A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

Supporting adaptiveness of cyber-physical processes through action-based formalisms

Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system

On developing open mobile fault tolerant agent systems

The paper introduces the CAMA (Context-Aware Mobile Agents) framework intended for developing large-scale mobile applications using the agent paradigm. CAMA provides a powerful set of abstractions, a supporting middleware and an adaptation layer allowing developers to address the main characteristics of the mobile applications: openness, asynchronous and anonymous communication, fault tolerance, and device mobility. It ensures recursive system structuring using location, scope, agent, and role abstractions. CAMA supports system fault tolerance through exception handling and structured agent coordination within nested scopes. The applicability of the framework is demonstrated using an ambient lecture scenario - the first part of an ongoing work on a series of ambient campus applications. This scenario is developed starting from a thorough definition of the traceable requirements including the fault tolerance requirements. This is followed by the design phase at which the CAMA abstractions are applied. At the implementation phase, the CAMA middleware services are used through a provided API. This work is part of the FP6 IST RODIN project on Rigorous Open Development Environment for Complex Systems

On using the CAMA framework for developing open mobile fault tolerant agent systems

The paper introduces the Cama (Context-Aware Mobile Agents) framework intended for developing large-scale mobile applications using the agent paradigm. Cama provides a powerful set of abstractions, a supporting middleware and an adaptation layer allowing developers to address the main characteristics of the mobile applications: openness, asynchronous and anonymous communication, fault tolerance, device mobility. It ensures recursive system structuring using location, scope, agent and role abstractions. Cama supports system fault tolerance through exception handling and structured agent coordination. The applicability of the framework is demonstrated using an ambient lecture scenario - the first part of an ongoing work on a series of ambient campus applications