Combining Multi-Agent Systems and Artificial Intelligence of Things: Technical challenges and gains

Abstract

A Multi-Agent System (MAS) usually refers to a network of autonomous agents that interact with each other to achieve a common objective. This system is therefore composed of several software components or hardware components (agents) that are simpler to construct and manage. Additionally, these agents can dynamically and swiftly adapt to changes in their environment. The MAS proves advantageous in addressing intricate issues by employing the divide-and-conquer approach. It finds application in diverse fields where the emphasis is on distributed computing and control, enabling the development of resilient, adaptable, and scalable systems.

MAS is not a substitute or rival for Artificial Intelligence (AI). Instead, AI techniques can be integrated within agents to enhance their computational and decision-making capabilities. The diversity or uniformity of goals, actions, domain knowledge, sensor inputs, and outputs among the agents in the MAS can determine whether each agent is heterogeneous or homogeneous.

The Internet of Things (IoT) and AI are two technologies that have been applied for a long time to the development of smart systems. These systems cover various areas, such as smart cities, energy management, autonomous cars, etc. Smart behavior, autonomy, and real-time monitoring are the fundamental elements that characterize these application areas. The convergence of AI and IoT, known as AIoT, allows these electronic devices to make more intelligent, autonomous, and automatic decisions. This integration leverages the power of MAS to enable intelligent communication and collaboration among various entities, while IoT provides a vast network of interconnected sensors and devices that collect and transmit real-time data. On the other hand, AI algorithms process and analyze these data to derive valuable insights and make informed decisions. The authors devoted their efforts to the critical analysis of AIoT research, highlighting specific areas with insufficient solutions and pointing out gaps for future advances. Essentially, the contribution of the authors is in the formulation of innovative research directions, which outline a clear guide for researchers and professionals in the expansion of knowledge in AIoT integration. The results of the research are significant contributions to the continuous advance of the area, enriching the understanding of the challenges and boosting the development of solutions and strategies in this technological convergence. Eleven research questions are considered at the beginning of the review, including typical research topics and application domains. From the SLR results, the research directions are: (i) Development of a methodology showing how to integrate the different applications independently of the scenarios in which they are deployed. Additionally, elaboration of the tools used in the integration process. (ii) Deployment of an agent in a microprocessor. (iii) How to implement and connect MAS technology and IoT devices (processors, controllers, sensors, and actuators).