Because of the increasing complexity of energy systems and their interconnectedness with other urban and industrial infrastructures, simulation is essential for their design and optimized operation. We present i7-AnyEnergy as a stand-alone simulation framework that enables rapid modelling and simulation of the entire system. It combines concepts from software engineering, agent-based and distributed simulation, and cellular energy systems. Key concepts include the abstraction of energy, information, and other commodity flows, as well as a component-based design with an interface and filter concept that avoids explicit navigation through the model structure. This enables the development of cellular energy systems with hierarchical and neighbourhood relationships. Local load compensation at all levels is available as an integrated mechanism, but can be replaced by any other centralized, decentralized or mixed control strategy. We demonstrate the modelling using an illustrative example and outline studies in which the framework has been used.

In the context of satellite communication, unique challenges arise, such as long delay links that can impact the network performance also at the Transport Layer. With regard to the Transmission Control Protocol (TCP), congestion control plays a crucial role in managing network traffic and ensuring efficient data transmission over the Internet. This paper wants to give an overview of how TCP congestion control mechanisms adapt to mitigate the effects of long link delays, highlighting the importance of optimizing protocols for these specific environments. Within this context, where signals travel extended distances, traditional TCP algorithms designed for terrestrial networks with relatively low latency, may struggle to maintain optimal performance. As a result, specialized congestion control algorithms, like TCP CUBIC, can be employed to address these challenges. Through the integration of TCP CUBIC with HyStart within a simulation framework based on OMNeT++ and INET, this study sheds light on the importance of tailoring congestion control strategies to suit specific network environments including satellite communication.

Starlink is the largest and most powerful low Earth orbit satellite megaconstellation built to date. Since its debut in 2021, there has been an enormous interest in performance evaluations. https://starlinkstatus.space is a crowdsourced measurement platform based on the Ookla speed test. This paper presents its design, user statistics, and results: Almost 1.7 million measurements from 309 users and 29 countries. The median goodput rates are between 100 Mbit/s and 200 Mbit/s in the forward link (download), and between 10 Mbit/s and 20 Mbit/s in the return link (upload). Goodput rates in North America are lower than in other countries, and the time of day matters, with peak time performance down to a median of 50 Mbit/s. Median round trip times are between 40 ms and 50 ms. Latency under load seems to have improved recently. The hardware version of the standard user terminal does not seem to have an impact on the performance.

Edge computing enhances cloud architectures through the integration of distributed platforms that operate closer to end-users. With dynamically changing network topologies, user mobility, multiple access networks, demand-driven service orchestration, and consequently several distributed service instances that are available, methods for selecting edge resources must adapt to these conditions. This consideration is particularly relevant for edge computing, where target applications often have strict Quality of Service (QoS) constraints. This work proposes an edge resource selection scheme that considers several QoS parameters, computing load, and service usage cost. These metrics are employed to derive a selection score through four distinct algorithms. The overall performance of each algorithm is evaluated by scoring examples, a sensitivity analysis, and in a system-level simulation of a fifth generation (5G) cellular network with user mobility and multiple Multi-Access Edge Computing (MEC) platforms. The results show that all approaches perform well under unambiguous QoS and load conditions, i.e., very good or very poor, but some lose accuracy under moderate, but still acceptable, conditions. For the goal of cost-optimized selection while meeting QoS constraints, one novel method presented is significantly superior to the others. Used in simulation, it generates 12% more computing load on cheaper platforms than its closest competitor, and maintains significantly lower loads on high-cost platforms.

This paper aims to solve the challenge of quantifying the perfor- mance of Hardware-in-the-Loop (HIL) computer systems used for data re-injection. The system can be represented as a multiple queue and server system that operates on a First-In, First-Out (FIFO) basis. The task at hand involves establishing tight bounds on end-to-end delay and system backlog. This is necessary to optimise buffer and pre-buffer time configurations. Network Calculus (NC) is chosen as the basic analytical framework to achieve this. In the literature, there are different techniques for estimating arrival and service curves from measurement data which can be used for NC calcu- lations. We have selected four of these methods to be applied to datasets of industrial Timestamp Logging (TL). The problem arises because these conventional methods often produce bounds that are much larger (by a factor of 1000 or more) than the measured maximum values, resulting in inefficient design of HIL system pa- rameters and inefficient resource usage. The proposed approach, called TBASCEM, introduces a reverse engineering approach based on linear NC equations for estimating the parameters of arrival and service curves. By imposing constraints on the equation variables and employing non-linear optimization, TBASCEM searches for a burst parameter estimation which derives tight global delay bounds. In addition, TBASCEM simplifies the run-time measurement pro- cess, supporting real-time data acquisition to evaluate and optimise HIL system performance, and enhancing observability to adapt the HIL configuration to new sensor data. The benefits of TBASCEM are clearly that it enables an efficient performance logging of arrival and service curve parameters and with deriving tighter bounds in HIL systems, compared to evaluated state-of-the-art methods, mak- ing TBASCEM an invaluable tool for optimising and monitoring streaming applications in non-hard-real-time environments.

Paths with high delay and packet loss are problematic for transport protocols, especially when loss-based congestion control algorithms are used. Our topology of interest is a geostationary satellite path (round trip time approx. 600 ms) with additional packet loss, which could be caused by a lossy wireless local area network. In geostationary satellite networks, Performance Enhancing Proxies split TCP connections to improve performance, but this is not possible in case of encrypted transport protocols (VPNs, QUIC). This motivates the use of local loss recovery, i.e., avoiding packet loss on specific path segments. We briefly discuss related concepts, architectures, and protocols. We then evaluate local loss recovery using a TCP tunnel on the lossy link. By means of a simple emulation setup, we demonstrate that local loss recovery has great performance benefits if CUBIC is used. It is also shown that BBR works well for high-delay and lossy paths even without local loss recovery.

With the increased use of software in modern vehicles, the need for updates and data communication rises. While many vehicles are equipped with cellular communication technology, monetary costs are adding up for vehicle manufacturers. Cooperative downloading is a communication scheme, in which many vehicles cooperatively download an update and share the data between them. Through the use of direct communication, only small amounts of data have to be transferred from the internet to the vehicle. Besides using cellular communication for the initial transfer of data, the opportunistic use of WiFi access points has been proposed. To accurately evaluate the usefulness of access points, it is imperative to model their availability, which requires measurement data. We have used an extensive data set and introduced a novel approach to model the reception of access points on a street network. We have shown that even reduced connectivity with the WiFi access points benefits the cooperative downloading scheme, while the deployment of direct communication in the vehicles has larger benefits for the overall update progress. However, if WiFi is used as an additional technology in a cooperative downloading scheme, even its opportunistic availability greatly improves the overall system.

The Multi-Access Edge Computing (MEC) paradigm enables services that have strict network requirements like a maximum tolerable latency or a minimum bandwidth requirement. This consequently leads to a tight coupling of networks and computing, since the service can only function correctly if its communication requirements are met. Numerous existing strategies for MEC service deployment or resource selection put the characteristics of computing demands correlated with the free capacity of nodes or inter-service dependencies in their focus. In contrast, our work is a complementary attempt to enable especially the consideration of the strict communication needs of MEC services. It shows how edge features, i.e., knowledge of edge network conditions and service requirements, can be used to support resource selection and orchestration strategies. We consider and discuss how our model meQ can be applied in real communication systems as well as in heterogeneous networks, where clients may have multiple uplink connections. To enable communication-optimized selection strategies, meQ consists of two major components. A sharable, graph-based network representation and online searches to determine resources. The model benefits from existing work and limits the search space with the use of edge features. A performed simulation study demonstrates the benefits of meQ. The results show that it is possible to significantly reduce the search space, leading to lower search efforts also in large network graphs. An additional system-level simulation proves integration and applicability under realistic conditions.

With the growth of self-driving technologies, the reliability analysis of automated driving systems has received considerable attention from both academia and industry. Safety of the intended functionality (SOTIF) serves as one of the primary standards to assure the reliability and safety of the automated driving system. One of its key issues is the performance limitations of perception sensor systems. Generally, the reliability of the perception sensor system depends on the different scenarios of the driving environment. In this work, we investigate the sensor features and dependencies of the front camera and the top LiDAR of the nuTonomy scenes (nuScenes) dataset with respect to scenarios (e.g., rain and night) and distance information (e.g., two distance-based regions of interest). In addition, we apply the obtained parameters to a proven analytical reliability model to examine the impact of scenario and distance information on the reliability assessment.

Ultra-reliable and low-latency communication has received significant research attention. A key part of this evolution are the Time-Sensitive Networking (TSN) standards, which extend Ethernet with real-time mechanisms. To guarantee high reliability, the standard IEEE 802.1CB-2017 Frame Replication and Elimination for Reliability enables redundant communication over disjoint paths. While this mechanism is essential for time-critical applications, the standard contains some fundamental limitations that can compromise safety. Although some of these limitations have been addressed, none of the previous works provide solutions to these problems. This paper presents solutions to four main limitations of the IEEE 802.1CB-2017 standard. These are 1) choosing match versus vector recovery algorithm, 2) defining the length of the sequence history, 3) setting a timer to reset the sequence history, and 4) dimensioning the burst size in case of link failures. We show how these challenges can be solved by using best- and worst-case path delays of the network. We have performed simulations to illustrate the impact of the limitations and prove the correctness of our solutions. Thereby, we demonstrate how our solutions can improve reliability in TSN networks and propose these methods as guidance for users of the IEEE 802.1CB standard.

With the vast increase in software and digital services in the vehicular domain, communication networks play an increasingly important role. While some applications rely on fast data transfers, other tasks can be offloaded and delayed. For those, cooperative downloading schemes have been proposed, which utilize opportunistic communication, heterogeneous networks, and extended deadlines in a best effort approach. The efficiency of such communication depends on the chosen protocol and strategies, which impact the performance. We, therefore, introduce a novel hybrid encoding scheme for the encoding of relevant data. Additionally, we study the impact of reacting to data requests and initial distribution of data. We introduce a protocol logic for cooperative downloading and evaluate the overall downloading efficiency in simulation. A comparison between strategies reveals improvements of up to 31.8% in downloading progress and a time reduction averaging 46.4% for the best set of parameters, while also reducing the channel load significantly.

Developing and testing automated driving functions on public roads or proving grounds is time-consuming and expensive. Driving in virtual environments is increasingly seen as an eligible approach to counteract these drawbacks. The virtual environment is provided by one or more simulation tools and is coupled with software modules of the automated driving function. This results in a feedback loop that controls a virtual vehicle. Since simulation runs are carried out on off-the-shelf computers, determinism issues may occur due to race conditions. However, determinism is a desirable property in order to be able to reasonably benchmark the automated driving function. This work identifies execution patterns of software modules and shows how they can be synchronized by means of mapping to discrete event semantics. The resulting simulation setup produces repeatable results independent of the available computing and network power. The results are demonstrated using an exemplary driving scenario and compared to paced real-time simulation.

5G is on the rise in both automated driving and industrial automation. So far, these application areas have been considered separately, although the automotive industry can apply them jointly. We examine the future car factory use case Self-Navigating Automotive Production (SNAP), which derives from Automated Valet Parking (AVP). While SNAP benefits from increased flexibility and efficiency, current issues include the lack of AVP standardization and higher system complexity in the factory due to additional cameras, software, as well as worker and machine collaboration. Our concept represents a novel solution for car production and combines both ecosystems.

Quantitative evaluation of stochastic models supports early verification of design choices and assessment of non-functional requirements. Model Driven Engineering (MDE) leverages automated derivation of formal stochastic models from semi-formal artifacts of the Unified Modeling Language (UML) to facilitate deployment of quantitative evaluation methods without disrupting industrial practices. As a major limitation, when generally distributed (GEN) temporal parameters are considered to enhance the model expressivity, the structure and complexity of the underlying stochastic process cannot be easily controlled, possibly impairing the model analyzability. We present a hierarchical modeling formalism based on UML statecharts with GEN durations, designed to guarantee ease of modeling and efficient evaluation of steady-state or transient behaviour until absorption. To this end, fairly lax restrictions are applied to the model syntax to enable separate analysis of the Semi-Markov Process (SMP) underlying each model component. Scalability of solution is assessed by analyzing a suite of synthetic models referred to the context of timed Failure Logic Analysis (FLA) of component-based systems, specifically designed to point out each factor of computational complexity. Notably, the analysis derives both the probability that the system is in each step before failure and the Cumulative Distribution Function (CDF) of the duration of the overall failure process. A challenging case study that significantly and jointly stresses the main factors of computational complexity is finally addressed, performing steady-state analysis of a non-Markovian variant of a server virtualized system from the literature on software rejuvenation.

Multi-sensor systems are the key components of automated driving functions. They enhance the quality of the driving experience and assisting in preventing traffic accidents. Due to the rapid evolution of sensor technologies, sensor data collection errors occur rarely. Nonetheless, according to Safety Of The Intended Functionality (SOTIF), an erroneous interpretation of the sensor data can also cause safety hazards. For example the front-camera may not understand the meaning of a traffic sign. Due to safety concerns it is essential to analyze the system reliability throughout the whole development process. In this work, we present an approach to explore the sensor’s features, such as the dependencies between successive sensor detection errors and the correlation between different sensors on the KITTI dataset quantitatively. Besides, we apply the learned parameters to a proven multi-sensor system model, which is based on Discrete-time Markov chains, to estimate the reliability of a hypothetical Stereo camera-LiDAR based sensor system.

Development and testing of automated driving functions is complex and costly. Experts accord that it is necessary to cover most of the process by simulation. While tools and standards are evolving to satisfy this need, it is still challenging to generate appropriate driving scenarios for the simulation. In this paper we present a method for processing real driving data in order to generate maneuver-based scenarios for resimulation. We propose an automatic extraction of sequential, parametrized maneuvers - expressed in a high level format such as OpenSCENARIO. This enables to intuitively vary maneuver parameters and automatically generate whole sets of new discrete test scenarios, providing a link between simulation and real driving tests. The application of the method shows promising results in respect to the creation of meaningful scenarios with little loss in precision at reproducing the original driving tests.

For testing and validation of automated driving functions, simulations are absolutely essential to manage the required test effort. Therefore, the simulation models need to be modeled adequately in order to use the simulation results for virtual validation. As the required accuracy in virtual environment models is not clearly defined, this contribution investigates and quantifies accuracy requirements for the static domain of virtual environment models. By the use of an appropriate sensitivity analysis and a unique metric for the evaluation of simulation results suitable parameters are identified and statistically analyzed for validity and sensitivity assessment for a highway scenario. The results reveal that influences on the creation of virtual environment descriptions for automated driving could be derived and used for defining requirements in the generation and updating of virtual test fields.

Multipath QUIC is a promising protocol for future multipath communications. One early and often cited Multipath QUIC implementation has been presented by Quentin De Coninck and Olivier Bonaventure in 2017. Their implementation is based on quic-go and results are compared to Multipath TCP Version 0.91, showing performance benefits of Multipath QUIC in various scenarios. In this short paper, we reproduce their experiments and update Multipath TCP to Version 0.92 and later. More recent MPTCP implementations perform much better than Multipath TCP Version 0.91 and thus the performance gain compared to the mentioned Multipath QUIC implementation is not that significant anymore.

Remote infrastructure is going to assist prospective mobile services, but requires reliable and efficient network resources. Multi-Access Edge Computing (MEC) and cellular networks starting from the fifth generation (5G) are two of the enabling technologies that are necessary to meet strong service level requirements. When service constraints cannot be met all the time, the service may be malfunctioning or even impossible. Impact factors can be user mobility, the network architecture, flexible topology changes, and orchestration decisions. We discuss these challenges while considering the technical integration possibilities. Mainly, we develop a MEC network simulation with a 5G cellular network and user mobility. Our aim is to respect and focus on network conditions for MEC-dependent services and to generate performance indicators for an example task offloading application. We vary the available 5G resources, the number of involved MEC nodes, and application-level behavior like the offloaded task complexity, i.e., computing duration and offloading interval. Additionally, we examine connection-oriented and stateless communication. Our results show that a dense MEC network is able to improve round-trip time (RTT) and jitter in high-load situations and can have a positive impact on service availability and reliability.

Proprietary field buses such as PROFINET IRT, EtherNet/IP, and EtherCAT are currently used to ensure realtime communication in industrial automation appliances. Despite the fact that they are mostly Ethernet-based, interoperability between them is limited or not feasible at all. In the Industry 4.0 context, the goal is to enable real-time communication with ultralow latencies, while also permitting non-critical traffic and ensuring interoperability between devices from different manufacturers. Time-Sensitive Networking (TSN) is a promising candidate to meet these requirements on layer 2 of the OSI reference model in conjunction with a suitable higher-level protocol. The IEEE 802.1 TSN Task Group has been developing extensions to the Ethernet technology to make it more deterministic and reliable since 2012. In this paper, we have evaluated the TSN mechanisms (1) Time-Aware Shaper (TAS) and (2) frame preemption (FP) and compared them to the existing strict priority scheduling (ST) mechanism in exemplary setups. The chosen comparison criteria are latency guarantee, jitter, and the influence on non-critical traffic. Since the support of TSN devices on the market is low, we have carried out the evaluation using the simulation framework OMNeT++ and the NeSTiNg library. Our evaluation shows that, all in all, TAS performs the best, but it introduces a great configuration overhead. FP and ST can keep up in certain scenarios and their performance can even be improved.

Precise environment perception, which consists of multi-sensor systems, ensures the safety of the automated driving functions (ADFs). With the rapid evolution of sensor technologies, sensor data collection errors occur rarely. Nevertheless, accurate interpretation of the sensor data context, such as 3D multi-object tracking, is still full of challenges. Safety of the Intended Functionality (SOTIF) takes concern of the vulnerability of perception systems. The research of quantitative SOTIF analysis is still ongoing. In this paper, we propose a multi-sensor system model to observe both false negative and false positive errors in different field-of-views. Besides, we also extend a proven Markov-based approach, which takes dependencies between successive sensor errors and correlation between two dependent sensors into account, to model three correlated sensors sharing the same region of interest. In the end, we present a numerical example to illustrate the quantitative reliability analysis of a multi-sensor perception system according to SOTIF.

The architecture of modern vehicles is shifting from multiple rather loosely collaborating single-core systems to fewer tightly collaborating high-performance multi-core systems. This architectural shift is intended to be capable of managing the increased computational and communication effort that is required to run advanced driver assistance and automated driving software. The most widespread real-time execution model is based on bounded worst-case execution times of tasks, which can result in weaknesses with regard to data determinism in multi-core systems. Therefore, an alternative execution model in which the execution time is abstracted by a logical time has emerged. This execution model proved its feasibility in practical use cases on multi-core systems under the assumption, that data transmission can be performed in zero time. However, this assumption is difficult to meet, especially for distributed realtime systems. In this paper, we propose an execution model in which a logical transmission time is introduced in addition to the logical execution time. The proposed execution model not only allows the prerequisite of zero read and write times to be discarded, but also the scheduling of read and write processes at a fixed point in time is no longer necessary. Consequently, we validate the proposed execution model in a case study and discuss advantages and disadvantages.

For the simulation of radio communication, the Line of Sight (LoS) signal is of special interest in path loss models as it strongly influences the received signal strength and therefore has direct impact on simulation results. Often, for the determination of the LoS a probability function is used which relies on the distance between sender and receiver. However, in simulations that provide information about the infrastructure between sender and receiver, such an approach may be too imprecise. In this case, it would be useful to determine the LoS according to this underlying information. For this paper, the OMNeT++ framework 5G-Sim-V2I/N was extended to be able to compare three different approaches for the determination of the LoS. The framework uses channel models from the ITU-R M.2412-0 guidelines for the calculation of the path loss and also the probabilistic determination of the LoS described therein. The framework has been extended so that the coordinates of buildings can be used for the determination of the LoS in 2D and 3D cases. The differences between the considered approaches are highlighted by measurements of performance indicators of the physical layer (attenuation) and of the application layer (reliability).

For the scenario-based development and testing of automated and connected driving an unknown huge number of different driving scenarios is needed. In this paper we propose an approach that extracts driving scenarios from real driving data without any requirement of predefinitions or rules. Instead of searching for specific scenarios in the data, we cluster recurring patterns and interpret the resulting clusters as potential scenario groups. The method shows promising results. In the exemplary clustering we are able to detect four main scenario groups and corner cases within the clusters. With an huge amount of data this method could be used in the future to set up a scenario database in an automatic manner.

The development and testing of automated driving functions in the real world is costly and time-consuming. For this reason, software for automated driving is at first developed and tested in a virtual environment. The provision of the virtual environment requires the coupling of several simulation tools and models. The result is a co-simulation with a feedback loop from the automated driving function to the virtual environment, that does not necessarily exhibit deterministic behavior. However, determinism is a desirable property for developers to be able to analyze the automated driving function and for testers to be able to implement test automation. This paper identifies sources of non-determinism in co-simulation setups for automated driving and proposes a master-less coupling approach that allows repeatable executions. The results are demonstrated using an exemplary driving scenario and compared to a traditional coupling method that uses physical clock synchronization. The presented approach adds some overhead to the simulation modeling and execution, but ensures repeatable simulation results in any case.

The increasing precision in simulation models leads to a high computational effort. In general, parallel and distributed simulation is one technique to speed up a simulation to an acceptable runtime. Depending on the simulation model, the suitability of a parallel or distributed simulation varies. This applies also to coupled V2X simulations, where distributed computation may help to increase performance drastically. In this work, we identify opportunities and possibilities regarding distributed V2X simulations. Therefore, we describe and evaluate an approach to distribute multiple instances of the well-known Veins framework that are interacting and running concurrently. The instances are coupled using a hybrid co-simulation framework based on the High Level Architecture. With increasing traffic load, a higher speedup can be measured.

Vehicle-to-Infrastructure (V2I) via mobile communication technologies such as Long Term Evolution (LTE) or 5G NR (New Radio) is a current research topic. In the context of the vision of autonomous driving many stakeholders are interested in the capabilities and shortcomings of these technologies. Several simulation frameworks are available which can be used for evaluation in large traffic scenarios. Especially the channel model used on the physical layer is of interest, because it is crucial for the assessment whether messages could be exchanged successfully. In this paper we look into the framework SimuLTE and its provided 4G channel models taken from International Telecommunication Union (ITU) guidelines (M.2135) and compare it with the latest corresponding ITU-guidelines (M.2412) for 5G NR simulating two sample scenarios and analyzing the different outcomes.

Recent topics of interest such as smart cities and autonomous driving are currently in focus of many research activities. In this context, simulations are used to evaluate new algorithms, performance of current technologies, or the impact of upcoming products. In particular, they allow finding errors and optimizing parameter sets prospectively, prior to a real-world implementation. Simulation models of many traffic problems need to handle large-scale scenarios, connect entities from different domains, and run in feasible time. In order to meet these challenges, an extendable multi-level traffic simulation approach is proposed in this paper. We briefly introduce existing traffic simulation techniques, name upcoming problems, available solution approaches, and topics regarding the development of our framework. As a first step, we coupled two different resolution levels of traffic simulation by using High Level Architecture (HLA) and evaluated this approach in light of simulation results and simulation performance.

Multipath communication with heterogeneous channels is very challenging. In this paper, we describe the combination of terrestrial and satellite internet access links. We review related concepts for such a setup (MPTCP, CMT-SCTP, MPQUIC, Layer 3 Bonding). We propose that dynamically switching a single flow among heterogeneous communication links is a simple and efficient scheduling strategy. Finally, architectures and protocols (MOBIKE, PMIPv6, LISP) are discussed regarding their eligibility for multipath communication and heterogeneous link bonding.

Modern driver assistance systems for self-driving cars often rely on data collected by different sensors to determine the necessary system decisions. To prevent system failures, different validation techniques are used. The development is often split between car manufacturers and suppliers, whereby the requested test effort is one main project acceptance criterion. Already available effort estimation methods are not applicable, because they rely on implementation details that do not exist at early phases or on project experiences or individual expert expectations, which are not reliable enough to be employed as trustworthy source. Therefore, we provide in this paper an analytic approach for the computation of the error probability of a multi-sensor system. Based on this, we can give estimations for the test effort such that with statistical confidence no errors of the sensor system can be expected during the tests. The approach is able to take both the dependencies between successive sensor errors and the correlation between different sensors into account, mainly by using discrete time Markov chains. The provided approach therefore allows to design multi-sensor systems such that a specified overall error probability can be met and to give an estimation for the upper bound of the test effort.

Modern driver assistance systems are often using a wide range of equipped sensors as primary data source. The reliability of each sensor is specified by the manufacturer and influences the system reliability distinctly. To prevent potential fatal system failures, diverse failure prevention mechanisms are included. Nevertheless, a certain level of reliability of the system has to be guaranteed to meet legal regulations. For this, extensive testing is required, which is costly due to the involved resources. To estimate and simulate the required test effort, an exact analytical method based on Markov Chains and an implementation realized in a common simulation framework is presented in this paper. It enables real automotive projects to estimate the test costs and simulate changes for various sensor setups.

This paper introduces a novel machine learning approach for performing binary decision-making tasks under uncertainty. Reducing the regression test effort of safety-critical black box systems is a safety-critical task as system failures would remain undetected if corresponding failing test cases are not executed. The uncertainty of potentially undetected system failures persists due to the lack of implementation knowledge of black-box systems. We refer to executing test cases as a costly labeling process due to required special test equipment and testing time. However, we model a binary classifier for selecting test cases. Accordingly, only fault revealing test cases should be selected and thus executed in order to reduce the overall cost of the regression test effort. On the one side, the classifier's specificity has to be maximized. On the other side, the classifier's sensitivity has to meet a specific quality-level since the number of undetected system failures should be limited. We will show in an industrial case study the benefits of our classifier where we reduce the regression test effort of safety-critical systems. The experimental results indicate that our implemented classifier outperforms other machine learning approaches in terms of sensitivity.

We consider regression testing of safety-critical systems consisting of black-box components. This scenario is common for automotive electronic systems where testing time is expensive and should be reduced without uncontrolled reduction of reliability. This requires a methodology to select test cases from a larger test suite for which a defined quality level of the resulting regression test cycle can be provided. With this in mind, we propose a method to select test cases based on a stochastic model. We are modeling test case failure probabilities as dependent random variables, therefore already observed test results have an influence on the estimation of further test case failure probabilities. Based on an information theoretical approach, we validate the mutual differential information degree between test case failure probabilities and compute a function which returns the risk for not selecting a test case, i.e., the probability of not selecting a failing test case. Depending on the mutual differential information significant reductions of testing time can be achieved while testing reliability is preserved at a quantifiable high level. We will validate theoretically our results and will show in an industrial case study the benefits of our method.

Simulation has become an important method for the evaluation of Vehicular Ad-Hoc Network (VANET) applications and protocols. These simulations often work on the link level with simulation time far off from wall clock time, making them impracticable to be deployed for the performance analysis of real hardware in Hardware-In-The-Loop (HIL) environments or prototype vehicles. Probabilistic communication models can help in this regard, however often suffer from considerably lower accuracy than link-level simulations. It was shown that determining whether a communication link is of Line-of-Sight (LOS) or Non-Line-of-Sight (NLOS) nature greatly increases the quality of probabilistic communication models. This paper presents such an LOS probability model for VANETs in urban and rural environments. We find that typical urban environments such as rural, urban, and industrial areas show similar characteristics across different German and European cities. We utilize this to derive a probabilistic LOS model that reliably predicts LOS constellations and outperforms related work. Our model therefore enables the development of accurate probabilistic packet success or packet arrival rate models to investigate VANET technology in real-time environments.

The switch from gasoline-powered vehicles to electric vehicles (EVs) is an important step to reduce greenhouse gas emissions. To this end, many European countries announced EV stock targets, e.g., Germany aims to have one million EVs on the roads by 2020. To achieve these goals and to handle the range limitation of EVs, a widespread publicly accessible charging infrastructure is needed. This paper provides a dynamic spatial and temporal simulation model for the building of charging infrastructure on a municipality scale. We evaluate empirical data about the timely utilization of different charging stations in the German federal state of Bavaria. This data is used to derive empirical models for the start time and duration of charging events as well as the popularity of charging stations. We develop a lightweight discrete event simulation model which can be used to investigate different expansion strategies, e.g., based on the load of charging stations or the number of successful and failed charging attempts. We show the applicability of our model using the German federal state of Bavaria as a use case.

The development of modern driver assistance systems in the automotive domain requires extensive testing, for safety as well as for legal reasons. This is especially the case for sensor-based systems that provide support for autonomous driving: if they fail, an accident may occur with fatal consequences. In the majority of cases, the required test effort is roughly estimated by means of previous project data, expert knowledge or based on economical factors. As an alternative, a general mathematical approach is presented in this paper, which is focused on black box systems with sensor data fusion. It enables new projects related with sensor data fusion to estimate the required test effort for a given scenario.

Nowadays factory automation customers require that industrial automation networks are capable of transmitting real-time I/O data and non-time-critical data. PROFINET, one of the most used industrial Ethernet standards available today [1], promises that “everything [can be transmitted] on one cable” [2]. Nevertheless, the standard also recommends that the non-time-critical data sent by a device is limited to 3 kilobytes per millisecond to avoid network overload [3]. In this paper we discuss the need for controlling the non-time-critical data sent to PROFINET networks. Since Microsoft Windows is an often used operating system in industrial automation today [4] we show that its built-in traffic shaping capabilities are not appropriate for industrial automation networks since it is not possible to limit the bursts sent by applications. Thus, we developed our own industrial traffic shaper that is able to limit the burst and the rate per non-time-critical flow. This enables us to apply Network Calculus to obtain guaranteed performance bounds for PROFINET networks that both transmit real-time I/O and non-time-critical data [5]. Both the analysis and the Windows traffic shaper are now provided by the Siemens AG as Siemens Network Planner (SINETPLAN) [6]. Our work is not limited to PROFINET but also applicable to Industrial Ethernet technologies based on IEEE 802.3 such as EtherNet/IP.

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Smart meters are a crucial part of the smart grid, yet they arise several privacy concerns. The fine-grained consumption readings, can expose information about the household such as types of devices inside the house. In this paper we introduce two methods which enable a household to hide private issues. The first method, which is called One Level Consumption (OLC), tries to exploit demand response components such as battery, elastic load and distributed generators to maintain a constant consumption throughout the whole day. The second method, which is called Maximum Difference (MD), obfuscates the smart meter readings before sending them to the utility company. Nevertheless, the transmitted values are valid for billing. Taking Electric Vehicle (EV) charging signal as an example, we have shown that it is possible to hide most charging signals.

Replacing combustion vehicles with electric vehicles is a major step towards reducing CO 2 emissions. With regard to this shift, in particular commercial or governmental vehicle fleets are believed to be early adopters. However, the electrification of vehicle fleets is a complex task since it can have a huge impact on companies' day-to-day-business. This article presents a lightweight simulation framework to support the planning process and to study important effects of vehicle fleet electrification. Our discrete event-based approach allows to investigate economical and environmental aspects, such as the impact on operating costs, the required electric energy, or the amount of CO 2 emissions. Based on a given trip schedule (consisting of start time, distance, and duration), the framework allows deriving all necessary information, among others to dimension both the vehicle fleet and the charging infrastructure. We show the usefulness and the capability of our model by presenting results for a mid-sized company's vehicle fleet comprising 100 vehicles.

Outline and present building blocks for a comprehensive open source location privacy evaluation framework to enable researchers to reproducibly assess the effectiveness of a given privacy protection algorithm. We also present a proof of concept evaluation. By extending the well established Veins simulation framework [4] that couples the traffic simulator SUMO and the network simulator OMNeT++ we allow for an easy setup and integration with existing simulation scenarios or already implemented protocols. We hope that our framework lowers the complexity of privacy evaluation and thereby makes certain protection measures more likely to be considered in future vehicular networks.

The transition from classic combustion engine vehicles to electric vehicles is a major step to reduce worldwide CO2 emissions. In order to correctly and efficiently investigate impacts on the electric grid and the dimensioning of charging infrastructure, or to explore new technologies for a further increase of the driving range, realistic simulation models are required. In this paper, we present an accurate yet computationally inexpensive battery and kinematic model to be used in microscopic traffic simulation to help study the performance of thousands of electric vehicles. Our model also supports recuperation and range extender modules while only relying on the vehicle's speed and a set of constant predefined parameters. It can therefore be easily coupled with current sophisticated traffic simulators. We show its applicability and correctness regarding the State of Charge and power flows using comprehensive real-life experiments.

Many reasons lead to embedded systems getting more complex than ever. The higher integration and interconnection and the additional effort spend for safety-critical functions, require new techniques to support the testing process. Beneath using model-driven test techniques, test engineers need support to take the right decisions on test case selection and prioritization during the whole development process. Available approaches on test selection and prioritization lack in adaptability to different testing techniques. We previously presented our approach of modeling components and dependences of a system, such as black-box systems, using Component-Dependency-Models (CDMs) as shown in earlier publications. In this paper we present our tool CDM-Suite in detail and show the way it can be used by test engineers. We therefore start with the presentation of our approach and discussing related tools. The next section describes the graphical user interface (GUI) and the different ways of interaction for data generation. This point includes the introduction of metrics build upon fuzzy logic and graph analysis. At last we show how to interpret analysis results and derive knowledge for test case selection and prioritization. We sum the paper up by giving a conclusion and a presentation outline.

Indoor hovering objects such as quadrotors need to be controlled continuously to hold their position in space. In order to support fully autonomous flights of these copters, the necessary position control including all related information transfers have to be provided in a fully decentralized and autonomous manner. We discuss challenges related to flight control based on our Autonomous Localization Framework (ALF), which provides scalable and decentralized localization in GPS-denied areas. Using a sensor network based on the IEEE 802.15.4 communication protocol, continuous position maintenance is feasible but, unfortunately, in no way stable. Therefore, we introduce a low-cost sensor array, which reduces the system dynamics and allows a robust position control of the platform.

Embedded systems in automotive engineering are getting more and more complex due to a higher rate of integration and shared usage of sensor signals. A common solution to testing these systems is deriving test cases from models, so called model-based testing. In practice, generated test suites are typically very huge and have to be reduced by methods of regression-test selection and prioritization. In our field of application, we additionally suffer from the lack of knowledge on system internals like the source code. Therefore, our approach is based on dependences between the components of embedded systems. The model we use is derived from the system architecture and system requirements. We are using graph algorithms for selecting and prioritizing test cases to run on a certain system version. First statistical evaluations and the current implementation already have shown promising reduction of test-cases for regression testing.

Increasingly intelligent energy-management and safety systems are developed to realize safe and economic automobiles. The realization of these systems is only possible with complex and distributed software. This development poses a challenge for verification and validation. Upcoming standards like ISO 26262 provide requirements for verification and validation during development phases. Advanced test methods are requested for safety critical functions. Formal specification of requirements and appropriate testing strategies in different stages of the development cycle are part of it. In this paper we present our approach to formalize the requirements specification by test models. These models serve as basis for the following testing activities, including the automated derivation of executable test cases from it. Test cases can be derived statistically, randomly on the basis of operational profiles, and deterministically in order to perform different testing strategies. We have applied our approach with a large German OEM in different development stages of active safety and energy management functionalities. The test cases were executed in model-in-the-loop and in hardware-in-the-loop simulation. Errors were identified with our approach both in the requirement specification and in the implementation that were not discovered before.

Many safety applications in Intelligent Transportation Systems (ITS) require vehicles to be aware of the presence of nearby cars, but - especially in urban and suburban regions - buildings and other obstacles may block radio transmissions. In the literature, multi-hop relaying by neighboring cars has been demonstrated to perform well at disseminating safety broadcasts in the presence of obstacles. At night, in areas with low traffic density, or when the penetration rate of Car-2-X devices is low, however, there are likely to be too few relaying cars available. This again leads to the problem that vehicles which are not in line-of-sight frequently cannot be sensed either. To the best of our knowledge, we are the first to help overcome this problem by utilizing parked cars as relay nodes. We study the effectiveness and the necessity of this approach with the help of extensive simulative studies and real life experiments. We show how, for scenarios with few equipped cars, the utilization of parked cars proves crucial to support safety applications. When disseminating safety critical events in a realistic scenario, parked cars can increase cooperative awareness by over 40% in total.

We present a fully self-organizing approach for creating and maintaining a reference coordinate system for self-localization in Sensor and Actor Networks (SANETs). GPS technology has become a de facto standard for outdoor localization, however, self-localization in GPS denied scenarios is still extremely challenging. Typically, anchor nodes or global state information are used to update the nodes' location information. In contrast, we present a fully self-organizing strategy to generate a distributed reference coordinate system. In particular, we use autonomous robot systems to span and maintain this coordinate system. In particular, we investigated the capabilities of the Mass-Spring-Relaxation (MSR) algorithm, which is frequently used for fault-tolerant and robust localization. Unfortunately, this algorithm needs certain globally valid state information. We extended the MSR algorithm in two ways: First, we made the algorithm independent of a priori global knowledge, and, secondly, we provide extensions that make the algorithm more reliable and robust, and to reduce the number of necessary information exchanges between the nodes. As can be seen from the simulation results, our advanced MSR is very accurate and clearly outperforms the classical MSR for increasing network sizes. We also validated the simulations in an experimental setting. The obtained results confirm the very high localization accuracy.

Embedded systems become increasingly complex and distributed. Although there is necessity for thourough testing, exhaustive validiation and verification is hardly possible in industry due to time and resource restrictions. In the past the reason for this has often been that it was to time-consuming to specify, to execute, and to evaluate test cases for the first design models and the integrated embedded system. In the meantime methods have become popular in industry that allow the automated generation, execution, and evaluation of test cases. In order to be able to automate these steps all necessary information must be integrated into the models that are the basis for the following steps. The complexity of the system, however, makes the evaluation and assessment of the behavior of the system even more complex. The growth of information needed for this comes along with it. The integration of this information into the model which is used for the generation of test cases is hardly feasible. In this paper we describe how this issue can be addressed by the combination of reference models in MATLAB/Simulink® with test models. Time Usage Models (TUM) are employed as test models and provide the basis to generate all possible test scenarios. Model based statistical testing with consideration of time and durations is supported by TUMs. The reference models are used like an executable specification, providing information for the evaluation of the system to be tested. The test model can therefore be kept generic in order to be able to derive virtually any test case from the model, taking account of the potentially infinite sequence of inputs reactive systems might process. We applied the presented approach with a german automotive OEM for the validation and verification of the energy management system.

The aviation community is currently working on the standardization of data communication systems for the future air traffic management. In this context, the International Civil Aviation Organization (ICAO) has initiated a work on standardization of an IPv6-based aeronautical telecommunications network and on future radio access technologies, respectively. In this paper, we integrate L-Band Digital Aeronautical Communications System Option 1 (L-DACS 1), which is one candidate for future radio access technologies, with realistic IPv6-based network layer functionality and analyze Deficit Round Robin (DRR) with fragmentation algorithm for the forward and return link in L-DACS 1. Our analysis mainly covers two application domains: file transfer and real-time services. We show that DRR with fragmentation scheduler provides good performance results in terms of throughput, delay, and bandwidth fairness.

We present the Virtual Cord Protocol (VCP), which exploits virtual coordinates to provide efficient and failure tolerant routing and data management in sensor networks. VCP maintains a virtual cord interconnecting all the nodes in the network and which, operating similar to a Distributed Hash Table (DHT), provides means for inserting data fragments into sensor nodes and retrieving them. Furthermore, it supports service discovery using indirections. VCP uses two mechanisms for finding paths to nodes and associated data items: First, it relies on the virtual cord that always provides a path toward the destination. Second, locally available neighborhood information is exploited for greedy routing. Our simulation results show that VCP is able to find paths close to the shortest path (achieving a stretch ratio of less than 125 percent) with very low overhead. We also extended VCP with data replication mechanisms to improve failure handling. The routing performance of VCP, which clearly outperforms other ad hoc routing protocols such as Dynamic MANET On Demand (DYMO), is similar to other virtual addressing schemes, e.g., Virtual Ring Routing (VRR). However, we improved VCP to handle frequent node failures in an optimized way. The presented results outline the capabilities of VCP to handle such cases more efficiently compared to other protocols. We also compared the capabilities to reliably store and retrieve data in the network to Geographic Hash Tables (GHTs). VCP, in the worst case, performs similar to GHTs, but outperforms this protocol in most cases, especially when complex routing is involved.

Recently, many efforts have been made to develop more efficient Inter-Vehicle Communication (IVC) protocols for on-demand route planning according to observed traffic congestion or incidents, as well as for safety applications. Because practical experiments are often not feasible, simulation of network protocol behavior in Vehicular Ad Hoc Network (VANET) scenarios is strongly demanded for evaluating the applicability of developed network protocols. In this work, we discuss the need for bidirectional coupling of network simulation and road traffic microsimulation for evaluating IVC protocols. As the selection of a mobility model influences the outcome of simulations to a great extent, the use of a representative model is necessary for producing meaningful evaluation results. Based on these observations, we developed the hybrid simulation framework Veins (Vehicles in Network Simulation), composed of the network simulator OMNeT++ and the road traffic simulator SUMO. In a proof-of-concept study, we demonstrate its advantages and the need for bidirectionally coupled simulation based on the evaluation of two protocols for incident warning over VANETs. With our developed methodology, we can advance the state-of-the-art in performance evaluation of IVC and provide means to evaluate developed protocols more accurately.

The aviation community is currently working on the standardization of data communication systems for the future air traffic management. In this context, the ICAO and EUROCONTROL are working on the standardization of IP-based aeronautical telecommunications network and future radio access technologies, respectively. With this work, for the first time, we integrate L-DACS 1, which is one candidate for future radio access technologies, with realistic IP-based network layer functionality and analyze the handover delay performance. We first investigate the effect of link layer retransmissions on handover delay performance. We realized that for regions with low signal-to-noise ratio (BER of 1e-3), link layer retransmissions improve the total handover delay (layer 2 and 3) by about 80%. Considering regions with high signal-to-noise ratio (BER of 1e-5), the benefit of link layer retransmissions becomes negligible due to the reduced number of packet losses. During our analysis, we notice frequent transmission of router advertisement messages causing significant overhead on L-DACS 1 and propose two approaches in order to decrease the overhead to an acceptable range. In the last section, we tackle the increase in handover delay due to a limited number of return link resource request opportunities in congested cells.

In the automotive industry requirements are often still composed of natural language text, spreadsheets, drawings, and formal models. Models are often used to describe partial aspects from the whole set of requirements. Hence, flaws and vagueness in requirements are common and hard to discover. Upcoming standards like ISO 26262 request the automotive industry to be more strict and formal on the requirements. Formal notation and unambiguitiy is explicitely stated. In the field of system and acceptance testing requirements are the basis for all activities. Although, requirements are often not close to testing. To overcome this we introduced the Timed Usage Model (TUM) as a formal representation of requirements specification. During the creation of the model the requirements are analyzed and brought into an unambiguous and formal representation. Traceability is achieved, as each path in the model must be based upon a requirement. The formulation of the requirements in form of an unambiguous model clarifies the requirements and helps to detect design errors. During the creation of the model omissions and flaws in the requirements are discovered. The model serves as a communication medium when functionality responsibles are involved to clarify these aspects. Timed Usage Models were created for power train functionality and the energy management. Moreover, the model as a formalized representation of the requirements served as the basis for the whole testing process, including test planning, test case generation, and test campaign analysis.

In embedded automotive real-time systems correct timing is required to effectively implement distributed applications, time-triggered communication, and integration of intelligent safety applications. We use formal schedulability analysis to validate the task timing for asynchronous real-time systems in an automotive context. Restricting the computational model to purely time-triggered task activation and applying application specific constraints enables us to perform exact response time analyses without pessimism. The analysis can be done efficiently since our computational model drastically limits the number of critical instant candidates to be considered for the worst-case scenario. Simulation results show that the original computational model incorporates a significant amount of response time overestimation and a high computational workload. The presented method can be applied in early design stages as well as in series development and helps to reduce development risks for electronic control units and embedded telematic systems.

The development of economic and hybrid vehicles is only possible with complex systems to control the energy management and engine. The amount of embedded electronics and software to realize these systems increases continuously, and hence the complexity of the integration. The functional validation and testing of these systems is of increasing importance, as these systems have an increasing impact on other systems like comfort and safety functionalities. So there is a need for thourough testing. However, exhaustive testing is not possible as testing in industry is limited by time and resources. So a method is needed to derive systematically the most significant test cases in order to be able to assess the behavior of the system. We applied test models to accomplish this task. A Timed Usage Model served as a formal requirement specification and was the basis for following test activities. The model provides the possibility to describe timing and data dependencies of the system to be tested. The test planning and test case generation was supported by the models. The appliance of models allowed the systematic generation of test cases and the assessment of the significance of the conducted test activities with respect to the coverage of requirements.

We discuss the need for taking the environmental impact as a primary metric for evaluating the quality of algorithms for Intelligent Transportation Systems (ITS). In many studies, the travel time, or its minimization, has been used to demonstrate the advantages of Inter-Vehicle Communication (IVC) solutions combined with dynamic rerouting. Such evaluations are frequently based on simulation experiments. Recently, much progress has been achieved in this domain by coupling network simulation with road traffic microsimulation. We now investigate the relationship between different metrics used for the evaluation, in particular the environmental impact represented by gas consumption and emissions versus the travel time, highlighting cases where both metrics are conflicting.

Abstract The IEEE 802.15.4 protocol has become the primary solution for many low‐rate wireless personal area network (LR‐WPAN) applications. This is especially the case for industrial sensor network applications such as automation control. We contribute to the better understanding of the protocol behavior by presenting a set of results of simulation experiments. Our results outline the capabilities of this protocol in the selected scenarios but also the limitations. In particular, we investigated the dependency of the protocol on protocol‐inherent parameters such as the beacon order and the superframe order but also to different traffic load. Our results can be used for planning and deploying IEEE 802.15.4‐ based sensor networks with specific performance demands. We put a special focus on application scenarios in industrial sensor network applications. The primary requirements are reduced end‐to‐end latency and energy consumption. Our studies are based on our new implementation of IEEE 802.15.4 developed for the simulation framework OMNeT++. Copyright © 2009 John Wiley & Sons, Ltd.

The use of wireless technology is continuously gaining interest in industrial automation. With the standardization of the IEEE 802.15.4 protocol, low-power sensor network protocols have been introduced in this field. Recently, we investigated the real-time communication capabilities of this protocol. This study is now extended by incorporating the security mechanisms as provided by the protocol standard. In contrast to other papers, which studied the effectiveness of these security techniques, we are interested in whether the real-time capabilities are affected by encryption and message authentication and to which extend. Based on extensive simulations, we investigated the interdependency of protocol parameters and available security options. The results can be used for optimally selecting such parameters according to the quality of service requirements of the application scenario.

Modern cars comprise tens of electronic control units, serving for various purposes like entertainment, driver's comfort or occupant protection. To connect these networked embedded systems inside the car, widespread bus systems like CAN or FlexRay are used, each one offering projecting abilities and specific limitations for certain application areas. In order to point out the suitability of a certain in-car network topology for the transmission of highly timeand safety-critical data content, we investigated effects of routing by a central gateway on the communication delay. In cooperation with the German car manufacturer Audi, we installed a prototype measurement infrastructure and collected meaningful communication data samples for up-to-date and future in-car networks. The results we obtained from the data samples allow for a sophisticated evaluation of the in-car communication network with respect to the demands of time- and safety-critical functions at an early stage of system development.

Debugging and analyzing wireless sensor networks (WSNs) are important tasks for improving the quality and performance of the network. In this paper, Pimoto is to be presented, which is a distributed passive monitoring system implemented for debugging and analyzing WSNs. It is based on a hierarchical structure allowing to monitor different networks simultaneously and to analyze the obtained information at a dedicated PC. The system relies on three components. The first element is the monitoring node. It intercepts the radio packets in the vicinity and sends received packet information to a gateway using a second radio interface in order to prevent intrinsic interactions with the sensor network operation. The gateway has the ability to communicate directly with the monitoring node and to transfer all the collected monitoring data to the third component, a dedicated PC (server), in the hierarchy using standard TCP/IP communication. The packets are analyzed and visualized on the server using the standard network monitoring and analyzing tool Wireshark. The most important characteristic of this monitoring concept is the passive operation, i.e. the normal operation in the WSN is not influenced by the analyzer.

Efficient data management techniques are needed in wireless sensor networks (WSNs) to counteract issues related to limited resources, e.g. energy, memory, bandwidth, as well as limited connectivity. Self-organizing and cooperative algorithms are thought to be the optimal solution to overcome these limitations. On an abstract level, structured peer-to-peer protocols provide O(1) complexity for storing and retrieving data in the network. However, they rely on underlayer routing techniques. In this paper, we present the virtual cord protocol (VCP), a virtual relative position based efficient routing protocol that also provides means for data management, e.g. insert, get, and delete, as known from typical distributed hash table (DHT) services. The key contributions of this protocol are independence of real location information by relying on relative positions of neighboring nodes, short virtual paths because successors and predecessors are in their vicinity, and high scalability because only information about direct neighbors is needed for routing. Furthermore, VCP inherently prevents dead-ends and it is easy to be implemented.

We present Virtual Cord Protocol (VCP), a virtual relative position based routing protocol for sensor networks that also provides methods for data management as known from standard DHT services. Self-organizing and cooperative algorithms are thought to be the optimal solution to overcome the inherent resource limitations in sensor networks. On an abstract level, DHT techniques offer O(1) complexity data lookup. Unfortunately, they usually rely on underlayer routing techniques. The key contributions of VCP are the independence of real location information by relying on relative positions of neighboring nodes, successors and predecessors in the cord are always in their vicinity, and the high scalability because only information about direct neighbors are needed for routing. Furthermore, VCP inherently prevents dead-ends and it is easy to be implemented.

Modern cars comprise a multitude of electronic features which are implemented in tens of communicating control units. To connect these in-car embedded systems, the CAN bus offers a sustainable performance, hence it is used as a widespread communication infrastructure, even for safety critical applications. However, CAN media access is priority based and performed competitive and non-preemptive. Thus, assessing the worst case end-to-end delay is inevitable in order to provide safe and efficient operation of functions with hard real-time properties. In this paper, we use the analytical method of network calculus to determine guaranteed upper bounds for transmission delays of all CAN priorities. We demonstrate the applicability of our approach by investigating current real-life CAN communication data from the German car manufacturer Audi.

This paper presents an efficient and numerically reliable method for the transient analysis of deterministic and stochastic Petri nets. The transient behavior is described by state equations derived by the method of supplementary variables. Significant features of the proposed solution algorithm of fourth order are an automatic stepsize control and a two-stage relative error control. Furthermore, a formal way of dealing with discontinuities in the transient state equations is developed. This resolves the problems posed by initially enabled deterministic transitions and also improves the accuracy of numerical results. Experiments with a queueing system with failure and repair illustrate the efficiency (with respect to both CPU-time and memory space) and the numerical quality of the new algorithm.

Stochastic Petri nets (SPNs) and extensions are a popular method for evaluating a wide variety of systems. In most cases, their numerical solution requires generating a state-level stochastic process, which captures the behavior of the SPN with respect to a set of specified performance measures. These measures are commonly defined at the net level by means of a reward variable. In this paper, we discuss issues regarding the generation of state-level reward models for systems specified as stochastic activity networks (SANs) with "step-based reward structures". Step-based reward structures are a generalization of previously proposed reward structures for SPNs and can represent all reward variables that can be defined on the marking behavior of a net. While discussing issues related to the generation of the underlying state-level reward model, we provide an algorithm to determine whether a given SAN is "well-specified" A SAN is well-specified if choices about which instantaneous activity completes among multiple simultaneously-enabled instantaneous activities do not matter, with respect to the probability of reaching next possible stable markings and the distribution of reward obtained upon completion of a timed activity. The fact that a SAN is well specified is both a necessary and sufficient condition for its behavior to be completely probabilistically specified, and hence is an important property to determine.

Non-Markovian models allow us to capture a very wide range of circumstances in which it is necessary to model phenomena whose times to occurrence is not exponentially distributed. Events such as timeouts in a protocol, service times at a machine performing the same task on each part, and memory access or instruction execution in a low-level h/w or s/w model, have durations which are constant or with a very low variance. Phase-type distributions can be used to approximate a non-exponential, but they increase the size of the state space.The analysis of stochastic systems with non-exponential timing is of increasing interest in the literature and requires the development of suitable modeling tools. Recently, some effort has been devoted to generalize the concept of Stochastic Petri Nets (SPN), by allowing the firing times to be generally distributed.A particular case of non-Markovian SPN, is the class of Deterministic and SPN (DSPN) [1]. A DSPN is a non-Markovian SPN where, in each marking, at most one transition is allowed to have a deterministic firing time with enabling memory policy.A new class of stochastic Petri nets has recently been defined [2, 3] by generalizing the deterministic firing times of the DSPN to generally distributed firing times. The underlying stochastic process for these classes of Petri nets is a Markov Regenerative Process (MRGP). This observation has opened a very fertile line of research aimed at the definition of solvable classes of models whose underlying marking process is an MRGP, and therefore referred to as Markov Regenerative Stochastic Petri Nets (MRSPN).Some of the results in this filed will be described in the session. In particular, Ciardo investigates stochastic confusion by defining the selection probability for transitions attempting to fire at the same time. German introduces the "method of supplementary variables" for the derivation of state equations describing the transient behavior of the marking process. Puliafito describes how, under some constraints, concurrent enabling of several generally distributed timed transitions is allowed. Bobbio and Telek discuss how age memory policy can be included to capture preemptive mechanisms of the resume (prs) type.

Stochastic Petri nets (SPN's) with generally distributed firing times can model a large class of systems, but simulation is the only feasible approach for their solution. We explore a hierarchy of SPN classes where modeling power is reduced in exchange for an increasingly efficient solution. Generalized stochastic Petri nets (GSPN's), deterministic and stochastic Petri nets (DSPN's), semi-Markovian stochastic Petri nets (SM-SPN's), timed Petri nets (TPN's), and generalized timed Petri nets (GTPN's) are particular entries in our hierarchy. Additional classes of SPN's for which we show how to compute an analytical solution are obtained by the method of the embedded Markov chain (DSPN's are just one example in this class) and state discretization, which we apply not only to the continuous-time case (PH-type distributions), but also to the discrete case.>