Accuracy Of Location Information Research Articles

AHP-FCE를 활용한 중국 피난안내도 최적화 설계 연구 - Chung Hua University(CHU) 도서관을 중심으로

In large indoor public spaces such as office buildings and shopping malls, the complex architectural layout and dense population increase the difficulty of emergency evacuation. In the event of a fire or other disaster, the lack of effective evacuation measures can lead to significant casualties and property loss. This paper studies fire evacuation maps in China based on the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE) methods, aiming to improve the cognitive efficiency and user experience of evacuation maps. The study first constructs a hierarchical index system for evacuation maps, determines the weights of various design elements, and forms a judgment matrix through evaluations by experts and users. Then, the optimized evacuation map undergoes a comprehensive evaluation analysis. The results show that clarity and functionality are the two aspects users are most concerned about, with the recognizability of signage and the accuracy of location information being particularly important. The optimized evacuation map shows significant improvements in clarity and information transmission, effectively guiding people to quickly escape in emergencies. This study validates the rationality and feasibility of the AHP-FCE method in the design of fire evacuation maps, providing new ideas and directions for further optimization of evacuation maps.

Cascaded matching based on detection box area for multi-object tracking

In the existing tracking-by-detection paradigm, advanced approaches rely on appearance features to establish associations between current detections and trajectories. However, these methods are often plagued by issues such as sluggish tracking performance and suboptimal results, particularly when confronted with the unreliability of the appearance features. Considering these challenges, we propose a novel cascaded matching algorithm called the detection box area-based tracking algorithm (DBAT), which groups the detection boxes by area size and associates detections within each group in a cascaded manner. To enhance the accuracy of grouping, we introduce two crucial components to enhance the quality of detections: the compressed self-decoding module (CSDM) and the task collaboration module (TCM). To acquire more precise location information and augment feature richness, CSDM decomposes the input features into two one-dimensional feature encodings and one two-dimensional feature encoding. Subsequently, these feature encodings perform feature aggregation along both spatial directions to capture long-range dependencies and refine the accuracy of location information. Ultimately, these aggregated features engage with the original features, facilitating information fusion and elevating the overall feature representation. To alleviate potential conflicts between various tasks and bolster task-specific representations, TCM combines disparate receptive fields and decouples features through self-relationship and cross-relationship mappings, thereby concurrently enhancing learning across different tasks. Extensive experiments demonstrate that our proposed method achieves performance comparable to state-of-the-art methods on the MOT17, MOT20 and DanceTrack benchmark tests.

Exploring the Impact of Big Data Analytics on Emergency Calls within Telecommunication Systems

Big Data Analytics is a critical function in Telecommunication Systems in order to monitor, measure and report the system’s performance for various services. Collecting data from various nodes, extracting the necessary fields, correlating the data according to system needs, storing the data and reporting the required information are the main functions of such Analytics Platforms in Telecommunication Systems. Emergency calls are mostly made from cell phones and location information can be sent along with them. With Big Data Analytics, it is possible to use the emergency call systems more effectively and make improvements to save lives. In this study, as an industry experience, the structure and working principles of an Analytics Platform which is used to measure and report the location information accuracy of Emergency 911 (E911) calls are going to be provided.

Accuracy Differences in Cannabis Retailer Information Ascertained from Webservices and Government-Maintained State Registries Across US States Legalizing the Sale of Cannabis in 2019.

Cannabis retailer locations used to investigate geographic cannabis access are frequently ascertained from two sources: 1) webservices which provide locations of cannabis retailers (e.g., Yelp) or 2) government-maintained registries. Characterizing the operating status and location information accuracy of cannabis retailer data sources on a state-by-state level can inform research examining the health implications of cannabis legalization policies. This study ascertained cannabis retailer name and location from webservices and government-maintained registries for 26 states and the District of Columbia legalizing cannabis sales in 2019. Validation subsamples were created using state-level sequential sampling. Phone surveys were conducted by trained researchers for webservice samples (n=790, November 2019 - May 2020) and government-maintained registry (n=859, February - June 2020) to ascertain information about operating status and location. Accuracy was calculated as the percent agreement among subsample and phone survey data. For operating status and location, webservice derived data was 78% (614/790) and 79% (484/611) accurate, whereas government-maintained registry derived data was 76% (657/859) and 95% (622/655) accurate, respectively. Fifty-nine percent (15/27) of states and the District of Columbia had over 80% accuracy for operating status and 48% (13/27) states had over 80% accuracy for location information with both data sources. However, government-maintained registry derived information was more accurate in 33% (9/27) states for operating status and 41% (11/27) states for location information. Both data sources had similar operating status accuracy. Research using spatial analysis may prefer government-maintained registry derived data due to high location information accuracy, whereas studies looking at broad trends across states may prefer webservice derived. State level COVID-19 restrictions had minimal impact on ascertainment of cannabis retailer operating status and location information via phone survey derived from webservices and government-maintained registries.

Self-Localization of IoT Devices Using Noisy Anchor Positions and RSSI Measurements

Location-enabled Internet of things (IoT) has attracted much attention from the scientific and industrial communities given its high relevance in application domains such as agriculture, wildlife management, and infectious disease control. The frequency and accuracy of location information plays an important role in the success of these applications. However, frequent and accurate self-localization of IoT devices is challenging due to their resource-constrained nature. In this paper, we propose a new algorithm for self-localization of IoT devices using noisy received signal strength indicator (RSSI) measurements and perturbed anchor node position estimates. In the proposed algorithm, we minimize a weighted sum-square-distance-error cost function in an iterative fashion utilizing the gradient-descent method. We calculate the weights using the statistical properties of the perturbations in the measurements. We assume log-normal distribution for the RSSI-induced distance estimates due to considering the log-distance path-loss model with normally-distributed perturbations for the RSSI measurements in the logarithmic scale. We also assume normally-distributed perturbation in the anchor position estimates. We compare the performance of the proposed algorithm with that of an existing algorithm that takes a similar approach but only accounts for the perturbations in the RSSI measurements. Our simulation results show that by taking into account the error in the anchor positions, a significant improvement in the localization accuracy can be achieved. The proposed algorithm uses only a single measurement of RSSI and one estimate of each anchor position. This makes the proposed algorithm suitable for frequent and accurate localization of IoT devices.

An Adaptive Location-Based Tracking Algorithm Using Wireless Sensor Network for Smart Factory Environment

In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results.

A City-Level Boundary Nodes Identification Algorithm Based on Bidirectional Approaching

Existing city-level boundary nodes identification methods need to locate all IP addresses on the path to differentiate which IP is the boundary node. However, these methods are susceptible to time-delay, the accuracy of location information and other factors, and the resource consumption of locating all IPes is tremendous. To improve the recognition rate and reduce the locating cost, this paper proposes an algorithm for city-level boundary node identification based on bidirectional approaching. Different from the existing methods based on time-delay information and location results, the proposed algorithm uses topological analysis to construct a set of candidate boundary nodes and then identifies the boundary nodes. The proposed algorithm can identify the boundary of the target city network without high-precision location information and dramatically reduces resource consumption compared with the traditional algorithm. Meanwhile, it can label some errors in the existing IP address database. Based on 45,182,326 measurement results from Zhengzhou, Chengdu and Hangzhou in China and New York, Los Angeles and Dallas in the United States, the experimental results show that: The algorithm can accurately identify the city boundary nodes using only 20.33% location resources, and more than 80.29% of the boundary nodes can be mined with a precision of more than 70.73%.

A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles

During the procedure, a location-based service (LBS) query, the real location provided by the vehicle user may results in the disclosure of vehicle location privacy. Moreover, the point of interest retrieval service requires high accuracy of location information. However, some privacy preservation methods based on anonymity or obfuscation will affect the service quality. Hence, we study the location privacy-preserving method based on dummy locations in this paper. We propose a vehicle location privacy-preservation method based on dummy locations under road restriction in Internet of vehicles (IoV). In order to improve the validity of selected dummy locations under road restriction, entropy is used to represent the degree of anonymity, and the effective distance is introduced to represent the characteristics of location distribution. We present a dummy location selection algorithm to maximize the anonymous entropy and the effective distance of candidate location set consisting of vehicle user’s location and dummy locations, which ensures the uncertainty and dispersion of selected dummy locations. The proposed location privacy-preservation method does not need a trustable third-party server, and it protects the location privacy of vehicles as well as guaranteeing the LBS quality. The performance analysis and simulation results show that the proposed location privacy-preservation method can improve the validity of dummy locations and enhance the preservation of location privacy compared with other methods based on dummy locations.

A k-Anonymous Location Privacy Protection Method of Polygon Based on Density Distribution

In order to solve the problem of out-off-balance caused by accuracy of location information between privacy protection security and query service quality, considering basic information comprehensively such as the environment and geographical features and so on, and adopting k- anonymous privacy protection mechanism, we present a k-anonymous location privacy protection method of polygon based on density distribution. Firstly, a k-anonymous irregular polygon region is structured in whole area. Then, according to the preset anonymous region and density threshold, the better effects of anonymous are obtained by expanding the region or adding the random dummy locations. Experimental results show that the proposed method improves the efficiency of anonymous and query accuracy. The balance between privacy protection security and query quality is achieved.

Distributed Group Location Update Algorithm for Massive Machine Type Communication

Frequent location updates of individual Internet of Things (IoT) devices can cause several problems (e.g., signaling overhead in networks and energy depletion of IoT devices) in massive machine type communication (mMTC) systems. To alleviate these problems, we design a distributed group location update algorithm (DGLU) in which geographically proximate IoT devices determine whether to conduct the location update in a distributed manner. To maximize the accuracy of the locations of IoT devices while maintaining a sufficiently small energy outage probability, we formulate a constrained stochastic game model. We then introduce a best response dynamics-based algorithm to obtain a multi-policy constrained Nash equilibrium. From the evaluation results, it is demonstrated that DGLU can achieve an accuracy of location information that is comparable with that of the individual location update scheme, with a sufficiently small energy outage probability.

Improving GPS Code Phase Positioning Accuracy in Urban Environments Using Machine Learning

The accuracy of location information, mainly provided by the global positioning system (GPS) sensor, is critical for Internet-of-Things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. These effects cause range errors that degrade GPS positioning accuracy. Enhancements in the design of antennae and receivers deliver a level of reduction of multipath. However, NLOS signal reception and residual effects of multipath are still to be mitigated sufficiently for improvements in range errors and positioning accuracy. Recent machine learning-based methods have shown promise in improving pseudorange-based position solutions by considering multiple variables from raw GPS measurements. However, positioning accuracy is limited by low accuracy signal reception classification. Unlike the existing methods, which use machine learning to directly predict the signal reception classification, we use a gradient boosting decision tree (GBDT)-based method to predict the pseudorange errors by considering the signal strength, satellite elevation angle and pseudorange residuals. With the predicted pseudorange errors, two variations of the algorithm are proposed to improve positioning accuracy. The first corrects pseudorange errors and the other either corrects or excludes the signals determined to contain the effects of multipath and NLOS signals. The results for a challenging urban environment characterized by high-rise buildings on one side, show that the 3-D positioning accuracy of the pseudorange error correction-based positioning measured in terms of the root mean square error is 23.3 m, an improvement of more than 70% over the conventional methods.

On vehicular safety message transmissions through LTE-Advanced networks

Long Term Evolution Advanced (LTE-A), which provides high data rates with low latencies, can be an alternative to IEEE 802.11p for supporting vehicular applications. In this paper, we propose two solutions for the uplink transmission of safety messages: a new scheduler and a safety message generation rate adaptation. These solutions consider the speed of vehicles for resource allocation or selection of message generation rates, to provide the same level of location information accuracy on the central server for all vehicles. For downlink, we investigate the possible methods for delivering safety messages to the relevant vehicles, and propose a resource-efficient scheme based on broadcasting.We also propose a scheme that uses both LTE-D2D and LTE-cellular communication for transmission of safety messages. Vehicles transmit their safety messages with longer periods to the central server. This location information is used to determine the possible D2D pairs, as well as centralized resource allocation for the following period. Vehicles use the allocated resources for transmission of their safety messages to their neighboring vehicles using D2D communication. The simulation results show the effectiveness of our solutions in the cellular-only approach. These results also show that the proposed D2D-cellular scheme can reduce the required resources, while satisfying the requirements of safety applications.

K-Anonymity Location Privacy Algorithm Based on Clustering

The accuracy of user location information is inversely proportional to the user’s privacy preserving degree $k$ , and is proportional to quality of query service. In order to balance the conflict between privacy preserving security and query quality caused by the accuracy of location information, a clustering algorithm aiming at eliminating outliers based on the k-anonymity location privacy preserving model is proposed, which is used to realize the establishment of anonymous group in the anonymous model. The distribution of user in the anonymous group is optimized. The idea of replacing the user location query by the center of the anonymous group is proposed. The number of repeated queries is reduced, and the quality of query service is improved on the premise of ensuring security through the experimental analysis and comparison with other schemes.

Location error resilient geographical routing for vehicular ad‐hoc networks

The efficiency and scalability of geographical routing depend on the accuracy of location information of vehicles. Each vehicle determines its location using global positioning system (GPS) or other positioning systems. Related literature in geographical routing implicitly assumes accurate location information. However, this assumption is unrealistic considering the accuracy limitation of GPS and obstruction of signals by road side environments. The inaccurate location information results in performance degradation of geographical routing protocols in vehicular environments. In this context, this study proposes a location error resilient geographical routing (LER-GR) protocol. Rayleigh distribution-based error calculation technique is utilised for assessing error in the location of neighbouring vehicles. Kalman filter-based location prediction and correction technique is developed to predict the location of the neighbouring vehicles. The next forwarding vehicle is selected based on the least error in location information. Simulations are carried out to evaluate the performance of LER-GR in realistic environments, considering junction-based as well as real map-based road networks. The comparative performance evaluation attests the location error resilient capability of LER-GR in a vehicular environment.

An Efficient Location Verification Scheme for Static Wireless Sensor Networks.

In wireless sensor networks (WSNs), the accuracy of location information is vital to support many interesting applications. Unfortunately, sensors have difficulty in estimating their location when malicious sensors attack the location estimation process. Even though secure localization schemes have been proposed to protect location estimation process from attacks, they are not enough to eliminate the wrong location estimations in some situations. The location verification can be the solution to the situations or be the second-line defense. The problem of most of the location verifications is the explicit involvement of many sensors in the verification process and requirements, such as special hardware, a dedicated verifier and the trusted third party, which causes more communication and computation overhead. In this paper, we propose an efficient location verification scheme for static WSN called mutually-shared region-based location verification (MSRLV), which reduces those overheads by utilizing the implicit involvement of sensors and eliminating several requirements. In order to achieve this, we use the mutually-shared region between location claimant and verifier for the location verification. The analysis shows that MSRLV reduces communication overhead by 77% and computation overhead by 92% on average, when compared with the other location verification schemes, in a single sensor verification. In addition, simulation results for the verification of the whole network show that MSRLV can detect the malicious sensors by over 90% when sensors in the network have five or more neighbors.

Ethical considerations and potential threats to validity for three methods commonly used to collect geographic information in studies among people who use drugs

BackgroundAnalyses with geographic data can be used to identify “hot spots” and “health service deserts”, examine associations between proximity to services and their use, and link contextual factors with individual-level data to better understand how environmental factors influence behaviors. Technological advancements in methods for collecting this information can improve the accuracy of contextually-relevant information; however, they have outpaced the development of ethical standards and guidance, particularly for research involving populations engaging in illicit/stigmatized behaviors. Thematic analysis identified ethical considerations for collecting geographic data using different methods and the extent to which these concerns could influence study compliance and data validity. MethodsIn-depth interviews with 15 Baltimore residents (6 recruited via flyers and 9 via peer-referral) reporting recent drug use explored comfort with and ethics of three methods for collecting geographic information: (1) surveys collecting self-reported addresses/cross-streets, (2) surveys using web-based maps to find/confirm locations, and (3) geographical momentary assessments (GMA), which collect spatiotemporally referenced behavioral data. ResultsSurvey methods for collecting geographic data (i.e., addresses/cross-streets and web-based maps) were generally acceptable; however, participants raised confidentiality concerns regarding exact addresses for illicit/stigmatized behaviors. Concerns specific to GMA included burden of carrying/safeguarding phones and responding to survey prompts, confidentiality, discomfort with being tracked, and noncompliance with study procedures. Overall, many felt that confidentiality concerns could influence the accuracy of location information collected for sensitive behaviors and study compliance. ConclusionsConcerns raised by participants could result in differential study participation and/or study compliance and questionable accuracy/validity of location data for sensitive behaviors.

Reputation-based secure sensor localization in wireless sensor networks.

Location information of sensor nodes in wireless sensor networks (WSNs) is very important, for it makes information that is collected and reported by the sensor nodes spatially meaningful for applications. Since most current sensor localization schemes rely on location information that is provided by beacon nodes for the regular sensor nodes to locate themselves, the accuracy of localization depends on the accuracy of location information from the beacon nodes. Therefore, the security and reliability of the beacon nodes become critical in the localization of regular sensor nodes. In this paper, we propose a reputation-based security scheme for sensor localization to improve the security and the accuracy of sensor localization in hostile or untrusted environments. In our proposed scheme, the reputation of each beacon node is evaluated based on a reputation evaluation model so that regular sensor nodes can get credible location information from highly reputable beacon nodes to accomplish localization. We also perform a set of simulation experiments to demonstrate the effectiveness of the proposed reputation-based security scheme. And our simulation results show that the proposed security scheme can enhance the security and, hence, improve the accuracy of sensor localization in hostile or untrusted environments.

Adaptive and Dynamic Beaconing Update Mechanism for Position-based Routing Protocols

Location information to be up to-date is a fundamental operation in position-based routing protocols for mobile ad hoc networks. The location information accuracy level depends on beacon sending frequency and the neighbors’ entries timeout period. This instigates the need for dynamic updating approach with improved technique to refresh neighbors’ entries in a node’s neighbors’ matrix (NLM). This paper presents a dynamic beaconing update mechanism (DBUM). In the DBUM, the mobile nodes dynamically adjust their beacon intervals based on pre-specified tolerance deviation distance by using the dynamic fuzzy logic controller. Moreover, with DBUM, a node applies the residual link lifetime with its neighboring node to refresh their entries in its NLM. Compared with the existing algorithms, evaluation results reveal that the proposed DBUM algorithm increases the accuracy of a node’s information in its NLM matrix and achieves high packet delivery ratio at the expense of negligible overhead increment.

Active-transport walking behavior: destinations, durations, distances

This paper fills a gap in our knowledge of active-transport (AT) walking, by presenting detailed aspects of walking behavior for a medium-sized North American city. It analyzes the frequency and length of walking episodes, categorized by origins, purposes, and destinations, and also investigates distance-decay functions for major destinations. The study employs day-after recall time diary and questionnaire data from the 2007–8 Space–Time Activity Research (STAR) survey conducted in Halifax, Canada. GPS co-ordinate data enhanced the accuracy of location information, start times, and end times of the 1790 AT walking episodes, while GIS software was used to compute a shortest-path distance between the origin and destination of each episode.Home is both the most common origin and destination for AT walks, and the most common purpose is travel-to-shop rather than travel-to-work. Most walks are to non-home locations, such as retail establishments and offices. Particularly important are restaurants and bars, grocery stores, shopping centers, banks, and other services. All major destinations show strong distance-decay effects: most walks are shorter than 600m, and very few exceed 1200m. The assumption employed in the walkability literature, that one should restrict the ‘neighborhood of opportunity’ to walking destinations within 1000m of the home, is seen to be well justified. However, a planning policy focus on the walker’s home neighborhood is revealed as questionable, since the majority of walking trips do not originate from the home. The relationship between urban land-use patterns and walkability may therefore require some rethinking.

"Right Time, Right Place" Health Communication on Twitter: Value and Accuracy of Location Information

BackgroundTwitter provides various types of location data, including exact Global Positioning System (GPS) coordinates, which could be used for infoveillance and infodemiology (ie, the study and monitoring of online health information), health communication, and interventions. Despite its potential, Twitter location information is not well understood or well documented, limiting its public health utility.ObjectiveThe objective of this study was to document and describe the various types of location information available in Twitter. The different types of location data that can be ascertained from Twitter users are described. This information is key to informing future research on the availability, usability, and limitations of such location data.MethodsLocation data was gathered directly from Twitter using its application programming interface (API). The maximum tweets allowed by Twitter were gathered (1% of the total tweets) over 2 separate weeks in October and November 2011. The final dataset consisted of 23.8 million tweets from 9.5 million unique users. Frequencies for each of the location options were calculated to determine the prevalence of the various location data options by region of the world, time zone, and state within the United States. Data from the US Census Bureau were also compiled to determine population proportions in each state, and Pearson correlation coefficients were used to compare each state’s population with the number of Twitter users who enable the GPS location option.ResultsThe GPS location data could be ascertained for 2.02% of tweets and 2.70% of unique users. Using a simple text-matching approach, 17.13% of user profiles in the 4 continental US time zones were able to be used to determine the user’s city and state. Agreement between GPS data and data from the text-matching approach was high (87.69%). Furthermore, there was a significant correlation between the number of Twitter users per state and the 2010 US Census state populations (r ≥ 0.97, P < .001).ConclusionsHealth researchers exploring ways to use Twitter data for disease surveillance should be aware that the majority of tweets are not currently associated with an identifiable geographic location. Location can be identified for approximately 4 times the number of tweets using a straightforward text-matching process compared to using the GPS location information available in Twitter. Given the strong correlation between both data gathering methods, future research may consider using more qualitative approaches with higher yields, such as text mining, to acquire information about Twitter users’ geographical location.