Privacy and Security of Big Data in AI Systems:A Research and Standards Perspective

in 2019 IEEE International Conference on Big Data (Big Data), 9-12 December 2019 (2020, February 24)

Automatic Software Tuning of Parallel Programs for Energy-Aware Executions

in Proc. of 13th Intl. Conf. on Parallel Processing and Applied Mathematics (PPAM 2019) (2019, December)

For large scale systems, such as data centers, energy efficiency has proven to be key for reducing capital, operational expenses and environmental impact. Power drainage of a system is closely related to ... [more ▼]

For large scale systems, such as data centers, energy efficiency has proven to be key for reducing capital, operational expenses and environmental impact. Power drainage of a system is closely related to the type and characteristics of workload that the device is running. For this reason, this paper presents an automatic software tuning method for parallel program generation able to adapt and exploit the hardware features available on a target computing system such as an HPC facility or a cloud system in a better way than traditional compiler infrastructures. We propose a search based approach combining both exact methods and approximated heuristics evolving programs in order to find optimized configurations relying on an ever-increasing number of tunable knobs i.e., code transformation and execution options (such as the num- ber of OpenMP threads and/or the CPU frequency settings). The main objective is to outperform the configurations generated by traditional compiling infrastructures for selected KPIs i.e., performance, energy and power usage (for both for the CPU and DRAM), as well as the runtime. First experimental results tied to the local optimization phase of the proposed framework are encouraging, demonstrating between 8% and 41% improvement for all considered metrics on a reference benchmark- ing application (i.e., Linpack). This brings novel perspectives for the global optimization step currently under investigation within the presented framework, with the ambition to pave the way toward automatic tuning of energy-aware applications beyond the performance of the current state-of-the-art compiler infrastructures. [less ▲]

A Multilayer Low-Altitude Airspace Model for UAV Traffic Management

in Samir Labib, Nader; Danoy, Grégoire; Musial, Jedrzej (Eds.) et al 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (DIVANet '19) (2019, November)

Over the recent years, Unmanned Aerial Vehicles' (UAVs) technology developed rapidly. In turn shedding light on a wide range of potential civil and commercial applications ranging from mapping and ... [more ▼]

Over the recent years, Unmanned Aerial Vehicles' (UAVs) technology developed rapidly. In turn shedding light on a wide range of potential civil and commercial applications ranging from mapping and surveillance, parcel delivery to more demanding ones that require UAVs to operate in heterogeneous swarms. However, with the great advantages UAVs bring, they are expected to soon dominate the shared, low-altitude airspace over populated cities, introducing multiple new research challenges in safely managing the unprecedented traffic demands. The main contribution of this work is addressing the complex problem of UAV traffic management at an abstract level by proposing a structure for the uncontrolled low-altitude airspace. The paper proposes a model of the airspace as a weighted multilayer network of nodes and airways and presents a set of experimental simulations of UAV traffic for the verification and validation of the model. Finally, the paper outlines our intended future work. [less ▲]

Link Definition ameliorating Community Detection in Collaboration Networks

in Frontiers in Big Data (2019), 2

Tackling Large-Scale and Combinatorial Bi-level Problems with a Genetic Programming Hyper-heuristic

in IEEE Transactions on Evolutionary Computation (2019)

Combinatorial bi-level optimization remains a challenging topic, especially when the lower-level is a NP-hard problem. In this work, we tackle large-scale and combinatorial bi-level problems using GP ... [more ▼]

Combinatorial bi-level optimization remains a challenging topic, especially when the lower-level is a NP-hard problem. In this work, we tackle large-scale and combinatorial bi-level problems using GP Hyper-heuristics, i.e., an approach that permits to train heuristics like a machine learning model. Our contribution aims at targeting the intensive and complex lower-level optimizations that occur when solving a large-scale and combinatorial bi-level problem. For this purpose, we consider hyper-heuristics through heuristic generation. Using a GP hyper-heuristic approach, we train greedy heuristics in order to make them more reliable when encountering unseen lower-level instances that could be generated during bi-level optimization. To validate our approach referred to as GA+AGH, we tackle instances from the Bi-level Cloud Pricing Optimization Problem (BCPOP) that model the trading interactions between a cloud service provider and cloud service customers. Numerical results demonstrate the abilities of the trained heuristics to cope with the inherent nested structure that makes bi-level optimization problems so hard. Furthermore, it has been shown that training heuristics for lower-level optimization permits to outperform human-based heuristics and metaheuristics which constitute an excellent outcome for bi-level optimization. [less ▲]

Toward Real-world Vehicle Placement Optimization in Round-trip Carsharing

in Proceedings of the Genetic and Evolutionary Computation Conference (2019)

Cloud Brokering with Bundles: Multi-objective Optimization of Services Selection

in Foundations of Computing and Decision Sciences (2019)

White Paper: Data Protection and Privacy in Smart ICT - Scientific Research and Technical Standardization

Report (2018)

On Standardised UAV Localisation and Tracking Systems in Smart Cities

Poster (2018, May)

In the near future, more than two thirds of the world’s population is expected to be living in cities and hence, with the aim of being proactive and finding innovative and sustainable solutions ... [more ▼]

In the near future, more than two thirds of the world’s population is expected to be living in cities and hence, with the aim of being proactive and finding innovative and sustainable solutions, governments have made smart cities one of their priority areas of research. Smart cities are sustainable, inclusive and prosperous greener cities that foster enabling smart Information and Communication Technologies (smart ICT) like Internet-of-Things (IoT), cloud computing and big data to facilitate services such as mobility, governance, utility and energy management. As these services depend heavily on data collected by sensors, Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising IoT devices for smart cities thanks to their mobility, agility and customizability of onboard sensors. UAVs found use in a wide array of applications expanding beyond military to more commercial ones, ranging from monitoring, surveillance, mapping to parcel delivery and more demanding applications that require UAVs to operate in heterogeneous swarms in a shared low-altitude airspace over populated cities. However, as the number of UAVs continues to grow and as their sensing, actuation, communication and control capabilities become increasingly sophisticated, UAV deployment in smart cities is faced with a set of fundamental challenges in their safe operation and management. These challenges emphasize the need for establishing globally-harmonised regulations and internationally-agreed-upon technical standards to govern the rapid technological advancements, as well as ensure a fair economy by encouraging market competition and lowering barriers to entry for newcomers. As various Standardisation Development Organisations (SDOs) recently recognised the need, importance and potential of such regulations, most have established dedicated working groups addressing UAVs. However, most current SDO committees focus on aspects such as vehicle categorisation, specifications and operational procedures, but one usually overlooked elementary topic is UAV localisation. Due to its importance and close relation to other technical subsystems, the lack of a resilient, scalable and efficient standardised UAV localisation and tracking system is one of the main obstructing barriers hindering the integration and interoperability of UAV swarms in smart cities and hence impeding the realisation of their vast application benefits. In this work, we focus on studying the fundamental technical requirements, specifications and functions of such UAV localisation and tracking system, and explore its relationship to and importance in 1) optimising path planning, flight scheduling and utilising shared airspace, 2) collision avoidance and conflict resolution in highly populated residential areas and 3) addressing privacy and data protection concerns that could arise from UAV monitoring and surveillance applications. Furthermore, for each of the three aspects, we analyse current SDOs efforts such as those put forth by EASA, EUCARE WG73 and ISO TC20/SC16 on UAV systems, ISO JTC1/SC41 on IoT and related technologies and ISO JTC1/SC27, EU Directive 95/46 EC and GDPR on security, privacy and data protection, in order to identify and prioritise future research questions in relation to UAV localisation, aiming to make a contribution towards narrowing the gap between research and existing technical standards by encouraging multimode standardisation. This research was conducted in collaboration with ILNAS - the Institut Luxembourgeois 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 \& 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 ▲]