Distributed scheduling with efficient collision detection for end-to-end delay optimization in 6TiSCH multi-hop wireless networks

Hosni, Ines

doi:10.1007/s12243-018-0671-2

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…Although the traditional detection method can also be simulated, when the complexity of the objects designed in the scene is relatively high, the traditional detection cannot meet the realtime requirements of the virtual system. With the promotion of Internet technology, personal computers have become very common, and virtual simulation technology has also begun to be used in all walks of life [13,14]. For example, in the development of RPS online games, developers need to use simulation to test the smoothness of the game.…”

Section: Overview Of Collision Detectionmentioning

confidence: 99%

Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Cheng¹,

2023

Mobile Information Systems

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Big data processing includes multiple processing flows of data. But data quality is the most important part of the entire process. Every data processing link will have an impact on the quality of big data. Collision detection is an important research content in many fields such as computer graphics and computer virtual reality. In layman's terms, it means that the computer detects the signal voltage on the channel while sending data. When the signal voltage swing value detected by a station exceeds a certain threshold value, it will be considered that at least two stations on the bus are sending data at the same time, indicating that a collision has occurred. As the number of questions increases, the level becomes higher, the target information becomes more diverse, and the algorithm becomes more complex. The traditional evolutionary algorithm is far from being able to deal with this situation effectively, and the optimization target algorithm emerges as the times require. Evolutionary computing is a mature global optimization method with high robustness and wide applicability. It has the characteristics of self-organization, self-adaptation, and self-learning. It is not limited by the nature of the problem and can effectively deal with complex problems that are difficult to solve by traditional optimization algorithms. This paper aims to study the optimization algorithm of real-time collision detection based on Snake model in the field of big data. It is expected that, with the support of big data technology, the efficiency of real-time collision detection of Snake model will be improved and time will be saved. This paper proposes a multiline swarm particle swarm algorithm and combines it with the Snake model to improve the detection efficiency of the collision detection algorithm. It verifies the detection performance of traditional algorithms and tests their effectiveness in detection. The experimental results of this paper show that the frame rate of the Snake model algorithm is 15, the frame rate of the K-DOPs algorithm is 6.7, and the error of the algorithm is 1.04. It shows that the frame rate of Snake model algorithm is better.

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Section: Overview Of Collision Detectionmentioning

confidence: 99%

Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Cheng¹,

2023

Mobile Information Systems

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“…We also excluded TSCH scheduling papers that were not autonomous, e.g. that relied on 6top [12] or other packet exchange, such as [13], [14]. This filtering yielded 10 papers [8], [9], [15]- [22].…”

Section: B Search Methodologymentioning

confidence: 99%

“…For example, let us assume a TSCH network that uses 15, 11, 19, 13 as its FHS. Then, the FHS of channel offset 0 will be 15,11,19,13, that of channel offset 1 will be 11,19,13,15, and that of channel offset 2 will be 19, 13, 15, 11. Thereby, the channel to be used at ASN time slot is:…”

Section: Literature Survey and Taxonomy A Background On Time Slomentioning

confidence: 99%

An Empirical Survey of Autonomous Scheduling Methods for TSCH

Elsts

Kim

et al. 2020

IEEE Access

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Time Slotted Channel Hopping (TSCH) is a link layer protocol defined in the IEEE 802.15.4 standard. Although it is designed to provide highly reliable and efficient service targeting industrial automation systems, scheduling TSCH transmissions in the time and frequency dimensions is left to the implementers. We evaluate the performance of existing autonomous scheduling approaches for TSCH on various traffic patterns and network configurations. We thoroughly investigate the pros and cons of each scheme; moreover, we propose the use of node based channel allocation to improve the performance of the best scheme, and demonstrate its practicality and reliability, with up to 6 percentage points better packet delivery ratio than the second best option while retaining a similar radio duty cycle. Finally, based on our extensive performance evaluation, we provide some guidelines on how to select a scheduler for a given network. INDEX TERMS IEEE 802.15.4, TSCH, autonomous scheduling, MAC, low-power wireless protocols.

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“…Since Layered needs to know the node depth in the RPL network, we add the hop count as an RPL metric container 3 . A similar approach is used in the collaborative scheduler by Hosni et al [27]. Note that this is not required if using hop count as a metric.…”

Section: A Layered Schedulermentioning

confidence: 99%

Experimental Evaluation of the Layered Flow-Based Autonomous TSCH Scheduler

2023

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The Industrial Internet of Things (IIoT) requires wireless connectivity that meets the strict industrial requirements on metrics such as reliability and latency. Promising approaches include Time Slotted Channel Hopping (TSCH) media access, where nodes operate according to a schedule. Autonomously built schedules typically rely on shared resources, where reliability and latency may suffer depending on traffic scenarios and topologies. We have earlier proposed the Layered scheduler, which belongs to a new category of autonomous schedulers: Flow-based scheduling. Layered allocates resources to traffic flows, and as opposed to typical autonomous schedulers, dedicated resources are guaranteed to be scheduled at every hop from source to destination in a convergecast scenario. In addition, Layered minimizes the number of channels required through the novel employment of autonomous spatial reuse. We extend earlier theoretical analysis and simulations by evaluating Layered using the FIT IoT-LAB testbed and compare it to Orchestra and 6TiSCH Minimal scheduler. The experiments demonstrate the feasibility of spatial reuse and that Layered retains performance independent of network topology and traffic intensity -a desirable feature in industrial scenarios. The performance comes at the expense of energy consumption, which in the worst case is 75 % higher compared to Orchestra and Minimal. We also present lessons learned, such as the crucial relationship between TSCH and RPL convergence, the benefits of black-listing on performance, and how co-located TSCH networks could be divided by channel offsets as opposed to physical channels. Lastly, we discuss flow-based scheduling in general, its properties, and future research areas.

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Distributed scheduling with efficient collision detection for end-to-end delay optimization in 6TiSCH multi-hop wireless networks

Cited by 5 publications

References 24 publications

Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

An Empirical Survey of Autonomous Scheduling Methods for TSCH

Experimental Evaluation of the Layered Flow-Based Autonomous TSCH Scheduler

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