Towards efficient real-time decision support at the edge

Kang, Kyoung Don

doi:10.1145/3318216.3363380

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Scheduling Techniques: Some authors have applied scheduling techniques to achieve low latency in stream processing [25][26][27][28]. One such technique includes edgewise; it achieves low latency and higher throughput by introducing engine-level scheduling.…”

Section: Related Workmentioning

confidence: 99%

“…Priority is given to the queues with large pending data thus resulting in balanced queue lengths [25]. In another approach, authors have utilized Earliest Deadline or Expiration First-Least Volatile First (EDEF-LVF) scheduling algorithm which schedules the common data accessing tasks to the same core to avoid redundant computations and repeated memory accesses [26].…”

Section: Related Workmentioning

confidence: 99%

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Fossel: Efficient Latency Reduction in Approximating Streaming Sensor Data

2020

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The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network traffic which leads to heavily delayed network operations. In streaming data analytics, the ability to obtain real time data insight is crucial for computational sustainability for many IoT enabled applications such as environmental monitors, pollution and climate surveillance, traffic control or even E-commerce applications. However, such network delays prevent us from achieving high quality real-time data analytics of environmental information. In order to address this challenge, we propose the Fog Sampling Node Selector (Fossel) technique that can significantly reduce the IoT network and processing delays by algorithmically selecting an optimal subset of fog nodes to perform the sensor data sampling. In addition, our technique performs a simple type of query executions within the fog nodes in order to further reduce the network delays by processing the data near the data producing devices. Our extensive evaluations show that Fossel technique outperforms the state-of-the-art in terms of latency reduction as well as in bandwidth consumption, network usage and energy consumption.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fossel: Efficient Latency Reduction in Approximating Streaming Sensor Data

2020

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“…More specifically, they aim to efficiently evaluate logic predicates that model alternative courses of action [8,12,13] and to effectively schedule decision making tasks [22][23][24]. The field of research on real-time decision making in IoT, however, is in an early stage and relatively little work has been done to review the area [25], even though closely related areas that form a basis for real-time decision making, such as wireless networking for IoT [6,[26][27][28][29] and sensor data analytics via machine learning [30][31][32], have been reviewed extensively.…”

Section: Introductionmentioning

confidence: 99%

A Review of Efficient Real-Time Decision Making in the Internet of Things

Kang

2022

Technologies

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Emerging applications of IoT (the Internet of Things), such as smart transportation, health, and energy, are envisioned to greatly enhance the societal infrastructure and quality of life of individuals. In such innovative IoT applications, cost-efficient real-time decision-making is critical to facilitate, for example, effective transportation management and healthcare. In this paper, we formally define real-time decision tasks in IoT, review cutting-edge approaches that aim to efficiently schedule real-time decision tasks to meet their timing and data freshness constraints, review state-of-the-art approaches for efficient sensor data analytics in IoT, and discuss future research directions.

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