An Energy Efficient Real-Time MAC Protocol

Kim, Kyeong Tae; Kim, Jeong-Nyeo

doi:10.1109/ictc.2018.8539677

Cited by 1 publication

(2 citation statements)

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“…With the process working upwards from the leaves, it achieves daisy-chaining and thus shorter latency. DeTAS [52] Wave [90] DiSCA [102] DeAMON [55] ReSF [78] LOST [54] Kim et al [75] DIVVY [103] LaDiS [104] SSAP [85] LDSF [81] CFDS [72] Similarly, schedulers seldom optimize for high throughput, and this typically requires short slotframes such that cells repeat often. However, most collaborative approaches avoid this because smaller slotframes increase the chance of scheduling collisions since there is no global coordination.…”

Section: Collaborative Schedulingmentioning

confidence: 99%

“…One local example is DeBraS, where nodes broadcast their schedules, while the recursive scheduler LOST [ 54 ] depend on nodes overhearing negotiations between other nodes. Knowledge of the neighborhood schedule may also be exploited to sequentially place cells towards the sink for reduced latency, as seen in recursive schedulers Wave and Kim et al [ 75 ]. However, these improvements typically come at the cost of overhead and increased complexity compared to local schedulers.…”

Section: Collaborative Schedulingmentioning

confidence: 99%

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A Survey of 802.15.4 TSCH Schedulers for a Standardized Industrial Internet of Things

Urke

Kure

Øvsthus

2021

Sensors

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Concepts such as Industry 4.0 and Cyber-Physical Systems may bring forward a new industrial revolution. These concepts require extensive connectivity far beyond what is provided by traditional industrial networks. The Industrial Internet of Things (IIoT) bridges this gap by employing wireless connectivity and IP networking. In order for wireless networks to meet the strict requirements of the industrial domain, the Time Slotted Channel Hopping (TSCH) MAC is often employed. The properties of a TSCH network are defined by the schedule, which dictates transmission opportunities for all nodes. We survey the literature for these schedulers, describe and organize them according to their operation: Centralized, Collaborative, Autonomous, Hybrid, and Static. For each category and the field as a whole, we provide a holistic view and describe historical trends, highlight key developments, and identify trends, such as the attention towards autonomous mechanisms. Each of the 76 schedulers is analyzed into their common components to allow for comparison between schedulers and a deeper understanding of functionality and key properties. This reveals trends such as increasing complexity and the utilization of centralized principles in several collaborative schedulers. Further, each scheduler is evaluated qualitatively to identify its objectives. Altogether this allows us to point out challenges in existing work and identify areas for future research, including fault tolerance, scalability, non-convergecast traffic patterns, and hybrid scheduling strategies.

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