This paper presents our effort of using model-driven engineering to establish a safety-assured implementation of Patient-Controlled Analgesic (PCA) infusion pump software based on the generic PCA reference model provided by the U.S. Food and Drug Administration (FDA). The reference model was first translated into a network of timed automata using the UPPAAL tool. Its safety properties were then assured according to the set of generic safety requirements also provided by the FDA. Once the safety of the reference model was established, we applied the TIMES tool to automatically generate platform-independent code as its preliminary implementation. The code was then equipped with auxiliary facilities to interface with pump hardware and deployed onto a real PCA pump. Experiments show that the code worked correctly and effectively with the real pump. To assure that the code does not introduce any violation of the safety requirements, we also developed a testbed to check the consistency between the reference model and the code through conformance testing. Challenges encountered and lessons learned during our work are also discussed in this paper. ABSTRACTThis paper presents our effort of using model-driven engineering to establish a safety-assured implementation of Patient-Controlled Analgesic (PCA) infusion pump software based on the generic PCA reference model provided by the U.S. Food and Drug Administration (FDA). The reference model was first translated into a network of timed automata using the UPPAAL tool. Its safety properties were then assured according to the set of generic safety requirements also provided by the FDA. Once the safety of the reference model was established, we applied the TIMES tool to automatically generate platform-independent code as its preliminary implementation. The code was then equipped with auxiliary facilities to interface with pump hardware and deployed onto a real PCA pump. Experiments show that the code worked correctly and effectively with the real pump. To assure that the code does not introduce any violation of the safety requirements, we also developed a testbed to check the consistency between the reference model and the code through conformance testing. Challenges encountered and lessons learned during our work are also discussed in this paper.

As vehicles playing an increasingly important role in people's daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have attracted widespread attentions from both academia and industry. These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications. Computation offloading techniques, such as cloud, edge, and fog computing, can help CVs process computationintensive and large-scale computing tasks. Additionally, different cloud/edge/fog computing architectures are suitable for supporting different types of CV applications with highly different QoS requirements, which demonstrates the importance of the computing architecture design. However, most of the existing surveys on cloud/edge/fog computing for CVs overlook the computing architecture design, where they (i) only focus on one specific computing architecture and (ii) lack discussions on benefits, research challenges, and system requirements of different architectural alternatives. In this paper, we provide a comprehensive survey on different architectural design alternatives based on cloud/edge/fog computing for CVs. The contributions of this paper are: (i) providing a comprehensive literature survey on existing proposed architectural design alternatives based on cloud/edge/fog computing for CVs, (ii) proposing a new classification of computing architectures based on cloud/edge/fog computing for CVs: computation-aided and computation-enabled architectures, (iii) presenting a holistic comparison among different cloud/edge/fog computing architectures for CVs based on functional requirements of CV systems, including advantages, disadvantages, and research challenges, (iv) presenting a holistic overview on the design of CV systems from both academia and industry perspectives, including activities in industry, functional requirements, service requirements, and design considerations, and (v) proposing several open research issues of designing cloud/edge/fog computing architectures for CVs.

Deep Neural Networks (DNNs) have tremendous potential in advancing the vision for self-driving cars. However, the security of DNN models in this context leads to major safety implications and needs to be better understood. We consider the case study of steering angle prediction from camera images, using the dataset from the 2014 Udacity challenge. We demonstrate for the first time adversarial testing-time attacks for this application for both classification and regression settings. We show that minor modifications to the camera image (an L2 distance of 0.82 for one of the considered models) result in mis-classification of an image to any class of attacker's choice. Furthermore, our regression attack results in a significant increase in Mean Square Error (MSE) -by a factor of 69 in the worst case. 1

Medical cyber-physical systems (MCPS) are lifecritical, context-aware, networked systems of medical devices. These systems are increasingly used in hospitals to provide highquality continuous care for patients. The need to design complex MCPS that are both safe and effective has presented numerous challenges, including achieving high assurance in system software, intoperability, context-aware intelligence, autonomy, security and privacy, and device certifiability. In this paper, we discuss these challenges in developing MCPS, some of our work in addressing them, and several open research issues.

No abstract