Lightweight Virtualization as Enabling Technology for Future Smart Cars

International audienceModern vehicles are equipped with several interconnectedsensors on board for monitoring and diagnosispurposes; their availability is a main driver for the developmentof novel applications in the smart vehicle domain. Inthis paper, we propose a Docker-based container platform asa virtualization solution to implement customized smart carapplications. Through a proof-of-concept prototype—developedon a Raspberry Pi3 board—we show that a container-basedvirtualization approach is not only viable but also effective andflexible to manage several parallel processes running on board.More specifically, the platform can take priority-based decisionsby handling multiple inputs, e.g., data from the CANbus basedon the OBD II codes, video from the on-board webcam, andso on. Results are promising for the development of in-vehiclevirtualization techniques in future vehicles

CONSERVE: A framework for the selection of techniques for monitoring containers security

Context:\ua0Container-based virtualization is gaining popularity in different domains, as it supports continuous development and improves the efficiency and reliability of run-time environments.\ua0Problem:\ua0Different techniques are proposed for monitoring the security of containers. However, there are no guidelines supporting the selection of suitable techniques for the tasks at hand.\ua0Objective:\ua0We aim to support the selection and design of techniques for monitoring container-based virtualization environments.\ua0Approach: First, we review the literature and identify techniques for monitoring containerized environments. Second, we classify these techniques according to a set of categories, such as technical characteristic, applicability, effectiveness, and evaluation. We further detail the pros and cons that are associated with each of the identified techniques.\ua0Result:\ua0As a result, we present CONSERVE, a multi-dimensional decision support framework for an informed and optimal selection of a suitable set of container monitoring techniques to be implemented in different application domains.\ua0Evaluation:\ua0A mix of eighteen researchers and practitioners evaluated the ease of use, understandability, usefulness, efficiency, applicability, and completeness of the framework. The evaluation shows a high level of interest, and points out to potential benefits

LEGIoT: a Lightweight Edge Gateway for the Internet of Things

International audienceThe stringent latency together with the higher bandwidth requirements of current Internet of Things (IoT) applications, are leading to the definition of new network-infrastructures, such as Multi-access Edge Computing (MEC). This emerging paradigm encompasses the execution of many network tasks at the edge and in particular on constrained gateways that have also to deal with the plethora of disparate technologies available in the IoT landscape. To cope with these issues, we introduce a Lightweight Edge Gateway for the Internet of Things (LEGIoT) architecture. It relies on the modular characteristic of microservices and the flexibility of lightweight virtualization technologies to guarantee an extensible and flexible solution. In particular, by combining the implementation of specific frameworks and the benefits of container-based virtualization, our proposal enhances the suitability of edge gateways towards a wide variety of IoT protocols/applications (for both downlink and uplink) enabling an optimized resource management and taking into account requirements such as energy efficiency, multi-tenancy, and interoperability. LEGIoT is designed to be hardware agnostic and its implementation has been tested within a real sensor network. Achieved results demonstrate its scalability and suitability to host different applications meant to provide a wide range of IoT services

MOVE: Mobile Observers Variants and Extensions

Traffic state estimation is a fundamental task of Intelligent Transportation Systems. Recent advances in sensor technology and emerging computer and vehicular communications paradigms have brought the task of estimating traffic state parameters in real-time within reach. This has led to the main research question of this thesis: Can a vehicle accurately estimate traffic parameters using onboard resources shared through CV technology in a lightweight manner without utilizing centralized or roadside infrastructure? In 1954 Wardrop and Charlesworth proposed the Moving Observer method to measure traffic parameters based on an observed number of vehicle passes. We start by proposing methods for detecting vehicle passes using both radar and V2X as a well as with V2X only. Next, a modernization of the Moving Observer method, called the MO1 method, using the capabilities of modern vehicles is proposed which mitigates some of the limitations of the original method. The results show our method is able to provide estimates comparable to stationary observer methods, even in low ow scenarios. The MO2 method also utilizes two vehicles traveling in the same direction to determine a density between the two vehicles. Again, the results show this method provides estimates comparable to stationary observer methods, even in low ow scenarios. The MO3 method is similar to the MO2 method; however, here the two vehicles travel in oncoming traffic. In doing so, the vehicles\u27 relative velocity is large, leading us to hypothesize that the method will work well in urban traffic. The results for the MO3 method in urban traffic did not meet our expectations, which inspired us to develop the MO3-Flow method. The MO3-Flow method aggregates the counts of multiple vehicles to determine flow. The MO3-Flow method requires additional roadside infrastructure. To remove this need, a Virtual Road Side Unit architecture is proposed. This architecture uses vehicles on the roadway to act in place of roadside infrastructure. We show this architecture provides ample service coverage if the data image is sufficiently small