scholarly journals Latency Reduction in Vehicular Sensing Applications by Dynamic 5G User Plane Function Allocation with Session Continuity

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7744
Author(s):  
Pablo Fondo-Ferreiro ◽  
David Candal-Ventureira ◽  
Francisco Javier González-Castaño ◽  
Felipe Gil-Castiñeira
Keyword(s):  
Situational Awareness ◽  
Sensor Data ◽  
Dynamic Allocation ◽  
Latency Reduction ◽  
Sensing Applications ◽  
Function Allocation ◽  
High Quality Information ◽  
Vehicle Automation ◽  
New Applications ◽  
Vehicular Sensing

Vehicle automation is driving the integration of advanced sensors and new applications that demand high-quality information, such as collaborative sensing for enhanced situational awareness. In this work, we considered a vehicular sensing scenario supported by 5G communications, in which vehicle sensor data need to be sent to edge computing resources with stringent latency constraints. To ensure low latency with the resources available, we propose an optimization framework that deploys User Plane Functions (UPFs) dynamically at the edge to minimize the number of network hops between the vehicles and them. The proposed framework relies on a practical Software-Defined-Networking (SDN)-based mechanism that allows seamless re-assignment of vehicles to UPFs while maintaining session and service continuity. We propose and evaluate different UPF allocation algorithms that reduce communications latency compared to static, random, and centralized deployment baselines. Our results demonstrated that the dynamic allocation of UPFs can support latency-critical applications that would be unfeasible otherwise.

scholarly journals SYNTHETIC VISION SYSTEM CALIBRATION FOR CONFORM PROJECTION ON THE PILOT’S HEAD-UP DISPLAY

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 575-581
Author(s):  
S. Danilov ◽  
M. Kozyrev ◽  
M. Grechanichenko ◽  
L. Grodzitskiy ◽  
V. Mizginov ◽  
...  
Keyword(s):  
Situational Awareness ◽  
Vision System ◽  
Digital Terrain Model ◽  
3D Models ◽  
Sensor Data ◽  
Training Dataset ◽  
Learning Approaches ◽  
Terrain Model ◽  
Front Window ◽  
Calibration Algorithm

Abstract. Situational awareness of the crew is critical for the safety of the air flight. Head-up display allows providing all required flight information in front of the pilot over the cockpit view visible through the cockpit’s front window. This device has been created for solving the problem of informational overload during piloting of an aircraft. While computer graphics such as scales and digital terrain model can be easily presented on the such display, errors in the Head-up display alignment for correct presenting of sensor data pose challenges. The main problem arises from the parallax between the pilot’s eyes and the position of the camera. This paper is focused on the development of an online calibration algorithm for conform projection of the 3D terrain and runway models on the pilot’s head-up display. The aim of our algorithm is to align the objects visible through the cockpit glass with their projections on the Head-up display. To improve the projection accuracy, we use an additional optical sensor installed on the aircraft. We combine classical photogrammetric techniques with modern deep learning approaches. Specifically, we use an object detection neural network model to find the runway area and align runway projection with its actual location. Secondly, we re-project the sensor’s image onto the 3D model of the terrain to eliminate errors caused by the parallax. We developed an environment simulator to evaluate our algorithm. Using the simulator we prepared a large training dataset. The dataset includes 2000 images of video sequences representing aircraft’s motion during takeoff, landing and taxi. The results of the evaluation are encouraging and demonstrate both qualitatively and quantitatively that the proposed algorithm is capable of precise alignment of the 3D models projected on a Head-up display.

scholarly journals Sensing as a Service for the Internet of Things

2016 ◽  
Vol 4 (3) ◽  
pp. 28
Author(s):  
Omar Subhi Aldabbas
Keyword(s):  
Internet Of Things ◽  
Cost Effective ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Heterogeneous Services ◽  
Coordination Languages ◽  
Spatio Temporal ◽  
Temporal Events ◽  
New Applications ◽  
Common Application

Internet of Things (IoT) is a ubiquitous embedded ecosystem known for its capability to perform common application functions through coordinating resources, which are distributed on-object or on-network domains. As new applications evolve, the challenge is in the analysis and usage of multimodal data streamed by diverse kinds of sensors. This paper presents a new service-centric approach for data collection and retrieval. This approach considers objects as highly decentralized, composite and cost effective services. Such services can be constructed from objects located within close geographical proximity to retrieve spatio-temporal events from the gathered sensor data. To achieve this, we advocate Coordination languages and models to fuse multimodal, heterogeneous services through interfacing with every service to achieve the network objective according to the data they gather and analyze. In this paper we give an application scenario that illustrates the implementation of the coordination models to provision successful collaboration among IoT objects to retrieve information. The proposed solution reduced the communication delay before service composition by up to 43% and improved the target detection accuracy by up to 70%, while maintaining energy consumption 20% lower than its best rivals in the literature.

scholarly journals Fossel: Efficient Latency Reduction in Approximating Streaming Sensor Data

2020 ◽  
Vol 12 (23) ◽  
pp. 10175
Author(s):  
Fatima Abdullah ◽  
Limei Peng ◽  
Byungchul Tak
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Streaming Data ◽  
Sensor Data ◽  
Simple Type ◽  
Data Sampling ◽  
Time Data ◽  
Latency Reduction ◽  
Network Delays ◽  
Real Time Data

The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network traffic which leads to heavily delayed network operations. In streaming data analytics, the ability to obtain real time data insight is crucial for computational sustainability for many IoT enabled applications such as environmental monitors, pollution and climate surveillance, traffic control or even E-commerce applications. However, such network delays prevent us from achieving high quality real-time data analytics of environmental information. In order to address this challenge, we propose the Fog Sampling Node Selector (Fossel) technique that can significantly reduce the IoT network and processing delays by algorithmically selecting an optimal subset of fog nodes to perform the sensor data sampling. In addition, our technique performs a simple type of query executions within the fog nodes in order to further reduce the network delays by processing the data near the data producing devices. Our extensive evaluations show that Fossel technique outperforms the state-of-the-art in terms of latency reduction as well as in bandwidth consumption, network usage and energy consumption.

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

Robotic transportation

1990 ◽  
Vol 36 (9) ◽  
pp. 1544-1550 ◽  
Author(s):  
W S Lob
Keyword(s):  
Information Systems ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Test Equipment ◽  
Sensor Data ◽  
Intelligent Sensor ◽  
Hospital Environments ◽  
Extensive Facility ◽  
New Applications ◽  
User Friendly

Abstract Mobile robots perform fetch-and-carry tasks autonomously. An intelligent, sensor-equipped mobile robot does not require dedicated pathways or extensive facility modification. In the hospital, mobile robots can be used to carry specimens, pharmaceuticals, meals, etc. between supply centers, patient areas, and laboratories. The HelpMate (Transitions Research Corp.) mobile robot was developed specifically for hospital environments. To reach a desired destination, Help-Mate navigates with an on-board computer that continuously polls a suite of sensors, matches the sensor data against a pre-programmed map of the environment, and issues drive commands and path corrections. A sender operates the robot with a user-friendly menu that prompts for payload insertion and desired destination(s). Upon arrival at its selected destination, the robot prompts the recipient for a security code or physical key and awaits acknowledgement of payload removal. In the future, the integration of HelpMate with robot manipulators, test equipment, and central institutional information systems will open new applications in more localized areas and should help overcome difficulties in filling transport staff positions.

scholarly journals Accuracy-Energy Configurable Sensor Processor and IoT Device for Long-Term Activity Monitoring in Rare-Event Sensing Applications

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Daejin Park ◽  
Jeonghun Cho
Keyword(s):  
Rare Event ◽  
Sensor Data ◽  
Event Generator ◽  
Signal Acquisition ◽  
Processing Unit ◽  
Sensor Signal ◽  
Single Chip ◽  
Sensing Applications ◽  
Tightly Coupled ◽  
Sensor Processor

A specially designed sensor processor used as a main processor in IoT (internet-of-thing) device for the rare-event sensing applications is proposed. The IoT device including the proposed sensor processor performs the event-driven sensor data processing based on an accuracy-energy configurable event-quantization in architectural level. The received sensor signal is converted into a sequence of atomic events, which is extracted by the signal-to-atomic-event generator (AEG). Using an event signal processing unit (EPU) as an accelerator, the extracted atomic events are analyzed to build the final event. Instead of the sampled raw data transmission via internet, the proposed method delays the communication with a host system until a semantic pattern of the signal is identified as a final event. The proposed processor is implemented on a single chip, which is tightly coupled in bus connection level with a microcontroller using a 0.18 μm CMOS embedded-flash process. For experimental results, we evaluated the proposed sensor processor by using an IR- (infrared radio-) based signal reflection and sensor signal acquisition system. We successfully demonstrated that the expected power consumption is in the range of 20% to 50% compared to the result of the basement in case of allowing 10% accuracy error.

Flooding applications enabled by high resolution, high cadence imagery from the Planet constellation of satellites

2020 ◽  
Author(s):  
Brittany Zajic ◽  
Samapriya Roy ◽  
Joseph Mascaro
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Financial Services ◽  
Situational Awareness ◽  
Private Companies ◽  
Spectral Bands ◽  
Global Cost ◽  
Earth Observation Satellites ◽  
New Applications ◽  
High Cadence

<p>Flooding is the most common and costliest global natural disaster, accounting for 43% of all recorded events in the last 20 years and increasing the global cost of flooding tenfold by 2030. Satellite imagery has proven beneficial for numerous flood use cases from historical modeling, situational awareness and extent, to risk forecasting. The addition of high resolution, high cadence satellite imagery from Planet has been widely adopted by the flood community, from researchers in academia to private companies in the insurance and financial services. </p><p>Planet Labs, Inc. currently operates over 140 satellites, comprising of the largest constellation of Earth observation satellites. The PlanetScope dataset consists of broad coverage, always-on imaging of the entire landmass by 120+ Dove satellites at 3.7 meter resolution. Complementary to PlanetScope, the SkySat dataset includes 15 high resolution satellites imaging at .72 meter resolution with the ability to image any location on Earth twice daily via tasking commands. Next-Generation PlanetScope imagery powered by SuperDove will introduce new spectral bands and interoperability positioned for the increased utilization of Planet imagery by the flood community for both existing and new applications.</p>

HomCam

2016 ◽  
Vol 60 (1) ◽  
pp. 800-804 ◽  
Author(s):  
Brian Peck ◽  
Stephen Gilbert ◽  
Eliot Winer ◽  
Robert C. Ray
Keyword(s):  
Real Time ◽  
Situation Awareness ◽  
Network Performance ◽  
Situational Awareness ◽  
Visual Display ◽  
Initial Test ◽  
Video System ◽  
Wearable Technologies ◽  
Remote Location ◽  
New Applications

The growth of mobile and wearable technologies has enabled a host of new applications, including remote situational awareness, in which a device worn by a remote partner can simulate being present in the remote location for an observer. We illustrate this idea by constructing the HomCam, a helmet-based omnidirectional video system that gives an observer the 360-degree perspective of a remote wearer. To our knowledge, the HomCam was the first wearable system that enabled real-time streaming of 360-degree video to a remote location built from commercial-of-the-shelf hardware. This paper describes related efforts, the HomCam prototype, its visual display, and an initial test of network performance. This prototype demonstrates some of the challenges of remote situation awareness and contributes to designers’ implementation of related systems.

scholarly journals A Low-Power Microcontroller with Accuracy-Controlled Event-Driven Signal Processing Unit for Rare-Event Activity-Sensing IoT Devices

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Daejin Park ◽  
Jonghee M. Youn ◽  
Jeonghun Cho
Keyword(s):  
Signal Processing ◽  
Data Processing ◽  
Energy Efficient ◽  
Rare Event ◽  
Sensor Data ◽  
Processing Unit ◽  
Sensing Applications ◽  
Event Driven ◽  
Iot Devices ◽  
Activity Sensing

A specially designed microcontroller with event-driven sensor data processing unit (EPU) is proposed to provide energy-efficient sensor data acquisition for Internet of Things (IoT) devices in rare-event human activity sensing applications. Rare-event sensing applications using a remotely installed IoT sensor device have a property of very long event-to-event distance, so that the inaccurate sensor data processing in a certain range of accuracy error is enough to extract appropriate events from the collected sensing data. The proposed signal-to-event converter (S2E) as a preprocessor of the conventional sensor interface extracts a set of atomic events with the specific features of interest and performs an early evaluation for the featured points of the incoming sensor signal. The conventional sensor data processing such as DSPs or software-driven algorithm to classify the meaningful event from the collected sensor data could be accomplished by the proposed event processing unit (EPU). The proposed microcontroller architecture enables an energy efficient signal processing for rare-event sensing applications. The implemented system-on-chip (SoC) including the proposed building blocks is fabricated with additional 7500 NAND gates and 1-KB SRAM tracer in 0.18 um CMOS process, consuming only 20% compared to the conventional sensor data processing method for human hand-gesture detection.

scholarly journals Distinguishing dopamine and calcium responses using XNA-nanotube sensors for improved neurochemical sensing

2021 ◽  
Author(s):  
Alice J. Gillen ◽  
Alessandra Antonucci ◽  
Melania Reggente ◽  
Daniel Morales ◽  
Ardemis A. Boghossian
Keyword(s):  
Neurological Diseases ◽  
Locked Nucleic Acid ◽  
Optical Biosensors ◽  
Label Free ◽  
Sensing Applications ◽  
Label Free Detection ◽  
Improved Stability ◽  
New Applications

AbstractTo date, the engineering of single-stranded DNA-SWCNT (DNA-SWCNT) optical biosensors have largely focused on creating sensors for new applications with little focus on optimising existing sensors for in vitro and in vivo conditions. Recent studies have shown that nanotube fluorescence can be severely impacted by changes in local cation concentrations. This is particularly problematic for neurotransmitter sensing applications as spatial and temporal fluctuations in the concentration of cations, such as Na+, K+, or Ca2+, play a central role in neuromodulation. This can lead to inaccuracies in the determination of neurotransmitter concentrations using DNA-SWCNT sensors, which limits their use for detecting and treating neurological diseases.Herein, we present new approaches using locked nucleic acid (LNA) to engineer SWCNT sensors with improved stability towards cation-induced fluorescence changes. By incorporating LNA bases into the (GT)15-DNA sequence, we create sensors that are not only more resistant towards undesirable fluorescence modulation in the presence of Ca2+ but that also retain their capabilities for the label-free detection of dopamine. The synthetic biology approach presented in this work therefore serves as a complementary means for enhancing nanotube optoelectronic behavior, unlocking previously unexplored possibilities for developing nano-bioengineered sensors with augmented capabilities.

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