An Event-based Local Action Paradigm to Improve Energy Efficiency in Queriable Wireless Sensor Actuator Networks

Wireless sensor networks (WSN) are deployed in a multitude of applications both in industrial and academic fields. In recent years, due to the emerge of Internet of Things (IoT) technologies and Vehicle2X communication scenarios, novel challenges for wireless sensor network platforms - regarding hardware and software - arose. Thus, challenges known from big data processing have reached the WSN scope and consequently approaches and methods have been devised to handle these. One such approach is queriable wireless sensor networks which enable their users the specification of sensing tasks in a declarative way without the need to re-program nodes in case the application requirements change. As many current WSN applications feature active parts with which nodes can directly influence their environment, the term wireless sensor actuator networks (WSAN) has been coined, setting such networks apart from solely passively measuring networks.In this article, we will present a short introduction to big data processing in wireless sensor networks which motivates the usage of queriable networks. We will show that in order to enable a WSAN to carry out actions energy-efficiently and in a timely manner, an event-based action model is favorable. Additionally, we will demonstrate how such an event system can be used to improve sub query performance in WSNs. We conclude with an evaluation regarding the benefit of combining this approach with wake-up receiver technologies based on a qualitative energy efficiency definition for WSN

Low Latency Reliable Data Sharing Mechanism for UAV Swarm Missions

The use of Unmanned Aerial Vehicle (UAV) swarms is increasing in many commercial applications as well as military applications (such as reconnaissance missions, search and rescue missions). Autonomous UAV swarm systems rely on multi-node interhost communication, which is used in coordination for complex tasks. Reliability and low latency in data transfer play an important role in the maintenance of UAV coordination for these tasks. In these applications, the control of UAVs is performed by autonomous software and any failure in data reception may have catastrophic consequences. On the other hand, there are lots of factors that affect communication link performance such as path loss, interference, etc. in communication technology (WIFI, 5G, etc.), transport layer protocol, network topology, and so on. Therefore, the necessity of reliable and low latency data sharing mechanisms among UAVs comes into prominence gradually. This paper examines available middleware solutions, transport layer protocols, and data serialization formats. Based on evaluation results, this research proposes a middleware concept for mobile wireless networks like UAV swarm systems

Towards Collection of Smart City Data for Cloud Storage Using UAVs

The article describes the methodology and process of collecting smart city data using drones for cities that do not have a sufficiently developed infrastructure. For storage and subsequent analysis of data, a cloud server is required; TUC DriveCloud is presented as an example of such a server in the article. Traffic analysis and building inspection are described as examples of drone data collection tasks. The advantages and disadvantages of collecting data using a thermal imaging camera are also discussed using the example of the problem of detecting and tracking the movement of people

A new evaluation model for e-learning programs

This paper deals with a measure theoretical model for evaluation of e-learning programs. Based on methods of general measure theory an evaluation model is developed which can be used for assessment of complex target structures in context of e-learning programs. With the presented rating function target structures can be evaluated by a scoring value which indicates how the targets in sense of a given logical target structure has been reached. A procedure is developed for the estimation of scoring values for target structures based on adapted assessment checklists

Automated Evaluation of Smart City Data from Cloud-System

Smart city data processing is an important taskfor the promotion and development of smart cities. The articledescribes and presents the types of smart city data, discusses theexisting modern methods and approaches to the processing ofsmart city data, such as pre-processing, assessment and analysis,and their tasks. This article contains architectural solutions andmethods used in the developed automated smart city dataevaluation system. There is also a detailed description of theintegration of the developed system with the DriveCloud cloudserver for receiving and storing smart city data

Editorial

This editorial introduces the second issue of the EmbeddedSelforganising Systems (ESS) journal with the focus on embedded,wireless sensor networks and upcoming, innovativeapplication scenarios

Editorial

This editorial introduces the third volume of the Embedded Selforganising Systems (ESS) journal

Automated Identification of Wood Surface Defects Based on Deep Learning

Wood plates are widely used in the interior design of houses primarily for their aesthetic value. However, considering its esthetical values, surface defect detection is necessary. The development of computer vision and CNN-based object detection methods has opened the way for wood surface defect detection process automation. This paper investigates deep-learning applications for automatic wood surface defect detection. It includes the evaluation of deep learning algorithms, including data generation and labeling, preprocessing, model training, and evaluation. Many adjustments regarding the dataset size, the model, and the modification of the neural network were made to evaluate the model's performance in the specified challenge. The results indicate that modifications can increase the YOLOv5s performance in detection. The model with GCNet added and trained in 4800 images has achieved 88.1% of mAP. The paper also evaluates the time performance of models based on different GPU units. The results show that in A100 40GB GPU, the maximum time to process a wood plate is 2.2 seconds. Finally, an Active learning approach for the continual increase in performance while detecting with the smaller size of manual labeling has been implemented. After detecting 500 images in 5 cycles, the model achieved 98.8% of mAP. This scientific paper concludes that YOLOv5s modified model is suitable for wood surface defect detection. It can perform with high accuracy in real time. Moreover, applying the active learning approach can facilitate the labeling process by increasing the performance during detection

Perception Through Image Processing Applications for Situation Awareness

This paper introduces a concept for the perception level of situation awareness through image processing applications. The perception of the information from the environment is lacking to achieve highly autonomous vehicles. This approach mainly focuses on collecting information from the environment using camera sensors. The frames from the camera are processed using multiple image processing algorithms, outputs converted into CAN message, and sent to the expert system. CE-Box custom hardware that consists of a Raspberry Pi 3b model counted with PiCAN 2 used to implement and evaluate this approach. The goal of this paper is to provide a conceptual method to make decisions based on the extracted information from the environment using image processing algorithms

Analysis of Machine Learning Approach for the modemodel in SWC Mapping in Automotive Systems

Automotive technologies are ever-increasinglybecoming digital. Highly autonomous driving togetherwith digital E/E control mechanisms include thousandsof software applications which are called as software components. Together with the industry requirements, and rigorous software development processes, mappingof components as a software pool becomes very difficult.This article analyses and discusses the integration possiblilities of machine learning approaches to our previously introduced concept of mapping of software components through a common software pool