A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

Abstract

The world is moving toward a new connected world in which millions of intelligent processing devices communicate with each other to provide services in transportation, telecommunication, and power grids in the future’s smart cities. Distributed computing is considered one of the efficient platforms for processing and management of massive amounts of data collected by smart devices. This can be implemented by utilizing multi-agent systems (MASs) with multiple autonomous computational entities by memory and computation capabilities and the possibility of message-passing between them. These systems provide a dynamic and self-adaptive platform for managing distributed large-scale systems, such as the Internet-of-Things (IoTs). Despite, the potential applicability of MASs in smart cities, very few practical systems have been deployed using agent-oriented systems. This research surveys the existing techniques presented in the literature that can be utilized for implementing adaptive multi-agent networks in smart cities. The related literature is categorized based on the steps of designing and controlling these adaptive systems. These steps cover the techniques required to define, monitor, plan, and evaluate the performance of an autonomous MAS. At the end, the challenges and barriers for the utilization of these systems in current smart cities, and insights and directions for future research in this domain, are presented.

Predictive consensus tracking of multi‐agent systems in the presence of Byzantine agents and connection loss of reference signals

“…Multi‐agent systems (MAS) are widely used in the various fields such as unmanned aerial vehicle (UAV), 1 autonomous vehicles, 2 traffic control, 3 smart cities, 4 microgrids, 5 power systems, 6 game theory, 7 video games, 8 and military applications 9 . Multi‐agent systems are used to model some real issues with a graph that has various nodes and edges.…”

Multi-agent Coordination with Deferred Asynchronous Messaging in a Distributed Coordination Space

“…To achieve this goal, various decision-making methods are being developed in multi-agent systems [2] and methods for coordinating the joint actions of agents solving a problem [3], including the usage of machine learning methods [4]. Multi-agent systems can be applied to solve important problems in many areas, such as business process automation, computer network management, distributed robotics, resource allocation and scheduling [5], smart city management [6], and many others.…”