Data Aggregation and Scheduling to Optimize Information Freshness In Multi-Hop IoT Networks
The rapid proliferation of Internet of Things (IoT) devices with sensing, monitoring, and control capabilities has fueled the emergence of diverse real-time IoT applications. These applications often require the efficient transmission of collected sensitive data to centralized or distributed process...
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10993432/ |
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| author | Xueling Wu Long Qu Maurice J. Khabbaz |
| author_facet | Xueling Wu Long Qu Maurice J. Khabbaz |
| author_sort | Xueling Wu |
| collection | DOAJ |
| description | The rapid proliferation of Internet of Things (IoT) devices with sensing, monitoring, and control capabilities has fueled the emergence of diverse real-time IoT applications. These applications often require the efficient transmission of collected sensitive data to centralized or distributed processing units, such as cloud servers, edge nodes, or fog computing platforms—to enable timely responses. Ensuring the freshness of information while maintaining sustainable data transmission under energy constraints remains a critical challenge. To address this issue, we quantify information freshness using Age of Aggregated Information (AoAI) and investigate its minimization within energy-harvesting multi-hop IoT networks. Specifically, we develop data transmission schedules that jointly consider energy harvesting and packet aggregation. This problem is formulated as a Mixed-Integer Linear Programming (MILP) model, integrating data scheduling, packet aggregation, link interference, and energy resource management across multiple nodes. To tackle the computational complexity of this NP-hard problem, we propose an Energy-Aware Column Generation Algorithm (CGA). The original problem is decomposed into a Restricted Master Problem (RMP) and a Pricing Problem (PP). The RMP models data aggregation at Base Station (BS), while the PP incorporates an Energy-Aware Scheduling Algorithm (EASA) to generate feasible scheduling solutions. Simulation results demonstrate that the proposed CGA consistently outperforms EASA, the Genetic Algorithm (GA), and the Juventas Algorithm, achieving an average deviation of only 1.84% from the global optimal solution. |
| format | Article |
| id | doaj-art-346a7152d63d44b0b81fcd866eee7bbc |
| institution | OA Journals |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-346a7152d63d44b0b81fcd866eee7bbc2025-08-20T02:32:22ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0164591460710.1109/OJCOMS.2025.356800510993432Data Aggregation and Scheduling to Optimize Information Freshness In Multi-Hop IoT NetworksXueling Wu0Long Qu1https://orcid.org/0000-0002-4246-7421Maurice J. Khabbaz2https://orcid.org/0000-0002-3472-8660Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, ChinaDepartment of Computer Science, Faculty of Arts and Science, American University of Beirut, Beirut, LebanonThe rapid proliferation of Internet of Things (IoT) devices with sensing, monitoring, and control capabilities has fueled the emergence of diverse real-time IoT applications. These applications often require the efficient transmission of collected sensitive data to centralized or distributed processing units, such as cloud servers, edge nodes, or fog computing platforms—to enable timely responses. Ensuring the freshness of information while maintaining sustainable data transmission under energy constraints remains a critical challenge. To address this issue, we quantify information freshness using Age of Aggregated Information (AoAI) and investigate its minimization within energy-harvesting multi-hop IoT networks. Specifically, we develop data transmission schedules that jointly consider energy harvesting and packet aggregation. This problem is formulated as a Mixed-Integer Linear Programming (MILP) model, integrating data scheduling, packet aggregation, link interference, and energy resource management across multiple nodes. To tackle the computational complexity of this NP-hard problem, we propose an Energy-Aware Column Generation Algorithm (CGA). The original problem is decomposed into a Restricted Master Problem (RMP) and a Pricing Problem (PP). The RMP models data aggregation at Base Station (BS), while the PP incorporates an Energy-Aware Scheduling Algorithm (EASA) to generate feasible scheduling solutions. Simulation results demonstrate that the proposed CGA consistently outperforms EASA, the Genetic Algorithm (GA), and the Juventas Algorithm, achieving an average deviation of only 1.84% from the global optimal solution.https://ieeexplore.ieee.org/document/10993432/Multi-hop IoT networksage of aggregated information (AoAI)mixed-integer linear programming (MILP)column generation algorithm (CGA)data aggregation |
| spellingShingle | Xueling Wu Long Qu Maurice J. Khabbaz Data Aggregation and Scheduling to Optimize Information Freshness In Multi-Hop IoT Networks IEEE Open Journal of the Communications Society Multi-hop IoT networks age of aggregated information (AoAI) mixed-integer linear programming (MILP) column generation algorithm (CGA) data aggregation |
| title | Data Aggregation and Scheduling to Optimize Information Freshness In Multi-Hop IoT Networks |
| title_full | Data Aggregation and Scheduling to Optimize Information Freshness In Multi-Hop IoT Networks |
| title_fullStr | Data Aggregation and Scheduling to Optimize Information Freshness In Multi-Hop IoT Networks |
| title_full_unstemmed | Data Aggregation and Scheduling to Optimize Information Freshness In Multi-Hop IoT Networks |
| title_short | Data Aggregation and Scheduling to Optimize Information Freshness In Multi-Hop IoT Networks |
| title_sort | data aggregation and scheduling to optimize information freshness in multi hop iot networks |
| topic | Multi-hop IoT networks age of aggregated information (AoAI) mixed-integer linear programming (MILP) column generation algorithm (CGA) data aggregation |
| url | https://ieeexplore.ieee.org/document/10993432/ |
| work_keys_str_mv | AT xuelingwu dataaggregationandschedulingtooptimizeinformationfreshnessinmultihopiotnetworks AT longqu dataaggregationandschedulingtooptimizeinformationfreshnessinmultihopiotnetworks AT mauricejkhabbaz dataaggregationandschedulingtooptimizeinformationfreshnessinmultihopiotnetworks |