Adaptive Cell Scheduling and Negotiation Techniques for 6TiSCH Networks Under Bursty Traffic

6TiSCH networks adopt the IEEE 802.15.4e-based TSCH protocol to support efficient and reliable communication in low-power and lossy network (LLN) environments. However, under bursty traffic conditions, the traditional minimal scheduling function (MSF)-based scheduling technique cannot effectively handle the traffic load and suffers from packet queue overflow. In this study, we propose two main techniques to solve these problems. The first technique, dynamic cell cycle adjustment, dynamically adjusts the cell addition and deletion cycles based on the link quality and packet queue utilization to prevent packet queue overflow and efficiently use limited cell resources. The second technique, the parent node 6P transaction forwarding technique, is designed to pre-forward cell addition requests to higher nodes along the path when the cell utilization exceeds a set threshold due to traffic spikes at the lower nodes, so that the higher nodes can perform 6P negotiation immediately without waiting for MAX_NUMCELLS cycles. This minimizes the cell addition delay and prevents packet queue overflow. The simulation results show that the proposed technique has a high packet delivery ratio (PDR), low latency, and energy efficiency compared to conventional MSF, IMSF, and LMSF in various traffic environments. In particular, it maintains stable performance while preventing packet overflow under bursty traffic conditions. This work contributes to the optimization of scheduling and cell negotiation in 6TiSCH networks to improve the network efficiency and reliability in IoT environments.

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Publication Details

Journal
Sensors
Published
2025-02-26
DOI
https://doi.org/10.3390/s25051418
Citations
1
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
1.49
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article

Adaptive Cell Scheduling and Negotiation Techniques for 6TiSCH Networks Under Bursty Traffic

Je-Hyeong Lee, Sang-Hwa Chung
1 citations
Sensors
IoT and Edge/Fog Computing
1.49
article

Adaptive Cell Scheduling and Negotiation Techniques for 6TiSCH Networks Under Bursty Traffic

Je-Hyeong Lee, Sang-Hwa Chung
article en
1 citations

Abstract

6TiSCH networks adopt the IEEE 802.15.4e-based TSCH protocol to support efficient and reliable communication in low-power and lossy network (LLN) environments. However, under bursty traffic conditions, the traditional minimal scheduling function (MSF)-based scheduling technique cannot effectively handle the traffic load and suffers from packet queue overflow. In this study, we propose two main techniques to solve these problems. The first technique, dynamic cell cycle adjustment, dynamically adjusts the cell addition and deletion cycles based on the link quality and packet queue utilization to prevent packet queue overflow and efficiently use limited cell resources. The second technique, the parent node 6P transaction forwarding technique, is designed to pre-forward cell addition requests to higher nodes along the path when the cell utilization exceeds a set threshold due to traffic spikes at the lower nodes, so that the higher nodes can perform 6P negotiation immediately without waiting for MAX_NUMCELLS cycles. This minimizes the cell addition delay and prevents packet queue overflow. The simulation results show that the proposed technique has a high packet delivery ratio (PDR), low latency, and energy efficiency compared to conventional MSF, IMSF, and LMSF in various traffic environments. In particular, it maintains stable performance while preventing packet overflow under bursty traffic conditions. This work contributes to the optimization of scheduling and cell negotiation in 6TiSCH networks to improve the network efficiency and reliability in IoT environments.

SensorsVol. 25(5)
Pusan National University (KR)
Affordable and clean energy
Openalex Percentile: Top 20%
IoT and Edge/Fog Computing
1.49
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Adaptive Cell Scheduling and Negotiation Techniques for 6TiSCH Networks Under Bursty Traffic — Je-Hyeong Lee, Sang-Hwa Chung · Sensors (2025) | TGRS Research Map | TGRS