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.
Highlights
Systems Science and Industrial Engineering Department, Binghamton University, 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
Deep learning algorithms such as graph neural networks (GNN), graph convolutional networks (GCN), graph autoencoders (GAE), graph recurrent neural networks (GRNN), or graph reinforcement learning were widely applied for processing the data collected from interconnected system elements or agents [114]
We focus on two main factors for this topic that include the main performance indicators applied for evaluating these systems and existing test platforms and datasets applied for this purpose
Summary
The smart city is a new notion that has rapidly gained ground in the agendas of city authorities all over the world. Integrating a diverse range of devices with different functionalities, computation capabilities, and data streams is extremely challenging [5] Scalability is another major issue for controlling IoTs systems, considering the highly dynamic and distributed nature of these networked systems [6]. The distributed processing and control of multi-agent systems (MAS) or agent-oriented programming (AOP) are some of the main technological paradigms for the efficient deployment of smart devices and services in the smart city [7]. These techniques are considered the best abstraction approaches for modeling the operations and functionalities of IoTs systems in all three layers of perception, network, and application [8]. The last section provides insights to existing gaps and open issues that can be addressed by this research community to achieve fully operative autonomous IoTs in future smart cities
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