Historical Location Data Research Articles

Lightweight APIT with Bat Optimization with Simulated Annealing Localization for Resource-Constrained Sensor Networks

In a wireless sensor network, information processing, and information acquisition, localization technology is the key to making it practically possible application. Approximate Point-in-Triangulation (APIT) is the most widely used localization estimation which has high accuracy in localizing the nodes and ease of deployment of nodes in the real-time environment. Though it has numerous key advantages, some of the drawbacks which make it a little setback in preference are the unevenness in the distribution of nodes. Tracking is more appropriate for mobile sensor nodes than tracking is for static sensor nodes. The two main types of localization algorithms are range-based and range-free techniques. In an indoor setting, the projected range (distance) between an anchor and an unknown node is very inaccurate. By utilizing a large number of already existing access points (APs) in the range-free localization approach, this issue can be overcome to a great extent. The utilization of multisensor data, such as magnetic, inertial, compass, gyroscope, ultrasonic, infrared, visual, and/or odometer, is stressed in recent research to further increase localization accuracy. The tracking system also makes location predictions for the future based on historical location data. To overcome this issue, the proposed localization algorithm of APIT with Bat-SA proves its efficiency. Due to its low localization error, the traditional Bat method is more accurate than APIT. The proposed Bat using the SA algorithm is found to perform better than the traditional APIT algorithm in terms of convergence of computing rate and success rate. In order to mimic the suggested APIT method, it is paired with the Bat-SA localization technique. Simulation evaluation proves the performance efficiency of the proposed algorithm. The performance metrics parameters are latency, node distribution map, positioning error map, and neighbor relationship diagram which are used to evaluate the proposed method.

COUNTERSAVIOR: AIoMT and IIoT-Enabled Adaptive Virus Outbreak Discovery Framework for Healthcare Informatics

In the current pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes global positioning system (GPS) spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject’s cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behavior, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behavior patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3-D tracker movements of individuals, 3-D contact analysis of COVID-19 and suspected individuals for 24 h, forecasting and risk classification of COVID-19, suspected and safe individuals.

A Context- and Trajectory-Based Destination Prediction of Public Transportation Users

Location prediction based on contextual information is the core of a plethora of personalized location-based services (LBSs). Several applications require the use of techniques for predicting travel destinations based on human movement. Network analyses of human behavioral data show how the spatial-temporal regularity of human movement can be harnessed for inferring human mobility patterns. However, techniques are often based on a limited number of contextual features, which may limit prediction accuracy, especially if only historical location data are used. Using movement data obtained from public transportation users, we investigate the utility of contextual features derived via the installation of Bluetooth beacons in transportation vehicles and software tools in end-users’ travel applications. Using a multiclass random forest classifier, we show that contextual information of a user’s past travel history and at journey onset goes beyond spatial information and boosts destination prediction accuracy. The likely destination and travel-path length obtained at journey onset can then serve as the input for a stochastic-based model to predict a destination based on acquired trajectory information. Here we show that previously predicted destinations boost the performance of a Markov chain network. Thus, various contextual information at the start of a journey provides information beyond the location information acquired during a journey’s progression that can be employed for destination prediction. These findings have strong implications for LBSs as they require accurate destination prediction at early stages of a journey while at the same time mitigating the privacy concerns associated with collection of location data.

Generating Synthetic Trajectory Data Using GRU

With the rise of mobile network, user location information plays an increasingly important role in various mobile services. The analysis of mobile users’ trajectories can help develop many novel services or applications, such as targeted advertising recommendations, location-based social networks, and intelligent navigation. However, privacy issues limit the sharing of such data. The release of location data resulted in disclosing users’ privacy, such as home addresses, medical records, and other living habits. That promotes the development of trajectory generators, which create synthetic trajectory data by simulating moving objects. At current, there are some disadvantages in the process of generation. The prediction of the following position in the trajectory generation is very dependent on the historical location data, but the relationship between trajectory positions tends to be ignored. Most commonly used methods only adopt the probability distribution of users’ positions to generate synthetic data. On the one hand, this type of statistical method is too rough, and on the other hand, it cannot bring more benefits in availability by increasing data volume. We propose a new trajectory generation method in this paper–Trajectory Generation Model with RNNs(TGMRNN), to address the deficiencies above. It adopts the RNN model to replace the traditional Markov model to generate trajectory data with higher availability. Meanwhile, it solves the problem that RNNs are unsuitable for continuous location data by representing trajectories as discretized data with the grid method. We have conducted experiments in a real data set. Compared with the Markov model, the results of TGMRNN demonstrate that it is superior to some existing methods.

Blockchain-Enabled Privacy-Preserving Location Sharing Scheme for LBSNs

The rise of Internet of Things (IoT) technology promotes the rapid development of location services industry. The idea of smart connectivity also provides a new direction for Location-Based Social Networks (LBSNs). However, due to limited calculate ability and internal storage space of IoT devices, historical location data of users is generally stored in the central server, which is likely to cause the disclosure of users’ private data. In this paper, we propose a Blockchain-enabled Privacy-Preserving Location Sharing (B-PPLS) scheme, which is a new framework that not only protects user location privacy but also provides effective location sharing services for users. For B-PPLS, location data owners can share the location area instead of location coordinates to Requesters, in order to realize the location privacy preserving. Also, the Merkle hash tree is utilized to divide the location area, so as to realize the multilevel privacy preserving. Furthermore, four algorithms are proposed to achieve the four stages of initialization, location record, location sharing, and location verification, respectively. Finally, we analyze the security of the proposed B-PPLS scheme and compare the performance with other related location privacy-preserving schemes by experimental evaluation.

Delay‐aware privacy‐preserving location‐based services under spatiotemporal constraints

SummaryThe ubiquitous use of location‐based services (LBS) through smart devices produces massive amounts of location data. An attacker, with an access to such data, can reveal sensitive information about users. In this paper, we study location inference attacks based on the probability distribution of historical location data, travel time information between locations using knowledge of a map, and short and long‐term observation of privacy‐preserving queries. We show that existing privacy‐preserving approaches are vulnerable to such attacks. In this context, we propose a novel location privacy‐preserving approach, called KLAP, based on the three fundamental obfuscation requirements: minimum k‐locations, l‐diversity, and privacy area preservation. KLAP adopts a personalized privacy preference for sporadic, frequent, and continuous LBS use cases. Specifically, it generates a secure concealing region (CR) to obfuscate the user's location and directs that CR to the service provider. The main contribution of this work is twofold. First, a CR pruning technique is devised to establish a balance between privacy and delay in LBS usage. Second, a new attack model called a long‐term obfuscated location tracking attack, and its countermeasure is proposed and evaluated both theoretically and empirically. We assess KLAP with two real‐world datasets. Experimental results show that it can achieve better privacy, reduced delay, and lower communication costs than existing state‐of‐the‐art methods.

Contact Tracing to Manage COVID-19 Spread—Balancing Personal Privacy and Public Health

Contact Tracing to Manage COVID-19 Spread—Balancing Personal Privacy and Public Health

Mechanism Design for Wireless Powered Spatial Crowdsourcing Networks

Wireless power transfer (WPT) is a promising technology to prolong the lifetime of the sensors and communication devices, i.e., workers, in completing crowdsourcing tasks by providing continuous and cost-effective energy supplies. In this paper, we propose a wireless powered spatial crowdsourcing framework which consists of two mutually dependent phases: task allocation phase and data crowdsourcing phase. In the task allocation phase, we propose a Stackelberg game based mechanism for the spatial crowdsourcing platform to efficiently allocate spatial tasks and wireless charging power to each worker. In the data crowdsourcing phase, the workers may have an incentive to misreport its real working location to improve its utility, which causes adverse effects to the spatial crowdsourcing platform. To address this issue, we present three strategyproof deployment mechanisms for the spatial crowdsourcing platform to place a mobile base station, e.g., vehicle or robot, which is responsible for transferring the wireless power and collecting the crowdsourced data. As the benchmark, we first apply the classical median mechanism and evaluate its worst-case performance. Then, we design a conventional strategyproof deployment mechanism to improve the expected utility of the spatial crowdsourcing platform under the condition that the workers' locations follow a known geographical distribution. For a more general case with only the historical location data available, we propose a deep learning based strategyproof deployment mechanism to maximize the spatial crowdsourcing platform's utility. Extensive experimental results based on synthetic and real-world datasets reveal the effectiveness of the proposed framework in allocating tasks and charging power to workers while avoiding the dishonest worker's manipulation.

The use of aircraft tracking GPS data to develop models of the use of airtankers in forest fire management

The Ontario Ministry of Natural Resources and Forestry uses amphibious airtankers to help control forest fires in the province of Ontario. Their airtankers are based at airports from which they are dispatched to fires to assist FireRanger crews that work on the ground. Airtanker systems can be modelled as spatial queueing systems with fires as customers that arrive at rates that vary over both time and space and airtankers as mobile servers that fight fires at spatially dependent service rates. In order to model the airtanker service process one must know how much time airtankers spend travelling to and from and dropping water on fires. We developed a methodology for processing historical fire, airport location and on-board airtanker tracking GPS data to identify the key events that take place while airtankers are involved in fire suppression operations. We used that data to develop detailed models that describe how airtankers participate in the initial attack process. Those models can be incorporated in strategic, tactical and operational airtanker management decision support systems. We conclude by suggesting that our methodology be extended to model the use of transport helicopters, trucks and heavy equipment in fire suppression, and the activities of FireRanger crews on the ground.

NextRoute: a lossless model for accurate mobility prediction

Mobility prediction of vehicles has become a key feature in mobility management in smart cities, mobile computing environments, intelligent transportation systems, vehicular networks, and location-based services. It has several important applications in traffic congestion forecasting, location-based routing protocol designs and targeting advertisements generation, etc. Mobility prediction (aka route prediction) consists of forecasting future routes to be traversed by a vehicle. Several models have been proposed for route prediction. These models are generally probabilistic models (e.g., Markov models) or data mining-based models (e.g., sequential rule-based models), and are trained with historical location data. Although these models were shown to perform well, one important drawback is that they are lossy. In other words, a large amount of information found in the location data is discarded by their training processes. Consequently, these models do not perform well for predicting routes where detailed information is required to perform accurate predictions. Besides, several prediction models assume the Markovian hypothesis that the next location of a user only depends on his/her current location and any previous movement of the user is ignored in prediction. This hypothesis has been employed, such as in Lian et al. (ACM Trans Intell Syst Technol 6:1–27, 2015. https://doi.org/10.1145/2629557), as a simplifying assumption because it allows building models having a small size. However, this assumption is often unrealistic and thus greatly decreases the accuracy of route prediction. Moreover, these models are noise sensitive as they do not tolerate the smallest deviation in location data principally prone to several disturbances and uncertainty issues. To address these limitations, this paper introduces a novel route prediction model named NextRoute. The proposed model is lossless as it compresses location data in a prediction tree without information loss, and it is designed to use all the relevant information contained in the training data to perform prediction. In contrast to some other proposals, NextRoute provides efficient noise tolerance strategy that loosened the similarity measure when matching the current trajectory of vehicle with historical data. An extensive experimental evaluation was conducted with real-world and synthetic datasets providing quite encouraging results.

On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies

Appropriately characterizing spatiotemporal individual mobility is important in many research areas, including epidemiological studies focusing on air pollution. However, in many retrospective air pollution health studies, exposure to air pollution is typically estimated at the subjects’ residential addresses. Individual mobility is often neglected due to lack of data, and exposure misclassification errors are expected. In this study, we demonstrate the potential of using location history data collected from smartphones by the Google Maps application for characterizing historical individual mobility and exposure. Here, one subject carried a smartphone installed with Google Maps, and a reference GPS data logger which was configured to record location every 10 s, for a period of one week. The retrieved Google Maps Location History (GMLH) data were then compared with the GPS data to evaluate their effectiveness and accuracy of the GMLH data to capture individual mobility. We also conducted an online survey (n = 284) to assess the availability of GMLH data among smartphone users in the US. We found the GMLH data reasonably captured the spatial movement of the subject during the one-week time period at up to 200 m resolution. We were able to accurately estimate the time the subject spent in different microenvironments, as well as the time the subject spent driving during the week. The estimated time-weighted daily exposures to ambient particulate matter using GMLH and the GPS data logger were also similar (error less than 1.2%). Survey results showed that GMLH data may be available for 61% of the survey sample. Considering the popularity of smartphones and the Google Maps application, detailed historical location data are expected to be available for large portion of the population, and results from this study highlight the potential of these location history data to improve exposure estimation for retrospective epidemiological studies.

Efficient Parcel Delivery by Predicting Customers’ Locations*

ABSTRACTAn important aspect of the growing e‐commerce sector involves the delivery of tangible goods to the end customer, the so‐called last mile. This final stage of the logistics chain remains highly inefficient due to the problem of failed deliveries. To address this problem, delivery service providers can apply data science to determine the optimal, customer‐centered location and time window for handover. In this article, we present a three‐step approach for location prediction, based on mobile location data, in order to support delivery planning. The first step is identifying a user's locations of interest through density‐based clustering. Next, the semantics (home or work) of the user's locations of interest are discovered, based on temporal assumptions. Finally, we predict future locations with a decision tree model that is trained on each user's historical location data. Though the problem of location prediction is not new, this work is the first to apply it to the field of parcel delivery with its corresponding implications. Moreover, we provide a novel and detailed evaluation on real‐world data from a parcel delivery service. The promising results indicate that our approach has the potential to help delivery service providers to gain insights into their customers’ optimal delivery time and location in order to support delivery planning. Eventually this will decrease last‐mile delivery costs and boost customer satisfaction.

Unsupervised WiFi-Enabled IoT Device-User Association for Personalized Location-Based Service

A fundamental building block toward personalized location-based service and context-aware service in smart buildings is the knowledge about the identity and mobility of users in indoor environments. Conventional user identification systems require the deployment of dedicated infrastructure or the active user involvement. Motivated by the widespread usage of the WiFi-enabled mobile device (MD), e.g., people usually carry at least one MD in their daily lives, in this paper, we propose WinDUA, a WiFi-enabled nonintrusive device and user association scheme to infer user identity and mobility via a novel unsupervised association learning algorithm. First, we utilize our WiFi-based indoor positioning system to obtain the historical location data of each MD using only existing WiFi infrastructure in a nonintrusive manner. Then, we classify all the MDs into two categories: 1) static device (SD) and 2) mobile phone (MP), according to their location variations and overnight presences. Subsequently, we estimate the correct mapping between each SD and its user through hierarchical clustering and location similarity matching between its location and user's personal space. Finally, we make possible pairs of MP and SD according to their duration of coexistence as well as the historical location similarity to associate the owner of each MP. Real-world experiments are conducted in an office, verifying that WinDUA is able to associate the MD to the correct users in a nonintrusive and unsupervised manner.

Anti-Theft Vehicle Tracking System Using GPS and Location Prediction

Currently the number of private vehicles is increasing day by day and hence the importance of tracking and theft prevention. Recently the vehicle tracking systems are getting wide popularity and can be used in tracking in case of stolen vehicles. Real-time applications like Vehicle Tracking System is developed using Arduino board with a microcontroller. We have developed a vehicle tracking system with a Smartphone which is less expensive and reliable when compared to the existing system as there is no need for extra hardware. The objective is to develop an application for tracking vehicles, which will help the cab owners to track their car all the time and to predict the location of the vehicle in the case of a failing GPS (Global Positioning System). Time series prediction algorithm is used to predict the location of the vehicle if GPS is in off mode. The vehicle tracking system installed will update the GPS coordinates of the vehicle continued to the cloud, and this data can be used for predicting the location of the vehicle in case of emergency. This system can also be used to generate the bills after finishing the freight in the form of an SMS based on the distance traveled, which can be calculated from the latitude and longitude data. The GPS data can be mapped to the Google maps to track the location in real time. Compared with the existing system, this system is having the advantage of location prediction from the historic location data, and the cost is reduced by almost half.

English

Author(s): Siegfried, Lucas J.; Fleenor, William E.; Lund, Jay R. | Abstract: Water use estimation is central to managing most water problems. To better understand water use in California’s Sacramento–San Joaquin Delta, a collaborative, integrated approach was used to predict Delta island diversion, consumption, and return of water on a more detailed temporal and spatial resolution. Fabian Tract and Staten Island were selected for this pilot study based on available data and island accessibility. Historical diversion and return location data, water rights claims, LiDAR digital elevation model data, and Google Earth were used to predict island diversion and return locations, which were tested and improved through ground-truthing. Soil and land-use characteristics as well as weather data were incorporated with the Integrated Water Flow Model Demand Calculator to estimate water use and runoff returns from input agricultural lands. For modeling, the islands were divided into grid cells forming subregions, representing fields, levees, ditches, and roads. The subregions were joined hydrographically to form diversion and return watersheds related to return and diversion locations. Diversions and returns were limited by physical capacities. Differences between initial model and measured results point to the importance of seepage into deeply subsided islands. The capabilities of the models presented far exceeded current knowledge of agricultural practices within the Delta, demonstrating the need for more data collection to enable improvements upon current Delta Island Consumptive Use estimates.

Can You See Me Now? Toward Reasonable Standards for Law Enforcement Access to Location Data That Congress Could Enact

The use of location information by law enforcement agencies is common and becoming more so as technological improvements enable collection of more accurate, precise location data. The legal mystery surrounding the proper law enforcement access standard for prospective location data remains unsolved. This mystery, along with conflicting rulings over the appropriate law enforcement access standards for both prospective and historical location data, has created a messy, inconsistent legal landscape where even judges in the same district may require law enforcement to meet different standards to compel location data. As courts struggle with these intertwined technology, privacy, and legal issues, some judges are expressing concern over the scope of the harms, from specific and personal to general and social, presented by unfettered government collection and use of location data and how to respond to them. Judges have sought to communicate the scope and gravity of these concerns through direct references to Orwell’s dystopia in 1984, as well as suggestive allusions to the “panoptic effect” observed by Jeremy Bentham and his later interpreters like Michel Foucault. Some have gone on to suggest that privacy issues raised by law enforcement access to location data might be addressed more effectively by the legislature.This Article proposes a legislative model for law enforcement access standards and downstream privacy protections for location information. This proposal attempts to (1) articulate clear rules for courts to apply and law enforcement agents and industry to follow; and (2) strike a reasonable balance among the interests of law enforcement, privacy, and industry with the ultimate goal of improving the position of all concerned when measured against the current state of the law.

Can You See Me Now?: Toward Reasonable Standards for Law Enforcement Access to Location Data that Congress Could Enact

The use of location information by law enforcement agencies is common and becoming more so as technological improvements enable collection of more accurate, precise location data. The legal mystery surrounding the proper law enforcement access standard for prospective location data remains unsolved. This mystery, along with conflicting rulings over the appropriate law enforcement access standards for both prospective and historical location data, has created a messy, inconsistent legal landscape where even judges in the same district may require law enforcement to meet different standards to compel location data. As courts struggle with these intertwined technology, privacy, and legal issues, some judges are expressing concern over the scope of the harms, from specific and personal to general and social, presented by unfettered government collection and use of location data and how to respond to them. Judges have sought to communicate the scope and gravity of these concerns through direct references to Orwell’s dystopia in 1984, as well as suggestive allusions to the “panoptic effect” observed by Jeremy Bentham and his later interpreters like Michel Foucault. Some have gone on to suggest that privacy issues raised by law enforcement access to location data might be addressed more effectively by the legislature.This Article proposes a legislative model for law enforcement access standards and downstream privacy protections for location information. This proposal attempts to (1) articulate clear rules for courts to apply and law enforcement agents and industry to follow; and (2) strike a reasonable balance among the interests of law enforcement, privacy, and industry with the ultimate goal of improving the position of all concerned when measured against the current state of the law.

AN ADAPTIVE LEARNING FRAMEWORK FOR FORECASTING SEASONAL WATER ALLOCATIONS IN IRRIGATED CATCHMENTS

This paper describes an adaptive learning framework for forecasting end-season water allocations using climate forecasts, historic allocation data, and results of other detailed hydrological models. The adaptive learning framework is based on artificial neural network (ANN) method, which can be trained using past data to predict future water allocations. Using this technique, it was possible to develop forecast models for end-irrigation-season water allocations from allocation data available from 1891 to 2005 based on the allocation level at the start of the irrigation season. The model forecasting skill was further improved by the incorporation of a set of correlating clusters of sea surface temperature (SST) and the Southern oscillation index (SOI) data. A key feature of the model is to include a risk factor for the end-season water allocations based on the start of the season water allocation. The interactive ANN model works in a risk-management context by providing probability of availability of water for allocation for the prediction month using historic data and/or with the incorporation of SST/SOI information from the previous months. All four developed ANN models (historic data only, SST incorporated, SOI incorporated, SST-SOI incorporated) demonstrated ANN capability of forecasting end-of-season water allocation provided sufficient data on historic allocation are available. SOI incorporated ANN model was the most promising forecasting tool that showed good performance during the field testing of the model.