Proximity Detection and Message Exchange in WLANs

Author(s):  
Sascha Schnaufer ◽  
Johannes Kiess ◽  
Hendrik Lemelson ◽  
Stephan Kopf ◽  
Sebastian Geiger ◽  
...  
IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 38891-38906
Author(s):  
Zhuoran Su ◽  
Kaveh Pahlavan ◽  
Emmanuel Agu

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Konstantin D. Pandl ◽  
Scott Thiebes ◽  
Manuel Schmidt-Kraepelin ◽  
Ali Sunyaev

AbstractTo combat the COVID-19 pandemic, many countries around the globe have adopted digital contact tracing apps. Various technologies exist to trace contacts that are potentially prone to different types of tracing errors. Here, we study the impact of different proximity detection ranges on the effectiveness and efficiency of digital contact tracing apps. Furthermore, we study a usage stop effect induced by a false positive quarantine. Our results reveal that policy makers should adjust digital contact tracing apps to the behavioral characteristics of a society. Based on this, the proximity detection range should at least cover the range of a disease spread, and be much wider in certain cases. The widely used Bluetooth Low Energy protocol may not necessarily be the most effective technology for contact tracing.


2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Yehia Abd Alrahman ◽  
Nir Piterman

AbstractWe propose a formalism to model and reason about reconfigurable multi-agent systems. In our formalism, agents interact and communicate in different modes so that they can pursue joint tasks; agents may dynamically synchronize, exchange data, adapt their behaviour, and reconfigure their communication interfaces. Inspired by existing multi-robot systems, we represent a system as a set of agents (each with local state), executing independently and only influence each other by means of message exchange. Agents are able to sense their local states and partially their surroundings. We extend ltl to be able to reason explicitly about the intentions of agents in the interaction and their communication protocols. We also study the complexity of satisfiability and model-checking of this extension.


Author(s):  
Matthew Westin ◽  
Ronald Dougherty ◽  
Christopher Depcik ◽  
Austin Hausmann ◽  
Charles Sprouse

The original use of the vehicle dashboard was to provide enough sensory information to inform the driver of the current engine and vehicle status and performance. Over time, it has evolved into an entertainment system that includes person-to-person communication, global positioning information, and the Internet, just to name a few. Each of these new features adds to the amount of information that drivers must absorb, leading to potential distraction and possible increases in the number and types of accidents. In order to provide an overview of these issues, this paper summarizes previous work on driver distraction and workload, demonstrating the importance of addressing those issues that compete for driver attention and action. In addition, a test platform vehicle is introduced which has the capability of assessing modified dashboards and consoles, as well as the ability to acquire relevant driving performance data. Future efforts with this test platform will be directed toward helping to resolve the critical tug-of-war between providing more information and entertainment while keeping drivers and their passengers safe. The long-term goal of this research is to evaluate the various technological innovations available for inclusion in the driving environment and determining how to optimize driver information delivery without excessive distraction and workload. The information presented herein is the first step in that effort of developing an adaptive distraction/workload management system that monitors performance metrics and provides selected feedback to drivers. The test platform (1973 VW Beetle converted to a plug-in series hybrid) can provide speed, location (GPS), 3-D acceleration, and rear proximity detection. The test drive route was a 2 km × 3 km city street circuit which took approximately 25 minutes to complete. Data is provided herein to demonstrate these capabilities. In addition, the platform has driver selectable layouts for the instrument cluster and console (LCD screens). The test platform is planned for use to determine driver preferences (e.g., dashboard/console configurations) and attention performance in addition to identifying optimal real-time feedback for drivers with different demographics.


Author(s):  
K. G. Yashchenkov ◽  
K. S. Dymko ◽  
N. O. Ukhanov ◽  
A. V. Khnykin

The issues of using data analysis methods to find and correct errors in the reports issued by meteorologists are considered. The features of processing various types of meteorological messages are studied. The advantages and disadvantages of existing methods of classification of text information are considered. The classification methods are compared in order to identify the optimal method that will be used in the developed algorithm for analyzing meteorological messages. The prospects of using each of the methods in the developed algorithm are described. An algorithm for processing the source data is proposed, which consists in using syntactic and logical analysis to preclean the data from various kinds of noise and determine format errors for each type of message. After preliminary preparation the classification method correlates the received set of message characteristics with the previously trained model to determine the error of the current weather report and output the corresponding message to the operator in real time. The software tools used in the algorithm development and implementation processes are described. A complete description of the process of processing a meteorological message is presented from the moment when the message is entered in a text editor until the message is sent to the international weather message exchange service. The developed software is demonstrated, in which the proposed algorithm is implemented, which allows to improve the quality of messages and, as a result, the quality of meteorological forecasts. The results of the implementation of the new algorithm are described by comparing the number of messages containing various types of errors before the implementation of the algorithm and after the implementation.


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