Proactive Scheduling and Resource Management for Connected Autonomous Vehicles: A Data Science Perspective

2021 ◽  
pp. 1-1
Author(s):  
Sayyam Malik ◽  
Hasan Ali Khattak ◽  
Zoobia Ameer ◽  
Umar Shoaib ◽  
Hafiz Tayyab Rauf ◽  
...  
Keyword(s):  
Resource Management ◽  
Autonomous Vehicles ◽  
Data Science ◽  
Proactive Scheduling

scholarly journals Detection of Road Traffic Anomalies Based on Computational Data Science

2022 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Data Science ◽  
Road Traffic ◽  
Smart Devices ◽  
Traffic Data ◽  
Forecasting Accuracy ◽  
Traffic Efficiency ◽  
Computational Data

Abstract The development of 5G has enabled the autonomous vehicles (AVs) to have full control over all functions. The AV acts autonomously and collects travel data based on various smart devices and sensors, with the goal of enabling it to operate under its own power. However, the collected data is affected by several sources that degrade the forecasting accuracy. To manage large amounts of traffic data in different formats, a computational data science approach (CDS) is proposed. The computational data science scheme introduced to detect anomalies in traffic data that negatively affect traffic efficiency. The combination of data science and advanced artificial intelligence techniques, such as deep leaning provides higher degree of data anomalies detection which leads to reduce traffic congestion and vehicular queuing. The main contribution of the CDS approach is summarized in detection of the factors that caused data anomalies early to avoid long- term traffic congestions. Moreover, CDS indicated a promoting results in various road traffic scenarios.

scholarly journals Positioning Methods and the Use of Location and Activity Data in Forests

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 458 ◽  
Author(s):  
Robert F. Keefe ◽  
Ann M. Wempe ◽  
Ryer M. Becker ◽  
Eloise G. Zimbelman ◽  
Emily S. Nagler ◽  
...  
Keyword(s):  
Big Data ◽  
Resource Management ◽  
Natural Resources ◽  
Data Science ◽  
Fire Management ◽  
Location Based Services ◽  
Future Research ◽  
Positioning Systems ◽  
Incident Command ◽  
Location Sharing

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms location-based services (LBS), geofences, wearable technology, activity recognition, mesh networking, the Internet of Things (IoT), and big data. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.

scholarly journals NEW SOURCE OF GEOSPATIAL DATA: CROWDSENSING BY ASSISTED AND AUTONOMOUS VEHICLE TECHNOLOGIES

2018 ◽  
Vol XLII-4/W8 ◽  
pp. 211-216 ◽  
Author(s):  
C. K. Toth ◽  
Z. Koppanyi ◽  
M. G. Lenzano
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Data Science ◽  
Information Dissemination ◽  
Autonomous Vehicle ◽  
Weather Conditions ◽  
Traffic Monitoring ◽  
Geospatial Data ◽  
Object Space ◽  
Vehicle Technologies

<p><strong>Abstract.</strong> The ongoing proliferation of remote sensing technologies in the consumer market has been rapidly reshaping the geospatial data acquisition world, and subsequently, the data processing as well as information dissemination processes. Smartphones have clearly established themselves as the primary crowdsourced data generators recently, and provide an incredible volume of remote sensed data with fairly good georeferencing. Besides the potential to map the environment of the smartphone users, they provide information to monitor the dynamic content of the object space. For example, real-time traffic monitoring is one of the most known and widely used real-time crowdsensed application, where the smartphones in vehicles jointly contribute to an unprecedentedly accurate traffic flow estimation. Now we are witnessing another milestone to happen, as driverless vehicle technologies will become another major source of crowdsensed data. Due to safety concerns, the requirements for sensing are higher, as the vehicles should sense other vehicles and the road infrastructure under any condition, not just daylight in favorable weather conditions, and at very fast speed. Furthermore, the sensing is based on using redundant and complementary sensor streams to achieve a robust object space reconstruction, needed to avoid collisions and maintain normal travel patterns. At this point, the remote sensed data in assisted and autonomous vehicles are discarded, or partially recorded for R&amp;amp;D purposes. However, in the long run, as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies mature, recording data will become a common place, and will provide an excellent source of geospatial information for road mapping, traffic monitoring, etc. This paper reviews the key characteristics of crowdsourced vehicle data based on experimental data, and then the processing aspects, including the Data Science and Deep Learning components.</p>

scholarly journals Multi-Cloud Resource Management Techniques for Cyber-Physical Systems

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8364
Author(s):  
Vlad Bucur ◽  
Liviu-Cristian Miclea
Keyword(s):  
Resource Management ◽  
Autonomous Vehicles ◽  
Cyber Physical Systems ◽  
Software Projects ◽  
Physical Systems ◽  
Cloud Resource Management ◽  
Human Driver ◽  
Resource Requirements ◽  
Self Driving Cars ◽  
Multi Cloud

Information technology is based on data management between various sources. Software projects, as varied as simple applications or as complex as self-driving cars, are heavily reliant on the amounts, and types, of data ingested by one or more interconnected systems. Data is not only consumed but is transformed or mutated which requires copious amounts of computing resources. One of the most exciting areas of cyber-physical systems, autonomous vehicles, makes heavy use of deep learning and AI to mimic the highly complex actions of a human driver. Attempting to map human behavior (a large and abstract concept) requires large amounts of data, used by AIs to increase their knowledge and better attempt to solve complex problems. This paper outlines a full-fledged solution for managing resources in a multi-cloud environment. The purpose of this API is to accommodate ever-increasing resource requirements by leveraging the multi-cloud and using commercially available tools to scale resources and make systems more resilient while remaining as cloud agnostic as possible. To that effect, the work herein will consist of an architectural breakdown of the resource management API, a low-level description of the implementation and an experiment aimed at proving the feasibility, and applicability of the systems described.

scholarly journals Extending 5G Capacity Planning Through Advanced Subscriber Behavior-Centric Clustering

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1385
Author(s):  
Gonçalves ◽  
Sebastião ◽  
Souto ◽  
Correia
Keyword(s):  
Resource Management ◽  
Capacity Planning ◽  
Data Science ◽  
Planning Process ◽  
Network Planning ◽  
Mobile Network ◽  
Network Capacity ◽  
5G Networks ◽  
Planning Processes ◽  
Mobile Network Operators

This work focuses on providing enhanced capacity planning and resource management for 5G networks through bridging data science concepts with usual network planning processes. For this purpose, we propose using a subscriber-centric clustering approach, based on subscribers’ behavior, leading to the concept of intelligent 5G networks, ultimately resulting in relevant advantages and improvements to the cellular planning process. Such advanced data-science-related techniques provide powerful insights into subscribers’ characteristics that can be extremely useful for mobile network operators. We demonstrate the advantages of using such techniques, focusing on the particular case of subscribers’ behavior, which has not yet been the subject of relevant studies. In this sense, we extend previously developed work, contributing further by showing that by applying advanced clustering, two new behavioral clusters appear, whose traffic generation and capacity demand profiles are very relevant for network planning and resource management and, therefore, should be taken into account by mobile network operators. As far as we are aware, for network, capacity, and resource management planning processes, it is the first time that such groups have been considered. We also contribute by demonstrating that there are extensive advantages for both operators and subscribers by performing advanced subscriber clustering and analytics.

Application of Image Processing and Big Data Science for Flood Label Detection

2021 ◽  
Author(s):  
Jaku Rabinder Rakshit Pally ◽  
Vidya Samadi
Keyword(s):  
Image Processing ◽  
Big Data ◽  
Object Detection ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Data Science ◽  
Image Annotation ◽  
Smart Phone ◽  
Identity Verification ◽  
Bounding Boxes

&lt;p&gt;Due to the importance of object detection in video analysis and image annotation, it is widely utilized in a number of computer vision tasks such as face recognition, autonomous vehicles, activity recognition, tracking objects and identity verification. Object detection does not only involve classification and identification of objects within images, but also involves localizing and tracing the objects by creating bounding boxes around the objects and labelling them with their respective prediction scores. Here, we leverage and discuss how connected vision systems can be used to embed cameras, image processing, Edge Artificial Intelligence (AI), and data connectivity capabilities for flood label detection. We favored the engineering definition of label detection that a label is a sequence of discrete measurable observations obtained using a capturing device such as web cameras, smart phone, etc. We built a Big Data service of around 1000 images (image annotation service) including the image geolocation information from various flooding events in the Carolinas (USA) with a total of eight different object categories. Our developed platform has several smart AI tools and task configurations that can detect objects&amp;#8217; edges or contours which can be manually adjusted with a threshold setting so as to best segment the image. The tool has the ability to train the dataset and predict the labels for large scale datasets which can be used as an object detector to drastically reduce the amount of time spent per object particularly for real-time image-based flood forecasting. &amp;#160;This research is funded by the US National Science Foundation (NSF).&lt;/p&gt;

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