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See detailA New Modelling Framework over Temporal Graphs for Collaborative Mobility Recommendation Systems
Toader, Bogdan UL; Moawad, Assaad UL; Fouquet, François UL et al

in A New Modelling Framework over Temporal Graphs for Collaborative Mobility Recommendation Systems (2018, March 15)

Over the years, collaborative mobility proved to be an important but challenging component of the smart cities paradigm. One of the biggest challenges in the smart mobility domain is the use of data ... [more ▼]

Over the years, collaborative mobility proved to be an important but challenging component of the smart cities paradigm. One of the biggest challenges in the smart mobility domain is the use of data science as an enabler for the implementation of large scale transportation sharing solutions. In particular, the next generation of Intelligent Transportation Systems (ITS) requires the combination of artificial intelligence and discrete simulations when exploring the effects of whatif decisions in complex scenarios with millions of users. In this paper, we address this challenge by presenting an innovative data modelling framework that can be used for ITS related problems. We demonstrate that the use of graphs and time series in multi-dimensional data models can satisfy the requirements of descriptive and predictive analytics in real-world case studies with massive amounts of continuously changing data. The features of the framework are explained in a case study of a complex collaborative mobility system that combines carpooling, carsharing and shared parking. The performance of the framework is tested with a large-scale dataset, performing machine learning tasks and interactive realtime data visualization. The outcome is a fast, efficient and complete architecture that can be easily deployed, tested and used for research as well in an industrial environment. [less ▲]

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See detailNear Real-Time Electric Load Approximation in Low Voltage Cables of Smart Grids with Models@run.time
Hartmann, Thomas UL; Moawad, Assaad UL; Fouquet, François UL et al

in 31st Annual ACM Symposium on Applied Computing (SAC'16) (2016, April)

Micro-generations and future grid usages, such as charging of electric cars, raises major challenges to monitor the electric load in low-voltage cables. Due to the highly interconnected nature, real-time ... [more ▼]

Micro-generations and future grid usages, such as charging of electric cars, raises major challenges to monitor the electric load in low-voltage cables. Due to the highly interconnected nature, real-time measurements are problematic, both economically and technically. This entails an overload risk in electricity networks when cables must be disconnected for maintenance reasons or are accidentally damaged. Therefore, it is of great interest for electricity grid providers to anticipate the load in networks and quicker detect failures. However, computing the electric load in cables requires computational intensive power flow calculations and live consumption measurements. Today’s view of the grid is usually based on on-field documentation of cables, fuses, and measurements by technicians and therefore often outdated. Thus, the electric load is usually only simulated in case of major topology variations. However, live measurements of smart meters provide new opportunities. In this paper we present a novel approach for a near real-time electric load approximation by deriving in live the current electric topology and cable loads from smart meter data. We leverage the models@run.time paradigm to combine live measurements with topology characteristics of the grid. Our approach enables to approximate the load in cables, not only for the current grid topology, but also to simulate topology changes for maintenance purposes. We showed that this allows a near real-time approximation while remaining very accurate (average deviation of 1.89% compared to offline power-flow calculation tools). Developed with a grid operator, this approach will be integrated in a monitoring and warning system and as an embeddable solution for on-field simulation. [less ▲]

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See detailTowards Ambient Intelligent Applications Using Models@run.time And Machine Learning For Context-Awareness
Moawad, Assaad UL

Doctoral thesis (2016)

Ambient Intelligence (AmI) constitutes a new paradigm of interaction among humans, smart objects and devices. AmI systems are expected to support humans in their every day tasks and activities. In order ... [more ▼]

Ambient Intelligence (AmI) constitutes a new paradigm of interaction among humans, smart objects and devices. AmI systems are expected to support humans in their every day tasks and activities. In order to achieve this goal, these systems require augmenting the environment with sensing, computing, communicating, and reasoning capabilities. Due to advances in technology, sensors are getting more powerful, cheaper and smaller, which stimulated large scale development and production. These sensors will generate a big amount of data and can easily lead to millions of values in a short amount of time, which can quickly reach the computation and storage limits when it comes to structuring and processing the data. For this problem, we propose a concept of continuous models that can handle highly-volatile data, and represent the continuous nature of sensor data in an efficient and compact way. We show on various AmI datasets that this can significantly improve storage and efficiency. One important goal of AmI systems is to transform living and working environments into intelligent spaces able to adapt to their users’ needs and desires in real-time. In this sense, we call AmI applications context-aware, meaning that they use environmental information to adaptively provide more relevant and better services to the user. However, AmI systems are composed from heterogeneous components, operating in an open and dynamic environment. Each of these components can have different storage and computation capabilities. They might not have all the information needed to derive context information, and they might not be reachable all the time for various reasons. In this thesis, we present a contextual reasoning solution adapted for component based platforms. Our solution can derive contextual information in a distributed way and can handle inconsistencies when contradictory information is received from several components. Other than the storage and computation efficiency, several qualities need to be satisfied according to the different contexts. Privacy is one of these qualities. AmI services will rely more and more on personal data that is vastly collected, stored, and exchanged with other third parties in order to provide added-value services. Such data are sensitive and often related to personal activities and therefore can lead to privacy risks, especially when data is shared with high precision and frequency. However, this privacy quality can be relaxed in some contexts, for example in an emergency situation in order to increase utility or efficiency. This leads to the need of developing an adaptive solution that is able to react to context changes in real-time and involve optimizing conflicting objectives. For this challenge, we propose to use blurring components as our main privacy preservation elements. The idea behind this approach is that, by gradually decreasing the data quality, a blurring component is able to hide some of the personal data delivered by sensors while still keeping the necessary information for the services to work. In order to find a good trade-off between these different conflicting objectives, we adapt a multi-objective evolutionary algorithm to run directly on top of domain specific models. We then apply it as our main optimization engine on models@run.time to keep adapting the different qualities, when the context change. Finally, AmI services are expected to be tailored for different users’ needs in a seamless and unobtrusive way. Meaning that they should be able to detect contexts and learn habits automatically with the least possible intervention of users. In order to achieve this, machine learning (ML) techniques need to be merged at the core of reasoning models. These techniques offer powerful tools to automatically detect patterns, categorize contexts, build usage profiles, represent data with compact mathematical hypothesis and provide statistical information vital for the intelligent aspect of AmI. This dissertation ends up by opening new directions on how to model and adapt machine learning techniques to fit for AmI platforms. Overall, this thesis provides solutions for the next leap of technology, where sensors become ubiquitous. Our solutions, implemented in an open source framework KMF, allow to create efficient and distributed, data and component models for IoT, adaptable at runtime leveraging multi-objective optimization to find good tradeoff between qualities for the current context, and machine learning techniques to derive contextual rules, profile and learn habits automatically. [less ▲]

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See detailPrivacy Challenges in Ambient Intelligence Systems
Caire, Patrice UL; Moawad, Assaad UL; Efthymiou, Vasileios UL et al

in Journal of Ambient Intelligence and Smart Environments (2016)

Today, privacy is a key concept. It is also one which is rapidly evolving with technological advances, and there is no consensus on a single definition for it. In fact, the concept of privacy has been ... [more ▼]

Today, privacy is a key concept. It is also one which is rapidly evolving with technological advances, and there is no consensus on a single definition for it. In fact, the concept of privacy has been defined in many different ways, ranging from the “right to be left alone” to being a “commodity” that can be bought and sold. In the same time, powerful Ambient Intelligence (AmI) systems are being developed, that deploy context-aware, personalised, adaptive and anticipatory services. In such systems personal data is vastly collected, stored, and distributed, making privacy preservation a critical issue. The human- centred focus of AmI systems has prompted the introduction of new kinds of technologies, e.g. Privacy Enhancing Technologies (PET), and methodologies, e.g. Privacy by Design (PbD), whereby privacy concerns are included in the design of the system. One particular application field, where privacy preservation is of critical importance is Ambient Assisted Living (AAL). Emerging from the continuous increase of the ageing population, AAL focuses on intelligent systems of assistance for a better, healthier and safer life in their living environment. In this paper, we first build on our previous work, in which we introduced a new tripartite categorisation of privacy as a right, an enabler, and a commodity. Second, we highlight the specific privacy issues raised in AAL. Third, we review and discuss current approaches for privacy preservation. Finally, drawing on lessons learned from AAL, we provide insights on the challenges and opportunities that lie ahead. Part of our methodology is a statistical analysis performed on the IEEE publications database. We illustrate our work with AAL scenarios elaborated in cooperation with the city of Luxembourg. [less ▲]

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See detailSuspicious Electric Consumption Detection Based on Multi-Profiling Using Live Machine Learning
Hartmann, Thomas UL; Moawad, Assaad UL; Fouquet, François UL et al

in 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm) (2015, November)

The transition from today’s electricity grid to the so-called smart grid relies heavily on the usage of modern information and communication technology to enable advanced features like two-way ... [more ▼]

The transition from today’s electricity grid to the so-called smart grid relies heavily on the usage of modern information and communication technology to enable advanced features like two-way communication, an automated control of devices, and automated meter reading. The digital backbone of the smart grid opens the door for advanced collecting, monitoring, and processing of customers’ energy consumption data. One promising approach is the automatic detection of suspicious consumption values, e.g., due to physically or digitally manipulated data or damaged devices. However, detecting suspicious values in the amount of meter data is challenging, especially because electric consumption heavily depends on the context. For instance, a customers energy consumption profile may change during vacation or weekends compared to normal working days. In this paper we present an advanced software monitoring and alerting system for suspicious consumption value detection based on live machine learning techniques. Our proposed system continuously learns context-dependent consumption profiles of customers, e.g., daily, weekly, and monthly profiles, classifies them and selects the most appropriate one according to the context, like date and weather. By learning not just one but several profiles per customer and in addition taking context parameters into account, our approach can minimize false alerts (low false positive rate). We evaluate our approach in terms of performance (live detection) and accuracy based on a data set from our partner, Creos Luxembourg S.A., the electricity grid operator in Luxembourg. [less ▲]

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See detailBeyond Discrete Modeling: A Continuous and Efficient Model for IoT
Moawad, Assaad UL; Hartmann, Thomas UL; Fouquet, François UL et al

in Lethbridge, Timothy; Cabot, Jordi; Egyed, Alexander (Eds.) 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS) (2015, September)

Internet of Things applications analyze our past habits through sensor measures to anticipate future trends. To yield accurate predictions, intelligent systems not only rely on single numerical values ... [more ▼]

Internet of Things applications analyze our past habits through sensor measures to anticipate future trends. To yield accurate predictions, intelligent systems not only rely on single numerical values, but also on structured models aggregated from different sensors. Computation theory, based on the discretization of observable data into timed events, can easily lead to millions of values. Time series and similar database structures can efficiently index the mere data, but quickly reach computation and storage limits when it comes to structuring and processing IoT data. We propose a concept of continuous models that can handle high-volatile IoT data by defining a new type of meta attribute, which represents the continuous nature of IoT data. On top of traditional discrete object-oriented modeling APIs, we enable models to represent very large sequences of sensor values by using mathematical polynomials. We show on various IoT datasets that this significantly improves storage and reasoning efficiency. [less ▲]

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See detailStream my Models: Reactive Peer-to-Peer Distributed Models@run.time
Hartmann, Thomas UL; Moawad, Assaad UL; Fouquet, François UL et al

in Lethbridge, Timothy; Cabot, Jordi; Egyed, Alexander (Eds.) 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS) (2015, September)

The models@run.time paradigm promotes the use of models during the execution of cyber-physical systems to represent their context and to reason about their runtime behaviour. However, current modeling ... [more ▼]

The models@run.time paradigm promotes the use of models during the execution of cyber-physical systems to represent their context and to reason about their runtime behaviour. However, current modeling techniques do not allow to cope at the same time with the large-scale, distributed, and constantly changing nature of these systems. In this paper, we introduce a distributed models@run.time approach, combining ideas from reactive programming, peer-to-peer distribution, and large-scale models@run.time. We define distributed models as observable streams of chunks that are exchanged between nodes in a peer-to-peer manner. lazy loading strategy allows to transparently access the complete virtual model from every node, although chunks are actually distributed across nodes. Observers and automatic reloading of chunks enable a reactive programming style. We integrated our approach into the Kevoree Modeling Framework and demonstrate that it enables frequently changing, reactive distributed models that can scale to millions of elements and several thousand nodes. [less ▲]

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See detailAdaptive Blurring of Sensor Data to balance Privacy and Utility for Ubiquitous Services
Moawad, Assaad UL; Hartmann, Thomas UL; Fouquet, François UL et al

in The 30th Annual ACM Symposium on Applied Computing (2015, April)

Given the trend towards mobile computing, the next generation of ubiquitous “smart” services will have to continuously analyze surrounding sensor data. More than ever, such services will rely on data ... [more ▼]

Given the trend towards mobile computing, the next generation of ubiquitous “smart” services will have to continuously analyze surrounding sensor data. More than ever, such services will rely on data potentially related to personal activities to perform their tasks, e.g. to predict urban traffic or local weather conditions. However, revealing personal data inevitably entails privacy risks, especially when data is shared with high precision and frequency. For example, by analyzing the precise electric consumption data, it can be inferred if a person is currently at home, however this can empower new services such as a smart heating system. Access control (forbid or grant access) or anonymization techniques are not able to deal with such trade-off because whether they completely prohibit access to data or lose source traceability. Blurring techniques, by tuning data quality, offer a wide range of trade-offs between privacy and utility for services. However, the amount of ubiquitous services and their data quality requirements lead to an explosion of possible configurations of blurring algorithms. To manage this complexity, in this paper we propose a platform that automatically adapts (at runtime) blurring components between data owners and data consumers (services). The platform searches the optimal trade-off between service utility and privacy risks using multi-objective evolutionary algorithms to adapt the underlying communication platform. We evaluate our approach on a sensor network gateway and show its suitability in terms of i) effectiveness to find an appropriate solution, ii) efficiency and scalability. [less ▲]

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See detailPolymer: A Model-Driven Approach for Simpler, Safer, and Evolutive Multi-Objective Optimization Development
Moawad, Assaad UL; Hartmann, Thomas UL; Fouquet, François UL et al

in Hammoudi, Slimane; Pires, Luis Ferreira; Desfray, Philippe (Eds.) et al MODELSWARD 2015 - Proceedings of the 3rd International Conference on Model-Driven Engineering and Software Development (2015, February)

Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully used to optimize various domains such as finance, science, engineering, logistics and software engineering. Nevertheless, MOEAs are ... [more ▼]

Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully used to optimize various domains such as finance, science, engineering, logistics and software engineering. Nevertheless, MOEAs are still very complex to apply and require detailed knowledge about problem encoding and mutation operators to obtain an effective implementation. Software engineering paradigms such as domain-driven design aim to tackle this complexity by allowing domain experts to focus on domain logic over technical details. Similarly, in order to handle MOEA complexity, we propose an approach, using model-driven software engineering (MDE) techniques, to define fitness functions and mutation operators without MOEA encoding knowledge. Integrated into an open source modelling framework, our approach can significantly simplify development and maintenance of multi-objective optimizations. By leveraging modeling methods, our approach allows reusable optimizations and seamlessly connects MOEA and MDE paradigms. We evaluate our approach on a cloud case study and show its suitability in terms of i) complexity to implement an MOO problem, ii) complexity to adapt (maintain) this implementation caused by changes in the domain model and/or optimization goals, and iii) show that the efficiency and effectiveness of our approach remains comparable to ad-hoc implementations. [less ▲]

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See detailConviviality-driven access control policy
El Kateb, Donia UL; Zannone, N.; Moawad, Assaad UL et al

in Requirements Engineering (2015), 20(4), 363-382

Nowadays many organizations experience security incidents due to unauthorized access to information. To reduce the risk of such incidents, security policies are often employed to regulate access to ... [more ▼]

Nowadays many organizations experience security incidents due to unauthorized access to information. To reduce the risk of such incidents, security policies are often employed to regulate access to information. Such policies, however, are often too restrictive, and users do not have the rights necessary to perform assigned duties. As a consequence, access control mechanisms are perceived by users as a barrier and thus bypassed, making the system insecure. In this paper, we draw a bridge between the social concept of conviviality and access control. Conviviality has been introduced as a social science concept for ambient intelligence and multi-agent systems to highlight soft qualitative requirements like user-friendliness of systems. To bridge the gap between conviviality and security, we propose a methodological framework for updating and adapting access control policies based on conviviality recommendations. Our methodology integrates and extends existing techniques to assist system designers in the derivation of access control policies from socio-technical requirements of the system, while taking into account the conviviality of the system. We illustrate our framework using the Ambient Assisted Living use case from the HotCity of Luxembourg. © 2014, Springer-Verlag London. [less ▲]

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See detailConviviality-Driven Access Control Policy
El Kateb, Donia UL; Zannone, Nicola; Moawad, Assaad UL et al

in Requirements Engineering (2014)

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See detailA Rule-based Contextual Reasoning Platform for Ambient Intelligence environments
Moawad, Assaad UL; Bikakis, Antonis; Caire, Patrice UL et al

in Theory, Practice, and Applications of Rules on the Web (2013, July 01)

The special characteristics and requirements of intelligent environments impose several challenges to the reasoning processes of Ambient Intelligence systems. Such systems must enable heterogeneous ... [more ▼]

The special characteristics and requirements of intelligent environments impose several challenges to the reasoning processes of Ambient Intelligence systems. Such systems must enable heterogeneous entities operating in open and dynamic environments to collectively rea- son with imperfect context information. Previously we introduced Con- textual Defeasible Logic (CDL) as a contextual reasoning model that addresses most of these challenges using the concepts of context, map- pings and contextual preferences. In this paper, we present a platform integrating CDL with Kevoree, a component-based software framework for Dynamically Adaptive Systems. We explain how the capabilities of Kevoree are exploited to overcome several technical issues, such as com- munication, information exchange and detection, and explain how the reasoning methods may be further extended. We illustrate our approach with a running example from Ambient Assisted Living. [less ▲]

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See detailR-CoRe: A Rule-based Contextual Reasoning Platform for AmI
Moawad, Assaad UL; Bikakis, Antonis; Caire, Patrice UL et al

in Joint Proceedings of the 7th International Rule Challenge, the Special Track on Human Language Technology and the 3rd RuleML Doctoral Consortium hosted at the 8th International Symposium on Rules (RuleML2013) (2013, July)

In this paper we present R-CoRe; a rule-based contextual reasoning platform for Ambient Intelligence environments. R-CoRe integrates Contextual Defeasible Logic (CDL) and Kevoree, a component-based ... [more ▼]

In this paper we present R-CoRe; a rule-based contextual reasoning platform for Ambient Intelligence environments. R-CoRe integrates Contextual Defeasible Logic (CDL) and Kevoree, a component-based software platform for Dynamically Adaptive Systems. Previously, we explained how this integration enables to overcome several reasoning and technical issues that arise from the imperfect nature of context knowledge, the open and dynamic nature of Ambient Intelligence environments, and the restrictions of wireless communications. Here, we focus more on technical aspects related to the architecture of R-Core, and demonstrate its use in Ambient Assisted Living. [less ▲]

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See detailIntroducing Conviviality as a New Paradigm for Interactions among IT Objects
Moawad, Assaad UL; Efthymiou, Vasileios UL; Caire, Patrice UL et al

in Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments (2012, August), 907

The Internet of Things allows people and objects to seamlessly interact, crossing the bridge between real and virtual worlds. Newly created spaces are heterogeneous; social relations naturally extend to ... [more ▼]

The Internet of Things allows people and objects to seamlessly interact, crossing the bridge between real and virtual worlds. Newly created spaces are heterogeneous; social relations naturally extend to smart objects. Conviviality has recently been introduced as a social science concept for ambient intelligent systems to highlight soft qualitative requirements like user friendliness of systems. Roughly, more opportunities to work with other people increase the conviviality. In this paper, we first propose the conviviality concept as a new interaction paradigm for social exchanges between humans and Information Technology (IT) objects, and extend it to IT objects among themselves. Second, we introduce a hierarchy for IT objects social interactions, from low-level one-way interactions to high-level complex interactions. Then, we propose a mapping of our hierarchy levels into dependence networks-based conviviality classes. In particular, low levels without cooperation among objects are mapped to lower conviviality classes, and high levels with complex cooperative IT objects are mapped to higher conviviality classes. Finally, we introduce new conviviality measures for the Internet of Things, and an iterative process to facilitate cooperation among IT objects, thereby the conviviality of the system. We use a smart home as a running example. [less ▲]

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