Location Of Mobile Device Research Articles

Pandemic-influenced human mobility on tribal lands in California: Data sparsity and analytical precision.

Human mobility datasets collected from personal mobile device locations are integral to understanding how states, counties, and cities have collectively adapted to pervasive social disruption stemming from the COVID-19 pandemic. However, while indigenous tribal communities in the United States have been disproportionately devastated by the pandemic, the relatively sparse populations and data available in these hard-hit tribal areas often exclude them from mobility studies. We explore the effects of sparse mobility data in untangling the often inter-correlated relationship between human mobility, distancing orders, and case growth throughout 2020 in tribal and rural areas of California. Our findings account for data sparsity imprecision to show: 1) Mobility through legal tribal boundaries was unusually low but still correlated highly with case growth; 2) Case growth correlated less strongly with mobility later in the the year in all areas; and 3) State-mandated distancing orders later in the year did not necessarily precede lower mobility medians, especially in tribal areas. It is our hope that with more timely feedback offered by mobile device datasets even in sparse areas, health policy makers can better plan health emergency responses that still keep the economy vibrant across all sectors.

Rethinking Activity Awareness: The Design, Evaluation and Implication of Integrating Activity Awareness into Mobile Messaging

Nowadays, different types of context information are integrated into mobile messaging to increase expressiveness and awareness, including mobile device setting, location, activity, and heart rate. Due to low recognition accuracy, sometimes users cannot accurately infer others’ status through activity awareness. Recently, activity recognition technology has advanced. However, the user behaviors of activity awareness with improved technology have not been studied. In this study, we design ActAware, a mobile instant messaging application that integrates activity awareness based on improved activity recognition technology, i.e., improved recognition accuracy and the addition of activity transition notification. We conduct a field study to explore user behaviors and found that activity awareness allows users to speculate on the reasons for chat interruption, plan communication, speculate on whether the chat partner is departing/arriving, and deepen the understanding of living patterns. Compared with disclosing other types of context information, users have fewer privacy concerns about disclosing activity information in ActAware. Based on these findings, we provide design recommendations for mobile messaging to better support activity awareness.

High-dimensional population inflow time series forecasting via an interpretable hierarchical transformer

Mobile device location data (MDLD) are emerging data sources in the transportation domain that contain large-scale, fine-grained information on population inflow. However, limited studies have built forecasting models based on large-scale MDLD-based population inflow time series. This task is challenging due to complex nonlinear temporal dynamics, high-dimensional time series structure (i.e. multiple time series with multi-shape inputs and outputs), and non-negligible impacts from various external factors. To address these challenges, this study introduces a deep learning framework, the Interpretable Hierarchical Transformer (IHTF), for nationwide county-level population inflow time series forecasting and interpretation. A variety of cutting-edge deep learning techniques are fused, including the variable selection network to incorporate external effects, the gated residual network to handle nonlinearity, and the transformer architecture to learn temporal dynamics. Different interior parameters, such as variable selection weight and temporal attention weight, are extracted to explain patterns learned by the framework. Numerical experiments show that IHTF outperforms extensive baseline models in forecasting accuracy. In addition, feature importance generated by IHTF is similar to the tree-based model, LightGBM, but exhibits a more even distribution, among which point-of-interests (POIs) count, county location, median household income, and percentage of accommodation and food services are the most important static variables. Moreover, attention weight demonstrates that IHTF can automatically learn the seasonality from time series. Taken together, this framework can serve as a reliable travel demand forecasting component in the transportation planning process that allows modeling the travel demand continuously instead of by snapshot.

Perturb and optimize users’ location privacy using geo-indistinguishability and location semantics

Location-based services (LBS) are capable of providing location-based information retrieval, traffic navigation, entertainment services, emergency rescues, and several similar services primarily on the premise of the geographic location of users or mobile devices. However, in the process of introducing a new user experience, it is also easy to expose users’ specific location which can result in more private information leakage. Hence, the protection of location privacy remains one of the critical issues of the location-based services. Moreover, the areas where humans work and live have different location semantics and sensitivities according to their different social functions. Although the privacy protection of a user’s real location can be achieved by the perturbation algorithm, the attackers may employ the semantics information of the perturbed location to infer a user’s real location semantics in an attempt to spy on a user’s privacy to certain extent. In order to mitigate the above semantics inference attack, and further improve the quality of the location-based services, this paper hereby proposes a user side location perturbation and optimization algorithm based on geo-indistinguishability and location semantics. The perturbation area satisfying geo-indistinguishability is thus generated according to the planar Laplace mechanism and optimized by combining the semantics information and time characteristics of the location. The optimum perturbed location that is able to satisfy the minimum loss of location-based service quality is selected via a linear programming method, and can be employed to replace the real location of the user so as to prevent the leakage of the privacy. Experimental comparison of the actual road network and location semantics dataset manifests that the proposed method reduces approximately 37% perturbation distance in contrast to the other state-of-the-art methods, maintains considerably lower similarity of location semantics, and improves region counting query accuracy by a margin of around 40%.

Residency and worker status identification based on mobile device location data

Mobile device location data (MDLD) have been widely recognized for their rich human mobility information and thus considered as a supplementary data source for the current travel data bank consisting of travel survey data and traffic monitoring data. However, the lack of ground truth information about the device owners raises concern about the biases and representativeness of the nonprobability MDLD sample and significantly limits the applications of MDLD. This paper focuses on identifying two important socio-demographic characteristics for the MDLD sample devices: residency and worker status, including four worker types (normal commuters, professional drivers, mobility-for-work workers, and nonworkers/home-based workers). Based on the spatial–temporal patterns of location sightings and derived trips from MDLD, a comprehensive imputation framework is proposed with parameters calibrated against public domain ground truth data. A national-level case study in the U.S. based on a commercial MDLD dataset further evaluates the performances of the proposed heuristic rules. The multi-level validation results indicate a satisfying match against the ground truth data and prove the effectiveness of the proposed methods. As one of the earliest efforts to identify the residency and worker status information for a large-scale national-level MDLD dataset, mobile workers—including professional drivers and mobility-for-work workers—are also identified from MDLD for the first time.

OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning.

In an indoor positioning system (IPS), transfer learning (TL) methods are commonly used to predict the location of mobile devices under the assumption that all training instances of the target domain are given in advance. However, this assumption has been criticized for its shortcomings in dealing with the problem of signal distribution variations, especially in a dynamic indoor environment. The reasons are: collecting a sufficient number of training instances is costly, the training instances may arrive online, the feature spaces of the target and source domains may be different, and negative knowledge may be transferred in the case of a redundant source domain. In this work, we proposed an online heterogeneous transfer learning (OHetTLAL) algorithm for IPS-based RSS fingerprinting to improve the positioning performance in the target domain by fusing both source and target domain knowledge. The source domain was refined based on the target domain to avoid negative knowledge transfer. The co-occurrence measure of the feature spaces (Cmip) was used to derive the homogeneous new feature spaces, and the features with higher weight values were selected for training the classifier because they could positively affect the location prediction of the target. Thus, the objective function was minimized over the new feature spaces. Extensive experiments were conducted on two real-world scenarios of datasets, and the predictive power of the different modeling techniques were evaluated for predicting the location of a mobile device. The results have revealed that the proposed algorithm outperforms the state-of-the-art methods for fingerprint-based indoor positioning and is found robust to changing environments. Moreover, the proposed algorithm is not only resilient to fluctuating environments but also mitigates the model's overfitting problem.

Exclusionary Effects of Campsite Allocation through Reservations in U.S. National Parks: Evidence from Mobile Device Location Data.

Campsites represent highly sought after recreational amenities in the national parks of the United States. Equitable allocation of scarce recreational resources has long been a key management issue in U.S. national parks, but has become increasingly difficult in an era of increasing demand. At present, a growing number of national park campsites are allocated through an online reservation system well in advance of a camper's arrival at a park. Compounding the challenge of allocating these campsites is a long history of exclusivity within national park camping-institutionalized through campground design and predicated on a legacy of the leisure class's affinity for camping in national parks. Given national park camping's history of exclusivity, this exploratory study seeks to explore how online reservation systems may impact the demographics of national park campers. Using mobile device location data, estimated demographics were calculated for campers in five national park campgrounds in the U.S. that each contained some sites requiring reservations and some sites available on a first-come, first-served basis. We detail results from analyses of variance between campsites requiring reservations and those that are available on a first-come, first-served basis. Results suggest that for each of the five campgrounds, those campers camping in sites that require reservations came from areas with higher median household incomes, on average. In three of the five campgrounds, this difference was significant. Additionally, in an urban-proximate setting, those camping in sites requiring reservations came from areas with a higher portion of White residency than those campers in campsites not requiring reservations, on average. We conclude with discussion that includes management implications concerning the growing prominence of online reservation systems for outdoor recreation amenities, and a brief research agenda for diversity, equity, and inclusion (DEI) as they relate to campgrounds. Principally, the former group of implications includes the realization that online reservation systems present the unintended consequence of excluding low-income, and perhaps non-White, would-be campers-a conclusion drawn from the results of this exploratory study. This discussion includes an analysis of the distributive justice of online reservation systems.

Social Acceptability in Context: Stereotypical Perception of Shape, Body Location, and Usage of Wearable Devices

Assessing social acceptability is vital when designing body-worn mobile devices. Previous research found evidence that using stereotyping content model (SCM) mobile devices can systematically predict ratings of the warmth and competence of their wearers. However, it is currently unknown if other contextual dimensions of mobile device usage can also systematically affect those ratings. In two studies, we investigate if and how shape and body location of a body-worn mobile device as well as the activity in which the device is being used can systematically influence stereotypical ratings. Our results suggest that this is evident in some but not all cases. We conclude that people further differentiate between the placement of the device, particularly devices in the user’s hand, and during an activity in which the device can contextually be misused. This indicates that users further differentiate the context and that more contexual information is helpful while operationalizing the SCM as a measure for social acceptability.

Extended Bayesian endemic-epidemic models to incorporate mobility data into COVID-19 forecasting.

Forecasting the number of daily COVID‐19 cases is critical in the short‐term planning of hospital and other public resources. One potentially important piece of information for forecasting COVID‐19 cases is mobile device location data that measure the amount of time an individual spends at home. Endemic–epidemic (EE) time series models are recently proposed autoregressive models where the current mean case count is modelled as a weighted average of past case counts multiplied by an autoregressive rate, plus an endemic component. We extend EE models to include a distributed‐lag model in order to investigate the association between mobility and the number of reported COVID‐19 cases; we additionally include a weekly first‐order random walk to capture additional temporal variation. Further, we introduce a shifted negative binomial weighting scheme for the past counts that is more flexible than previously proposed weighting schemes. We perform inference under a Bayesian framework to incorporate parameter uncertainty into model forecasts. We illustrate our methods using data from four US counties.

Enabling Efficient Scheduling in Large-Scale UAV-Assisted Mobile-Edge Computing via Hierarchical Reinforcement Learning

Due to the high maneuverability and flexibility, unmanned aerial vehicles (UAVs) have been considered as a promising paradigm to assist mobile edge computing (MEC) in many scenarios including disaster rescue and field operation. Most existing research focuses on the study of trajectory and computation-offloading scheduling for UAV-assisted MEC in stationary environments, and could face challenges in dynamic environments where the locations of UAVs and mobile devices (MDs) vary significantly. Some latest research attempts to develop scheduling policies for dynamic environments by means of reinforcement learning (RL). However, as these need to explore in high-dimensional state and action space, they may fail to cover in large-scale networks where multiple UAVs serve numerous MDs. To address this challenge, we leverage the idea of “divide-and-conquer” and propose HT3O, a scalable scheduling approach for large-scale UAV-assisted MEC. First, HT3O is built with neural networks via deep RL to obtain real-time scheduling policies for MEC in dynamic environments. More importantly, to make HT3O more scalable, we decompose the scheduling problem into two-layered subproblems and optimize them alternately via hierarchical RL. This not only substantially reduces the complexity of each subproblem, but also improves the convergence efficiency. Experimental results show that HT3O can achieve promising performance improvements over state-of-the-art approaches.

DHCLoc: A Device-Heterogeneity-Tolerant and Channel-Adaptive Passive WiFi Localization Method Based on DNN

Passive WiFi localization refers to determining the location of WiFi-enabled mobile devices by deploying dedicated WiFi access points to sniff WiFi packets transmitted by these mobile devices and measure the corresponding received signal strengths (RSSs) for the use in localization. However, most existing studies fail to consider the effect of multiple channels where WiFi packets are transmitted and sniffed. The problem is further exacerbated by device heterogeneity occurring across various mobile devices. In this article, we present a unified deep neural network (DNN)-based solution, termed DHCLoc, to address these two challenges. To be specific, a Cramer–Rao lower bound (CRLB)-based analysis reveals that utilizing multichannel information will benefit localization, motivating us to include channel information into DHCLoc. Moreover, a novel maximum likelihood estimation (MLE)-based localization framework is introduced by incorporating a new variable to characterize the RSS measurement offsets caused by device heterogeneity, inspiring us to apply adversarial training to adopt such offsets against device heterogeneity. Extensive experiments using two real-world data sets are conducted, and show that, in comparison with several existing methods, DHCLoc can improve the localization accuracy by at least 25.2% and 25.8%, respectively.

Strategizing COVID-19 lockdowns using mobility patterns.

During the COVID-19 pandemic, governments have attempted to control infections within their territories by implementing border controls and lockdowns. While large-scale quarantine has been the most successful short-term policy, the enormous costs exerted by lockdowns over long periods are unsustainable. As such, developing more flexible policies that limit transmission without requiring large-scale quarantine is an urgent priority. Here, the dynamics of dismantled community mobility structures within US society during the COVID-19 outbreak are analysed by applying the Louvain method with modularity optimization to weekly datasets of mobile device locations. Our networks are built based on individuals' movements from February to May 2020. In a multi-scale community detection process using the locations of confirmed cases, natural break points from mobility patterns as well as high risk areas for contagion are identified at three scales. Deviations from administrative boundaries were observed in detected communities, indicating that policies informed by assumptions of disease containment within administrative boundaries do not account for high risk patterns of movement across and through these boundaries. We have designed a multi-level quarantine process that takes these deviations into account based on the heterogeneity in mobility patterns. For communities with high numbers of confirmed cases, contact tracing and associated quarantine policies informed by underlying dismantled community mobility structures is of increasing importance.

RESEARCH OF THE PROCEDURE OF INITIAL ACCESS TO THE FIFTH GENERATION COMMUNICATION NETWORK

Traditional mobile networks have bandwidth and infrastructure limitations. Each base station has a limited number of users and data capacity. 5G technology uses advanced technologies, such as massive MIMO and beamforming, to increase data rates and spectrum efficiency. These features, and the large number of small cells, allow many 5G users to receive low latency and high traffic without overloading the base station. Small cells work the same way as regular cells, but use new technology and millimetre waves (5G New Radio). Low-power base stations can be easily deployed. Cell search is a procedure by which the user equipment (UE) obtains time and frequency synchronization with the base station and decodes the cell ID of the mobile device. System information is broadcast information that is periodically transmitted by the base station on the downlink. This is very important information for the mobile device (UE) needed to connect to the base station (gNB). The UE reads system information to determine the location of mobile devices when turned on, to select and re-select a cell. System information provides the following information: system frame number, system bandwidth, PLMN, mobile subscriber selection and redial thresholds, etc., required to access the network. Physical Cell ID (PCI) is used to distinguish mobile devices in the network radio communication. PLMN (Public land Mobile Network) – mobile network service area. Is a set of all mobile devices of one cellular network operator. The UE scans the bandwidth in the sync raster. The synchronization raster indicates the frequency positions of the synchronization unit that can be used by the UE to detect the system when there is no explicit indication of the position of the synchronization unit. In the next step, the UE decodes the primary and secondary synchronization signals to obtain the Physical Cell ID (PCI) and the DMRS PBCH. The base station transmits SSB packets that contain one SSB per beam. The beam direction changes for each SSB transmission. The following steps are performed to complete the synchronization procedure. The UE selects the preferred beam and decodes the PBCH MIB information, such as SFN, SSB index, raster offset, default DL numerology, RMSI configuration, DM-RS location, and cell prohibition information. If the cell is not locked, the UE attempts to decode the physical downlink control channels (PDCCH) to obtain the frequency location of the PDSCH resources. A study of the model of restoring the synchronization of symbols and frames in the QPSK system, caused by a variable synchronization error. A study of the formation of the radio channel at the physical level.

Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform.

The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities, using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables, including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy, and scaled to the entire population of each county and state. The research team is making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.

A data-driven travel mode share estimation framework based on mobile device location data.

Mobile device location data (MDLD) contains abundant travel behavior information to support travel demand analysis. Compared to traditional travel surveys, MDLD has larger spatiotemporal coverage of the population and its mobility. However, ground truth information such as trip origins and destinations, travel modes, and trip purposes are not included by default. Such important attributes must be imputed to maximize the usefulness of the data. This paper targets at studying the capability of MDLD on estimating travel mode share at aggregated levels. A data-driven framework is proposed to extract travel behavior information from MDLD. The proposed framework first identifies trip ends with a modified Spatiotemporal Density-based Spatial Clustering of Applications with Noise algorithm. Then three types of features are extracted for each trip to impute travel modes using machine learning models. A labeled MDLD dataset with ground truth information is used to train the proposed models, resulting in a 95% recall rate in identifying trip ends and over 93% tenfold cross-validation accuracy in imputing the five travel modes (drive, rail, bus, bike and walk) with a random forest (RF) classifier. The proposed framework is then applied to two large-scale MDLD datasets, covering the Baltimore-Washington metropolitan area and the United States, respectively. The estimated trip distance, trip time, trip rate distribution, and travel mode share are compared against travel surveys at different geographies. The results suggest that the proposed framework can be readily applied in different states and metropolitan regions with low cost in order to study multimodal travel demand, understand mobility trends, and support decision making.

Semantic framework of event detection in emergency situations for smart buildings

Multimedia Sensor Networks (MSNs) have enhanced the ability to analyze the environment and provide responses based on its current status. Generally, MSNs are composed of scalar and multimedia sensors that have fixed locations. However, given the advancement of smart mobile device technologies, it is currently possible to dynamically integrate mobile sensors into MSNs. In this paper, we propose a formal platform to manage MSNs and the data gathered from them to detect complex events. Our main contributions include: M2SSN ​− ​Onto, a Mobile and Multimedia Semantic Sensor Networks Ontology; Py-CEMiD, an engine for detecting complex events and generate reactions to them; a mobile device location engine to locate mobile sensors; and a proof-of-concept in the context of detecting emergency situations in smart buildings. Several scenarios are validated for emergency events, combining simulated sensor measurements with real measurements of mobile devices. Results show complex events can be detected in near real time (less than 1 ​s).

The Impact of the Coronavirus Lockdown on Domestic Violence

We use 911 call records and mobile device location data to study the impact of the coronavirus lockdown on domestic violence. The percent of people at home sharply increased at all hours, and nearly doubled during regular working hours, from 45% to 85%. Domestic violence increased 12% on average and 20% during working hours. Using neighborhood-level identifiers, we show that the rate of first-time abuse likely increased even more: 16% on average and 23% during working hours. Our results contribute to an urgent need to quantify the physical and psychological burdens of prolonged lockdown policies.

Gated Recurrent Unit with RSSIs from Heterogeneous Network for Mobile Positioning

Recently, research studies on Location-Based Services (LBSs) based on networks including cellular network and Wi-Fi network have gradually become popular. Received Signal Strength Indicators (RSSIs) from the network can be detected and collected by mobile devices to estimate the locations without adopting the Global Positioning System (GPS). Previous research studies utilized the RSSIs of only cellular network or only Wi-Fi network to estimate location, which leads to a two-fold predicament involving error limits of cellular network-based methods and environmental constraints of Wi-Fi network-based methods. In addition, accommodating a highly temporal dependence of RSSI series data, this paper proposed a mobile positioning system based on Gated Recurrent Unit (GRU) with RSSIs from the heterogeneous network. GRU learns the temporal correlation of RSSIs and the relationship between RSSIs and GPS coordinates to estimate the locations of mobile devices. A large number of real experiments have been carried out to verify the performance of the proposed method, and experimental results demonstrate that the proposed method has lower errors (i.e., 5.86 m and 75% of errors within 4 m) compared with Neural Network (NN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM).

An Improved Hybrid Framework for Evaluating a Mobile Device Location in a Wireless Network

An Improved Hybrid Framework for Evaluating a Mobile Device Location in a Wireless Network

The effect of human mobility and control measures on traffic safety during COVID-19 pandemic.

As mobile device location data become increasingly available, new analyses are revealing the significant changes of mobility pattern when an unplanned event happened. With different control policies from local and state government, the COVID-19 outbreak has dramatically changed mobility behavior in affected cities. This study has been investigating the impact of COVID-19 on the number of people involved in crashes accounting for the intensity of different control measures using Negative Binomial (NB) method. Based on a comprehensive dataset of people involved in crashes aggregated in New York City during January 1, 2020 to May 24, 2020, people involved in crashes with respect to travel behavior, traffic characteristics and socio-demographic characteristics are found. The results show that the average person miles traveled on the main traffic mode per person per day, percentage of work trip have positive effect on person involved in crashes. On the contrary, unemployment rate and inflation rate have negative effects on person involved in crashes. Interestingly, different level of control policies during COVID-19 outbreak are closely associated with safety awareness, driving and travel behavior, and thus has an indirect influence on the frequency of crashes. Comparing to other three control policies including emergence declare, limits on mass gatherings, and ban on all nonessential gathering, the negative relationship between stay-at-home policy implemented in New York City from March 20, 2020 and the number of people involved crashes is found in our study.