{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:41:03Z","timestamp":1760060463195,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The increasing elderly population presents major challenges to traditional healthcare due to the need for continuous care, a shortage of skilled professionals, and increasing medical costs. To address this, smart elderly care homes where multiple residents live with the support of caregivers and IoT-based assistive technologies have emerged as a promising solution. For their effective operation, a reliable high speed network like 5G is essential, along with intelligent resource allocation to ensure efficient service delivery. This study proposes a deep reinforcement learning (DRL)-based resource management framework for smart elderly homes, formulated as a Markov decision process. The framework dynamically allocates computing and network resources in response to real-time application demands and system constraints. We implement and compare two DRL algorithms, emphasizing their strengths in optimizing edge utilization and throughput. System performance is evaluated across balanced, high-demand, and resource-constrained scenarios. The results demonstrate that the proposed DRL approach effectively learns adaptive resource management policies, making it a promising solution for next-generation intelligent elderly care environments.<\/jats:p>","DOI":"10.3390\/fi17090402","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T15:02:57Z","timestamp":1756825377000},"page":"402","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Resource Management in 5G-Enabled Smart Elderly Care Using Deep Reinforcement Learning"],"prefix":"10.3390","volume":"17","author":[{"given":"Krishnapriya V.","family":"Shaji","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Clappana 690525, India"}]},{"given":"Srilakshmi S.","family":"Rethy","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Clappana 690525, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7018-9476","authenticated-orcid":false,"given":"Simi","family":"Surendran","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Clappana 690525, India"}]},{"given":"Livya","family":"George","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Clappana 690525, India"}]},{"given":"Namita","family":"Suresh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Clappana 690525, India"}]},{"given":"Hrishika","family":"Dayan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Clappana 690525, India"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"ref_1","unstructured":"(2021). World Population Ageing 2020: Highlights: Living Arrangements of Older Persons, UN."},{"key":"ref_2","unstructured":"Mayhew, L.D. (2000). Health and Elderly Care Expenditure in an Aging World, IIASA. IIASA Research Report."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ogura, S., and Jakovljevic, M.M. (2018). Global population aging-health care, social and economic consequences. Front. Public Health, 6.","DOI":"10.3389\/fpubh.2018.00335"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ahmed, S., Irfan, S., Kiran, N., Masood, N., Anjum, N., and Ramzan, N. (2023). Remote health monitoring systems for elderly people: A survey. Sensors, 23.","DOI":"10.3390\/s23167095"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Majumder, S., Aghayi, E., Noferesti, M., Memarzadeh-Tehran, H., Mondal, T., Pang, Z., and Deen, M.J. (2017). Smart homes for elderly healthcare\u2014Recent advances and research challenges. Sensors, 17.","DOI":"10.3390\/s17112496"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hung, J. (2022). Smart elderly care services in China: Challenges, progress, and policy development. Sustainability, 15.","DOI":"10.3390\/su15010178"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"100614","DOI":"10.1016\/j.measen.2022.100614","article-title":"AI based elderly fall prediction system using wearable sensors: A smart home-care technology with IOT","volume":"25","author":"Kulurkar","year":"2023","journal-title":"Meas. Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shi, J., Zhang, N., Wu, K., and Wang, Z. (2025). Application Status, Challenges, and Development Prospects of Smart Technologies in Home-Based Elder Care. Electronics, 14.","DOI":"10.3390\/electronics14122463"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"S14","DOI":"10.3349\/ymj.2022.63.S14","article-title":"Scoping review of the literature on smart healthcare for older adults","volume":"63","author":"Ji","year":"2022","journal-title":"Yonsei Med. J."},{"key":"ref_10","first-page":"188519","article-title":"Big data and AI algorithms for sustainable development goals: A topic modeling analysis","volume":"12","author":"Nedungadi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_11","unstructured":"Choukou, M.A., and Syed-Abdul, S. (2021). Smart Home Technologies and Services for Geriatric Rehabilitation, Academic Press."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.30574\/ijsra.2024.12.2.1381","article-title":"Technological trends in 5G networks for IoT-enabled smart healthcare: A review","volume":"12","author":"Hoque","year":"2024","journal-title":"Int. J. Sci. Res. Arch."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Peralta-Ochoa, A.M., Chaca-Asmal, P.A., Guerrero-V\u00e1squez, L.F., Ordo\u00f1ez-Ordo\u00f1ez, J.O., and Coronel-Gonz\u00e1lez, E.J. (2023). Smart healthcare applications over 5G networks: A systematic review. Appl. Sci., 13.","DOI":"10.3390\/app13031469"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112290","DOI":"10.1109\/ACCESS.2021.3099845","article-title":"5G new radio key performance indicators evaluation for IMT-2020 radio interface technology","volume":"9","author":"Reddy","year":"2021","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1007\/s00464-023-10585-x","article-title":"Telemedicine network latency management system in 5G telesurgery: A feasibility and effectiveness study","volume":"38","author":"Li","year":"2024","journal-title":"Surg. Endosc."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Aswanth, A., Manoj, E., Rajendran, K., EM, S.K., and Duttagupta, S. (October, January 30). Meeting Delay guarantee in Telemedicine service using SDN framework. Proceedings of the 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), Bangalore, India.","DOI":"10.1109\/R10-HTC53172.2021.9641695"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nayak, S., and Patgiri, R. (2021). 6G communication technology: A vision on intelligent healthcare. Health Informatics: A Computational Perspective in Healthcare, Springer.","DOI":"10.1007\/978-981-15-9735-0_1"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ahad, A., Tahir, M., Aman Sheikh, M., Ahmed, K.I., Mughees, A., and Numani, A. (2020). Technologies trend towards 5G network for smart health-care using IoT: A review. Sensors, 20.","DOI":"10.3390\/s20144047"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mamdiwar, S.D., Shakruwala, Z., Chadha, U., Srinivasan, K., and Chang, C.Y. (2021). Recent advances on IoT-assisted wearable sensor systems for healthcare monitoring. Biosensors, 11.","DOI":"10.3390\/bios11100372"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Abdulmalek, S., Nasir, A., Jabbar, W.A., Almuhaya, M.A., Bairagi, A.K., Khan, M.A.M., and Kee, S.H. (2022). IoT-based healthcare-monitoring system towards improving quality of life: A review. Healthcare, 10.","DOI":"10.3390\/healthcare10101993"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bisht, N.S., and Duttagupta, S. (2022, January 16\u201318). Deploying a Federated Learning Based AI Solution in a Hierarchical Edge Architecture. Proceedings of the 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), Hyderabad, India.","DOI":"10.1109\/R10-HTC54060.2022.9929526"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/s42979-024-03606-6","article-title":"A Reinforcement Learning Approach for Routing in Marine Communication Network of Fishing Vessels","volume":"6","author":"Surendran","year":"2025","journal-title":"SN Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Surendran, S., Montresor, A., Ramesh, M.V., and Casari, P. (2022, January 18\u201321). Reinforcement Learning-Based Connectivity Restoration in an Ocean Network of Fishing Vessels. Proceedings of the 2022 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Gandhinagar, India.","DOI":"10.1109\/ANTS56424.2022.10227794"},{"key":"ref_24","unstructured":"Gopalakrishnan, A., and Duttagupta, S. (2020, January 14\u201317). Scheduling in time-sensitive networks using deep reinforcement learning. Proceedings of the International Conference on Applied Soft Computing and Communication Networks, Chennai, India."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e4929","DOI":"10.1002\/ett.4929","article-title":"Resource allocation in 5G cloud-RAN using deep reinforcement learning algorithms: A review","volume":"35","author":"Khani","year":"2024","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, Z., Yuan, Y., and Guan, X. (2025). Distributed Real-Time and Fair Resource Allocation for 5G Dense Cellular Networks Based on Deep Reinforcement Learning. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2025.3572715"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MVT.2019.2903655","article-title":"Deep reinforcement learning for mobile 5G and beyond: Fundamentals, applications, and challenges","volume":"14","author":"Xiong","year":"2019","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1109\/COMST.2024.3410295","article-title":"A survey on beyond 5g network slicing for smart cities applications","volume":"27","author":"Rafique","year":"2024","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1109\/OJCOMS.2021.3057679","article-title":"The road towards 6G: A comprehensive survey","volume":"2","author":"Jiang","year":"2021","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3527","DOI":"10.1109\/TNSM.2021.3066625","article-title":"AI-based resource provisioning of IoE services in 6G: A deep reinforcement learning approach","volume":"18","author":"Sami","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"65156","DOI":"10.1109\/ACCESS.2022.3183647","article-title":"Reinforcement learning-empowered mobile edge computing for 6G edge intelligence","volume":"10","author":"Wei","year":"2022","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.future.2019.01.059","article-title":"Task migration for mobile edge computing using deep reinforcement learning","volume":"96","author":"Zhang","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"106","DOI":"10.3390\/network2010008","article-title":"A dynamic service placement based on deep reinforcement learning in mobile edge computing","volume":"2","author":"Lu","year":"2022","journal-title":"Network"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"103058","DOI":"10.1016\/j.jnca.2021.103058","article-title":"Service migration in multi-access edge computing: A joint state adaptation and reinforcement learning mechanism","volume":"183","author":"Rui","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"16742","DOI":"10.1109\/JIOT.2022.3164441","article-title":"Dynamic task allocation and service migration in edge-cloud iot system based on deep reinforcement learning","volume":"9","author":"Chen","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11341","DOI":"10.1109\/JIOT.2023.3332421","article-title":"Joint service migration and resource allocation in edge IoT system based on deep reinforcement learning","volume":"11","author":"Liu","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.comcom.2021.04.028","article-title":"Deep reinforcement learning for computation offloading in mobile edge computing environment","volume":"175","author":"Chen","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"17508","DOI":"10.1109\/JIOT.2021.3081694","article-title":"A DRL agent for jointly optimizing computation offloading and resource allocation in MEC","volume":"8","author":"Chen","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103669","DOI":"10.1016\/j.jnca.2023.103669","article-title":"A comprehensive survey on reinforcement-learning-based computation offloading techniques in Edge Computing Systems","volume":"216","author":"Hortelano","year":"2023","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1186\/s13638-020-01801-6","article-title":"Decentralized computation offloading for multi-user mobile edge computing: A deep reinforcement learning approach","volume":"2020","author":"Chen","year":"2020","journal-title":"Eurasip J. Wirel. Commun. Netw."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MWC.006.2200407","article-title":"Machine learning for 6G enhanced ultra-reliable and low-latency services","volume":"30","author":"Liu","year":"2023","journal-title":"IEEE Wirel. Commun."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Doke, A.R., and Sangeeta, K. (2018, January 16\u201318). Deep reinforcement learning based load balancing policy for balancing network traffic in datacenter environment. Proceedings of the 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), Bangalore, India.","DOI":"10.1109\/ICGCIoT.2018.8752969"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/s13677-023-00461-3","article-title":"Optimizing task offloading and resource allocation in edge-cloud networks: A DRL approach","volume":"12","author":"Ullah","year":"2023","journal-title":"J. Cloud Comput."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pradhan, R., Dash, A.K., and Jena, B. (2021). Resource management challenges in IoT based healthcare system. Smart Healthcare Analytics: State of the Art, Springer.","DOI":"10.1007\/978-981-16-5304-9_4"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Mutlag, A.A., Ghani, M.K.A., and Mohammed, M.A. (2021). A healthcare resource management optimization framework for ECG biomedical sensors. Efficient Data Handling for Massive Internet of Medical Things: Healthcare Data Analytics, Springer.","DOI":"10.1007\/978-3-030-66633-0_10"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lv, J., Chen, C.M., Kumari, S., and Li, K. (Digit. Commun. Netw., 2025). Resource allocation for AI-native healthcare systems in 6G dense networks using deep reinforcement learning, Digit. Commun. Netw., in press.","DOI":"10.1016\/j.dcan.2025.06.011"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/j.dcan.2024.06.008","article-title":"Deep reinforcement learning based latency-energy minimization in smart healthcare network","volume":"11","author":"Su","year":"2025","journal-title":"Digit. Commun. Netw."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Naseer, F., Addas, A., Tahir, M., Khan, M.N., and Sattar, N. (2025). Integrating generative adversarial networks with IoT for adaptive AI-powered personalized elderly care in smart homes. Front. Artif. Intell., 8.","DOI":"10.3389\/frai.2025.1520592"},{"key":"ref_49","unstructured":"Rhee, I., Shin, M., Hong, S., Lee, K., Kim, S., and Chong, S. (2025, July 29). CRAWDAD ncsu\/mobilitymodels. IEEE Dataport. Available online: https:\/\/ieee-dataport.org\/open-access\/crawdad-ncsumobilitymodels."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/9\/402\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:38:18Z","timestamp":1760035098000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/9\/402"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,2]]},"references-count":49,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["fi17090402"],"URL":"https:\/\/doi.org\/10.3390\/fi17090402","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2025,9,2]]}}}