A Time-Sensitive IoT Data Analysis Framework

Korala, Harindu; Georgakopoulos, Dimitrios; Jayaraman, Prem Prakash; Yavari, Ali

doi:10.24251/hicss.2021.865

Cited by 5 publications

(17 citation statements)

References 17 publications

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“…To overcome this shortcoming, in this paper, we propose a novel Time-Sensitive IoT Data Analysis platform called TIDA that includes a task management technique, called DTDA, which utilizes the computing resources available in the IoT devices, edge computers, and the cloud for meeting the time-bound requirements of each TS-IoT application when the entire pool of available computing resources is sufficient to collectively achieve this. Specifically, this paper significantly extends our earlier work [3] and makes the following novel contributions:…”

Section: Introductionsupporting

confidence: 55%

“…In these distributed real-time systems, attributes of the execution environment, such as latencies of networks, data generation rates, and available computing resources, can be determined in advance [23,24]. On the contrary, although the IoT environment is highly distributed, it has unique characteristics that impose additional challenges for the execution of TS-IoT applications in such a manner that their time-bound requirements are met compared to traditional distributed real-time systems [3,8,25]. We further discuss these additional challenges of the IoT environment in Section 3 via a motivation scenario, and in Section 4 by presenting a formal model for the IoT environment.…”

Section: Related Workmentioning

confidence: 99%

“…The Internet of Things (IoT) is a novel expansion of the Internet that forms a network of heterogeneous physical objects such as sensors, video cameras, mobile phones, and industrial machines (all of which we refer to as IoT devices) that can communicate and exchange data with each other over the Internet [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…A greedy task distribution technique that distributes and executes the TS-IoT application in the IoT environment, which was presented in [3].…”

mentioning

confidence: 99%

“…Our earlier work [3] presented a greedy task distribution technique that is unable to dynamically adapt the task distribution in real-time to cope with the volatile changes in the IoT environment. Hence, the proposed DTDA technique presented in this paper aims to address this key limitation of the greedy task distribution technique.…”

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confidence: 99%

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Managing Time-Sensitive IoT Applications via Dynamic Application Task Distribution and Adaptation

et al. 2021

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The recent proliferation of the Internet of Things has led to the pervasion of networked IoT devices such as sensors, video cameras, mobile phones, and industrial machines. This has fueled the growth of Time-Sensitive IoT (TS-IoT) applications that must complete the tasks of (1) collecting sensor observations they need from appropriate IoT devices and (2) analyzing the data within application-specific time-bounds. If this is not achieved, the value of these applications and the results they produce depreciates. At present, TS-IoT applications are executed in a distributed IoT environment that consists of heterogeneous computing and networking resources. Due to the heterogeneous and volatile nature (e.g., unpredictable data rates and sudden disconnections) of the IoT environment, it has become a major challenge to ensure the time-bounds of TS-IoT applications. Many existing task management techniques (i.e., techniques that are used to manage the execution of IoT applications in distributed computing resources) that have been proposed to support TS-IoT applications to meet their time-bounds do not provide a sophisticated and complete solution to manage the TS-IoT applications in a manner in which their time-bounds are guaranteed. This paper proposes TIDA, a comprehensive platform for managing TS-IoT applications that includes a task management technique, called DTDA, which incorporates novel task sizing, distribution, and dynamic adaptation techniques. DTDA’s task sizing technique measures the computing resources required to complete each task of the TS-IoT application at hand in each available IoT device, edge computer (e.g., network gateways), and cloud virtual machine. DTDA’s task distribution technique distributes and executes the tasks of each TS-IoT application in a manner that their time-bound requirements are met. Finally, DTDA includes a task adaptation technique that dynamically adapts the distribution of tasks (i.e., redistributes TS-IoT application tasks) when it detects a potential application time-bound violation. The paper describes a proof-of-concept implementation of TIDA that uses Microsoft’s Orleans Actor Framework. Finally, the paper demonstrates that the DTDA task management technique of TIDA meets the time-bound requirements of TS-IoT applications by presenting an experimental evaluation involving real time-sensitive IoT applications from the smart city domain.

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Section: Introductionsupporting

confidence: 55%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…A greedy task distribution technique that distributes and executes the TS-IoT application in the IoT environment, which was presented in [3].…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Managing Time-Sensitive IoT Applications via Dynamic Application Task Distribution and Adaptation

et al. 2021

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MVE-based Reinforcement Learning Framework with Explainability for improving Quality of Experience of Application Placement in Fog Computing

Krishnamurthy

Shiva

Das

2022

2022 IEEE World AI IoT Congress (AIIoT)

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A Survey of Techniques for Fulfilling the Time-Bound Requirements of Time-Sensitive IoT Applications

et al. 2022

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This paper surveys existing techniques for meeting the time-bound requirements of time-sensitive applications in the Internet of Things (IoT). To provide the foundation for identifying and classifying relevant techniques, we present three sample time-sensitive IoT applications and their time-bound requirements, describe the main computation and network resources in IoT that can be used to process such applications, and identify the main challenges in meeting their time-bound requirements. Based on these, the paper presents a comprehensive literature review of existing techniques and tools that can help meet application-specific time-bound requirements in IoT. The paper also includes a gap analysis in existing research outcomes and proposes research directions for bridging the remaining research gaps in supporting time-sensitive IoT applications.

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A Time-Sensitive IoT Data Analysis Framework

Cited by 5 publications

References 17 publications

Managing Time-Sensitive IoT Applications via Dynamic Application Task Distribution and Adaptation

Managing Time-Sensitive IoT Applications via Dynamic Application Task Distribution and Adaptation

MVE-based Reinforcement Learning Framework with Explainability for improving Quality of Experience of Application Placement in Fog Computing

A Survey of Techniques for Fulfilling the Time-Bound Requirements of Time-Sensitive IoT Applications

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