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Javed, A., Yousefnezhad, N., Robert, J., Heljanko, K. & Främling, K. (2019). Access Time Improvement Framework for Standardized IoT Gateways. In: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops): . Paper presented at 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 11–15 March 2019, Kyoto, Japan (pp. 220-226). IEEE
Open this publication in new window or tab >>Access Time Improvement Framework for Standardized IoT Gateways
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2019 (English)In: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, 2019, p. 220-226Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Internet of Things, microservices, consistent hashing, distributed system, gateway, big data
National Category
Software Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-160142 (URN)10.1109/PERCOMW.2019.8730867 (DOI)
Conference
2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 11–15 March 2019, Kyoto, Japan
Available from: 2019-06-13 Created: 2019-06-13 Last updated: 2019-06-14Bibliographically approved
Kampik, T., Malhi, A. & Främling, K. (2019). Agent-based Business Process Orchestration for IoT. In: Payam Barnaghi, Georg Gottlob, Yannis Manolopoulos, Theodoros Tzouramanis, Athena Vakali (Ed.), WI '19 IEEE/WIC/ACM International Conference on Web Intelligence: . Paper presented at IEEE/WIC/ACM International Conference on Web Intelligence, October 14–17, 2019, Thessaloniki, Greece (pp. 393-397). New York: ACM Press
Open this publication in new window or tab >>Agent-based Business Process Orchestration for IoT
2019 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
New York: ACM Press, 2019
Keywords
Business Process Management, Internet of Things, Orchestration
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-164713 (URN)10.1145/3350546.3352554 (DOI)978-1-4503-6934-3 (ISBN)
Conference
IEEE/WIC/ACM International Conference on Web Intelligence, October 14–17, 2019, Thessaloniki, Greece
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2019-10-28 Created: 2019-10-28 Last updated: 2019-11-04Bibliographically approved
Mualla, Y., Najjar, A., Galland, S., Nicolle, C., Tchappi, I. H., Yasar, A.-U. & Främling, K. (2019). Between the Megalopolis and the Deep Blue Sky: Challenges of Transport with UAVs in Future Smart Cities. In: AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS. Paper presented at 18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), May 13-17, 2019, Montréal, Canada (pp. 1649-1653). ASSOC COMPUTING MACHINERY
Open this publication in new window or tab >>Between the Megalopolis and the Deep Blue Sky: Challenges of Transport with UAVs in Future Smart Cities
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2019 (English)In: AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS, ASSOC COMPUTING MACHINERY , 2019, p. 1649-1653Conference paper, Published 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.

Place, publisher, year, edition, pages
ASSOC COMPUTING MACHINERY, 2019
Keywords
Multiagent Systems, Unmanned Aerial Vehicles, Intelligent Transport Systems, Smart Cities
National Category
Human Geography
Identifiers
urn:nbn:se:umu:diva-162359 (URN)000474345000190 ()978-1-4503-6309-9 (ISBN)
Conference
18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), May 13-17, 2019, Montréal, Canada
Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-09-10Bibliographically approved
Madhikermi, M. & Främling, K. (2019). Data discovery method for Extract-Transform-Load. In: 2019 IEEE 10th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT): . Paper presented at 2019 IEEE 10th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT 2019), Cape Town, South Africa, 15–17 February, 2019 (pp. 174-181). IEEE
Open this publication in new window or tab >>Data discovery method for Extract-Transform-Load
2019 (English)In: 2019 IEEE 10th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT), IEEE, 2019, p. 174-181Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
ETL, Database, Trigger, Reverse Engineering, Data Warehouse, Information System, Information Retrieval, Process Mapping, Data Discovery
National Category
Information Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-160141 (URN)10.1109/ICMIMT.2019.8712027 (DOI)2-s2.0-85066473340 (Scopus ID)978-1-5386-7972-2 (ISBN)
Conference
2019 IEEE 10th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT 2019), Cape Town, South Africa, 15–17 February, 2019
Available from: 2019-06-13 Created: 2019-06-13 Last updated: 2019-06-18Bibliographically approved
Anjomshoae, S., Najjar, A., Calvaresi, D. & Främling, K. (2019). Explainable Agents and Robots: Results from a Systematic Literature Review. In: N. Agmon, M. E. Taylor, E. Elkind, M. Veloso (Ed.), Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems: . Paper presented at the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), Montreal, Canada, May 13–17, 2019 (pp. 1078-1088). International Foundation for Autonomous Agents and MultiAgent Systems
Open this publication in new window or tab >>Explainable Agents and Robots: Results from a Systematic Literature Review
2019 (English)In: 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, Published 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

Place, publisher, year, edition, pages
International Foundation for Autonomous Agents and MultiAgent Systems, 2019
Series
Proceedings, ISSN 2523-5699
Keywords
Explainable AI, goal-based XAI, autonomous agents, human-robot interaction
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:umu:diva-158024 (URN)978-1-4503-6309-9 (ISBN)
Conference
the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), Montreal, Canada, May 13–17, 2019
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-07-16Bibliographically approved
Madhikermi, M., Malhi, A. & Främling, K. (2019). Explainable artificial intelligence based heat recycler fault detection in air handling unit. In: Davide Calvaresi, Amro Najjar, Michael Schumacher, Kary Främling (Ed.), Lecture Notes in Computer Science, Vol. 11763 LNAI: . Paper presented at Explainable, Transparent Autonomous Agents and Multi-Agent Systems - 1st International Workshop, EXTRAAMAS 2019, Montreal, Canada, May 13-14, 2019 (pp. 110-125). Springer-Verlag New York
Open this publication in new window or tab >>Explainable artificial intelligence based heat recycler fault detection in air handling unit
2019 (English)In: Lecture Notes in Computer Science, Vol. 11763 LNAI / [ed] Davide Calvaresi, Amro Najjar, Michael Schumacher, Kary Främling, Springer-Verlag New York, 2019, p. 110-125Conference paper, Published paper (Refereed)
Abstract [en]

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 understand. The current AI applications inability to explain is decisions and actions to end users have limited its effectiveness. The explainable AI will enable the users to understand, accordingly trust and effectively manage the decisions made by machine learning models. The heat recycler’s fault detection in Air Handling Unit (AHU) has been explained with explainable artificial intelligence since the fault detection is particularly burdensome because the reason for its failure is mostly unknown and unique. The key requirement of such systems is the early diagnosis of such faults for its economic and functional efficiency. The machine learning models, Support Vector Machine and Neural Networks have been used for the diagnosis of the fault and explainable artificial intelligence has been used to explain the models’ behaviour.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11763 LNAI
Keywords
Explainable artificial intelligence, Heat recycler unit, Neural networks, Support vector machine
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-165194 (URN)10.1007/978-3-030-30391-4_7 (DOI)978-3-030-30390-7 (ISBN)978-3-030-30391-4 (ISBN)
Conference
Explainable, Transparent Autonomous Agents and Multi-Agent Systems - 1st International Workshop, EXTRAAMAS 2019, Montreal, Canada, May 13-14, 2019
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2019-11-13 Created: 2019-11-13 Last updated: 2019-11-14Bibliographically approved
Anjomshoae, S., Främling, K. & Najjar, A. (2019). Explanations of black-box model predictions by contextual importance and utility. In: Davide Calvaresi, Amro Najjar, Michael Schumacher, Kary Främling (Ed.), Explainable, transparent autonomous agents and multi-agent systems: first international workshop, EXTRAAMAS 2019, Montreal, QC, Canada, May 13–14, 2019, revised selected papers (pp. 95-109). Springer
Open this publication in new window or tab >>Explanations of black-box model predictions by contextual importance and utility
2019 (English)In: 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.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 11763
Keywords
Explainable AI, Black-box models, Contextual importance, Contextual utility, Contrastive explanations
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-163549 (URN)10.1007/978-3-030-30391-4_6 (DOI)9783030303907 (ISBN)9783030303914 (ISBN)
Note

First International Workshop, EXTRAAMAS 2019, Montreal, QC, Canada, May 13–14, 2019

Available from: 2019-09-25 Created: 2019-09-25 Last updated: 2019-09-25Bibliographically approved
Rana, A., Malhi, A. & Främling, K. (2019). Exploring numerical calculations with CalcNet. In: : . Paper presented at IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI 2019), Portland, Oregon, November 4-6, 2019.
Open this publication in new window or tab >>Exploring numerical calculations with CalcNet
2019 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

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 produces best 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.

Keywords
Neural networks, Neural Arithmetic Logic Unit, Neural Accumulators, CalcNet
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-165293 (URN)DOI 10.1109/ICTAI.2019.00-73 (DOI)
Conference
IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI 2019), Portland, Oregon, November 4-6, 2019
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP), 570011220
Available from: 2019-11-19 Created: 2019-11-19 Last updated: 2019-11-20
Dave, B., Buda, A., Nurminen, A. & Främling, K. (2018). A framework for integrating BIM and IoT through open standards. Automation in Construction, 95, 35-45
Open this publication in new window or tab >>A framework for integrating BIM and IoT through open standards
2018 (English)In: Automation in Construction, ISSN 0926-5805, E-ISSN 1872-7891, Vol. 95, p. 35-45Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Internet of Things, BIM, SmartCampus, Open standards
National Category
Information Systems
Research subject
computer and systems sciences
Identifiers
urn:nbn:se:umu:diva-154885 (URN)10.1016/j.autcon.2018.07.022 (DOI)000446286800004 ()
Funder
EU, Horizon 2020
Available from: 2019-01-04 Created: 2019-01-04 Last updated: 2019-01-16Bibliographically approved
Javed, A., Heljanko, K., Buda, A. & Främling, K. (2018). CEFIoT: A Fault Tolerant IoT Architecture for Edge and Cloud. In: 2018 IEEE 4th World Forum on Internet of Things (WF-IoT): . Paper presented at 4th IEEE World Forum on Internet of Things, Singapore, February 5-8, 2018. IEEE
Open this publication in new window or tab >>CEFIoT: A Fault Tolerant IoT Architecture for Edge and Cloud
2018 (English)In: 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), IEEE, 2018Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
internet of things, fault tolerance, edge, cloud, container virtualization, kubernetes, kafka
National Category
Information Systems
Identifiers
urn:nbn:se:umu:diva-154888 (URN)10.1109/WF-IoT.2018.8355149 (DOI)
Conference
4th IEEE World Forum on Internet of Things, Singapore, February 5-8, 2018
Funder
EU, Horizon 2020, 688203
Available from: 2019-01-04 Created: 2019-01-04 Last updated: 2019-06-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8078-5172

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