Yongchareon, Sira2025-10-142025-10-142025-10-09Internet of Things (Netherlands), ISSN: 2543-1536 (Print); 2542-6605 (Online), Elsevier, 34, 101775-101775. doi: 10.1016/j.iot.2025.1017752543-15362542-6605http://hdl.handle.net/10292/19944Fog computing extends cloud capabilities to the network edge, enabling Internet-of-Things (IoT) devices to offload computation to nearby fog nodes rather than a remote cloud. Offloading aggregation tasks reduces data redundancy and accelerates analytics while easing device energy use and backhaul load. Yet end-to-end completion time—comprising execution, transmission, and queueing—can still be substantial, creating a challenging time-energy trade-off. We formulate data-aggregation offloading as a multi-objective optimization problem that jointly minimizes latency (makespan) and energy under compute and bandwidth constraints. To solve it, we develop an NSGA-III-based method that searches for Pareto-optimal offloading and scheduling decisions across sensor and fog nodes. Comprehensive simulations and systematic experiments demonstrate that our approach consistently outperforms state-of-the-art baselines, delivering lower latency and energy consumption with better scalability.© 2025 The Author. Published by Elsevier B.V. Creative Commons. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article. To request permission for a type of use not listed, please contact Elsevier Global Rights Department.4605 Data Management and Data Science4606 Distributed Computing and Systems Software46 Information and Computing Sciences7 Affordable and Clean Energy46 Information and computing sciencesJournal ArticleOpenAccess10.1016/j.iot.2025.101775