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Alawadi, Sadi
Publications (5 of 5) Show all publications
Alawadi, S., Mera, D., Fernandez-Delgado, M., Alkhabbas, F., Olsson, C. M. & Davidsson, P. (2020). A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings. Energy Systems, Springer Verlag, 13, 689-705
Open this publication in new window or tab >>A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings
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2020 (English)In: Energy Systems, Springer Verlag, ISSN 1868-3967, E-ISSN 1868-3975, Vol. 13, p. 689-705Article in journal (Refereed) Published
Abstract [en]

The international community has largely recognized that the Earth's climate is changing. Mitigating its global effects requires international actions. The European Union (EU) is leading several initiatives focused on reducing the problems. Specifically, the Climate Action tries to both decrease EU greenhouse gas emissions and improve energy efficiency by reducing the amount of primary energy consumed, and it has pointed to the development of efficient building energy management systems as key. In traditional buildings, households are responsible for continuously monitoring and controlling the installed Heating, Ventilation, and Air Conditioning (HVAC) system. Unnecessary energy consumption might occur due to, for example, forgetting devices turned on, which overwhelms users due to the need to tune the devices manually. Nowadays, smart buildings are automating this process by automatically tuning HVAC systems according to user preferences in order to improve user satisfaction and optimize energy consumption. Towards achieving this goal, in this paper, we compare 36 Machine Learning algorithms that could be used to forecast indoor temperature in a smart building. More specifically, we run experiments using real data to compare their accuracy in terms of R-coefficient and Root Mean Squared Error and their performance in terms of Friedman rank. The results reveal that the ExtraTrees regressor has obtained the highest average accuracy (0.97%) and performance (0,058%) over all horizons.

Place, publisher, year, edition, pages
Springer, 2020
National Category
Energy Systems
Identifiers
urn:nbn:se:mau:diva-13827 (URN)10.1007/s12667-020-00376-x (DOI)000509132000001 ()2-s2.0-85078337875 (Scopus ID)
Available from: 2020-03-24 Created: 2020-03-24 Last updated: 2024-02-05Bibliographically approved
Alkhabbas, F., Alawadi, S., Spalazzese, R. & Davidsson, P. (2020). Activity Recognition and User Preference Learning for Automated Configuration of IoT Environments. In: IoT '20: Proceedings of the 10th International Conference on the Internet of Things. Paper presented at IoT '20: 10th International Conference on the Internet of Things, Malmö Sweden 6-9 October, 2020 (pp. 1-8). New York, United States: ACM Digital Library, Article ID 3.
Open this publication in new window or tab >>Activity Recognition and User Preference Learning for Automated Configuration of IoT Environments
2020 (English)In: IoT '20: Proceedings of the 10th International Conference on the Internet of Things, New York, United States: ACM Digital Library, 2020, p. 1-8, article id 3Conference paper, Published paper (Refereed)
Abstract [en]

Internet of Things (IoT) environments encompass different types of devices and objects that offer a wide range of services. The dynamicity and uncertainty of those environments, including the mobility of users and devices, make it hard to foresee at design time available devices, objects, and services. For the users to benefit from such environments, they should be proposed services that are relevant to the specific context and can be provided by available things. Moreover, environments should be configured automatically based on users' preferences. To address these challenges, we propose an approach that leverages Artificial Intelligence techniques to recognize users' activities and provides relevant services to support users to perform their activities. Moreover, our approach learns users' preferences and configures their environments accordingly by dynamically forming, enacting, and adapting goal-driven IoT systems. In this paper, we present a conceptual model, a multi-tier architecture, and processes of our approach. Moreover, we report about how we validated the feasibility and evaluated the scalability of the approach through a prototype that we developed and used.

Place, publisher, year, edition, pages
New York, United States: ACM Digital Library, 2020
National Category
Information Systems, Social aspects
Identifiers
urn:nbn:se:mau:diva-36986 (URN)10.1145/3410992.3411003 (DOI)2-s2.0-85123041965 (Scopus ID)978-1-4503-8758-3 (ISBN)
Conference
IoT '20: 10th International Conference on the Internet of Things, Malmö Sweden 6-9 October, 2020
Available from: 2020-11-26 Created: 2020-11-26 Last updated: 2024-02-05Bibliographically approved
Kebande, V. R., Alawadi, S., Bugeja, J., Persson, J. A. & Olsson, C. M. (2020). Leveraging Federated Learning & Blockchain to counter Adversarial Attacks in Incremental Learning. In: IoT '20 Companion: 10th International Conference on the Internet of Things Companion. Paper presented at 10th International Conference on the Internet of Things Companion, October 6-9, 2020, Malmö Sweden (pp. 1-5). ACM Digital Library, Article ID 2.
Open this publication in new window or tab >>Leveraging Federated Learning & Blockchain to counter Adversarial Attacks in Incremental Learning
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2020 (English)In: IoT '20 Companion: 10th International Conference on the Internet of Things Companion, ACM Digital Library, 2020, p. 1-5, article id 2Conference paper, Published paper (Refereed)
Abstract [en]

Whereas data labelling in IoT applications is costly, it is also time consuming to train a supervised Machine Learning (ML) algorithm. Hence, a human oracle is required to gradually annotate the data patterns at run-time to improve the models’ learning behavior, through an active learning strategy in form of User Feedback Process (UFP). Consequently, it is worth to note that during UFP there may exist malicious content that may subject the learning model to be vulnerable to adversarial attacks, more so, manipulative attacks. We argue in this position paper, that there are instances during incremental learning, where the local data model may present wrong output, if retraining is done using data that has already been subjected to adversarial attack. We propose a Distributed Interactive Secure Federated Learning (DISFL) framework that utilizes UFP in the edge and fog node, that subsequently increases the amount of labelled personal local data for the ML model during incremental training. Furthermore, the DISFL framework addresses data privacy by leveraging federated learning, where only the model's knowledge is moved to a global unit, herein referred to as Collective Intelligence Node (CIN). During incremental learning, this would then allow the creation of an immutable chain of data that has to be trained, which in its entirety is tamper-free while increasing trust between parties. With a degree of certainty, this approach counters adversarial manipulation during incremental learning in active learning context at the same time strengthens data privacy, while reducing the computation costs.

Place, publisher, year, edition, pages
ACM Digital Library, 2020
Keywords
Federated learning, adversarial, blockchain, privacy, incremental training
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-48196 (URN)10.1145/3423423.3423425 (DOI)001062649200002 ()2-s2.0-85117542476 (Scopus ID)9781450388207 (ISBN)
Conference
10th International Conference on the Internet of Things Companion, October 6-9, 2020, Malmö Sweden
Funder
Knowledge Foundation
Available from: 2021-12-15 Created: 2021-12-15 Last updated: 2023-12-13Bibliographically approved
Kebande, V. R., Ikuesan, R., Karie, N., Alawadi, S., Kim-Kwang, R. C. & Al-Dhaqm, A. (2020). Quantifying the need for supervised machine learning in conducting liveforensic analysis of emergent configurations (ECO) in IoT environments. Forensic Science International: Reports, 2, Article ID 100122.
Open this publication in new window or tab >>Quantifying the need for supervised machine learning in conducting liveforensic analysis of emergent configurations (ECO) in IoT environments
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2020 (English)In: Forensic Science International: Reports, ISSN 2665-9107, Vol. 2, article id 100122Article in journal, Editorial material (Other academic) Published
Abstract [en]

Machine learning has been shown as a promising approach to mine larger datasets, such as those that comprise datafrom a broad range of Internet of Things devices, across complex environment(s) to solve different problems. Thispaper surveys existing literature on the potential of using supervised classical machine learning techniques, such asK-Nearest Neigbour, Support Vector Machines, Naive Bayes and Random Forest algorithms, in performing livedigital forensics for different IoT configurations. There are also a number of challenges associated with the use ofmachine learning techniques, as discussed in this paper.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Supervised machine, Learning, Live forensics, Emergent configurations, IoT
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mau:diva-37145 (URN)10.1016/j.fsir.2020.100122 (DOI)2-s2.0-85099007368 (Scopus ID)
Available from: 2020-12-06 Created: 2020-12-06 Last updated: 2024-02-05Bibliographically approved
Aladwan, M. N., Awaysheh, F. M., Alawadi, S., Alazab, M., Pena, T. F. & Cabaleiro, J. C. (2020). TrustE-VC: Trustworthy Evaluation Framework for Industrial Connected Vehicles in the Cloud. IEEE Transactions on Industrial Informatics, 16(9), 6203-6213
Open this publication in new window or tab >>TrustE-VC: Trustworthy Evaluation Framework for Industrial Connected Vehicles in the Cloud
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2020 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 16, no 9, p. 6203-6213Article in journal (Refereed) Published
Abstract [en]

The integration between cloud computing and vehicular ad hoc networks, namely, vehicular clouds (VCs), has become a significant research area. This integration was proposed to accelerate the adoption of intelligent transportation systems. The trustworthiness in VCs is expected to carry more computing capabilities that manage large-scale collected data. This trend requires a security evaluation framework that ensures data privacy protection, integrity of information, and availability of resources. To the best of our knowledge, this is the first study that proposes a robust trustworthiness evaluation of vehicular cloud for security criteria evaluation and selection. This article proposes three-level security features in order to develop effectiveness and trustworthiness in VCs. To assess and evaluate these security features, our evaluation framework consists of three main interconnected components: 1) an aggregation of the security evaluation values of the security criteria for each level; 2) a fuzzy multicriteria decision-making algorithm; and 3) a simple additive weight associated with the importance-performance analysis and performance rate to visualize the framework findings. The evaluation results of the security criteria based on the average performance rate and global weight suggest that data residency, data privacy, and data ownership are the most pressing challenges in assessing data protection in a VC environment. Overall, this article paves the way for a secure VC using an evaluation of effective security features and underscores directions and challenges facing the VC community. This article sheds light on the importance of security by design, emphasizing multiple layers of security when implementing industrial VCs.

Place, publisher, year, edition, pages
IEEE, 2020
Keywords
Security, Cloud computing, Informatics, Connected vehicles, Data privacy, Sensors, Decision making, Decision-making (DM), industrial connected vehicles (CVs), industrial Internet of Things (IIoT), security analysis, security by design, vehicular clouds (VCs)
National Category
Computer Systems
Identifiers
urn:nbn:se:mau:diva-17820 (URN)10.1109/TII.2020.2966288 (DOI)000542966300058 ()2-s2.0-85086070923 (Scopus ID)
Available from: 2020-07-21 Created: 2020-07-21 Last updated: 2024-02-05Bibliographically approved
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