Browsing by Author "Framling, Kary"

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Access Time Improvement Framework for Standardized IoT Gateways
(2019-03-01) Javed, Asad; Yousefnezhad, Narges; Robert, Jeremy; Heljanko, Keijo; Framling, Kary
A4 Artikkeli konferenssijulkaisussa
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.
bIoTope: Building an IoT Open Innovation Ecosystem for Smart Cities
(2020) Javed, Asad; Kubler, Sylvain; Malhi, Avleen; Nurminen, Antti; Robert, Jeremy; Framling, Kary
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The Internet of Things (IoT) has led towards a digital world in which everything becomes connected. Unfortunately, most of the currently marketed connected devices feed vertically-oriented closed systems (commonly referred to as vertical silos) which prevent the development of a unified global IoT. This issue is all the more valid in complex environments, such as smart cities, in which exceedingly large amounts of heterogeneous sensor data are collected, and in which platforms and stakeholders should also be able to interact and cooperate. Therefore, it is of utmost importance to move towards the creation of open IoT ecosystems to support efficient smart city service integration, discovery and composition. This paper contributes to the specifications of such an ecosystem, which has been developed as part of the EU's H2020 bIoTope project. The novelty of this ecosystem compared with the current literature is threefold: (i) it is based on the extensive use of open communication and data standards, notably O-MI and O-DF standards, that foster technical, syntactic and semantic interoperability over domains; (ii) it proposes an innovative service marketplace for data/service publication, discovery and incentivization; (iii) it integrates security functionalities at the IoT gateway level. The practicability of our ecosystem has been validated through several smart city proofs-of-concept set up in three distinct cities: Helsinki, Lyon and Brussels. Given the five major themes defined in the CITYKeys (a smart city performance indicator framework), namely People, Planet, Prosperity, Governance and Propagation, bIoTope mainly contributes to Prosperity-related metrics, as discussed in this paper.
A Clean Air Journey Planner for pedestrians using high resolution near real time air quality data
(2020-07) Nurminen, Antti; Malhi, Avleen; Johansson, Lasse; Framling, Kary
A4 Artikkeli konferenssijulkaisussa
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.
Data Exchange Standard for Industrial Internet of Things
(2019-04-11) Madhikermi, Manik; Yousefnezhad, Narges; Framling, Kary
A4 Artikkeli konferenssijulkaisussa
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.
Edge Computing-based Fault-Tolerant Framework: A Case Study on Vehicular Networks
(2020-06) Javed, Asad; Malhi, Avleen; Framling, Kary
A4 Artikkeli konferenssijulkaisussa
With the evolution of vehicular networks, the Intelligent Transportation System (ITS) has emerged as a promising technology for autonomous road transport. For a successful deployment of ITS, security and reliability are the most challenging factors to be tackled to ensure Vehicle-to-Infrastructure (V2I) and Infrastructure-to-Infrastructure (I2I) communications. Due to unreliable communications in vehicular networks, implementing fault-tolerant techniques for the Road Side Unit (RSU) infrastructure is an imperial need. Within this context, the contributions of this paper are twofold: (i) we propose a distributed fault-tolerant framework for V2I and I2I communications based on edge computing to resolve hardware- and network connectivity-based failures. The fault tolerance issue is addressed by employing open messaging standards as a subscription-based data replication solution at the edge. We also adopt Kubernetes for the fault-tolerant management, combined with high-availability mechanism, allowing automatic reconfiguration of the data processing pipeline; and (ii) we implement a demonstrator system for vehicular networks-based smart mobility to assess fault tolerance capabilities. The experimental results show that our proposed framework dynamically tolerates RSU-related failures during the vehicular communication phase.
Explainable Artificial Intelligence for Human Decision Support System in the Medical Domain
(2021-09) Knapic, Samanta; Malhi, Avleen; Saluja, Rohit; Framling, Kary
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
In this paper, we present the potential of Explainable Artificial Intelligence methods for decision support in medical image analysis scenarios. Using three types of explainable methods applied to the same medical image data set, we aimed to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN). In vivo gastral images obtained by a video capsule endoscopy (VCE) were the subject of visual explanations, with the goal of increasing health professionals' trust in black-box predictions. We implemented two post hoc interpretable machine learning methods, called Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), and an alternative explanation approach, the Contextual Importance and Utility (CIU) method. The produced explanations were assessed by human evaluation. We conducted three user studies based on explanations provided by LIME, SHAP and CIU. Users from different non-medical backgrounds carried out a series of tests in a web-based survey setting and stated their experience and understanding of the given explanations. Three user groups (n = 20, 20, 20) with three distinct forms of explanations were quantitatively analyzed. We found that, as hypothesized, the CIU-explainable method performed better than both LIME and SHAP methods in terms of improving support for human decision-making and being more transparent and thus understandable to users. Additionally, CIU outperformed LIME and SHAP by generating explanations more rapidly. Our findings suggest that there are notable differences in human decision-making between various explanation support settings. In line with that, we present three potential explainable methods that, with future improvements in implementation, can be generalized to different medical data sets and can provide effective decision support to medical experts.
Malware detection technique in IoT with Data mining methods
(2018-11-07) Addy, Christian
Perustieteiden korkeakoulu | Master's thesis
Malware plays a major role as a threat to the security of computer systems. As the Internet of things and its systems of connectivity increase all around the world, it has led to an astronomical increase of malware that target these IoT devices. From DDoS attacks to crytomining malware, companies and industries nowadays encounter problems through malware attack that were not existent a few years ago or have evolved to the new environment of IoT, taking advantage of its vulnerabilities such as the inadequate security monitoring and protection systems. This thesis research surveys the types of attack that are common to IoT technology, current detection techniques, learning techniques and machine learning algorithms that are popularly used for malware detection. This paper then further continues to use a dataset of extracted network traffic features from benign and malicious trace data. With the aid of tools such as Rapid Miner and the use of algorithms such as Artificial Neural Network, statistical analysis of data is evaluated with clear evidence of anomaly detection and a proposed model for network anomaly detection with a low false positive rate and high detection accuracy is presented.
Metrics and Evaluations of Time Series Explanations: An Application in Affect Computing
(2022) Fouladgar, Nazanin; Alirezaie, Marjan; Framling, Kary
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Explainable artificial intelligence (XAI) has shed light on enormous applications by clarifying why neural models make specific decisions. However, it remains challenging to measure how sensitive XAI solutions are to the explanations of neural models. Although different evaluation metrics have been proposed to measure sensitivity, the main focus has been on the visual and textual data. There is insufficient attention devoted to the sensitivity metrics tailored for time series data. In this paper, we formulate several metrics, including max short-term sensitivity (MSS), max long-term sensitivity (MLS), average short-term sensitivity (ASS) and average long-term sensitivity (ALS), that target the sensitivity of XAI models with respect to the generated and real time series. Our hypothesis is that for close series with the same labels, we obtain similar explanations. We evaluate three XAI models, LIME, integrated gradient (IG), and SmoothGrad (SG), on CN-Waterfall, a deep convolutional network. This network is a highly accurate time series classifier in affect computing. Our experiments rely on data-, metric- and XAI hyperparameter- related settings on the WESAD and MAHNOB-HCI datasets. The results reveal that (i) IG and LIME provide a lower sensitivity scale than SG in all the metrics and settings, potentially due to the lower scale of important scores generated by IG and LIME, (ii) the XAI models show higher sensitivities for a smaller window of data, (iii) the sensitivities of XAI models fluctuate when the network parameters and data properties change, and (iv) the XAI models provide unstable sensitivities under different settings of hyperparameters.
One-to-Many Negotiation QoE Management Mechanism for End-User Satisfaction
(2021) Najjar, Amro; Mualla, Yazan; Singh, Kamal Deep; Picard, Gauthier; Calvaresi, Davide; Malhi, Avleen; Galland, Stephane; Framling, Kary
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
Proposal of a Closed Loop Framework for the Improvement of Industrial Systems' Life Cycle Performances: Experiences from the LinkedDesign Project
(2015) Cerri, Daniele; Taisch, Marco; Terzi, Sergio; Buda, Andrea; Framling, Kary; El Kaddiri, Soumaya; Milicic, Ana; Kiritsis, Dimitris; Parrotta, Simone; Peukert, Eric
A4 Artikkeli konferenssijulkaisussa
The context where European manufacturers of industrial systems operate has dramatically changed over recent years: the pressure of emerging countries they have to face, policy makers’ environmental laws and industrial companies’ interests are pushing towards sustainable manufacturing and a holistic view of industrial systems. Designers and system engineers are the main actors involved, because they have high influence on product life cycle costs and environmental impacts. However they need tools to pursue a holistic view. The aim of this paper is to propose a closed loop framework to improve life cycle performances of industrial systems, focusing on the automotive sector.