{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T06:23:16Z","timestamp":1776752596730,"version":"3.51.2"},"reference-count":58,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T00:00:00Z","timestamp":1645401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["RSP-2021\/260"],"award-info":[{"award-number":["RSP-2021\/260"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The search algorithm based on symbiotic organisms\u2019 interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm\u2019s mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61\u201320.08% and 1.92\u201325.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO.<\/jats:p>","DOI":"10.3390\/s22041674","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T20:48:41Z","timestamp":1645476521000},"page":"1674","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8607-2627","authenticated-orcid":false,"given":"Ajoze Abdulraheem","family":"Zubair","sequence":"first","affiliation":[{"name":"Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8824-6069","authenticated-orcid":false,"given":"Shukor Abd","family":"Razak","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md. Asri","family":"Ngadi","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0729-2654","authenticated-orcid":false,"given":"Arafat","family":"Al-Dhaqm","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2842-9736","authenticated-orcid":false,"given":"Wael M. S.","family":"Yafooz","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7730-9693","authenticated-orcid":false,"given":"Abdel-Hamid M.","family":"Emara","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia"},{"name":"Department of Computers and Systems Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11884, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aldosary","family":"Saad","sequence":"additional","affiliation":[{"name":"Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1920-7418","authenticated-orcid":false,"given":"Hussain","family":"Al-Aqrabi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1007\/s42235-019-0030-7","article-title":"Energy-efficient Virtual Machine Allocation Technique Using Flower Pollination Algorithm in Cloud Datacenter: A Panacea to Green Computing","volume":"16","author":"Usman","year":"2019","journal-title":"J. Bionic Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"594","DOI":"10.3844\/jcssp.2019.594.611","article-title":"Metaheuristic algorithms for independent task scheduling in symmetric and asymmetric cloud computing environment","volume":"15","author":"Samee","year":"2019","journal-title":"J. Comput. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1007\/s00521-019-04119-7","article-title":"An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments","volume":"32","author":"Zhou","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_4","first-page":"653","article-title":"Dynamic heterogeneous shortest job first (DHSJF): A task scheduling approach for heterogeneous cloud computing systems","volume":"11","author":"Seth","year":"2019","journal-title":"Int. J. Inf. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1109\/TCC.2018.2889956","article-title":"SLA-Based Profit Optimization Resource Scheduling for Big Data Analytics-as-a-Service Platforms in Cloud Computing Environments","volume":"9","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.comcom.2019.12.050","article-title":"Multi objective task scheduling algorithm based on SLA and processing time suitable for cloud environment","volume":"151","author":"Lavanya","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"12459","DOI":"10.1007\/s10586-017-1657-y","article-title":"Monitoring IaaS using various cloud monitors","volume":"22","author":"Stephen","year":"2019","journal-title":"Cluster Comput."},{"key":"ref_8","first-page":"3516","article-title":"Improved hybrid symbiotic organism search task-scheduling algorithm for cloud computing","volume":"12","author":"Choe","year":"2018","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_9","first-page":"1","article-title":"Enhanced fault identification and optimal task prediction (EFIOTP) algorithm during multi-resource utilization in cloud-based knowledge and personal computing","volume":"1","author":"Nandhini","year":"2019","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1504\/IJBIC.2018.092799","article-title":"An evolutionary approach to schedule deadline constrained bag of tasks in a cloud","volume":"11","author":"Sindhu","year":"2018","journal-title":"Int. J. Bio-Inspired Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.knosys.2019.01.023","article-title":"Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution","volume":"169","author":"Elaziz","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1007\/s11227-020-03317-8","article-title":"Dynamic scheduling of tasks in cloud computing applying dragonfly algorithm, biogeography-based optimization algorithm and Mexican hat wavelet","volume":"77","author":"Shirani","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zubair, A.A., Razak, S.B.A., Ngadi, M.A.B., Ahmed, A., and Madni, S.H.H. (2020). Convergence-based task scheduling techniques in cloud computing: A review. IRICT, AISC.","DOI":"10.1007\/978-3-030-33582-3_22"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s10586-018-2856-x","article-title":"Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment","volume":"22","author":"Madni","year":"2019","journal-title":"Cluster Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s10586-018-1823-x","article-title":"Chaotic social spider algorithm for load balance aware task scheduling in cloud computing","volume":"22","author":"Annadurai","year":"2019","journal-title":"Cluster Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.future.2015.08.006","article-title":"Symbiotic Organism Search optimization based task scheduling in cloud computing environment","volume":"56","author":"Abdullahi","year":"2016","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2483","DOI":"10.1007\/s10586-019-03022-z","article-title":"A hybrid multi-objective artificial bee colony algorithm for flexible task scheduling problems in cloud computing system","volume":"23","author":"Li","year":"2019","journal-title":"Cluster Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1007\/s10586-017-1479-y","article-title":"Task scheduling of cloud computing using integrated particle swarm algorithm and ant colony algorithm","volume":"22","author":"Chen","year":"2019","journal-title":"Cluster Comput."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mubeen, A., Ibrahim, M., Bibi, N., Baz, M., Hamam, H., and Cheikhrouhou, O. (2021). Alts: An adaptive Load Balanced Task Scheduling Approach for Cloud Computing. Processes, 9.","DOI":"10.3390\/pr9091514"},{"key":"ref_20","first-page":"435","article-title":"Hybrid Cat Swarm Optimization and Simulated Annealing for dynamic task scheduling on Cloud Computing Environment","volume":"3","author":"Gabi","year":"2018","journal-title":"J. Inf. Commun. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.jnca.2019.02.005","article-title":"An efficient symbiotic organisms search algorithm with chaotic optimization strategy for multi-objective task scheduling problems in cloud computing environment","volume":"133","author":"Abdullahi","year":"2019","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"14817","DOI":"10.1007\/s00521-020-04834-6","article-title":"Cloud customers service selection scheme based on improved conventional cat swarm optimization","volume":"32","author":"Gabi","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1016\/j.procs.2015.09.064","article-title":"Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments","volume":"65","author":"Awad","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_24","first-page":"5","article-title":"Particle Swarm Optimisation","volume":"927","author":"Okwu","year":"2021","journal-title":"Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.compstruc.2014.03.007","article-title":"Symbiotic Organisms Search: A new metaheuristic optimization algorithm","volume":"139","author":"Cheng","year":"2014","journal-title":"Comput. Struct."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.asoc.2016.04.030","article-title":"A Symbiotic Organisms Search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems","volume":"46","author":"Panda","year":"2016","journal-title":"Appl. Soft. Comput. J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Banerjee, S., and Chattopadhyay, S. (2016, January 16\u201318). Optimization of Three-Dimensional Turbo Code using Novel Symbiotic Organism Search Algorithm. Proceedings of the Conference: 2016 IEEE Annual India Conference (INDICON), Bangalore, India.","DOI":"10.1109\/INDICON.2016.7838874"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.eswa.2017.06.007","article-title":"Discrete symbiotic organisms search algorithm for travelling salesman problem","volume":"87","author":"Ezugwu","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Abdullahi, M., and Ngadi, M.A. (2016). Hybrid symbiotic organisms search optimization algorithm for scheduling of tasks on cloud computing environment. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0162054"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ezugwu, A.E., Adeleke, O.J., and Viriri, S. (2018). Symbiotic organisms search algorithm for the unrelated parallel machines scheduling with sequence-dependent setup times. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0200030"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.asoc.2016.10.006","article-title":"Symbiotic organisms search and two solution representations for solving the capacitated vehicle routing problem","volume":"52","author":"Yu","year":"2017","journal-title":"Appl. Soft. Comput. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"106067","DOI":"10.1016\/j.asoc.2020.106067","article-title":"A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for optimal allocation of DG in radial distribution networks","volume":"88","author":"Truong","year":"2020","journal-title":"Appl. Soft. Comput. J."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rodrigues, L.R., Gomes, P.P., Neto, A.R.R., and Junior, A.H.S. (2018, January 8\u201313). A Modified Symbiotic Organisms Search Algorithm Applied to Flow Shop Scheduling Problems. Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil.","DOI":"10.1109\/CEC.2018.8477846"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101104","DOI":"10.1016\/j.jocs.2020.101104","article-title":"Nature-inspired optimization algorithms: Challenges and open problems","volume":"46","author":"Yang","year":"2020","journal-title":"J. Comput. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.1007\/s13369-018-3602-7","article-title":"Multi-objective-Oriented Cuckoo Search Optimization-Based Resource Scheduling Algorithm for Clouds","volume":"44","author":"Madni","year":"2019","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_36","first-page":"446","article-title":"Current Perspective of Symbiotic Organisms Search Technique in Cloud Computing Environment: A Review","volume":"12","author":"Zubair","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.eswa.2018.10.045","article-title":"Symbiotic Organisms Search Algorithm: Theory, recent advances and applications","volume":"119","author":"Ezugwu","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"361","DOI":"10.5267\/j.dsl.2016.2.004","article-title":"Improved symbiotic organisms search algorithm for solving unconstrained function optimization","volume":"5","author":"Nama","year":"2016","journal-title":"Decis. Sci. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1007\/s11277-016-3586-0","article-title":"Power Optimization of Three Dimensional Turbo Code Using a Novel Modified Symbiotic Organism Search (MSOS) Algorithm","volume":"92","author":"Banerjee","year":"2017","journal-title":"Wirel. Pers. Commun."},{"key":"ref_40","first-page":"226","article-title":"Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization","volume":"3","author":"Tejani","year":"2016","journal-title":"J. Comput. Des. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1080\/01605682.2017.1418151","article-title":"A modified symbiotic organisms search algorithm for unmanned combat aerial vehicle route planning problem","volume":"70","author":"Miao","year":"2018","journal-title":"J. Oper. Res. Soc."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sa\u2019ad, S., Muhammed, A., Abdullahi, M., Abdullah, A., and Ayob, F.H. (2021). An enhanced discrete symbiotic organism search algorithm for optimal task scheduling in the cloud. Algorithms, 14.","DOI":"10.3390\/a14070200"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.future.2020.08.036","article-title":"A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers","volume":"115","author":"Alboaneen","year":"2021","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Madni, S.H.H., Abd Latiff, M.S., Abdullahi, M., Abdulhamid, S.M., and Usman, M.J. (2017). Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0176321"},{"key":"ref_45","first-page":"448","article-title":"Quality of service task scheduling algorithm for time-cost trade off scheduling problem in cloud computing environment","volume":"18","author":"Gabi","year":"2019","journal-title":"Int. J. Intell. Syst. Technol. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.future.2016.04.014","article-title":"Multi-objective scheduling of Scientific Workflows in multisite clouds","volume":"63","author":"Liu","year":"2016","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_47","first-page":"185","article-title":"A parallel task scheduling optimization algorithm based on clonal operator in green cloud computing","volume":"11","author":"Liu","year":"2016","journal-title":"J. Commun."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"9589","DOI":"10.1007\/s10586-017-1268-7","article-title":"A strategic performance of virtual task scheduling in multi cloud environment","volume":"22","author":"Thirumalaiselvan","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Panda, S.K., Member, I., Jana, P.K., and Member, I.S. (2015, January 29\u201330). A Multi-Objective Task Scheduling Algorithm for Heterogeneous Multi-Cloud Environment. Proceedings of the IEEE 2015 International Conference on Electronic Design, Computer Networks & Automated Verification (EDCAV), Shillong, India.","DOI":"10.1109\/EDCAV.2015.7060544"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Srivastava, D., and Kalra, M. (2019, January 21\u201323). Improved Symbiotic Organism Search Based Approach for Scheduling Jobs in Cloud. Proceedings of the 2019 5th International Conference on Innovation and Industrial Logistics (ICIIL 2019), Paris, France.","DOI":"10.1007\/978-981-15-3020-3_39"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"71","DOI":"10.11113\/sh.v9n1-3.1145","article-title":"Optimal Resource Scheduling for IaaS Cloud Computing using Cuckoo","volume":"9","author":"Madni","year":"2017","journal-title":"Sains Humanika"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.compositesb.2018.09.087","article-title":"Material optimization of functionally graded plates using deep neural network and modified symbiotic organisms search for eigenvalue problems","volume":"159","author":"Do","year":"2019","journal-title":"Compos. Part B Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.apm.2020.06.002","article-title":"Material optimization of tri-directional functionally graded plates by using deep neural network and isogeometric multimesh design approach","volume":"87","author":"Do","year":"2020","journal-title":"Appl. Math. Model."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1002\/spe.995","article-title":"CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms","volume":"41","author":"Calheiros","year":"2011","journal-title":"Softw. Pract. Exp."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3390\/diagnostics11020241","article-title":"Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine","volume":"11","author":"Lahoura","year":"2021","journal-title":"Diagnostics"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Garg, H., Gupta, N., Agrawal, R., Shivani, S., and Sharma, B. (2022). A real time cloud-based framework for glaucoma screening using EfficientNet. Multimed. Tools Appl., 1\u201322.","DOI":"10.1007\/s11042-021-11559-8"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chand, D.T., and Sharma, B. (2015, January 1\u20134). HRCCTP: A Hybrid Reliable and Congestion Control Transport Protocol for Wireless Sensor Networks. Proceedings of the 2015 IEEE SENSORS, Busan, Korea.","DOI":"10.1109\/ICSENS.2015.7370446"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Bajaj, K., Sharma, B., and Singh, R. (2021). Implementation analysis of IoT-based offloading frameworks on cloud\/edge computing for sensor generated big data. Complex Intell. Syst., 1\u201318.","DOI":"10.1007\/s40747-021-00434-6"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1674\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:24:00Z","timestamp":1760135040000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1674"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,21]]},"references-count":58,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22041674"],"URL":"https:\/\/doi.org\/10.3390\/s22041674","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,21]]}}}