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Online Social Network Services (OSNSs) are changing the fabric of our society, impacting almost every aspect of it. Over the past few decades, an aggressive market rivalry has led to the emergence of multiple competing, “closed” OSNSs. As a result, users are trapped in the walled gardens of their OSNS, encountering restrictions about what they can do with their personal data, the people they can interact with, and the information they get access to. As an alternative to the platform lock-in, “open” OSNSs promote the adoption of open, standardized APIs. However, users still massively adopt closed OSNSs to benefit from the services’ advanced functionalities and/or follow their “friends,” although the users’ virtual social sphere is ultimately limited by the OSNSs they join. Our work aims at overcoming such a limitation by enabling users to meet and interact beyond the boundary of their OSNSs, including reaching out to “friends” of distinct closed OSNSs. We specifically introduce Universal Social Network Bus (USNB) , which revisits the “service bus” paradigm that enables interoperability across computing systems to address the requirements of “ social interoperability .” USNB features synthetic profiles and personae for interaction across the boundaries of closed and open and profile- and non-profile-based OSNSs through a reference social interaction service . We ran a 1-day workshop with a panel of users who experimented with the USNB prototype to assess the potential benefits of social interoperability for social network users. Results show the positive evaluation of users for USNB, especially as an enabler of applications for civic participation. This further opens up new perspectives for future work, among which includes enforcing security and privacy guarantees.

International audience; Real-time event detection and targeted decision making for emerging mission-critical applications require systems that extract and process relevant data from IoT sources in smart spaces. Oftentimes, this data is heterogeneous in size, relevance, and urgency, which creates a challenge when considering that different groups of stakeholders (e.g., first responders, medical staff, government officials, etc) require such data to be delivered in a reliable and timely manner. Furthermore, in mission-critical settings, networks can become constrained due to lossy channels and failed components, which ultimately add to the complexity of the problem. In this paper, we propose PrioDeX, a cross-layer middleware system that enables timely and reliable delivery of mission-critical data from IoT sources to relevant consumers through the prioritization of messages. It integrates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics through a queueing analytical model. PrioDeX proposes novel algorithms that utilize the results of this analysis to tune data exchange configurations (event priorities and dropping policies), which is necessary for satisfying situational awareness requirements and resource constraints. PrioDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate our approach using both simulated and prototype-based experiments in a smart building fire response scenario. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization.

International audience; Numerous middleware application programming interfaces (APIs) and protocols were introduced in the literature in order to facilitate the application development of the Internet of Things (IoT). Such applications are built on reliable or even unreliable protocols that may implement different quality-of-service (QoS) delivery modes. The exploitation of these protocols, APIs and QoS modes, can satisfy QoS requirements in critical IoT applications (e.g., emergency response operations). To study QoS in IoT applications, it is essential to leverage a performance analysis methodology. Queueing-network models offer a modeling and analysis framework that can be adopted for the IoT interactions of QoS representation through either analytical or simulation models. In this paper, various types of queueing models are presented that can be used for the representation of various QoS settings of IoT interactions. In particular, we propose queueing models to represent message-drop probabilities, intermittent mobile connectivity, message availability or validity, the prioritization of important information, and the processing or transmission of messages. Our simulation models demonstrate the significant effect on delivery success rates and response times when QoS settings are varied.

International audience; Crowdsensing is an essential element of the IoT; it allows gathering massive data across time and space to feed our environmental knowledge, and to link such knowledge to user behavior. However, there are major obstacles to crowdsensing, including the preservation of privacy. The consideration of privacy in crowdsensing systems has led to two main approaches, sometimes combined, which are, respectively, to trade privacy for rewards, and to take advantage of privacy-enhancing technologies "anonymizing" the collected data. Although relevant, we claim that these approaches do not sufficiently take into account the users' own tolerance to the use of the data provided, so that the crowdsensing system guarantees users the expected level of confidentiality as well as fosters the use of crowdsensing data for different tasks. To this end, we introduce the-completeness property, which ensures that the data provided can be used for all the tasks to which their owners consent as long as they are analyzed with − 1 other sources, and that no privacy violations can occur due to the related contribution of users with less stringent privacy requirements. The challenge, therefore, is to ensure-completeness when analyzing the data while allowing the data to be used for as many tasks as possible and promoting the accuracy of the resulting knowledge. We address this challenge with a clustering algorithm sensitive to the data distribution, which is shown to optimize data reuse and utility using a dataset from a deployed crowdsensing application.

International audience; Crowdsensing allows citizens to contribute to the monitoring of their living environment using the sensors embedded in their mobile devices, e.g., smartphones. However, crowdsensing at scale involves significant communication, computation, and financial costs due to the dependence on cloud infrastructures for the analysis (e.g., interpolation and aggregation) of spatio-temporal data. This limits the adoption of crowdsensing by activists although sorely needed to inform our knowledge of the environment. As an alternative to the centralized analysis of crowdsensed observations, this paper introduces a fully distributed interpolation-mediated aggregation approach running on smartphones. To achieve so efficiently, we model the interpolation as a distributed tensor completion problem, and we introduce a lightweight aggregation strategy that anticipates the likelihood of future encounters according to the quality of the interpolation. Our approach thus shifts the centralized postprocessing of crowdsensed data to distributed pre-processing on the move, based on opportunistic encounters of crowdsensors through state-of-the-art D2D networking. The evaluation using a dataset of quantitative environmental measurements collected from 550 crowdsensors over 1 year shows that our solution significantly reduces-and may even eliminate-the dependence on the cloud infrastructure, while it incurs a limited resource cost on end devices. Meanwhile, the overall data accuracy remains comparable to that of the centralized approach.

International audience; Digital technologies have impacted almost every aspect of our society, including how people participate in activities that matter to them. Indeed, digital participation allows people to be involved in different societal activities at an unprecedented scale through the use of Information and Communication Technologies (ICT). Still, enabling participation at scale requires making it seamless for people to: interact with a variety of software platforms, get information from connected physical objects and software services, and communicate and collaborate with their peers. Toward this objective, this paper introduces and formalizes the concept of Social Participation Network, which captures the diverse participation relationships-between people, digital services and connected things-supporting participatory processes. The paper further presents the design of an associated online service to support the creation and management of Social Participation Networks. The design advocates the instantiation of Social Participation Networks within distinct participation contexts-spanning, e.g., private institutions, neighbor communities, and governmental institutions-so that the participants' information and contributions to participation remain isolated and private within the given context.

International audience; Our work aims to make opportunistic crowdsensing a reliable means of detecting urban phenomena, as a component of smart city development. We believe that the optimal method for achieving this is by enforcing the cost-effective collection of high quality data. We then investigate a supporting middleware solution that reduces both the network traffic and computation at the cloud. To this end, our research focuses on defining a set of protocols that together implement "context-aware in-network collaborative mobile crowdsensing" by combining: (i) The inference of the crowdsensors' physical context so as to characterize the gathered data; (ii) The context-aware grouping of crowdsensors to share the workload and filter out low quality data; and (iii) Data aggregation at the edge to enhance the knowledge transferred to the cloud.

International audience; Opportunistic crowdsensing empowers citizens carrying hand-held devices to sense physical phenomena of common interest at a large and fine-grained scale without requiring the citizens' active involvement. However, the resulting uncontrolled collection and upload of the massive amount of contributed raw data incur significant resource consumption, from the end device to the server, as well as challenge the quality of the collected observations. This paper tackles both challenges raised by opportunistic crowdsensing, that is, enabling the resource-efficient gathering of relevant observations. To achieve so, we introduce the BeTogether middleware fostering context-aware, collaborative crowdsensing at the edge so that co-located crowdsensors operating in the same context, group together to share the work load in a cost- and quality-effective way. We evaluate the proposed solution using an implementation-driven evaluation that leverages a dataset embedding nearly 1 million entries contributed by 550 crowdsensors over a year. Results show that BeTogether increases the quality of the collected data while reducing the overall resource cost compared to the cloud-centric approach.

International audience; To enable direct Internet connectivity of Things, complete protocol stacks need to be deployed on resource-constrained devices. Such protocol stacks typically build on lightweight IPv6 adaptations and may even include a middleware layer supporting high-level application development. However, the profusion of IoT middleware-layer interaction protocols has introduced technology diversity and high fragmentation in the IoT systems landscape with siloed vertical solutions. To enable the interconnection of heterogeneous Things across these barriers, advanced interoperability solutions at the middleware layer are required. In this paper, we introduce a solution for the automated synthesis of protocol mediators that support the interconnection of heterogeneous Things. Our systematic approach relies on the Data eXchange (DeX) connector model, which comprehensively abstracts and represents existing and potentially future IoT middleware protocols. Thanks to DeX, Things seamlessly interconnect through lightweight mediators. We validate our solution with respect to: (i) the support to developers when developing heterogeneous IoT applications; (ii) the runtime performance of the synthesized mediators.

The data gluttony of AI is well known: Data fuels the artificial intelligence. Technologies that help to gather the needed data are then essential, among which the IoT. However, the deployment of IoT solutions raises significant challenges, especially regarding the resource and financial costs at stake. It is our view that mobile crowdsensing, aka phone sensing, has a major role to play because it potentially contributes massive data at a relatively low cost. Still, crowdsensing is useless, and even harmful, if the contributed data are not properly analyzed. This paper surveys our work on the development of systems facing this challenge, which also illustrates the virtuous circles of AI. We specifically focus on how intelligent crowdsensing middleware leverages on-device machine learning to enhance the reported physical observations. Keywords: Crowdsensing, Middleware, Online learning.

International audience; Emerging applications in smart cities and communities require massive IoT deployments using sensors/actuators (things) that can enhance citizens' quality of life and public safety. However, budget constraints often lead to limited instrumentation and/or the use of low-cost sensors that are subject to drift and bias. This raises concerns of robustness and accuracy of the decisions made on uncertain data. To enable effective decision-making while fully exploiting the potential of low-cost sensors, we propose to send mobile units (e.g., trained personnel) equipped with high-quality (more expensive) and freshly-calibrated reference sensors so as to carry out calibration in the field. We design and implement an efficient cooperative approach to solve the calibration planning problem, which aims at minimizing the cost of the recurring calibration of multiple sensor types in the long-term operation. We propose a two-phase solution that consists of a sensor selection phase that minimizes the average cost of recurring calibration, and a path planning phase that minimizes the travel cost of multiple calibrators which have load constraints. We provide fast and effective heuristics for both phases. We further build a prototype that facilitates the mapping of the deployment field and provides navigation guidance to mobile calibrators. Extensive use-case-driven simulations show our proposed approach significantly reduces the average cost compared to naive approaches: up to 30% in a moderate-sized indoor case, and higher in outdoor cases depending on the scale.

International audience; Understanding the sensing context of raw data is crucial for assessing the quality of large crowdsourced spatio-temporal datasets and supporting context-augmented personal trajectories. Detecting sensing contexts in the wild is a challenging task and requires features from smartphone sensors that are not always available. In this paper, we propose three heuristic algorithms for detecting sensing contexts such as in/out-pocket, under/over-ground, and in/out-door for crowdsourced spatio-temporal datasets. These are unsupervised binary classifiers with a small memory footprint and execution time. Using a segment of the Ambiciti real dataset-a feature-limited crowdsourced dataset-we report that our algorithms perform equally well in terms of balanced accuracy (within 4.3%) when compared to machine learning (ML) models reported by an AutoML tool.

International audience; Mobile crowdsensing is a powerful mechanism to aggregate hyper-local knowledge about the environment. Indeed, users may contribute valuable observations across time and space using the sensors embedded in their smartphones. However, the relevance of the provided measurements depends on the adequacy of the sensing context with respect to the phenomena that are analyzed. This paper concentrates more specifically on assessing the sensing context when gathering observations about the physical environment beyond its geographical position in the Euclidean space, i.e., whether the phone is in-/out-pocket, in-/out-door and on-/under-ground. We introduce an online learning approach to the local inference of the sensing context so as to overcome the disparity of the classification performance due to the heterogeneity of the sensing devices as well as the diversity of user behavior and novel usage scenarios. Our approach specifically features a hierarchical algorithm for inference that requires few opportunistic feedbacks from the user, while increasing the accuracy of the context inference per user.

International audience; Civic engagement refers to any collective action towards the identification and solving of public issues. Current civic technologies are traditional Web-or mobile-based platforms that make difficult, or just impossible, the participation of citizens via different communication technologies. Moreover, connected objects sensing physical-world data can nourish participatory processes by providing physical evidence to citizens; however, leveraging these data is not direct and still a time-consuming process for civic technologies developers. This paper introduces the concept of social middleware for civic engagement. Social middleware allows citizens to engage in participatory processes-supported by civic technologies-via their favorite communication tools, and to interact not only with other citizens but also with relevant connected objects and software platforms. The mission of social middleware goes beyond the connection of all these heterogeneous entities. It aims at easing the implementation of distributed applications oriented toward civic engagement by featuring dedicated built-in services.

International audience; This poster presents our prototype implementation of FireDeX, a cross-layer middleware that supports timely delivery of mission-critical messages (i.e. events) over an IoT data exchange service. Emergency scenarios may challenge/congest the network infrastructure. FireDeX addresses these situations by prioritizing event delivery and by dropping some low priority events.

International audience; Real-time event detection and targeted decision making for emerging mission-critical applications, e.g. smart fire fighting, requires systems that extract and process relevant data from connected IoT devices in the environment. In this paper, we propose FireDeX, a cross-layer middleware that facilitates timely and effective exchange of data for coordinating emergency response activities. FireDeX adopts a publish-subscribe data exchange paradigm with brokers at the network edge to manage prioritized delivery of mission-critical data from IoT sources to relevant subscribers. It incorporates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics (e.g. delays, success rates). We design an extensible queueing theoretic model that abstracts these cross-layer interactions as a network of queues, thereby making it amenable for rapid analysis. We propose novel algorithms that utilize results of this analysis to tune data exchange configurations (event priorities and dropping policies) while meeting situational awareness requirements and resource constraints. FireDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate its performance through simulated experiments in a smart building fire response scenario. Our results demonstrate significant improvement to mission-critical data delivery under a variety of conditions. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization.

International audience; Mobile Internet of Things (IoT) applications are typically deployed on resource-constrained devices with intermittent network connectivity. To support the deployment of such applications, the Publish/Subscribe (pub/sub) interaction paradigm is often employed, as it decouples mobile peers in time and space. Furthermore, pub/sub middleware protocols and APIs consider the Things' hardware limitations and support the development of effective applications by providing Quality of Service (QoS) features. These features aim to enable developers to tune an application by switching different levels of response times and delivery success rates. However, the profusion of pub/sub middleware protocols coupled with intermittent network connectivity result in non-trivial application tuning. In this paper, we model the performance of middleware protocols found in IoT, which are classified within the pub/sub interaction paradigm-both reliable and unreliable underlying network layers are considered. We model reliable and unreliable protocols, by considering QoS semantics for data validity, buffer capacities as well as the intermittent availability of peers. Finally, we perform statistical analysis by varying these QoS semantics, demonstrating their significant effect on the rate of successful interactions. We showcase the application of our analysis in concrete scenarios relating to Traffic Information Management systems, that integrate both reliable and unreliable participants. The consequent PerfMP performance modeling pattern may be tailored for a variety of deployments, in order to control fine-grained QoS policies.

International audience; To facilitate the development of Internet of Things (IoT) applications, numerous middleware protocols and APIs have been introduced. Such applications built atop reliable or unreliable protocols and they expose different characteristics. Additionally, with regard to the application context (e.g., emergency response operations), several Quality of Service (QoS) requirements must be satisfied. To study QoS in IoT applications, the provision of a generic performance analysis methodology is required. Queueing network models offer a simple modeling environment, which can be used to represent IoT interactions by combining multiple queueing model types for building queueing networks. The resulting networks can be used for performance analysis through analytical or simulation models. In this paper, we present several types of queueing models that represent different QoS settings of IoT interactions, such as intermittent mobile connectivity, message drop probabilities, message availability/validity and resource constrained devices. Using MobileJINQS, we simulate our models demonstrating the significant effect on response times and message success rates when varying QoS settings.

The widespread deployment of connected Things in our cities allows for the enhanced management of the urban space. However, due to environmental constraints, the connected Things are often battery-powered, which affects the lifetime of the $\mathrm {I}\mathrm {o}\mathrm {T}$ Networks. We believe that the urban $\mathrm {I}\mathrm {o}\mathrm {T}$ networks must leverage the power of the crowd, leading to combine the fixed urban $\mathrm {I}\mathrm {o}\mathrm {T}$ networks with the crowdsensing capabilities of the citizens' smartphones. That is, we integrate the offloading of a portion of the traffic generated by the fixed network to mobile crowdsensing devices that also contribute with relevant urban observations (e.g., for noise pollution monitoring). This paper highlights the key technical features of the resulting solution, whose evaluation shows that we are able to extend the lifetime of the battery-operated $\mathrm {I}\mathrm {o}\mathrm {T}$ network by a up to 7 factor.

International audience; Mobile Phone Sensing offers a great opportunity toward the large scale monitoring of urban phenomena, such as the exposure of the population to environmental noise. Our research aims to make available a supporting mobile application together with the associated platform for the analysis of the contributed observations. The technological issues arising have been partly solved but a gap remains between the need for the massive collection of relevant data, and the quantity and accuracy of the measurements that are actually gathered. This paper presents our iterative research process to tackle this challenge, which combines technological innovation and social design. The presented research results contribute to a better understanding of why and how people use mobile phone sensing applications; the results also inform how to best leverage mobile crowd-sensing in the development of smart cities and how it may serve addressing urban challenges related to, e.g., public health or urban planning.; Le recueil de données via les smartphones ouvre des perspectives importantes pour le suivi à grande échelle de phénomènes urbains, dont l'exposition de la population au bruit. Notre recherche vise à rendre disponible une application pour smartphone et sa plateforme associée, pour l'analyse et la gestion des observations recueillies. Si les défis technologiques ont en partie été levés, un écart significatif persiste entre, d'une part la nécessité d'un recueil massif de données significatives et, d'autre part la quantité et la précision des données de mesure actuellement produites et récoltées. Cet article présente notre processus de recherche itératif qui, afin de relever ces défis, combine innovations technologiques et design social. Les résultats présentés ici contribuent à une meilleure compréhension des raisons et conditions dans lesquelles le public utilise des applications mobiles de recueil de données; les résultats nous informent également sur les possibilités d'amélioration du crowd-sensing via smartphones pour le développement de projets de villes intelligentes et pour la santé publique et l'aménagement urbain.

The possibilities of new mobile distributed systems have reached unprecedented levels. Such systems are dynamically composed of networked resources in the environment, which may span from the immediate neighborhood of the users - as advocated by pervasive computing - up to the entire globe - as envisioned by the Future Internet and one of its major constituents, the Internet of Things. This paper more specifically concentrates on urban participatory mobile distributed systems where people get involved in producing new knowledge about the urban environment. Service-oriented and cloud computing are evident baseline technologies for the target mobile distributed systems. Service orientation provides the abstraction to deal with the assembly of the relevant heterogeneous component systems. The cloud provides the infrastructure to deal with the gathering and analyses of the observations coming from the sensing infrastructure, including from people. However, cloud-based centralized solutions come at a price, regarding both resource consumption and privacy risk. Further, the high heterogeneity of the participating nodes results in diverse levels of sensing accuracy. This paper provides an overview of our past and ongoing research to overcome the challenges facing urban participatory mobile distributed systems, which leverages mobile collaborative sensing, networking and computing. The experience with the Ambiciti platform and associated mobile app for monitoring the individual and collective exposure to environmental pollution serves as an illustrative use case.

International audience; Abstract —Online social network services (OSNSs) have become an integral part of our daily lives. At the same time, the aggressive market competition has led to the emergence of multiple competing siloed OSNSs that cannot interoperate. As a consequence, peopleface the burden of creating and managing multiple OSNS accounts and learning how to use them to stay connected. This paper is concerned with relieving users from such a burden by enabling universal online social interactions. The contributions of this paper span: (1)a model of the universal social network bus (USNB) for OSNS interoperability; (2) a prototype for universal online social interactions that builds upon the proposed model; and (3) a preliminary experimental evaluation involving 50 participants. Results show that people are positive about the solution as they are able to reach out a larger community of users independently of the OSNSs they use

International audience; This paper analyzes existing practices and supporting technologies for Participatory Budgeting (PB), with a special focus on US-related initiatives, as a mean to understand the current and future design space of ICT for participatory democracy. We suggest new design opportunities for ICT to facilitate citizen collaboration in the PB process, and by extension, to reflect on how these technologies could better foster deliberative decision-making at a scale that is both small and large.

International audience; We revisit core Service-Oriented Architecture –SOA– concepts to integrate advanced continuous processing techniques. The resulting architecture allows overcoming the high heterogeneity and the very large data scale that arise in the Internet of Things. Further, we discuss how to leverage the CoAP protocol so as to enable the resource-efficient deployment of the proposed architecture over the large diversity of sensor networks.

International audience; Internet of Things (IoT) applications consist of diverse Things (sensors and devices) in terms of hardware resources. Furthermore, such applications are characterized by the Things' mobility and multiple interaction types, such as synchronous, asynchronous, and streaming. Middleware IoT protocols consider the above limitations and support the development of effective applications by providing several Quality of Service features. These features aim to enable application developers to tune an application by switching different levels of response times and delivery success rates. However, the profusion of the developed IoT protocols and the intermittent connectivity of mobile Things, result to a non-trivial application tuning. In this paper, we model the performance of the middleware overlay infrastructure using Queueing Network Models. To represent the mobile Thing's connections/disconnections, we model and solve analytically an ON/OFF queueing center. We apply our approach to Streaming interactions with mobile peers. Finally, we validate our model using simulations. The deviations between the performance results foreseen by the analytical model and the ones provided by the simulator are shown to be less than 5%.

International audience; Computer-mediated communication can be defined as any form of human communication achieved through computer technology. From its beginnings, it has been shaping the way humans interact with each other, and it has influenced many areas of society. There exist a plethora of communication services enabling computer-mediated social communication (e.g., Skype, Facebook Messenger, Telegram, WhatsApp, Twitter, Slack, etc.). Based on personal preferences, users may prefer a communication service rather than another. As a result, users sharing same interests may not be able to interact since they are using incompatible technologies. To tackle this interoperability barrier, we propose the Social Communication Bus, a middleware solution targeted to enable the interaction between heterogeneous communication services. More precisely, the contribution of this paper is threefold: (i), we propose a survey of the various forms of computer-mediated social communication, and we make an analogy with the computing communication paradigms; (ii), we revisit the eXtensible Service Bus (XSB) that supports interoperability across computing interaction paradigms to provide a solution for computer-mediated social communication interoperability; and (iii), we present Social-MQ, an implementation of the Social Communication Bus that has been integrated into the AppCivist platform for participatory democracy.

International audience; Systems deployed in mobile environments are typically characterized by intermittent connectivity and asynchronous sending/reception of data. To create effective mobile systems for such environments, it is essential to guarantee acceptable levels of timeliness between sending and receiving mobile users. In order to provide QoS guarantees in different application scenarios and contexts, it is necessary to model the system performance by incorporating the intermittent connectivity. Queueing Network Models (QNMs) offer a simple modeling environment, which can be used to represent various application scenarios, and provide accurate analytical solutions for performance metrics, such as system response time. In this paper, we provide an analytical solution regarding the end-to-end response time between users sending and receiving data by modeling the intermittent connectivity of mobile users with QNMs. We utilize the publish/subscribe (pub/sub) middleware as the underlying communication infrastructure for the mobile users. To represent the user's connections/disconnections, we model and solve analytically an ON/OFF queueing system by applying a mean value approach. Finally, we validate our model using simulations with real-world workload traces. The deviations between the performance results foreseen by the analytical model and the ones provided by the simulator are shown to be less than 5% for a variety of scenarios.

Nowadays, various static wireless sensor networks (WSN) are deployed in the environment for many purposes: traffic control, pollution monitoring, and so on. The willingness to open these legacy WSNs to the users is emerging, by integrating them to the Internet network as part of the future Internet of Things (IoT), for example, in the context of smart cities and open data policies. While legacy sensors cannot be directly connected to the Internet in general, emerging standards such as 6LoWPAN are aimed at solving this issue but require us to update or replace the existing devices. As a solution to connect legacy sensors to the IoT, we propose to take advantage of the multi-modal connectivity as well as the mobility of smartphones to use phones as opportunistic proxies , that is, mobile proxies that opportunistically discover closeby static sensors and act as intermediaries with the IoT, with the additional benefit of bringing fresh information about the environment to the smartphones’ owners. However, this requires us to monitor the smartphone’s mobility and further infer when to discover and register the sensors to guarantee the efficiency and reliability of opportunistic proxies. To that end, we introduce and evaluate an approach based on mobility analysis that uses a novel path prediction technique to predict when and where the user is not moving, and thereby serves to anticipate the registration of sensors within communication range. We show that this technique enables the deployment of low-cost resource-efficient mobile proxies to connect legacy WSNs with the IoT.

Supporting the execution of service-oriented applications over ubiquitous networks specifically calls for a service-oriented middleware (SOM), which effectively enables ubiquitous networking while benefiting from the diversity and richness of the networking infrastructure. However, developing ubiquitous applications that exploit the specific features offered by a SOM might be a time-consuming task, which demands a deep knowledge spanning from the application domain concepts down to the underlying middleware technicalities. In this paper, first we present the model-driven development process underpinning ubiSOAP, a SOM for the ubiquitous networking domain. Then, based on the domain concepts defined by the conceptual model of ubiSOAP, its architecture and its technicalities, we propose a domain-specific environment, called ubiDSE, that aids the development of applications that exploits the ubiSOAP features, from design to implementation. ubiDSE allows developers to focus on the main behavior of the modeled systems, rather than on complex details inherent to ubiquitous environments. As part of ubiDSE, specific tools are provided to automatically generate skeleton code for service-oriented applications to be executed on ubiSOAP-enabled devices, hence facilitating the exploitation of ubiSOAP by developers.

International audience; Mobile phone sensing contributes to changing the way we approach science: massive amount of data is being contributed across places and time, and paves the way for advanced analyses of numerous phenomena at an unprecedented scale. Still, despite the extensive research work on enabling resource-efficient mobile phone sensing with a very-large crowd, key challenges remain. One challenge is facing the introduction of a new heterogeneity dimension in the traditional middleware research landscape. The middleware must deal with the heterogeneity of the contributing crowd in addition to the system's technical heterogeneities. In order to tackle these two heterogeneity dimensions together, we have been conducting a large-scale empirical study in cooperation with the city of Paris. Our experiment revolves around the public release of a mobile app for urban pollution monitoring that builds upon a dedicated mobile crowd-sensing middleware. In this paper, we report on the empirical analysis of the resulting mobile phone sensing efficiency from both technical and social perspectives, in face of a large and highly heterogeneous population of participants. We concentrate on the data originating from the 20 most popular phone models of our user base, which represent contributions from over 2,000 users with 23 million observations collected over 10 months. Following our analysis, we introduce a few recommendations to overcome-technical and crowd-heterogeneities in the implementation of mobile phone sensing applications and supporting middleware.

After a thorough analysis of existing Internet of Things (IoT) related ontologies, in this paper we propose a holistic and a light-weight ontology that aims to achieve semantic interoperability among various fragmented testbeds (that store data in their proprietary format) in IoT domain. Such ontology currently finds its implementation in the EU H2020 FIESTA-IoT project that aims to provide federation and interoperability to testbeds using semantics. The proposed ontology leverages from the Noy et al. methodology of building ontology. With the aim of including more ontologies in the future, if needed, our ontology currently reuses core concepts from various popular ontologies and taxonomies, such as Semantic Sensor Network (SSN), M3-lite (a lite version of M3 and also an outcome of this study), WGS84, IoT-lite and DUL. In addition to this, we also introduce a set of tools that aims to help external testbeds adapt their respective datasets to the common ontology defined throughout this paper. This work is partially funded by the European project \Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT)" from the European Union's Horizon 2020 Programme with the Grant Agreement No. CNECTICT-643943. The Authors would like to also thank the FIESTA-IoT consortium for fruitful discussions.

International audience; By bridging the physical and the virtual worlds, the Internet of Things (IoT) impacts a multitude of application domains, among which smart cities, smart factories, resource management, intelligent transportation, health and well-being to name a few. However, leveraging the IoT within software applications raises tremendous challenges from the networking up to the application layers, in particular due to the ultra-large scale, the extreme heterogeneity and the dynamics of the IoT. This paper more specifically explores how the service-oriented architecture paradigm may be revisited to address challenges posed by the IoT for the development of distributed applications. Drawing from our past and ongoing work within the MiMove team at Inria Paris, the paper discusses the evolution of the supporting middleware solutions spanning the introduction of: probabilistic protocols to face scale, cross-paradigm interactions to face heterogeneity, and streaming-based interactions to support the inherent sensing functionality brought in by the IoT.

International audience; Public transportation is essential for sustainable and economical development of cities. Several transport organizations aim to provide service information to commuters through web and mobile apps. This information includes possible routes between two stations, estimated travel and arrival times, and real-time updates about traffic conditions. However, this information is currently not personalized according to commuter preferences. In this work, we emphasize the need for personalized transit service information to commuters and present a vision of our work in this direction. Our final goal is to develop a fully-functional personalized route recommendation system for public transit commuters. This involves identifying commuter preferences and suitable recommendation techniques, and developing a platform to communicate this information to the commuters. We identify the requirements for the development of this platform, and propose an architecture for our system. As a proof of concept, we present an Android participatory sensing application - MetroCognition, which acquires feedback on convenience experienced by commuters in public transit.

The worldwide use of the Internet and social networking has transformed the constraints of time and space in human interaction: we can now be heard at a massive scale unprecedented in human history. As a result, information and communication technologies may enable citizens to undertake both government through direct assembly and collective action at a scale and an efficacy previously considered impossible. Our research concerns this opportunity to leverage a new sort of political life. We focus specifically on how software systems may enable participatory democracy, that is, the participation of citizens in democratic assembly, action, and governance. As an initial step, we have developed a service-oriented software platform, called AppCivist-PB, focused on a specific, yet representative use case of participatory democracy, namely, Participatory Budgeting (PB for short). PB is an allocation process used in many cities around the world through which they commit a percentage of their annual budget (often 5%) to implement citizen-proposed projects. In PB, residents of a city (or a higher level territorial organization), brainstorm, develop, and select project proposals that local government institutions are required to fund and implement. The key contribution of AppCivist-PB is to enable the cohesive creation of both citizen and software assemblies that together implement a given participatory budgeting campaign.

International audience; The massive deployment of mobile networks together with the emergence of powerful portable devices has given rise to pervasive computing environments. In such environments, mobile users may discover and access services offered in surrounding networks using Service Discovery Protocols (SDPs). However, several SDPs are currently in use, each one designed for a specific target network architecture and setting. As a result, in today's multi-radio networking environment, SDPs do not equally suit each radio interface. In order to provide effective service discovery in multi-radio networks, the most resource-efficient radio interface should be chosen with respect to two main criteria: the adequacy of the interface against the SDP to be used, and energy saving, which is crucial for battery-powered devices. Toward this goal, this article assesses how to effectively take advantage of the multiple radio interfaces now embedded within wireless devices with respect to energy consumption, from the standpoint of service discovery and access. It further investigates the adequacy of legacy SDPs with the various networks, so as to classify the most appropriate networks for each SDP. Exploitation of these results is finally investigated with the description of an energy-efficient algorithm for SDP-based context sensing in a multi-radio pervasive environment.

International audience; Internet-of-Things (IoT) protocols are constantly increasing in the research and industrial landscape. However, the current standardization efforts limit the incorporation of Things as first-class entities into choreographies. To tackle this interoperability barrier, we propose and demonstrate the eVolution Service Bus (VSB), a middleware solution targeted to enable the interaction between Things-based and business-oriented services. Particularly, we demonstrate the incorporation of a service/Thing into the following choreographies: i) temperature sensors interacting with a business-oriented service, and ii) business-oriented services interacting with a route planner service.

International audience; Modern distributed systems and Systems of Systems (SoS) are built as a composition of existing components and services. As a result, systems communicate (either internally, locally or over networks) using protocol stacks of ever-increasing complexity whose messages need to be translated (i.e., interpreted, generated, analyzed and transformed) by third-party systems such as services dedicated to security or interoperability. We observe that current approaches in software engineering are unable to provide an efficient solution towards reusing message translators associated with the message formats composed in protocol stacks. Instead, developers must write ad hoc “glue-code” whenever composing two or more message translators. In addition, the data structures of the output must be integrated/harmonized with the target system.In this paper we propose a solution to the above that enables the composition of message translators according to a high-level user-provided query. While the composition scheme we propose is simple, the inference of the resulting data structures is a problem that has not been solved up to present. This leads us to contribute with a novel data type inference mechanism, which generates a data-schema using tree automata, based on the aforementioned user query.

International audience; —Software cohesion concerns the degree to which the elements of a module belong together. Cohesive software is easier to understand, test and maintain. In the context of service-oriented development, cohesion refers to the degree to which the operations of a service interface belong together. In the state of the art, software cohesion is improved based on refactoring methods that rely on information, extracted from the software implementation. This is a main limitation towards using these methods in the case of Web services: Web services do not expose their implementation; instead all that they export is the Web service interface specification. To deal with this problem, we propose an approach that enables the cohesion-driven decomposition of service interfaces, without information on how the services are implemented. Our approach progressive decomposes a given service interface into more cohesive interfaces; the backbone of the approach is a suite of cohesion metrics that rely on information, extracted solely from the specification of the service interface. We validate the approach in 22 real-world services, provided by Amazon and Yahoo. We assess the effectiveness of the proposed approach, concerning the cohesion improvement, and the number of interfaces that result from the decomposition of the examined interfaces. Moreover, we show the usefulness of the approach in a user study, where developers assessed the quality of the produced interfaces.

International audience; Task mapping, which basically consists of mapping a set of tasks onto a set of nodes, is a well-known problem in distributed computing research. As a particular case of distributed systems, the Internet of Things (IoT) poses a set of renewed challenges, because of its scale, heterogeneity and properties traditionally associated with wireless sensor networks (WSN), shared sensing, continous processing and real time computing. To handle IoT features, we present a formalization of the task mapping problem that captures the varying consumption of resources and various constraints (location, capabilities, QoS) in order to compute a mapping that guarantees the lifetime of the concurrent tasks inside the network and the fair allocation of tasks among the nodes. It results in a binary programming problem for which we provide an efficient heuristic that allows its resolution in polynomial time. Our experiments show that our heuristic: (i) gives solutions that are close to optimal and (ii) can be implemented on reasonably powerful Things and performed directly within the network, without requiring any centralized infrastructure.

Online social networks (OSNs) are increasingly turning mobile and further calling for decentralized social data management. This trend is only going to increase in the near future, based on the increased activity, both by established players like Facebook and new players in the domain such as Google, Instagram, and Pinterest. The increasing adoption of social networks in the workplace has further led to the development of corporate social networks such as those provided by Yammer, which was recently acquired by Microsoft. As individuals from different companies will need to interact as part of joint teams in these federated social networks, questions of privacy and access control arise. This chapter identifies the challenges concerning the above aspects, surveys the state of the art, and identifies directions of future research.

International audience; The Internet of Things (IoT) is a promising concept toward pervasive computing as it may radically change the way people interact with the physical world, by connecting sensors to the Internet and, at a higher level, to the Web, thereby enacting a Web of Things (WoT). One of the challenges raised by the WoT is the in-network continuous processing of data streams presented by Things, which must be investigated urgently because it affects the future data models of the IoT, and is critical regarding the scalability and the sustainability required by the IoT. This cross-cutting concern has been previously studied in the context of Wireless Sensor Networks (WSN) given the focus on the acquisition and in-network processing of sensed data. However, proposed solutions feature various proprietary and highly specialized technologies that are difficult to integrate and complex to use, which represents a hurdle to their wide deployment. At the other end of the spectrum, cloud-based solutions introduce a too high energy cost for the envisioned IoT scale, considering the energy cost of communication over computation. There is thus a need for a distributed middleware solution for data stream management that leverages existing WSN work, while integrating it with today's Web technologies in order to support the required flexibility and the interoperability of the IoT.Toward that goal, this paper introduces Dioptase, a lightweight Data Stream Management System for the WoT, which aims to integrate the Things and their streams into today's Web by presenting sensors and actuators as Web services. The middleware specifically provides a way to describe complex fully-distributed stream-based mashups and to deploy them dynamically, at any time, as task graphs, over available Things of the network, including resource-constrained ones.

International audience; The computing and networking capabilities of today's wireless mobile devices allow for seamlessly-networked, ubiquitous services, which may be dynamically composed at run-time to accomplish complex tasks. This vision, however, remains challenged by the inherent mobility of such devices, which makes services highly volatile. These issues call for a service-oriented middleware that should (i) deal with the run-time growth of the application in terms of involved services (flexibility), (ii) accommodate heterogeneous and unforeseen services into the running application (genericity), and (iii) discover new services at run time and rearrange the application accordingly (dynamism). This chapter discusses the design and implementation of ubiREST, a service-oriented middleware that leverages REST principles to effectively enable the ubiquitous networking of Services. ubiREST specifically defines a layered communication middleware supporting RESTful Services while exploiting nowadays ubiquitous connectivity.

International audience; The essential issue of interoperability in distributed systems is becoming even more pressing in the Future Internet, where complex applications will be composed from extremely heterogeneous systems. Open system integration paradigms, such as service oriented architecture (SOA) and enterprise service bus (ESB), have provided answers to the interoperability requirement. However, when it comes to integrating systems featuring heterogeneous interaction paradigms, such as client-service, publish-subscribe and tuple space, existing solutions are typically ad hoc and partial, applying to specific interaction protocol technologies. In this paper, we introduce an interoperability solution based on abstraction and merging of the common high-level semantics of interaction paradigms, which is sufficiently general and extensible to accommodate many different protocol technologies. We apply this solution to revisit the SOA- and ESB-based integration of heterogeneous distributed systems.

International audience; The sensor networking field is evolving into the Internet of Things~(IoT), owing in large part to the increased availability of consumer sensing devices, including modern smart phones. However, application development in the IoT still remains challenging, since it involves dealing with several related issues, such as lack of proper identification of roles of various stakeholders, as well as lack of suitable (high-level) abstractions to address the large scale and heterogeneity in IoT systems. Although the software engineering community has proposed several approaches to address the above in the general case, existing approaches for IoT application development only cover limited subsets of above mentioned challenges. In this paper, we propose a multi-stage model-driven approach for IoT application development based on a precise definition of the role to be played by each stakeholder involved in the process -- domain expert, application designer, application developer, device developer, and network manager. The abstractions provided to each stakeholder are further customized using the inputs provided in the earlier stages by other stakeholders. We have also implemented code-generation and task-mapping techniques to support our approach. Our initial evaluation based on two realistic scenarios shows that the use of our techniques/framework succeeds in improving productivity in the IoT application development process.

International audience; Quality of Service (QoS) in orchestrated web services compositions have been well studied with probabilistic and multi-dimensional models. Choreographies that involve message passing among services, on the other hand, require further analysis. In this paper, we begin with the set of QoS domains that may be studied in case of choreographies and the algebraic rules for their composition. As choreographies manage QoS composition in a distributed fashion, techniques to enrich functional specifications with QoS are examined using the model proposed in the CHOReOS project. These are further analyzed with choreographies that may reconfigure due to functional or QoS requirements. Studies on the effects of such reconfiguration on multiple QoS domains can lead to better understanding of optimal runtime configurations along with associated tradeoffs. A goal programming approach is also proposed to choose Pareto optimal solutions with respect to diverse QoS domains.

International audience; In recent years, there has been a growing interest towards the application of artificial intelligence approaches in software engineering (SE) processes. In the specific area of SE for self-adaptive systems (SASs) there is a growing research awareness about the synergy between SE and AI. However, just few significant results have been published. This paper briefly studies uncertainty in SASs and surveys techniques that have been developed to engineer SASs in order to tackle uncertainty. In particular, we highlight techniques that use AI concepts. We also report and discuss our own experience using Dynamic Decision Networks (DDNs) to model and support decision-making in SASs while explicitly taking into account uncertainty. We think that Bayesian inference, and specifically DDNs, provide a useful formalism to engineer systems that dynamically adapt themselves at runtime as more information about the environment and the execution context is discovered during execution. We also discuss partial results, challenges and future research avenues.

International audience; One of the main benefits of mobile participatory sensing becoming a reality is the increased knowledge it will provide about the real world, as it is expected to rely on a large number of smart and mobile devices. Nowadays, those devices have the ability to host different types of sensors that will be incorporated in every aspect of our daily lives. However, given the constantly increasing number of capable mobile devices, any participatory sensing approach should be, first and foremost, scalable. To address this challenge, we present an approach to decrease the participation of devices (in sensing tasks) in a manner that does not compromise the accuracy of the real-world information while increasing the efficiency of the overall system. To reduce the number of the devices involved, we present a probabilistic registration approach enabling only a subset of devices to register their services while still providing adequate coverage of the deployment area. Our techniques, based on a re- alistic human mobility model, allow devices to decide whether or not to register their sensing services depending on the probability of other, equivalent devices being present at the locations of their expected path. We present the design and implementation of a registration middleware based on our techniques, using which mobile devices can base their registration decision. Through experiments performed on real and simulated datasets, we show that our approach scales, while not sacrificing significant amounts of sensing coverage.

Software systems are increasingly composed of independently-developed components, which are often systems by their own. This composition is possible only if the components are interoperable, i.e., are able to work together in order to achieve some user tasks. However, interoperability is often hampered by the differences in the data types, communication protocols, and middleware technologies used by the components involved. In order to enable components to interoperate despite these differences, mediators that perform the necessary data translations and coordinate the components' behaviours appropriately, have been introduced. Still, interoperability remains a critical challenge for today's and even more tomorrow's distributed systems that are highly heterogeneous and dynamic. This chapter introduces the fundamental principles and solutions underlaying interoperability in software systems with a special focus on protocols. First, we take a software architecture perspective and present the fundamentals for reasoning about interoperability and bring out mediators as a key solution to achieve protocol interoperability. Then, we review the solutions proposed for the implementation, synthesis, and dynamic deployment of mediators. We show how these solutions still fall short in automatically solving the interoperability problem in the context of systems of systems. This leads us to present the solution elaborated in the context of the European Connect project, which revolves around the notion of emergent middleware, whereby mediators are synthesised on the fly. We consider the GMES Global Monitoring of Environment and Security initiative and use it to illustrate the different solutions presented.

International audience; he success of pervasive computing depends on the ability to compose a multitude of networked applications dynamically in order to achieve user goals. However, applications from different providers are not able to interoperate due to incompatible interaction protocols or disparate data models. Instant messaging is a representative example of the current situation, where various competing applications keep emerging. To enforce interoperability at runtime and in a non-intrusive manner, mediators are used to perform the necessary translations and coordination between the heterogeneous applications. Nevertheless, the design of mediators requires considerable knowledge about each application as well as a substantial development effort. In this paper we present an approach based on ontology reasoning and model checking in order to generate correct-by-construction mediators automatically. We demonstrate the feasibility of our approach through a prototype tool and show that it synthesises mediators that achieve efficient interoperation of instant messaging applications.