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

Adaptive Mobile Chargers Scheduling Scheme based on AHP-MCDM for WRSN

Wireless Sensor Networks (WSNs) are used to sense and monitor physical conditions in various services and applications.However, there are a number of challenges in deploying WSNs, especially those pertaining to energy replenishment. Using the current solutions, when a significant number of sensors need to replenish their energy, this would be costly in terms of time, efforts and resources. Thus, this paper aims to solve this problem by efficiently deploying wireless power transfer technologies and scheduling Mobile Charging Vehicles (MCVs) in WRSN. The proposed method deploys multi-criteria decision-making (i.e., Analytical Hierarchy Process (AHP)) to schedule the charging tasks. To the best of our knowledge, this paper is the first to depend solely on AHP in MCVs scheduling. The paper demonstrates the validity of the proposed method by illustrating that the matrices that are created are within the accepted values of consistency ratio. In addition, the paper proposes a method of partitioning the values of our criteria to avoid the problem of different criteria having different measurement units. Unlike existing works, the paper aims to schedule an MCV for charging based on both the distance and residual energy of the sensor. The proposed method exhibits superiority in terms of the average remaining energy available in the system, having the shortest queue length, shorter MCV response time, shorter charging duration, and shorter queue waiting time against the state-of-the-art methods. Our study paves the way for next generation efficient charging and MCV scheduling

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

Wireless Power Transfer Technologies, Applications, and Future Trends: A Review

Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energy- limited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., energy harvesting) and expand for deployment in many of our daily-life applications, including but not limited to healthcare, transportation, automation, and smart cities. As a new rising technology, WPT has attracted many researchers from academia and industry in terms of technologies and charging scheduling within a plethora of services and applications. Thus, in this paper, we review the most recent studies related to WPT, including the classifications, advantages, disadvantages, and main application domains. Furthermore, we review the recently designed wireless charging scheduling algorithms and schemes for wireless sensor networks. Our study provides a detailed survey of wireless charging scheduling schemes covering the main scheme classifications, evaluation metrics, application domains, advantages, and disadvantages of each charging scheme. We further summarize trends and opportunities for applying WPT at some intersections

Early MTS Forecasting for Dynamic Stock Prediction: A Double Q-Learning Ensemble Approach

Multivariate time series (MTS) forecasting is a rapidly expanding field with diverse and futuristic applications. However, traditional statistical learning models need more prediction accuracy when faced with dynamic variability, non-linearity, and non-stationarity, as well as the challenge of selecting MTS data for classification. Moreover, the existing methods for early classification of multivariate time series data suffer from numerous severe challenges, including evaluating the length of testing the MTS data component, which must be equal to the training MTS data component, and the availability of faulty data components in MTS. To address this issue, we propose a novel framework for early MTS forecasting using double Q-learning-based ensemble techniques to improve prediction accuracy. Our framework uses Q-learning agents to select optimal actions, which results in maximum rewards and accurate prediction. We investigate the ensemble behavior of learned agents using double Q-learning and Gaussian Process Classifiers (GPC) for early forecasting of MTS data. We also determine the minimum required time-series length for classifying faulty data components using the probabilistic Auto-Regressive Integrated Moving Average (ARIMA) model, enhancing framework robustness and mitigating miss-classification accuracy. Our proposed framework achieves 99.89% accuracy for early forecasting, surpassing existing methods based on different benchmark settings and publicly available multivariate time-series datasets. The framework provides a promising solution to the challenges of accurate MTS forecasting and offers insights into the early prediction of recent stock market trading data

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