Unmanned Micro-aerial vehicles (MAVs) enjoy high popularity in various application fields. They are capable of flying autonomously, following the instructions received from a controlling ground entity. One physical limitation of all mobile robotic vehicles is the restricted energy storage capacity they are able to carry. All processes in a robotic system, prominently in-air movements, consume energy and are thereby defining the overall operation time limit. This paper presents results of an analysis of the energy consumption in various discrete movement states of a multicopter, measured for two different systems. Findings regarding a systematic relation between system and movement parameters and the energy consumption levels are discussed. Furthermore, a generic energy consumption profile model is presented and validated.

Communication is often considered as the most costly component of a wireless sensor node. As a result, a variety of technologies and protocols aim to reduce the energy consumption for the communication especially in the Internet of Things context. In order to select the best suitable technology for a given use case, a tool that allows the comparison of these options is needed. The goal of this work is to introduce a new modular modeling framework that enables a comparison of various technologies based on analytical calculations. We chose to model the cost for a single data transfer of arbitrary application data amounts in order to provide flexibility regarding the data amount and traffic patterns. The modeling approach covers the stack traversal of application data and thus in comparison to other approaches includes the required protocol overhead directly. By applying our models to different data amounts, we are able to show trade-offs between various technologies and enable comparisons for different scenarios. In addition, our results reveal the impact of design decisions that can help to identify future development challenges.

This work examines the applicability of the relatively young radio standard Bluetooth Low Energy (Bluetooth Smart) for mesh-enabled applications. To achieve this, a demonstrator application is used in multiple measurements to analyze the packet delivery ratio of different setups. Since no official mesh implementation for Bluetooth Low Energy (BLE) has been released to this date, the proprietary, BLE-based CSRmesh protocol is used in this study. Besides the measurement campaign, several simulations were performed in order to study the suitability of Bluetooth Mesh in large-scale networks. Our results show that Bluetooth Mesh is a promising technology for mesh applications but additional effort is required during the ongoing standardization process to exploit its full potential.

Almost all rotating machinery in the industry has bearings as their key building block and most of these machines run 24 × 7. This makes bearing health prediction an active research area for predictive maintenance solutions. Many state of the art Deep Neural Network (DNN) models have been proposed to solve this. However, most of these high performance models are computationally expensive and have high memory requirements. This limits their use to very specific industrial applications with powerful hardwares deployed close the the machinery. In order to bring DNN-based solutions to a potential use in the industry, we need to deploy these models on Microcontroller Units (MCUs) which are cost effective and energy efficient. However, this step is typically neglected in literature as it poses new challenges. The primary concern when inferencing the DNN models on MCUs is the on chip memory of the MCU that has to fit the model, the data and additional code to run the system. Almost all the state of the art models fail this litmus test since they feature too many parameters. In this paper, we show the challenges related to the deployment, review possible solutions and evaluate one of them showing how the deployment can be realized and what steps are needed. The focus is on the steps required for the actual deployment rather than finding the optimal solution. This paper is among the first to show the deployment on MCUs for a predictive maintenance use case. We first analyze the gap between State Of The Art benchmark DNN models for bearing defect classification and the memory constraint of two MCU variants. Additionally, we review options to reduce the model size such as pruning and quantization. Afterwards, we evaluate a solution to deploy the DNN models by pruning them in order to fit them into microcontrollers. Our results show that most models under test can be reduced to fit MCU memory for a maximum loss of $3\%$ in average accuracy of the pruned models in comparison to the original models. Based on the results, we also discuss which methods are promising and which combination of model and feature work best for the given classification problem.

The Internet of Things (IoT) enables users to gather and analyze data from a large number of devices. Knowledge obtained by these systems is valuable in order to understand, control, and enhance the monitored process. The mass of information to process leads however to new challenges related to required resources for both data processing and data transportation. Two critical metrics are latency and consumed energy to complete a given task. Both metrics might be exceed if all processing is done locally at the sensor device level. Cloud and Fog computing concepts can help to mitigate this effect. However, using such offloading concepts add complexity and overhead to the system. In this paper, we study the latency for processing and communication tasks in a distributed IoT systems with respect to cloud or fog offloading and derive characteristic cost functions for the studied tasks. Our results give valuable insights into the tradeoffs and constraint within our example scenario. The developed characterization methodology can however be applied to any kind of IoT system and thus allowing more general analysis.

Vehicle-to-Pedestrian (V2P) communication can help to prevent crashes between vehicles and pedestrians by extending the detection range of participants on collision course. This can be achieved by exploiting the inherent scanning and detecting features of the communication systems. However, there are several challenges to be solved if a communication-based approach is used to detect and actively prevent possible crashes. In this paper, we first discuss these challenges and then focus on communication aspects. More specifically, the focus is on the required reliability of the system that has to ensure the successful and timely delivery of crucial messages. Therefore, we evaluate the performance of V2P communication based on safety beaconing mechanisms in a typical intersection scenario in terms of delivery ratio and channel access. Both metrics describe the current networking situation and can have significant impact on the reliability of a crash prevention system. Our results show that the required beaconing intervals for early detection can add a significant load to the network and thus the delivery of crucial messages cannot be guaranteed with the default V2P setup due to increasing channel congestion. Finally, we discuss various options to limit the congestion.