Time-varying Mobile Research Articles

Relative deprivation, inequality and the Covid-19 pandemic

There is a growing concern that inequalities are hindering health outcomes. This paper's primary objective is to investigate the role of relative deprivation and inequality in explaining the daily spread of the Covid-19 pandemic. For this purpose, we use secondary cross-sectional data across 119 (developed and developing) countries from January 2020 – to April 2021. For the empirical analysis, we use a recent dynamic panel data modelling approach that allows us to identify the role of time-invariant variables such as degree of globalisation, political freedom and income inequality on the dynamics of the pandemic and fatality rates across countries. We find that new cases per million and fatality rates are highly persistent processes. After controlling for time-varying mobility statistics from the Google mobility database and region-specific dummy variables, the two significant factors that explain the severity of Covid-19 spread in a country are per-capita Gross Domestic Product (GDP) and Yitzhaki's relative income deprivation index. Lagged value of new cases per million significantly explains cross-country variations in the daily case fatality rates. A higher proportion of the older population and pollution increased fatality rates while better medical infrastructure reduced it.

A multilayer network model for studying the impact of non-pharmaceutical interventions implemented in response to COVID-19

Non-pharmaceutical interventions (NPIs) are essential for the effective prevention and control of the COVID-19 pandemic. However, the scenarios for disease transmission are complicated and varied, and it remains unclear how real-world networks respond to the changes in NPIs. Here, we propose a multi-layer network combining structurally fixed social contact networks with a time-varying mobility network, select the COVID-19 outbreak in two metropolitans in China as case studies, and assess the effectiveness of NPIs. Human mobility, both in relatively fixed places and in urban commuting, is considered. Enclosed places are simulated by three different types of social contact networks, while urban commuting is represented by a time-varying commute network. We provide a composite framework that captures the heterogeneity and time variation of the real world and enables us to simulate large populations with low computational costs. We give out a thorough evaluation of the effectiveness of NPIs (i.e., work from home, school closure, close-off management, public transit limitation, quarantine, and mask use) under certain vaccine coverage varying with implementation timing and intensity. Our results highlight the strong correlation between the NPI pattern and the epidemic mitigation effect and suggest important operational strategies for epidemic control.

Particulate matter and COVID-19 excess deaths: Decomposing long-term exposure and short-term effects

We investigate the time-varying effect of particulate matter (PM) on COVID-19 deaths in Italian municipalities. We find that the lagged moving averages of PM2.5 and PM10 are significantly related to higher excess deceases during the first wave of the disease, after controlling, among other factors, for time-varying mobility, regional and municipality fixed effects, the nonlinear contagion trend, and lockdown effects. Our findings are confirmed after accounting for potential endogeneity, heterogeneous pandemic dynamics, and spatial correlation through pooled and fixed-effect instrumental variable estimates using municipal and provincial data. In addition, we decompose the overall PM effect and find that both pre-COVID long-term exposure and short-term variation during the pandemic matter. In terms of magnitude, we observe that a 1 μg/m3 increase in PM2.5 can lead to up to 20% more deaths in Italian municipalities, which is equivalent to a 5.9% increase in mortality rate.

Understanding COVID-19 dynamics and the effects of interventions in the Philippines: A mathematical modelling study

BackgroundCOVID-19 initially caused less severe outbreaks in many low- and middle-income countries (LMIC) compared with many high-income countries, possibly because of differing demographics, socioeconomics, surveillance, and policy responses. Here, we investigate the role of multiple factors on COVID-19 dynamics in the Philippines, a LMIC that has had a relatively severe COVID-19 outbreak.MethodsWe applied an age-structured compartmental model that incorporated time-varying mobility, testing, and personal protective behaviors (through a “Minimum Health Standards” policy, MHS) to represent the first wave of the Philippines COVID-19 epidemic nationally and for three highly affected regions (Calabarzon, Central Visayas, and the National Capital Region). We estimated effects of control measures, key epidemiological parameters, and interventions.FindingsPopulation age structure, contact rates, mobility, testing, and MHS were sufficient to explain the Philippines epidemic based on the good fit between modelled and reported cases, hospitalisations, and deaths. The model indicated that MHS reduced the probability of transmission per contact by 13-27%. The February 2021 case detection rate was estimated at ~8%, population recovered at ~9%, and scenario projections indicated high sensitivity to MHS adherence.InterpretationCOVID-19 dynamics in the Philippines are driven by age, contact structure, mobility, and MHS adherence. Continued compliance with low-cost MHS should help the Philippines control the epidemic until vaccines are widely distributed, but disease resurgence may be occurring due to a combination of low population immunity and detection rates and new variants of concern.FundingThis work was supported by the World Health Organization Regional Office for the Western Pacific. Tagalog translation of the abstract (Appendix 2).

Understanding COVID-19 Dynamics and the Effects of Interventions in the Philippines: A Mathematical Modelling Study

Background: COVID-19 appears to have caused less severe outbreaks in many low- and middle-income countries (LMIC) compared with high-income countries, possibly because of differing demographics, socio-economics, climate, surveillance, and policy responses. The Philippines is a LMIC that has had a relatively severe COVID-19 outbreak but has recently curtailed transmission while gradually easing interventions.Methods: We applied an age-structured compartmental model that incorporated time-varying mobility, testing, and personal protective behaviors (through a “Minimum Health Standards” policy, MHS) to represent the Philippines COVID-19 epidemic nationally and for three highly affected regions (Calabarzon, Central Visayas, and the National Capital Region). We estimated effects of control measures, key epidemiological parameters, and projected the impacts of easing interventions.Findings: Population age structure, contact rates, mobility, testing, and MHS were sufficient to explain the Philippines epidemic based on the good fit between modelled and reported cases, hospitalisations, and deaths. Several of the fitted epidemiological parameters were consistent with those reported in high-income settings. The model indicated that MHS reduced the probability of transmission per contact by 15-32%. The December 2020 case detection rate was estimated at ~14%, population recovered at ~12%, and scenario projections indicated high sensitivity to MHS adherence.Interpretation: COVID-19 dynamics in the Philippines are driven by age, contact structure, and mobility, and the epidemic can be understood within a similar framework as for high-income settings. Continued compliance with low-cost MHS measures should allow the Philippines to maintain epidemic control, but disease resurgence remains a threat due to low population immunity and detection rates.Funding Statement: This work was supported by the World Health Organization Regional Office for the Western Pacific to provide modelling advice to Member States. JMS is supported by a NASA Ecological Forecasting grant (NNX17AI21G). EDLT, TRT, MRJEE, and RFRS are supported by a project grant from the Philippine Council for Health Research and Development, Department of Science and Technology, Philippines. JMT is supported by an Early Career Fellowship from the National Health and Medical Research Council (APP1142638).Declaration of Interests: The authors declare no conflicts of interest.

Modelling Time-varying Mobility Flows Using Function-on-Function Regression: Analysis of a Bike Sharing System in the City of Milan

Abstract In today's world, bike sharing systems are becoming increasingly common in all main cities around the world. To understand the spatiotemporal patterns of how people move by bike through the city of Milan, we apply functional data analysis to study the flows of a bike sharing mobility network. We introduce a complete pipeline to properly analyse and model functional data through a concurrent functional-on-functional model taking into account the effects of weather conditions and calendar on the bike flows. In the end, we develop an interactive interface to explore the results of the analyses.

Time-varying mobility and turnover of actomyosin ring components during cytokinesis in Schizosaccharomyces pombe.

Cytokinesis in many eukaryotes is dependent on a contractile actomyosin ring (AMR), composed of F-actin, myosin II, and other actin and myosin II regulators. Through fluorescence recovery after photobleaching experiments, many components of the AMR have been shown to be mobile and to undergo constant exchange with the cytosolic pools. However, how the mobility of its components changes at distinct stages of mitosis and cytokinesis has not been addressed. Here, we describe the mobility of eight Schizosaccharomyces pombe AMR proteins at different stages of mitosis and cytokinesis using an approach we have developed. We identified three classes of proteins, which showed 1) high (Ain1, Myo2, Myo51), 2) low (Rng2, Mid1, Myp2, Cdc12), and 3) cell cycle–dependent (Cdc15) mobile fractions. We observed that the F-BAR protein Cdc15 undergoes a 20–30% reduction in its mobile fraction after spindle breakdown and initiation of AMR contraction. Moreover, our data indicate that this change in Cdc15 mobility is dependent on the septation initiation network (SIN). Our work offers a novel strategy for estimating cell cycle–dependent mobile protein fractions in cellular structures and provides a valuable dataset, that is of interest to researchers working on cytokinesis.

The origin of the oppositional patterns of cultural diversity in different levels

The evolution of cultural diversity in some social systems can be observed as the oppositional patterns in different levels, namely, the directions of the trends of diversity in different levels are opposite. In this paper, we propose a payoff-driven metapopulation modeling framework to investigate the origin of the oppositional patterns in the trends of cultural diversity. This model considers the mobile agents in multiple communities, and the cultural characteristics of an agent are represented by a vector. The diffusion of cultural characteristics is driven by two opposite effects: the communication cost positively depending on cultural difference between agents, and the cultural attractiveness single-peak-functional correlating on cultural difference. In the numerical simulations of the model, the polarization is observed in the case with the homophily principle, and two types of the oppositional patterns can emerge in the case with time-varying mobility of agents. These findings efficiently explain the emergence of the oppositional patterns and help to understand the evolution of diversity under the dynamics of both synchronization and variation.

Stochastic QoE-aware optimization of multisource multimedia content delivery for mobile cloud

The increasing popularity of mobile video streaming in wireless networks has stimulated growing demands for efficient video streaming services. However, due to the time-varying throughput and user mobility, it is still difficult to provide high quality video services for mobile users. Our proposed optimization method considers key factors such as video quality, bitrate level, and quality variations to enhance quality of experience over wireless networks. The mobile network and device parameters are estimated in order to deliver the best quality video for the mobile user. We develop a rate adaptation algorithm using Lyapunov optimization for multi-source multimedia content delivery to minimize the video rate switches and provide higher video quality. The multi-source manager algorithm is developed to select the best stream based on the path quality for each path. The node joining and cluster head election mechanism update the node information. As the proposed approach selects the optimal path, it also achieves fairness and stability among clients. The quality of experience feature metrics like bitrate level, rebuffering events, and bitrate switch frequency are employed to assess video quality. We also employ objective video quality assessment methods like VQM, MS-SSIM, and SSIMplus for video quality measurement closer to human visual assessment. Numerical results show the effectiveness of the proposed method as compared to the existing state-of-the-art methods in providing quality of experience and bandwidth utilization.

Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas.

Assessment of Traffic-Related Air Pollution: Case Study of Pregnant Women in South Texas.

Population groups vulnerable to adverse effects of traffic-related air pollution correspond to children, pregnant women and elderly. Despite these effects, literature is limited in terms of studies focusing on these groups and a reason often cited is the limited information on their mobility important for exposure assessment. The current study presents a method for assessing individual-level exposure to traffic-related air pollution by integrating mobility patterns tracked by global positioning system (GPS) devices with dynamics of air pollutant concentrations. The study is based on a pool of 17 pregnant women residing in Hidalgo County, Texas. The traffic-related particulate matter with diameter of less than 2.5 micrometer (PM2.5) emissions and air pollutant concentrations are predicted using MOVES and AERMOD models, respectively. The daily average traffic-related PM2.5 concentration was found to be 0.32 µg/m3, with the highest concentration observed in transit (0.56 µg/m3), followed by indoors (0.29 µg/m3), and outdoor (0.26 µg/m3) microenvironment. The obtained exposure levels exhibited considerable variation between time periods, with higher levels during peak commuting periods, close to the US–Mexico border region and lower levels observed during midday periods. The study also assessed if there is any difference between traffic-related dynamic exposure, based on time-varying mobility patterns, and static exposure, based solely on residential locations, and found a difference of 9%, which could be attributed to the participants’ activity patterns being focused mostly indoors.

Data offloading in IoT environments: modeling, analysis, and verification

Recent years have seen a significant growth in Internet of Things (IoT) technology consisting of a large number of devices embedded with sensors and deployed to perform monitoring and actuation tasks. The IoT devices collect large volumes of data that is usually uploaded to cloud to perform analytics and predictions. One of the main challenges in IoT is the transportation of large-scale data collected over a period of time at a remote site. Cellular networks are already facing explosive growth of mobile data traffic due to the proliferation of smart devices and traffic-intensive applications. An alternate solution is to perform the data offloading, where a portion of data can be offloaded from primary links and transferred using opportunistic terminal-to-terminal (T2T) network that relies on direct communication between mobile users, without any need for an infrastructure backbone. However, such approach may lead to data loss and delay if dynamics of time-varying topology and mobility of nodes is not taken care of. To address this challenge, we propose three prediction-based offloading schemes that exploit the mobility patterns and temporal contacts of nodes to predict future data transfer opportunities. We have utilized the High-level Petri Nets to model and formally analyzed the communication processes of the proposed schemes. The new symbolic model verifier (NuSMV) has been employed for the verification of the three schemes against the identified constraints. The verification results affirm the correctness and scalability of the models. The protocols are evaluated with performance metrics, such as the delivery ratio, latency, and overhead. Our results indicate significant improvement in performance compared to existing approaches.

Enabling Flexible Link Capacity for eCPRI-Based Fronthaul With Load-Adaptive Quantization Resolution

Bandwidth-efficient 5G optical fronthaul interfaces, such as the Ethernet-based common public radio interface (eCPRI), with novel low layer split (LLS) are being actively investigated. Regarding the uplink eCPRI, the burdened wireless resource blocks (RBs) are delivered and the traffic aggregation is leveraged; therefore, the eCPRI traffic is highly dynamic depending on the time-varying mobile traffic load. This dynamic property will lower the average fiber link utilization and complicate the sizing of link bandwidth for the deployment of low-latency fronthaul. To tackle this issue, we propose a load-adaptive quantization resolution scheme that enables elastic fronthaul capacity. By adjusting the quantization resolution of the resource elements in RBs, the fronthaul link capacity, measured by the amount of bearable RBs, can be scaled to fit the mobile traffic load. Specifically, a full resolution is applied during low-load period, while for high-load case, to boost the link capacity, a stringent resolution is performed by removing the least significant quantization bits (LSQBs). Besides, to minimize the signal fidelity deterioration caused by the decline of resolution, the resolution redundancy is evaluated based on the detected wireless signal quality at central unit, and the location of LSQBs is fed back to the radio unit through the eCPRI control plane. With the enhanced link flexibility, the required fronthaul bandwidth can be significantly reduced, while the user experience is barely compromised. Based on our developed low-MAC and PHY-layer wireless system model following 3GPP specifications and the 25-Gb/λ experimental fiber transmission, the bandwidth of eCPRI user data can be saved by 40%.

Channel Estimation for TDS-OFDM Systems in Rapidly Time-Varying Mobile Channels

This paper explores the performance of time-domain synchronous orthogonal frequency-division multiplexing (TDS-OFDM) systems operated under rapidly time-varying mobile channels. Since a rapidly time-varying channel contains more unknown channel coefficients than the number of observations, the mobile channel can conveniently be modeled with the discrete Legendre polynomial basis expansion model to reduce the number of unknowns. The linear minimum mean square error (LMMSE) estimate can be exploited for channel estimation on inter-block-interference-free received signal samples owing to transmitting pseudo-noise (PN) sequences. In conventional TDS-OFDM systems, the channel estimation performance is limited due to estimating channel responses only from the beginning part of the channel. Therefore, a new system model named “ partitioned TDS-OFDM system ” is proposed to improve the system performance by inserting multiple PN sequences to the middle and end parts of the channel as well. In addition to providing the reconstruction error performance, Bayesian Cramer–Rao lower bound is derived analytically. Also, the LMMSE-based symbol detection is employed. To alleviate the negative effects of inter-carrier-interference (ICI) occuring in mobile channels, ICI cancellation is applied to enhance the detection performance. The simulation results demonstrate that the proposed TDS-OFDM system is superior to the conventional system and its corresponding performance is able to approach the achievable lower performance bound.

Energy-Aware Temporal Reachability Graphs for Time-Varying Mobile Opportunistic Networks

With the rapid emergence of applications in Mobile Opportunistic Networks (MONs), understanding and characterizing their properties becomes extremely important. A fundamental model for MONs is time-varying graph, which currently remains poorly understood since many well-recognized properties of static graphs have no obvious counterpart in dynamic ones. In MONs, the dynamical links change opportunistically and usually the system energy is very limited, which results in unique and unknown properties about the network connectivity and reachability. In this paper, taking the communication energy into account, we introduce the concept of Energy-aware Temporal Reachability Graphs (ETRG), which characterizes the connectivity of MONs with the consideration of communication consumed energy, and consequently reveals the communication capabilities of MONs with the given of real-world system parameters of data size, tolerable delay, and energy budget. We come up with efficient algorithm to calculate ETRG from the corresponding time-varying graphs. By applying ETRG to several mobile networks recorded by real-life human and vehicular mobility traces, we characterize their network connectivity properties in terms of average reachability, communication asymmetry, and stability. Moreover, utilizing ETRG that places upper bounds of the communications capabilities, we reveal the fundamental relations and tradeoffs among large-scale variability of the system metrics of energy budget, tolerable delay, and data size on the system performance.

A Game Theory Based Approach for Power Efficient Vehicular Ad Hoc Networks

Green communications are playing critical roles in vehicular ad hoc networks (VANETs), while the deployment of a power efficient VANET is quite challenging in practice. To add more greens into such kind of complicated and time-varying mobile network, we specifically investigate the throughput and transmission delay performances for real-time and delay sensitive services through a repeated game theoretic solution. This paper has employed Nash Equilibrium in the noncooperative game model and analyzes its efficiency. Simulation results have shown an obvious improvement on power efficiency through such efforts.

Interpolation based pilot‐aided channel estimation for STBC spatial modulation and performance analysis under imperfect CSI

A combination of space-time block coding and spatial modulation (STBC-SM) has been recently proposed for multiple input-multiple output systems to obtain both spatial diversity gain and more capacity simultaneously while assuming the perfect channel state information (P-CSI) was available at the receiver. However, in practical scenarios the CSI is unknown to the receiver and should be estimated in order to detect the transmitted data in a reliable way. Therefore, channel estimation (CE) is a major challenge in designing the STBC-SM systems. In this study, the problem of CE is investigated and a new pilot-aided channel estimation (PA-CE) technique, coupled with an interpolation, is proposed for the STBC-SM systems operating in the presence of rapidly time-varying mobile channels. Several interpolation schemes such as the linear, nearest neighbour, piecewise cubic Hermite and the low-pass interpolations are applied and their performances are compared to determine the best suitable interpolation technique to be employed in STBC-SM systems. Bit error rate (BER) performance of the proposed CE technique is then investigated in time-varying channels with different modulation. Moreover, the pairwise error probability of the STBC-SM scheme is derived and its average bit error probability is evaluated analytically in the presence of CE errors.

Constructed Data Pilot-Assisted Channel Estimators for Mobile Environments

Nowadays, more and more communication systems are deployed under mobile environments with very high velocity. This paper focuses on the channel estimation problem in vehicular scenarios, which is very challenging in view of the extremely time-varying characteristics of mobile channels. Specifically, we propose a novel channel estimator named constructed data pilot (CDP) estimator for the current communication standards by fully exploiting the channel correlation characteristics across two concatenated symbols. On the basis of the CDP estimator, we further resort to two efficient techniques to improve its performance over the entire signal-to-noise-ratio (SNR) region. For the first technique, the time-variant mobile channel is modeled as a first-order Markov process so that the exact autocorrelation value of the two adjacent symbols can be derived. For the second technique, the SNR is estimated and serves as a priori information. Simulation results reveal that our proposed channel estimators outperform existing alternatives with lower computational complexity.

Optimal Set of Multiple Relays and Distributed Self-Selection in Cooperative Networks

In this paper we derive analytically the optimal set of relays for the maximal destination signal-to-noise ratio (SNR) in a two-hop amplify-and-forward cooperative network with frequency-selective fading channels. Simple rules are derived to determine the optimal relays from all available candidates. Our results show that a node either participates in relaying with full power or does not participate in relaying at all, and that a node is a valid relay if and only if its SNR is higher than the optimal destination SNR. In addition, we develop a simple distributed algorithm for each node to determine whether participating in relaying by comparing its own SNR with the broadcasted destination SNR. This algorithm has extremely low overhead, and is shown to converge to the optimal solution fast and exactly within a finite number of iterations. The extremely high efficiency makes it especially suitable to time-varying mobile networks.

Reconfigurable performance measurement system-on-a-chip for baseband wireless algorithm design and verification

Modern wireless technology applies several powerful but complex techniques - such as multiple-antenna systems, orthogonal frequency-division multiplexing, and capacity-approaching forward error correcting codes - to provide reliable broadband digital communications over the time-varying mobile radio channel. Such techniques appear in most recent wireless standards such as IEEE 802.11n Wi-Fi, IEEE 802.16 WiMax, and 3GPP LTE. Bit error rate simulation continues to be the workhorse design verification and performance optimization methodology; however, the growing complexity of modern wireless systems and the tendency of wireless standards to evolve even after product development has started have greatly increased the required simulation times to the extent that software-based simulation is now the major bottleneck in wireless system development. This article describes how baseband performance verification of wireless systems can be accelerated by over four orders of magnitude using compact, accurate, and high-throughput emulators implemented on reconfigurable field programmable gate arrays.