Remote Sensing, Vol. 13, Pages 4148: Managing Time-Sensitive IoT Applications via Dynamic Application Task Distribution and Adaptation (Remote Sensing)

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.