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VLDB Endowment, Copenhagen, 16/08/2021; International audience; This paper explores the data cleaning challenges that arise in using WiFi connectivity data to locate users to semantic indoor locations such as buildings, regions, rooms. WiFi connectivity data consists of sporadic connections between devices and nearby WiFi access points (APs), each of which may cover a relatively large area within a building. Our system, entitled semantic LOCATion cleanER (LOCATER), postulates semantic localization as a series of data cleaning tasks-first, it treats the problem of determining the AP to which a device is connected between any two of its connection events as a missing value detection and repair problem. It then associates the device with the semantic subregion (e.g., a conference room in the region) by postulating it as a location disambiguation problem. LOCATER uses a bootstrapping semi-supervised learning method for coarse localization and a probabilistic method to achieve finer localization. The paper shows that LOCATER can achieve significantly high accuracy at both the coarse and fine levels.

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; This paper describes the collaborative effort between privacy and security researchers at nine different institutions along with researchers at the Naval Information Warfare Center to deploy, test, and demonstrate privacy-preserving technologies in creating sensor-based awareness using the Internet of Things (IoT) aboard naval vessels in the context of the US Navy's Trident Warrior 2019 exercise. Funded by DARPA through the Brandeis program, the team built an integrated IoT data management middleware, entitled TIPPERS, that supports privacy by design and integrates a variety of Privacy Enhancing Technologies (PETs), including differential privacy, computation on encrypted data, and fine-grained policies. We describe the architecture of TIPPERS and its use in creating a smart ship that offers IoT-enabled services such as occupancy analysis, fall detection, detection of unauthorized access to spaces, and other situational awareness scenarios. We describe the privacy implications of creating IoT spaces that collect data that might include individuals' data (e.g., location) and analyze the tradeoff between privacy and utility of the supported PETs in this context.

International audience; Messaging and communication is a critical aspect of next generation Internet-of-Things (IoT) systems where interactions among devices, software systems/services and end-users is the expected mode of operation. Given the diverse and changing communication needs of entities, the data exchange interactions may assume different protocols (MQTT, CoAP, HTTP) and interaction paradigms (point to point, multicast, unicast). In this paper, we address the issue of supporting adaptive communications in IoT systems through a mediation-based architecture for data exchange. Here, components called mediators support protocol translation to bridge the heterogeneity gap. Aiming to provide a placement of mediators to nodes, we introduce an integer linear programming solution that takes as input: a set of Edge nodes, IoT devices, and networking semantics. Our proposed solution achieves adaptive placement resulting in timely interactions between IoT devices for larger topologies of IoT spaces.

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.

Internet of Things (IoT) aim to leverage data from multiple sensors, actuators and devices for improving peoples' daily life and safety. Multiple data sources must be integrated, analyzed from the corresponding application and notify interested stakeholders. To support the data exchange between data sources and stakeholders, the publish/subscribe (pub/sub) middleware is often employed. Pub/sub provides additional mechanisms such as reliable messaging, event dropping, prioritization, etc. The event dropping mechanism is often used to satisfy Quality of Service (QoS) requirements and ensure system stability. To enable event dropping, basic approaches apply finite buffers or data validity periods and more sophisticated ones are information-aware. In this paper, we introduce a pub/sub mechanism for probabilistic event dropping by considering the stakeholders' intermittent connectivity and QoS requirements. We model the pub/sub middleware as a network of queues which includes a novel ON/OFF queueing model that enables the definition of join probabilities. We validate our analytical model via simulation and compare our mechanism with existing ones. Experimental results can be used as insights for developing hybrid dropping mechanisms.

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; Resource allocation for distributed systems is a well-known approach to deal with quality of service requirements. However, existing approaches do not consider the effects of resource allocation at the different levels of a system, especially when considering the end-to-end behavior of distributed compositions such as service choreographies. Based on our previous research on the optimized and QoS-aware resource allocation for service choreographies and publish-subscribe brokers, we outline a novel approach that integrates the treatment of resource allocation at both levels in orderto yield a more precise control over end-to-end QoS. The paper presents an early architecture and discusses some of the main challenges towards realizing the approach.

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.

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; 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.

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.

Dendritic morphology is a key determinant of how individual neurons acquire a unique signal processing profile. The highly branched dendritic structure that originates from the cell body, explores the surrounding 3D space in a fractal-like manner, until it reaches a certain amount of complexity. Its shape undergoes significant alterations under various physiological or neuropathological conditions. Yet, despite the profound effect that these alterations can have on neuronal function, the causal relationship between the two remains largely elusive. The lack of a systematic approach for remodeling neural cells and their dendritic trees is a key limitation that contributes to this problem. Such causal relationships can be inferred via the use of large-scale neuronal models whereby the anatomical plasticity of neurons is accounted for, in order to enhance their biological relevance and hence their predictive performance. To facilitate this effort, we developed a computational tool named REMOD that allows the structural remodeling of any type of virtual neuron. REMOD is written in Python and can be accessed through a dedicated web interface that guides the user through various options to manipulate selected neuronal morphologies. REMOD can also be used to extract meaningful morphology statistics for one or multiple reconstructions, including features such as sholl analysis, total dendritic length and area, path length to the soma, centrifugal branch order, diameter tapering and more. As such, the tool can be used both for the analysis and/or the remodeling of neuronal morphologies of any type.

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.

International audience; The smart cities of the future are expected to be serviced by advanced, personalized multimodal transit systems, charged with timely transport of citizens. Optimizing routes on such networks is a complex problem, in part due to the fact that simple metrics such as latency by themselves are not sufficient to find the best routes. In this paper, we focus on the problem of providing commuters with personalized routes with the most convenience. We present our mathematical model of user convenience during a multi-leg journey, and the overview of a middleware for enabling convenient transit (including ensuring acceptable network connectivity to mobile apps) by using crowdsourcing. We also report on initial insights obtained through empirical studies on network connectivity and user-perception of convenience in Delhi, India, and Paris, France.