UL HPC Tutorial: Basic and Advanced scientific computing using MATLAB

Presentation (2018, June)

UL HPC Tutorial: Performance engineering - HPC debugging and profilling

Presentation (2018, June)

A Competitive Approach for Bi-Level Co-Evolution

in 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (2018, May 25)

A Standardized Broker Model in Smart Cities

Poster (2018, May)

As urban residents are expected to represent more than 60 per cent of the world’s population by 2050, the current developments and interests in the “Smart City” concept are essential to enable the ... [more ▼]

As urban residents are expected to represent more than 60 per cent of the world’s population by 2050, the current developments and interests in the “Smart City” concept are essential to enable the successful transition to this new era. This paradigm relies on the integration of emerging Information and Communication Technologies (ICT), such as the Internet of Things (IoT), Cloud Computing, Big Data to manage assets and resources efficiently while facilitating the planning, construction, management and smart services within cities. While smart cities aim to enhance the quality, performance and interactivity of urban services at reduced cost, their realization is faced by many regulatory and technical challenges. Among these challenges, is the integration of renewable energy resources to the utility system of smart cities motivated by the increasingclimate change concerns. Adding further to its complexity, is the challenge of incorporating multiple renewable energy retailers in the same region each with their own pricing strategies due to the lack of a standardized metering indicator and billing system. These challenges create a need for an intelligent and standardized cloud-based energy broker to satisfying the end-user requests, and minimize expenses by efficiently selecting the most suitable energy retailer. In our work, a particular focus is raised towards the optimization of such energy brokering service which is motivated by the orchestration of a brokering role aiming to improve user experience and interaction with smart city services. Hence our main contribution is proposing a standardized intelligent broker model with smart trading strategies to cope with the dynamics and complexity of the energy retail market, while allocating energy resources based on endusers’ demands. This is achieved through the following steps: 1) studying a complete model of the broker service and involved parties within the exposed framework. 2) proposing a multiobjective heuristic to provide a dynamic optimization of the grid operations and resources, with full cyber-security, within the boundaries of the city. 3) analyzing the gaps among industry practices, market requirements and current technical standardization efforts at ISO/IEC JTC 1/ SC 38 (Cloud Computing and distributed platform) in order to pave the way to establishing standards in metering indicators and billing principles for cloud services this while keeping in mind privacy and data protection risks and regulations enforced by ISO JT1/SC 27 and EU General Data Protection Regulation effective May 2018. This research was conducted in collaboration with ILNAS - the Institut Luxem- bourgeois de la Normalisation, de l’Accréditation, de la Sécurité et qualité des produits et services (ILNAS) under the authority of the Minister of Economy, Luxembourg. [less ▲]

Collision Avoidance Effects on the Mobility of a UAV Swarm Using Chaotic Ant Colony with Model Predictive Control

in Journal of Intelligent and Robotic Systems (2018)

The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance the area coverage of such UAV swarms, a ... [more ▼]

The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance the area coverage of such UAV swarms, a novel mobility model has been presented in previous work, combining an Ant Colony algorithm with chaotic dynamics (CACOC). This work is extending CACOC by a Collision Avoidance (CA) mechanism and testing its efficiency in terms of area coverage by the UAV swarm. For this purpose, CACOC is used to compute UAV target waypoints which are tracked by model predictively controlled UAVs. The UAVs are represented by realistic motion models within the virtual robot experimentation platform (V-Rep). This environment is used to evaluate the performance of the proposed CACOC with CA algorithm in an area exploration scenario with 3 UAVs. Finally, its performance is analyzed using metrics. [less ▲]

Profiling Energy Efficiency of Mobile Crowdsensing Data Collection Frameworks for Smart City Applications

in The 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (IEEE Mobile Cloud 2018) (2018, March)

Mobile crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. In MCS, citizens actively participate in the sensing process by contributing ... [more ▼]

Mobile crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. In MCS, citizens actively participate in the sensing process by contributing data with their smartphones, tablets, wearables and other mobile devices to a collector. As citizens sustain costs while contributing data, i.e., the energy spent from the batteries for sensing and reporting, devising energy efficient data collection frameworks (DCFs) is essential. In this work, we compare the energy efficiency of several DCFs through CrowdSenSim, which allows to perform large-scale simulation experiments in realistic urban environments. Specifically, the DCFs under analysis differ one with each other by the data reporting mechanism implemented and the signaling between users and the collector needed for sensing and reporting decisions. Results reveal that the key criterion differentiating DCFs' energy consumption is the data reporting mechanism. In principle, continuous reporting to the collector should be more energy consuming than probabilistic reporting. However, DCFs with continuous reporting that implement mechanisms to block sensing and data delivery after a certain amount of contribution are more effective in harvesting data from the crowd. [less ▲]

Proceedings - 2017 ILILAS Distinguished Lectures

Report (2018)

The Proceedings summarizes the 12 lectures that have taken place within the ILIAS Dinstguished Lecture series 2017. It contains a brief abstract of the talks as well as some additional information about ... [more ▼]

The Proceedings summarizes the 12 lectures that have taken place within the ILIAS Dinstguished Lecture series 2017. It contains a brief abstract of the talks as well as some additional information about each speaker. [less ▲]

Lightweight Key Agreement for Wireless Sensor Networks

in 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) (2018)

A Degenerate Agglomerative Hierarchical Clustering Algorithm for Community Detection

in Nguyen, Ngoc Thanh; Hoang, Duong Hung; Hong, Tzung-Pei (Eds.) et al Intelligent Information and Database Systems (2018)

Community detection consists of grouping related vertices that usually show high intra-cluster connectivity and low inter-cluster connectivity. This is an important feature that many networks exhibit and ... [more ▼]

Community detection consists of grouping related vertices that usually show high intra-cluster connectivity and low inter-cluster connectivity. This is an important feature that many networks exhibit and detecting such communities can be challenging, especially when they are densely connected. The method we propose is a degenerate agglomerative hierarchical clustering algorithm (DAHCA) that aims at finding a community structure in networks. We tested this method using common classes of graph benchmarks and compared it to some state-of-the-art community detection algorithms. [less ▲]

Clustering approaches for visual knowledge exploration in molecular interaction networks.

in BMC bioinformatics (2018), 19(1), 308

BACKGROUND: Biomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and ... [more ▼]

BACKGROUND: Biomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and established ontologies. Combining these structured information sources is an important computational challenge, as large graphs are difficult to analyze visually. RESULTS: We investigate knowledge discovery in manually curated and annotated molecular interaction diagrams. To evaluate similarity of content we use: i) Euclidean distance in expert-drawn diagrams, ii) shortest path distance using the underlying network and iii) ontology-based distance. We employ clustering with these metrics used separately and in pairwise combinations. We propose a novel bi-level optimization approach together with an evolutionary algorithm for informative combination of distance metrics. We compare the enrichment of the obtained clusters between the solutions and with expert knowledge. We calculate the number of Gene and Disease Ontology terms discovered by different solutions as a measure of cluster quality. Our results show that combining distance metrics can improve clustering accuracy, based on the comparison with expert-provided clusters. Also, the performance of specific combinations of distance functions depends on the clustering depth (number of clusters). By employing bi-level optimization approach we evaluated relative importance of distance functions and we found that indeed the order by which they are combined affects clustering performance. Next, with the enrichment analysis of clustering results we found that both hierarchical and bi-level clustering schemes discovered more Gene and Disease Ontology terms than expert-provided clusters for the same knowledge repository. Moreover, bi-level clustering found more enriched terms than the best hierarchical clustering solution for three distinct distance metric combinations in three different instances of disease maps. CONCLUSIONS: In this work we examined the impact of different distance functions on clustering of a visual biomedical knowledge repository. We found that combining distance functions may be beneficial for clustering, and improve exploration of such repositories. We proposed bi-level optimization to evaluate the importance of order by which the distance functions are combined. Both combination and order of these functions affected clustering quality and knowledge recognition in the considered benchmarks. We propose that multiple dimensions can be utilized simultaneously for visual knowledge exploration. [less ▲]