A simulation framework for assessing and optimizing IoT service and resource allocation: SimulateIoT-Services

Abstract

The Internet of Things (IoT) is increasingly being applied across various domains, including smart cities, smart buildings, agriculture, and connected vehicles, also known as the Internet of Vehicles (IoV), as well as industry, where it is commonly referred to as the Industrial IoT (IIoT). The ultimate goal of these systems is to provide services to end users. Services can be deployed across the computing layers of IoT systems, such as the fog or cloud layers, in a process known as service allocation. Similarly, hardware resources can be configured for each node within these layers, a process referred to as resource allocation. Properly executing these processes is crucial to achieving optimal performance in IoT systems. However, these processes are complex, and no single universally accepted method exists for carrying them out. Instead, the literature contains numerous proposals aimed at optimizing system performance by refining the execution of these tasks. In this work, to provide tools for optimizing service and resource allocation, SimulateIoT, an IoT simulator based on Model-Driven Development, has been extended to support these concepts. This extension, named SimulateIoT-Services, enables users to model, validate, generate, deploy, and test IoT systems, assessing their performance and evaluating how different service and resource allocation strategies impact overall system performance. Finally, this study not only extends SimulateIoT towards SimulateIot-Services, but also showcases its practical application. Namely, in the design and evaluation of scalable IoT systems and services through a case study centered on the IoV and efficient parking within a smart city context.