19 research outputs found
Climate-Resilient UAVs: Enhancing Energy-Efficient B5G Communication in Harsh Environments
This paper explores the crucial role of Unmanned Aerial Vehicles (UAVs) in
advancing Beyond Fifth Generation (B5G) communication networks, especially in
adverse weather conditions like rain, fog, and snow.
The study investigates the synergy between climate-resilient UAVs and
energy-efficient B5G communication.
Key findings include the impact of weather elements on UAV coverage and
communication dynamics. The research demonstrates significant enhancements in
energy efficiency, reduced interference, increased data transmission rates, and
optimal channel gain under various weather conditions.
Overall, this paper emphasizes the potential of climate-resilient UAVs to
improve energy-efficient B5G communication and highlights technology's role in
mitigating climate change's impact on communication systems, promoting
sustainability and resilience
Big Data Testing Techniques: Taxonomy, Challenges and Future Trends
Big Data is reforming many industrial domains by providing decision support
through analyzing large data volumes. Big Data testing aims to ensure that Big
Data systems run smoothly and error-free while maintaining the performance and
quality of data. However, because of the diversity and complexity of data,
testing Big Data is challenging. Though numerous research efforts deal with Big
Data testing, a comprehensive review to address testing techniques and
challenges of Big Data is not available as yet. Therefore, we have
systematically reviewed the Big Data testing techniques evidence occurring in
the period 2010-2021. This paper discusses testing data processing by
highlighting the techniques used in every processing phase. Furthermore, we
discuss the challenges and future directions. Our findings show that diverse
functional, non-functional and combined (functional and non-functional) testing
techniques have been used to solve specific problems related to Big Data. At
the same time, most of the testing challenges have been faced during the
MapReduce validation phase. In addition, the combinatorial testing technique is
one of the most applied techniques in combination with other techniques (i.e.,
random testing, mutation testing, input space partitioning and equivalence
testing) to find various functional faults through Big Data testing.Comment: 32 page
Flexible Beamforming in B5G for Improving Tethered UAV Coverage over Smart Environments
Unmanned Aerial Vehicles (UAVs) are being used for wireless communications in
smart environments. However, the need for mobility, scalability of data
transmission over wide areas, and the required coverage area make UAV
beamforming essential for better coverage and user experience. To this end, we
propose a flexible beamforming approach to improve tethered UAV coverage
quality and maximize the number of users experiencing the minimum required rate
in any target environment. Our solution demonstrates a significant achievement
in flexible beamforming in smart environments, including urban, suburban,
dense, and high-rise urban. Furthermore, the beamforming gain is mainly
concentrated in the target to improve the coverage area based on various
scenarios. Simulation results show that the proposed approach can achieve a
significantly received flexible power beam that focuses the transmitted signal
towards the receiver and improves received power by reducing signal power
spread. In the case of no beamforming, signal power spreads out as distance
increases, reducing the signal strength. Furthermore, our proposed solution is
suitable for improving UAV coverage and reliability in smart and harsh
environments.Comment: 6 pages, 7 figure
Low computational complexity for optimizing energy efficiency in mm-wave hybrid precoding system for 5G
Millimeter-wave (mm-wave) communication is the spectral frontier to meet the anticipated
significant volume of high data traffic processing in next-generation systems. The primary challenges in
mm-wave can be overcome by reducing complexity and power consumption by large antenna arrays for
massive multiple-input multiple-output (mMIMO) systems. However, the circuit power consumption is
expected to increase rapidly. The precoding in mm-wave mMIMO systems cannot be successfully achieved
at baseband using digital precoders, owing to the high cost and power consumption of signal mixers and
analog-to-digital converters. Nevertheless, hybrid analog–digital precoders are considered a cost-effective
solution. In this work, we introduce a novel method for optimizing energy efficiency (EE) in the upper-bound
multiuser (MU) - mMIMO system and the cost efficiency of quantized hybrid precoding (HP) design.
We propose effective alternating minimization algorithms based on the zero gradient method to establish
fully-connected structures (FCSs) and partially-connected structures (PCSs). In the alternating minimization
algorithms, low complexity is proposed by enforcing an orthogonal constraint on the digital precoders
to realize the joint optimization of computational complexity and communication power. Therefore, the
alternating minimization algorithm enhances HP by improving the performance of the FCS through
advanced phase extraction, which involves high complexity. Meanwhile, the alternating minimization
algorithm develops a PCS to achieve low complexity using HP. The simulation results demonstrate that the
proposed algorithm for MU - mMIMO systems improves EE. The power-saving ratio is also enhanced for
PCS and FCS by 48.3% and 17.12%, respectively
Long-baseline, sub-decimeter kinematic GPS positioning of moving object, with potential application to monitor ocean surface wave
Precise relative kinematic positioning of moving platforms using GPS carrier phase
observables has numerous applications. One prominent application is utilization of highly
stabilized GPS technology mounted on the buoy, which is specially designed for detecting
tsunami wave at open sea. The essential point of this research is to investigate a potential use of a
GPS tsunami buoy for the purpose of tsunami early warning system with long-baseline kinematic
GPS processing method.
The rule of thumb GPS positioning concept, GPS position results are affected by. baseline
length mostly due to de-correlation of atmospheric errors. As baseline lengths increase, position
results degrade due to the difficulty to correctly fix the cariier phase ambiguity to its integer
value. carrier phase fixed ambiguity solutions are more accurate that float arnbiguify solutions. It
is generally accepted that carrier phase can be successfUlly fixed for baselines of up to 10 km.
After that, fixing ambiguities becomes more difficult and risky. It would be certainty more
advantageous to have a reliable float solution rather than an unreliable fixed solution.
In this study, we have developed a new quasi-real time long-baseline kinematic analysis
method using dual-frequency carrier phase with floated ambiguities, implemented in the Bernese
GPS Software Version 5.0. We demonstrate that early detection of a damaging tsunami can be
achieved by tracking the anomalous changes in sea surface height. The movements of a GPS
buoy relative to a base station with baseline length of 500 km have been monitored in quasi-real
time mode, and the tsunami waves caused by the 5th September 2004 Off Kii Peninsula
earthquake, Japan, have been successful detected as they went by, even though these were only 15 cm high. The filtered record of the solution closely resembles that of short baseline, with
RMS of 3.4 cm over 2.5 hours.
To test the robustness of our Iong-baseline kinematic GPS method under various
meteorological, we conducted the GPS tsunami buoy data analysis continuously for 8 days to
monitor the motion of the buoy. The average scatterings of GPS buoy heights by the low-pass
filtered 1 -Hz positioning result after tidal correction are about 3.4 cm and 1.2 cm under both
typhoon and calm weather conditions. This accuracy is precise enough to be applicable to a
tsunami early warning system. Since our long-baseline kinematic GPS analysis is effective to a
long baseline up to 500 km, we can place a GPS buoy far offshore, which ensures an adequate
evacuation time even, for people living on the coast
Shape Memory Alloy-Based Wearables: A Review, and Conceptual Frameworks on HCI and HRI in Industry 4.0
Ever since its discovery, the applications of Shape Memory Alloys (SMA) can be found across a range of application domains, from structural design to medical technology. This is based upon the unique and inherent characteristics such as thermal Shape Memory Effect (SME) and Superelasticity (or Pseudoelasticity). While thermal SME is used for shape morphing applications wherein temperature change can govern the shape and dimension of the SMA, Superelasticity allows the alloy to withstand a comparatively very high magnitude of loads without undergoing plastic deformation at higher temperatures. These unique properties in wearables have revolutionized the field, and from fabrics to exoskeletons, SMA has found its place in robotics and cobotics. This review article focuses on the most recent research work in the field of SMA-based smart wearables paired with robotic applications for human-robot interaction. The literature is categorized based on SMA property incorporated and on actuator or sensor-based concept. Further, use-cases or conceptual frameworks for SMA fiber in fabric for ‘Smart Jacket’ and SMA springs in the shoe soles for ‘Smart Shoes’ are proposed. The conceptual frameworks are built upon existing technologies; however, their utility in a smart factory concept is emphasized, and algorithms to achieve the same are proposed. The integration of the two concepts with the Industrial Internet of Things (IIoT) is discussed, specifically regarding minimizing hazards for the worker/user in Industry 5.0. The article aims to propel a discussion regarding the multi-faceted applications of SMAs in human-robot interaction and Industry 5.0. Furthermore, the challenges and the limitations of the smart alloy and the technological barriers restricting the growth of SMA applications in the field of smart wearables are observed and elaborated
Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
The fundamental issue for Beyond fifth Generation (B5G) is providing a pervasive connection to heterogeneous and various devices in smart environments. Therefore, Drones play a vital role in the B5G, allowing for wireless broadcast and high-speed communications. In addition, the drone offers several advantages compared to fixed terrestrial communications, including flexible deployment, robust Line of Sight (LoS) connections, and more design degrees of freedom due to controlled mobility. Drones can provide reliable and high data rate connectivity to users irrespective of their location. However, atmospheric disturbances impact the signal quality between drones and users and degrade the system performance. Considering practical implementation, the location of drones makes the drone–user communication susceptible to several environmental disturbances. In this paper, we evaluate the performance of drone-user connectivity during atmospheric disturbances. Further, a Machine Learning (ML)-assisted algorithm is proposed to adapt to a modulation technique that offers optimal performance during atmospheric disturbances. The results show that, with the algorithm, the system switches to a lower order modulation scheme during higher rain rate and provides reliable communication with optimized data rate and error performance
Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
In recent years, the area of Unmanned Aerial Vehicles (UAVs) has seen rapid growth. There has been a trend to build and produce UAVs that can carry out planetary exploration throughout the past decade. The technology of UAVs has tremendous potential to support various successful space mission solutions. In general, different techniques for observing space objects are available, such as telescopes, probes, and flying spacecraft, orbiters, landers, and rovers. However, a detailed analysis has been carried out due to the benefits of UAVs relative to other planetary exploration techniques. The deployment of UAVs to other solar bodies has been considered by numerous space agencies worldwide, including NASA. This article contributes to investigating the types of UAVs that have been considered for various planetary explorations. This study further investigates the behaviour of UAV prototypes on Mars’ surface in particular. It has been discovered that a prototype UAV flight on Mars has a higher chance of success. In this research, a prototype UAV has been successfully simulated to fly on Mars’ surface. This article discusses the opportunities, challenges, and future scope of deploying UAVs on Mars
Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
In recent years, the area of Unmanned Aerial Vehicles (UAVs) has seen rapid growth. There has been a trend to build and produce UAVs that can carry out planetary exploration throughout the past decade. The technology of UAVs has tremendous potential to support various successful space mission solutions. In general, different techniques for observing space objects are available, such as telescopes, probes, and flying spacecraft, orbiters, landers, and rovers. However, a detailed analysis has been carried out due to the benefits of UAVs relative to other planetary exploration techniques. The deployment of UAVs to other solar bodies has been considered by numerous space agencies worldwide, including NASA. This article contributes to investigating the types of UAVs that have been considered for various planetary explorations. This study further investigates the behaviour of UAV prototypes on Mars’ surface in particular. It has been discovered that a prototype UAV flight on Mars has a higher chance of success. In this research, a prototype UAV has been successfully simulated to fly on Mars’ surface. This article discusses the opportunities, challenges, and future scope of deploying UAVs on Mars
Blockchain for decentralized multi-drone to combat COVID-19 and future pandemics: Framework and proposed solutions
Currently, drones represent a promising technology for combating Coronavirus disease 2019 (COVID-19) due to the transport of goods, medical supplies to a given target location in the quarantine areas experiencing an epidemic outbreak. Drone missions will increasingly rely on drone collaboration, which requires the drones to reduce communication complexity and be controlled in a decentralized fashion. Blockchain technology becomes a must in industrial applications because it provides decentralized data, accessibility, immutability, and irreversibility. Therefore, Blockchain makes data public for all drones and enables drones to log information concerning world states, time, location, resources, delivery data, and drone relation to all neighbors drones. This paper introduces decentralized independent multi-drones to accomplish the task collaboratively. Improving blockchain with a consensus algorithm can improve network partitioning and scalability in order to combat COVID-19. The multi-drones task is to combat COVID-19 via monitoring and detecting, social distancing, sanitization, data analysis, delivering goods and medical supplies, and announcement while avoiding collisions with one another. We discuss End to End (E2E) delivery application of combination blockchain and multi-drone in combating COVID-19 and beyond future pandemics. Furthermore, the challenges and opportunities of our proposed framework are highlighted