AutoPlug: An Automotive Test-bed for Electronic Controller Unit Testing and Verification

In 2010, over 20.3 million vehicles were recalled. Software issues related to automotive controls such as cruise control, anti-lock braking system, traction control and stability control, account for an increasingly large percentage of the overall vehicles recalled. There is a need for new and scalable methods to evaluate automotive controls in a realistic and open setting. We have developed AutoPlug, an automotive Electronic Controller Unit (ECU) test-bed to diagnose, test, update and verify controls software. AutoPlug consists of multiple ECUs interconnected by a CAN bus, a race car driving simulator which behaves as the plant model and a vehicle controls monitor in Matlab. As the ECUs drive the simulated vehicle, the physicsbased simulation provides feedback to the controllers in terms of acceleration, yaw, friction and vehicle stability. This closedloop platform is then used to evaluate multiple vehicle control software modules such as traction, stability and cruise control. With this test-bed we highlight approaches for runtime ECU software diagnosis and testing of the stability and performance of the vehicle. Code updates can be executed via a smart phone so drivers may remotely “patch” their vehicle. This closedloop automotive control test-bed allows the automotive research community to explore the capabilities and challenges of safe and secure remote code updates for vehicle recalls management

Adaptive Distributed Caching for Scalable Machine Learning Services

<p>Applications for Internet-enabled devices use machine learning to process captured data to make intelligent decisions or provide information to users. Typically, the computation to process the data is executed in cloud-based backends. The devices are used for sensing data, offloading it to the cloud, receiving responses and acting upon them. However, this approach leads to high end-to-end latency due to communication over the Internet. This dissertation proposes reducing this response time by minimizing offloading, and pushing computation close to the source of the data, i.e. to edge servers and devices themselves. To adapt to the resource constrained environment at the edge, it presents an approach that leverages spatiotemporal locality to push subparts of the model to the edge. This approach is embodied in a distributed caching framework, Cachier. Cachier is built upon a novel caching model for recognition, and is distributed across edge servers and devices. The analytical caching model for recognition provides a formulation for expected latency for recognition requests in Cachier. The formulation incorporates the effects of compute time and accuracy. It also incorporates network conditions, thus providing a method to compute expected response times under various conditions. This is utilized as a cost function by Cachier, at edge servers and devices. By analyzing requests at the edge server, Cachier caches relevant parts of the trained model at edge servers, which is used to respond to requests, minimizing the number of requests that go to the cloud. Then, Cachier uses context-aware prediction to prefetch parts of the trained model onto devices. The requests can then be processed on the devices, thus minimizing the number of offloaded requests. Finally, Cachier enables cooperation between nearby devices to allow exchanging prefetched data, reducing the dependence on remote servers even further. The efficacy of Cachier is evaluated by using it with an art recognition application. The application is driven using real world traces gathered at museums. By conducting a large-scale study with different control variables, we show that Cachier can lower latency, increase scalability and decrease infrastructure resource usage, while maintaining high accuracy.</p

Benchmarking Wireless Protocols for Feasibility in Supporting Crowdsourced Mobile Computing

International audienceRecent advances in mobile device technology have triggered research on using their aggregate computational and/or storage resources to form edge-clouds. Whilst traditionally viewed as simple clients, smartphones and tablets today have hardware resources that allow more sophisticated software to be installed, and can be used as thick clients or even thin servers. Simultaneously, new standards and protocols, such as Wi-Fi Direct and Wi-Fi TDLS (Tunneled Direct Link Setup), have been established that allow mobile devices to talk directly with each other, as opposed to over the Internet or across Wi-Fi access points. This can, potentially, lead to ubiquitous, low-latency, device-to-device (D2D) communication. In this paper, we study whether D2D protocols can support mobile-edge clouds by benchmarking different protocols and configurations for a specific application. The results show that decentralized device-to-device techniques can be used to efficiently disseminate multi- media contents while diminishing contention in the wireless infrastructure, allowing for up to 65 % traffic reduction at the access points