URBANCONTEXT: A MANAGEMENT MODEL FOR PERVASIVE ENVIRONMENTS IN USER-ORIENTED URBAN COMPUTING

Nowadays, urban computing has gained a lot of interest for guiding the evolution of citiesinto intelligent environments. These environments are appropriated for individuals’ inter-actions changing in their behaviors. These changes require new approaches that allow theunderstanding of how urban computing systems should be modeled.In this work we present UrbanContext, a new model for designing of urban computingplatforms that applies the theory of roles to manage the individual’s context in urban envi-ronments. The theory of roles helps to understand the individual’s behavior within a socialenvironment, allowing to model urban computing systems able to adapt to individuals statesand their needs.UrbanContext collects data in urban atmospheres and classifies individuals’ behaviorsaccording to their change of roles, to optimize social interaction and offer secure services.Likewise, UrbanContext serves as a generic model to provide interoperability, and to facilitatethe design, implementation and expansion of urban computing systems

A Context-Aware Adaptive Streaming Media Distribution System in a Heterogeneous Network with Multiple Terminals

We consider the problem of streaming media transmission in a heterogeneous network from a multisource server to home multiple terminals. In wired network, the transmission performance is limited by network state (e.g., the bandwidth variation, jitter, and packet loss). In wireless network, the multiple user terminals can cause bandwidth competition. Thus, the streaming media distribution in a heterogeneous network becomes a severe challenge which is critical for QoS guarantee. In this paper, we propose a context-aware adaptive streaming media distribution system (CAASS), which implements the context-aware module to perceive the environment parameters and use the strategy analysis (SA) module to deduce the most suitable service level. This approach is able to improve the video quality for guarantying streaming QoS. We formulate the optimization problem of QoS relationship with the environment parameters based on the QoS testing algorithm for IPTV in ITU-T G.1070. We evaluate the performance of the proposed CAASS through 12 types of experimental environments using a prototype system. Experimental results show that CAASS can dynamically adjust the service level according to the environment variation (e.g., network state and terminal performances) and outperforms the existing streaming approaches in adaptive streaming media distribution according to peak signal-to-noise ratio (PSNR)

On the potentials of object group localization in the internet of things

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A Statically Typed Logic Context Query Language With Parametric Polymorphism and Subtyping

The objective of this thesis is programming language support for context-sensitive program adaptations. Driven by the requirements for context-aware adaptation languages, a statically typed Object-oriented logic Context Query Language  (OCQL) was developed, which is suitable for integration with adaptation languages based on the Java type system. The ambient information considered in context-aware applications often originates from several, potentially distributed sources. OCQL employs the Semantic Web-language RDF Schema to structure and combine distributed context information. OCQL offers parametric polymorphism, subtyping, and a fixed set of meta-predicates. Its type system is based on mode analysis and a subset of Java Generics. For this reason a mode-inference approach for normal logic programs that considers variable aliasing and sharing was extended to cover all-solution predicates. OCQL is complemented by a service-oriented context-management infrastructure that supports the integration of OCQL with runtime adaptation approaches. The applicability of the language and its infrastructure were demonstrated with the context-aware aspect language CSLogicAJ. CSLogicAJ aspects encapsulate context-aware behavior and define in which contextual situation and program execution state the behavior is woven into the running program. The thesis concludes with a case study analyzing how runtime adaptation of mobile applications can be supported by pure object-, service- and context-aware aspect-orientation. Our study has shown that CSLogicAJ can improve the modularization of context-aware applications and reduce anticipation of runtime adaptations when compared to other approaches

La contextualisation en entreprise (mettre en avant utilisateurs et développeurs)

Les applications contextuelles doivent gérer un flux contenu de contexte selon une logique approprié. Les travaux de recherche en contextualisation se limitent à proposer des plateformes de développement proposant des mécanismes d adaptation prédéfinie. Cette thèse se propose d étende l état de l art en proposant des nouveaux concepts formant la fondation pour la création d application contextuelles en adoptant des principes de l ingénierie logicielle et une décomposition fonctionnelle. Aussi, cela permet l intégration de comportements contextualisés à des applications non initialement conçus pour cela. La thèse propose une autre manière centrée-contexte permettant de séparer la représentation du contexte de son interprétation, offrant encore plus de flexibilité à la gestion de contexte. Les propositions sont analysées aux lumières d étude de cas et de simulations. Le résultat de la thèse est l introduction de nouvelle approche de création d applications contextuelles qui met en avant le développeur mais aussi l utilisateurContext-aware applications must manage a continuous stream of context according to dedicated business logic. Research was limited on proposing frameworks and platforms that have predefined behavior toward applications. This thesis attempts to extend background works by proposing new concepts serving as foundation for a flexible approach for building context-aware applications. The thesis examines the state of the art of context-aware computing, then adopts well-established software design principles and a functional decomposition for designing a reference model for context management enabling seamless integration of context-awareness into applications. Also, the thesis studies the use of context in common applications and proposes a context-centric modeling approach which allows the creation of a graph-based representation where entities are connected to each other through links representing context. Furthermore, the context graph decouples the presentation and the semantics of context, leaving each application to manage the appropriate semantic for their context data. Case studies are conducted for the evaluation of the proposed system in terms of its support for the creation of applications enhanced with context-awareness. A simulation study is performed to analyze the performance properties of the proposed system. The result of this thesis is the introduction of a novel approach for supporting the creation of context-aware applications that supports the integration of context-awareness to existing applications. It empowers developers as well as users to participate in the creation process, thereby reducing usability issuesEVRY-INT (912282302) / SudocSudocFranceF

Privacy-preserving data analytics in cloud computing

The evolution of digital content and rapid expansion of data sources has raised the need for streamlined monitoring, collection, storage and analysis of massive, heterogeneous data to extract useful knowledge and support decision-making mechanisms. In this context, cloud computing o↵ers extensive, cost-e↵ective and on demand computing resources that improve the quality of services for users and also help service providers (enterprises, governments and individuals). Service providers can avoid the expense of acquiring and maintaining IT resources while migrating data and remotely managing processes including aggregation, monitoring and analysis in cloud servers. However, privacy and security concerns of cloud computing services, especially in storing sensitive data (e.g. personal, healthcare and financial) are major challenges to the adoption of these services. To overcome such barriers, several privacy-preserving techniques have been developed to protect outsourced data in the cloud. Cryptography is a well-known mechanism that can ensure data confidentiality in the cloud. Traditional cryptography techniques have the ability to protect the data through encryption in cloud servers and data owners can retrieve and decrypt data for their processing purposes. However, in this case, cloud users can use the cloud resources for data storage but they cannot take full advantage of cloud-based processing services. This raises the need to develop advanced cryptosystems that can protect data privacy, both while in storage and in processing in the cloud. Homomorphic Encryption (HE) has gained attention recently because it can preserve the privacy of data while it is stored and processed in the cloud servers and data owners can retrieve and decrypt their processed data to their own secure side. Therefore, HE o↵ers an end-to-end security mechanism that is a preferable feature in cloud-based applications. In this thesis, we developed innovative privacy-preserving cloud-based models based on HE cryptosystems. This allowed us to build secure and advanced analytic models in various fields. We began by designing and implementing a secure analytic cloud-based model based on a lightweight HE cryptosystem. We used a private resident cloud entity, called ”privacy manager”, as an intermediate communication server between data owners and public cloud servers. The privacy manager handles analytical tasks that cannot be accomplished by the lightweight HE cryptosystem. This model is convenient for several application domains that require real-time responses. Data owners delegate their processing tasks to the privacy manager, which then helps to automate analysis tasks without the need to interact with data owners. We then developed a comprehensive, secure analytical model based on a Fully Homomorphic Encryption (FHE), that has more computational capability than the lightweight HE. Although FHE can automate analysis tasks and avoid the use of the privacy manager entity, it also leads to massive computational overhead. To overcome this issue, we took the advantage of the massive cloud resources by designing a MapReduce model that massively parallelises HE analytical tasks. Our parallelisation approach significantly speeds up the performance of analysis computations based on FHE. We then considered distributed analytic models where the data is generated from distributed heterogeneous sources such as healthcare and industrial sensors that are attached to people or installed in a distributed-based manner. We developed a secure distributed analytic model by re-designing several analytic algorithms (centroid-based and distribution-based clustering) to adapt them into a secure distributed-based models based on FHE. Our distributed analytic model was developed not only for distributed-based applications, but also it eliminates FHE overhead obstacle by achieving high efficiency in FHE computations. Furthermore, the distributed approach is scalable across three factors: analysis accuracy, execution time and the amount of resources used. This scalability feature enables users to consider the requirements of their analysis tasks based on these factors (e.g. users may have limited resources or time constrains to accomplish their analysis tasks). Finally, we designed and implemented two privacy-preserving real-time cloud-based applications to demonstrate the capabilities of HE cryptosystems, in terms of both efficiency and computational capabilities for applications that require timely and reliable delivery of services. First, we developed a secure cloud-based billing model for a sensor-enabled smart grid infrastructure by using lightweight HE. This model handled billing analysis tasks for individual users in a secure manner without the need to interact with any trusted parties. Second, we built a real-time secure health surveillance model for smarter health communities in the cloud. We developed a secure change detection model based on an exponential smoothing technique to predict future changes in health vital signs based on FHE. Moreover, we built an innovative technique to parallelise FHE computations which significantly reduces computational overhead

An Activity Theory-based Architecture To Enhance Context-aware Collaboration In Software Development In The Cloud

This research study reviews collaborative software development and assesses the impact of cloud computing in this domain. This is with a view towards identifying challenges to effective context-aware collaboration, as well as opportunities, risks, and potential benefits that could come from a well-defined structured leverage of cloud capabilities. Findings from systematic review of literature indicate that adoption of cloud computing played a significant part in bringing about trends such as: movement of traditional applications and processes to the cloud; cloud development environments; increased distribution in teams and resources; increased diversity in requirements; changes in how software is developed, tested, deployed, accessed, and maintained. These trends have in turn introduced factors such as: massive scale; additional layers of complexity in abstraction levels, entity characteristics and entity relationships within the development process. This additional layer of complexity translates into increase in contexts i.e., information that can be used to characterize states of entities. This is in addition to existing traditional complexity i.e., measure of proportionality of activities and tasks within the process. Some notable efforts towards improving collaboration in software development in the cloud include: transitioning development environments, tools and teams to the cloud; provision of code repositories and version control functionality to support collaboration between developers; provision of platforms to enhance collaboration between developers and end-users in early stages of the process via registered project campaigns and targeted questionnaires; provision of platforms with integrated social networking tools. However, an essential missing piece for more effective context-aware collaboration in the process is, the need for ways of addressing resultant complexity from cloud adoption and capturing actionable contexts. Capturing and communicating contextual information can help improve awareness and understanding and facilitate role-based coordination of distributed team members including users, and not just developers. This would ensure all stakeholders are always on the same page even if not in same location, across all phases of development. The main aim of this research study is to apply a new architecture framework underpinned by the right theoretical foundations, capable of leveraging cloud capabilities, harnessing contexts and addressing complexity to enhance context-aware collaboration in cloud-based software development. To achieve this aim, knowledge gleaned from the systematic literature review and the gap-impact analysis was thematized and synthesized to provide optimal recommendations to serve as roadmap guide for the development and evaluation carried out, and subsequent knowledge contributions. Key dimensions were adapted, along with development of classifications for approaches to enhancing collaboration in software development in the cloud. The key dimensions created were for - assessing collaboration needs; definition of context data and levels; collecting, categorizing, analysing, and applying contextual information to tasks, activities, and stages within software development in the cloud. These dimensions and classifications are useful for identification of reliable ways of measuring collaboration and success factors, as well as managing complexity and ensuring synchronous regularity of process and understanding within the development process in the cloud. A formal process was proposed to aid selection of an appropriate theoretical basis and assembling of a theoretical framework and methodology to underpin the architecture for enhancing context-aware collaboration in cloud-based software development. This was necessary due to the current lack of a de-facto architecture method for cloud-based software development. An activity theory-based architecture has been designed and developed, along with a Proof-of-Concept (POC) implementation that leverages cloud capabilities, for evaluation of the architecture. This architecture presents a novel approach for enhancing collaboration in software development in the cloud due to its underlying activity theory-based tenets that considers ‘activity’ as the unit of analysis, and ideal for activity systems and ease of identification of congruencies and contradictions present or capable impacting related components of the activity system and its ecosystem. The conclusions for this research study, limitations and future research directions have been discussed at the end of this thesis work