Scheduling in IEEE 802.15.4e Time Slotted Channel Hopping: A Survey

Hammoudi, Sarra; Bentaleb, Abdelhak; Harous, Saad; Aliouat, Zibouda

doi:10.1109/uemcon51285.2020.9298043

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Clearly, different scheduling algorithms would have direct impact on the energy consumption of the nodes, as illustrated in [55]. It has also been shown that despite of the potential of TSCH mode, the TSCH scheduling faces serious challenges [56]. On the other hand, during a recent international competition of the evaluation of state-of-the-art low-power wireless MAC protocols, the competition results revealed that while TSCH had better latency and reliability it actually had worse energy consumption, compared to asynchronous low-power listening MAC [57].…”

Section: Tischmentioning

confidence: 99%

Energy-efficient and balanced routing in low-power wireless sensor networks for data collection

2022

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Section: Tischmentioning

confidence: 99%

Energy-efficient and balanced routing in low-power wireless sensor networks for data collection

2022

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“…The standard does not define how the TSCH packet transmission schedule is defined. In [ 3 ], a thorough analysis was conducted on several existing TSCH scheduling protocols, bringing attention to their respective advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Time-Slotted Channel Hopping Schedule Optimization Using Priority-Based Customized Differential Evolution Algorithm in Heterogeneous IoT Networks

Vatankhah,

Liscano

2024

Sensors

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The Time-Slotted Channel Hopping (TSCH) protocol is known for its suitability in highly reliable applications within industrial wireless sensor networks. One of the most significant challenges in TSCH is determining a schedule with a minimal slotframe size that can meet the required throughput for a heterogeneous network. We proposed a Priority-based Customized Differential Evolution (PCDE) algorithm based on the determination of a collision- and interference-free transmission graph. Our schedule can encompass sensors with different data rates in the given slotframe size. This study presents a comprehensive performance evaluation of our proposed algorithm and compares the results to the Traffic-Aware Scheduling Algorithm (TASA). Sufficient simulations were performed to evaluate different metrics such as the slotframe size, throughput, delay, time complexity, and Packet Delivery Ratio (PDR) to prove that our approach achieves a significant result compared with this method.

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“…A MAC layer protocol called TSCH (Time-Slotted Channel Hopping) combines channel hopping and TDMA [29]. Nodes synchronize their schedules to send and receive within the time windows that are allocated for this purpose.…”

mentioning

confidence: 99%

ML-Based Adaptive MAC Protocol for Real Time Data Transmission in Wireless Sensor Networks

Kakade

2024

Preprint

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Wireless Sensor Networks (WSNs) are expressively utilized in various real-time control and monitoring applications. WSNs have been expanded considering the necessities in industrial time-bounded applications to support the dependable, time-bound delivery of data. Recently, Machine Learning (ML) algorithms have been used to address various WSN-related issues. The use of machine learning techniques supports dynamically modifying MAC settings based on traffic patterns and network conditions. In WSNs to control the communication between a large number of tiny, low-power sensor nodes while preserving energy and reducing latency, effective MAC protocols are essential. This paper addresses the ML-based Adaptive MAC (ML-MAC) protocol to provide a priority-based transmission system. In this research, depending upon the predictions of the machine learning model, the MAC parameters are dynamically adjusted to find priority-based channel access and the optimal routing path to meet the deadline of critical data packets. From the result analysis, the average throughput and delay of the proposed ML-MAC algorithm is improved as compared to the existing I-MAC protocol. © 2017 Elsevier Inc. All rights reserved.

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Scheduling in IEEE 802.15.4e Time Slotted Channel Hopping: A Survey

Cited by 7 publications

References 23 publications

Energy-efficient and balanced routing in low-power wireless sensor networks for data collection

Energy-efficient and balanced routing in low-power wireless sensor networks for data collection

Comparative Analysis of Time-Slotted Channel Hopping Schedule Optimization Using Priority-Based Customized Differential Evolution Algorithm in Heterogeneous IoT Networks

ML-Based Adaptive MAC Protocol for Real Time Data Transmission in Wireless Sensor Networks

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