{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T13:17:11Z","timestamp":1786281431393,"version":"build-2736575974"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T00:00:00Z","timestamp":1610582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FRGS\/1\/2018\/TK04\/UKM\/02\/17","award":["UKM DIP-2018-040"],"award-info":[{"award-number":["UKM DIP-2018-040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fog computing is an emerging technology. It has the potential of enabling various wireless networks to offer computational services based on certain requirements given by the user. Typically, the users give their computing tasks to the network manager that has the responsibility of allocating needed fog nodes optimally for conducting the computation effectively. The optimal allocation of nodes with respect to various metrics is essential for fast execution and stable, energy-efficient, balanced, and cost-effective allocation. This article aims to optimize multiple objectives using fog computing by developing multi-objective optimization with high exploitive searching. The developed algorithm is an evolutionary genetic type designated as Hyper Angle Exploitative Searching (HAES). It uses hyper angle along with crowding distance for prioritizing solutions within the same rank and selecting the highest priority solutions. The approach was evaluated on multi-objective mathematical problems and its superiority was revealed by comparing its performance with benchmark approaches. A framework of multi-criteria optimization for fog computing was proposed, the Fog Computing Closed Loop Model (FCCL). Results have shown that HAES outperforms other relevant benchmarks in terms of non-domination and optimality metrics with over 70% confidence of the t-test for rejecting the null-hypothesis of non-superiority in terms of the domination metric set coverage.<\/jats:p>","DOI":"10.3390\/s21020558","type":"journal-article","created":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T01:33:29Z","timestamp":1610674409000},"page":"558","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Hyper-Angle Exploitative Searching for Enabling Multi-Objective Optimization of Fog Computing"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9708-4161","authenticated-orcid":false,"given":"Taj-Aldeen","family":"Naser Abdali","sequence":"first","affiliation":[{"name":"Centre for Cyber Security, Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1348-5804","authenticated-orcid":false,"given":"Rosilah","family":"Hassan","sequence":"additional","affiliation":[{"name":"Centre for Cyber Security, Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7337-6736","authenticated-orcid":false,"given":"Azana Hafizah","family":"Mohd Aman","sequence":"additional","affiliation":[{"name":"Centre for Cyber Security, Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0941-7318","authenticated-orcid":false,"given":"Quang Ngoc","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Communications and Computer Engineering, Faculty of Science and Engineering, Waseda University, Tokyo 169-8050, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6746-6011","authenticated-orcid":false,"given":"Ahmed Salih","family":"Al-Khaleefa","sequence":"additional","affiliation":[{"name":"Centre for Cyber Security, Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Badii, C., Bellini, P., Difino, A., and Nesi, P. (2019). Sii-Mobility: An IoT\/IoE architecture to enhance smart city mobility and transportation services. Sensors, 19.","DOI":"10.3390\/s19010001"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wu, F., Wu, T., and Yuce, M.R. (2019). An internet-of-things (IoT) network system for connected safety and health monitoring applications. Sensors, 19.","DOI":"10.3390\/s19010021"},{"key":"ref_3","first-page":"1","article-title":"The Implementation of Internet of Things using Test Bed in the UKMnet Environment","volume":"8","author":"Ibrahim","year":"2019","journal-title":"Asia Pac. J. Inf. Technol. Multimed"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1109\/COMST.2018.2844341","article-title":"Deep learning for IoT big data and streaming analytics: A survey","volume":"20","author":"Mohammadi","year":"2018","journal-title":"Ieee Commun. Surv. Tutor."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sadeq, A.S., Hassan, R., Al-rawi, S.S., Jubair, A.M., and Aman, A.H.M. (2019, January 25\u201326). A Qos Approach For Internet Of Things (Iot) Environment Using Mqtt Protocol. Proceedings of the 2019 International Conference on Cybersecurity (ICoCSec), Negeri Sembilan, Malaysia.","DOI":"10.1109\/ICoCSec47621.2019.8971097"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4252","DOI":"10.1109\/JIOT.2018.2875743","article-title":"Toward improved offloading efficiency of data transmission in the IoT-cloud by leveraging secure truncating OFDM","volume":"6","author":"Jia","year":"2018","journal-title":"Ieee Internet Things J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111763","DOI":"10.1109\/ACCESS.2020.3002932","article-title":"A survey on trend and classification of internet of things reviews","volume":"8","author":"Aman","year":"2020","journal-title":"Ieee Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1016\/j.future.2016.11.031","article-title":"Secure integration of IoT and cloud computing","volume":"78","author":"Stergiou","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hassan, R., Jubair, A.M., Azmi, K., and Bakar, A. (2016, January 26\u201328). Adaptive congestion control mechanism in CoAP application protocol for internet of things (IoT). Proceedings of the 2016 International Conference on Signal Processing and Communication (ICSC), Noida, India.","DOI":"10.1109\/ICSPCom.2016.7980560"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Iyer, G.N. (2020). Evolutionary games for cloud, fog and edge computing\u2014A comprehensive study. Computational Intelligence in Data Mining, Springer.","DOI":"10.1007\/978-981-13-8676-3_27"},{"key":"ref_11","unstructured":"Cisco Systems (2015). Fog Computing and the Internet of Things: Extend the Cloud to Where the Things Are, Cisco Systems. White paper."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11277-020-07230-4","article-title":"Symmetric Key-Based Secure Storage and Retrieval of IoT Data on a Semi-trusted Cloud Server","volume":"113","author":"Shabisha","year":"2020","journal-title":"Wirel. Pers. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4150","DOI":"10.1109\/JIOT.2018.2875520","article-title":"Folo: Latency and quality optimized task allocation in vehicular fog computing","volume":"6","author":"Zhu","year":"2018","journal-title":"Ieee Internet Things J."},{"key":"ref_14","first-page":"1","article-title":"Computing the interleaving distance is NP-hard","volume":"20","author":"Bjerkevik","year":"2019","journal-title":"Found. Comput. Math."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s40747-017-0053-9","article-title":"A mini-review on preference modeling and articulation in multi-objective optimization: Current status and challenges","volume":"3","author":"Wang","year":"2017","journal-title":"Complex Intell. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Han, D., Li, Y., Song, T., and Liu, Z. (2020). Multi-Objective Optimization of Loop Closure Detection Parameters for Indoor 2D Simultaneous Localization and Mapping. Sensors, 20.","DOI":"10.3390\/s20071906"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115058","DOI":"10.1016\/j.apenergy.2020.115058","article-title":"Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm","volume":"269","author":"Mayer","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Albadr, M.A., Tiun, S., Ayob, M., and AL-Dhief, F. (2020). Genetic Algorithm Based on Natural Selection Theory for Optimization Problems. Symmetry, 12.","DOI":"10.3390\/sym12111758"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Abdali, T.-A.N., Hassan, R., Muniyandi, R.C., Mohd Aman, A.H., Nguyen, Q.N., and Al-Khaleefa, A.S. (2020). Optimized Particle Swarm Optimization Algorithm for the Realization of an Enhanced Energy-Aware Location-Aided Routing Protocol in MANET. Information, 11.","DOI":"10.3390\/info11110529"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mai, Y., Shi, H., Liao, Q., Sheng, Z., Zhao, S., Ni, Q., and Zhang, W. (2020). Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts. Sensors, 20.","DOI":"10.3390\/s20082230"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"22920","DOI":"10.1109\/ACCESS.2018.2829262","article-title":"A multi-objective genetic algorithm based on fitting and interpolation","volume":"6","author":"Han","year":"2018","journal-title":"Ieee Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.jocs.2017.09.015","article-title":"A novel non-dominated sorting algorithm for evolutionary multi-objective optimization","volume":"23","author":"Bao","year":"2017","journal-title":"J. Comput. Sci."},{"key":"ref_23","first-page":"119","article-title":"Evaluation of Final Product Integrated with Intelligent Systems in Architectural Education Studios","volume":"5","author":"Arslan","year":"2017","journal-title":"Online J. Art Des."},{"key":"ref_24","first-page":"9697104","article-title":"An improved multiobjective algorithm: DNSGA2-PSA","volume":"2018","author":"Qu","year":"2018","journal-title":"J. Robot."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"51253","DOI":"10.1109\/ACCESS.2018.2869732","article-title":"NSGAII with local search based heavy perturbation for bi-objective weighted clique problem","volume":"6","author":"Cai","year":"2018","journal-title":"Ieee Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"66964","DOI":"10.1109\/ACCESS.2019.2917838","article-title":"A new evolutionary multiobjective model for traveling salesman problem","volume":"7","author":"Chen","year":"2019","journal-title":"Ieee Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Roy, P.C., Islam, M.M., and Deb, K. (2016, January 20\u201324). Best order sort: A new algorithm to non-dominated sorting for evolutionary multi-objective optimization. Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, Denver, CO, USA.","DOI":"10.1145\/2908961.2931684"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"Ieee Trans. Evol.. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2729974","article-title":"Many-objective software remodularization using NSGA-III","volume":"24","author":"Mkaouer","year":"2015","journal-title":"Acm Trans. Softw. Eng. Methodol. (Tosem)"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"10196","DOI":"10.1109\/ACCESS.2018.2890461","article-title":"Searching with direction awareness: Multi-objective genetic algorithm based on angle quantization and crowding distance MOGA-AQCD","volume":"7","author":"Metiaf","year":"2019","journal-title":"Ieee Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mahmud, R., Kotagiri, R., and Buyya, R. (2018). Fog computing: A taxonomy, survey and future directions. Internet of Everything, Springer.","DOI":"10.1007\/978-981-10-5861-5_5"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1007\/s11277-017-5200-5","article-title":"Multi-objective optimization of resource scheduling in fog computing using an improved NSGA-II","volume":"102","author":"Sun","year":"2018","journal-title":"Wirel. Pers. Commun."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1109\/JIOT.2017.2780236","article-title":"Multiobjective optimization for computation offloading in fog computing","volume":"5","author":"Liu","year":"2017","journal-title":"Ieee Internet Things J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4791","DOI":"10.1109\/JIOT.2018.2869226","article-title":"Joint optimization of energy consumption and latency in mobile edge computing for Internet of Things","volume":"6","author":"Cui","year":"2018","journal-title":"Ieee Internet Things J."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zahoor, S., Javaid, S., Javaid, N., Ashraf, M., Ishmanov, F., and Afzal, M.K. (2018). Cloud\u2013fog\u2013based smart grid model for efficient resource management. Sustainability, 10.","DOI":"10.3390\/su10062079"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.ins.2019.09.060","article-title":"Memory based self-adaptive sampling for noisy multi-objective optimization","volume":"511","author":"Rakshit","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/4235.797969","article-title":"Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach","volume":"3","author":"Zitzler","year":"1999","journal-title":"Ieee Trans. Evol. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TEVC.2011.2125968","article-title":"Solving multicommodity capacitated network design problems using multiobjective evolutionary algorithms","volume":"16","author":"Kleeman","year":"2012","journal-title":"Ieee Trans. Evol. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/3468.650319","article-title":"Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation","volume":"28","author":"Fonseca","year":"1998","journal-title":"Ieee Trans. Syst. Man Cybern.-Part A Syst. Hum."},{"key":"ref_40","unstructured":"Kursawe, F. (2016, January 17\u201321). A variant of evolution strategies for vector optimization. Proceedings of the International Conference on Parallel Problem Solving from Nature, Edinburgh, Scotland."},{"key":"ref_41","unstructured":"Winter, G., Periaux, J., Galan, M., and Cuesta, P. (1997). Hybrid GA for Multi Objective Aerodynamic Shape Optimisation, Genetic Algorithms in Engineering and Computer Science."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"12779","DOI":"10.1007\/s00500-019-03829-3","article-title":"Hiding sensitive itemsets with multiple objective optimization","volume":"23","author":"Lin","year":"2019","journal-title":"Soft Comput."},{"key":"ref_43","unstructured":"Deb, K. (2001). Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/558\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:11:13Z","timestamp":1760159473000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/558"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,14]]},"references-count":43,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["s21020558"],"URL":"https:\/\/doi.org\/10.3390\/s21020558","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,14]]}}}