Age of Information for Energy Harvesting-Driven LoRa Short-Packet Communication Networks

XIAO Shuyu; SUN Xinghua; YUAN Anshan; ZHAN Wen; CHEN Xiang

doi:10.11999/JEIT250814

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XIAO Shuyu, SUN Xinghua, YUAN Anshan, ZHAN Wen, CHEN Xiang. Age of Information for Energy Harvesting-Driven LoRa Short-Packet Communication Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250814

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XIAO Shuyu, SUN Xinghua, YUAN Anshan, ZHAN Wen, CHEN Xiang. Age of Information for Energy Harvesting-Driven LoRa Short-Packet Communication Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250814

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Age of Information for Energy Harvesting-Driven LoRa Short-Packet Communication Networks

doi: 10.11999/JEIT250814 cstr: 32379.14.JEIT250814

1.
School of Electronics and Communication Engineering, Sun Yat-sen Univ., Shenzhen 518107, China
2.
School of Electronics and Information Technology, Sun Yat-sen Univ., Guangzhou 510000, China

Funds: Shenzhen Science and Technology Program (CJGJZD20240729142100001), Guangdong Basic and Applied Basic Research Foundation (2024A1515012015)

Accepted Date: 2025-12-29
Rev Recd Date: 2025-12-29

Available Online: 2026-01-04

Abstract

Abstract

Objective In short packet communication scenarios of the Industrial Internet of Things (IIoT), devices face limited energy while some require timely transmissions, making real-time performance challenging. To address this challenge, this paper analyzes the information freshness in energy-harvesting (EH) networks, revealing the influence of energy storage, access strategies, and packet block length on the age of information (AoI). The goal is to develop effective optimization schemes for practical IIoT communication system design. Methods This paper develops an accurate system model based on short packet communication theory, random access mechanisms, and EH models. By characterizing the charging and discharging process of the energy queue as a Markov chain, the steady-state distribution of different energy states is derived, and a general expression for the average AoI is further obtained. Based on this, a mathematical optimization problem is formulated to minimize the average AoI. To enhance practical relevance, two extreme cases of battery capacity are examined. For the minimum battery capacity, a closed-form analytical solution for the optimal packet generation probability is derived. For the ideal infinite battery capacity, the packet generation probability and packet block length are jointly optimized, yielding closed-form optimal solutions for both parameters. Finally, extensive simulation results illustrate the average AoI performance under various network parameter settings, thereby demonstrating the performance gains achieved through parameter optimization. Results and Discussions This paper derives an analytical expression for the average AoI and investigates its optimization under two extreme cases of battery capacity. Under minimum battery capacity, the optimal probability must balance update frequency and channel collision (Fig. 5). The optimal packet generation probability decreases with increasing network size, significantly improving the average AoI (Theorem 1; Fig. 6). In the ideal infinite battery scenario, both block length and packet generation probability affect the average AoI (Fig. 7). With a fixed packet generation probability, optimizing the block length reduces AoI, indicating the existence of an optimal block length that balances reliability and energy consumption. Similarly, when the block length is fixed, a low generation probability results in infrequent and delayed updates, whereas a high probability increases the collision under the energy-sufficient regime (ESR) but facilitates more efficient utilization of energy and channel resources under the energy-limited regime (ELR). The joint optimization of block length and packet generation probability is consistent with the optimal solution obtained via exhaustive search (Theorem 2; Fig. 8). The optimal block length increases with network size; and under ELR, the optimal packet generation probability remains 1, while it decreases with network size to balance update frequency and collision risk (Fig. 9, Fig. 10). Finally, the average AoI varies with the energy arrival rate and discusses the impact of battery capacity and packet generation probability on system performance (Fig. 11). Conclusions Under minimum battery capacity, the average AoI can be minimized by adjusting the packet generation probability to its theoretical optimal value. In the scenario of ideal infinite battery capacity, both the packet generation probability and the block length must be set simultaneously to their respective theoretical optimal values to achieve the optimal average AoI. Theoretical analysis indicates that the selection of the optimal block length requires a trade-off between error rate and energy consumption. Within the ELR, if the optimal block length of the packet is preconfigured, energy buffer supporting only one transmission is sufficient, allowing nodes to effectively adapt to external energy supply limitations. Furthermore, nodes should actively access the channel to fully utilize the available energy and ensure timely information updates, thereby achieving the optimal average AoI. In contrast, if energy is abundant or in large scale networks, nodes need to balance the trade-off between channel collision and update frequency by adjusting the packet generation probability to its optimal value in order to minimize the average AoI. Simulation results further validate the theoretical optimization strategy, showing that the optimized LoRa network significantly improves timeliness, providing theoretical guidance for the design of low-power short packet communication systems.
- Age of Information (AoI),
- Energy Harvesting (EH),
- Short-Packet Communications,
- Slotted Aloha,
- Industrial Internet of Things (IIoT)

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References(16)

References

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