Safety-Assured Model-Based Development of Real-Time Embedded Software for the Gpca Infusion Pump

Many safety-critical embedded systems must meet safety requirements associated with timing constraints. Not only shall a system read/write correct input or output values, but also those operations shall be performed with the right timing. Failing to meet those timing constraints results in serious safety issues (e.g., medical device malfunctions may harm patients). It is difficult to develop complex embedded software in a correct way without rigorous and systematic handling of various sources that affect the timed behavior of a system. We propose the model-based development framework that enables timing aspects of a system to be formally modeled, verified, and further implemented in a systematic way. The fundamental idea is to separate the timing concerns of the platform-independent and the platform-dependent aspects of a system. In the platform-independent development phase, input and output timed interactions between a system and its environment is modeled and verified using state-transition formalism (e.g., UPPAAL) by hiding platform-dependent timing details. In the platform-dependent development phase, such platform-dependent timing details are modeled using architectural modeling languages (e.g., AADL) that are necessary to execute the platform-independent code on a particular platform, such as internal interactions among software components (e.g., threads) and hardware components (e.g., sensors and actuators). The platform-independent code and the platform-dependent code are independently developed from the different levels of timing abstractions, and composed together in the integration phase. In this phase, we propose a way to systematically extend the platform-independent model into different platform-specific models, which formally characterize the implementation-level timed behavior that can be verified for timing requirement conformance. In case this verification step fails, we propose a way to adjust the timing parameters of the platform-independent code by compensating for the platform-dependent processing delays in such a way that the resulting implementation meets the timing requirements verified in the platform-independent model. Applicability of this development approach was demonstrated by developing software running on several Patient-Controlled Analgesia (PCA) infusion pump systems. We hope that this approach is also applicable to other safety-critical domains where generic software needs to be developed independently of a particular platform, and integrated with many different platforms in a way that conforms to timing requirements

Platform-Specific Code Generation from Platform-Independent Timed Models

Many safety-critical real-time embedded systems need to meet stringent timing constraints such as preserving delay bounds between input and output events. In model-based development, a system is often implemented by using a code generator to automatically generate source code from system models, and integrating the generated source code with a platform. It is challenging to guarantee that the implemented systems preserve required timing constraints, because the timed behavior of the source code and the platform is closely intertwined. In this paper, we address this challenge by proposing a model transformation approach for the code generation. Our approach compensates the platform-processing delays by adjusting the timing parameters in system models, based on an Integer Linear Programming problem formulation. We demonstrate the usefulness of our approach via a case study of infusion pump systems. Experimental results show that the code generated using our approach can better preserve the timing constraints

Real-Time Dynamic Map with Crowdsourcing Vehicles in Edge Computing

Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definition map, we investigate to share perception information among connected and automated vehicles. However, it is challenging to achieve real time perception sharing under varying network dynamics in automotive edge computing. In this paper, we propose a novel real time dynamic map, named LiveMap to detect, match, and track objects on the road. We design the data plane of LiveMap to efficiently process individual vehicle data with multiple sequential computation components, including detection, projection, extraction, matching and combination. We design the control plane of LiveMap to achieve adaptive vehicular offloading with two new algorithms (central and distributed) to balance the latency and coverage performance based on deep reinforcement learning techniques. We conduct extensive evaluation through both realistic experiments on a small-scale physical testbed and network simulations on an edge network simulator. The results suggest that LiveMap significantly outperforms existing solutions in terms of latency, coverage, and accurac