACCESS.2020.2999829","article-title":"Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges","volume":"8","author":"Alkinani","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8183","DOI":"10.1109\/TVT.2019.2922452","article-title":"Driver-Behavior-Based Adaptive Steering Robust Nonlinear Control of Unmanned Driving Robotic Vehicle with Modeling Uncertainties and Disturbance Observer","volume":"68","author":"Chen","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106284","DOI":"10.1109\/ACCESS.2019.2930762","article-title":"Using Asymmetric Theory to Identify Heterogeneous Drivers\u2019 Behavior Characteristics Through Traffic Oscillation","volume":"7","author":"Wan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8157","DOI":"10.1109\/JIOT.2020.2980954","article-title":"Toward Energy-Aware Caching for Intelligent Connected Vehicles","volume":"7","author":"Wu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2171","DOI":"10.1109\/TITS.2018.2864637","article-title":"Advanced Driving Behavior Analytics for an Improved Safety Assessment and Driver Fingerprinting","volume":"20","author":"Bouhoute","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","first-page":"2168","article-title":"Dynamic Resource Orchestration for Service Capability Maximization in Fog-Enabled Connected Vehicle Networks","volume":"6","author":"Vu","year":"2020","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103009","DOI":"10.1016\/j.ergon.2020.103009","article-title":"Driv0065r\u2019s distracted behavior: The contribution of compensatory beliefs increases with higher perceived risk","volume":"80","author":"Zhou","year":"2020","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.1109\/TITS.2018.2832219","article-title":"Scheduling the Operation of a Connected Vehicular Network Using Deep Reinforcement Learning","volume":"20","author":"Atallah","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11613","DOI":"10.1109\/ACCESS.2018.2886420","article-title":"Moth Flame Clustering Algorithm for Internet of Vehicle (MFCA-IoV)","volume":"7","author":"Khan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5648","DOI":"10.1109\/TII.2019.2906886","article-title":"ACO-Based Dynamic Decision Making for Connected Vehicles in IoT System","volume":"15","author":"Bui","year":"2019","journal-title":"IEEE Trans. Ind. Informatics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3822","DOI":"10.1109\/JIOT.2020.2969209","article-title":"A Cooperative Driving Strategy Based on Velocity Prediction for Connected Vehicles with Robust Path-Following Control","volume":"7","author":"Chen","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TITS.2005.858624","article-title":"Dynamic Driving Risk Potential Field Model Under the Connected and Automated Vehicles Environment and Its Application in Car-Following Modeling","volume":"7","author":"Li","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_22","unstructured":"Chehreghani, M.H. (2020). Hierarchical Correlation Clustering and Tree Preserving Embedding. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6424","DOI":"10.1109\/JSTARS.2020.3035382","article-title":"Attention-Based Domain Adaptation Using Residual Network for Hyperspectral Image Classification","volume":"13","author":"Mdrafi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100911","DOI":"10.1109\/ACCESS.2021.3097006","article-title":"Optimal Power Flow Solution Based on Jellyfish Search Optimization Considering Uncertainty of Renewable Energy Sources","volume":"9","author":"Farhat","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"11289","DOI":"10.1109\/ACCESS.2020.2964648","article-title":"Cluster Analysis for Driving Behavior of Dangerous Goods Transportation Based on Data Mining","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11454","DOI":"10.1109\/JSEN.2020.2995921","article-title":"Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components","volume":"20","author":"Escano","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"135037","DOI":"10.1109\/ACCESS.2020.3011003","article-title":"An Effective Bio-Signal-Based Driver Behavior Monitoring System Using a Generalized Deep Learning Approach","volume":"8","author":"Alamri","year":"2020","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5213","DOI":"10.1109\/TITS.2020.2982186","article-title":"Adaptive Computation Offloading with Edge for 5G-Envisioned Internet of Connected Vehicles","volume":"22","author":"Xu","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1109\/TITS.2019.2918328","article-title":"Methodology and Mobile Application for Driver Behavior Analysis and Accident Prevention","volume":"21","author":"Kashevnik","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"83745","DOI":"10.1109\/ACCESS.2020.2992058","article-title":"Effective Edge-Based Approach for Promoting the Spreading of Information","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105754","DOI":"10.1016\/j.aap.2020.105754","article-title":"Evaluating the impact of Heavy Goods Vehicle driver monitoring and coaching to reduce risky behaviour","volume":"146","author":"Mase","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"125501","DOI":"10.1109\/ACCESS.2020.3007151","article-title":"Determination of Risk Perception of Drivers Using Fuzzy-Clustering Analysis for Road Safety","volume":"8","author":"Ni","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"109335","DOI":"10.1109\/ACCESS.2020.3001159","article-title":"HCF: A Hybrid CNN Framework for Behavior Detection of Distracted Drivers","volume":"8","author":"Huang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3281","DOI":"10.1109\/TITS.2019.2925510","article-title":"Transfer Learning for Driver Model Adaptation in Lane-Changing Scenarios Using Manifold Alignment","volume":"21","author":"Lu","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Fu, R. (2020). A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning. Sensors, 20.","DOI":"10.3390\/s20174887"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8859891","DOI":"10.1155\/2020\/8859891","article-title":"Development of Driver-Behavior Model Based onWOA-RBM Deep Learning Network","volume":"2020","author":"Liu","year":"2020","journal-title":"J. Adv. Transp."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wu, R., Zheng, X., Xu, Y., Wu, W., Li, G., Xu, Q., and Nie, Z. (2019). Modified Driving Safety Field Based on Trajectory Prediction Model for Pedestrian\u2013Vehicle Collision. Sustainability, 11.","DOI":"10.3390\/su11226254"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Woo, H., Madokoro, H., Sato, K., Tamura, Y., Yamashita, A., and Asama, H. (2019). Advanced Adaptive Cruise Control Based on Operation Characteristic Estimation and Trajectory Prediction. Appl. Sci., 9.","DOI":"10.3390\/app9224875"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, F., Ke, H., Wang, L.-L., and Xu, C.-C. (2019). A Driver\u2019s Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model. Sensors, 19.","DOI":"10.3390\/s19122670"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5232","DOI":"10.1109\/TITS.2020.2997472","article-title":"Clustering-Learning-Based Long-Term Predictive Localization in 5G-Envisioned Internet of Connected Vehicles","volume":"22","author":"Lin","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/MWC.001.1900489","article-title":"Intelligent Task Offloading in Vehicular Edge Computing Networks","volume":"27","author":"Guo","year":"2020","journal-title":"IEEE Wirel. Commun."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105460","DOI":"10.1016\/j.aap.2020.105460","article-title":"A driver behavior assessment and recommendation system for connected vehicles to produce safer driving environments through a \u201cfollow the leader\u201d approach","volume":"139","author":"Hong","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ter\u00e1n, J., Navarro, L., Quintero M., C.G., and Pardo, M. (2020). Intelligent Driving Assistant Based on Road Accident Risk Map Analysis and Vehicle Telemetry. Sensors, 20.","DOI":"10.3390\/s20061763"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"102285","DOI":"10.1016\/j.adhoc.2020.102285","article-title":"Fuzzy-based beaconless probabilistic broadcasting for information dissemination in urban VANET","volume":"108","author":"Srivastava","year":"2020","journal-title":"Ad Hoc Networks"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Matousek, M., El-Zohairy, M., Al-Momani, A., Kargl, F., and B\u00f6sch, C. (2019, January 9\u201312). Detecting Anomalous Driving Behavior using Neural Networks. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8814246"},{"key":"ref_46","unstructured":"Behrisch, M., Bieker, L., Erdmann, J., and Krajzewicz, D. (2011, January 23\u201328). SUMO\u2014Simulation of urban mobility: An overview. Proceedings of the Third International Conference on Advances in System Simulation. ThinkMind (SIMUL 2011), Barcelona, Spain."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/1\/12\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:53:49Z","timestamp":1760169229000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/1\/12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,27]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["fi14010012"],"URL":"https:\/\/doi.org\/10.3390\/fi14010012","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,27]]}}}