Publications
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Lakhan, Abdullah; Grønli, Tor-Morten; Soylu, Ahmet; Muhammad, Ghulam; Mastoi, Qurat Ul Ain & Wu, Huaming
(2025).
Transfer Learning-Enabled System for Drone Medicine Delivery Based on Spatio-Temporal Remote Sensing Data in Edge Cloud Networks.
IEEE Transactions on Cloud Computing.
ISSN 2168-7161.
doi:
10.1109/TCC.2025.3639073.
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Majchrzak, Tim A.; Mateos, Cristian; Hirsch, Matias; Grønli, Tor-Morten; Engan, Sina Camilla Gausel & Henningsen, Jon Stareng
(2025).
The Adoption of Digital Twins: Enablers, Challenges and Benefits.
In Bui, Tung (Eds.),
Proceedings of the 58th Hawaii International Conference on System Sciences (HICSS), Hilton Waikoloa Village, January 7-10, 2025.
University of Hawai'i at Manoa.
ISSN 9780998133188.
p. 7391–7400.
doi:
10.24251/hicss.2025.884.
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Skretting, Anders; Grønli, Tor-Morten & Mukkamala, Raghava Rao
(2025).
Feature importance from novel dataset for on-device transport mode detection.
Journal of Intelligent Transportation Systems: Technology, Planning, and Operations.
ISSN 1547-2450.
doi:
10.1080/15472450.2025.2515567.
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Assres, Gebremariam; Bhandari, Guru; Shalaginov, Andrii; Gronli, Tor-Morten & Ghinea, Gheorghita
(2025).
State-of-the-Art and Challenges of Engineering ML- Enabled Software Systems in the Deep Learning Era.
ACM Computing Surveys.
ISSN 0360-0300.
57(10),
p. 35–35.
doi:
10.1145/3731597.
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Emerging from the software crisis of the 1960s, conventional software systems have vastly improved through Software Engineering (SE) practices. Simultaneously, Artificial Intelligence (AI) endeavors to augment or replace human decision-making. In the contemporary landscape, Machine Learning (ML), a subset of AI, leverages extensive data from diverse sources, fostering the development of ML-enabled (intelligent) software systems. While ML is increasingly utilized in conventional software development, the integration of SE practices in developing ML-enabled systems, especially across typical Software Development Life Cycle (SDLC) phases and methodologies in the post-2010 Deep Learning (DL) era, remains underexplored. Our survey of existing literature unveils insights into current practices, emphasizing the interdisciplinary collaboration challenges of developing ML-enabled software, including data quality, ethics, explainability, continuous monitoring and adaptation, and security. The study underscores the imperative for ongoing research and development with focus on data-driven hypotheses, non-functional requirements, established design principles, ML-first integration, automation, specialized testing, and use of agile methods.
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Weldeslasie, Dawit Teklu; Assres, Gebremariam; Abdurahman, Mehamed Ahmed; Grønli, Tor-Morten & Ghinea, Gheorghita
(2025).
Utilizing AI for viral infection diagnosis: a case study in Tigray, Ethiopia.
Discover Health Systems.
4(1).
doi:
10.1007/s44250-025-00190-1.
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In recent years, the emergence of viral infections has posed significant challenges to healthcare systems worldwide. Timely and accurate detection of viral infections is essential for effective patient care and outbreak management. Although the World Health Organization has declared an end to the emergency phase of the Covid-19 pandemic, it is still impacting countries like Ethiopia, particularly in the war-ridden Tigray region. Today, viral infection testing in Tigray is still expensive, less accessible and time consuming. In this study, we introduce a novel approach for intelligent detection of viral infections, particularly Covid-19 from similar other diseases. A rule-based system, called CSDIS, was developed using tree to identify symptoms of the viruses and analyze the behavior of each diseases. Accuracy and usability of the system was evaluated by employing a dataset of 100 patient cases. We use 70% of the dataset for training, and the remaining 30% for testing the model. Additionally, standard RT-PCR tests are employed to confirm the model prediction results. Accordingly, the developed model proved successful in accurately identifying each class of disease with 98% accuracy, and 96% acceptance rating from the local community. This suggests that the system can empower individual healthcare professionals and minimize the shortage of manpower and resources. Overall, this research not only enhances the efficiency and precision of viral infection detection but also has the capacity to transform how healthcare professionals, researchers, and policymakers respond to viral outbreaks.
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Sinkie, Mekuria; Grønli, Tor-Morten; Midekso, Dida & Lakhan, Abdullah
(2025).
DESIGNING AND IMPLEMENTING A MULTI-CRITERIA INTELLIGENT FRAMEWORK TO ENSURE THE RELEVANCE OF REQUIREMENTS SOURCED FROM SOCIAL NETWORK SITES.
Journal of Theoretical and Applied Information Technology.
ISSN 1992-8645.
103(7),
p. 2677–2708.
doi:
https:/www.jatit.org/volumes/hundredthree7.php.
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Lakhan, Abdullah; Grønli, Tor-Morten; Bellavista, Paolo; Memon, Sajida; Alharby, Maher & Thinnukool, Orawit
(2024).
IoT workload offloading efficient intelligent transport system in federated ACNN integrated cooperated edge-cloud networks.
Journal of Cloud Computing.
13(1).
doi:
10.1186/s13677-024-00640-w.
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Skretting, Anders; Grønli, Tor-Morten; Majchrzak, Tim A.; Mateos, Cristian & Hirsch, Matías
(2024).
Enhanced Transport Mode Recognition on Mobile Devices.
In Bui, Tung X. (Eds.),
Proceedings of the 57th Hawaii International Conference on System Sciences.
The Hawaii International Conference on System Sciences (HICSS).
ISSN 9780998133171.
p. 7740–7749.
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Grønli, Tor-Morten; Lakhan, Abdullah & Younas, Muhammad
(2024).
Quantum-Blockchain Healthcare System for Invasive and No-Invasive-IoMT Data,
20th International Conference, MobiWIS 2024, Vienna, Austria, August 19–21, 2024, Proceedings.
Springer Nature.
ISSN 9783031680045.
p. 175–186.
doi:
10.1007/978-3-031-68005-2_13.
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Grønli, Tor-Morten; Lakhan, Abdullah & Younas, Muhammad
(2024).
Federated Learning Enabled Green Edge Computing System for IIoT Applications,
20th International Conference, MobiWIS 2024, Vienna, Austria, August 19–21, 2024, Proceedings.
Springer Nature.
ISSN 9783031680045.
p. 19–31.
doi:
10.1007/978-3-031-68005-2_2.
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Bhandari, Guru Prasad; Assres, Gebremariam Mesfin; Gavric, Nikola; Shalaginov, Andrii & Grønli, Tor-Morten
(2024).
IoTvulCode: AI-enabled vulnerability detection in software products designed for IoT applications.
International Journal of Information Security.
ISSN 1615-5262.
doi:
10.1007/s10207-024-00848-6.
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Jat, Avnish Singh; Grønli, Tor-Morten & Ghinea, George
(2024).
Navigating Design Science Research in mHealth Applications: A Guide to Best Practices.
IEEE transactions on engineering management.
ISSN 0018-9391.
71,
p. 14472–14484.
doi:
10.1109/TEM.2024.3450178.
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Yannibelli, Virginia; Hirsch, Matias; Toloza, Juan; Majchrzak, Tim A.; Grønli, Tor-Morten & Zunino, Alejandro
[Show all 7 contributors for this article]
(2024).
BAGESS: A software module based on a genetic algorithm to sequentially order load-balancing evaluation scenarios over smartphone-based clusters at the Edge.
IEEE Access.
12,
p. 145893–145919.
doi:
10.1109/ACCESS.2024.3469641.
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Due to the increasing interest in employing smartphones as first-class citizens in high-performance Edge computing environments, the necessity of software to facilitate the evaluation of load-balancing strategies for smartphone-based clusters has emerged. Regarding this, to select the best strategy for a cluster with m smartphones, usually a number of g candidate strategies are evaluated based on a number of r scenarios that contain these smartphones, which differ in terms of the start battery levels required for these smartphones. Thus, each of the r scenarios must be prepared before evaluating each of the g strategies on each ri , so that the smartphones have the required start battery levels pre-configured for ri , which requires discharging or charging smartphones. This leads to a number of e=r∗g scenario preparation events that must be sequentially developed, considering that the time required to develop each event depends on the previous event. Thus, the single-objective problem addressed here implies finding out the sequential order in which the events should be developed, so that the total time required to develop them is minimized. This problem is modeled as the ATSP (Asymmetric Traveling Salesman Problem), since defining the sequential order to develop the events is equivalent to defining the sequential order to visit the cities, and therefore, is an NP-Hard problem. Given the complexity of this problem, the novel software module BAGESS (Battery Aware Green Edge Scenario Sequencer) is proposed, which uses a genetic algorithm for defining the sequential order to develop the events. BAGESS’s performance outperforms those of the methods currently used for the problem, reaching significant savings regarding the time required to develop the events in the range [12, 85]%.
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Lakhan, Abdullah; Grønli, Tor-Morten; Muhammad, Ghulam & Tiwari, Prayag
(2024).
Evolutionary Meta-Heuristic Offloading and Scheduling Schemes Enabled Industrial Cyber-Physical System.
IEEE Systems Journal.
ISSN 1932-8184.
18(2),
p. 826–835.
doi:
10.1109/JSYST.2023.3347523.
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Jat, Avnish Singh; Grønli, Tor-Morten & Ghinea, George
(2024).
Technological Utilization in Remote Healthcare: Factors Influencing Healthcare Professionals' Adoption and Use.
Journal of Health Informatics in Developing Countries.
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Background: This study focuses on evaluating the perceptions, competencies, and acceptance of remote healthcare monitoring technology among healthcare professionals in developing countries, specifically India and Bhutan, with an emphasis on sustainability as a crucial factor. The study aims to understand the impact of perceived ease of use, usefulness, and sustainability on the adoption of such technologies.
Methods: This research utilized the TAM framework to analyze the responses of 44 healthcare professionals, primarily doctors, from developing countries. The study examined participants' attitudes towards the ease of use and effectiveness of remote healthcare technologies, their intentions to use such technologies, and their perceptions of sustainable design in these technologies.
Results: The findings indicate a generally favorable stance towards the utility and ease of use of remote monitoring technologies in telemedicine among healthcare professionals. Participants showed positive intentions toward adopting remote healthcare technologies and acknowledged the role of technology in enhancing remote monitoring. The preference for sustainably designed technologies suggests that sustainability principles can positively influence technology acceptance. However, concerns regarding security, privacy, and network coverage emerged as significant barriers to technology adoption.
Conclusion: The study underscores the potential of remote healthcare monitoring technologies to improve healthcare access, especially in remote and underserved areas of developing countries. It highlights the importance of addressing security, privacy, and network infrastructure issues to enhance technology adoption. Furthermore, incorporating sustainability into the TAM provides a broader understanding of the factors influencing the acceptance and use of remote healthcare technologies among healthcare professionals.
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Jat, Avnish Singh; Grønli, Tor-Morten; Ghinea, George & Assres, Gebremariam Mesfin
(2024).
Evolving Software Architecture Design in Telemedicine: A PRISMA-based Systematic Review.
Healthcare Informatics Research.
ISSN 2093-3681.
30(3),
p. 184–193.
doi:
10.4258/hir.2024.30.3.184.
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Objectives
This article presents a systematic review of recent advancements in telemedicine architectures for continuous monitoring, providing a comprehensive overview of the evolving software engineering practices underpinning these systems. The review aims to illuminate the critical role of telemedicine in delivering healthcare services, especially during global health crises, and to emphasize the importance of effectiveness, security, interoperability, and scalability in these systems.
Methods
A systematic review methodology was employed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. As the primary research method, the PubMed, IEEE Xplore, and Scopus databases were searched to identify articles relevant to telemedicine architectures for continuous monitoring. Seventeen articles were selected for analysis, and a methodical approach was employed to investigate and synthesize the findings.
Results
The review identified a notable trend towards the integration of emerging technologies into telemedicine architectures. Key areas of focus include interoperability, security, and scalability. Innovations such as cognitive radio technology, behavior-based control architectures, Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) standards, cloud computing, decentralized systems, and blockchain technology are addressing challenges in remote healthcare delivery and continuous monitoring.
Conclusions
This review highlights major advancements in telemedicine architectures, emphasizing the integration of advanced technologies to improve interoperability, security, and scalability. The findings underscore the successful application of cognitive radio technology, behavior-based control, HL7 FHIR standards, cloud computing, decentralized systems, and blockchain in advancing remote healthcare delivery.
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Lakhan, Abdullah; Grønli, Tor-Morten; Wu, Huaming; Younas, Muhammad & Ghinea, George
(2024).
A Novel Homomorphic Blockchain Scheme for Intelligent Transport Services in Fog/Cloud and IoT Networks.
IEEE Transactions on Intelligent Transportation Systems (T-ITS).
ISSN 1524-9050.
doi:
10.1109/TITS.2024.3493452.
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Saleem, Ahmed; Grønli, Tor-Morten; Lakhan, Abdullah; Chen, Yi & Liang, Guoxi
(2023).
A reinforcement federated learning based strategy for urinary disease dataset processing.
Computers in Biology and Medicine.
ISSN 0010-4825.
163,
p. 1–13.
doi:
10.1016/j.compbiomed.2023.107210.
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Lakhan, Abdullah; Grønli, Tor-Morten; Muhammad, Ghulam & Tiwari, Prayag
(2023).
EDCNNS: Federated learning enabled evolutionary deep convolutional neural network for Alzheimer disease detection.
Applied Soft Computing.
ISSN 1568-4946.
147.
doi:
10.1016/j.asoc.2023.110804.
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Tom, ali El; Bogdan, Christian; Majchrzak, Tim A. & Grønli, Tor-Morten
(2023).
Criteria Based Evaluation of Cross-Platform Development Frameworks.
In Bui, Tung X. (Eds.),
Proceedings of the 56thAnnual Hawaii International
Conference on System Sciences.
HICSS Conference Office.
ISSN 9780998133164.
p. 6944–6953.
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Hogsnes, Mathilde; Grønli, Tor-Morten & Hansen, Kjeld Steenbjerg
(2023).
Unconsciously Influential Understanding sociotechnical influence on social media.
Scandinavian Journal of Information Systems (SJIS).
ISSN 0905-0167.
35(2),
p. 3–36.
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Jat, Avnish Singh; Grønli, Tor-Morten & Lakhan, Abdullah
(2023).
Towards Next-Generation Healthcare: Architectural Insights into an AI-Driven, Smartwatch-Compatible mHealth Application.
International Journal of Computer Trends and Technology.
ISSN 2349-0829.
71(10),
p. 92–106.
doi:
10.14445/22312803/IJCTT-V71I10P111.
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With the rise of telemedicine and wearable technology, mHealth (mobile health) applications are becoming increasingly important in providing real-time, personalized healthcare solutions. However, integrating these technologies effectively to deliver high-quality care is a significant challenge. This paper proposes a novel system architecture for an AI-assisted mHealth application that seamlessly integrates video conferencing platforms, smartwatch APIs, and AI algorithms. Our proposed architecture facilitates real-time health monitoring during video consultations by allowing healthcare providers to access patients' health data gathered from their wearable smartwatches. Furthermore, integrating AI algorithms provides personalized health recommendations based on patterns identified from the collected data. Additionally, this research delves deep into the implementation considerations and cloud architectural paradigms, underscoring the challenges and discrepancies between proposed designs and real-world application feasibility. The proposed architecture addresses the needs of modern healthcare services and offers the potential for further enhancements in the realm of AI-assisted telemedicine.
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Weldeslasie, D. T.; Ahmed, Mohamed; Assres, Gebremariam; Grønli, Tor-Morten & Ghinea, Gheorghita
(2023).
Intelligent Identification of Respiratory Diseases: Covid-19 and Similar Virus Cases.
In Younas, Muhammad; Awan, Irfan & Grønli, Tor-Morten (Ed.),
Mobile Web and Intelligent Information Systems: 19th International Conference, MobiWIS 2023, Marrakech, Morocco, August 14–16, 2023. Proceedings.
Springer Nature.
ISSN 9783031397646.
p. 83–95.
doi:
10.1007/978-3-031-39764-6_6.
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The World Health Organization (WHO) has declared an end to the emergency phase of the COVID-19 pandemic. However, it is still negatively impacting countries like Ethiopia. Thus, exploring the identification and treatment mechanisms for COVID-19 from similar disease has still paramount importance.
COVID-19 testing is still expensive, not easily available and time consuming, particularly in the conflict hit Tigray region. In this study, our main goal is to explore,
design, implement and evaluate intelligent software that identifiesCOVID-19 from similar diseases, namely, Common Cold, Measles, Flu, COVID-19, and Chicken Pox. Furthermore, we point out the benefits of implementing such software to
replace shortage of manpower given the circumstances in Tigray region. The software, hereafter named CKBS (COVID-19 Knowledge-based System), has been evaluated to ensure whether it is accurate and usable. In this study, 25 patients
participated for each of the five different diseases to test the effectiveness of the developed knowledge base. The dataset was split in such a way that 70% is used for training while the remaining 30% for testing. Accordingly, we found out that the resulting software can successfully identify the five diseases at 98% accuracy and getting 96% community acceptance rate. This implies that our use of the tree model in the development was effective and resulting software has been accepted to be used by the local community to tackle the COVID-19 and related challenges that Ethiopia (particularly Tigray) is still facing.
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Bhandari, Guru; Lyth, Andreas; Shalaginov, Andrii & Grønli, Tor-Morten
(2023).
Distributed Deep Neural-Network-Based Middleware for Cyber-Attacks Detection in Smart IoT Ecosystem: A Novel Framework and Performance Evaluation Approach.
Electronics (Basel).
12(2).
doi:
10.3390/electronics12020298.
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Cyberattacks always remain the major threats and challenging issues in the modern digital world. With the increase in the number of internet of things (IoT) devices, security challenges in these devices, such as lack of encryption, malware, ransomware, and IoT botnets, leave the devices vulnerable to attackers that can access and manipulate the important data, threaten the system, and demand ransom. The lessons from the earlier experiences of cyberattacks demand the development of the best-practices benchmark of cybersecurity, especially in modern Smart Environments. In this study, we propose an approach with a framework to discover malware attacks by using artificial intelligence (AI) methods to cover diverse and distributed scenarios. The new method facilitates proactively tracking network traffic data to detect malware and attacks in the IoT ecosystem. Moreover, the novel approach makes Smart Environments more secure and aware of possible future threats. The performance and concurrency testing of the deep neural network (DNN) model deployed in IoT devices are computed to validate the possibility of in-production implementation. By deploying the DNN model on two selected IoT gateways, we observed very promising results, with less than 30 kb/s increase in network bandwidth on average, and just a 2% increase in CPU consumption. Similarly, we noticed minimal physical memory and power consumption, with 0.42 GB and 0.2 GB memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device with the deployed model. The ML models were able to demonstrate nearly 93% of detection accuracy and 92% f1-score on both utilized datasets. The result of the models shows that our framework detects malware and attacks in Smart Environments accurately and efficiently.
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Jat, Avnish Singh & Grønli, Tor-Morten
(2023).
Virtual Reality in Digital Health: A Literature Review.
CEUR Workshop Proceedings.
3382.
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Jat, Avnish Singh & Grønli, Tor-Morten
(2023).
Harnessing the Digital Revolution: A Comprehensive Review of mHealth Applications for Remote Monitoring in Transforming Healthcare Delivery.
Lecture Notes in Computer Science (LNCS).
ISSN 0302-9743.
13977,
p. 55–67.
doi:
10.1007/978-3-031-39764-6_4.
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Grønli, Tor-Morten; Lakhan, Abdullah & Younas, Muhammad
(2023).
RSITS: Road Safety Intelligent Transport System in Deep Federated Learning Assisted Fog Cloud Networks.
In Younas, Muhammad; Awan, Irfan & Grønli, Tor-Morten (Ed.),
Mobile Web and Intelligent Information Systems: 19th International Conference, MobiWIS 2023, Marrakech, Morocco, August 14–16, 2023. Proceedings.
Springer Nature.
ISSN 9783031397646.
p. 20–37.
doi:
10.1007/978-3-031-39764-6_2.
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This book constitutes the refereed proceedings of the 19th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2023, held in Marrakech, Morocco, during August 14–16, 2023.
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Mathisen, Inger Elisabeth; Mohan, Kanika; Grønli, Tor-Morten; Serif, Tacha & Ghinea, Gheorghita
(2023).
User Acceptance of Contact Tracing Apps: A Study During the Covid-19 Pandemic.
Lecture Notes in Networks and Systems.
ISSN 2367-3370.
p. 541–550.
doi:
10.1007/978-981-99-3932-9_46.
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Lakhan, Abdullah; Thinnukool, Orawit; Grønli, Tor-Morten & Khuwuthyakorn, Pattaraporn
(2023).
RBEF: Ransomware Efficient Public Blockchain Framework for Digital Healthcare Application.
Sensors.
23(11).
doi:
10.3390/s23115256.
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Biørn-Hansen, Andreas; Grønli, Tor-Morten; Majchrzak, Tim Alexander; Kaindl, Hermann & Gheorghita, Ghinea
(2022).
The Use of Cross-Platform Frameworks for Google Play Store Apps.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
2022,
p. 7772–7781.
doi:
10.24251/HICSS.2022.934.
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In this paper, we describe the harnessing and analyses of a large sample (n = 661705) of Android apps and associated metadata available on the Google Play Store. The analyses and scrutiny are in the context of cross-platform mobile development, as we report on the technologies used to develop apps for the Android ecosystem. Specifically, we quantify the use of 13 technical frameworks for cross-platform development, identify their distribution across Google Play Store categories, present an overview of framework usage from 2008 to 2019, app file size (.apk size), and lastly discuss our findings in the context of current industry trends and directions. Our findings indicate that cross-platform apps account for approximately 15% (n = 99304) of the dataset, and that all overarching development approaches are present.
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Lakhan, Abdullah; Grønli, Tor-Morten; Majumdar, Arnab; Khuwuthyakorn, Pattaraporn; Khoso, Fida Hussain & Thinnukool, Orawit
(2022).
Potent Blockchain-Enabled Socket RPC Internet of Healthcare Things (IoHT) Framework for Medical Enterprises.
Sensors.
22(12).
doi:
10.3390/s22124346.
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Bieler, Malte; Mukkamala, Raghava Rao & Grønli, Tor-Morten
(2022).
A Context- and Trajectory-Based Destination Prediction of Public Transportation Users.
IEEE Intelligent Transportation Systems Magazine (ITSM).
ISSN 1939-1390.
15(1),
p. 300–317.
doi:
10.1109/MITS.2021.3132772.
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Location prediction based on contextual information is the core of a plethora of personalized locationbased services (LBSs). Several applications require the use of techniques for predicting travel destinations based on human movement. Network analyses of human behavioral data show how the spatial-temporal regularity of human movement can be harnessed for inferring human mobility patterns. However, techniques are often based on a limited number of contextual features, which may limit prediction accuracy, especially if only historical location data are used. Using movement data obtained from public transportation users, we investigate the utility of contextual features derived via the installation of Bluetooth beacons in transportation vehicles and software tools in end-users’ travel applications. Using a multiclass random forest classifier, we show that contextual information of a user’s past travel history and at journey onset goes beyond spatial information and boosts destination prediction accuracy. The likely destination and travel-path length obtained at journey onset can then serve as the input for a stochastic-based model to predict a destination based on acquired trajectory information. Here we show that previously predicted destinations boost the performance of a Markov chain network. Thus, various contextual information at the start of a journey provides information beyond the location information acquired during a journey’s progression that can be employed for destination prediction. These findings have strong implications for LBSs as they require accurate destination prediction at early stages of a journey while at the same time mitigating the privacy concerns associated with collection of location data
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Sodhro, Ali Hassan; Lakhan, Abdullah; Pirbhulal, Sandeep; Grønli, Tor-Morten & Abie, Habtamu
(2022).
A Lightweight Security Scheme for Failure Detection in Microservices IoT-Edge Networks.
In Suryadevara, Nagender Kumar; George, Boby; Jayasundera, Krishanthi P.; Roy, Joyanta Kumar & Mukhopadhyay, Subhas Chandra (Ed.),
Sensing Technology. Proceedings of ICST 2022.
Springer.
ISSN 9783030988852.
doi:
10.1007/978-3-030-98886-9_31.
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Nowadays, microservices-based applications such as E-Business, E-Healthcare, 3D-Gaming, and Augmented Reality have latterly drawn attention in the research area. The microservices enabled applications are different from traditional monolithic applications with high demand of security and fault detection, therefore, a lightweight secure and failure detection enabled schemes widely required for the new applications. This paper proposes a new lightweight microservices mobile cloud (Mob-Cloud) framework that replaces the heavyweight virtual machine (VM) based on mobile cloud computing (MCC). The study devises MFHE (Modified Fully Homomorphism Encryption) and WATFA (Workload Assignment Transient Fault Aware) schemes to deal with security and failures. Simulation results show that the proposals are practical for the considered problem.
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Bieler, Malte; Skretting, Anders; Büdinger, Philippe & Grønli, Tor-Morten
(2022).
Survey of Automated Fare Collection Solutions in Public Transportation.
IEEE Transactions on Intelligent Transportation Systems (T-ITS).
ISSN 1524-9050.
23(9),
p. 14248–14266.
doi:
10.1109/TITS.2022.3161606.
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Public transportation is expensive to operate and maintain and is often unsatisfactory. The attractiveness of public transportation can be enhanced by making it more seamless, which, in turn, would reduce financial constraints and inefficiencies. The adoption of mobile devices for ticketing solutions is promising. However, current solutions are often inflexible and require manual interactions that produce evanescent data. Therefore, using leading-edge technologies and infrastructure, it is desirable to develop a solution to fully automate fare collection. In this paper, we provide a comprehensive literature review to understand the state of public transportation and to facilitate the development and implementation of automated fare collection solutions. First, we discuss existing mobile technologies and their common ticketing implementations. Second, we provide a predictive behavior model with sensor analytics to better understand customer needs. Finally, we highlight how machine learning can harness transactional ticketing data to create valuable business intelligence. Overall, developing and implementing automated fare collection solutions in urban transportation is expected to have a significant positive impact on customer experiences, the emergence of new business models and the reduction of pollutant emissions.
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Majchrzak, Tim A.; Grønli, Tor-Morten & Kaindl, Hermann
(2022).
Introduction to the HICSS-55 Software Development for Mobile Devices, the Internet-of-Things, and Cyber-Physical Systems Minitrack.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
doi:
10.24251/HICSS.2022.931.
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Wu, Chunhui; Lakhan, Abdullah & Grønli, Tor-Morten
(2022).
Microservices architectural based secure and failure aware task assignment schemes in fog-cloud assisted Internet of things.
International Journal of Intelligent Systems.
ISSN 0884-8173.
37(11),
p. 8696–8729.
doi:
10.1002/int.22964.
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Skretting, Anders & Grønli, Tor-Morten
(2022).
Distributed Sensor Data Collection Using Mobile Clouds for Public Transportation.
In Nedevschi, Sergiu; Potolea, Rodica & Slavescu, Radu Razvan (Ed.),
Proceedings - 2021 IEEE 17th International Conference
on Intelligent Computer Communication and Processing (ICCP).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9781665409759.
p. 61–68.
doi:
10.1109/ICCP53602.2021.9733560.
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Today mobile solutions are expected to streamline
all kinds of daily tasks from entertainment and communication
to more specific tasks such as ordering food or buying a ticket for public transport. Social networking solutions might entail frequent image processing while mobility solutions might activate sensors in order to provide public transport operators with insights into travel patterns. All of these tasks lead to an elevation in resource expenditure which becomes a problem for everyday mobile users, especially in terms of battery life. This work proposes the usage of mobile clouds in mobility related solutions in order to offload tasks and share context specific data with nearby devices. The proposed solution is a mobile application
capable of ranking nearby nodes based on their performance
characteristics, and then request these nodes to participate in a
shared sensor data collection according to computing principles
of mobile clouds. The proposed solution can be used to both
reduce energy consumption of devices and increase the amount
of sensor data collected. We evaluate the solution based on both energy consumption and data loss. We measure and compare
both battery drainage and data loss where the latter is evaluated by measuring sensor events sent versus received.
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Hidalgo, Alexandra; Serif, Tacha; Grønli, Tor-Morten & Ghinea, Gheorghita
(2022).
On User Experience in The Internet of Things,
Proceedings of the IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022.
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798350396454.
doi:
10.1109/IoTaIS56727.2022.9975911.
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The IoT (Internet-of-Things) era is in full swing,
with the transformation of all everyday objects extending the
internet to communicate. The need to focus on UX (user
experience) design of IoT services stems from the complex
sequence of interactions between users from physical systems to
virtual ones. This study aims to contribute to the definition of new IoT UX standards that go beyond the standard website and
mobile guides that exist today. In attaining a better knowledge
in the domain of UX and IoT, we focus on learning how UX
designers are approaching IoT solutions and how user
interactions are evolving with the use of IoT systems.
Accordingly a study exploring the perspectives on the topic
encompassing qualitative interviews with five UX designers and
questionnaires administered to 65 IoT users was undertaken.
Based on this, a series of guidelines are drafted and reviewed by UX designers to gain validity of the results produced thus
creating a positive impact for not only users of IoT services but the designers behind the scenes creating the experiences.
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Önder, Onurcan; Ghinea, Gheorghita; Grønli, Tor-Morten & Serif, Tacha
(2022).
Indoor Navigation Using Hybrid Personal Pedestrian Dead Reckoning (Hybrid P-PDR).
Proceedings of the IEEE.
ISSN 0018-9219.
doi:
10.1109/IoTaIS56727.2022.9975916.
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The proliferation of smart devices has dramatically changed how people live their daily lives. Today, on top of their initial communicator role, smart devices act as guides,
companions, and aids. For a long time, people have been using
navigation systems and mobile phones as navigators in their
cars. Indeed, there have been interests in implementing similar indoor navigation systems using technologies such as Wi-Fi, Bluetooth, and ultra-wideband. However, the proposed indoor navigation solutions were either too expensive to implement and maintain, or not accurate enough for a wider acceptance. Accordingly, this paper proposes a hybrid pedestrian dead reckoning (PDR) for indoor navigation, which utilizes the builtin sensors of smart devices. As part of this study, the authors implement three approaches to pedestrian dead-reckoning - namely PDR, Personal PDR, and Hybrid P- PDR - and evaluate in a real-world setting. The findings of the the evaluation shows that the Hybrid P-PDR approach, which harnesses the user’s walking pattern and signals from low-energy beacons, can navigate users in an indoor environment with a minimum of 0.77 and maximum of 1.35-meter average distance error.
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Lakhan, Abdullah; Dootio, Mazhar Ali; Grønli, Tor-Morten; Sodhro, Ali Hassan & Khokhar, Muhammad Saddam
(2021).
Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks.
Electronics (Basel).
10(14).
doi:
10.3390/electronics10141719.
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These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, E-Train and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, round-trip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.
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Hirsch, Matias; Mateos, Cristian; Zunino, Alejandro; Majchrzak, Tim A.; Grønli, Tor-Morten & Kaindl, Hermann
(2021).
A Simulation-based Performance Evaluation of Heuristics for Dew Computing.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
p. 7207–7216.
doi:
10.24251/HICSS.2021.868.
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The evolution of smartphones allows the continuous exploitation of computing resources. This increasingly applies also to distributed environments as exemplified through utilization of network router loads in edge computing and fog computing. Combining cloud computing and mobile smart devices in dew computing contexts enables new techniques for resource utilization, data collection and data processing. However, new challenges regarding job scheduling arise. Smartphones may be used in ad-hoc networks in this context, but their heterogeneity and energy usage must be considered. We propose novel heuristics for performance measuring of distributed computing systems integrated with mobile devices and compare them with previous heuristics in a simulation environment. Our results show an overall improvement in job completion and load balancing metrics compared to previous approaches. They highlight the usefulness of pursuing this research stream for aiming at industrial implementation and evaluation.
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Weldeslasie, Dawit Teklu; Assres, Gebremariam; Grønli, Tor-Morten & Ghinea, Gheorghita
(2021).
Automated Climate Monitoring System: the Case of Greenhouse Industries in Ethiopia.
Internet of Things: Engineering Cyber Physical Human Systems.
15.
doi:
10.1016/j.iot.2021.100426.
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Technological interventions can play a significant role to keep optimal growing conditions in greenhouse industries with varying seasons and improve environmental performance by maintaining heating, cooling, and humidity levels. The research reported in this paper aims to integrate an Automated Climate Monitoring System (ACMS) for greenhouse in Ethiopia. Green- houses in Ethiopia require constant monitoring to properly maintain optimal climate conditions to grow plants all year round. To this end, they apply environment monitoring systems like timing devices, ON/OFF controllers and manual (human) monitoring which are prone to error. We propose a flexible ACMS prototype which can be applied without a user having the knowledge of how the system is made. The prototype system uses Advantech PC- cards and maintains the optimal climate condition (temperature, humidity and light intensity) of the greenhouses. Moreover, the easy to use graphical user interface provided by the system enables to combine the hardware and software functionalities for optimal manipulation of the environmental conditions. The system is centralized in that anyone can control several de- vices spread in the environment being monitored from one Computer. The performance of the system is found to be 88.89% which is “accurate and acceptable” result according to our confusion matrix. This indicates that better environmental supervision can be achieved when using the system. Moreover, the mean user acceptance (by the flower growers) of the ACMS and timing devices, ON/OFF implementations is 84.6% and 73.2%, respectively, highlighting the potential for integrating the ACMS into the greenhouse industry.
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Lakhan, Abdullah; Dootio, Mazhar Ali; Grønli, Tor-Morten; Sodhro, Ali Hassan & Khokhar, Muhammad Saddam
(2021).
Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks.
Electronics (Basel).
10(14).
doi:
10.3390/electronics10141719.
Full text in Research Archive
Show summary
These days, with the emerging developments in wireless communication technologies,
such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications
has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, ETrain and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network.
Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay,
round-trip delay and migration during the workload in the cloudlet-based cloud network. However,
the distributed execution of workloads at different computing nodes during the assignment is a
challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, roundtrip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the
workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different
components, such as the Q-Learning aware assignment and the Iterative method, which distribute
workload in a dynamic environment where runtime changes of overloading and overheating remain
controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to
minimize the average response time of applications. Simulation results demonstrate that MLAWAS
earns the minimum average response time as compared with the two other existing strategies.
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Grønli, Tor-Morten & Skretting, Anders
(2021).
Baseline for Performance Prediction of Android Applications.
In Wu, Xintao (Eds.),
2020 IEEE International Conference on Big Data.
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9781728162515.
p. 3304–3310.
doi:
10.1109/BigData50022.2020.9377882.
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Abstract—Millions of applications are being developed around the world with a promise of increasing our productivity, teaching us new skills, improving our health and providing us with entertainment. In order to cater to the users needs, ever more intricate and advanced applications are being developed and with more sophisticated solutions, an increase in resource expenditure is likely to occur. Smartphones have limited resources in terms of both computational performance and battery capacity and most developers today tries to optimise their applications in order to provide the best user experience with as low a resource cost as possible. In this paper, large amounts of performance data has been gathered from different Android applications and games on different devices in order to establish a baseline which in turn can be used for training of machine learning algorithms for usage in automatic testing and real-time performance assessment solutions.
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Dootio, Mazhar Ali; Lakhan, Abdullah; Sodhro, Ali Hassan; Grønli, Tor-Morten; Bawany, Narmeen Zakaria & Kumar, Samrat Schmiem
(2021).
Secure and failure hybrid delay enabled a lightweight RPC and SHDS schemes in Industry 4.0 aware IIoHT enabled fog computing.
Mathematical Biosciences and Engineering.
ISSN 1547-1063.
19(1),
p. 513–536.
doi:
10.3934/mbe.2022024.
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These days, the Industrial Internet of Healthcare Things (IIT) enabled applications have been growing progressively in practice. These applications are ubiquitous and run onto the different computing nodes for healthcare goals. The applications have these tasks such as online healthcare monitoring, live heartbeat streaming, and blood pressure monitoring and need a lot of resources for execution. In IIoHT, remote procedure call (RPC) mechanism-based applications have been widely designed with the network and computational delay constraints to run healthcare applications. However, there are many requirements of IIoHT applications such as security, network and computation, and failure efficient RPC with optimizing the quality of services of applications. In this study, the work devised the lightweight RPC mechanism for IIoHT applications and considered the hybrid constraints in the system. The study suggests the secure hybrid delay scheme (SHDS), which schedules all healthcare workloads under their deadlines. For the scheduling problem, the study formulated this problem based on linear integer programming, where all constraints are integer, as shown in the mathematical model. Simulation results show that the proposed SHDS scheme and lightweight RPC outperformed the hybrid for IIoHT applications and minimized 50% delays compared to existing RPC and their schemes.
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Parker, Alice; Grønli, Tor-Morten & Younas, Muhammad
(2021).
A Game of Fog and Mirrors: Privacy in the World of Internet of Things.
In Bentahar, Jamal; Awan, Irfan & Grønli, Tor-Morten (Ed.),
Mobile Web and Intelligent Information Systems: 17th International Conference, MobiWIS 2021, Virtual Event, August 23–25, 2021, Proceedings.
Springer.
ISSN 9783030831646.
p. 163–174.
doi:
10.1007/978-3-030-83164-6_13.
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Leithe, Wasana; Grønli, Tor-Morten & Younas, Muhammad
(2021).
Analysis of Distance Sensor in Lego Mindstorm.
In Bentahar, Jamal; Awan, Irfan & Grønli, Tor-Morten (Ed.),
Mobile Web and Intelligent Information Systems: 17th International Conference, MobiWIS 2021, Virtual Event, August 23–25, 2021, Proceedings.
Springer.
ISSN 9783030831646.
p. 185–194.
doi:
10.1007/978-3-030-83164-6_15.
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Lakhan, Abdullah; Dootio, Mazhar Ali; Sodhro, Ali Hassan; Pirbhulal, Sandeep; Grønli, Tor-Morten & Khokhar, Muhammad Saddam
[Show all 7 contributors for this article]
(2021).
Cost-efficient service selection and execution and blockchain-enabled serverless network for internet of medical things.
Mathematical Biosciences and Engineering.
ISSN 1547-1063.
18(6),
p. 7344–7362.
doi:
10.3934/mbe.2021363.
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These days, healthcare applications on the Internet of Medical Things (IoMT) network have been growing to deal with different diseases via different sensors. These healthcare sensors are connecting to the various healthcare fog servers. The hospitals are geographically distributed and offer different services to the patients from any ubiquitous network. However, due to the full offloading of data to the insecure servers, two main challenges exist in the IoMT network. (i) Data security of workflows healthcare applications between different fog healthcare nodes. (ii) The cost-efficient and QoS efficient scheduling of healthcare applications in the IoMT system. This paper devises the Cost-Efficient Service Selection and Execution and Blockchain-Enabled Serverless Network for Internet of Medical Things system. The goal is to choose cost-efficient services and schedule all tasks based on their QoS and minimum execution cost. Simulation results show that the proposed outperform all existing schemes regarding data security, validation by 10%, and cost of application execution by 33% in IoMT.
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Hirsch, Matías; Mateos, Cristian; Zunino, Alejandro; Majchrzak, Tim A.; Grønli, Tor-Morten & Kaindl, Hermann
(2021).
A task execution scheme for dew computing with state-of-the-art smartphones.
Electronics (Basel).
10(16),
p. 1–22.
doi:
10.3390/electronics10162006.
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The computing resources of today’s smartphones are underutilized most of the time. Using these resources could be highly beneficial in edge computing and fog computing contexts, for example, to support urban services for citizens. However, new challenges, especially regarding job scheduling, arise. Smartphones may form ad hoc networks, but individual devices highly differ in computational capabilities and (tolerable) energy usage. We take into account these particularities to validate a task execution scheme that relies on the computing power that clusters of mobile devices could provide. In this paper, we expand the study of several practical heuristics for job scheduling including execution scenarios with state-of-the-art smartphones. With the results of new simulated scenarios, we confirm previous findings and better comprehend the baseline approaches already proposed for the problem. This study also sheds some light on the capabilities of small-sized clusters comprising mid-range and low-end smartphones when the objective is to achieve real-time stream processing using Tensorflow object recognition models as edge jobs. Ultimately, we strive for industry applications to improve task scheduling for dew computing contexts. Heuristics such as ours plus supporting dew middleware could improve citizen participation by allowing a much wider use of dew computing resources, especially in urban contexts in order to help build smart cities.
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Lakhan, Abdullah; Li, Jin; Groenli, Tor Morten; Sodhro, Ali Hassan; Zardari, Nawaz Ali & Imran, Ali Shariq
[Show all 8 contributors for this article]
(2021).
Dynamic application partitioning and task-scheduling secure schemes for biosensor healthcare workload in mobile edge cloud.
Electronics (Basel).
10(22),
p. 1–30.
doi:
10.3390/electronics10222797.
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Currently, the use of biosensor-enabled mobile healthcare workflow applications in mobile edge-cloud-enabled systems is increasing progressively. These applications are heavyweight and divided between a thin client mobile device and a thick server edge cloud for execution. Application partitioning is a mechanism in which applications are divided based on resource and energy parameters. However, existing application-partitioning schemes widely ignore security aspects for healthcare applications. This study devises a dynamic application-partitioning workload task-scheduling-secure (DAPWTS) algorithm framework that consists of different schemes, such as min-cut algorithm, searching node, energy-enabled scheduling, failure scheduling, and security schemes. The goal is to minimize the energy consumption of nodes and divide the application between local nodes and edge nodes by applying the secure min-cut algorithm. Furthermore, the study devises the secure-min-cut algorithm, which aims to migrate data between nodes in a secure form during application partitioning in the system. After partitioning the applications, the node-search algorithm searches optimally to run applications under their deadlines. The energy and failure schemes maintain the energy consumption of the nodes and the failure of the system. Simulation results show that DAPWTS outperforms existing baseline approaches by 30% in terms of energy consumption, deadline, and failure of applications in the system.
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Staaby, Anne; Hansen, Kjeld S. & Grønli, Tor-Morten
(2021).
Automation of Routine Work: A Case Study of Employees' Experiences of Work Meaningfulness.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
54,
p. 156–165.
doi:
10.24251/HICSS.2021.018.
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The idea of automation replacing humans in the workplace has been given considerable attention, while less attention has been afforded to how people develop a meaningful work life with automation of routine work. In this study we investigate how employees, who have had their routine work automated with RPA, have experienced its influence on their work and its meaningfulness. Concretely, we conduct a case study of how employees experience this process in three different case organizations in Oslo, Norway. We make theoretical contributions by combining automation of work, RPA and work meaningfulness literature to understand what opportunities and pitfalls the organizations experience, when they seek to harness value from their human resources in the process. We also contribute with implications for practice, by suggesting organizations to focus on creating autonomy and job crafting opportunities for employees, when they automate routine work with RPA.
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Biørn-Hansen, Andreas; Rieger, Christoph; Grønli, Tor-Morten; Majchrzak, Tim A. & Ghinea, Gheorghita
(2020).
An Empirical Investigation of Performance Overhead in Cross-Platform Mobile Development Frameworks.
Empirical Software Engineering.
ISSN 1382-3256.
25.
doi:
10.1007/s10664-020-09827-6.
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The heterogeneity of the leading mobile platforms in terms of user interfaces, user experience, programming language, and ecosystem have made cross-platform development frameworks popular.
These aid the creation of mobile applications – apps – that can be executed across the target platforms (typically Android and iOS) with minimal to no platform-specific code.
Due to the cost- and time-saving possibilities introduced through adopting such a framework, researchers and practitioners alike have taken an interest in the underlying technologies.
Examining the body of knowledge, we, nonetheless, frequently encounter discussions on the drawbacks of these frameworks, especially with regard to the performance of the apps they generate.
Motivated by the ongoing discourse and a lack of empirical evidence, we scrutinised the essential piece of the cross-platform frameworks:
the bridge enabling cross-platform code to communicate with the underlying operating system and device hardware APIs.
The study we present in the article benchmarks and measures the performance of this bridge to reveal its associated overhead in Android apps.
The development of the artifacts for this experiment was conducted using five cross-platform development frameworks to generate Android apps, in addition to a baseline native Android app implementation.
Our results indicate that – for Android apps – the use of cross-platform frameworks for the development of mobile apps may lead to decreased performance compared to the native development approach.
Nevertheless, certain cross-platform frameworks can perform equally well or even better than native on certain metrics which highlights the importance of well-defined technical requirements and specifications for deliberate selection of a cross-platform framework or overall development approach.
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Klavestad, Sindre; Assres, Gebremariam; Fagernes, Siri & Grønli, Tor-Morten
(2020).
Monitoring Activities of Daily Living Using UWB Radar Technology: A Contactless Approach.
IoT.
1(2),
p. 320–336.
doi:
10.3390/iot1020019.
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Klavestad, Sindre; Assres, Gebremariam Mesfin; Fagernes, Siri & Grønli, Tor-Morten
(2020).
Monitoring Activities of Daily Living Using UWB Radar Technology: A Contactless Approach.
IoT.
1,
p. 320–336.
doi:
10.3390/iot1020019.
Full text in Research Archive
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Thambawita, Vajira Lasantha Bandara; Hicks, Steven; Borglia, Hanna; Stensland, Håkon Kvale; Jha, Debesh & Svensen, Martin Kristoffer
[Show all 26 contributors for this article]
(2020).
PMData: a sports logging dataset.
In Alay, Özgü & Toni, Laura (Ed.),
MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference.
Association for Computing Machinery (ACM).
ISSN 9781450368452.
p. 231–236.
doi:
10.1145/3339825.3394926.
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In this paper, we present PMData: a dataset that combines traditional lifelogging data with sports-activity data. Our dataset enables the development of novel data analysis and machine-learning applications where, for instance, additional sports data is used to predict and analyze everyday developments, like a person's weight and sleep patterns; and applications where traditional lifelog data is used in a sports context to predict athletes' performance. PMData combines input from Fitbit Versa 2 smartwatch wristbands, the PMSys sports logging smartphone application, and Google forms. Logging data has been collected from 16 persons for five months. Our initial experiments show that novel analyses are possible, but there is still room for improvement.
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Mikkelsen, Anders; Grønli, Tor-Morten; Tamburri, Damian Andrew & Kazman, Rick
(2020).
Architectural Principles for Autonomous Microservices.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
p. 6569–6578.
doi:
10.24251/hicss.2020.804.
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Fagernes, Siri & Grønli, Tor-Morten
(2020).
The Live Programming Lecturing Technique: A Study of the Student Experience in Introductory and Advanced Programming Courses.
NIKT: Norsk IKT-konferanse for forskning og utdanning.
ISSN 1892-0713.
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This paper investigates the topic of teaching programming in higher education. The teaching method often referred to as live programming has become a widely applied lecturing strategy for teaching programming subjects in an interactive fashion. Lectures based on live programming normally involve live demonstrations, explanations and interaction with the students. Although this technique seems to be very popular amongst students and instructors, we hypothesise that it also involves potential challenges. In this paper, we investigate the perceived difficulty and promise of following such an approach from a student perspective. We present results from interviews with 1st and 2nd year IT Bachelor students about their experience with live programming. Our results indicate that students’ engagement and desire to learn through active learning techniques still are very much valid also in introductory and advanced programming courses. Furthermore, we also interpret from our findings a suggested model of a repeated cycle of lecture, demo and exercise as highly beneficial to the student learning process.
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Grønli, Tor-Morten; Majchrzak, Tim A. & Kaindl, Hermann
(2020).
A Bibliometric Analysis of the HICSS Software Technology Track.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
p. 6671–6680.
doi:
10.24251/hicss.2020.816.
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The HICSS Software Technology track has a long tradition and many papers have been published as part of its history. Its impact in terms of citations, paper contributions, author share and community impact does not yet seem to have been investigated, though. In particular, software technology has evolved, and as it stands, it is of primary importance for mobile computing, the Internet-of-Things and Cyber-physical Systems. Hence, the development of these topics and the related impact of this track are of particular interest. In this paper, we present a bibliographic analysis as a first step towards such an investigation. We found that the history of the track is indeed noteworthy. Our results include finding a few extremely much cited papers, some curious tendencies, and a generally favourable outlook for HICSS. We found that indeed many papers in the history of this HICSS track addressed mobile technology and are highly cited. Together with providing insight into track, paper and author impact, we also raise questions worth of further investigation to understand the role of the HICSS Software Technology track and its impact on the academic field and society.
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Lizarralde, Ignacio; Mateos, Cristian; Zunino, Alejandro; Majchrzak, Tim A. & Grønli, Tor-Morten
(2020).
Discovering web services in social web service repositories using deep variational autoencoders.
Information Processing & Management.
ISSN 0306-4573.
57(4).
doi:
10.1016/j.ipm.2020.102231.
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Mikkelsen, Anders; Grønli, Tor-Morten & Kazman, Rick
(2019).
Immutable Infrastructure Calls for Immutable Architecture:
Deploying a Changeless Architecture in the Cloud.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
p. 7058–7066.
doi:
10.24251/HICSS.2019.846.
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Neto, Benedito Medeiros; Ishikawa, Edison; Grønli, Tor-Morten & Ghinea, Gheorghita
(2019).
Newsroom 3.0: Managing Technological and Media Convergence in
Contemporary Newsrooms.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
p. 2407–2416.
doi:
10.24251/HICSS.2019.290.
Full text in Research Archive
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Biørn-Hansen, Andreas; Grønli, Tor-Morten; Ghinea, Gheorghita & Alouneh, Sahel
(2019).
An Empirical Study of Cross-Platform Mobile Development in Industry.
Wireless Communications & Mobile Computing.
ISSN 1530-8669.
2019,
p. 1–12.
doi:
10.1155/2019/5743892.
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The purpose of this study is to report on the industry’s perspectives and opinions on cross-platform mobile development, with an emphasis on the popularity, adoption, and arising issues related to the use of technical development frameworks and tools. We designed and conducted an online survey questionnaire, for which 101 participants were recruited from various developer-oriented online forums and websites. A total of five questions are reported in this study, of which two employed a Likert scale instrument, while three were based on multiple choice. In terms of technical frameworks, we find that PhoneGap, the Ionic Framework, and React Native were the most popular in use, both in hobby projects and in professional settings. The participants report an awareness of trade-offs when embracing cross-platform technologies and consider penalties in performance and user experience to be expected. This is also in line with what is reported in academic research. We find patterns in the reported perceived issues which match both older and newer research, thus rendering the findings a point of departure for further endevours.
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Weldeslasie, Dawit; Mesfin, Gebremariam; Grønli, Tor-Morten; Younas, Muhammad & Ghinea, Gheorghita
(2019).
An Expert System Approach for the Diagnosis and Rectification of Yarn Faults.
Lecture Notes in Computer Science (LNCS).
ISSN 0302-9743.
11673,
p. 230–242.
doi:
10.1007/978-3-030-27192-3_18.
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Grønli, Tor-Morten; Biørn-Hansen, Andreas & Majchrzak, Tim A.
(2019).
Software Development for Mobile Computing, the Internet of Things and Wearable Devices: Inspecting the Past to Understand the Future.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
p. 7451–7460.
doi:
10.24251/HICSS.2019.896.
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Currently a convergence in software development for mobile computing (including mobile devices and special technology such as wearables) and the Internet of Things (IoT) can be observed. Devices from the fields are becoming part of a joint ecosystem and share the same infrastructure. Moreover, development processes have changed and user requirements have become very heterogeneous. We have been studying this development for a while, also by offering the fitting HICSS minitrack. In this article, we look into the past of software development for mobile devices, in the context of IoT, and for wearables. We analyse joint characteristics and show differences. Then we invite to a discussion that leads to a research outlook. While neither for industry nor for academia the journey is over, the convergence of fields should offer many new possibilities, prevent problems we faced in the past, but also introduce novel challenges.
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Biørn-Hansen, Andreas; Grønli, Tor-Morten & Ghinea, Gheorghita
(2019).
Animations in Cross-Platform Mobile Applications: An Evaluation of Tools, Metrics and Performance.
Sensors.
19(9).
doi:
10.3390/s19092081.
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Along with the proliferation of high-end and performant mobile devices, we find that the inclusion of visually animated user interfaces are commonplace, but that research on their performance is scarce. Thus, for this study, eight mobile apps have been developed for scrutiny and assessment to report on the device hardware impact and penalties caused by transitions and animations, with an emphasis on apps generated using cross-platform development frameworks. The tasks we employ for animation performance measuring, are those of (i) a complex animation consisting of multiple elements, (ii) the opening sequence of a side menu navigation pattern, and (iii) a transition animation during in-app page navigation. We employ multiple performance profiling tools, and scrutinize metrics including frames per second (FPS), CPU usage, device memory usage and GPU memory usage, all to uncover the impact caused by executing transitions and animations. We uncover important differences in device hardware utilization during animations across the different cross-platform technologies employed. Additionally, Android and iOS are found to differ greatly in terms of memory consumption, CPU usage and rendered FPS, a discrepancy that is true for both the native and cross-platform apps. The findings we report are indeed factors contributing to the complexity of app development.
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Biørn-Hansen, Andreas; Grønli, Tor-Morten & Ghinea, Gheorghita
(2019).
A Survey and Taxonomy of Core Concepts and Research Challenges in Cross-Platform Mobile Development.
ACM Computing Surveys.
ISSN 0360-0300.
51(5).
doi:
10.1145/3241739.
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Developing applications targeting mobile devices is a complex task involving numerous options, technologies, and trade-offs, mostly due to the proliferation and fragmentation of devices and platforms. As a result of this, cross-platform app development has enjoyed the attention of practitioners and academia for the previous decade. Throughout this review, we assess the academic body of knowledge and report on the state of research on the field. We do so with a particular emphasis on core concepts, including those of user experience, device features, performance, and security. Our findings illustrate that the state of research demand for empirical verification of an array of unbacked claims, and that a particular focus on qualitative user-oriented research is essential. Through our outlined taxonomy and state of research overview, we identify research gaps and challenges, and provide numerous suggestions for further research.
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Soylu, Ahmet; Liu, Fan; Mitra, Karan; Zhang, Yan & Grønli, Tor-Morten
(2026).
Mobile and Ubiquitous Systems: Computing, Networking and Services - 21st EAI International Conference, MobiQuitous 2024, Oslo, Norway, November 12–14, 2024, Proceedings.
Springer Nature.
ISBN 9783032105547.
633 p.
doi:
https:/link.springer.com/book/10.1007/978-3-032-10554-7.
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Younas, Muhammad; Awan, Irfan & Grønli, Tor-Morten
(2023).
Mobile Web and Intelligent Information Systems: 19th International Conference, MobiWIS 2023, Marrakech, Morocco, August 14–16, 2023. Proceedings.
Springer Nature.
ISBN 9783031397646.
282 p.
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This book constitutes the refereed proceedings of the 19th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2023, held in Marrakech, Morocco, during August 14–16, 2023.
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Bentahar, Jamal; Awan, Irfan; Younas, Muhammad & Grønli, Tor-Morten
(2021).
Mobile Web and Intelligent Information Systems: 17th International Conference, MobiWIS 2021, Virtual Event, August 23–25, 2021, Proceedings.
Springer.
ISBN 9783030831646.
205 p.
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Majchrzak, Tim A.; Grønli, Tor-Morten & Kaindl, Hermann
(2025).
Introduction to the HICSS-58 Software Technology and Software Development Minitrack.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
p. 7379–7380.
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Majchrzak, Tim A.; Grønli, Tor-Morten & Kaindl, Hermann
(2024).
Introduction to the HICSS-57 Software Technology and Software Development Minitrack.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
p. 7697–7699.
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Majchrzak, Tim A.; Grønli, Tor-Morten & Kaindl, Hermann
(2023).
Introduction to the HICSS-56 Software Development for Mobile Devices, the Internet-of-Things, and Cyber-Physical Systems Minitrack.
In Bui, Tung X. (Eds.),
Proceedings of the 56thAnnual Hawaii International
Conference on System Sciences.
HICSS Conference Office.
ISSN 9780998133164.
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Tiwari, Prayag; Lakhan, Abdullah; Jhaveri, Rutvij H. & Grønli, Tor-Morten
(2023).
Consumer-Centric Internet of Medical Things for Cyborg Applications based on Federated Reinforcement Learning.
IEEE transactions on consumer electronics.
ISSN 0098-3063.
69(4),
p. 756–764.
doi:
10.1109/TCE.2023.3242375.
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Majchrzak, Tim A.; Grønli, Tor-Morten & Kaindl, Hermann
(2023).
Introduction to the HICSS-56 Software Development for Mobile Devices, the Internet-of-Things, and Cyber-Physical Systems Minitrack.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
2023-,
p. 6893–6894.
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Sodhro, Ali Hassan; Lakhan, Abdullah; Pirbhulal, Sandeep; Grønli, Tor-Morten & Abie, Habtamu
(2022).
A Lightweight Security Scheme for Failure Detection in Microservices IoT-Edge Networks.
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Nowadays, microservices-based applications such as E-Business, E-Healthcare, 3D-Gaming,
and Augmented Reality has latterly drawn attention in the research area. The microservices enabled
applications are different from traditional monolithic
applications with the high demand of security and fault
detection, therefore, a lightweight secure and failure detection enabled schemes widely required for
the new applications. This paper proposes a new
lightweight microservices mobile cloud (Mob-Cloud)
the framework that replaces the heavyweight virtual
machine (VM) based on mobile cloud computing
(MCC). The study devises MFHE (Modified Fully
Homomorphism Encryption) and WATFA (Workload
Assignment Transient Fault Aware) schemes to deal
with security and failures. Simulation results show
that the proposals are practical for the considered
problem
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Bhandari, Guru; Lyth, Andreas; Shalaginov, Andrii & Grønli, Tor-Morten
(2022).
Artificial Intelligence Enabled Middleware for Distributed Cyberattacks Detection in IoT-based Smart Environments.
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This work is a part of the ENViSEC project which has received funding from the European Union’s Horizon 2020 research and innovation program within the framework of the NGI POINTER Project funded under grant agreement# 871528.
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Majchrzak, Tim A.; Grønli, Tor-Morten & Kaindl, Hermann
(2021).
Introduction to the HICSS-54 Software Development for Mobile Devices, the Internet-of-Things, and Cyber-Physical Systems Minitrack.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
p. 7195–7196.
doi:
10.24251/HICSS.2021.866.
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Shalaginov, Andrii & Grønli, Tor-Morten
(2021).
Workshop on Building Next Generation Internet: Artificial Intelligence-enabled Cybersecurity for IoT in Future Smart Environments.
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Shalaginov, Andrii & Grønli, Tor-Morten
(2021).
ENViSEC: Artificial Intelligence- enabled Cybersecurity for Future Smart Environments.
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Rapid boosted digitalization through the last decade, also during 2021 in addition to a worldwide orientation towards smarter and greener environments has resulted in many cities becoming more intelligent when it comes to automation and natural resources utilization. This includes faster development of 5G networks, Wi-Fi 6, blockchain and versatile application of AI. Smart Environment is a technology-enabled phenomenon that offers better, user-friendly and more efficient IoT-based environments with a specific focus on greener and more sustainable future both in rural and city-wise contexts.
IoT devices often lack the means for implementing necessary cybersecurity measures capable of handling adversarial actions both due to limited standardization and weak computation capabilities. Mirai IoT botnet back in 2016 was a frightening example of how such low-power devices can target large companies and cause DDoS attacks. The reason for this is mainly limited integration of data-in-transit, data-at-rest encryption and security practices as shown in 2017 in \#WatchOut study by the Norwegian Consumer Council. Machine Learning has proved efficiency in detecting cyber-attacks before, however, deploying Machine Learning on the Edge devices (like AVR and ARM) under the current state of the art is considered to be highly inefficient. Hence, the functionality of IoT devices might get potentially compromised due to their need for more power and time to complete their tasks. This potentially will lead to compromising the main function of the IoT devices requiring more power and time to complete the task the device was developed for. The overall vision of ENViSEC project is to enhance Smart Environments cybersecurity by introducing intelligent multi-agent data handling, cyber threats sharing, situational awareness and data streams aggregation from Edge devices. The project will utilise insights into novel solutions from recent developments in not only edge computing, but also look to combined approaches merging cloudlets, edge, fog and mist computing. Our ambition is to offer a resilient response to cyber-attacks as well as to ensure early detection of adversarial actions. Our new method enables multi-level data collection and off-chip Machine Learning model training to reduce the overhead and latency of the Internet of Things (IoT) components. It will contribute towards hardening cybersecurity and building an efficient infrastructure in a resource-constrained environment.
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Hansen, Kjeld Steenbjerg; Staaby, Anne & Grønli, Tor-Morten
(2021).
What if your work was automated?
Kunnskapsmagasinet Kristiania.
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Majchrzak, Tim A.; Grønli, Tor-Morten & Kaindl, Hermann
(2020).
Introduction to the HICSS-53 software development for mobile devices, the internet-of-things, and cyber-physical systems MiniTrack.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
2020-,
p. 6629–6630.
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Berihu, zemichael; Assres, Gebremariam; Atsbaha, Mulugeta & Grønli, Tor-Morten
(2020).
Enhancing Bi-directional English-Tigrigna Machine Translation Using Hybrid Approach.
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Majchrzak, Tim A. & Grønli, Tor-Morten
(2019).
Introduction to the HICSS-52 Software Development for Mobile Devices, Wearables, and the Internet-of-Things Minitrack.
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
ISSN 1530-1605.
p. 7399–7400.
doi:
10.10125/60380.
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Majchrzak, Tim A.; Mateos, Cristian; Poggi, Fransesco & Grønli, Tor-Morten
(2019).
Towards integrated web, mobile, and iot technology: Foreword.
Lecture Notes in Business Information Processing.
ISSN 1865-1348.
347,
p. v–vii.
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Mar. 24, 2021 4:43 PM
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Mar. 24, 2021 4:43 PM