Learning mechanism for RT task scheduling

Rao, Adithya; Agarwal, Swathi; Srinivas, K.; Rani, B. Kavitha

doi:10.1109/iccic.2015.7435795

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…Random ()* 60) + 30) and its values are shown in the table 6.1. Invoke task cluster algorithm for generating different clusters [5] and processor cluster [8] from ETM (i, j). The <task, processor> pairs can be generated by comparing task cluster and processor cluster using feasible conditions as shown in the figure 2.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The ETM (i,j) generates the processor cluster and the task cluster generated from the COBWEB algorithm. These two clusters are mapped using the proposed [1], [8].…”

Section: B Mapping Processmentioning

confidence: 99%

“…The IoT applications environment generates multiple tasks from network-connected devices, embedded devices in the physical environment gathers information from different sensors. The execution results of all tasks are recorded in ETM (i, j) matrix that forms the processor cluster [5] and the task cluster generated using COBWEB algorithms [5,8].…”

Section: Iot Task Modelmentioning

confidence: 99%

See 2 more Smart Citations

Formulation of Control Strategies for IoT Task Scheduling

A*¹,

Reddy²,

C³

et al. 2019

IJRTE

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The various Internets of Things (IoT) application tasks are difficult to schedule due to heterogeneity properties of IoT. So an efficient algorithm is required that forms < task, processor> pair appropriately. This paper presents a more sensible model for varying execution times of tasks and deviation in task parameters for building a schedule is allowed. The system provides an adaptive learning mechanism called Expected Time Matrix ETM (i, j) . When the environment of the system changes dynamically, the system learns and adapts itself to the new changes automatically, since the learning mechanism has been incorporated in the system. ETM (i, j) concepts allows the system to learn from past instances as well. The work is supported by simulations that highlight the viability of concepts proposed. The key objective of this paper is to present the developed scheduling algorithm that is self-configurable and dynamic

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Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The ETM (i,j) generates the processor cluster and the task cluster generated from the COBWEB algorithm. These two clusters are mapped using the proposed [1], [8].…”

Section: B Mapping Processmentioning

confidence: 99%

Section: Iot Task Modelmentioning

confidence: 99%

See 1 more Smart Citation

Formulation of Control Strategies for IoT Task Scheduling

A*¹,

Reddy²,

C³

et al. 2019

IJRTE

View full text Add to dashboard Cite

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Adaptive Control Strategies for Task Scheduler Using Internet of Things

Rao

2018

Exploring the Convergence of Big Data and the Internet of Things

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The Internet of Things comprises billions of devices that can sense, communicate, compute and potentially actuate. The data generated by the Internet of Things are valuable and have the potential to drive innovative and novel applications which are one of the sources of Big Data. IoT connects real world objects to the internet using tiny sensors or embedded devices. One of the biggest advantages of the IoT is the increasing number of low-cost sensors available for many different kinds of functionalities. These sensors include a variety of devices and solutions. The trend is moving towards multi-sensor platforms that incorporate several sensing elements. In such environment, discovering, identifying, connecting and configuring sensor hardware are critical issues. The cloud-based IoT platforms can retrieve data from sensors. Therefore, IoT is a comprehensive inter-disciplinary technology, So, this chapter presents Better scheduling decisions should result in saving the time, utilization of resources and enable to meet the time constraints.

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Learning mechanism for RT task scheduling

Cited by 2 publications

References 5 publications

Formulation of Control Strategies for IoT Task Scheduling

Formulation of Control Strategies for IoT Task Scheduling

Adaptive Control Strategies for Task Scheduler Using Internet of Things

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