Introduction to the thematic issue on Intelligent systems, applications and environments for the industry of the future

Recent advances in the area of ubiquitous computing, ambient intelligence and intelligent environments are making inroads in business-oriented application domains. This issue of JAISE addresses core topics on the design, use and evaluation of smart applications and systems for the factory of the future, an emerging trend perhaps better known as Industry 4.0. The digital transformation in the enterprise envisioned by Industry 4.0 will entwine the cyber-physical world and real world of manufacturing to deliver networked production with enhanced process transparency. Production systems, data analytics and cloud-enabled business processes will interact directly with customers to realize the ambitious goal of single lot individualized manufacturing. This thematic issue features a survey and 5 research articles which address the modeling, designing, implementation, assessment and management of intelligent systems, applications and environments that will shape and advance the smart industry of the future.status: publishe

Efficiency and Security of Process Transparency in Production Networks - A View of Expectations, Obstacles and Potentials

Much of the resilience and flexibility of production networks lies in the transparency of processes that allows timely perception of actual process states and adequate decisions or intervention at the proper point of the production system. Such degree of observability and permeability do, however, bear risks of malevolent tapping or interference with the information stream which, in the case of production systems, can put both business and physical processes at risk, requiring careful exploration of security threats in horizontal and vertical integration, and individual end-to-end connections likewise. Also, different levels of networked production present specific needs—high throughput and low time lag on the shop-floor level, or tolerances for confidence, gambling and bounded-rational views in cross-company relations—that may conflict with security policies. The paper presents a systematic summary of such apparently contradicting preferences, and possible approaches of reconciliation currently perceived to be relevant on various abstraction levels of production networks.status: publishe

Policy reconciliation for access control in dynamic cross-enterprise collaborations

In dynamic cross-enterprise collaborations, different enterprises form a – possibly temporary – business relationship. To integrate their business processes, enterprises may need to grant each other limited access to their information systems. Authentication and authorization are key to secure information handling. However, access control policies often rely on non-standardized attributes to describe the roles and permissions of their employees which convolutes cross-organizational authorization when business relationships evolve quickly. Our framework addresses the managerial overhead of continuous updates to access control policies for enterprise information systems to accommodate disparate attribute usage. By inferring attribute relationships, our framework facilitates attribute and policy reconciliation, and automatically aligns dynamic entitlements during the evaluation of authorization decisions. We validate our framework with a Industry 4.0 motivating scenario on networked production where such dynamic cross-enterprise collaborations are quintessential. The evaluation reveals the capabilities and performance of our framework, and illustrates the feasibility of liberating the security administrator from manually provisioning and aligning attributes, and verifying the consistency of access control policies for cross-enterprise collaborations.status: publishe

Data Modeling for Ambient Home Care Systems

Ambient assisted living (AAL) services are usually designed to work on the assumption that real-time context information about the user and his environment is available. Systems handling acquisition and context inference need to use a versatile data model, expressive and scalable enough to handle complex context and heterogeneous data sources. In this paper, we describe an ontology to be used in a system providing AAL services. The ontology reuses previous ontologies and models the partners in the value chain and their service offering. With our proposal, we aim at having an effective AAL data model, easily adaptable to specific domain needs and services

Robust Digital Twin Compositions for Industry 4.0 Smart Manufacturing Systems

Industry 4.0 is an emerging business paradigm that is reaping the benefits of enabling technologies driving intelligent systems and environments. By acquiring, processing and acting upon various kinds of relevant context information, smart automated manufacturing systems can make well-informed decisions to adapt and optimize their production processes at runtime. To manage this complexity, the manufacturing world is proposing the ‘Digital Twin’ model to represent physical products in the real space and their virtual counterparts in the virtual space, with data connections to tie the virtual and real products together for an augmented view of the manufacturing workflow. The benefits of such representations are simplified process simulations and efficiency optimizations, predictions, early warnings, etc. However, the robustness and fidelity of digital twins are a critical concern, especially when independently developed production systems and corresponding digital twins interfere with one another in a manufacturing workflow and jeopardize the proper behavior of production systems. We therefore evaluate the addition of safeguards to digital twins for smart cyber-physical production systems (CPPS) in an Industry 4.0 manufacturing workflow in the form of feature toggles that are managed at runtime by software circuit breakers. Our evaluation shows how these improvements can increase the robustness of interacting digital twins by avoiding local errors from cascading through the distributed production or manufacturing workflow.status: publishe

DogOnt - Ontology Modeling for Intelligent Domotic Environments

Abstract. Home automation has recently gained a new momentum thanks to the ever-increasing commercial availability of domotic components. In this context, researchers are working to provide interoperation mechanisms and to add intelligence on top of them. For supporting intelligent behaviors, house modeling is an essential requirement to understand current and future house states and to possibly drive more complex actions. In this paper we propose a new house modeling ontology designed to fit real world domotic system capabilities and to support interoperation between currently available and future solutions. Taking advantage of technologies developed in the context of the Semantic Web, the DogOnt ontology supports device/network independent description of houses, including both “controllable ” and architectural elements. States and functionalities are automatically associated to the modeled elements through proper inheritance mechanisms and by means of properly defined SWRL auto-completion rules which ease the modeling process, while automatic device recognition is achieved through classification reasoning.

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases