Throughput Maximization Scheduling Algorithm in TSCH Networks with Deadline Constraints

Ojo, Mike; Giordano, Stefano; Portaluri, Giuseppe; Adami, Davide

doi:10.1109/glocomw.2017.8269114

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…In almost all previous TSCH scheduling, authors either assume that the impact of link qualities or channel variations on performance are negligible (such as [7][8][9][10][11][12][13][14]), or that the instantaneous and complete channel information is available (such as [15][16][17]), or else the channel is modeled quasi-static (such as [18][19][20]). For example, in [15 and 16], it is assumed that the instantaneous CSI is fully measurable and available, therefore one can use an offline method and calculate the optimal scheduling for a TSCH network by the exact rate of packets to be sent over each link.…”

Section: B Related Workmentioning

confidence: 99%

“…To the best of our knowledge, there are many studies which present new scheduling methods for TSCH networks without considering the impact of CSI (such as [7][8][9][10][11][12][13][14]). We have seen that in some schemes authors assume that perfect non-causal knowledge of CSI is available for the scheduling process (such as [15][16][17]) and in others prior statistical knowledge of link qualities is utilized in order to improve performance (Such as [18][19][20]). Although such assumptions in earlier scheduling algorithms, where channel status is available or constant, enable mathematical tractability of the scheduling problem, they are not realizable in real-world systems due to the difficulty in predicting random and timevarying wireless channel conditions.…”

Section: Motivationmentioning

confidence: 99%

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IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach

2020

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In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction, and maintenance of the packet transmission schedules are not defined. Moreover, to ensure optimal throughput, most of the existing scheduling methods are based on the assumption that instantaneous and accurate Channel State Information (CSI) is available. However, due to the inevitable errors in the channel estimation process, this assumption cannot be materialized in many practical scenarios. In this paper, we propose two alternative and realistic approaches. In our first approach, we assume that only the statistical knowledge of CSI is available a priori. Armed with this knowledge, the average packet rate on each link is computed and then, using the results, the throughput-optimal schedule for the assignment of (slotframe) cells to links can be formulated as a max-weight bipartite matching problem, which can be solved efficiently using the well-known Hungarian algorithm. In the second approach, we assume that no CSI knowledge (even statistical) is available at the design stage. For this zeroknowledge setting, we introduce a machine learning-based algorithm by formally modeling the scheduling problem in terms of a combinatorial multi-armed bandit (CMAB) process. Our CMAB-based scheme is widely applicable to many real operational environments, thanks to its reduced reliance on design-time knowledge. Simulation results show that the average throughput obtained by the statistical CSIbased method is within the margin of 15% from the theoretical upper bound associated with perfect instantaneous CSI. The aforesaid margin is around 18% for our learning-theoretic solution.Index Terms-IEEE 802.15.4e, TSCH, Scheduling, CSI, CMAB.

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Section: B Related Workmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach

2020

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“…It ensures high reliability and efficiency while delivering the data packets. |In [21], an efficient scheduling technique is adopted in TSCH networks to provide high throughput and minimum delay considering the deadline constraints. A scheduler based on genetic algorithm is proposed to provide optimal throughput and minimum complexity.…”

Section: Related Workmentioning

confidence: 99%

Efficiency enhancement using optimized static scheduling technique in TSCH networks

Asuti¹,

Basarkod²

2020

IJECE

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In recent times, the reliable and real-time data transmission becomes a mandatory requirement for various industries and organizations due to the large utilization of Internet of Things (IoT) devices. However, the IoT devices need high reliability, precise data exchange and low power utilization which cannot be achieved by the conventional Medium Access Control (MAC) protocols due to link failures and high interferences in the network. Therefore, the Time-Slotted Channel Hopping (TSCH) networks can be used for link scheduling under the IEEE 802.15.4e standard. In this paper, we propose an Optimized Static Scheduling Technique (OSST) for the link scheduling in IEEE 802.15.4e based TSCH networks. In OSST the link schedule is optimized by considering the packet latency information during transmission by checking the status of the transmitted packets as well as keeping track of the lost data packets from source to destination nodes. We evaluate the proposed OSST model using 6TiSCH Simulator and compare the different performance metrics with Simple distributed TSCH Scheduling.

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“…The scheduling algorithms depend upon noncausal information about instantaneous channel qualities, such as the signal-to-noise ratio (SNR) [12]. Furthermore, the TSCH-based scheduling is performed based on the maximization of the network's throughput [13,14]. In addition, some other scheduling algorithms utilize previous statistical information on link qualities, such as the expected number of transmissions (ETX) or the packet error rate (PER), to improve the average packet delivery ratio (PDR) [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…However, in real applications of wireless networks, the channels exhibit random behaviours with time-varying conditions. In some papers, we have found that the authors considered the variation of channel status, which means that the CSI was considered for performing the scheduling tasks [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Scheduling Scheme for IEEE 802.15.4e TSCH Network

Haque

Lee

Koo

2022

Wireless Communications and Mobile Computing

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IEEE 802.15.4e time-slotted channel hopping (TSCH) is one of the most reliable resources of the Industrial Internet of Things (IIoT). TSCH operates on the slot-frame structure consisting of multiple channel-offsets and multiple slot-offsets. It is gaining acceptance due to its simple architecture and consume low power in industrial applications. The performance of TSCH is mainly dominated by the media access control (MAC) mechanism, which covers the refitment, enumeration, composition, and data transmission. However, in many cases, the data transmission schedules are not accurately prescribed. Therefore, most researchers are trying to define many pragmatic scenarios of scheduling. Their fundamental approach is to schedule TSCH network in a centralized way while framing scheduling based on network performance such as throughput and delay. In this work, a deep learning (DL)-based scheme has been proposed. TSCH network schedules for links to cell assignment of a slot-frame can be constructed as a maximum edge weighted bipartite matching approach. In this paper, we design bipartite edge weight to be composed of throughput and delay, and we use the Hungarian algorithm for proper cell assignment. With the Hungarian scheduling algorithm, we generate the training data and train a deep neural network (DNN) accordingly. In the simulation, we consider a simple TSCH network with 5 nodes where 12 links are formulated, and we consider 16 cells for the link assignment. The simulation results show that the proposed deep learning-based scheduling scheme can provide performance similar to the Hungarian algorithm-based scheduling scheme with above 90% accuracy and nearly 80% execution time reduction.

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Throughput Maximization Scheduling Algorithm in TSCH Networks with Deadline Constraints

Cited by 5 publications

References 13 publications

IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach

IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach

Efficiency enhancement using optimized static scheduling technique in TSCH networks

Deep Learning-Based Scheduling Scheme for IEEE 802.15.4e TSCH Network

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