{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:14:26Z","timestamp":1775470466048,"version":"3.50.1"},"reference-count":165,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Water and Energy Center of the United Arab Emirates University","award":["31R215"],"award-info":[{"award-number":["31R215"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The recent upsurge of smart cities\u2019 applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged the design of the upcoming 6G network generation, due to their stringent requirements in terms of the quality of services (QoS), availability, and dependability to satisfy a Service-Level-Agreement (SLA) for the end users. Industries and academia have started to design 6G networks and propose the use of AI in its protocols and operations. Published papers on the topic discuss either the requirements of applications via a top-down approach or the network requirements in terms of agility, performance, and energy saving using a down-top perspective. In contrast, this paper adopts a holistic outlook, considering the applications, the middleware, the underlying technologies, and the 6G network systems towards an intelligent and integrated computing, communication, coordination, and decision-making ecosystem. In particular, we discuss the temporal evolution of the wireless network generations\u2019 development to capture the applications, middleware, and technological requirements that led to the development of the network generation systems from 1G to AI-enabled 6G and its employed self-learning models. We provide a taxonomy of the technology-enabled smart city applications\u2019 systems and present insights into those systems for the realization of a trustworthy and efficient smart city ecosystem. We propose future research directions in 6G networks for smart city applications.<\/jats:p>","DOI":"10.3390\/s22155750","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T23:49:27Z","timestamp":1659397767000},"page":"5750","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-0946-1818","authenticated-orcid":false,"given":"Leila","family":"Ismail","sequence":"first","affiliation":[{"name":"Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory, Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates"},{"name":"National Water and Energy Center, United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates"}]},{"given":"Rajkumar","family":"Buyya","sequence":"additional","affiliation":[{"name":"Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Parkville, VIC 3010, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"key":"ref_1","unstructured":"Buyya, R., and Dastjerdi, A.V. (2016). Internet of Things: Principles and Paradigms, Elsevier Science."},{"key":"ref_2","unstructured":"Russell, S., and Norvig, P. (2002). Artificial Intelligence: A Modern Approach, Prentice Hall."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1109\/JIOT.2020.3030072","article-title":"A survey on federated learning: The journey from centralized to distributed on-site learning and beyond","volume":"8","author":"AbdulRahman","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11036-013-0489-0","article-title":"Big data: A survey","volume":"19","author":"Chen","year":"2014","journal-title":"Mob. Netw. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ismail, L., and Materwala, H. (2019). A Review of Blockchain Architecture and Consensus Protocols: Use Cases, Challenges, and Solutions. Symmetry, 11.","DOI":"10.20944\/preprints201908.0311.v1"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7806","DOI":"10.1109\/TII.2021.3073066","article-title":"A Survey on Edge and Edge-Cloud Computing Assisted Cyber-Physical Systems","volume":"17","author":"Cao","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1177\/0042098020975982","article-title":"Smart cities: Between worlding and provincialising","volume":"58","author":"Burns","year":"2021","journal-title":"Urban Stud."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.future.2022.04.009","article-title":"Energy-SLA-aware genetic algorithm for edge\u2013cloud integrated computation offloading in vehicular networks","volume":"135","author":"Materwala","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3383464","article-title":"SLA Management for Big Data Analytical Applications in Clouds: A Taxonomy Study","volume":"53","author":"Zeng","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ismail, L., Materwala, H., and Hassanein, H.S. (2022). QoS-SLA-Aware Adaptive Genetic Algorithm for Multi-Request Offloading in Integrated Edge-Cloud Computing in Internet of Vehicles. arXiv.","DOI":"10.36227\/techrxiv.19603591"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ismail, L., and Materwala, H. (2020, January 28\u201329). IoT-Edge-Cloud Computing Framework for QoS-Aware Computation Offloading in Autonomous Mobile Agents: Modeling and Simulation. Proceedings of the International Conference on Mobile Secure, and Programmable Networking, Paris, France.","DOI":"10.1007\/978-3-030-67550-9_11"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3390605","article-title":"Computing Server Power Modeling in a Data Center: Survey, Taxonomy, and Performance Evaluation","volume":"53","author":"Ismail","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_13","first-page":"22","article-title":"Energy consumption and emission mitigation prediction based on data center traffic and PUE for global data centers","volume":"3","author":"Liu","year":"2020","journal-title":"Glob. Energy Interconnect."},{"key":"ref_14","unstructured":"Sunbird (2022, June 12). How Much Does It Cost to Power One Rack in a Data Center?. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/www.sunbirddcim.com\/blog\/how-much-does-it-cost-power-one-rack-data-center."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.jclepro.2017.12.239","article-title":"Assessing ICT global emissions footprint: Trends to 2040 & recommendations","volume":"177","author":"Belkhir","year":"2018","journal-title":"Clean. Prod. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13673-020-00258-2","article-title":"The shift to 6G communications: Vision and requirements","volume":"10","author":"Akhtar","year":"2020","journal-title":"Hum.-Cent. Comput. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1109\/JPROC.2021.3061701","article-title":"6G wireless systems: Vision, requirements, challenges, insights, and opportunities","volume":"109","author":"Tataria","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1109\/MNET.011.2000195","article-title":"Artificial-intelligence-enabled intelligent 6G networks","volume":"34","author":"Yang","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MCOM.2019.1900271","article-title":"The roadmap to 6G: AI empowered wireless networks","volume":"57","author":"Letaief","year":"2019","journal-title":"IEEE Commun. Mag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/JSAC.2021.3126076","article-title":"Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications","volume":"40","author":"Letaief","year":"2021","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10755","DOI":"10.1016\/j.comnet.2020.107556","article-title":"Towards artificial intelligence enabled 6G: State of the art, challenges, and opportunities","volume":"183","author":"Zhang","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_22","first-page":"1124","article-title":"Survey of cellular mobile radio localization methods: From 1G to 5G","volume":"20","author":"Raulefs","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_23","first-page":"3130","article-title":"An overview on evolution of mobile wireless communication networks: 1G\u20136G","volume":"3","author":"Gawas","year":"2015","journal-title":"Int. J. Recent Innov. Trends Comput. Commun."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MMM.2013.2296207","article-title":"Wireless communications: Present and future: Introduction to focused issue articles","volume":"15","author":"Niehenke","year":"2014","journal-title":"IEEE Microw. Mag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1145\/1247001.1247003","article-title":"Evolution and emerging issues in mobile wireless networks","volume":"50","author":"Dekleva","year":"2007","journal-title":"Commun. ACM"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/MCOM.2003.1252799","article-title":"Challenges in the migration to 4G mobile systems","volume":"41","author":"Hui","year":"2003","journal-title":"IEEE Commun. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/65.898820","article-title":"Toward an all-IP-based UMTS system architecture","volume":"15","author":"Bos","year":"2001","journal-title":"IEEE Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2009\/472124","article-title":"3GPP LTE and LTE-Advanced","volume":"2009","author":"Clerckx","year":"2009","journal-title":"EURASIP Wirel. Commun. Netw. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1109\/ACCESS.2015.2461602","article-title":"A survey of 5G network: Architecture and emerging technologies","volume":"3","author":"Gupta","year":"2015","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1109\/JSAC.2017.2692307","article-title":"5G: A tutorial overview of standards, trials, challenges, deployment, and practice","volume":"35","author":"Shafi","year":"2017","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_31","unstructured":"Huawei (2022, March 24). Huawei 5.5G. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/www.huawei.com\/en\/news\/2020\/11\/mbbf-shanghai-huawei-david-wang-5dot5g."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MVT.2019.2921208","article-title":"6G wireless networks: Vision, requirements, architecture, and key technologies","volume":"14","author":"Zhang","year":"2019","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_33","first-page":"1","article-title":"Evaluation of the mobile agents technology: Comparison with the Client\/Server Paradigm","volume":"19","author":"Ismail","year":"2000","journal-title":"Inf. Sci. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ismail, L., and Belkkhouche, B. (2009, January 18\u201323). Full and autonomic mobility management for Mobile agents. Proceedings of the First International Conference on Advances in Future Internet, Athens, Greece.","DOI":"10.1109\/AFIN.2009.13"},{"key":"ref_35","first-page":"1223","article-title":"Agents mobiles et client\/serveur: \u00c9valuation de performance et comparaison","volume":"19","author":"Hagimont","year":"2000","journal-title":"Tech. Sci. Inform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3133","DOI":"10.1109\/COMST.2019.2916583","article-title":"Applications of deep reinforcement learning in communications and networking: A survey","volume":"21","author":"Luong","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MVT.2019.2921162","article-title":"6G: The next frontier: From holographic messaging to artificial intelligence using subterahertz and visible light communication","volume":"14","author":"Strinati","year":"2019","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.phycom.2014.01.006","article-title":"Terahertz band: Next frontier for wireless communications","volume":"12","author":"Akyildiz","year":"2014","journal-title":"Phys. Commun."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2040","DOI":"10.1109\/JSAC.2019.2929455","article-title":"Terahertz-band ultra-massive spatial modulation MIMO","volume":"37","author":"Sarieddeen","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Basar, E. (2019, January 18\u201321). Transmission through large intelligent surfaces: A new frontier in wireless communications. Proceedings of the 2019 European Conference on Networks and Communications (EuCNC), Valencia, Spain.","DOI":"10.1109\/EuCNC.2019.8801961"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/LWC.2017.2757490","article-title":"Power of deep learning for channel estimation and signal detection in OFDM systems","volume":"7","author":"Ye","year":"2017","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1109\/TWC.2021.3107452","article-title":"An attention-aided deep learning framework for massive MIMO channel estimation","volume":"21","author":"Gao","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_43","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zeng, Y., Han, Z., and Gong, Y. (2018, January 25\u201328). Automatic modulation recognition using deep learning architectures. Proceedings of the 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Kalamata, Greece.","DOI":"10.1109\/SPAWC.2018.8446021"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"109063","DOI":"10.1109\/ACCESS.2019.2933448","article-title":"Deep learning aided method for automatic modulation recognition","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2537546","DOI":"10.1155\/2021\/2537546","article-title":"Signal Modulation Recognition Method Based on Differential Privacy Federated Learning","volume":"2021","author":"Shi","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1109\/SURV.2013.100613.00161","article-title":"A survey of payload-based traffic classification approaches","volume":"16","author":"Finsterbusch","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"114363","DOI":"10.1016\/j.eswa.2020.114363","article-title":"Tree-RNN: Tree structural recurrent neural network for network traffic classification","volume":"167","author":"Ren","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"18042","DOI":"10.1109\/ACCESS.2017.2747560","article-title":"Network traffic classifier with convolutional and recurrent neural networks for Internet of Things","volume":"5","author":"Carro","year":"2017","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Vinayakumar, R., Soman, K., and Poornachandran, P. (2017, January 13\u201316). Applying deep learning approaches for network traffic prediction. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Manipal, India.","DOI":"10.1109\/ICACCI.2017.8126198"},{"key":"ref_51","first-page":"46","article-title":"Deep learning based network traffic matrix prediction","volume":"2","author":"Aloraifan","year":"2021","journal-title":"Int. J. Intell. Netw."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MCOM.2016.7565183","article-title":"Caching at the wireless edge: Design aspects, challenges, and future directions","volume":"54","author":"Liu","year":"2016","journal-title":"IEEE Commun. Mag."},{"key":"ref_53","unstructured":"Ismail, L. (2008, January 12\u201314). Implementation and performance of a dynamic-content based cache for a backend database server. Proceedings of the IASTED International Conference on Software Engineering, Innsbruck, Austria."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"97505","DOI":"10.1109\/ACCESS.2019.2927836","article-title":"Deep Q-learning-based content caching with update strategy for fog radio access networks","volume":"7","author":"Jiang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Yu, Z., Hu, J., Min, G., Wang, Z., Miao, W., and Li, S. (2021). Privacy-preserving federated deep learning for cooperative hierarchical caching in fog computing. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2021.3081480"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/MWC.2017.1700244","article-title":"On removing routing protocol from future wireless networks: A real-time deep learning approach for intelligent traffic control","volume":"25","author":"Tang","year":"2017","journal-title":"IEEE Wirel. Commun."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"102865","DOI":"10.1016\/j.jnca.2020.102865","article-title":"DRL-R: Deep reinforcement learning approach for intelligent routing in software-defined data-center networks","volume":"177","author":"Liu","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/JSAC.2020.3036965","article-title":"Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis","volume":"39","author":"Shen","year":"2020","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.1109\/TNSE.2020.3004333","article-title":"Deep learning based radio resource management in NOMA networks: User association, subchannel and power allocation","volume":"7","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_60","unstructured":"Regin, R., Rajest, S.S., and Singh, B. (2021). Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms. EAI Trans. Scalable Inf. Syst."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.dcan.2018.02.001","article-title":"Multifault diagnosis in WSN using a hybrid metaheuristic trained neural network","volume":"6","author":"Swain","year":"2020","journal-title":"Digit. Commun. Netw."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kumar, Y., Farooq, H., and Imran, A. (2017, January 26\u201330). Fault prediction and reliability analysis in a real cellular network. Proceedings of the 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, Spain.","DOI":"10.1109\/IWCMC.2017.7986437"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1109\/TNSM.2020.3034482","article-title":"Mobility management with transferable reinforcement learning trajectory prediction","volume":"17","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Klus, R., Klus, L., Solomitckii, D., Talvitie, J., and Valkama, M. (2020). Deep learning-based cell-level and beam-level mobility management system. Sensors, 20.","DOI":"10.3390\/s20247124"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1109\/TWC.2017.2769644","article-title":"User scheduling and resource allocation in HetNets with hybrid energy supply: An actor-critic reinforcement learning approach","volume":"17","author":"Wei","year":"2017","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_66","unstructured":"Kong, P.-Y., and Panaitopol, D. (2013, January 8\u201311). Reinforcement learning approach to dynamic activation of base station resources in wireless networks. Proceedings of the 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), London, UK."},{"key":"ref_67","first-page":"42","article-title":"Intrusion Detection Based on Joint of K-Means and KNN","volume":"10","author":"Sharifi","year":"2015","journal-title":"J. Converg. Inf. Technol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","article-title":"A deep learning approach for intrusion detection using recurrent neural networks","volume":"5","author":"Yin","year":"2017","journal-title":"IEEE Access"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.eswa.2018.04.004","article-title":"Web traffic anomaly detection using C-LSTM neural networks","volume":"106","author":"Kim","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"48231","DOI":"10.1109\/ACCESS.2018.2863036","article-title":"Enhanced network anomaly detection based on deep neural networks","volume":"6","author":"Naseer","year":"2018","journal-title":"IEEE Access"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2768","DOI":"10.1109\/COMST.2017.2749442","article-title":"Botnet communication patterns","volume":"19","author":"Vormayr","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","article-title":"Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset","volume":"100","author":"Koroniotis","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Injadat, M., Moubayed, A., and Shami, A. (2020, January 14\u201317). Detecting botnet attacks in IoT environments: An optimized machine learning approach. Proceedings of the 2020 32nd International Conference on Microelectronics (ICM), Aqaba, Jordan.","DOI":"10.1109\/ICM50269.2020.9331794"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"4944","DOI":"10.1109\/JIOT.2020.3034156","article-title":"Hybrid deep learning for botnet attack detection in the internet-of-things networks","volume":"8","author":"Popoola","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.eswa.2019.05.014","article-title":"The Internet of Things: Review and theoretical framework","volume":"133","author":"Nord","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_76","first-page":"100182","article-title":"A survey on internet of vehicles: Applications, security issues & solutions","volume":"20","author":"Sharma","year":"2019","journal-title":"Veh. Commun."},{"key":"ref_77","unstructured":"Allied Market Research (2022, March 25). Internet of Vehicle Market Growth. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/www.alliedmarketresearch.com\/internet-of-vehicles-market#:~:text=The%20global%20internet%20of%20vehicles,18.00%25%20from%202018%20to%202024."},{"key":"ref_78","unstructured":"Heath, R., and Gonzalez-Prelcic, N. (2020, January 7\u201311). Vehicle-to-everything (V2X) communication in 5G and beyond. Proceedings of the IEEE International Conference on Communications (ICC), online."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Schotten, H.D., Sattiraju, R., Serrano, D.G., Ren, Z., and Fertl, P. (2014, January 23\u201326). Availability indication as key enabler for ultra-reliable communication in 5G. Proceedings of the 2014 European Conference on Networks and Communications (EuCNC), Bologna, Italy.","DOI":"10.1109\/EuCNC.2014.6882630"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Katsaros, K., and Dianati, M. (2017). A conceptual 5G vehicular networking architecture. 5G Mobile Communications, Springer.","DOI":"10.1007\/978-3-319-34208-5_22"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1109\/MCOM.2018.1700467","article-title":"5G for vehicular communications","volume":"56","author":"Shah","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_82","unstructured":"Precedence Research (2022, March 25). Internet of Medical Things Market Growth. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/www.precedenceresearch.com\/internet-of-medical-things-market."},{"key":"ref_83","first-page":"10288","article-title":"IoMT amid COVID-19 pandemic: Application, architecture, technology, and security","volume":"174","author":"Aman","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"3034","DOI":"10.1109\/COMST.2018.2851452","article-title":"Toward haptic communications over the 5G tactile Internet","volume":"20","author":"Antonakoglou","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MVT.2013.2295069","article-title":"The tactile internet: Applications and challenges","volume":"9","author":"Fettweis","year":"2014","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MWC.2016.1500157RP","article-title":"Realizing the tactile Internet: Haptic communications over next generation 5G cellular networks","volume":"24","author":"Aijaz","year":"2016","journal-title":"IEEE Wirel. Commun."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"9489","DOI":"10.1109\/ACCESS.2017.2647747","article-title":"Internet of robotic things: Concept, technologies, and challenges","volume":"4","author":"Ray","year":"2016","journal-title":"IEEE Access"},{"key":"ref_88","unstructured":"Research and Markets (2022, March 25). Internet of Robotic Things Market Growth. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/www.researchandmarkets.com\/reports\/3873998\/internet-of-robotic-things-market-by-component."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"104","DOI":"10.3389\/frobt.2020.00104","article-title":"Internet of robotic things intelligent connectivity and platforms","volume":"7","author":"Vermesan","year":"2020","journal-title":"Front. Robot. AI"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"25532","DOI":"10.1109\/JSEN.2021.3114266","article-title":"Applications, deployments, and integration of internet of drones (iod): A review","volume":"21","author":"Abualigah","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_91","unstructured":"IMARC Group (2022, March 25). Drones Market Growth. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/www.imarcgroup.com\/drones-market."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"102600","DOI":"10.1016\/j.adhoc.2021.102600","article-title":"An extensive survey on the Internet of Drones","volume":"122","author":"Boccadoro","year":"2021","journal-title":"Ad Hoc Netw."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"106522","DOI":"10.1016\/j.compeleceng.2019.106522","article-title":"Industrial internet of things: Recent advances, enabling technologies and open challenges","volume":"81","author":"Khan","year":"2020","journal-title":"Comput. Electr. Eng."},{"key":"ref_94","unstructured":"Allied Market Research (2022, March 25). Industrial Internet of Things Market Growth. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/www.alliedmarketresearch.com\/industrial-internet-of-things-IIOT-market."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"4724","DOI":"10.1109\/TII.2018.2852491","article-title":"Industrial internet of things: Challenges, opportunities, and directions","volume":"14","author":"Sisinni","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_96","unstructured":"Vega, M.T., Mehmli, T., van der Hooft, J., Wauters, T., and De Turck, F. (2018, January 5\u20139). Enabling virtual reality for the tactile Internet: Hurdles and opportunities. Proceedings of the 2018 14th International Conference on Network and Service Management (CNSM), Rome, Italy."},{"key":"ref_97","unstructured":"Allied Market Research (2022, March 25). Holographic Display Market Growth. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/www.alliedmarketresearch.com\/holographic-display-market-A12501."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1109\/MCOM.001.1900272","article-title":"Toward truly immersive holographic-type communication: Challenges and solutions","volume":"58","author":"Clemm","year":"2020","journal-title":"IEEE Commun. Mag."},{"key":"ref_99","unstructured":"Li, R. (2018, January 2). Network 2030: Market Drivers and Prospects. Proceedings of the 1st ITU Workshop on Network 2030, Brooklyn, NY, USA."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Matsubayashi, A., Makino, Y., and Shinoda, H. (2019, January 4\u20139). Direct finger manipulation of 3D object image with ultrasound haptic feedback. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK.","DOI":"10.1145\/3290605.3300317"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1109\/TOH.2018.2818134","article-title":"A systematic review of multilateral teleoperation systems","volume":"11","author":"Shahbazi","year":"2018","journal-title":"IEEE Trans. Haptics"},{"key":"ref_102","first-page":"1","article-title":"6G: A comprehensive survey on technologies, applications, challenges, and research problems","volume":"32","author":"Mahmoud","year":"2021","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1109\/MNET.001.1900287","article-title":"A vision of 6G wireless systems: Applications, trends, technologies, and open research problems","volume":"34","author":"Saad","year":"2019","journal-title":"IEEE Netw."},{"key":"ref_104","first-page":"9109300","article-title":"Rules of Smart IoT Networks within Smart Cities towards Blockchain Standardization","volume":"2022","author":"Alasbali","year":"2022","journal-title":"Mob. Inf. Syst."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Ismail, L., and Barka, E. (2008, January 16\u201319). Key distribution framework for a mobile agent platform. Proceedings of the 2008 The Second International Conference on Next Generation Mobile Applications, Services, and Technologies, Cardiff, UK.","DOI":"10.1109\/NGMAST.2008.61"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Ismail, L. (2007, January 10\u201313). Authentication Mechanisms for Mobile Agents. Proceedings of the Second International Conference on Availability, Reliability and Security (ARES\u201907), Vienna, Austria.","DOI":"10.1109\/ARES.2007.47"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Ismail, L. (2007, January 1\u201313). Evaluation of Authentication Mechanisms for Mobile Agents on top of Java. Proceedings of the 6th IEEE\/ACIS International Conference on Computer and Information Science, Melbourne, Australia.","DOI":"10.1109\/ICIS.2007.97"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Ismail, L., Materwala, H., and Hennebelle, A. (2021). A Scoping Review of Integrated Blockchain-Cloud (BcC) Architecture for Healthcare: Applications, Challenges and Solutions. Sensors, 21.","DOI":"10.3390\/s21113753"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/MITP.2021.3071534","article-title":"Healthcare Insurance Frauds: Taxonomy and Blockchain-based Detection Framework (Block-HI)","volume":"23","author":"Ismail","year":"2021","journal-title":"IT Prof."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.jnca.2019.02.027","article-title":"Blockchain in healthcare applications: Research challenges and opportunities","volume":"135","author":"McGhin","year":"2019","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Ismail, L., and Materwala, H. (2020, January 22\u201324). BlockHR: A Blockchain-based Framework for Health Records Management. Proceedings of the 12th International Conference on Computer Modeling and Simulation, Brisbane, Australia.","DOI":"10.1145\/3408066.3408106"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Ismail, L., Materwala, H., and Khan, M.A. (2020, January 8\u201310). Performance Evaluation of a Patient-Centric Blockchain-based Healthcare Records Management Framework. Proceedings of the 2020 2nd International Electronics Communication Conference, Singapore.","DOI":"10.1145\/3409934.3409941"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Peng, W.C., Gao, L., Zhang, J., Yau, K.-L.A., and Ji, Y. (2020). Blockchain for Vehicular Internet of Things: Recent Advances and Open Issues. Sensors, 20.","DOI":"10.3390\/s20185079"},{"key":"ref_114","first-page":"100392","article-title":"Proof of accumulated trust: A new consensus protocol for the security of the IoV","volume":"32","author":"Mershad","year":"2021","journal-title":"Veh. Commun."},{"key":"ref_115","first-page":"3690","article-title":"Consortium blockchain for secure energy trading in industrial internet of things","volume":"14","author":"Li","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Materwala, H., and Ismail, L. (2021, January 23\u201325). Secure and Privacy-Preserving Lightweight Blockchain for Energy Trading. Proceedings of the 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud), Rome, Italy.","DOI":"10.1109\/FiCloud49777.2021.00064"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"5112","DOI":"10.1109\/ACCESS.2018.2789929","article-title":"EduCTX: A Blockchain-Based Higher Education Credit Platform","volume":"6","year":"2018","journal-title":"IEEE Access"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Ismail, L., Heba, H., AlShamsi, M., AlHammadi, M., and AlDhanhani, N. (2019, January 14\u201317). Towards a Blockchain Deployment at UAE University: Performance Evaluation and Blockchain Taxonomy. Proceedings of the 2019 International Conference on Blockchain Technology, Atlanta, GA, USA.","DOI":"10.1145\/3320154.3320156"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"120397","DOI":"10.1016\/j.techfore.2020.120397","article-title":"Peace engineering: The contribution of blockchain systems to the e-voting process","volume":"162","author":"Baudier","year":"2021","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"44411","DOI":"10.1109\/ACCESS.2021.3066184","article-title":"The Notarial Office in E-government: A Blockchain-Based Solution","volume":"9","author":"Gao","year":"2021","journal-title":"IEEE Access"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Ismail, L. (2010, January 5\u20138). Communication issues in parallel conjugate gradient method using a star-based network. Proceedings of the International Conference on Computer Applications and Industrial Electronics, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICCAIE.2010.5735102"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Ismail, L., and Shuaib, K. (2010, January 26\u201328). Empirical Study for Communication Cost of Parallel Conjugate Gradient on a Star-Based Network. Proceedings of the Fourth Asia International Conference on Mathematical\/Analytical Modelling and Computer Simulation, Kota Kinabalu, Malaysia.","DOI":"10.1109\/AMS.2010.101"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Ismail, L., Materwala, H., and Sharaf, Y. (2020, January 16\u201318). BlockHR\u2013A Blockchain-based Healthcare Records Management Framework: Performance Evaluation and Comparison with Client\/Server Architecture. Proceedings of the 2020 International Symposium on Networks, Computers and Communications (ISNCC), Montreal, QC, Canada.","DOI":"10.1109\/ISNCC49221.2020.9297216"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"175003","DOI":"10.1109\/ACCESS.2019.2956881","article-title":"Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and Evaluation","volume":"7","author":"Ismail","year":"2019","journal-title":"IEEE Access"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Ismail, L., and Materwala, H. (2021). IDMPF: Intelligent diabetes mellitus prediction framework using machine learning. Appl. Comput. Inform.","DOI":"10.1108\/ACI-10-2020-0094"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s11831-021-09582-x","article-title":"Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation","volume":"29","author":"Ismail","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Ismail, L., and Materwala, H. (2020, January 16\u201318). Comparative Analysis of Machine Learning Models for Diabetes Mellitus Type 2 Prediction. Proceedings of the 2020 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.","DOI":"10.1109\/CSCI51800.2020.00095"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"33132","DOI":"10.1109\/ACCESS.2021.3061368","article-title":"Explainable Student Performance Prediction Models: A Systematic Review","volume":"9","author":"Alamri","year":"2021","journal-title":"IEEE Access"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Ismail, L., Materwala, H., and Hennebelle, A. (2021, January 19\u201321). Comparative Analysis of Machine Learning Models for Students\u2019 Performance Prediction. Proceedings of the International Conference on Advances in Digital Science Salvador, Salvador, Brazil.","DOI":"10.1007\/978-3-030-71782-7_14"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1109\/TE.2020.3008751","article-title":"Student performance prediction based on blended learning","volume":"64","author":"Xu","year":"2021","journal-title":"IEEE Trans. Educ."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Ismail, L., and Zhang, L. (2018). Information Innovation Technology in Smart Cities, Springer.","DOI":"10.1007\/978-981-10-1741-4"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.indmarman.2019.11.001","article-title":"Big data analytics for supply chain relationship in banking","volume":"86","author":"Hung","year":"2020","journal-title":"Ind. Mark. Manag."},{"key":"ref_133","first-page":"10040","article-title":"Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019","volume":"40","author":"Magalingam","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.egyr.2017.11.002","article-title":"Power systems big data analytics: An assessment of paradigm shift barriers and prospects","volume":"4","year":"2018","journal-title":"Energy Rep."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Chac\u00f3n, R., Luna-Romera, J.M., Troncoso, A., Mart\u00ednez-\u00c1lvarez, F., and Riquelme, J.C. (2018). Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities. Energies, 11.","DOI":"10.3390\/en11030683"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0281-5","article-title":"Big data monetization throughout Big Data Value Chain: A comprehensive review","volume":"7","author":"Faroukhi","year":"2020","journal-title":"J. Big Data"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1016\/j.indmarman.2019.09.001","article-title":"Real-time big data processing for instantaneous marketing decisions: A problematization approach","volume":"90","author":"Jabbar","year":"2020","journal-title":"Ind. Mark. Manag."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Ismail, L., Masud, M.M., and Khan, L. (July, January 27). FSBD: A framework for scheduling of big data mining in cloud computing. Proceedings of the 2014 IEEE International Congress on Big Data (BigData Congress), Anchorage, AK, USA.","DOI":"10.1109\/BigData.Congress.2014.81"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Materwala, H., and Ismail, L. (2021, January 23\u201325). Energy-Aware Edge-Cloud Computation Offloading for Smart Connected Health. Proceedings of the 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud), Rome, Italy.","DOI":"10.1109\/FiCloud49777.2021.00028"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.procs.2021.07.044","article-title":"Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks","volume":"191","author":"Ismail","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Ismail, L., and Materwala, H. (2021). ESCOVE: Energy-SLA-Aware Edge-Cloud Computation Offloading in Vehicular Networks. Sensors, 21.","DOI":"10.3390\/s21155233"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.procs.2021.12.137","article-title":"Performance and Energy-Aware Bi-objective Tasks Scheduling for Cloud Data Centers","volume":"197","author":"Materwala","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Liu, F., Huang, Z., and Wang, L. (2019). Energy-efficient collaborative task computation offloading in cloud-assisted edge computing for IoT sensors. Sensors, 19.","DOI":"10.3390\/s19051105"},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Ismail, L., and Materwala, H. (2020, January 17\u201319). Artificial Intelligent Agent for Energy Savings in Cloud Computing Environment: Implementation and Performance Evaluation. Proceedings of the Agents and Multi-Agent Systems: Technologies and Applications, Split, Croatia.","DOI":"10.1007\/978-981-15-5764-4_12"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.procs.2018.08.172","article-title":"EATSVM: Energy-Aware Task Scheduling on Cloud Virtual Machines","volume":"135","author":"Ismail","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_146","first-page":"37","article-title":"Energy-Aware Task Scheduling (EATS) Framework for Efficient Energy in Smart Cities Cloud Computing Infrastructures","volume":"13","author":"Ismail","year":"2016","journal-title":"Int. J. Therm. Environ. Eng."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1109\/JIOT.2020.3033521","article-title":"EEDTO: An energy-efficient dynamic task offloading algorithm for blockchain-enabled IoT-edge-cloud orchestrated computing","volume":"8","author":"Wu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1109\/TPDS.2017.2688445","article-title":"Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy","volume":"29","author":"Li","year":"2018","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_149","unstructured":"Bruce, M., and Alain, H. (2008, January 6\u20138). A formal model of dynamic resource allocation in Grid computing environment. Proceedings of the 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel\/Distributed Computing, Phuket, Thailand."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Ismail, L. (2007, January 21\u201323). Dynamic Resource Allocation Mechanisms for Grid Computing Environment. Proceedings of the 2007 3rd International Conference on Testbeds and Research Infrastructure for the Development of Networks and Communities, Orlando, FL, USA.","DOI":"10.1109\/TRIDENTCOM.2007.4444737"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.future.2018.11.010","article-title":"Profile-based power-aware workflow scheduling framework for energy-efficient data centers","volume":"94","author":"Qureshi","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Han, G., Que, W., Jia, G., and Shu, L. (2016). An efficient virtual machine consolidation scheme for multimedia cloud computing. Sensors, 16.","DOI":"10.3390\/s16020246"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"2237","DOI":"10.1002\/cpe.1180","article-title":"Multi-installment divisible load processing in heterogeneous distributed systems","volume":"19","author":"Drozdowski","year":"2007","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Ismail, L., Abou-Kassem, J., and Qamar, B. (2014, January 22\u201324). Implementation and performance analysis of a parallel oil reservoir simulator tool using a CG method on a GPU-based system. Proceedings of the 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, Orlando, FL, USA.","DOI":"10.1109\/UKSim.2014.113"},{"key":"ref_155","unstructured":"Ismail, L., and Abou-Kassem, J.H. (July, January 29). Toward an automatic Load balanced distribution model in Conjugate gradient method for one-dimensional one-phase oil Reservoir simulation. Proceedings of the 10th IEEE International Conference on Computer and Information Technology, Bradford, UK."},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Aali, N., Shahhosseini, H.S., and Bagherzadeh, N. (2018, January 21\u201323). Divisible Load Scheduling of Image Processing Applications on the Heterogeneous Star Network Using a new Genetic Algorithm. Proceedings of the 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), Cambridge, UK.","DOI":"10.1109\/PDP2018.2018.00019"},{"key":"ref_157","first-page":"25","article-title":"Performance versus Cost of a Parallel Conjugate Gradient Method in Cloud and Commodity Clusters","volume":"12","author":"Ismail","year":"2012","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Ismail, L., and Fardoun, A.A. (2017, January 24\u201327). Towards energy-aware task scheduling (EATS) framework for divisible-load applications in cloud computing infrastructure. Proceedings of the Annual IEEE International Systems Conference, Montreal, QC, Canada.","DOI":"10.1109\/SYSCON.2017.7934791"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"e5498","DOI":"10.1002\/cpe.5498","article-title":"Divisible load scheduling of image processing applications on the heterogeneous star and tree networks using a new genetic algorithm","volume":"32","author":"Aali","year":"2019","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Ismail, L., and Zhang, L. (2010, January 17\u201319). Modeling and Performance Analysis to Predict the Behaviour of a Divisible Load Application in a Star Network Cloud. Proceedings of the 2010 Fourth UKSim European Symposium on Computer Modeling and Simulation, Pisa, Italy.","DOI":"10.1109\/EMS.2010.67"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"289","DOI":"10.3390\/a5020289","article-title":"Modeling and performance analysis to predict the behavior of a divisible load application in a cloud computing environment","volume":"5","author":"Ismail","year":"2012","journal-title":"Algorithms"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1002\/spe.2112","article-title":"Implementation and Performance Evaluation of a Distributed Conjugate Gradient Method in a Cloud Computing Environment","volume":"43","author":"Ismail","year":"2012","journal-title":"Softw. Pract. Exp."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1109\/TPDS.2010.70","article-title":"Performance evaluation of convolution on the Cell Broadband Engine processor","volume":"22","author":"Ismail","year":"2011","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_164","first-page":"85","article-title":"A ring-based parallel oil reservoir simulator","volume":"13","author":"Ismail","year":"2012","journal-title":"Scalable Comput. Pract. Exp."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"38","DOI":"10.3389\/frsc.2020.00038","article-title":"Urban artificial intelligence: From automation to autonomy in the smart city","volume":"2","author":"Cugurullo","year":"2020","journal-title":"Front. Sustain. Cities"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/22\/15\/5750\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:00:47Z","timestamp":1760140847000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/22\/15\/5750"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,1]]},"references-count":165,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155750"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/s22155750","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,1]]}}}