Showing papers by "Christian Bonnet published in 2017"

Vehicles as Connected Resources: Opportunities and Challenges for the Future

Abstract: With the introduction of smartphones, cloud and edge computing, and mobile Internet, the automotive ecosystem is shifting toward the Internet of Vehicles (IoV). This article looks at the evolution leading to the IoV and identifies related research and engineering challenges, including 1) coexistence of cloud, edge computing, and data caching strategies at the edge; 2) integration of data processing and management as IoV services; and 3) seamless interoperability among vehicular sensors, computing platforms, and consumer devices. To address these challenges, we present an Internet of Things (IoT) architecture that considers vehicles as IoT resources and provides 1) mechanisms to integrate them in an IoV ecosystem and 2) seamless interoperation among components (e.g., vehicular sensors, computational platform, and consumers). The functional elements and operational stages of the architecture also assist in maintaining interoperability among the components.

Low latency MEC framework for SDN-based LTE/LTE-A networks

Abstract: Mobile Edge Computing (MEC) consists of deploying computing resources (CPU, storage) at the edge of mobile networks; typically near or with eNodeBs. Besides easing the deployment of applications and services requiring low access to the remote server, such as Virtual Reality and Vehicular IoT, MEC will enable the development of context-aware and context-optimized applications, thanks to the Radio API (e.g. information on user channel quality) exposed by eNodeBs. Although ETSI is defining the architecture specifications, solutions to integrate MEC to the current 3GPP architecture are still open. In this paper, we fill this gap by proposing and implementing a Software Defined Networking (SDN)-based MEC framework, compliant with both ETSI and 3GPP architectures. It provides the required data-plane flexibility and programmability, which can on-the-fly improve the latency as a function of the network deployment and conditions. To illustrate the benefit of using SDN concept for the MEC framework, we present the details of software architecture as well as performance evaluations.

An edge computing architecture integrating virtual IoT devices

Abstract: The current IoT ecosystem is Cloud centric which can not handle a diverse set of IoT applications and services, especially those demanding real time response. This paper proposes an Edge Computing (EC) architecture that provides an intermediate computing layer for IoT data. The proposed architecture utilizes Virtual IoT Devices (VID) for local data processing, management of physical IoT devices and quick reaction using actuators. A prototype of the EC system is developed and early performance results are reported. Applications of the EC system for roadside assistance services and emerging autonomous vehicles are outlined.

Extending DataTweet IoT Architecture for Virtual IoT Devices

Abstract: Virtual sensors promise numerous benefits to the Internet of Things (IoT) in terms of enhanced computing on sensor data, sensor fusion and overall cost reduction. But a wide deployment of this concept in the IoT system is not witnessed due to a lack of uniform approach to create and maintain them. Also, virtual actuators are not fully explored yet. This paper proposes a uniform mechanism to create and operate a virtual IoT device (VID) that can represent either a virtual sensor or a virtual actuator. VID integration into an IoT architecture is presented and operational phases are explained. Utilization of the virtual IoT devices in an horizon IoT application involving connected vehicles and Smart City is outlined. The main novel aspects of the work are - (i) remote creation of VID by a system administrator and/or a consumer, (ii) standard web technology based implementation of VID promoting interoperability in IoT architecture, (iii) generic and flexible deployment, (iv) support of discovery and management of VIDs through the IoT architecture and (v) ability to communicate to both smart and legacy IoT devices.

A scalable IoT framework to design logical data flow using virtual sensor

Abstract: During recent years, we have witnessed an explosion in the Internet of Thing (IoT) in terms of the number and types of physical devices. However, there are many limitations of these devices regarding their computing power, storage, and connection capabilities. They affect on-device processing of sensed data significantly. Centralized treatment of IoT data has proven challenging for many use cases demanding real time response. This paper aims at augmenting sensor data processing using the concept of virtual sensors. We propose a scalable virtual sensor framework that supports building a logical data-flow (LDF) by visualizing either physical sensors or custom virtual sensors. The process produces high-level information from the sensed data that can be easily perceived by machines and humans. A web-based virtual sensor editor (VSE) is also implemented on the top of the framework to simplify creation and configuration of the LDF. The VSE supports cross-platform and real-time verification for composed LDF. The paper also presents a catalog of supported virtual sensor type along with preliminary performance study.

Braking strategy for an autonomous vehicle in a mixed traffic scenario

Abstract: During the early deployment phase of autonomous vehicles, autonomous vehicles will share roads with conventional manually driven vehicles. They will be required to adjust their driving dynamically taking into account not only preceding but also following conventional manually driven vehicles. This paper addresses the challenges of adaptive braking to avoid front-end and rear-end collisions, where an autonomous vehicle is followed by a conventional manually driven vehicle. We illustrate via simulations the consequences of independent braking in terms of collisions, on both autonomous and conventional vehicles, and propose an adaptive braking strategy for autonomous vehicles to coordinate with conventional manually driven vehicles to avoid front and rear-end collisions.

Impact of localization errors on automated vehicle control strategies

Abstract: Coordinated vehicle control strategies aim at optimizing driving dynamics to increase traffic flow without impacting safety. These control strategies are based on the knowledge of the vehicles' state information like position and velocity obtained through Vehicle-to-everything (V2X) communications. Literature on control strategies yet assumes perfect positions, whereas position errors are in fact present and non negligible (e.g. GPS). As a consequence, these localization errors impact the control strategies by introducing uncertainty, which must be accounted for to minimize the probability of accidents. This paper qualifies and quantifies such uncertainty and proposes strategies to reduce it in a collision avoidance scenario. We notably relate these strategies to their impacts on traffic flow. More specifically, we model coordinated automated vehicles as a Model Predictive Control (MPC), integrate localization errors and evaluate its impact of the output to avoid accident. We then propose possibilities to mitigate accident-prone controls and quantify them on traffic flow. Our study illustrates that localization errors impact traffic flow by forcing future automated vehicles to increase gaps or reduce speed.

A collision mitigation strategy for intelligent vehicles to compensate for human factors affecting manually driven vehicles

Abstract: Human factors include human errors and human weaknesses such as long reaction time, limited visibility, limited jerk sustainability, etc. Such human factors account for a large portion of vehicular accidents involving manually driven vehicles (MDVs). Future Intelligent Vehicles (IVs) are expected to have higher perception ranges, faster reactions and coordinated mobility capability. At early market penetration, IVs will share road with MDVs and will adjust its driving dynamics according to the dynamics of other neighboring vehicles. In such a scenario, we investigate how IVs can compensate for human factors impacting MDVs and accordingly improve traffic safety in mixed traffic conditions. We model a centralized Model Predictive Control (MPC) based controller and integrate constraints derived from human factors. We show that IVs may not only reduce collisions, but also increase traffic density without negatively impacting traffic safety. We finally assess the impact of a gradual penetration of IVs in a mixed vehicle scenario.

An industrial IoT framework to simplify connection process using system-generated connector

Abstract: Industrial Internet of Thing (IIoT) promises a lot of positive impacts on manufacturing, process transformation and digital value acceleration. IIoT is considered to have the potential to launch fourth industrial revolution and related economies. However, since IIoT is at its early stage, its benefits are limited to connected devices and open data sources to enrich the measurements and analysis. In this paper, we propose an innovative IIoT framework that could automate the process of creating cloud-based middleware connector for things used in industrial settings. The framework significantly accelerates the configuration process for heterogeneous connections by using a light-weight and convenient connector template and supports the common set of protocols. Interoperability with other such implementations is preserved using ongoing IoT standardization.

An IoT framework for intelligent roadside assistance system

Abstract: The connected road infrastructure and roadside assistance services constitute an important consumer market segment in the Intelligent Transportation System (ITS) and Smart Cities. A closer look at available such services reveal the presence of data silos, heterogeneity and lack of interoperability. They affect the overall consumer experience and increase the cost of service development & maintenance. This paper proposes an IoT framework for next generation, intelligent roadside assistance system. A data centric architecture is presented along with solutions of the mentioned challenges.

Demonstrating Named Data Networking integration into DataTweet IoT Architecture

Abstract: Content Centric Networking (CCN) is an important aspect of the Future Internet Architecture CCN investigates the evolution from current host-centric network architecture of IP to data centric networking architecture This paper aims at demonstrating the integration of Named Data Networking (NDN) mechanism, an instantiation of CCN, in previously developed DataTweet Internet of Things (IoT) architecture The main accomplishment is in achieving and demonstrating interworking between the NDN and IoT paradigms We briefly describe the identified challenges, their solutions and a real prototype showcasing the integration of NDN into the DataTweet IoT Architecture Early results about performance evaluation and future works are outlined

Audio analysis using edge computing for road side assistance systems

Abstract: Although Cloud Computing remains de-facto solution for IoT data processing, Edge Computing (EC) has started to gain its ground. EC systems are closer to devices generating various data types (audio, video, text etc.) and have capabilities to provide quick data validation and analysis. This paper explores one such application of EC for audio analysis in road side assistance services. A noise cancellation algorithm running in the EC system is described. A prototype of the overall scenario is developed and reported.