Coverage Performance Research Articles

Artificial Noise Aided Secure NOMA Communications in STAR-RIS Networks

Combination of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple access (NOMA) is a win-win strategy which can significantly enhance the coverage performance. However, eavesdroppers may enjoy similar performance gains as the legitimate users. To solve this problem, an artificial noise (AN) assisted secure communication strategy is proposed to maximize the secrecy rate. An alternating optimization (AO) based iterative algorithm leveraging the classical successive convex approximation (SCA) and the semidefinite relaxation (SDR) techniques is proposed to derive the optimal AN model and the RIS parameters. It is found that the proposed algorithm provides better secrecy performance with less AN power compared with the benchmark schemes. More RIS elements help reducing the AN power, while this effect shrinks when the number of RIS elements is sufficiently large. Increasing the number of transmit antennas reduces the AN power if the eavesdropper is quite close to the transmitter, while improves it when the eavesdropper is far away.

Stochastic Analysis of Cooperative Satellite-UAV Communications

This paper considers a dual-hop cooperative satellite-unmanned aerial vehicle (UAV) communication system including a satellite (S) and a group of cluster headers (CHs) which are respectively with a group of uniformly distributed UAVs. Specifically, the CHs serve as aerial decode-and-forward relays to forward the information transmitted by the S to the UAVs. Moreover, free-space optical (FSO) and radio frequency (RF) technologies are respectively adopted over S-CH and CH-UAV links to exploit the high directivity of FSO over long-distance transmission and the omnidirectional coverage ability of RF. The positions of the CHs in the three-dimensional space follow the Matérn hard-core point processes type-II, in which each CH cannot be closer to another by less than a predefined distance. Three different cases over CH-UAV links are considered during the performance modeling: interference-free, interference-dominated, and interference-and-noise. Then, the coverage performance of the S-CH link and the CH-UAV link in the aforementioned three cases is studied and the closed-form analytical expressions of the coverage probability (CP) over both two links are derived. Additionally, the asymptotic expressions for the CP over S-CH link and CH-UAV link in the interference-free case are derived. Finally, numerical results are provided to validate our proposed analytical models and thus some meaningful conclusions are achieved.

Node Collaborative Sensing-Based Redundant Path Construction for Multiarea Coverage in MWSNs

Mobile wireless sensor networks (MWSNs) have been widely used in environmental safety monitoring and other applications due to their many characteristics, especially in hazardous and harsh workplaces. This paper studies how to construct a covered redundant path between the Original Monitoring Area (OMA) and the Extended Area (EA) to provide continuous coverage and communication. To avoid the shortage of multi-area coverage control studies in MWSNs, we adopt a new Node Cooperative Sensing (NCS) model, and define the NCS-based Multi-area Coverage Redundant Path Construction (NCS-MCRPC) problem, intending to minimize the communication interruption of the path and ensure the monitoring of hazardous substances. To solve the NCS-MCRPC problem, we propose a Rapid Sensor Redeployment-based Redundant Path Construction (RSR-RPC) strategy. RSR-RPC includes the initialization phase, redeployment phase, working phase, and repair phase. To verify the effectiveness of the proposed mechanism, we conducted a series of experiments. The simulation results show that RSR-RPC outperforms the peer-to-peer algorithms in terms of path coverage performance and interruption probability.

Complete coverage path planning and performance factor analysis for autonomous bulldozer

AbstractWith the development of intelligent machinery, path planning of autonomous bulldozers plays an important role to solve the problems of efficiency in future construction sites. How to plan the path that meets the construction requirements is the key problem. This study investigates the standardized construction workmanship and moving rules of the bulldozer to add rules and path selection strategies to the algorithm. This study establishes the path planning model of bulldozer's leveling, considering the limitations of the traditional bioinspired neural network algorithm, an improved complete coverage path planning method is proposed. An integrated framework is proposed for an automatic bulldozer with a sensor system for a field experiment to verify the feasibility of the method. According to the comparative statistical simulation results, the effectiveness of the method is verified, and the effects of different factors on the path planning results are analyzed.

Olimpismo: The Olympic Movement in the Making of Latin America and the Caribbean

Olimpismo: The Olympic Movement in the Making of Latin America and the Caribbean

Simulation of proton exchange membrane electrolyzer: Influence of bubble covering

The bubble covering phenomenon has been considered one of the most critical factors affecting Proton Exchange Membrane Electrolysis Cell (PEMEC) at high current densities. However, the relationship between bubble dynamics and electrochemistry has not been clearly defined. This study analyzes the bubble coverage and PEMEC performance under different input conditions and develops a mathematical model of PEMEC incorporating bubble dynamics. The model successfully predicted the polarization curves and coincided with the experimental data. The results show that bubble coverage increases with increasing current density, bubble detachment radius, and temperature. It decreases with increasing pressure and water inlet velocity. Bubble coverage is influenced by temperature, pressure, wettability, current density, and water inlet velocity. Meanwhile, bubbles covering the electrode deteriorate the performance of the PEMEC, leading to higher overpotentials and lower efficiencies, which becomes more apparent with increasing current density. This paper elucidates the relationship between bubble growth/detachment, bubble coverage, and electrochemistry for the first time, and the results can provide a reference for the development and optimization of high-performance PEMEC.

The Near-Infrared Spectrograph (NIRSpec) on the James Webb Space Telescope

The Near-Inrared Spectrograph (NIRSpec) on theJames WebbSpace Telescope (JWST) is a very versatile instrument, offering mul-tiobject and integral field spectroscopy with varying spectral resolution (~30 to ~3000) over a wide wavelength range from 0.6 to 5.3 micron, enabling scientists to study many science themes ranging from the first galaxies to bodies in our own Solar System. In addition to its integral field unit and support for multiobject spectroscopy, NIRSpec features several fixed slits and a wide aperture specifically designed to enable high precision time-series and transit as well as eclipse observations of exoplanets. In this paper we present its capabilities regarding time-series observations, in general, and transit and eclipse spectroscopy of exoplanets in particular. Due to JWST’s large collecting area and NIRSpec’s excellent throughput, spectral coverage, and detector performance, this mode will allow scientists to characterize the atmosphere of exoplanets with unprecedented sensitivity.

Coverage analyses on directional transmissions ultra-dense networks with imperfect beam alignment

Blockage and imperfect beam alignment are two principal difficulties in high-frequency bands directional transmissions. In this paper, the coverage performance of downlink directional transmissions in ultra-dense networks is analyzed, with the consideration of beam alignment error and link blockage through stochastic geometry. Numerical experiments demonstrate that narrower beam leads to higher coverage probability with perfect beam alignment, but it is not the case with imperfect beam alignment. Therefore, the optimal beamwidth that maximize the coverage probability is characterized and a closed-form approximation of the optimal beamwidth is derived under imperfect beam alignment, accordingly. Furthermore, the optimal beamwidth is a monotonically increasing function of the standard deviation of the beam alignment error, and a monotonically decreasing function of the beamwidth of correspondent communication end, indicating that the beamwidth of the communication pairs ought to be jointly designed.

Dynamic coverage performance of wind-assisted balloons mesh based on Voronoi partition and energy constraint

Efficiently navigating a fleet of several thousand high-altitude balloons to provide communication network and information system to the entire Earth is a promising technology. Here we proposed the use of balloons with altitude control system to coverage the entire Earth by exploiting the natural time-varying wind field. The energy constraints are deduced based on the energy collecting-consuming model and the energy management strategy of the wind-assisted balloon. The Voronoi partition is introduced to optimize the configuration. Then a dynamic coverage algorithm for multi-balloons system is developed. Three schemes of 1500 balloons in two launch sites are simulated to show the effectiveness of the algorithm. The results demonstrate that Voronoi partition with energy constraints is an effective solution to spread the wind-assisted balloons out over the entire Earth within a short time and then maintain the optimal dynamic coverage configuration for quite a long time. This can serve as a guideline for the design and flight test of the serviceable wind-assisted balloons coverage mesh.

Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout

Background and objectiveSocial media sentiment analysis based on Twitter data can facilitate real-time monitoring of COVID-19 vaccine-related concerns. Thus, the governments can adopt proactive measures to address misinformation and inappropriate behaviors surrounding the COVID-19 vaccine, threatening the success of the national vaccination campaign. This study aims to identify the correlation between COVID-19 vaccine sentiments expressed on Twitter and COVID-19 vaccination coverage, case increase, and case fatality rate in Indonesia. MethodsWe retrieved COVID-19 vaccine-related tweets collected from Indonesian Twitter users between October 15, 2020, to April 12, 2021, using Drone Emprit Academic (DEA) platform. We collected the daily trend of COVID-19 vaccine coverage and the rate of case increase and case fatality from the Ministry of Health (MoH) official website and the KawalCOVID19 database, respectively. We identified the public sentiments, emotions, word usage, and trend of all filtered tweets 90 days before and after the national vaccination rollout in Indonesia. ResultsUsing a total of 555,892 COVID-19 vaccine-related tweets, we observed the negative sentiments outnumbered positive sentiments for 59 days (65.50%), with the predominant emotion of anticipation among 90 days of the beginning of the study period. However, after the vaccination rollout, the positive sentiments outnumbered negative sentiments for 56 days (62.20%) with the growth of trust emotion, which is consistent with the positive appeals of the recent news about COVID-19 vaccine safety and the government's proactive risk communication. In addition, there was a statistically significant trend of vaccination sentiment scores, which strongly correlated with the increase of vaccination coverage (r = 0.71, P<.0001 both first and second doses) and the decreasing of case increase rate (r = -0.70, P<.0001) and case fatality rate (r = -0.74, P<.0001). ConclusionsOur results highlight the utility of social media sentiment analysis as government communication strategies to build public trust, affecting individual willingness to get vaccinated. This finding will be useful for countries to identify and develop strategies for speed up the vaccination rate by monitoring the dynamic netizens' reactions and expression in social media, especially Twitter, using sentiment analysis.

Enhancing Trustworthiness of Internet of Vehicles in Space–Air–Ground-Integrated Networks: Attestation Approach

The integration of the space–air–ground-integrated network and the Internet of Vehicles (IoV) enables the IoV to achieve full network coverage and better network performance. However, the large scale of the network and the complex cooperation mechanism make the credibility of the nodes in the network and the service delivery questioned. In this article, the hardware trusted module is used as the root of trust to build the trust chain and the trusted running environment and provide protection and trusted state attestation for services. In order to overcome the large-scale and high-concurrency performance bottlenecks in the remote verification of trusted states in the IoV, a novel batch remote approach for trusted states is proposed. The simulation results show that the proposed approach can effectively attest to the trusted state of each network node and virtual service in the IoV and enhance the trustworthiness of the network.

Reinforcement Learning-Enabled Resampling Particle Swarm Optimization for Sensor Relocation in Reconfigurable WSNs

Aiming to maximize coverage performance and reduce the number of sensors deployed in the reconfigurable wireless sensor networks (RWSNs), in this paper, we first formulate a new cooperative sensing coverage control problem based on the confident information coverage model. Then, inspired by the reinforcement learning and resampling technology, a novel learning automata-based resampling particle swarm optimization (RPSOLA) algorithm is proposed to solve complex multi-peak optimization problem and optimize the cooperative sensing coverage control problem of RWSNs. Experimental results demonstrate that the RPSOLA considerably outperforms other three peer schemes, the RPSO, BASPSO and PSO, in terms of the convergence, coverage rate and sensor redundancy.

Metaheuristic based navigation of a reconfigurable robot through narrow spaces with shape changing ability

Emerging technologies and robotics solutions for cleaning applications are rapidly progressing nowadays. Reconfigurable robots that can change shapes are introduced to overcome the limitations of fixed-shape robots. Reconfigurable robots should be capable of moving through narrow spaces with the help of continuous shape-changing to improve the coverage performance. In this regard, a reconfigurable robot requires perceiving the narrow spaces in between obstacles and generating the path to navigate. Most importantly, the robot should decide and change the shape configurations appropriately to move on that path without collisions. Furthermore, such a reconfiguration ability could be used for covering around obstacles. Therefore, this paper proposes a method to navigate a reconfigurable robot through narrow spaces or cover obstacles with the aid of continuous reconfiguration. In the first step, the method analyzes the obstacle clusters in a metric map to determine the appropriate paths to navigate. In the second step, a sequence of shapes for reconfiguration that allows the robot’s movement on the path without collisions is determined. Metaheuristics, Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Simulated Annealing (SA), are proposed to determine the shape sequence. Simulations have been conducted to validate the performance and behavior of the proposed method. The results confirm that the developed method is effective for navigating a reconfigurable robot through narrow spaces or covering obstacles with continuous shape-changing. According to the statistical outcomes, GWO requires less computational time compared to GA and SA.

Precise 3D Indoor Localization and Trajectory Optimization Based on Sparse Wi-Fi FTM Anchors and Built-In Sensors

Indoor location-based services have become more and more important due to their potential applications in a wide range of personalized services in recent years. The accuracy of smartphone based 3D indoor localization is subjected to the poor performance of low-cost sensors and limited coverage of location sources. In order to solve these problems, this paper proposes a precise 3D indoor localization and trajectory optimization framework that uses the combination of sparse Wi-Fi Fine Time Measurement (FTM) anchors and built-in sensors (3D-LOWS). The inertial navigation system (INS) mechanization, multi-level constraints and observed values are integrated by the adaptive unscented Kalman filter to eliminate effects of cumulative error, indoor magnetic interference, and diversity of handheld modes. The Wi-Fi based ranging and landmark detection information is used to provide an accurate absolute reference to the built-in sensors based method. In addition, this paper proposes and evaluates two different trajectory optimization algorithms and compares the improved localization performance. The comprehensive experiments indicate that the proposed 3D-LOWS is proved to achieve accurate and stable 3D indoor positioning and trajectory optimization performance under complex indoor environments using sparse wireless stations.

Cooperation in Space: HAPS-Aided Optical Inter-Satellite Connectivity With Opportunistic Scheduling

Issues with tracking, precision pointing, and Doppler shift are the major sources of performance loss in laser inter-satellite communication that can severely decrease the coverage and overall performance of satellite constellations. As a solution to these problems, we propose a cooperation strategy in which a high altitude platform station (HAPS) staying at a quasi-stationary position contributes to the inter-satellite connectivity. In this setup, the HAPS node uses two different scheduling approaches: one that relies on the zenith angle; the other on instantaneous signal-to-noise ratio. To quantify the performance of the proposed scheme, overall outage probabilities for the two scheduling methods are obtained. In addition, guidelines for the design of practical inter-satellite networks are provided.

Improving IoT-Over-Satellite Connectivity Using Frame Repetition Technique

Through Non-Terrestrial Networks (NTN), a global coverage connecting areas with minimal or no terrestrial services is envisaged using satellite and air-borne platforms. However, one of the challenges to delivering NTN communications is the increased levels of path-loss, relative to typical terrestrial scenarios, due to the vast communication distance towards satellite platforms. Typically, cellular IoT technologies adopt frame repetitions where many redundant data transmissions are included to enhance its success rate. This can be also extended to satellite links to counter the effect of long distance communications on the link budget. In this paper, we put forth an analytic framework that captures the repetition behavior based on the probability of line-of-sight (LoS), as it heavily influences the propagation conditions. We analyze the coverage performance in terms of the frame success rate, which allows us to tune the repetitions behavior to suit a given satellite admittance region. Furthermore, we obtain an optimal global average success rate by considering the availability of satellites. The presented framework can facilitate coverage enhancements for IoT-over-Satellite networks.

Improved genetic algorithm based 3-D deployment of UAVs

Unmanned aerial vehicles (UAVs) are widely used as aerial base stations (BSs) to provide flexible connectivity and coverage for ground users in various scenarios such as disaster relief, traffic offloading, and so on. Especially, UAV deployment is an important issue that directly affects the coverage performance of the UAV network. In this paper, we propose a novel heuristic algorithm based three-dimensional (3-D) UAV deployment scheme while guaranteeing the connectivity of the UAV network in both static and dynamic user scenarios. For the static user scenario, we aim to deploy the minimum number of UAVs to provide coverage for the users from the perspective of deployment cost. To reduce the deployment complexity, we decouple the 3-D UAV deployment problem from the vertical and horizontal dimensions. Specifically, we firstly determine the optimal vertical height of UAVs based on the air-to-ground (A2G) model. Then, to alleviate the premature convergence of standard genetic algorithm (SGA), we design an improved genetic algorithm (IGA) to obtain the optimal horizontal locations of UAVs. On this basis, when the users move or increase, i.e., the dynamic user scenario, the already deployed UAVs cannot provide effective coverage. For this scenario, we propose a UAV redeployment scheme to maximize the number of covered users without increasing the number of UAVs. To further reduce the cost of redeployment, we firstly modify the proposed IGA to obtain a feasible set of two-dimensional (2-D) redeployed locations of the UAVs. Then, we design a backtracking algorithm (BA) based UAV movement strategy to minimize the total flying distance of the UAVs. The simulation results show that the effectiveness and convergence of our proposed schemes.

Coverage Control Algorithm for DSNs Based on Improved Gravitational Search

In directional sensor networks (DSNs), coverage control is an important way to ensure efficient communication and reliable data transmission. Aiming at enhancing the DSNs coverage performance, a coverage control algorithm based on improved gravitational search (IGSCCA) was proposed. Firstly, in the coverage enhancement phase, aiming at the defects of gravitational search algorithm that it is easy to fall into local optimum and slow convergence speed when solving the coverage enhancement problem, cosine decay function is used to improve the gravitational function, the global optimum position is introduced into the velocity updating of gravitational search algorithm, and use the uncovered grid points attractive force to disturb the worst particles. Secondly, in the node sleep phase, the node sleep strategy based on security set is introduced to reduce the network redundancy rate. Finally, the simulation results showed that this paper has great advantages in coverage rate, energy utilization rate and redundancy rate.

Coverage Probability of Relay-Assisted NOMA Millimeter Wave Networks with Steerable-Beam

In this paper, we consider a relay-assisted non-standalone (NSA) 5G network, where source (S) intends to transmit signals to users by assistance with a relay R, which receives the signals from S operating in microwave frequency and transfers to target users by utilizing steerable-beam non-orthogonal multiple access (NOMA) scheme operating in millimeter-wave (mmWave) frequency. The steerable-beam NOMA scheme can flexibly switch between single-beam and multi-beam modes according to angle difference of users in NOMA group, i.e., when at least one angle difference is smaller than mmWave beamwidth of main lobe, R adopts a single-beam for achieving NOMA transmission, otherwise multi-beam scheme is used to complete NOMA transmission. For insight into the performance of steerable-beam NOMA scheme, we analyze the coverage probability of the system in-depth for deriving the analytical expressions and approximately closed-form solutions of target users’ coverage probabilities within a NOMA group, where the first user Uo is selected with the shortest distance to the S and the paired user Up is determined based on the minimum angle difference to the first user. However, when R adopts multi-beam, the user with the smallest angle difference to the Uo may not be the best paired user due to the influence of path attenuation and antenna allocation. We then propose a two-stage scheme to obtain optimal user pairing by jointly considering the angle, distance, and sum-rate, while optimizing power allocation subject to maximize the sum-rate. Simulation results illustrate the coverage performance of the system with steerable-beam NOMA scheme for different parameters, and demonstrate the advantages of the proposed two-stage strategy offers a significant improvement in terms of average sum-rate compared to other schemes.

Three-dimensional feature matching improves coverage for single-cell proteomics based on ion mobility filtering.

SUMMARYSingle-cell proteomics (scProteomics) promises to advance our understanding of cell functions within complex biological systems. However, a major challenge of current methods is their inability to identify and provide accurate quantitative information for low-abundance proteins. Herein, we describe an ion-mobility-enhanced mass spectrometry acquisition and peptide identification method, transferring identification based on FAIMS filtering (TIFF), to improve the sensitivity and accuracy of label-free scProteomics. TIFF extends the ion accumulation times for peptide ions by filtering out singly charged ions. The peptide identities are assigned by a three-dimensional MS1 feature matching approach (retention time, accurate mass, and FAIMS compensation voltage). The TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells, with >1,100 proteins consistently identified. As a demonstration, we applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during lipopolysaccharide stimulation and identified time-dependent proteome changes. A record of this paper’s transparent peer review process is included in the supplemental information.