To enhance the prediction accuracy and efficiency of the wireless outdoor heatmap, we propose a novel federated Gaussian Process (GP) approach combined with Bayesian Model Averaging (BMA). Traditional centralized GP models need extensive communication between distributed sensors and a central server, which leads to inefficiencies, increased computational costs, and potential privacy risks. Additionally, the GP model's log-likelihood function is optimized using the entire dataset, which makes it incompatible with standard federated aggregation techniques such as Federated Averaging (FedAvg). To overcome these challenges, our approach enables each sensor to process its data locally with GP algorithms by mapping Received Signal Strength (RSS) to corresponding locations. At the central server, BMA predicts pseudo-labels from limited global data to create a pseudo-labeled set for knowledge distillation. This allows the central server to train a global student GP model that updates parameters rather than averaging local models directly as in FedAvg. The global model is then sent back to sensors for further iterations. We evaluate our approach using real-world RSS data from the National Science Foundation (NSF) funded Platform for Open Wireless Data-driven Experimental Research (POWDER) at the University of Utah. The experiment results demonstrate that the proposed federated GP model significantly outperforms existing methods, including the federated GP schemes using FedAvg and classical Alternating Direction of Multipliers Method (cADMM) and federated Neural Network (NN)-based scheme.

Future wireless networks demand a more accurate understanding of channel behavior to enable efficient communication with reduced interference. Uncrewed Aerial Vehicles (UAVs) are poised to play an integral role in these networks, offering versatile applications and flexible deployment options. However, accurately characterizing the shadow fading (SF) behavior in UAV communications remains a challenge. Traditional SF correlation models rely on spatial distance and neglect the UAV's 3D orientation and elevation angle. Yet even slight variations in pitch angle (5-10 degrees) can significantly affect the signal strength observed by a UAV. In this study, we investigate the impact of UAV pitch and elevation geometry on SF and propose an elevation- and tilt-aware spatial correlation model. We use a real-world fixed-altitude UAV measurement dataset collected in a rural environment at 3.32 GHz with a 125 kHz bandwidth. Results show that a 10 degrees tilt-angle separation and a 20 degrees elevation-angle separation can reduce the SF correlation by up to 15% and 40%, respectively. In addition, integrating the proposed correlation model into the ordinary Kriging (OK) framework for signal strength prediction yields an approximate 1.5 dB improvement in median RMSE relative to the traditional correlation model that ignores UAV orientation and elevation.

The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.

Dielectric polymer like pure poly methyl methacrylate (PMMA) and its composites with nanoparticles (NPs) are interesting for industrial application such as solar cell, energy storage devices etc. Reinforcement of PMMA enhances its own properties and promotes unique applications in different areas. On the other hand, Co3O4 NPs are semiconducting in nature and find versatile applications in the optoelectronics field. When Co3O4 NPs-PMMA composites are formed, the properties of PMMA get modified. Therefore, before realizing the applications, it is necessary to understand the basic properties of this composite. With this motivation we have synthesized pure PMMA and Co3O4 NPs-PMMA composite self-standing films via solution casting method and studied their dielectric properties in terms of variation in dielectric constant (ε’) and Dielectric loss (ε’’) as a function of filler particles (Co3O4 NPs) concentration. To investigate the modification in bonding behavior of PMMA in presence of metal oxide nanoparticles, Fourier transform infrared (FTIR) spectroscopy was employed, which showed that beyond 1 wt

Silver-doped Bi1/2Na1/2-xAgxTiO3 (BNAT) ceramics (x = 0.0, 0.025, 0.075, and 0.1) were synthesized using the solid-state reaction (SSR) technique. The structural analysis was performed using the X-ray diffraction (XRD) technique, which revealed the formation of a polycrystalline sample with R3c symmetry. Pristine Bi0.5Na0.5TiO3 (BNT) ceramics exhibited an average crystallite size of 25.372 nm. Doping a small amount of Ag+ ions in place of Na+ ions resulted in an improved average crystallite size of 26.365 nm, as calculated by Debye-Scherrer’s formula. Raman spectra were employed to investigate the vibrational modes of the materials. The FTIR spectra of Ag+-doped BNT ceramics displayed two strong peaks at 971 and 537 cm−1, attributed to the presence of metal-oxygen bonds. Room temperature dielectric constant (ε′) and dielectric loss (tan δ) analyses were conducted in the frequency range of 20 Hz to 1 MHz. Complex impedance and modulus spectroscopic analyses indicated the presence of grain boundary effects alongside the bulk contribution and also confirmed the presence of non-Debye relaxations in the materials.

We present a novel Bayesian learning approach to outdoor radio heatmap construction utilizing deep Gaussian Process (GP). The proposed approach employs a two-layer hierarchy that is capable of modeling more complex input-output relations than the standard single-layer GP. Since deriving the exact model likelihood is challenging, a lower bound is optimized instead to find the optimal model parameters. Typically, inducing points are used to facilitate low-rank approximation of covariance (kernel) matrices for computation speedup. However, the inaccuracy induced by inducing points can accumulate when stacking multiple layers of GP which may degrade the performance of deep GP. Moreover, since inducing points need to be learned, having them at all layers of deep GP also incurs computational burden. To overcome the above challenges, in contrast to the canonical deep GP model, we use a modified architecture where a full standard GP resides in the first layer and inducing points are only introduced for the second layer. This modified architecture strikes a balance between model accuracy and training complexity. In the proposed model, the noise parameter of the first GP layer is also eliminated to improve the training efficiency as the noise parameter at the output of the second layer suffices to model the uncertainty in the output. The proposed approach is evaluated on real-world datasets, collected from the Platform for Open Wireless Data-driven Experimental Research (POWDER) located at the campus of the University of Utah. Experimental results show that the proposed approach can achieve superior performance on various training and testing data configurations compared to canonical deep GP schemes, DNN-based and GP-based methods.

The research demonstrates the application of machine learning (ML) while keeping the information security aspect of IoT devices in mind. It explains the significance of the Internet of Things (IoT) and expands on associated challenges, in line with the goal of transitioning to "Society 5.0." It shows how machine learning can be used to maintain IoT security using a quantitative approach. The research design includes machine learning-based data analysis of 95 samples collected from IoT devices, as well as anomaly detection methods to identify compromised data. The study goes on to explain how to use machine learning for the security of IoT devices, highlighting anomaly detection as an essential technique in the process. Because the use of IoT in sensitive areas necessitates careful consideration, the research suggests deploying machine learning as a security measure. Finally, it reflects on how successful anomaly detection in machine learning can assist organizations in detecting and responding to information security risks in real time.

Wireless Internet of Things (WIoT), based on the concept of ubiquitous computing, attempts to create communication networks by embedding microchips in everyday consumer electronic devices and to set those devices up to share data with each other to provide innovative connected solutions. With the explosion in the number and variety of wireless applications in the last two decades, all of them face a major challenge of availability of frequency spectrum. This challenge can be addressed by resorting to cognitive radio. The cognitive radio scheme uses a radio system that can make an informed decision to dynamically fine-tune its radio operation metrics as it can sense and is mindful of its operational setting. Combining the cognitive radio technology with WIoT can make the application design more robust and universal. In this paper, we discuss the possibilities and challenges involving such cognitive sensors, used for environmental sensing and healthcare applications.

Tourist attractions are often centered on mega sports events. Since the mega events have the potential to generate new revenue and enhance the appeal of the destination similarly, the FIFA World Cup 2022 in Qatar presented a unique opportunity for Oman to leverage the mega sporting event to promote its tourism industry globally. Oman used this mega event to brand its tourism industry by collaborating with FIFA, hosting fan zones, effectively using social and print media, highlighting accessibility and accommodation, and promoting local tourism attractions. The mega event resulted in increased international arrivals, revenue generation, and an enhanced image of Oman in the Middle East. The chapter is an attempt to explore various initiatives taken by the Sultanate of Oman to attract international visitors. Further details will review the methods adopted by public and private sectors in Oman in leveraging the recent mega event of FIFA World Cup 2022 to become a preferred global destination.

Job-related stress and its influence on work-life have emerged as a challenging task at all levels of management. With the emergence of COVID-19, stress has become one of the major problems for the workforce. The main purpose of this chapter is to determine the impact of job-related stress experienced by higher education institutions (HEIs) employees on their professional and personal life during the period of the coronavirus pandemic. For this purpose, several variables are examined during the COVID-19 pandemic, such as Demographics, Health Conditions, Workplace Stress, and Work-Life Balance (WLB). After descriptive statistics and bivariate correlations between the study variables were presented, the analysis of variance and structural equation modelling analysis resulted in the test of the hypotheses under study. The study's findings demonstrate the amount of job-related stress experienced by HEIs employees and its impact on their professional and personal lives during the pandemic, based on their self-evaluation. Results from sample data reveal that only the health condition of the respondents significantly controls the level of stress and WLB, irrespective of their demographic profile. The HEIs employees are experiencing both negative and positive stress, which are mutually independent.

This chapter focuses on understanding the post-COVID-19 pandemic travel preferences and perception of Omani travelers, which is a mix of citizens and expatriate residents. Primary and secondary data methodology was employed for collecting and analyzing data. Data from both qualitative and quantitative sources were gathered and analyzed. The sample survey includes 425 respondents from the Sultanate of Oman, consisting of a mixed population of citizens and expatriate residents. The findings conclude that most of the respondents are willing to travel to reunite with their families and friends post-COVID-19. They are more inclined toward staycations and want to go for domestic and local trips. Though they will be very interested in traveling post-pandemic, the sales campaign and promotional deals may not be the primary motivator for them to travel. Overall, safety, hygiene, dependable health, and sanitation arrangement will play a crucial role in the residents' decisions about traveling post-COVID-19 pandemic.

Healthcare, lifestyle, and medical applications of Internet of Things (IoT) involve the use of wearable technology that employs sensors of various kinds to sense human physiological parameters such as steps walked, body temperature, blood pressure, heart rate and other cardiac parameters. Such sensors and associated actuators can be worn as gadgets, embedded in clothing, worn as patches in contact with the body and could even be implanted inside the body. These sensors are electronic, and any electronic activity during their sensing, processing and wireless transmission is associated with the generation of heat. This dissipated heat can cause discomfort to the subject and has the potential of damaging healthy living tissue and cells. In the proposed work, the author does a performance check on the intrinsic safety aspects of an IoT healthcare network with respect to the functioning of the wireless sensors involved and routing of sensor data samples. The author also suggests an optimized thermal and energy aware framework to address the issue of temperature rise due to processing and data transmission from sensors through signal processing approaches that help in reducing thermal hazards and simultaneously enhancing the network lifetime through energy conservation.

The overall significance of tourism's role in the nation's holistic development is now a common phenomenon world over. That is why the Government of various countries are according top-most priority towards tourism development. The luxury tourism is that niche segment which is growing intense day by day and the craving of luxury traveller is uninfluenced by any economic turmoil or the crises. Tourism is now widely acknowledged as the strategic tool for economic diversification in the Sultanate of Oman. The Government is fully geared towards turning Oman into a prime luxury tourism destination by incorporating tourism in its vision 2040 strategy. The Government is actively tapping Oman's luxury tourism potential with improved tourism product development, increased projects funding, thoughtful marketing and brand promotion to reap benefits from tourism investments. Oman's unparalleled beauty, rich historic grandeur and authentic hospitality complement the desire of luxury travellers to seek unique quality and comfort, exclusivity and less ostentation. With the slogan ‘Beauty has an address’, the laudable effort of Oman has placed its tourism offering as an ideal upscale and luxury destination in the Middle East. The chapter explores the prospects of beautiful attractions and various services and facilities offered by Oman to qualify as a luxury destination. It also identifies the challenges faced by Oman in luxury tourism destination development.

In this research work, we report synthesis, structural and other physical properties of Co1 − xCuxFe2O4 [x = 0.00, 0.25, 0.5, 0.75, 1.00] spinel ferrites. These materials were synthesized using double calcination solid-state method. To confirm the crystal structure acquired by the samples, we carried out X-ray diffraction characterization and the diffraction data was Rietveld refined. The crystallization of all the samples into cubic (Fd-3 m) phase was ascertained from refinement and the structural parameters obtained there. We examined these spinel ferrite samples for room temperature magnetic properties and from the analysis of magnetic data (M–H Curve), these samples were found to exhibit super-paramagnetic behaviour. The Mossbauer spectral analysis confirms the super-paramagnetic nature and the six-line magnetic pattern arising from the super-exchange interaction among the magnetic ions at tetrahedral and octahedral sites have been witnessed. In addition, the room-temperature frequency-dependent dielectric properties were investigated and the data analysis revealed that these materials exhibit better dielectric properties where dielectric constant is high and dielectric loss values are relatively low. Impedance data study reveals that the samples under observation exhibit deviation from the ideal Debye behaviour and inherit a distribution of time constants. All the characterizations reported here were performed at room temperature.

In this piece of research, we throw light on the synthesis, structure and other physical properties of Co(1-x)CuxFe(2)O(4) [x = 0.00, 0.25, 0.5, 0.75, 1.00] spinel ferrites. These materials were synthesized by employ-ing the double calcination solid-state method. To confirm structured acquired by the synthesized sam-ples, we characterized our samples by X-ray diffraction technique, FTIR spectroscopy and inelastic Raman scattering technique. XRD studies reveal that all the samples are present in cubic crystal structure (Fd-3m). The desired phase formation was confirmed from Raman and FTIR spectra analysis also. To emphasize on electrical properties, we carried out room temperature frequency dependent ac conductiv-ity measurements also.Copyright (C) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of 2022 International Confer-ence on Recent Advances in Engineering Materials.

IJTP is an important journal that encourages critical examination of the emerging discourses in tourism policy. This quantitative study analyses the research papers published between 2007 and 2020 using a bibliometric approach to commemorate the 15th anniversary of IJTP. The statistical tool - bibliometrix was used to identify the leading trends and themes in the journal. This study operationalises extensive bibliometric performance and relational indicators. The results revealed that authors from different regions of the world published their work in IJTP. Tourism, tourism policy, sustainable tourism, and tourism planning were the most common keywords used. The quantitative approach has been primarily adopted in the papers of IJTP. Around 69% of the researches were completed through collaboration. The analysis conducted in the current study is in line with the other bibliometric studies considering individual journals. Therefore, this study may be relevant for researchers in tourism and hospitality scholarship.

LaCoO3 based compounds with 5% Cr and Fe ion as impurity at Co site were prepared by standard solid state reaction method. These sample were characterized for structural confirmation via XRD technique and for microstructural and compositional studies through SEM/EDAX characterization. In addition, these materials were investigated for dielectric properties. The X-ray diffraction studies infer that the sample are pure and single phased. These samples have acquired trigonal structure. The XRD result was verified via Rietveld refinement of pristine LaCoO3 compound. FTIR technique has been used to confirm the required oxide formation. The SEM images of these samples reveals that average grain size acquired after sintering is very high however the images present the porosity in the samples. EDAX data study confirmed that all the integral elements of the samples were retained and witnesses the absence of any foreign impurity. Dielectric study of these samples witness the samples exhibit high dielectric constant (capacitance) however the loss values are comparatively high subject to the modifications in microstructure.

In the present study, the impact of Cr ion substitution on Mn–Zn soft nanoferrites has enhanced the dielectric, electrical conductivity, and impedance properties. The nanoferrites have been synthesized via a non-conventional wet chemical-based co-precipitation technique. Raman scattering confirms the spinel nature and also reveals a positive frequency shift with the Cr ion substitution. As Cr ion concentration increases, the dielectric constant (ε′) increases significantly at room temperature. At 100 Hz, x = 0.5 [Mn0.5Cr0.5Fe2O4] resulted in higher value of ε′ ~ 104 and a lower value of loss (tan δ) ~ 3.9. Frequency modulated ac conductivity rises with increasing Cr substitution in Mn–Zn nanoferrites. Electric modulus, impedance spectra, and conduction nature were found to improve with increasing Cr ions. The Nyquist plot shows two semicircle responses in the high and mid-frequency regions, which is due to a conduction mechanism of charges (Fe2+ ↔ Fe3+) that is related to bulk grains and grain boundary contribution, respectively. High dielectric constants and minimum electric loss in soft nanoferrite materials are useful for electronic device applications.

In this first 2021 issue, the Mobile Communications and Networks Series welcomes the New Year by bringing to the readers of this magazine a collection of very interesting articles on mainstream topics in wireless communications. The COVID-19 pandemic that battered many parts of the world in the past year imposed health-preserving physical distancing rules that hampered many of our activities. The pandemic crisis made us realize how vital it is to be digitally connected and brought out an urgent need for further enhanced mobile services and applications. The most recent generations of wireless technologies, which are now becoming operational in an increasing number of regions, already address the need for enhanced performance and new features. At the same time, researchers in mobile communications are readily looking into the potential solutions that will boost performance and features of next generation networks. The article “The Road to 6G: Ten Physical Layer Challenges for Communications Engineers” looks at future 6G wireless networks from a physical layer perspective, highlighting key enabling technologies, e.g., intelligent reflecting surfaces, cell-free massive MIMO and terahertz communications, and related major challenges. The article discusses theoretical modeling challenges, hardware implementation issues and scalability, and concludes by delineating the critical role of signal processing.

In this report, we discuss the structural and dielectric properties of TM0.7Zn0.3Fe2O4 (TM = Ni, Mn) spinel ferrites. The ferrite materials under study were synthesized by the solid state reaction method. For structural elucidation, these materials were characterized by X-ray diffraction technique and Raman scattering Method. The analysis of XRD data obtained in the angular range of 10 degrees - 80 degrees revealed that both the samples under study have crystallized in cubic structure (Fd3m). The XRD results were verified by Raman spectral study from the display of fingerprint modes of vibration related to cubic structure acquired by these ferrite materials. The dielectric study infers the samples are extremely good dielectric materials with high dielectric constant and comparatively low loss value. The dielectric constant of Zn doped of MnFe2O4 is higher. However, the resonance like behaviour is observed for Zn doped NiFe2O4. The high dielectric constant and relatively low loss value infer their feasibility in advanced electronic device applications. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Multifunctional Nanomaterials.