Location Of Mobile Device Research Articles

Fingerprinting‐Based Indoor Localization in a 3 × 3 Meter Grid Using OFDM Signals at Sub‐6 GHz

ABSTRACTAccurately determining the indoor location of mobile devices has garnered significant interest due to the complex challenges posed by non‐line‐of‐sight (NLOS) propagation and multipath effects. To address this challenge, this paper proposes a new approach to indoor positioning that utilises channel state information (CSI) and machine learning (ML) techniques to improve accuracy. The proposed method extracts the amplitude and phase differences of the subcarriers from the CSI data to create fingerprints. ML algorithms and network architecture are utilised to train the CSI data from two antennas, in the form of phase and amplitude. Experiments conducted in a standard indoor environment demonstrate the effectiveness of the proposed method.

US urban land-use reform: a strategy for energy sufficiency

Policy approaches to the global energy transition often focus on technology-based solutions while ignoring challenges of overall energy demand. A sufficiency-first approach aims to limit superfluous consumption while achieving wellbeing for all. This study focuses on US built environment mechanisms of sufficiency under urban land-use policy. The historical context of US exclusionary and car-oriented planning is reviewed with an order-of-magnitude assessment of the effects on greenhouse gas emissions (GHGE). Using national vehicle-miles traveled (VMT) data derived from mobile device locations (Replica) and validated here with federal data, a hypothetical scenario explores the potential for state urban land-use reforms to enable energy sufficiency. Tenth percentile-VMT (per capita) neighborhoods are defined by state: in 47 states, the typical such neighborhood has less than 33% of its housing units in structures larger than four units. Assuming each state redresses its housing shortage while matching this VMT, 31 Mt CO2e (direct GHGE) and about 38 Mt CO2e (indirect and life-cycle GHGE) would be avoided in 2033. Texas, California, and Florida have the largest absolute emissions reduction opportunity. Urban land-use reforms comprise a logical starting point for a US sufficiency agenda. Key priorities for research, data collection, and technology and policy innovation are proposed. Policy relevance International climate policy is increasingly focused on enabling people to consume less energy: not just technological ‘efficiency’ but ‘sufficiency’ is needed. However, sufficiency has seen little uptake in the US. It may be more relevant to US policymakers if related to the growing momentum for reforming land-use planning and housing policy to address the housing shortage and affordability crisis. This crisis stems in part from the US prevalence of single-family zoning and car-centric planning, rooted in a history of racial segregation; these same laws effectively mandate people to maintain more polluting lifestyles. This study estimates how much climate pollution could be avoided with state-led land-use reform. If states committed to solving the housing shortage while building new housing in neighborhoods where people can drive less, the savings could be comparable with expanding electric vehicle policies. Policymakers and practitioners can enable these reforms while supporting complementary policy goals.

A sequential transit network design algorithm with optimal learning under correlated beliefs

Mobility service route design requires demand information to operate in a service region. Transit planners and operators can access various data sources including household travel survey data and mobile device location logs. However, when implementing a mobility system with emerging technologies, estimating demand becomes harder because of limited data resulting in uncertainty. This study proposes an artificial intelligence-driven algorithm that combines sequential transit network design with optimal learning to address the operation under limited data. An operator gradually expands its route system to avoid risks from inconsistency between designed routes and actual travel demand. At the same time, observed information is archived to update the knowledge that the operator currently uses. Three learning policies are compared within the algorithm: multi-armed bandit, knowledge gradient, and knowledge gradient with correlated beliefs. For validation, a new route system is designed on an artificial network based on public use microdata areas in New York City. Prior knowledge is reproduced from the regional household travel survey data. The results suggest that exploration considering correlations can achieve better performance compared to greedy choices and other independent belief-based techniques in general. In future work, the problem may incorporate more complexities such as demand elasticity to travel time, no limitations to the number of transfers, and costs for expansion.

An infrastructure‐less emergency location detection system using basic cellular device‐to‐device connection

AbstractOne of the most time‐sensitive and critical tasks for first responders in the aftermath of a disaster is to locate people for effective and successful rescue operations. The existing solutions for large‐scale location detection of citizens depend on expensive equipment that may not be readily available in many urban areas and also cannot cover a vast area quickly. To address this challenge, the present study proposes a novel location estimation method by creating a Mobile Ad Hoc Network (MANET) using citizens' mobile phones. The proposed solution can accurately estimate the location of mobile devices in a MANET based on their Received Signal Strength (RSS) and communicate the estimated location coordinates to first responders without any dependencies on telecommunication infrastructures. This system will help first responders locate and map mobile devices in the affected area in a matter of seconds to improve the efficiency and effectiveness of postdisaster rescue operations. The envisioned solution offers unique capabilities that differentiate it from previously existing methods. The proposed method directly estimates device locations using their basic cellular signal strength without any dependency on GPS and relies only on basic cellphone signals. Therefore, nonsmartphones can also join and function in the envisioned MANET system. It also does not impose any computational operations or requirements for installing an app on citizens' phones. The performance of the proposed system was empirically evaluated using a simulation analysis. The results indicated that the method could efficiently and accurately discover more than 93% of mobile devices and estimate their locations.

Pipelined Neural Network Assisted Mobility Speed Estimation Over Doubly-Selective Fading Channels

The speed estimation has been widely used for tracking mobile device locations, providing essential information in location/mobility-aware communications, enhancing received signal quality/robustness, and reducing energy consumption and latency. Deep learning can be used to improve the performance constrained by signal/system model. This work focuses on the issues on machine learning (ML) based speed estimation using primary synchronous signal (PSS), which is embedded in the 5G standards, over general time-variant multipath channels. Aiming to reduce the complexity involved in the ML algorithms for the speed estimation in mobile networks, we propose a pipelined ML algorithm to decompose the original ML model into several smaller ones. The advantages of the proposed convolutional neural network (CNN) based approach have been verified by simulations.

Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years

Many studies have used mobile device location data to model SARS-CoV-2 dynamics, yet relationships between mobility behavior and endemic respiratory pathogens are less understood. We studied the effects of population mobility on the transmission of 17 endemic viruses and SARS-CoV-2 in Seattle over a 4-year period, 2018-2022. Before 2020, visits to schools and daycares, within-city mixing, and visitor inflow preceded or coincided with seasonal outbreaks of endemic viruses. Pathogen circulation dropped substantially after the initiation of COVID-19 stay-at-home orders in March 2020. During this period, mobility was a positive, leading indicator of transmission of all endemic viruses and lagging and negatively correlated with SARS-CoV-2 activity. Mobility was briefly predictive of SARS-CoV-2 transmission when restrictions relaxed but associations weakened in subsequent waves. The rebound of endemic viruses was heterogeneously timed but exhibited stronger, longer-lasting relationships with mobility than SARS-CoV-2. Overall, mobility is most predictive of respiratory virus transmission during periods of dramatic behavioral change and at the beginning of epidemic waves.

Rapid determination of seismic influence field based on mobile communication big data-A case study of the Luding Ms 6.8 earthquake in Sichuan, China.

Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal-spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution.

One-key based cryptographically generated address for location update in next generation IP mobility

Mobile devices have risen due to internet growth in recent years. The next generation of internet protocol is evolving for mobile devices to generate their addresses and get continuous services across networks to support the enormous number of addresses in network-based mobility. The mobile device updates its current location to its home network and the correspondent users through a binding update scheme in the visited network. Numerous studies have investigated binding update schemes to verify the reachability of the mobile device at its home network. However, most schemes endure security threats due to the incompetence of authenticating user identity and concealing the temporary location of mobile devices. To address these issues, this paper proposes a secure and efficient binding update scheme (One-CLU) by incorporating a one-key-based cryptographically generated address (CGA) to validate and conceal the address ownership of mobile devices with minimal computations. The security correctness of the proposed One-CLU scheme is verified using AVISPA – a model checker. Finally, the simulation and the numerical results showthat the proposed scheme significantly reduces communication payloads and costs for the binding update, binding refresh, and packet delivery.

Unmasking outdoor preferences: an analysis of the effects of COVID-19 mandates on recreation behaviour in Idaho

ABSTRACT A number of studies have examined park visitation patterns and consumer preferences using available national and state park visitation data. However, municipal park visitation remains largely understudied due to the difficulty and costliness associated with data collection and analysis. This study utilizes high frequency mobile device location data to measure changes in visitation to municipal and state parks and other outdoor recreation points of interest (POIs) after COVID-19 mask mandates were instituted. We exploit spatial and temporal variation in COVID-19 mandates at the county level in the U.S. state of Idaho. We find an increase in POI visitation in areas without mask mandates. Visitation rates were approximately seven percent lower in areas with a mask mandate compared to non-mandate counties. Our research brings insight on the behavioural response to restrictions and on recreational choice behaviour.

Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Resulting from COVID-19

Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Resulting from COVID-19

Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications

Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users’ context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).

Elaborated Framework for Duplicate Device Detection from Multisourced Mobile Device Location Data

Mobile device location data (MDLD) have been popularly utilized in various fields. Yet large-scale applications are limited because of either biased or insufficient spatial coverage of the data from individual data vendors. One approach to improve the data coverage is to leverage the data from different data vendors and integrate them to build a more representative dataset. To extract reliable statistics from MDLD, certain data preprocessing steps are crucial to ensure the accuracy of the analysis. One of these steps is the development of a framework to remove duplicated devices or several devices that belong to the same data subject. This treatment is especially necessary when using a multiplicity of data sources, as the same device may be captured by more than one data provider. We propose a data integration methodology for multisourced data to investigate the feasibility of integrating data from several sources. By leveraging the uniqueness of travel pattern of each device, duplicate devices are identified. The proposed methodology is shown to be cost-effective through a national-level analysis. The method is successfully applied to a dataset from January 2020 consisting of more than 270 million raw devices nationwide. Our findings suggest that devices sharing the same imputed home location and the same top-five most-visited locations during a month can represent the same user in the MDLD. It is shown that more than 99.6% of the sample devices having the aforementioned attribute in common are observed at the same location simultaneously.

Adaptive Path Loss Model for BLE Indoor Positioning System

Indoor positioning systems (IPSs) allow the location and tracking of mobile devices in indoor environments where the Global Positioning System (GPS) does not provide satisfactory results. In model-based IPSs, it is common to use signal propagation models to estimate distances between anchor nodes and mobile devices using the Received Signal Strength Indicator (RSSI). However, using fixed parameters in the path loss model to characterize the signal in large-scale scenarios results in the degradation of the positioning accuracy. In this paper, we propose the ADAM (Adaptive Model) Positioning System, a model-based IPS that chooses the best anchor nodes to benefit the positioning computation and uses different parameters for the log-distance model to represent the signal in different regions and conditions of the scenario. Then, we estimate a single, more precise position using a data fusion technique. Our proposal does not require training nor prior knowledge of the best parameters for each region. We evaluated the performance of our proposed system in a real-world, large-scale environment using Bluetooth-based mobile devices. Our results clearly show that ADAM can locate mobile devices with an average error of 2.93 m in relation to the real position, which is 23% better than literature-based models using fixed parameters for the entire environment.

Spatial, temporal, and social dynamics in visitation to U.S. national parks: A big data approach

The degree to which many benefits of national parks are realized hinges on public access. Traditional methods in estimating park visitation can be time-consuming, costly, and labor-intensive. Fortunately, the growing availability of ‘big data’ offers new opportunities for rapid and large-scale estimation. This study investigated the spatiotemporal dynamics in visitation to all National Park Service (NPS) units in the contiguous U.S. and explored associated sociodemographic factors using fine-grained SafeGraph mobile device location data. We found that people tend to travel less frequently and in a shorter distance to visit NPS units in both 2020 and 2021 vs. 2019. During the pandemic, people preferred visiting national parks and nature-based NPS units over culture-oriented ones relative to pre-pandemic levels. Most importantly, this study highlights a long-standing social inequity – the groups of minorities (i.e., Blacks, Asians, and Hispanics) and lower-income were less likely to visit NPS units than Whites and higher-income, respectively.

Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data

Vehicle volume serves as a critical metric and the fundamental basis for traffic signal control, transportation project prioritization, road maintenance planning, and more. Traditional methods of quantifying vehicle volume rely on manual counting, video cameras, and loop detectors at a limited number of locations. These efforts require significant labor and cost for expansions. Researchers and private sector companies have also explored alternative solutions, such as probe vehicle data, although this still suffers from a low penetration rate. In recent years, along with the technological advancement in mobile sensors and mobile networks, the quantity of mobile device location data (MDLD) has been growing dramatically in spatiotemporal coverage of the population and its mobility. This paper presents a big-data driven framework that can ingest terabytes of MDLD and estimate vehicle volume over a larger geographical area with a larger sample size. The proposed framework first employs a series of cloud-based computational algorithms to extract multimodal trajectories and trip rosters. A scalable map matching and routing algorithm is then applied to snap and route vehicle trajectories to the roadway network. The observed vehicle counts on each roadway segment are weighted and calibrated against ground truth control totals, that is, annual vehicle-miles traveled and annual average daily traffic. The proposed framework is implemented on the all-street network in the State of Maryland using MDLD for the entire year of 2019. The results demonstrate that our proposed framework produces reliable vehicle volume and also its transferability and generalization ability.

Quantifying the impact of COVID-19 on travel behavior in different socio-economic segments

The COVID-19 pandemic has resulted in substantial negative impacts on social equity. To investigate transport inequities in communities with varying medical resources and COVID controlling measures during the COVID pandemic and to develop transport-related policies for the post-COVID-19 world, it is necessary to evaluate how the pandemic has affected travel behavior patterns in different socio-economic segments (SES). We first analyze the travel behavior change percentage due to COVID, e.g., increased working from home (WFH), decreased in-person shopping trips, decreased public transit trips, and canceled overnight trips of individuals with varying age, gender, education levels, and household income, based on the most recent US Household Pulse Survey census data during Aug 2020 ∼ Dec 2021. We then quantify the impact of COVID-19 on travel behavior of different socio-economic segments, using integrated mobile device location data in the USA over the period 1 Jan 2020–20 Apr 2021. Fixed-effect panel regression models are proposed to statistically estimate the impact of COVID monitoring measures and medical resources on travel behavior such as nonwork/work trips, travel miles, out-of-state trips, and the incidence of WFH for low SES and high SES. We find that as exposure to COVID increases, the number of trips, traveling miles, and overnight trips started to bounce back to pre-COVID levels, while the incidence of WFH remained relatively stable and did not tend to return to pre-COVID level. We find that the increase in new COVID cases has a significant impact on the number of work trips in the low SES but has little impact on the number of work trips in the high SES. We find that the fewer medical resources there are, the fewer mobility behavior changes that individuals in the low SES will undertake. The findings have implications for understanding the heterogeneous mobility response of individuals in different SES to various COVID waves and thus provide insights into the equitable transport governance and resiliency of the transport system in the “post-COVID” era.

Spatiotemporal Optimization for the Placement of Automated External Defibrillators Using Mobile Phone Data.

With over 350,000 cases occurring each year, out-of-hospital cardiac arrest (OHCA) remains a severe public health concern in the United States. The correct and timely use of automated external defibrillators (AEDs) has been widely acknowledged as an effective measure to improve the survival rate of OHCA. While general guidelines have been provided by the American Heart Association (AHA) for AED deployment, the lack of detailed instructions hindered the adoption of such guidelines under dynamic scenarios with various time and space distributions. Formulating the AED deployment as a location optimization problem under budget and resource constraints, we proposed an overlayed spatio-temporal optimization (OSTO) method, which accounted for the spatiotemporal heterogeneity of potential OHCAs. To highlight the effectiveness of the proposed model, we applied the proposed method to Washington DC using user-generated anonymized mobile device location data. The results demonstrated that optimization-based planning provided an improved AED coverage level. We further evaluated the effectiveness of adding additional AEDs by analyzing the cost-coverage increment curve. In general, our framework provides a systematic approach for municipalities to integrate inclusive planning and budget-limited efficiency into their final decision-making. Given the high practicality and adaptability of the framework, the OSTO is highly amenable to different healthcare facilities' deployment tasks with flexible demand and resource restraints.

Mean Field Game and Broadcast Encryption-based Joint Data Freshness Optimization and Privacy Preservation for Mobile Crowdsensing

Recently, due to the benefits of system extensibility, deployment cost, and implementation flexibility, mobile crowdsensing (MCS) is a popular method for sensing data collection. This paper aims at developing a timely and secure model for MCS by realizing the joint Age of Information (AoI) performance optimization and privacy preservation. First, based on the queuing theory, the AoI expression is derived in the closed form. Next, through the mean field game (MFG)-based sensing rate control policy, the AoI of the sensing data is optimized. Meanwhile, by adopting the broadcast and EIGamal encryption schemes, sensing task, data content, location, and identities of mobile devices are protected. Finally, simulation results under different parameter settings show that the proposed scheme is effective in improving the timeliness and security performance of the MCS system.

Deployment of Ultra Dense Network Devices in Space. Part 2. Search Using Geometric Factor

This work is a continuation of the study devoted to mathematical and simulation modeling of methods for searching for locations of gNB base station devices in space according to the criterion of the geometric factor of reducing the positioning accuracy of the user device UE. The task in this formulation is relevant and in demand for ultra-dense networks of the millimeter (30–300 GHz) and submillimeter or terahertz band (0.3–3 THz) of the fifth and subsequent generations, respectively. As a criterion for the spatial placement of the infrastructure of fixed network devices acting as a base station, access point and / or repeater / router, the geometric factor of reducing the positioning accuracy of the DOP (Dilution of Precision) of the user device is used. Based on the algorithmic, methodological and software, formalized in the previous work for searching for gNB placements in space according to the criterion of geometric factors for reducing positioning accuracy in the horizontal plane HDOP (Horizontal DOP), the vertical plane VDOP (Vertical DOP) and by location in the PDOP (Position DOP) space, this study removes the restriction on the fixed placement of UEs in space, and also provides for an increase in the number of gNBs more than five, for which placements are searched. Subject of study. The article is devoted to mathematical and simulation modeling of methods for searching for locations of base station devices in space according to the criterion of a geometric factor for reducing the positioning accuracy of a user device. Method. Iterative enumeration of all possible locations of stationary base stations and mobile user devices with a given step in space to minimize the geometric factor of reducing positioning accuracy in the selected service area. Main results. The result of this work is the formalization of algorithmic, methodological and software to obtain the final configuration of locations for a given number of stationary base stations in the selected service area. Practical significance. The results obtained make it possible to search for and justify the locations of base stations and/or routers in scenarios of ultra-dense placement of user devices in a space saturated with biomass.

Bayesian location estimation of mobile devices using a signal strength model

Mobile network operator (MNO) data are a rich data source for various topics in official statistics, such as present population, mobility, migration, and tourism. Estimating the geographic location of mobile devices is an essential step for statistical inference. Most studies use Voronoi tessellation for this, which is based on the assumption that mobile devices are always connected to the nearest radio cell. We propose an alternative location estimation method following a Bayesian approach and using a physical model for the received signal strength. Our Bayesian framework allows for different modules of prior knowledge about where devices are expected to be, and different modules for the likelihood of connection given a geographic location. We discuss and compare the use of several prior modules, including one that is based on land use. For the likelihood module we propose a signal strength model using radio cell properties such as antenna height, propagation direction, and power. Using Bayes' rule, we derive a posterior probability distribution that is an estimate of the geographic location, which can be used for further statistical inference. We describe the method and provide illustrations of a fictional example that resembles a real-world situation. The method has been implemented in the R packages mobloc and mobvis, which are briefly described.