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 (2025A1515010235, 2024A1515012015)

Received Date: 2025-08-28
Accepted Date: 2025-12-29
Rev Recd Date: 2025-12-29

Available Online: 2026-01-04

Abstract

Abstract

Objective In short-packet communication scenarios for the Industrial Internet of Things (IIoT), devices operate under stringent energy constraints, whereas certain applications require timely data delivery, which makes real-time performance difficult to guarantee. To address this issue, this study analyzes information freshness in Energy Harvesting (EH) networks and examines the effects of energy storage capacity, random access strategies, and packet block length on the Age of Information (AoI). The objective is to provide effective optimization guidelines for the design of practical IIoT communication systems. Methods An accurate system model is established based on short-packet communication theory, random access mechanisms, and EH models. The charging and discharging processes of the energy queue are characterized as a Markov chain, from which the steady-state distribution of energy states is derived, followed by a general expression for the average AoI. A mathematical optimization problem is then formulated to minimize the average AoI. To improve practical applicability, two extreme battery-capacity scenarios are considered. For the minimum battery capacity case, a closed-form analytical solution for the optimal packet generation probability is obtained. For the ideal infinite battery capacity case, the packet generation probability and packet block length are jointly optimized, yielding closed-form optimal solutions for both parameters. Extensive simulations are conducted to evaluate the average AoI under different network parameter settings and to verify the effectiveness of the proposed optimization strategies. Results and Discussions An analytical expression for the average AoI is derived, and its optimization is investigated under two extreme battery-capacity conditions. For the minimum battery capacity case, the optimal packet generation probability balances update frequency and channel collision (Fig. 5). As the network size increases, the optimal packet generation probability decreases, which significantly improves the average AoI (Theorem 1; Fig. 6). For the ideal infinite battery capacity case, both packet block length and packet generation probability affect the average AoI (Fig. 7). With a fixed packet generation probability, optimizing the packet block length reduces the AoI, which indicates the existence of an optimal block length that balances transmission reliability and energy consumption. When the packet block length is fixed, a low packet generation probability leads to infrequent updates and increased delay, whereas a high probability increases collision in the Energy-Sufficient Regime (ESR) but enables more efficient utilization of energy and channel resources in the Energy-Limited Regime (ELR). Joint optimization of the packet block length and packet generation probability is consistent with the solution obtained via exhaustive search (Theorem 2; Fig. 8). The optimal packet block length increases with network size. In the ELR, the optimal packet generation probability remains equal to one, whereas it decreases with network size to balance update frequency and collision risk (Fig. 9, Fig. 10). In addition, the average AoI varies with the energy arrival rate, which reveals the effects of battery capacity and packet generation probability on overall system performance (Fig. 11). Conclusions For the minimum battery capacity case, the average AoI is minimized when the packet generation probability is set to its theoretical optimal value. Under ideal infinite battery capacity, both the packet generation probability and the packet block length must be jointly configured to their respective theoretical optimal values to achieve the minimum average AoI. Theoretical analysis shows that the selection of the optimal packet block length requires a trade-off between decoding error probability and energy consumption. In the ELR, when the packet block length is preconfigured to its optimal value, an energy buffer supporting a single transmission is sufficient, which allows network nodes to adapt effectively to external energy supply limitations. Network nodes should actively access the channel to fully utilize harvested energy and maintain timely information updates, thereby achieving the optimal average AoI. In contrast, under abundant energy conditions or in large-scale networks, network nodes should adjust the packet generation probability to balance channel collision and update frequency. Simulation results further confirm the proposed optimization strategy and demonstrate that the optimized LoRa network significantly improves information timeliness, which provides 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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