An Energy-Efficient Scheme for Waking Co-Channel TDMA in LoRa Networks via the Integration of Bidirectional Timestamp Correction and Address Recognition

To address the issues of high energy consumption, data collisions, and invalid wake-ups of nontarget nodes in large-scale node-deployment scenarios of long-range (LoRa) star networks, this paper proposes an energy-saving wake-up scheme that combines (i) time-division multiple access (TDMA) slot allocation based on bidirectional timestamp correction with (ii) a sensing and communication integrated (ISAC) scheme proposed for physical address identification of LoRa nodes operating on the same channel. The scheme incorporates parameter estimation of the LoRa channel, which effectively enhances the identification accuracy and improves the system’s robustness. The proposed scheme consists of two parts: First, in case nodes briefly lose power, a bidirectional timestamp calibration algorithm and GPS-assisted timing are used to synchronize the gateway and each node with high precision, ensuring the accurate scheduling of the TDMA mechanism. Second, based on time synchronization, a “slot–LoRa module address” mapping table is constructed to set the communication time points between the gateway and each node. The gateway can wake the target nodes at specific, precise communication time points. Experimental results show that the proposed method maintains the error range within ±1 ms. The significant decrease in the rate of unnecessary node wake-up decreases data collisions and energy waste in the same channel environment. Energy savings scale with network size, thereby significantly extending the network life cycle.

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Publication Details

Journal
Future Internet
Published
2025-08-14
DOI
https://doi.org/10.3390/fi17080369
Citations
2
Primary Topic
IoT Networks and Protocols
Type
article
Field-Weighted Citation Impact
1.46

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article

An Energy-Efficient Scheme for Waking Co-Channel TDMA in LoRa Networks via the Integration of Bidirectional Timestamp Correction and Address Recognition

Hao Jiang, Guicai Yu, Yingcong Luo, XU Zong-liang
2 citations
Future Internet
IoT Networks and Protocols
1.46
article

An Energy-Efficient Scheme for Waking Co-Channel TDMA in LoRa Networks via the Integration of Bidirectional Timestamp Correction and Address Recognition

Hao Jiang, Guicai Yu, Yingcong Luo, XU Zong-liang
article en
2 citations

Abstract

To address the issues of high energy consumption, data collisions, and invalid wake-ups of nontarget nodes in large-scale node-deployment scenarios of long-range (LoRa) star networks, this paper proposes an energy-saving wake-up scheme that combines (i) time-division multiple access (TDMA) slot allocation based on bidirectional timestamp correction with (ii) a sensing and communication integrated (ISAC) scheme proposed for physical address identification of LoRa nodes operating on the same channel. The scheme incorporates parameter estimation of the LoRa channel, which effectively enhances the identification accuracy and improves the system’s robustness. The proposed scheme consists of two parts: First, in case nodes briefly lose power, a bidirectional timestamp calibration algorithm and GPS-assisted timing are used to synchronize the gateway and each node with high precision, ensuring the accurate scheduling of the TDMA mechanism. Second, based on time synchronization, a “slot–LoRa module address” mapping table is constructed to set the communication time points between the gateway and each node. The gateway can wake the target nodes at specific, precise communication time points. Experimental results show that the proposed method maintains the error range within ±1 ms. The significant decrease in the rate of unnecessary node wake-up decreases data collisions and energy waste in the same channel environment. Energy savings scale with network size, thereby significantly extending the network life cycle.

Future InternetVol. 17(8)
Qinghai University (CN), Qinghai University for Nationalities (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 15%
IoT Networks and Protocols
1.46
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