{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T12:38:25Z","timestamp":1772195905674,"version":"3.50.1"},"reference-count":13,"publisher":"International Association of Online Engineering (IAOE)","issue":"04","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Interact. Mob. Technol."],"abstract":"<jats:p>The rapid expansion of mobile applications has led to increased energy consumption and carbon emissions, which are now regulated by more stringent national and international environmental laws. This paper presents GREEAM (Green and Energy-Efficient Mobile Architecture Model), a novel framework designed to help mobile ecosystems comply with evolving environmental regulations while maintaining high performance. GREEAM incorporates context-aware task scheduling, intelligent workload offloading, and renewable energy-based optimization to reduce power consumption and carbon emissions without compromising service quality. Simulations conducted in Internet of Things (IoT), mobile healthcare, and smart city scenarios demonstrate that GREEAM decreases energy consumption by 28%, reduces latency by 22%, and lowers device carbon emissions by up to 31% compared to conventional systems. These advancements support the attainment of mandated carbon-reduction and energy-efficiency objectives. By integrating regulatory compliance into its foundational design, GREEAM provides a practical, deployable solution for sustainable mobile ecosystems that meet both technical and legal requirements.<\/jats:p>","DOI":"10.3991\/ijim.v20i04.60069","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T11:19:49Z","timestamp":1772191189000},"source":"Crossref","is-referenced-by-count":0,"title":["GREEAM: A Green and Energy-Efficient Mobile Architecture Model for Sustainable Mobile Ecosystems Regulation"],"prefix":"10.3991","volume":"20","author":[{"family":"Shatha Abdul Jalil Hasan Ismaeel","sequence":"first","affiliation":[]},{"family":"R. Madhubala","sequence":"additional","affiliation":[]},{"family":"T.Padmapriya","sequence":"additional","affiliation":[]},{"family":"S. V. Manikanthan","sequence":"additional","affiliation":[]},{"family":"A. Joshi","sequence":"additional","affiliation":[]}],"member":"2371","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"30013","doi-asserted-by":"crossref","unstructured":"[1] Tahir, H. M., & Mkpojiogu, E. O. (2018). Towards Secure Data Circulation in Mobile Cloud Computing. IIRJET, 4(1), 18-23.","DOI":"10.32595\/iirjet.org\/v4i1.2018.69"},{"key":"30015","doi-asserted-by":"crossref","unstructured":"[2] Salma, S., Begum, A., & Syed, H. (2024). Practical and Innovative Applications of IoT and IoT Networks (Smart Cities, Smart Mobility, Smart Home, Smart Health, Smart Grid, etc.). In AI for Climate Change and Environmental Sustainability (pp. 121-144). CRC Press.","DOI":"10.1201\/9781003452393-10"},{"key":"30017","doi-asserted-by":"crossref","unstructured":"[3] Ser\u00f4dio, C., Cunha, J., Candela, G., Rodriguez, S., Sousa, X. R., & Branco, F. (2023). The 6G ecosystem as support for IoE and private networks: Vision, requirements, and challenges. 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The Journal of Supercomputing, 79(10), 10977-11006.","DOI":"10.1007\/s11227-023-05093-7"},{"key":"30031","doi-asserted-by":"crossref","unstructured":"[10] Huang, L., & Yu, Q. (2024). Mobility-aware and energy-efficient offloading for mobile edge computing in cellular networks. Ad Hoc Networks, 158, 103472.","DOI":"10.1016\/j.adhoc.2024.103472"},{"key":"30033","doi-asserted-by":"crossref","unstructured":"[11] Madiyev, A., Bulegenov, D., Karzhaubayev, A., Murzabulatov, M., & Bui, D. M. (2025). Energy-efficient offloading framework for mobile edge\/cloud computing based on convex optimization and Deep Q-Network. The Journal of Supercomputing, 81(11), 1-49.","DOI":"10.1007\/s11227-025-07647-3"},{"key":"30035","doi-asserted-by":"crossref","unstructured":"[12] Huang, L., & Yu, Q. (2024). Mobility-aware and energy-efficient offloading for mobile edge computing in cellular networks. 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