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Affective computing solutions, in the literature, mainly rely on machine learning methods designed to accurately detect human affective states. Nevertheless, many of the proposed methods are based on handcrafted features, requiring sufficient expert knowledge in the realm of signal processing. With the advent of deep learning methods, attention has turned toward reduced feature engineering and more end-to-end machine learning. However, most of the proposed models rely on late fusion in a multimodal context. Meanwhile, addressing interrelations between modalities for intermediate-level data representation has been largely neglected. In this paper, we propose a novel deep convolutional neural network, called CN-Waterfall, consisting of two modules: Base and General. While the Base module focuses on the low-level representation of data from each single modality, the General module provides further information, indicating relations between modalities in the intermediate- and high-level data representations. The latter module has been designed based on theoretically grounded concepts in the Explainable AI (XAI) domain, consisting of four different fusions. These fusions are mainly tailored to correlation- and non-correlation-based modalities. To validate our model, we conduct an exhaustive experiment on WESAD and MAHNOB-HCI, two publicly and academically available datasets in the context of multimodal affective computing. We demonstrate that our proposed model significantly improves the performance of physiological-based multimodal affect detection.

The safe deployment of autonomous physical systems in real-world scenarios requires them to be explainable and trustworthy, especially in critical domains. In contrast with g€black-box' systems, explainable and trustworthy autonomous physical systems will lend themselves to easy assessments by system designers and regulators. This promises to pave ways for easy improvements that can lead to enhanced performance, and as well, increased public trust. In this one-day virtual workshop, we aim to gather a globally distributed group of researchers and practitioners to discuss the opportunities and social challenges in the design, implementation, and deployment of explainable and trustworthy autonomous physical systems, especially in a post-pandemic era. Interactions will be fostered through panel discussions and a series of spotlight talks. To ensure lasting impact of the workshop, we will conduct a pre-workshop survey which will examine the public perception of the trustworthiness of autonomous physical systems. Further, we will publish a summary report providing details about the survey as well as the identified challenges resulting from the workshop's panel discussions.

It is of extreme importance to monitor and manage the battery health to enhance the performance and decrease the maintenance cost of operating electric vehicles. This paper concerns the machine-learning-enabled state-of-health (SoH) prognosis for Li-ion batteries in electric trucks, where they are used as energy sources. The paper proposes methods to calculate SoH and cycle life for the battery packs. We propose autoregressive integrated modeling average (ARIMA) and supervised learning (bagging with decision tree as the base estimator; BAG) for forecasting the battery SoH in order to maximize the battery availability for forklift operations. As the use of data-driven methods for battery prognostics is increasing, we demonstrate the capabilities of ARIMA and under circumstances when there is little prior information available about the batteries. For this work, we had a unique data set of 31 lithium-ion battery packs from forklifts in commercial operations. On the one hand, results indicate that the developed ARIMA model provided relevant tools to analyze the data from several batteries. On the other hand, BAG model results suggest that the developed supervised learning model using decision trees as base estimator yields better forecast accuracy in the presence of large variation in data for one battery., This paper has first been published at the Proceedings of the ASME 2020 International Mechanical Engineering Congress and Exposition IMECE2020 held on November 16-19, 2020, Portland, OR, USA

Collecting and utilizing product life-cycle data is both difficult and expensive for products that move between different industrial settings at various points of the product life-cycle. Product-centric approaches that present effective solutions in tightly integrated environments have been problematic to deploy across multiple industries and over longer timespans. Addressing deployment costs, incentives, and governance, this paper explores a blockchain-based approach for the deployment of product-centric information systems. Through explorative design science and systematic combining, the deployment of a permissionless blockchain system for collecting product life-cycle data is conceptualized, demonstrated, and evaluated by experts. The purpose of the blockchain-based solution is to manage product data interactions, to maintain an accurate single state of product information, and to provide an economic incentive structure for the provision and the deployment of the solution. The evaluation by knowledgeable researchers and practitioners identifies the aspects limiting blockchain-based deployment of solutions in the current industrial landscape. Combining theory and practice, the paper lays the foundation for a blockchain-based approach to product information management, placing design priority on inter-industrial and self-sustained deployment.

Many techniques have been proposed in recent years that attempt to explain results of image classifiers, notably for the case when the classifier is a deep neural network. This paper presents an implementation of the Contextual Importance and Utility method for explaining image classifications. It is an R package that can be used with the most usual image classification models. The paper shows results for typical benchmark images, as well as for a medical data set of gastro-enterological images. For comparison, results produced by the LIME method are included. Results show that CIU produces similar or better results than LIME with significantly shorter calculation times. However, the main purpose of this paper is to bring the existence of this package to general knowledge and use, rather than comparing with other explanation methods.

Publisher Copyright: © 2013 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved. Quality of Experience (QoE) is defined as the measure of end-user satisfaction with the service. Most of the existing works addressing QoE-management rely on a binary vision of end-user satisfaction. This vision has been criticized by the growing empirical evidence showing that QoE is rather a degree. This article aims to go beyond the binary vision and propose a QoE management mechanism. We propose a one-to-many negotiation mechanism allowing the provider to undertake satisfaction management: to meet fine-grained user QoE goals, while still minimizing the costs. This problem is formulated as an optimization problem, for which a linear model is proposed. For reference, a generic linear program solver is used to find the optimal solution, and an alternative heuristic algorithm is devised to improve the responsiveness when the system has to scale up with a fast-growing number of users. Both are implemented and experimentally evaluated against state-of-the-art one-to-many negotiation frameworks.

openaire: EC/H2020/688203/EU//bIoTope | openaire: EC/H2020/856602/EU//FINEST TWINS In an Internet of Things (IoT) environment, a large volume of potentially confidential data might be leaked from sensors installed everywhere. To ensure the authenticity of such sensitive data, it is important to initially verify the source of data and its identity. Practically, IoT device identification is the primary step toward a secure IoT system. An appropriate device identification approach can counteract malicious activities such as sending false data that trigger irreparable security issues in vital or emergency situations. Recent research indicates that primary identity metrics such as Internet Protocol (IP) or Media Access Control (MAC) addresses are insufficient due to their instability or easy accessibility. Thus, to identify an IoT device, analysis of the header information of packets by the sensors is of imperative consideration. This paper proposes a combination of sensor measurement and statistical feature sets in addition to a header feature set using a classification-based device identification framework. Various machine Learning algorithms have been adopted to identify different combinations of these feature sets to provide enhanced security in IoT devices. The proposed method has been evaluated through normal and under-attack circumstances by collecting real-time data from IoT devices connected in a lab setting to show the system robustness.

Explaining the result of machine learning models is an active research topic in Artificial Intelligence (AI) domain with an objective to provide mechanisms to understand and interpret the results of the underlying black-box model in a human-understandable form. With this objective, several eXplainable Artificial Intelligence (XAI) methods have been designed and developed based on varied fundamental principles. Some methods such as Local interpretable model agnostic explanations (LIME), SHAP (SHapley Additive exPlanations) are based on the surrogate model while others such as Contextual Importance and Utility (CIU) do not create or rely on the surrogate model to generate its explanation. Despite the difference in underlying principles, these methods use different sampling techniques such as uniform sampling, weighted sampling for generating explanations. CIU, which emphasizes a context-aware decision explanation, employs a uniform sampling method for the generation of representative samples. In this research, we target uniform sampling methods which generate representative samples that do not guarantee to be representative in the presence of strong non-linearities or exceptional input feature value combinations. The objective of this research is to develop a sampling method that addresses these concerns. To address this need, a new adaptive weighted sampling method has been proposed. In order to verify its efficacy in generating explanations, the proposed method has been integrated with CIU, and tested by deploying the special test case

By the ubiquitous usage of machine learning models with their inherent black-box nature, the necessity of explaining the decisions made by these models has become crucial. Although outcome explanation has been recently taken into account as a solution to the transparency issue in many areas, affect computing is one of the domains with the least dedicated effort on the practice of explainable AI, particularly over different machine learning models. The aim of this work is to evaluate the outcome explanations of two black-box models, namely neural network (NN) and linear discriminant analysis (LDA), to understand individuals affective states measured by wearable sensors. Emphasizing on context-aware decision explanations of these models, the two concepts of Contextual Importance (CI) and Contextual Utility (CU) are employed as a model-agnostic outcome explanation approach. We conduct our experiments on the two multimodal affect computing datasets, namely WESAD and MAHNOB-HCI. The results of applying a neural-based model on the first dataset reveal that the electrodermal activity, respiration as well as accelorometer sensors contribute significantly in the detection of “meditation” state for a particular participant. However, the respiration sensor does not intervene in the LDA decision of the same state. On the course of second dataset and the neural network model, the importance and utility of electrocardiogram and respiration sensors are shown as the dominant features in the detection of an individual “surprised” state, while the LDA model does not rely on the respiration sensor to detect this mental state.

openaire: EC/H2020/732240/EU//SynchroniCity Air pollution is a severe health issue. In urban environments, traffic is the main pollution source. Pollution disperses from main roads to the environment depending on weather conditions and city structure. Given dense air quality data, one could create routes that optimize journeys to avoid polluted air. We provide a methodology for this, and have implemented a Clean Air Journey Planner for the City of Helsinki. We have done this by modifying the existing Open Source journey planner (the Digitransit platform), extended by integrating high resolution (13m grid size) air quality data generated hourly with the ENFUSER dissipation model by the Finnish Meteorological Institute. The Planner is suited for pedestrians and allows citizens to find routes with less pollution. It is the first to utilize near real time updated high resolution air quality data directly in the routing core of a widely used Open Source journey planner.

Neural networks are not great generalizers outside their training range i.e. they are good at capturing bias but might miss the overall concept. Important issues with neural networks is that when testing data goes outside training range they fail to predict accurate results. Hence, they loose the ability to generalize a concept. For systematic numeric exploration neural accumulators (NAC) and neural arithmetic logic unit(NALU) are proposed which performs excellent for simple arithmetic operations. But, major limitation with these units is that they can't handle complex mathematical operations \& equations. For example, NALU can predict accurate results for multiplication operation but not for factorial function which is essentially composition of multiplication operations only. It is unable to comprehend pattern behind an expression when composition of operations are involved. Hence, we propose a new neural network structure effectively which takes in complex compositional mathematical operations and producesbest possible results with small NALU based neural networks as its pluggable modules which evaluates these expression at unitary level in a bottom-up manner. We call this effective neural network as CalcNet, as it helps in predicting accurate calculations for complex numerical expressions even for values that are out of training range. As part of our study we applied this network on numerically approximating complex equations, evaluating biquadratic equations and tested reusability of these modules. We arrived at far better generalizations for complex arithmetic extrapolation tasks as compare to both only NALU layer based neural networks and simple feed forward neural networks. Also, we achieved even better results for our golden ratio based modified NAC and NALU structures for both interpolating and extrapolating tasks in all evaluation experiments. Finally, from reusability standpoint this model demonstrate strong invariance for making predictions on different tasks.

A key aspect for the forklifts is the state-of-health (SoH) assessment to ensure the safety and the reliability of uninterrupted power source. Forecasting the battery SoH well is imperative to enable preventive maintenance and hence to reduce the costs. This paper demonstrates the capabilities of gradient boosting regression for predicting the SoH timeseries under circumstances when there is little prior information available about the batteries. We compared the gradient boosting method with light gradient boosting, extra trees, extreme gradient boosting, random forests, long short-term memory networks and with combined convolutional neural network and long short-term memory networks methods. We used multiple predictors and lagged target signal decomposition results as additional predictors and compared the yielded prediction results with different sets of predictors for each method. For this work, we are in possession of a unique data set of 45 lithium-ion battery packs with large variation in the data. The best model that we derived was validated by a novel walk-forward algorithm that also calculates point-wise confidence intervals for the predictions; we yielded reasonable predictions and confidence intervals for the predictions. Furthermore, we verified this model against five other lithium-ion battery packs; the best model generalised to greater extent to this set of battery packs. The results about the final model suggest that we were able to enhance the results in respect to previously developed models. Moreover, we further validated the model for extracting cycle counts presented in our previous work with data from new forklifts; their battery packs completed around 3000 cycles in a 10-year service period, which corresponds to the cycle life for commercial Nickel-Cobalt-Manganese (NMC) cells., Comment: 16 pages, 10 figures and 10 tables

Multivariate time series with missing data is ubiquitous when the streaming data is collected by sensors or any other recording instruments. For instance, the outdoor sensors gathering different meteorological variables may encounter low material sensitivity to specific situations, leading to incomplete information gathering. This is problematic in time series prediction with massive missingness and different missing rate of variables. Contribution addressing this problem on the regression task of meteorological datasets by employing Long Short-Term Memory (LSTM), capable of controlling the information flow with its memory unit, is still missing. In this paper, we propose a novel model called forward and backward variable-sensitive LSTM (FBVS-LSTM) consisting of two decay mechanisms and some informative data. The model inputs are mainly the missing indicator, time intervals of missingness in both forward and backward direction and missing rate of each variable. We employ this information to address the so-called missing not at random (MNAR) mechanism. Separately learning the features of each parameter, the model becomes adapted to deal with massive missingness. We conduct our experiment on three real-world datasets for the air pollution forecasting. The results demonstrate that our model performed well along with other LSTM-derivation models in terms of prediction accuracy.

Tuhosin alkuperäisen hyvin vajaan tietueen. Siinä oli mukana tiedosto, mutta se ei auennut. The Internet of Things (IoT) security and privacy remain a major challenge, especially due to the highly scalable and distributed nature of IoT networks. IoT security has an exceptionally wide scope which also leads to the most demanding requirements needed for the widespread realization of many IoT visions. It includes the tasks such as trusted sensing, computation, communication, privacy in terms of security scope. A large number of industry-driven domain specific standards has been developed which hinder the development of a single IoT ecosystem. O-MI and O-DF standards were previously proposed to address the challenge of interoperability in IoT devices. This paper highlights and discusses the measures taken to enhance the security of these standards by integration of OAuth 2.0 service provider functionality with the O-MI authentication module along with the existing security module. The security architecture has been proposed which enhances the security features by providing authorization without use of certificates. The implementation details of the plug-in module for envisioned security model developed for the O-MI and O-DF standards have been discussed.

As autonomous agents become more self-governing, ubiquitous and sophisticated, it is vital that humans should have effective interactions with them. Agents often use Machine Learning (ML) for acqui ...

Explainability has been a core research topic in AI for decades and therefore it is surprising that the current concept of Explain- able AI (XAI) seems to have been launched as late as 2016. This is a problem with current XAI research because it tends to ignore existing knowledge and wisdom gathered over decades or even centuries by other relevant domains. This paper presents the notion of Contextual Impor- tance and Utility (CIU), which is based on known notions and methods of Decision Theory. CIU extends the notions of importance and utility for the non-linear models of AI systems and notably those produced by Machine Learning methods. CIU provides a universal and model-agnostic foundation for XAI. WASP-AS

This paper proposes an explainable machine learning tool that can potentially be used for decision support in medical image analysis scenarios. For a decision-support system it is important to be able to reverse-engineer the impact of features on the final decision outcome. In the medical domain, such functionality is typically required to allow applying machine learning to clinical decision making. In this paper, we present initial experiments that have been performed on in-vivo gastral images obtained from capsule endoscopy. Quantitative analysis has been performed to evaluate the utility of the proposed method. Convolutional neural networks have been used for training the validating of the image data set to provide the bleeding classifications. The visual explanations have been provided in the images to help health professionals trust the black box predictions. While the paper focuses on the in-vivo gastral image use case, most findings are generalizable.

The so-called Internet of Things is of increasing importance for facilitating productivity across industries, i.e., by connecting sensors with manufacturing lines and IT system landscapes with an increasing degree of autonomy. In this context, a common challenge is enabling reasonable trade-offs between structure and control on the one hand and flexibility and human-like intelligent behavior on the other hand. To address this challenge, we establish the need for and requirements of a hybrid IoT-/agent-based business process orchestration architecture that utilizes open standards. We propose a four-layered architecture, which integrates autonomous agents and business process orchestration for IoT/agents, and provide a running example for a supply chain management (purchasing) use case. CCS CONCEPTS• Applied computing → Business process management; • Information systems; • Software and its engineering → Software as a service orchestration system

openaire: EC/H2020/688203/EU//BIoTope Information Systems (ISs) are fundamental to streamline operations and support processes of any modern enterprise. Being able to perform analytics over the data managed in various enterprise ISs is becoming increasingly important for organisational growth. Extract, Transform, and Load (ETL) are the necessary pre-processing steps of any data mining activity. Due to the complexity of modern IS, extracting data is becoming increasingly complicated and time-consuming. In order to ease the process, this paper proposes a methodology and a pilot implementation, that aims to simplify data extraction process by leveraging the end-users' knowledge and understanding of the specific IS. This paper first provides a brief introduction and the current state of the art regarding existing ETL process and techniques. Then, it explains in details the proposed methodology. Finally, test results of typical data-extraction tasks from four commercial ISs are reported.

openaire: EC/H2020/688203/EU//BIoTope Industrial Internet of things is becoming a boon to Original Equipment Manufacturers (OEMs) offering after-sales services such as condition-based maintenance and extended warranty for their products. These companies leverage novel digital information infrastructures to improve daily industrial activities, including data collection, remote monitoring and advanced condition-based maintenance services. The emergence of digital infrastructure and new business prospects via servi-tization and quality services encourage companies to collect vast amounts of data that have been generated in different stages of product lifecycles. Despite of the potential benefits, companies are unable to fully harness the opportunities presented by digital information infrastructure because there exist several platforms with variations in technologies and standards resulting in interoperability challenges. This becomes particularly critical when a company sells its products to several clients with different technologies. To overcome such challenges, we investigate the Open Messaging Interface (O-MI) and Open Data Format (O-DF), flexible messaging and data exchange standards that enable seamless integration of different systems. These standards enable interoperability and support time-centric, event-centric, and rate-centric modes of data exchange.

openaire: EC/H2020/688203/EU//BIoTope Internet of Things (IoT) is a computing infrastructure underlying powerful systems and applications, enabling autonomous interconnection of people, vehicles, devices, and information systems. Many IoT sectors such as smart grid or smart mobility will benefit from the recent evolutions of the smart city initiatives for building more advanced IoT services, from the collection of human- and machine-generated data to their storage and analysis. It is therefore of utmost importance to manage the volume, velocity, and variety of the data, in particular at the IoT gateways level, where data are published and consumed. This paper proposes an access time improvement framework to optimize the publication and consumption steps, the storage and retrieval of data at the gateways level to be more precise. This new distributed framework relies on a consistent hashing mechanism and modular characteristics of microservices to ensure a flexible and scalable solution. Applied and assessed on a real case study, experimental results show how the proposed framework improves data access time for standardized IoT gateways.

We are entering a new age of AI applications where machine learning is the core technology but machine learning models are generally non-intuitive, opaque and usually complicated for people to unde ...

The significant advances in autonomous systems together with an immensely wider application domain have increased the need for trustable intelligent systems. Explainable artificial intelligence is ga ...

Internet of Things (IoT), the emerging computing infrastructure that refers to the networked interconnection of physical objects, incorporates a plethora of digital systems that are being developed by means of a large number of applications. Many of these applications administer data collection on the edge and offer data storage and analytics capabilities in the cloud. This raises the following problems: (i) the processing stages in IoT applications need to have separate implementations for both the edge and the cloud, (ii) the placement of computation is inflexible with separate software stacks, as the optimal deployment decisions need to be made at runtime, and (iii) unified fault tolerance is essential in case of intermittent long-distance network connectivity problems, malicious harming of edge devices, or harsh environments. This paper proposes a novel fault-tolerant architecture CEFIoT for IoT applications by adopting state-of-the-art cloud technologies and deploying them also for edge computing. We solve the data fault tolerance issue by exploiting the Apache Kafka publish/subscribe platform as the unified high-performance data replication solution offering a common software stack for both the edge and the cloud. We also deploy Kubernetes for fault-tolerant management and the advanced functionality allowing on-the-fly automatic reconfiguration of the processing pipeline to handle both hardware and network connectivity based failures., 6 pages, 6 figures

openaire: EC/H2020/688203/EU//BIoTope Many domains are trying to integrate with the Internet of Things (IoT) ecosystem, such aspublic administrations starting Smart City initiatives all over the world. Cities are becomingsmart in many ways: smart mobility, smart buildings, smart environment and so on. However, theproblem of non-interoperability in IoT hinders the seamless communication between all kinds ofIoT devices. Different domain specific IoT applications use different interoperability standards. These standards are usually not interoperable with each other. IoT applications and ecosystems therefore tend to use a vertical communication model that does not allow to share data horizontally across the different IoT ecosystems. In 2014, The Open Group published two domain-independent IoT messaging standards O-MI and O-DF aiming to solve the interoperability problem.In this article we want to describe the practical use of O-MI/O-DF standards in a mobile application for the smart city context, in particular for the Smart Mobility domain, electric vehicle (EV) charging use case. The proof-of-concept of the mobile application for EV charging was developed as a part of an EU (Horizon 2020) Project bIoTope.

The built environment provides significant opportunities for IoT (Internet of Things) deployment, and can be singled out as one of the most important aspects for IoT related research. While the IoT deployment in the built environment is growing exponentially, there exists a gap in integrating these two in a systematic way through open standards and systems. From technological perspective, there is a need for convergence of diverse fields ranging from Building Information Systems and Building Services to Building Automation Systems, and IoT devices and finally the end user services to develop smart, user oriented applications. This paper outlines the efforts to develop a platform that integrates the built environment data with IoT sensors in a campus wide, web based system called Otaniemi3D that provides information about energy usage, occupancy and user comfort by integrating Building Information Models and IoT devices through open messaging standards (O-MI and O-DF) and IFC models. The paper describes the design criteria, the system architecture, the workflow and a proof of concept with potential use cases that integrate IoT with the built environment. Initial results show that both the end users and other research groups can benefit from such platforms by either consuming the data in their daily life or using the data for more advance research.

openaire: EC/H2020/688203/EU//BIoTope Information Systems (ISs) are fundamental to streamline operations and support processes of any modern enterprise. Being able to perform analytics over the data managed in various enterprise ISs is becoming increasingly important for organisational growth. Extract, Transform, and Load (ETL) are the necessary pre-processing steps of any data mining activity. Due to the complexity of modern IS, extracting data is becoming increasingly complicated and time-consuming. In order to ease the process, this paper proposes a methodology and a pilot implementation, that aims to simplify data extraction process by leveraging the end-users’ knowledge and understanding of the specific IS. This paper first provides a brief introduction and the current state of the art regarding existing ETL process and techniques. Then, it explains in details the proposed methodology. Finally, test results of typical data-extraction tasks from 4 commercial ISs are reported.

IoT systems may provide information from different sensors that may reveal potentially confidential data, such as a person’s presence or not. The primary question to address is how we can identify the sensors and other devices in a reliable way before receiving data from them and using or sharing it. In other words, we need to verify the identity of sensors and devices. A malicious device could claim that it is the legitimate sensor and trigger security problems. For instance, it might send false data about the environment, harmfully affecting the outputs and behavior of the system. For this purpose, using only primary identity values such as IP address, MAC address, and even the public-key cryptography key pair is not enough since IPs can be dynamic, MACs can be spoofed, and cryptography key pairs can be stolen. Therefore, the server requires supplementary security considerations such as contextual features to verify the device identity. This paper presents a measurement-based method to detect and alert false data reports during the reception process by means of sensor behavior. As a proof of concept, we develop a classification-based methodology for device identification, which can be implemented in a real IoT scenario.

Part 5: Industry 4.0 - Digital Twin; International audience; Maintenance is a complicated task that encompasses various activities including fault detection, fault diagnosis, and fault reparation. The advancement of Computer Aided Engineering (CAE) has increased challenges in maintenance as modern assets have became complex mixes of systems and sub systems with complex interaction. Among maintenance activities, fault diagnosis is particularly cumbersome as the reason of failures on the system is often neither obvious in terms of their source nor unique. Early detection and diagnosis of such faults is turning to one of the key requirements for economical and functional asset efficiency. Several methods have been investigated to detect machine faults for a number of years that are relevant for many application domains. In this paper, we present the process history-based method adopting nominal efficiency of Air Handling Unit (AHU) to detect heat recovery failure using Principle Component Analysis (PCA) in combination of the logistic regression method.

Internet of Things (IoT) is intended to provide a network where information flows could easily be set up between any kinds of products, devices, users and information systems in general. This vision is getting closer to become real due to the continuous development of new information system concepts and technologies. Nonetheless, this new reality requires special attention on particular aspects of the IoT such as security and mobility. First, people and companies want to secure their assets/data using firewalls, which inevitably leads to a challenging conflict between data security and usability. Second, products are becoming increasingly mobile, operating in environments where it can be difficult to contact them directly using their IP address (e.g., due to the presence of NAT or to access restrictions). It might therefore be necessary in some IoT applications to enable two-way communications through any type of firewall, e.g. to enable real-time control and maintenance. Quantum Lifecycle Management (QLM) messaging standards have been designed to provide generic and standardized application-level interfaces for the IoT that make it possible, among other things, to achieve such two-way communication. This paper provides a high-level description of QLM messaging standards with a particular focus on this QLM feature, along with proofs-of-concept through real-life implementations in building and automotive domains. A high-level description of new IoT standard named QLM ?is introduced.The QLM piggybacking property to deal with firewall and mobility policies is presented.A home automation case study dealing with firewall policies and using QLM is presented.A car automation case study dealing with mobility policies and using QLM is presented.

openaire: EC/H2020/688203/EU//BIoTope In today's competitive and fluctuating market, original equipment manufacturers (OEMs) must be able to offer aftersales services along with their products, such as condition based maintenance, extended warranty services etc. Condition based maintenance requires detailed understanding about products' operational behaviour, to detect problems before they occur, and react accordingly. Typically, Condition based maintenance consists of data collection, data analysis, and maintenance decision stages. Within this context, data quality is one of the key drivers in the knowledge acquisition process since poor data quality impacts the downstream maintenance processes, and reciprocally, high data quality will foster good decision making. The prospect of new business opportunities and better services to customers encourages companies to collect large amounts of data that have been generated in different stages of product lifecycle. Despite of availability of data, as well as advanced statistical and analytical tools, companies are still struggling to provide effective service by reducing maintenance cost and improving uptime. This paper highlights data related pitfalls that hinder organisations to improve maintenance services. These pitfalls are based on case studies of two globally operating Finnish manufacturing companies where maintenance is one of the major streams of income.

openaire: EC/H2020/688203/EU//BIoTope The number of Internet of Things (IoT) vendors is rapidly growing, providing solutions for all levels of the IoT stack. Despite the universal agreement on the need for a standardized technology stack, following the model of the world-wide-web, a large number of industry-driven domain specific standards hinder the development of a single IoT ecosystem. An attempt to solve this challenge is the introduction of O-MI (Open Messaging Interface) and O-DF (Open Data Format), two domain independent standards published by Open Group. Despite their good compatibility, they define no specific security model. This paper takes the first step of defining a security model for these standards by proposing suitable access control and authentication mechanisms that can regulate the rights of different principles and operations defined in these standards. First, a brief introduction is provided of the O-MI and O-DF standards, including a comparison with existing standards. Second, the envisioned security model is presented, together with the implementation details of the plug-in module developed for the O-MI and O-DF reference implementation.

Purpose – The purpose of this paper is to propose a collaborative tracking and tracing (T&T) system that enables supply chain actors to join and leave the system in run-time without disrupting the function of the system. Design/methodology/approach – A collaborative T&T system is introduced using a conceptual approach. Supporting empirical data are collected through a deductive case study of a Swedish automotive supply chain. Findings – A collaborative T&T system based on composite and observer design patterns is an appropriate solution for inter-organizational T&T. Research limitations/implications – This research contributes to the body of knowledge in the field of logistics T&T. The conceptually developed collaborative T&T system needs to be validated empirically. Practical implications – The collaborative T&T system developed conceptually here could be used as a model for the practical development of inter-organizational T&T systems. Originality/value – The introduction of a collaborative T&T system, adopting the theories of software engineering and product data management, represents an original contribution to the field of T&T in logistics and transportation.

According to our original vision of the Internet of Things (IoT), it should be possible to create ad hoc and loosely coupled information flows between any kinds of products, devices, computers, users, and information systems in general when and as needed. However, this is still challenging to achieve in practice due to the lack of sufficiently generic and standardized interfaces for creating the needed information flows between all devices and systems that the IoT is composed of. The paper presents necessary requirements for such interfaces, as well as proposed interface standards that fulfill those requirements. The paper describes the design principles and provides a high-level description of the proposed standards, followed by real-life implementations that illustrate why such standards are needed and how they are applied.

In our vision of the Internet of Things, it is possible to easily query and set up information flows between any kinds of products, devices, computers, users and information systems in general. However, for the moment it is not possible due to the lack of sufficiently generic and standardized application-level interfaces for exchanging the kind of information required by an Internet of Things. The Quantum Lifecycle Management (QLM) messaging standards have been developed and are proposed as a standard application-level interface that would fulfill such requirements. The article presents the design principles and provides a high-level description of QLM messaging. Real-life implementations illustrate why such a messaging interface is needed and compares it with related standards.

International audience; Intelligent products may be considered as information vectors, conveying their own information from a phase of its life cycle to another. Considering that such a product could be made with "communicating material", in previous work, a data dissemination process has been developed with the aim of selecting information from a database, that should be stored on the product according to the current stage of its life cycle. This selection is based on an indicator of relevance which depends on a multitude of criteria, whose weights are evaluated by a group of experts. The set of expert opinions must be formalized in accordance with a mathematical theory and then, synthesized by a suitable aggregation method. In this framework, three aggregation methods are introduced and compared. This comparison is carried out based on the scenario of data dissemination when using intelligent products. Results enable to assess the goodness of fit of the three aggregation methods.

The basis for Sustainable PLM is enabling Intelligent Products to communicate between themselves and with other systems in order to reduce CO"2 emissions, energy usage, and environmental damage. This type of communication between Intelligent Products and other information systems with the objective of reducing environmental impacts is an example of closed-loop product lifecycle management. The objective of the paper is to outline an architectural information system model for implementing Sustainable PLM based on experiences from a number of real-life applications. The technological challenge for developing Sustainable PLM is the high number and variety of information systems that need to communicate over organizational limits and over time. In the paper we describe how Sustainable PLM has been implemented using Intelligent Products in a number of representative real-life applications.

Developments in the area of radio frequency identification (RFID) and sensor network technologies have created new possibilities for product lifecycle management (PLM). These technologies are enabling building blocks upon which various business applications for product management can be built. A significant aspect in the through-life management of products is the gathering and management of data related to the product during the various phases of its lifecycle. Both RFID and wireless sensor technologies have created novel levels of product status visibility and automatic identification with granularity to the level of individual components. We consider three approaches that leverage automatic identification technologies and miniaturised wireless sensors to support PLM strategies at the product instance level based on a product centric computing paradigm. The paper draws upon a number of case studies focusing on the various phases of a product's lifecycle: Beginning of Life, Middle of Life and End of Life. We used the case studies to extract technical, capability and information requirements needed to support PLM strategies. We compared the ability and suitability of existing architectural approaches to meet the key requirements we identified. Furthermore we assessed them to evaluate their level of support for managing product instances at different lifecycle phases. Finally, we present an extension to significantly improve the ability of the most promising architecture for supporting PLM strategies.

The significance of tracking in operations management has been overshadowed, theoretically by the concept of supply chain visibility, and practically by the attention grabbing radio frequency identification (RFID) technology. This paper describes how uses of tracking for operations management, specifically, to improve the management of project and service supply chains, were explored and developed in a ten-year research program. Key results of the program are synthesized and linked to the relevant academic discourse in operations management using design science methodology.

PurposeThe purpose of the paper is to evaluate the feasibility of implementing a tracking based inventory management system in temporary storage locations of a project delivery chain. To describe the use of tracking based inventory information for improved logistics control in equipment delivery and installation.Design/methodology/approachA solution design experiment was carried out in 16 temporary storage locations with one original equipment manufacturer and four installation partners.FindingsIt is feasible to implement tracking based inventory management in temporary storage locations. The challenge is to ensure that installation partners adopt the system. The benefit is improved logistics control of equipment delivery and installation for the original equipment manufacturer.Research limitations/implicationsTracking information is more useful than conventional stock keeping in project delivery. By monitoring the dwell time of delivery items it is possible to identify and resolve problems in project execution.Practical implicationsInventory management in temporary storage locations help project management, project logistics, and central logistics organizations carry out their work more effectively. Implementation can be based on tracking.Originality/valueThe paper's value lies in empirical tests and evaluation of tracking based inventory management in temporary storage locations.

Service business renewal to support global fleet operations is one of the critical competitiveness factors of technology industries. This paper describes preliminary results of defining an IoT-based framework for fleet management systems (FMS). The main contribution is to provide a systematic outline to the ingredients of the framework and discussing their impact in an industrial setting. The overall aim of the research is to define a technical framework for fleet management solutions and to define the necessary set of functionalities that the framework should support as a base for FMS systems. The framework will be implemented and validated in collaboration with industrial partners.

Managing product information for product items during their whole lifetime is challenging, especially during their usage and end-of-life phases. The main difficulty is to maintain a communication link between the product item and its associated information as the product item moves over organizational borders and between different users. As network access will typically not be continuous during the whole product-item lifecycle, it is necessary to embed at least a globally unique product identifier (GUPI) that makes it possible to identify the product item anytime during its lifecycle. A GUPI also has to provide a linking mechanism to product information that may be stored in backend systems of different organizations. GUPIs are thereby a cornerstone for enabling the Internet of Things, where 'intelligent products' can communicate over the Internet. In the current paper, we analyse and compare the three main currently known approaches for achieving such functionality, i.e. the EPC Network, DIALOG and WWAI.

Reinforcement learning is based on exploration of the environment and receiving reward that indicates which actions taken by the agent are good and which ones are bad. In many applications receiving even the first reward may require long exploration, during which the agent has no information about its progress. This paper presents an approach that makes it possible to use pre-existing knowledge about the task for guiding exploration through the state space. Concepts of short- and long-term memory combine guidance by pre-existing knowledge with reinforcement learning methods for value function estimation in order to make learning faster while allowing the agent to converge towards a good policy.

A manufactured product usually has a great amount of design knowledge and other product information associated with it in CAD drawings and other technical documentation. Today, such product information mainly enables preventive maintenance of products. By collecting usage, performance and maintenance information about the product, it could be possible to perform more predictive maintenance. The key issue is to document product information in a way that would enable analysing the relation between usage parameters and actual product failures. Semantic networks are here proposed as an intermediate knowledge representation format for combining such static and dynamic product information.

In the so-called "Internet of Things" (IoT), mobile users and objects will be able to dynamically discover and impromptu interact with heterogeneous computing, physical resources, as well as virtual data. It is a fact today that more standardized formats, protocols and interfaces must be built in the IoT to provide more interoperable systems. However, even if a protocol would become the de facto IoT standard for data exchange, this would not solve all issues related to system and information integration into the IoT. Indeed, a key challenge is to allow integrating all types of existing domain- or vendor-specific application with such a standard. In this regard, this paper develops a generic framework for such an integration that consists in using the basic communication interfaces supported by the domain-specific application (e.g., basic read and write operations) to extend them into more advanced interfaces provided by IoT standards (e.g., to support subscription mechanisms). In this paper, the set of standardized interfaces defined in a recent IoT standard proposal, referred to as Quantum Lifecycle Management (QLM) messaging standards, are considered for developing the integration framework. A case study using a building-specific application, namely openHAB, is then presented to validate the framework practicability.

Building Information Management systems have started to leverage new kinds of digital information infrastructures that integrate activities related to design, budgeting, scheduling, analysis, material management, and human resources. Companies implementing such systems have no other option today than exchanging information among themselves in a proper and efficient way. Industry foundation classes (IFCs) provide useful information structures for such a data sharing. However they do not specify how to capture, exchange and synchronize the information between distinct entities (i.e. information systems like sensors, servers, smart products…) throughout the building lifecycle. Accordingly, this paper investigates existing messaging protocols in order to identify which one is the most appropriate for supporting building lifecycle management; i.e a flexible protocol that provides sufficiently generic communication interfaces. A platform set up on a university campus based on the selected messaging protocol is then presented. Within this context, the research agenda that should be pursued to develop new techniques and algorithms for optimizing the building energy management is announced.

International audience; The so-called Internet of Things (IoT) presents a tremendous opportunity for multitude of users to be connected to anything, whenever needed, wherever needed, by whoever needs it. Such opportunities, nonetheless, require more advanced and standardized communication interfaces to facilitate interactions between any types of "things" (smart devices, sensors, systems, networks, etc.). Recent standard proposals as the Quantum Lifecycle Management (QLM) messaging standards were developed and are proposed to provide such standardized interfaces for IoT data exchange. These standards make it possible to develop innovative and standardized techniques to address other issues in the IoT such as the management of replicas of a same data between distinct objects in a peer-to-peer, loosely coupled ways. This paper investigates new strategies-based agent models for peer-to-peer data synchronization in the IoT using the standardized interfaces provided by the QLM standards. This enables the development of models not designed domain-, vendor-or application-specific, as is often the case. Our synchronization models are implemented and assessed considering a real home automation platform.