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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.

The papers in this special section focus on secure and emerging collaborative computing and intelligent systems. The Internet, coupled with recent advances in computing and information technologies, such as IoT, mobile edge/ cloud computing, cyber-physical-social systems, and artificial intelligence/machine learning/deep learning, have paved the way for creating next-generation smart and intelligent systems and applications that can have transformative impact in our society while accelerating rapid scientific discoveries and innovations. Unprecedented cyber-social and cyber-physical infrastructures and systems that span geographic boundaries are possible because of the Internet and the growing number of collaboration-enabling technologies. With newer technologies and paradigms getting increasingly embedded in the computing platforms and networked information systems/ infrastructures that form the digital foundation for our personal, organizational, and social processes and activities, it is increasingly becoming critical that the trust, privacy, and security issues in such digital environments are holistically addressed to ensure the safety and well-being of individuals as well as our society.

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; Environmental noise is a major pollutant in contemporary cities and calls for the ac-10 tive monitoring of noise levels to spot the locations where it most affects the people's health and well-being. However, due to the complex relationship between environmental noise and its perception by the citizens, it is not sufficient to quantitatively measure environmental noise. We need to collect and aggregate contextualized-both quantitative and qualitative-data about the urban environmental noise so as to be 15 able to study the objective and subjective relationships between sound and living beings. This complex knowledge is a prerequisite for making efficient territorial public policies for soundscapes that are inclined towards living beings welfare. In this paper, we investigate how Mobile Phone Sensing (MPS)-aka crowdsensing-enables the gathering of such knowledge, provided the implementation of sensing protocols 20 that are customized according to the context of use and the intended exploitation of the data. Through three case studies that we carried out in France and Finland, we show that MPS is not solely a tool that contributes to sensitizing citizens and decision-makers about noise pollution; it also contributes to increasing our knowledge about the impact of the environmental noise on people's health and well-being 25 in relation to its physical and subjective perception.

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; Noise maps are a key asset in the elaboration of urban noise mitigation policies. However, simulation-based noise maps are subject to high uncertainties, and the estimation of population exposition to noise pollution generally relies on static averages over an extended period of time. This paper introduces a method to produce hourly noise maps based on temporally averaged simulation maps and mobile phone audio recordings. The data assimilation method produces an analysis noise map which is the so-called best linear unbiased estimator: it merges the simulated map and the measurements based on respective uncertainties so that the analysis map has minimum error variance. The method is illustrated through a neighborhood-wide experiment. A systematic study of the errors associated with both the simulation map and the observations (measurement error, temporal representativeness error, location error) is carried out. Two LAeq,1h maps are produced, corresponding, respectively, to a morning and an evening time slot. The analysis maps achieve a reduction of at least 25% of root-mean-square error. The a posteriori error variance of the maps are generally around 50% of the a priori error variance in the vicinity of the observed locations.

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.

Seven research leaders in software engineering for Internet computing present their viewpoints on important issues that will shape this field's future. They discuss opportunities and challenges for the shifting software paradigm; stepping outside the comfort zone to revisit issues such as software correctness; improving Internet software dependability and programmability; addressing software engineering issues for the Internet of Things; exploring relationships among the Internet of Things, people, and software services; supporting a participatory culture of software development; and rethinking logging in online services.

International audience; The increasing number and quality of sensors integrated in mobile phones have paved the way for sensing schemes driven by city dwellers. The sensing quality can drastically depend on the mobile phone, and appropriate calibration strategies are needed. This paper evaluates the quality of noise measurements acquired by a variety of Android phones. The Ambiciti application was developed so as to acquire a larger control over the acquisition process. Pink and narrowband noises were used to evaluate the phones' accuracy at levels ranging from background noise to 90 dB(A) inside the lab. Conclusions of this evaluation lead to the proposition of a calibration strategy that has been embedded in Ambiciti and applied to more than 50 devices during public events. A performance analysis addressed the range, accuracy, precision, and reproducibility of measurements. After identification and removal of a bias, the measurement error standard deviation is below 1.2 dB(A) within a wide range of noise levels [45 to 75 dB(A)], for 12 out of 15 phones calibrated in the lab. In the perspective of citizens-driven noise sensing, in situ experiments were carried out, while additional tests helped to produce recommendations regarding the sensing context (grip, orientation, moving speed, mitigation, frictions, wind).

While bringing massive-scale sensing at low cost, mobile participatory sensing is challenged by the low accuracy of the sensors embedded in and/or connected to the smartphones. The mobile measurements that are collected need to be corrected so as to accurately match the phenomena being observed. This paper addresses this challenge by introducing a multi-hop, multiparty calibration method that operates in the background in an automated way. Using our method, sensors that are within a relevant sensing (and communication) range coordinate so that the observations of the participating (previously) calibrated sensors serve calibrating the other participants. As a result, our method is particularly well suited for participatory sensing within crowd meetings, as for instance within public spaces. Our solution leverages multivariate linear regression, together with robust regression so as to discard the measurements that are of too low quality for being meaningful. To the best of our knowledge, we are the first to introduce a multiparty calibration algorithm, while previous work in the area focused on pairwise calibration. The paper further introduces a supporting prototype implemented over Android, and related experiment in the context of noise sensing.We show that the proposed multiparty calibration system enhances the accuracy of the mobile noise sensing application.

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.

L'expansion des reseaux sans fil temoigne de la demande croissante des utilisateurs pour un acces a des services, a tout moment. L'un des enjeux majeurs vise a decouvrir a la vo- lee les services en vue de leur utilisation. A cette fin, des protocoles de decouverte de services, toujours plus nombreux, ont ete proposes et standardises. A l'origine de nouvelles incompatibi- lites, ces protocoles demeurent inexploitables dans les reseaux ad hoc. Aussi, nous introduisons un intergiciel integrant un protocole de decouverte de services web pour reseaux ad hoc ainsi qu'un systeme de traduction de protocoles de decouverte de services afin d'offrir aux utilisa- teurs un acces transparent aux services tout en garantissant leur interoperabilite. ABSTRACT. The abundance of networked devices circumvents the need for providing access to services, anywhere, anytime. In such a context, one of the major issues lies in discovering services dynamically. For this purpose, a wide range of protocols have been proposed. While creating new sources of incompatibilities, these protocols cannot be directly deployed over MANETs, and, hence remain a major obstacle to the development of MANET-enabled services. In order to overcome these issues, we present a middleware that enables ubiquitous access to heterogeneous services based on automatic service discovery and dynamic translation of dis- covery protocols. This middleware includes a service discovery protocol customised to operate in MANETs, along with a translation component guaranteeing service interoperability.

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; With the emergence of Future Internet applications that connect web services, sensor-actuator networks and service feeds, scalability and heterogeneity support of interaction paradigms are of critical importance. Heterogeneous interactions can be abstractly represented by client-service, publish-subscribe and tuple space middleware connectors that are interconnected via bridging mechanisms providing interoperability among the services. In this paper, we make use of the eXtensible Service Bus (XSB), proposed in the CHOReOS project as the connector enabling interoperability among heterogeneous choreography participants. XSB models transactions among peers through generic post and get operations that represent peer behavior with varying time/space coupling. Nevertheless, the heterogeneous lease and timeout constraints of these operations severely affect latency and success rates of transactions. By precisely studying the related timing thresholds using timed automata models, we verify conditions for successful transactions with XSB connectors. Furthermore, we statistically analyze through simulations, the effect of varying lease and timeout periods to ensure higher probabilities of successful transactions. Simulation experiments are compared with experiments run on the XSB implementation testbed to evaluate the accuracy of results. This work can provide application developers with precise design time information when setting these timing thresholds in order to ensure accurate runtime behavior.

International audience; Large-scale mobile environments are characterized by, among others, a large number of mobile users, intermittent connectivity and non-homogeneous arrival rate of data to the users, depending on the region's context. Multiple application scenarios in major cities need to address the above situation for the creation of robust mobile systems. Towards this, it is fundamental to enable system designers to tune a communication infrastructure using various parameters depending on the specific context. In this paper, we take a first step towards enabling an application platform for large-scale information management relying on mobile social crowd-sourcing. To inform the stakeholders of expected loads and costs, we model a large-scale mobile pub/sub system as a queueing network. We introduce additional timing constraints such as i) mobile user's intermittent connectivity period; and ii) data validity lifetime period (e.g. that of sensor data). Using our MobileJINQS simulator, we parameterize our model with realistic input loads derived from the D4D dataset (CDR) and varied lifetime periods in order to analyze the effect on response time. This work provides system designers with coarse grain design time information when setting realistic loads and time constraints.