Abstract
In recent years, the intense urbanization, and consequently the traffic congestion, has been a major concern of large cities. In this context, the development of smart parkings is a suitable solution to deal with this problem. However, the complexity and requirements imposed by such large-scale systems are an obstacle to its easy implementation. In this sense, it is fundamental to adopt emergent ICT and Artificial Intelligence technologies that are capable to address the imposed requirements. Multi-Agent Systems (MAS) is a suitable approach to face this challenge by providing modularity, flexibility, reconfigurability and fast response to condition change based on its decentralized nature. The use of such agent-based solutions to control physical assets, create novel systems entitled Cyber-Physical Systems (CPS) where the interconnection between the cyber and the physical parts is a crucial issue. This paper focuses the interface between the software agents of a smart parking system with the physical control devices of the parking spots. For this purpose, different interface practices were implemented and tested, considering different interaction schemes and technologies. These alternative interface practices were analyzed taking into consideration the response time, scalability and re-usability parameters.

Abstract

Abstract
The increase volume of vehicles circulating in large cities and the limited space for parking are factors that motivate the adoption of systems capable of dealing with such problems. In this context, smart parking systems are suitable solutions to avoid the traffic congestion, the air pollution and the long search to find a free parking spot. The inclusion of emergent ICT technologies and artificial intelligence techniques, and particularly using multi-agent systems, combined under the scope of Cyber-Physical Systems (CPS), ensure flexibility, modularity, adaptability and the decentralization of intelligence through autonomous, cooperative and proactive entities. Such smart parking systems can be easily adapted to any type of vehicle to be parked and scalable in terms of the number of parking spots and drivers/vehicles. A fundamental issue in these agent-based CPS parking systems is the interconnection between the cyber and physical counterparts, i.e. between the software agents and the physical asset controllers to access the parking spots. This paper focuses on developing an agent-based CPS for a smart parking system and particularly addressing how the software agents are interconnected with the physical asset controllers using proper Internet of Things technologies. The proposed approach was implemented in two distinct parking systems, one for bicycles and another for cars, showing an efficient, modular, adaptable and scalable operation.

Abstract
The 4th industrial revolution advent promotes the reorganization of the traditional hierarchical automation systems towards decentralized Cyber-Physical Systems (CPS). In this context, Artificial Intelligence (AI) can address the new requirements through the use of data-driven and distributed problem solving approaches, such those based on Machine-Learning and Multi-agent Systems. Although their promising perspectives to enable and manage intelligent Internet of Things environments, the traditional Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitive. The solution lies in taking advantage of Edge and Fog computing to create a decentralized multi-level data analysis computing infrastructure that supports the development of industrial CPS. However, this is not a straightforward task, posing several challenges and demanding new approaches and technologies. In this context, this work discusses the distribution of intelligence along Cloud, Fog and Edge computing layers in industrial CPS, leveraging some research challenges and future directions. Copyright

Abstract
In a global market characterized by strong competition and quickly changing boundary conditions, flexible and reconfigurable production systems can rapidly react to both endogenous and exogenous drivers. To this extent, it is necessary to define a new production system model, which can combine the most significant key business factors (KBFs), in order to meet the specified objectives and the relevant KPIs and to control the system. The model can be used within a cyber-physical system, to properly support the different functions and take the right decisions through simulation ICT tools. This research task is part of PERFoRM (Production harmonizEd Reconfiguration of Flexible Robots and Machinery), a European funded project, which aims at developing an innovative manufacturing system based on a new agile concept introducing the implementation of methods, methodologies and strategies for transforming existing production systems into plug-and-produce production ones based on Cyber-Physical Systems technologies. In particular, this paper aims at describing the relationships among the KBFs (Key Business Factors), namely the drivers of the production system, and the relevant KPIs. The model has been validated through an industrial use case, in order to gain important information about constraints and opportunities for improvement in other contexts. © 2017 IEEE.

Abstract
Disturbance handling is a crucial issue in the development of intelligent and reconfigurable manufacturing control systems, supporting the fast and promptly response to the occurrence of unexpected disturbances. In holonic manufacturing control systems, the disturbance handling functions are distributed by the several autonomous control units. In this paper, a predictive disturbance handling approach is presented, transforming the traditional "fail and recover" practices into "predict and prevent" practices, allowing improving the control system performance.