CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture

Decentralized Identity and Access Management Framework for Internet of Things Devices

The emerging Internet of Things (IoT) domain is about connecting people and devices and systems together via sensors and actuators, to collect meaningful information from the devices surrounding environment and take actions to enhance productivity and efficiency. The proliferation of IoT devices from around few billion devices today to over 25 billion in the next few years spanning over heterogeneous networks defines a new paradigm shift for many industrial and smart connectivity applications. The existing IoT networks faces a number of operational challenges linked to devices management and the capability of devices’ mutual authentication and authorization. While significant progress has been made in adopting existing connectivity and management frameworks, most of these frameworks are designed to work for unconstrained devices connected in centralized networks. On the other hand, IoT devices are constrained devices with tendency to work and operate in decentralized and peer-to-peer arrangement. This tendency towards peer-to-peer service exchange resulted that many of the existing frameworks fails to address the main challenges faced by the need to offer ownership of devices and the generated data to the actual users. Moreover, the diversified list of devices and offered services impose that more granular access control mechanisms are required to limit the exposure of the devices to external threats and provide finer access control policies under control of the device owner without the need for a middleman. This work addresses these challenges by utilizing the concepts of decentralization introduced in Distributed Ledger (DLT) technologies and capability of automating business flows through smart contracts. The proposed work utilizes the concepts of decentralized identifiers (DIDs) for establishing a decentralized devices identity management framework and exploits Blockchain tokenization through both fungible and non-fungible tokens (NFTs) to build a self-controlled and self-contained access control policy based on capability-based access control model (CapBAC). The defined framework provides a layered approach that builds on identity management as the foundation to enable authentication and authorization processes and establish a mechanism for accounting through the adoption of standardized DLT tokenization structure. The proposed framework is demonstrated through implementing a number of use cases that addresses issues related identity management in industries that suffer losses in billions of dollars due to counterfeiting and lack of global and immutable identity records. The framework extension to support applications for building verifiable data paths in the application layer were addressed through two simple examples. The system has been analyzed in the case of issuing authorization tokens where it is expected that DLT consensus mechanisms will introduce major performance hurdles. A proof of concept emulating establishing concurrent connections to a single device presented no timed-out requests at 200 concurrent connections and a rise in the timed-out requests ratio to 5% at 600 connections. The analysis showed also that a considerable overhead in the data link budget of 10.4% is recorded due to the use of self-contained policy token which is a trade-off between building self-contained access tokens with no middleman and link cost

A Novel Method for Adaptive Control of Manufacturing Equipment in Cloud Environments

The ability to adaptively control manufacturing equipment, both in local and distributed environments, is becoming increasingly more important for many manufacturing companies. One important reason for this is that manufacturing companies are facing increasing levels of changes, variations and uncertainty, caused by both internal and external factors, which can negatively impact their performance. Frequently changing consumer requirements and market demands usually lead to variations in manufacturing quantities, product design and shorter product life-cycles. Variations in manufacturing capability and functionality, such as equipment breakdowns, missing/worn/broken tools and delays, also contribute to a high level of uncertainty. The result is unpredictable manufacturing system performance, with an increased number of unforeseen events occurring in these systems. Events which are difficult for traditional planning and control systems to satisfactorily manage. For manufacturing scenarios such as these, the use of real-time manufacturing information and intelligence is necessary to enable manufacturing activities to be performed according to actual manufacturing conditions and requirements, and not according to a pre-determined process plan. Therefore, there is a need for an event-driven control approach to facilitate adaptive decision-making and dynamic control capabilities. Another reason driving the move for adaptive control of manufacturing equipment is the trend of increasing globalization, which forces manufacturing industry to focus on more cost-effective manufacturing systems and collaboration within global supply chains and manufacturing networks. Cloud Manufacturing is evolving as a new manufacturing paradigm to match this trend, enabling the mutually advantageous sharing of resources, knowledge and information between distributed companies and manufacturing units. One of the crucial objectives for Cloud Manufacturing is the coordinated planning, control and execution of discrete manufacturing operations in collaborative and networked environments. Therefore, there is also a need that such an event-driven control approach supports the control of distributed manufacturing equipment. The aim of this research study is to define and verify a novel and comprehensive method for adaptive control of manufacturing equipment in cloud environments. The presented research follows the Design Science Research methodology. From a review of research literature, problems regarding adaptive manufacturing equipment control have been identified. A control approach, building on a structure of event-driven Manufacturing Feature Function Blocks, supported by an Information Framework, has been formulated. The Function Block structure is constructed to generate real-time control instructions, triggered by events from the manufacturing environment. The Information Framework uses the concept of Ontologies and The Semantic Web to enable description and matching of manufacturing resource capabilities and manufacturing task requests in distributed environments, e.g. within Cloud Manufacturing. The suggested control approach has been designed and instantiated, implemented as prototype systems for both local and distributed manufacturing scenarios, in both real and virtual applications. In these systems, event-driven Assembly Feature Function Blocks for adaptive control of robotic assembly tasks have been used to demonstrate the applicability of the control approach. The utility and performance of these prototype systems have been tested, verified and evaluated for different assembly scenarios. The proposed control approach has many promising characteristics for use within both local and distributed environments, such as cloud environments. The biggest advantage compared to traditional control is that the required control is created at run-time according to actual manufacturing conditions. The biggest obstacle for being applicable to its full extent is manufacturing equipment controlled by proprietary control systems, with native control languages. To take the full advantage of the IEC Function Block control approach, controllers which can interface, interpret and execute these Function Blocks directly, are necessary

Key performance indicators for sustainable manufacturing evaluation in automotive companies

The automotive industry is regarded as one of the most important and strategic industry in manufacturing sector. It is the largest manufacturing enterprise in the world and one of the most resource intensive industries of all major industrial system. However, its products and processes are a significant source of environmental impact. Thus, there is a need to evaluate sustainable manufacturing performance in this industry. This paper proposes a set of initial key performance indicators (KPIs) for sustainable manufacturing evaluation believed to be appropriate to automotive companies, consisting of three factors divided into nine dimensions and a total of 41 sub-dimensions. A survey will be conducted to confirm the adaptability of the initial KPIs with the industry practices. Future research will focus on developing an evaluation tool to assess sustainable manufacturing performance in automotive companies

Value creation mechanisms of cloud computing: a conceptual framework

The management literature has analysed Cloud Computing, mainly focusing on the impact of its technical properties (e.g. accessibility, elasticity, scaling) on firms' dynamics, without explicitly addressing the dynamic generation of value streams. With this paper we fill this gap, linking the unexplored potential sources of Cloud Computing with the literature on business model value creation. We define a conceptual model able to integrate existent technical knowledge on Cloud Computing with the understudied part on the value creation mechanisms, dynamically representing their interaction. Our approach is based on a mixed methodology built on three pillars: 1) systematic literature review of the properties of Cloud Computing with an impact on firms’ management in order to identify possible gaps, using value generation within business models as the unit of analysis; 2) multiple case studies to inductively derive the emerging properties using Gioia methodology, analysing 20 startups in the AWS business case repository; 3) dynamic representation between technical properties extracted by literature review and emergent properties, focusing on the value streams generation. Results confirm how the leveraging potentiality of Cloud Computing goes well beyond technical advantages, deeply inserting in the business model system and enabling different sources of value creation

Hybrid modeling to support the smart manufacturing: concepts, theoretic contributions and real-case applications about Hybrid and Wisdom-based Systems

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