{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T16:36:04Z","timestamp":1726850164507},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,27]],"date-time":"2020-03-27T00:00:00Z","timestamp":1585267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome\/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks\u2019 priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.<\/jats:p>","DOI":"10.3390\/s20071853","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T07:44:13Z","timestamp":1585727053000},"page":"1853","source":"Crossref","is-referenced-by-count":84,"title":["MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-4966-0232","authenticated-orcid":false,"given":"Ammar Awad","family":"Mutlag","sequence":"first","affiliation":[{"name":"Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Melaka, Malaysia"},{"name":"Ministry of education\/general directorate of curricula, pure science department, Baghdad 10065, Iraq"}]},{"given":"Mohd","family":"Khanapi Abd Ghani","sequence":"additional","affiliation":[{"name":"Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Melaka, Malaysia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9030-8102","authenticated-orcid":false,"given":"Mazin Abed","family":"Mohammed","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Technology, University of Anbar, 11, Ramadi 55431, Anbar, Iraq"}]},{"given":"Mashael S.","family":"Maashi","sequence":"additional","affiliation":[{"name":"Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia"}]},{"given":"Othman","family":"Mohd","sequence":"additional","affiliation":[{"name":"Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Melaka, Malaysia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5348-502X","authenticated-orcid":false,"given":"Salama A.","family":"Mostafa","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor 86400, Malaysia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7302-2049","authenticated-orcid":false,"given":"Karrar Hameed","family":"Abdulkareem","sequence":"additional","affiliation":[{"name":"College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5834-6571","authenticated-orcid":false,"given":"Gon\u00e7alo","family":"Marques","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade da Beira Interior, 6201-001 Covilh\u00e3, Portugal"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3134-7720","authenticated-orcid":false,"given":"Isabel","family":"de la Torre D\u00edez","sequence":"additional","affiliation":[{"name":"Department of Signal Theory and Communications, University of Valladolid, 47011 Valladolid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.comnet.2018.12.008","article-title":"Internet of Things applications: A systematic review","volume":"148","author":"Asghari","year":"2019","journal-title":"Comput. Netw."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mostafa, S.A., Gunasekaran, S.S., Mustapha, A., Mohammed, M.A., and Abduallah, W.M. (2019, January 24\u201328). Modelling an Adjustable Autonomous Multi-agent Internet of Things System for Elderly Smart Home. Proceedings of the International Conference on Applied Human Factors and Ergonomics, Washington, DC, USA.","DOI":"10.1007\/978-3-030-20473-0_29"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.future.2018.07.049","article-title":"Enabling technologies for fog computing in healthcare IoT systems","volume":"90","author":"Mutlag","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"153123","DOI":"10.1109\/ACCESS.2019.2947542","article-title":"A review of Fog computing and machine learning: Concepts, applications, challenges, and open issues","volume":"7","author":"Abdulkareem","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Parygin, D., Nikitsky, N., Kamaev, V., Matokhina, A., Finogeev, A., and Finogeev, A. (2016, January 25\u201327). Multi-agent approach to distributed processing big sensor data based on Fog computing model for the monitoring of the urban infrastructure systems. Proceedings of the 5th International Conference on System Modeling and Advancement in Research Trends 2016, SMART 2016, Moradabad, India.","DOI":"10.1109\/SYSMART.2016.7894540"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"28573","DOI":"10.1109\/ACCESS.2018.2831228","article-title":"Multi-Agent Systems: A Survey","volume":"6","author":"Dorri","year":"2018","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Alam, M.G.R., Tun, Y.K., and Hong, C.S. (2016, January 23\u201325). Multi-agent and reinforcement learning based code offloading in mobile Fog. Proceedings of the International Conference on Information Networking 2016, Crans-Montana, Switzerland.","DOI":"10.1109\/ICOIN.2016.7427078"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.simpat.2018.10.013","article-title":"An agent-Based self-organizing model for large-scale biosurveillance systems using mobile edge computing","volume":"93","author":"Jararweh","year":"2019","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hoque, S., Brito MSDe Willner, A., Keil, O., and Magedanz, T. (2017, January 27\u201329). Towards Container Orchestration in Fog Computing Infrastructures. Proceedings of the International Conference on Advanced Information Networking and Applications, Taipei, Taiwan.","DOI":"10.1109\/COMPSAC.2017.248"},{"key":"ref_10","unstructured":"Barg-Walkow, L.H., and Rogers, W.A. (2017, January 9\u201313). Modeling task scheduling in complex healthcare environments: Identifying relevant factors. Proceedings of the Human Factors and Ergonomics Society 2017, Austin, TX, USA."},{"key":"ref_11","first-page":"65","article-title":"The role of Information and Communication Technologies in healthcare: Taxonomies, perspectives, and challenges","volume":"93","author":"Aceto","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.comcom.2017.07.009","article-title":"Distributed computational model for shared processing on Cyber-Physical System environments","volume":"111","author":"Mora","year":"2017","journal-title":"Comput. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.future.2018.06.042","article-title":"CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey","volume":"90","author":"Fei","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Young, R., Fallon, S., and Jacob, P. (2018, January 16\u201318). Dynamic collaboration of centralized & edge processing for coordinated data management in an IoT paradigm. Proceedings of the International Conference on Advanced Information Networking and Applications 2018, AINA, Krakow, Poland.","DOI":"10.1109\/AINA.2018.00105"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jnca.2019.06.006","article-title":"A comprehensive survey for scheduling techniques in Cloud computing","volume":"143","author":"Kumar","year":"2019","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1002\/spe.2509","article-title":"IFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments","volume":"47","author":"Gupta","year":"2017","journal-title":"Softw. Pract. Exp."},{"key":"ref_17","first-page":"1312","article-title":"An Energy-Aware and Load-balancing Routing Scheme for Wireless Sensor Networks","volume":"12","author":"Mahdi","year":"2018","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"51691","DOI":"10.1109\/ACCESS.2019.2908998","article-title":"Comprehensive review of artificial intelligence and statistical approaches in distributed denial of service attack and defense methods","volume":"7","author":"Khalaf","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Amini Motlagh, A., Movaghar, A., and Rahmani, A.M. (2019). Task scheduling mechanisms in cloud computing: A systematic review. Int. J. Commun. Syst., e4302.","DOI":"10.1002\/dac.4302"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, L., Qi, D., Zhou, N., and Wu, Y. (2018). A Task Scheduling Algorithm Based on Classification Mining in Fog Computing Environment. Wirel. Commun. Mob. Comput.","DOI":"10.1155\/2018\/2102348"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.future.2019.12.029","article-title":"FaaVPP: Fog as a virtual power plant service for community energy management","volume":"105","author":"Aldegheishem","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cristescu, G., Dobrescu, R., Chenaru, O., and Florea, G. (2019, January 28\u201330). DEW: A New Edge Computing Component for Distributed Dynamic Networks. Proceedings of the 22nd International Conference on Control Systems and Computer Science (CSCS), Bucharest, Romania.","DOI":"10.1109\/CSCS.2019.00100"},{"key":"ref_23","unstructured":"Auluck, N., Rana, O., Nepal, S., Jones, A., and Singh, A. (2019). Scheduling Real Time Security Aware tasks in Fog Networks. IEEE Trans. Serv. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Al Ridhawi, I., Mostafa, N., Kotb, Y., Aloqaily, M., and Abualhaol, I. (2017, January 8\u201313). Data Caching and Selection in 5G Networks Using F2F Communication. Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Montreal, QC, Canada.","DOI":"10.1109\/PIMRC.2017.8292681"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Welsh, T., and Benkhelifa, E. (2019). Bio-Inspired Multi-agent Embryonic Architecture for Resilient Edge Networks. IEEE Trans. Ind. Inform.","DOI":"10.1109\/TII.2019.2916094"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tajalli, S.Z., Tajalli, S.A.M., Kavousi-Fard, A., Niknam, T., Dabbaghjamanesh, M., and Mehraeen, S. (2019, January 7\u20138). A Secure Distributed Cloud-Fog Based Framework for Economic Operation of Microgrids. Proceedings of the 2019 IEEE Texas Power and Energy Conference (TPEC), College Station, TX, USA.","DOI":"10.1109\/TPEC.2019.8662201"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ramakrishna, S., Dubey, A., Burruss, M.P., Hartsell, C., Mahadevan, N., Nannapaneni, S., Laszka, A., and Karsai, G. (2019, January 7\u20139). Augmenting learning components for safety in resource constrained autonomous robots. Proceedings of the 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC), Valencia, Spain.","DOI":"10.1109\/ISORC.2019.00032"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/TNSM.2019.2928698","article-title":"A Proactive Context-Aware Service Replication Scheme for Adhoc IoT Scenarios","volume":"16","author":"Choudhury","year":"2019","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.jpdc.2018.03.004","article-title":"Quality of Experience (QoE)-aware placement of applications in Fog computing environments","volume":"132","author":"Mahmud","year":"2019","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kalysh, I., Kenzhina, M., Kaiyrbekov, N., Kumar Nunna, H.S.V.S., Dadlani, A., and Doolla, S. (2019, January 10\u201312). Machine Learning-based Service Restoration Scheme for Smart Distribution Systems with DGs and High Priority Loads. Proceedings of the 2019 International Conference on Smart Energy Systems and Technologies (SEST), Sevilla, Spain.","DOI":"10.1109\/SEST.2019.8849002"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mehta, M., Kavitha, V., and Hemachandra, N. (May, January 26). Price of fairness for opportunistic and priority schedulers. Proceedings of the 2015 IEEE Conference on Computer Communications, Kowloon, Hong Kong.","DOI":"10.1109\/INFOCOM.2015.7218488"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Barthes, J.P., Callebert, L., and Lourdeaux, D. (2016, January 4\u20136). Priority-based contextual local decision making in multi-agent systems. Proceedings of the 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2016, Nanchang, China.","DOI":"10.1109\/CSCWD.2016.7565986"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yoon, Y.S., Ko, H., Han, S.W., and Youn, H.Y. (2007, January 2\u20135). Priority-based message scheduling for the multi-agent system in ubiquitous environment. Proceedings of the 2007 IEEE\/WIC\/ACM International Conference on Web Intelligence and Intelligent Agent Technology\u2014Workshops 2007, WI-IAT Workshops 2007, Silicon Valley, CA, USA.","DOI":"10.1109\/WIIATW.2007.4427615"},{"key":"ref_34","unstructured":"Yin, R.K. (2017). Case Study Research and Applications: Design and Methods, Sage Publications."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"45","DOI":"10.2753\/MIS0742-1222240302","article-title":"A design science research methodology for information systems research","volume":"24","author":"Peffers","year":"2007","journal-title":"J. Manag. Inf. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Keeney, R.L., and Raiffa, H. (1993). Decisions with Multiple Objectives: Preferences and Value Trade-Offs, Cambridge University Press.","DOI":"10.1017\/CBO9781139174084"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rahbari, D., and Nickray, M. (2018, January 13\u201316). Scheduling of Fog networks with optimized knapsack by symbiotic organisms search. Proceedings of the Conference of Open Innovation Association, Bologna, Italy.","DOI":"10.23919\/FRUCT.2017.8250193"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kabirzadeh, S., Rahbari, D., and Nickray, M. (2018, January 13\u201316). A hyper heuristic algorithm for scheduling of Fog networks. Proceedings of the Conference of Open Innovation Association, Bologna, Italy.","DOI":"10.23919\/FRUCT.2017.8250177"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.future.2019.05.015","article-title":"Improving fog computing performance via fog-2-fog collaboration","volume":"100","author":"Baker","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Goutam, S., and Yadav, A.K. (2015, January 19\u201321). Preemptable priority based dynamic resource allocation in Cloud computing with fault tolerance. Proceedings of the 2015 International Conference on Communication Networks (ICCN), Gwalior, India.","DOI":"10.1109\/ICCN.2015.54"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, J., Qiu, M., Niu, J.W., Chen, Y., and Ming, Z. (December, January 29). Adaptive resource allocation for preemptable jobs in Cloud systems. Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications 2010, ISDA\u201910, Cairo, Egypt.","DOI":"10.1109\/ISDA.2010.5687294"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/1853\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T01:30:45Z","timestamp":1719365445000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/1853"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,27]]},"references-count":41,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["s20071853"],"URL":"https:\/\/doi.org\/10.3390\/s20071853","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,27]]}}}