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<p>Integrating Internet of Things (IoT) devices with fifth-generation (5&#xa0;G) networks has significantly increased data traffic, posing novel challenges for efficient network management. The intermittent and unpredictable nature of IoT data, coupled with the mobility of 5&#xa0;G users, leads to increased latency and reduced Quality of Service (QoS). This paper proposes innovative caching and scheduling algorithms to address these challenges by leveraging the unique characteristics of transient IoT data and user mobility. The proposed methods include the scheduling algorithms TYPE and TS, as well as the caching algorithms LRUD, LFUD, and RATE. Furthermore, by combining the proposed scheduling and caching algorithms, hybrid algorithms are developed to enhance the overall performance, such as TS-LRUD, TS-LFUD, and TS-RATE. These hybrid methods leverage the strengths of both scheduling and caching to improve resource allocation and reduce latency. These approaches prioritize data based on type, size, and freshness, ensuring efficient resource utilization and reduced latency. Specifically, the TS-RATE method integrates cooperative caching strategies such as NEIGHBOUR, SIBLING, and FILTERING, resulting in significantly lower data retrieval delays and higher Cache Hit Ratio (CHR). Unlike previous approaches that treated all data uniformly or focused solely on static data, the proposed methods guarantee real-time delivery of critical IoT data while improving overall network performance. Comprehensive performance evaluations validate the superiority of these algorithms, demonstrating their effectiveness in reducing latency and enhancing network performance in dynamic and heterogeneous environments.</p>

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IoT data caching and scheduling in heterogeneous mobile networks by exploiting mobility and transient data characteristics

  • 期刊论文

  • 发表日期：

    2025年5月6日

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  • Seyedeh Maryam Mousavi Jalali,
  • Fatemeh Rezaei

摘要

Integrating Internet of Things (IoT) devices with fifth-generation (5 G) networks has significantly increased data traffic, posing novel challenges for efficient network management. The intermittent and unpredictable nature of IoT data, coupled with the mobility of 5 G users, leads to increased latency and reduced Quality of Service (QoS). This paper proposes innovative caching and scheduling algorithms to address these challenges by leveraging the unique characteristics of transient IoT data and user mobility. The proposed methods include the scheduling algorithms TYPE and TS, as well as the caching algorithms LRUD, LFUD, and RATE. Furthermore, by combining the proposed scheduling and caching algorithms, hybrid algorithms are developed to enhance the overall performance, such as TS-LRUD, TS-LFUD, and TS-RATE. These hybrid methods leverage the strengths of both scheduling and caching to improve resource allocation and reduce latency. These approaches prioritize data based on type, size, and freshness, ensuring efficient resource utilization and reduced latency. Specifically, the TS-RATE method integrates cooperative caching strategies such as NEIGHBOUR, SIBLING, and FILTERING, resulting in significantly lower data retrieval delays and higher Cache Hit Ratio (CHR). Unlike previous approaches that treated all data uniformly or focused solely on static data, the proposed methods guarantee real-time delivery of critical IoT data while improving overall network performance. Comprehensive performance evaluations validate the superiority of these algorithms, demonstrating their effectiveness in reducing latency and enhancing network performance in dynamic and heterogeneous environments.