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The IEEE802.11p standard describes a protocol for car-to-X and mainly for car-to-car-communication. It has found its place in hardware and firmware implementations and is currently tested in various field tests. In the research project Ko-TAG, which is part of the research initiative Ko-FAS, cooperative sensor technology is developed and its benefit for traffic safety applications is evaluated. A secondary radar principle based on communication signals enables localization of objects with simultaneous data transmission. It mainly concentrates on the detection of pedestrians and other vulnerable road users (VRU), but also supports pre crash safety applications. The Ko-TAG proposal enriches the current IEEE802.11p real-time characteristics needed for precise time-of-flight real-time localization. This contribution describes the development of a subsystem, which extends the functionality of IEEE802.11p and fits into the regulatory schemes. It discusses the approach for definition and verification of the protocol design, while maintaining the close coexistence with existing IEEE802.11p subsystems. System simulations were performed and hardware was implemented. The next step will be field measurements to verify the simulation results.
In recent years, the topic of embedded machine learning has become very popular in AI research. With the help of various compression techniques such as pruning, quantization and others compression techniques, it became possible to run neural networks on embedded devices. These techniques have opened up a whole new application area for machine learning. They range from smart products such as voice assistants to smart sensors that are needed in robotics. Despite the achievements in embedded machine learning, efficient algorithms for training neural networks in constrained domains are still lacking. Training on embedded devices will open up further fields of applications. Efficient training algorithms would enable federated learning on embedded devices, in which the data remains where it was collected, or retraining of neural networks in different domains. In this paper, we summarize techniques that make training on embedded devices possible. We first describe the need and requirements for such algorithms. Then we examine existing techniques that address training in resource-constrained environments as well as techniques that are also suitable for training on embedded devices, such as incremental learning. At the end, we also discuss which problems and open questions still need to be solved in these areas.
The Metering Bus, also known as M-Bus, is a European standard EN13757-3 for reading out metering devices, like electricity, water, gas, or heat meters. Although real-life M-Bus networks can reach a significant size and complexity, only very simple protocol analyzers are available to observe and maintain such networks. In order to provide developers and installers with the ability to analyze the real bus signals easily, a web-based monitoring tool for the M-Bus has been designed and implemented. Combined with a physical bus interface it allows for measuring and recording the bus signals. For this at first a circuit has been developed, which transforms the voltage and current-modulated M-Bus signals to a voltage signal that can be read by a standard ADC and processed by an MCU. The bus signals and packets are displayed using a web server, which analyzes and classifies the frame fragments. As an additional feature an oscilloscope functionality is included in order to visualize the physical signal on the bus. This paper describes the development of the read-out circuit for the Wired M-Bus and the data recovery.
Institute of Reliable Embedded Systems and Communication Electronics, Offenburg University of Applied Sciences, Germany has developed an automated testing environment, Automated Physical TestBeds (APTB), for analyzing the performance of wireless systems and its supporting protocols. Wireless physical networking nodes can connect to this APTB and the antenna output of this attaches with the RF waveguides. To model the RF environment this RF waveguides then establish wired connection among RF elements like splitters, attenuators and switches. In such kind of set up it’s well possible to vary the path characteristics by altering the attenuators and switches. The major advantage of using APTB is the possibility of isolated, well controlled, repeatable test environment in various conditions to run statistical analysis and even to execute regression tests. This paper provides an overview of the design and implementation of APTB, demonstrates its ability to automate test cases, and its efficiency.
In this paper, we study the runtime performance of symmetric cryptographic algorithms on an embedded ARM Cortex-M4 platform. Symmetric cryptographic algorithms can serve to protect the integrity and optionally, if supported by the algorithm, the confidentiality of data. A broad range of well-established algorithms exists, where the different algorithms typically have different properties and come with different computational complexity. On deeply embedded systems, the overhead imposed by cryptographic operations may be significant. We execute the algorithms AES-GCM, ChaCha20-Poly1305, HMAC-SHA256, KMAC, and SipHash on an STM32 embedded microcontroller and benchmark the execution times of the algorithms as a function of the input lengths.
To demonstrate how deep learning can be applied to industrial applications with limited training data, deep learning methodologies are used in three different applications. In this paper, we perform unsupervised deep learning utilizing variational autoencoders and demonstrate that federated learning is a communication efficient concept for machine learning that protects data privacy. As an example, variational autoencoders are utilized to cluster and visualize data from a microelectromechanical systems foundry. Federated learning is used in a predictive maintenance scenario using the C-MAPSS dataset.
The efficient support of Hardwae-In-theLoop (HIL) in the design process of hardwaresoftware-co-designed systems is an ongoing challenge. This paper presents a network-based integration of hardware elements into the softwarebased image processing tool „ADTF“, based on a high-performance Gigabit Ethernet MAC and a highly-efficient TCP/IP-stack. The MAC has been designed in VHDL. It was verified in a SystemCsimulation environment and tested on several Altera FPGAs.
Automatic Meter Reading (AMR) is a major enabler for the upcoming smart grid. Potentially, it will be one of the first really large-scale M2M-communication solutions for sensor applications.
To date, the definition of the standardized communication stacks for Local Metrological Network (LMN) in AMR is still ongoing. This holds true both for ZigBee Smart Energy Profile and for Wireless M-Bus according to EN 13757. During this process, there is the necessity for flexible, albeit optimized solutions, which support the different existing and upcoming versions of the communication protocols. In the case of Wireless M-Bus, the major contender for European and possibly Asian installations, this is valid not only for the different operation modes (C-, N-, P-, Q-, R-, S-, and T-modes), which work in different frequencies (i.e. 868 MHz, 433 MHz, and 169 MHz) but also for the application layer, where additional bodies, like EN137575, Open Metering System (OMS) Group, or national bodies follow their approaches.
This contribution describes requirements, design techniques and experiences from the development of highly efficient Wireless M-Bus protocol stacks with support of good flexibility and portability between microcontroller platforms and RF-transceivers. The presented approach is not limited to the use of modern software engineering design processes, as such, but also includes essential additional features like testing or simulation, as well as tools for commissioning and monitoring.
The research project Ko-TAG [2], as part of the research initiative Ko-FAS [1], funded by the German Ministry of Economics and Technologies (BMWi), deals with the development of a wireless cooperative sensor system that shall pro-vide a benefit to current driver assistance systems (DAS) and traffic safety applications (TSA). The system’s primary function is the localization of vulnerable road users (VRU) e.g. pedestrians and powered two-wheelers, using communication signals, but can also serve as pre-crash (surround) safety system among vehicles. The main difference of this project, compared to previous ones that dealt with this topic, e.g. the AMULETT project, is an underlying FPGA based Hardware-Software co-design. The platform drives a real-time capable communication protocol that enables highly scalable network topologies fulfilling the hard real-time requirements of the single localization processes. Additionally it allows the exchange of further data (e.g. sensor data) to support the accident pre-diction process and the channel arbitration, and thus supports true cooperative sensing. This paper gives an overview of the project’s current system design as well as of the implementations of the key HDL entities supporting the software parts of the communication protocol. Furthermore, an approach for the dynamic reconfiguration of the devices is described, which provides several topology setups using a single PCB design.
The Thread protocol is a recent development based on 6LoWPAN (IPv6 over IEEE 802.15.4), but with extensions regarding a more media independent approach, which – additionally – also promises true interoperability. To evaluate and analyse the operation of a Thread network a given open source 6LoWPAN stack for embedded devices (emb::6) has been extended in order to comply with the Thread specification. The implementation covers Mesh Link Establishment (MLE) and network layer functionality as well as 6LoWPAN mesh under routing mechanism based on MAC short addresses. The development has been verified on a virtualization platform and allows dynamical establishment of network topologies based on Thread's partitioning algorithm.