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  • 1.
    Anjomshoae, Sule
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University.
    Främling, Kary
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University.
    Najjar, Amro
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University.
    Explanations of black-box model predictions by contextual importance and utility2019In: Explainable, transparent autonomous agents and multi-agent systems: first international workshop, EXTRAAMAS 2019, Montreal, QC, Canada, May 13–14, 2019, revised selected papers / [ed] Davide Calvaresi, Amro Najjar, Michael Schumacher, Kary Främling, Springer, 2019, p. 95-109Chapter in book (Refereed)
    Abstract [en]

    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 gaining considerable attention among researchers and developers to address this requirement. Although there is an increasing number of works on interpretable and trans- parent machine learning algorithms, they are mostly intended for the technical users. Explanations for the end-user have been neglected in many usable and practical applications. In this work, we present the Contextual Importance (CI) and Contextual Utility (CU) concepts to extract explanations that are easily understandable by experts as well as novice users. This method explains the prediction results without transforming the model into an interpretable one. We present an example of providing explanations for linear and non-linear models to demonstrate the generalizability of the method. CI and CU are numerical values that can be represented to the user in visuals and natural language form to justify actions and explain reasoning for individual instances, situations, and contexts. We show the utility of explanations in car selection example and Iris flower classification by presenting complete (i.e. the causes of an individual prediction) and contrastive explanation (i.e. contrasting instance against the instance of interest). The experimental results show the feasibility and validity of the provided explanation methods.

  • 2.
    Anjomshoae, Sule
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Najjar, Amro
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Calvaresi, Davide
    Främling, Kary
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Explainable Agents and Robots: Results from a Systematic Literature Review2019In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems / [ed] N. Agmon, M. E. Taylor, E. Elkind, M. Veloso, International Foundation for Autonomous Agents and MultiAgent Systems , 2019, p. 1078-1088Conference paper (Refereed)
    Abstract [en]

    Humans are increasingly relying on complex systems that heavily adopts Artificial Intelligence (AI) techniques. Such systems are employed in a growing number of domains, and making them explainable is an impelling priority. Recently, the domain of eXplainable Artificial Intelligence (XAI) emerged with the aims of fostering transparency and trustworthiness. Several reviews have been conducted. Nevertheless, most of them deal with data-driven XAI to overcome the opaqueness of black-box algorithms. Contributions addressing goal-driven XAI (e.g., explainable agency for robots and agents) are still missing. This paper aims at filling this gap, proposing a Systematic Literature Review. The main findings are (i) a considerable portion of the papers propose conceptual studies, or lack evaluations or tackle relatively simple scenarios; (ii) almost all of the studied papers deal with robots/agents explaining their behaviors to the human users, and very few works addressed inter-robot (inter-agent) explainability. Finally, (iii) while providing explanations to non-expert users has been outlined as a necessity, only a few works addressed the issues of personalization and context-awareness

  • 3. Dave, Bhargav
    et al.
    Buda, Andrea
    Nurminen, Antti
    Främling, Kary
    Department of Computer Science, Aalto University, Espoo, Finland.
    A framework for integrating BIM and IoT through open standards2018In: Automation in Construction, ISSN 0926-5805, E-ISSN 1872-7891, Vol. 95, p. 35-45Article in journal (Refereed)
    Abstract [en]

    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.

  • 4.
    Främling, Kary
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Heat Recovery Unit Failure Detection in Air Handling Unit2018In: IFIP International Conference on Advances in Production Management Systems: APMS 2018: Advances in Production Management Systems. Smart Manufacturing for Industry 4.0, Springer, 2018, p. 343-350Conference paper (Refereed)
    Abstract [en]

    Maintenance is a complicated task that encompasses various activities including fault detection, fault diagnosis, and fault reparation. With advancement of Computer Aided Engineering (CAE), maintenance task becomes even more challenging as modern assets 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 are often neither obvious in terms of their source nor unique. Early detection and diagnosis of such fault is turning to one of the key requirements for economical and functional eciency of assets. Several methods have been studied to detect machine faults for a number of years and were relevant for many application domains. In this paper, we present process-history based method utilising nominal eciency of Air Handling Unit (AHU) to detect heat recovery failure using Principle Component Analysis (PCA) in combination of logistic regression method.

  • 5. Hefnawy, Ahmed
    et al.
    Elhariri, Taha
    Cherifi, Chantal
    Robert, Jérémy
    Bouras, Abdelaziz
    Kubler, Sylvain
    Främling, Kary
    Aalto School of Science and Technology, Aalto University, Finland.
    Combined Use of Lifecycle Management and IoT in Smart Cities2017In: 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), IEEE conference proceedings, 2017Conference paper (Refereed)
    Abstract [en]

    IoT-enabled smart city service systems are well recognized to address issues of urbanization in the city environment. In most cases, those systems are vertically locked; and from lifecycle perspective, independently designed, built and operated. Therefore, there is a need for a global vision to horizontally integrate those systems to ensure smooth flow of information across different domains. To ensure interoperability and better management across different phases of smart city lifecycle, this paper highlights the need for smooth exchange of two types of data/ information: (i) generated data from IoT data sources; (ii) lifecycle system related information. This paper proposes the use of The Open Group IoT standards to ensure interoperability and smooth data exchange; and the use of lifecycle management system to create Bill of Materials; export Objects' Tree and exchange lifecycle system related information. It also demonstrates the interaction between the proposed lifecycle system and IoT platform within smart parking use-case.

  • 6. Javed, Asad
    et al.
    Heljanko, Keijo
    Buda, Andrea
    Främling, Kary
    Department of Computer Science, Aalto University, Espoo, Finland.
    CEFIoT: A Fault Tolerant IoT Architecture for Edge and Cloud2018In: 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Internet of Things (IoT) is the emerging computing infrastructure that refers to the networked interconnection of physical objects, thus incorporating a huge plethora of applica- tions. Many of these applications contain data collection on the edge and data storage and analytics capabilities in the cloud. This raises a problem that (i) the processing stages in IoT application need to have separate implementation for both edge and the cloud, (ii) the placement of computation might not be flexible with separate software stacks and the optimal deployment decisions need to be done at runtime, and (iii) unified fault tolerance needs to be deployed in case of broken Internet connectivity, malicious harming of edge devices, or harsh environmental conditions. This paper proposes a novel fault tolerant layered 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 utilizing Apache Kafka publish/subscribe platform as the unified high performance data replication solution offering common software stack for both edge and cloud, and deploying Kubernetes for fault tolerant management and the advanced functionality of allowing on-the-fly automatic reconfiguration of the processing pipeline to handle both hardware and network connectivity based failures.

  • 7. Javed, Asad
    et al.
    Yousefnezhad, Narges
    Robert, Jérémy
    Heljanko, Keijo
    Främling, Kary
    Department of Computer Science, Aalto University, Espoo, Finland.
    Access Time Improvement Framework for Standardized IoT Gateways2019In: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, 2019, p. 220-226Conference paper (Refereed)
    Abstract [en]

    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.

  • 8.
    Kampik, Timotheus
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Signavio GmbH, Berlin, Germany.
    Malhi, Avleen
    Department of Computer Science, Aalto University, Helsinki, Finland.
    Främling, Kary
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Agent-based Business Process Orchestration for IoT2019In: WI '19 IEEE/WIC/ACM International Conference on Web Intelligence / [ed] Payam Barnaghi, Georg Gottlob, Yannis Manolopoulos, Theodoros Tzouramanis, Athena Vakali, New York: ACM Press, 2019, p. 393-397Conference paper (Refereed)
    Abstract [en]

    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.

  • 9. Karpenko, Anastasiia
    et al.
    Kinnunen, Tuomas
    Främling, Kary
    Dept. of Computer Science, Aalto University School of Science, Espoo, Finland .
    Dave, Bhargav
    Open IoT Ecosystem for Smart EV Charging2018In: 2018 Global Internet of Things Summit (GIoTS), IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Many domains are trying to integrate with the Internet of Things (IoT) ecosystem, such as public administrations starting Smart City initiatives all over the world. Cities are becoming smart in many ways: smart mobility, smart buildings, smart environment and so on. However, the problem of non-interoperability in IoT hinders the seamless communication between all kinds of IoT 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.

  • 10. Karpenko, Anastasiia
    et al.
    Kinnunen, Tuomas
    Madhikermi, Manik
    Robert, Jeremy
    Främling, Kary
    Department of Computer Science, Aalto University, 02150 Espoo, Finland.
    Dave, Bhargav
    Nurminen, Antti
    Data Exchange Interoperability in IoT Ecosystem for Smart Parking and EV Charging2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 12Article in journal (Refereed)
    Abstract [en]

    Many domains are trying to integrate with the Internet of Things (IoT) ecosystem, such as public administrations starting smart city initiatives all over the world. Cities are becoming smart in many ways: smart mobility, smart buildings, smart environment and so on. However, the problem of non-interoperability in the IoT hinders the seamless communication between all kinds of IoT 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 data sharing horizontally across 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 describe the practical use of O-MI/O-DF standards for reaching interoperability in a mobile application for the smart city context, in particular for the Smart Mobility domain, electric vehicle (EV) charging case study. The proof-of-concept of the smart EV charging ecosystem with mobile application user interface was developed as a part of an EU (Horizon 2020) Project bIoTope.

  • 11. Madhikermi, Manik
    et al.
    Buda, Andrea
    Dave, Bhargav
    Främling, Kary
    School of Science Aalto University P.O. Box 15400, FI-00076 Aalto, Espoo, Finland.
    Data Model Logger - Data Discovery for Extract-Transform-Load2017In: 2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), IEEE, 2017Conference paper (Refereed)
    Abstract [en]

    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.

  • 12. Madhikermi, Manik
    et al.
    Buda, Andrea
    Dave, Bhargav
    Främling, Kary
    School of Science Aalto University P.O. Box 15400, FI-00076 Aalto, Espoo, Finland.
    Key data quality pitfalls for condition based maintenance2017In: 2017 2nd International Conference on System Reliability and Safety (ICSRS), IEEE, 2017, p. 474-480Conference paper (Refereed)
    Abstract [en]

    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.

  • 13. Madhikermi, Manik
    et al.
    Främling, Kary
    School of Science, Aalto University, Espoo, Finland.
    Data discovery method for Extract-Transform-Load2019In: 2019 IEEE 10th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT), IEEE, 2019, p. 174-181Conference paper (Refereed)
    Abstract [en]

    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.

  • 14. Madhikermi, Manik
    et al.
    Yousefnezhad, Narges
    Främling, Kary
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Data Exchange Standard for Industrial Internet of Things2018In: 2018 3rd International Conference on System Reliability and Safety (ICSRS), IEEE, 2018, p. 53-61Conference paper (Refereed)
    Abstract [en]

    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 servitization 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.

  • 15. Mualla, Yazan
    et al.
    Najjar, Amro
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Galland, Stephane
    Nicolle, Christophe
    Tchappi, Igor Haman
    Yasar, Ansar-Ul-Haque
    Främling, Kary
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Between the Megalopolis and the Deep Blue Sky: Challenges of Transport with UAVs in Future Smart Cities2019In: AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS, ASSOC COMPUTING MACHINERY , 2019, p. 1649-1653Conference paper (Refereed)
    Abstract [en]

    With the rapid increase of the world's urban population, the infrastructure of the constantly expanding metropolitan areas is undergoing an immense pressure. To meet the growing demands of sustainable urban environments and improve the quality of life for citizens, municipalities will increasingly rely on novel transport solutions. In particular, Unmanned Aerial Vehicles (UAVs) are expected to have a crucial role in the future smart cities thanks to their interesting features such as autonomy, flexibility, mobility, adaptive altitude, and small dimensions. However, densely populated megalopolises of the future are administrated by several municipals, governmental and civil society actors, where vivid economic activities involving a multitude of individual stakeholders take place. In such megalopolises, the use of agents for UAVs is gaining more interest especially in complex application scenarios where coordination and cooperation are necessary. This paper sketches a visionary view of the UAVs' role in the transport domain of future smart cities. Additionally, four challenging research directions are highlighted including problems related to autonomy, explainability, security and validation & verification of the UAVs behavior.

  • 16. Robert, Jérémy
    et al.
    Kubler, Sylvain
    Kolbe, Niklas
    Cerioni, Alessandro
    Gastaud, Emmanuel
    Främling, Kary
    School of Science and Technology, Aalto University, P.O. Box 15500, Aalto 00076, Finland.
    Open IoT ecosystem for enhanced interoperability in smart cities - Example of Métropole de Lyon2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 12, article id 2849Article in journal (Refereed)
    Abstract [en]

    The Internet of Things (IoT) has promised a future where everything gets connected. Unfortunately, building a single global ecosystem of Things that communicate with each other seamlessly is virtually impossible today. The reason is that the IoT is essentially a collection of isolated “Intranets of Things”, also referred to as “vertical silos”, which cannot easily and efficiently interact with each other. Smart cities are perhaps the most striking examples of this problem since they comprise a wide range of stakeholders and service providers who must work together, including urban planners, financial organisations, public and private service providers, telecommunication providers, industries, citizens, and so forth. Within this context, the contribution of this paper is threefold: (i) discuss business and technological implications as well as challenges of creating successful open innovation ecosystems, (ii) present the technological building blocks underlying an IoT ecosystem developed in the framework of the EU Horizon 2020 programme, (iii) present a smart city pilot (Heat Wave Mitigation in Métropole de Lyon) for which the proposed ecosystem significantly contributes to improving interoperability between a number of system components, and reducing regulatory barriers for joint service co-creation practices.

  • 17. Yousefnezhad, Narges
    et al.
    Filippov, Roman
    Javed, Asad
    Buda, Andrea
    Madhikermi, Manik
    Främling, Kary
    Department of Computer Science Aalto University Espoo, Finland.
    Authentication and Access Control for Open Messaging Interface Standard2017In: MobiQuitous 2017: Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, ACM Digital Library, 2017Conference paper (Refereed)
    Abstract [en]

    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.

  • 18. Yousefnezhad, Narges
    et al.
    Madhikermi, Manik
    Främling, Kary
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    MeDI: Measurement-based Device Identification Framework for Internet of Things2018In: 2018 IEEE 16TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), IEEE , 2018, p. 95-100Conference paper (Refereed)
    Abstract [en]

    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.

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