Abstract
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.
Highlights
Impact of Task Management Techniques on the Total Application Execution Time under Different Computing Resources and Data Sizes. For this experimental evaluation scenario, we considered the of total application execution time as the summation of data processing time and the24data communication delay of the passenger counting Internet of Things (IoT) application
Results presented in cluster 2 show that both greedy and Distribution and Adaptation (DTDA) techniques performed well for each of the three data sizes and reduced the average data processing time compared to the binpack and random techniques as follows: by 11.11% when data size was
The results showed that DTDA, which is our proposed task management technique of the time-sensitive IoT data analysis (TIDA) platform, successfully fulfilled the time-bound requirements of the passenger counting IoT application with varying sizes of passenger onboarding data when the passenger counting IoT application was executed in cluster 3 and cluster 4, whereas the three other task management techniques failed to meet the time-bound requirements
Summary
Many existing studies in IoT have proposed various task management techniques (i.e., techniques that are used to manage the execution of IoT applications in distributed computing resources) to address the research challenge of supporting the time-bound requirements of TS-IoT applications. There is a lack of comprehensive task management techniques that integrate task sizing, task distribution, and task adaptation techniques to collectively facilitate the TS-IoT applications to meet their time-bound requirements To overcome this shortcoming, in this paper, we propose a novel Time-Sensitive IoT. A comprehensive experimental evaluation using a real-world smart city use case and related dataset that includes: (a) an experimental setup that forms four clusters of computing resources with heterogeneous system configurations to validate the DTDA task management technique; and (b) a comprehensive comparison with two current state-of-the-art task management techniques that shows how well the TIDA platform, which implements the above techniques, meets the time-bound requirements of TS-IoT applications.
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