Constrained Proximity Attacks on Mobile Targets

Location privacy has mostly focused on scenarios where users remain static. However, investigating scenarios where the victims present a particular mobility pattern is more realistic. In this paper, we consider abstract attacks on services that provide location information on other users in the proximity. In that setting, we quantify the required effort of the attacker to localize a particular mobile victim. We prove upper and lower bounds for the effort of an optimal attacker. We experimentally show that a Linear Jump Strategy (LJS) practically achieves the upper bounds for almost uniform initial distributions of victims. To improve performance for less uniform distributions known to the attacker, we propose a Greedy Updating Attack Strategy (GUAS). Finally, we derive a realistic mobility model from a real-world dataset and discuss the performance of our strategies in that setting.

COVID-19, activity and mobility patterns in Bogotá. Are we ready for a ‘15-minute city’?

The need of accurate and reliable positioning in various location-aware safety critical transportation applications is increasing day by day. The Global Positioning System (GPS) is not able to provide lane-level vehicle localization without the aid of differential corrections. It also suffers from signal outages in urban areas resulting in a complete loss of location information. Therefore, GPS independent localization methods are now being developed. In this domain, inertial sensors along with a terrain map have been successfully deployed to achieve sub-meter level accuracy in the longitudinal direction of the vehicle in an urban environment. However, lateral localization of the vehicle with good accuracy and computational efficiency remains a challenging topic. Existing algorithms are computationally intensive, and do not provide location information during the process of lane change by the vehicle. This information is very crucial as the risk of potential conflict with nearby vehicles is higher during lane changes. In this paper, we present a computationally efficient method for achieving lane-level localization in a multi-lane scenario by combining the particle filter with dead- reckoning. The particle filter provides the location information about a single lane while location information during the lane change maneuvers is provided by dead-reckoning. Lane- change maneuvers are detected by constantly observing the yaw rate of the vehicle. Developing a computationally efficient algorithm enables the GPS independent localization algorithm to be run on low cost micro-controllers making its deployment feasible for packaged devices. Experiments performed on an instrumented vehicle show the superiority of the proposed algorithm on the existing ones.

The pervasiveness of GPS-enabled mobile devices and the widespread use of location-based services have resulted in the generation of massive amounts of geo-tagged data. In recent times, the data analysis now has access to more sources, including reviews, news, and images, which also raises questions about the reliability of Point-of-Interest (POI) data sources. While previous research attempted to detect fake POI data through various security mechanisms, the current work attempts to capture the fake POI data in a much simpler way. The proposed work is focused on supervised learning methods and their capability to find hidden patterns in location-based data. The ground truth labels are obtained through real-world data, and the fake data is generated using an API, so we get a dataset with both the real and fake labels on the location data. The objective is to predict the truth about a POI using the Multi-Layer Perceptron (MLP) method. In the proposed work, MLP based on data classification technique is used to classify location data accurately. The proposed method is compared with traditional classification and robust and recent deep neural methods. The results show that the proposed method is better than the baseline methods.

A major feature of the emerging geo-social networks is the ability to notify a user when any of his friends (also called buddies) happens to be geographically in proximity. This proximity service is usually offered by the network itself or by a third party service provider (SP) using location data acquired from the users. This paper provides a rigorous theoretical and experimental analysis of the existing solutions for the location privacy problem in proximity services. This is a serious problem for users who do not trust the SP to handle their location data and would only like to release their location information in a generalized form to participating buddies. The paper presents two new protocols providing complete privacy with respect to the SP and controllable privacy with respect to the buddies. The analytical and experimental analysis of the protocols takes into account privacy, service precision, and computation and communication costs, showing the superiority of the new protocols compared to those appeared in the literature to date. The proposed protocols have also been tested in a full system implementation of the proximity service.

Zero-carbon microgrid: Real-world cases, trends, challenges, and future research prospects

This research introduces a novel mathematical framework for understanding collective human mobility patterns, integrating mathematical modeling and data analysis. It focuses on latent-variable networks to investigate the dynamics of human mobility using stochastic models. By analyzing origin–destination data, the study uncovers scaling relations and explores the economic implications of mobility patterns, particularly regarding the income elasticity of travel demand. The mathematical analysis begins with the development of a stochastic model based on inhomogeneous random graphs, constructing a visitation model with multipurpose drivers for travel demand. Through this model, the study gains insights into the structural properties and dynamic correlations of human mobility networks, deriving analytical solutions for key network metrics: visit distribution, assortativity behavior and clustering coefficient. Empirically, the study validates the model’s assumptions and reveals scaling behaviors in origin–destination flows within a region, reproducing statistical regularities observed in real-world cases. Notably, the model’s application to estimating income elasticity of travel demand provides significant implications for urban and transport economics. Overall, this research contributes to a deeper understanding of the interplay between human mobility and regional demographics and economics. It sheds light on critical scaling relations across various aspects of collective human mobility and underscores the importance of incorporating latent-variable structures into mobility modeling for accurate economic analysis and decision-making in urban and transportation planning.

BackgroundThe emergence of smartphones, wearable sensor technologies, and smart homes allows the nonintrusive collection of activity data. Thus, health-related events, such as activities of daily living (ADLs; eg, mobility patterns, feeding, sleeping, ...) can be captured without patients’ active participation. We designed a system to detect changes in the mobility patterns based on the smartphone’s native sensors and advanced machine learning and signal processing techniques.ObjectiveThe principal objective of this work is to assess the feasibility of detecting mobility pattern changes in a sample of outpatients with depression using the smartphone’s sensors. The proposed method processed the data acquired by the smartphone using an unsupervised detection technique.MethodsIn this study, 38 outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) participated. The Evidence-Based Behavior (eB2) app was downloaded by patients on the day of recruitment and configured with the assistance of a physician. The app captured the following data: inertial sensors, physical activity, phone calls and message logs, app usage, nearby Bluetooth and Wi-Fi connections, and location. We applied a change-point detection technique to location data on a sample of 9 outpatients recruited between April 6, 2017 and December 14, 2017. The change-point detection was based only on location information, but the eB2 platform allowed for an easy integration of additional data. The app remained running in the background on patients’ smartphone during the study participation.ResultsThe principal outcome measure was the identification of mobility pattern changes based on an unsupervised detection technique applied to the smartphone’s native sensors data. Here, results from 5 patients’ records are presented as a case series. The eB2 system detected specific mobility pattern changes according to the patients’ activity, which may be used as indicators of behavioral and clinical state changes.ConclusionsThe proposed technique could automatically detect changes in the mobility patterns of outpatients who took part in this study. Assuming these mobility pattern changes correlated with behavioral changes, we have developed a technique that may identify possible relapses or clinical changes. Nevertheless, it is important to point out that the detected changes are not always related to relapses and that some clinical changes cannot be detected by the proposed method.

Comparing aggregation methods in large-scale group AHP: Time for the shift to distance-based aggregation

GPU-enhanced Finite Volume Shallow Water solver for fast flood simulations

To take full advantage of the promise of ubiquitous computing requires the use of location information, yet people should have control over who may know their whereabouts. We present an architecture that achieves these goals for an interesting set of applications. Personal information is managed by User Agents, and a partially decentralized Location Query Service is used to facilitate location-based operations. This architecture gives users primary control over their location information, at the cost of making more expensive certain queries, such as those wherein location and identity closely interact. We also discuss various extensions to our architecture that offer users additional trade-offs between privacy and efficiency. Finally, we report some measurements of the unextended system in operation, focusing on how well the system is actually able to track people. Our system uses two kinds of location information, which turn out to provide partial and complementary coverage.

트위터는 다른 SNS와 대비되는 정보의 빠른 전파력과 확산성을 갖고 있다. 따라서 트위터를 이용하여 현실에서 발생한 이벤트를 탐지하는 여러 연구가 진행되고 있다. 트위터 사용자 개개인을 하나의 센서로 가정하고 그들이 작성한 트윗 텍스트를 분석하여 이벤트 탐지에 이용하는 것이다. 이와 관련된 연구들은 이미 많은 성과를 보이며 진행되어 왔으나 여러 가지 문제점들로 인해 새로운 한계에 직면했다. 특히 선행 연구의 대다수가 이벤트의 발생 위치를 추적하기 위해 GPS좌표를 이용한다. 그러나 이는 최근 트위터 사용자들이 위치정보 공개에 회의적인 점을 감안하면 명확한 한계점으로 제시될 수 있다. 이에 본 논문에서는 트위터에서 제공하는 위치정보를 이용하지 않고 트윗 텍스트에서 위치정보를 추적하는 방법을 제시하였다. 트윗 텍스트에서 키워드를 추출하여 키워드간의 관계를 고려해 연관단어를 군집화 하였다. 본 논문에서 제안한 알고리즘을 적용한 실험을 통해 이벤트가 발생한 지역과 실제로 발생한 이벤트의 탐지여부를 확인하였다. 또한 본 논문에서 제안한 기법이 기존 매체들보다 빠른 탐지를 보임으로써 제안된 기법의 우수성을 입증하였다. Twitter has the fast propagation and diffusion of information compare to other SNS. Therefore, many researches about detecting real-time event using twitter are progressing. Twitter real-time event detecting system assumes every twitter user as a sensor and analyzes their written tweet in order to detect the event. Researches that are related to this twitter have already obtained good results but confronted the limits because of some problems. Especially, many existing researches are using the method that can trace an event location by using GPS coordinate. However, it can be suggested a definite limitation through the present user's skeptical responses about making personal location information public. Therefore, this paper suggests the method that traces the location information in tweet contents text without using the provided location information from twitter. Associated words were grouped by using the keyword that extracted in tweet contents text. The place that the events have occurred and whether the events have surely occurred are detected by this experiment using this algorithm. Furthermore, this experiment demonstrated the necessity of the suggested methods by showing faster detection compare to the other existing media.

Many routing protocols in vehicular ad hoc networks (VANETs) utilize location information to find a route to the destination. However, tracking the location information of other nodes is very challenging in highly dynamic VANETs. We propose a location service which can provide location information with low overhead and low delay. The location service periodically disseminates the location of each vehicle to 3-hop distance for every second with very low overhead. By eliminating location errors by taking account of the velocity of a vehicle, the proposed protocol can provide accurate position information. The location service also provides a lightweight location query mechanism for longer distance destination nodes. We show the effectiveness of the protocol by using theoretical analysis and computer simulations.

To take full advantage of the promise of ubiquitous computing requires the use of location information, yet people should have control over who may know their whereabouts. We present an architecture that achieves these goals for an interesting set of applications. Personal information is managed by User Agents, and a partially decentralized Location Query Service is used to facilitate location-based operations. This architecture gives users primary control over their location information, at the cost of making more expensive certain queries, such as those wherein location and identity closely interact. We also discuss various extensions to our architecture that offer users additional trade-offs between privacy and efficiency. Finally, we report some measurements of the unextended system in operation, focusing on how well the system is actually able to track people. Our system uses two kinds of location information, which turn out to provide partial and complementary coverage.

Recently, the development of mobile edge computing has enabled exhilarating edge artificial intelligence (AI) with fast response and low communication costs. The location information of edge devices is essential to support the edge AI in many scenarios, like smart home, intelligent transportation systems, and integrated health care. Taking advantage of deep learning intelligence, the centralized machine learning (ML)-based positioning technique has received heated attention from both academia and industry. However, some potential issues, such as location information leakage and huge data traffic, limit its application. Fortunately, a newly emerging privacy-preserving distributed ML mechanism, named federated learning (FL), is expected to alleviate these concerns. In this article, we illustrate a framework of FL-based localization systems as well as the involved entities at edge networks. Moreover, the advantages of such a system are elaborated. On the practical implementation of it, we investigate the field-specific issues associated with system-level solutions, which are further demonstrated over a real-word database. Moreover, future challenging open problems in this field are outlined.