Location-based Services Research Articles

Enhancing the Competitiveness of Subsidized Housing through VRIO, PESTEL, and Porter’s Five Forces Analysis

This study aims to identify the internal and external factors influencing the development of the subsidized housing business in Kuningan City View, Kuningan Regency. The research methods used include qualitative and quantitative approaches, with primary data obtained through observation, in-depth interviews, and questionnaires, as well as secondary data from relevant literature. The analysis was conducted using the VRIO tool for internal factors and PESTEL and Porter’s Five Forces for external factors. The research results show that the main strengths are the strategic location and high-quality after-sales service, while the main threats are intense competition and rising land prices. The conclusion is that the most effective strategy is to highlight high-quality after-sales service as an added value in promotions to maintain customer trust even as property prices rise. This study provides insights for subsidized housing developers in improving operational efficiency and competitiveness in a dynamic market. Keywords: After-Sales Service, Competitive Advantage, Kuningan City View, PESTEL Analysis, Porter’s Five Forces, Subsidized Housing, VRIO Analysis

WKNN-Based Wi-Fi Fingerprinting with Deep Distance Metric Learning via Siamese Triplet Network for Indoor Positioning

Weighted k-nearest neighbor (WKNN)-based Wi-Fi fingerprinting is popular in indoor location-based services due to its ease of implementation and low computational cost. KNN-based methods rely on distance metrics to select the nearest neighbors. However, traditional metrics often fail to capture the complexity of indoor environments and have limitations in identifying non-linear relationships. To address these issues, we propose a novel WKNN-based Wi-Fi fingerprinting method that incorporates distance metric learning. In the offline phase, our method utilizes a Siamese network with a triplet loss function to learn a meaningful distance metric from training fingerprints (FPs). This process employs a unique triplet mining strategy to handle the inherent noise in FPs. Subsequently, in the online phase, the learned metric is used to calculate the embedding distance, followed by a signal-space distance filtering step to optimally select neighbors and estimate the user’s location. The filtering step mitigates issues from an overfitted distance metric influenced by hard triplets, which could lead to incorrect neighbor selection. We evaluate the proposed method on three benchmark datasets, UJIIndoorLoc, Tampere, and UTSIndoorLoc, and compare it with four WKNN models. The results show a mean positioning error reduction of 3.55% on UJIIndoorLoc, 16.21% on Tampere, and 16.49% on UTSIndoorLoc, demonstrating enhanced positioning accuracy.

Data mining on uncertain data based on frequent probability

Uncertain data exists in many applications, such as sensor monitoring systems, location-based services, and biological databases. To handle a large amount of uncertain information, probabilistic databases have been proposed. The main research involves interpreting probabilistic databases using possible world semantics and discovering frequent patterns within probabilistic databases. Since the possible worlds in probabilistic databases grow exponentially, this mining process is a technical challenge.

PCDP-CRLPPM: a classified regional location privacy-protection model based on personalized clustering with differential privacy in data management

Abstract Location data management plays a crucial role in facilitating data collection and supporting location-based services. However, the escalating volume of transportation big data has given rise to increased concerns regarding privacy and security issues in data management, potentially posing threats to the lives and property of users. At present, there are two possible attacks in data management, namely Reverse-clustering Inference Attack and Mobile-spatiotemporal Feature Inference Attack. Additionally, the dynamic allocation of privacy budgets emerges as an NP-hard problem. To protect data privacy and maintain utility in data management, a novel protection model for location privacy information in data management, Classified Regional Location Privacy-Protection Model based on Personalized Clustering with Differential Privacy (PCDP-CRLPPM), is proposed. Firstly, a twice-clustering algorithm combined with gridding is proposed, which divides continuous locations into different clusters based on the different privacy protection needs of different users. Subsequently, these clusters are categorized into different spatiotemporal feature regions. Then, a Sensitive-priority algorithm is proposed to allocate privacy budgets adaptively for each region. Finally, a Regional-fuzzy algorithm is presented to introduce Laplacian noise into the centroids of the regions, thereby safeguarding users’ location privacy. The experimental results demonstrate that, compared to other models, PCDP-CRLPPM exhibits superior resistance against two specific attack models and achieves high levels of data utility while preserving privacy effectively.

Leveraging machine learning for the detection of structured interference in Global Navigation Satellite Systems

Radio frequency interference disrupts services offered by Global Navigation Satellite Systems (GNSS). Spoofing is the transmission of structured interference signals intended to deceive GNSS location and timing services. The identification of spoofing is vital, especially for safety-of-life aviation services, since the receiver is unaware of counterfeit signals. Although numerous spoofing detection and mitigation techniques have been developed, spoofing attacks are becoming more sophisticated, limiting most of these methods. This study explores the application of machine learning techniques for discerning authentic signals from counterfeit ones. The investigation particularly focuses on the secure code estimation and replay (SCER) spoofing attack, one of the most challenging type of spoofing attacks, ds8 scenario of the Texas Spoofing Test Battery (TEXBAT) dataset. The proposed framework uses tracking data from delay lock loop correlators as intrinsic features to train four distinct machine learning (ML) models: logistic regression, support vector machines (SVM) classifier, K-nearest neighbors (KNN), and decision tree. The models are trained employing a random six-fold cross-validation methodology. It can be observed that both logistic regression and SVM can detect spoofing with a mean F1-score of 94%. However, logistic regression provides 165dB gain in terms of time efficiency as compared to SVM and 3 better than decision tree-based classifier. These performance metrics as well as receiver operating characteristic curve analysis make logistic regression the desirable approach for identifying SCER structured interference.

Travel route recommendation with a trajectory learning model

This study addresses a critical issue in location-based services: travel route recommendation. It leverages historical trajectory data to predict the actual route on a road network from a starting point to a destination, given a specific departure time. However, capturing the latent patterns in complex trajectory data for accurate route planning presents a significant challenge. Existing route recommendation methods commonly face two major problems: first, inadequate integration of multi-source data, which fails to fully consider the potential factors affecting route choice; and second, limited capability to capture road network characteristics, which restricts the effective application of node features and negatively impacts recommendation accuracy. To address these issues, this research introduces a Trajectory Learning Model for Route Recommendation (TLMR) based on deep learning techniques. TLMR enhances the understanding of user route choice behavior in complex environments by integrating multi-source data. Moreover, by incorporating road network features, TLMR more effectively captures and utilizes the structural and dynamic information of the road network. Specifically, TLMR first employs a Position-aware Graph Neural Network to learn features of intersections from the road network, incorporating context features like weather and traffic conditions. Then, it integrates this information through neural networks to predict the next intersection. Finally, a beam search algorithm is applied to generate and recommend multiple candidate routes. Extensive experiments on four large real-world datasets demonstrate that TLMR outperforms existing methods in four key performance metrics. These results prove the effectiveness and superiority of TLMR in route recommendation.

"We're Preventing People From Going Into the Most Expensive Level of Care in Every Way that We Can": Barriers and Recommendations for Use of Local Funds.

Most older Americans do not financially qualify for Medicaid and support provided through the Older Americans Act is quite limited. In response, Ohio has generated funds for services for older adults through the use of local county tax levy programs. Ohio currently leads the nation in the utilization and support of local levy funding, generating more than US$200 million annually. This study conducted qualitative descriptive interviews with 15 locally funded programs across the state. Thematic analysis revealed that local programs encounter an array of implementation barriers, including increased state and federal eligibility requirements, staffing issues, lack of access to services in rural locations, and low reimbursement rates. These barriers are coupled with added pressure from county officials to move individuals onto state-funded programs, rather than locally funded programs. This paper also provides recommendations for how these programs can be better supported to ensure their sustainability for the future.

Home Services Recommendation System using Machine Learning for Urban Areas

The Kotlin-based on-demand home service system streamlines household tasks by offering services such as repairs, cleaning, gardening, and plumbing. In today's fast-paced world, it provides quick solutions for maintaining a clean and hygienic living environment. The Housekeeper Finder app is a prominent player in this space, utilizing advanced machine learning algorithms to deliver personalized service recommendations based on user preferences. It includes essential services like cooking, laundry, and elder care, along with a robust feedback system to improve service quality. Users can easily book services from anywhere, leveraging GPS technology to connect them with nearby providers. The "Domestic Android Application for Home Services" further enhances accessibility through location-based service requests and mapping capabilities for a seamless user experience across platforms.A s cities transition into Smart Cities, integrated information systems open new avenues for urban service recommendations, fostering liveability and sustainability. Recommendation systems analyse user behaviour to provide tailored suggestions using techniques like collaborative filtering and hybrid methods. In response to the challenges of modern work culture, the e-commerce landscape now offers easy access to essential services via mobile applications, ultimately transforming how households address their needs and promoting safer, cleaner living space.

Lightweight consortium blockchain-enabled secured Vehicular ad Hoc Network using certificateless conditional privacy-preserving authentication mechanism.

Towards the intelligent transportation systems, Location Based Service (LBS) are widely engaged in Vehicular Ad Hoc Networks (VANETs) that are becoming as significant application that change the human driving experience in today's world. LBS systems facilitate the users with intelligent services by collecting an accurate location information. Due to the frequent exchange rate of the location information in an open environment, VANETs are inherently susceptible to privacy and security attacks. In past, many schemes have been proposed to ensure the privacy and security of exchanged location information; but fail to deploy in practical VANETs. At the same time, system efficiency is compromised which is another primary requirement of VANETs. Leveraging the semi-decentralized and lightweight nature of consortium blockchain technology, and Certificateless conditional privacy protection scheme to reduce the node authentication overhead, this paper introduces Consortium Blockchain assisted Certificateless Conditional Privacy Protection scheme to address the aforementioned challenges. Additionally, the proposed scheme has ability to develop anonymous regions for a particular time stamp ensuring the location privacy of vehicles. Rigorous security analysis and experiments show the practicality and resilience to various attack models, and achieve ADP 83% with maximum malicious attacks. Comparing with existing state of the art methods, the proposed scheme exhibits the privacy improvement and low computational complexity.

A Review of Indoor Localization Methods Leveraging Smartphone Sensors and Spatial Context.

As Location-Based Services (LBSs) rapidly develop, indoor localization technology is garnering significant attention as a critical component. Smartphones have become tools for indoor localization due to their highly integrated sensors, fast-evolving computational capabilities, and widespread user adoption. With the rapid advancement of smartphones, methods for smartphone-based indoor localization have increasingly attracted attention. Although there are reviews on indoor localization, there is still a lack of systematic reviews focused on smartphone-based indoor localization methods. In particular, existing reviews have not systematically analyzed smartphone-based indoor localization methods or considered the combination of smartphone sensor data with prior knowledge of the indoor environment to enhance localization performance. In this study, through systematic retrieval and analysis, the existing research was first categorized into three types to dissect the strengths and weaknesses based on the types of data sources integrated, i.e., single sensor data sources, multi-sensor data fusion, and the combination of spatial context with sensor data. Then, four key issues are discussed and the research gaps in this field are summarized. Finally, a comprehensive conclusion is provided. This paper offers a systematic reference for research and technological applications related to smartphone-based indoor localization methods.

A Gas Cylinder Delivery Service in Oman

Gas is one of the basic requirements that must be available in every home, purchased from gas stations and street vendors. However, issues may arise in reaching gas stations or finding sellers in certain areas when gas is needed urgently. Our project aims to create a desktop application for a gas cylinder delivery service, enhancing the community's experience by facilitating the timely delivery of suitable gas cylinders. The application lists gas cylinder owners in Omani society, providing customer data like name, phone number, city, and governorate. Customers can pay using cash and choose cylinder size and price. The app also determines delivery time based on location. It facilitates communication between buyers and sellers in Arabic and English. The gas cylinder delivery service application requires login information like username, phone number, and city, then opens a location service home page. The home page features an "Order Now" button, allowing users to choose from a list of gas cylinder sellers, specifying size and price. After confirmation, customers can choose payment method, delivery location, and expected delivery time. Then the seller receives an order containing the buyer's data and a gas cylinder, and the application confirms the delivery to the customer after completion. Oman's technological advancements have made society accustomed to mobile phones, software, and applications, making the location of the gas cylinder delivery service to people easier than the traditional method of finding sellers.

Theories and Methods for Indoor Positioning Systems: A Comparative Analysis, Challenges, and Prospective Measures.

In the era of the Internet of Things (IoT), the demand for accurate positioning services has become increasingly critical, as location-based services (LBSs) depend on users' location data to deliver contextual functionalities. While the Global Positioning System (GPS) is widely regarded as the standard for outdoor localization due to its reliability and comprehensive coverage, its effectiveness in indoor positioning systems (IPSs) is limited by the inherent complexity of indoor environments. This paper examines the various measurement techniques and technological solutions that address the unique challenges posed by indoor environments. We specifically focus on three key aspects: (i) a comparative analysis of the different wireless technologies proposed for IPSs based on various methodologies, (ii) the challenges of IPSs, and (iii) forward-looking strategies for future research. In particular, we provide an in-depth evaluation of current IPSs, assessing them through multidimensional matrices that capture diverse architectural and design considerations, as well as evaluation metrics established in the literature. We further examine the challenges that impede the widespread deployment of IPSs and highlight the potential risk that these systems may not be recognized with a single, universally accepted standard method, unlike GPS for outdoor localization, which serves as the golden standard for positioning. Moreover, we outline several promising approaches that could address the existing challenges of IPSs. These include the application of transfer learning, feature engineering, data fusion, multisensory technologies, hybrid techniques, and ensemble learning methods, all of which hold the potential to significantly enhance the accuracy and reliability of IPSs. By leveraging these advanced methodologies, we aim to improve the overall performance of IPSs, thus paving the way for more robust and dependable LBSs in indoor environments.

An Approach to Checking Map Tiles by Annotation Recognition

Abstract. As the capabilities of geographic information services increase, web geographic information services represented by internet mapping have become increasingly popular, widely used in various location-based services. It is precisely because of the surge in demand that higher requirements have been put forward for the updating and quality control of internet map data. The quality inspection of geographic vector data is relatively well-developed, with many inspection rules and automated processes, but there is a clear deficiency in the quality inspection of map tiles, which mainly relies on manual visual inspection, restricting the rapid updating and release of map tiles. To solve this problem, this paper proposes an automated map tile inspection method. First, web crawling technology is used to capture map tiles, and to avoid the problem of incomplete display of annotations across tiles, adjacent tiles are all captured and merged into one image; second, OCR technology is used for image text detection and text recognition, and finally, the newly added recognition results are stored in the database and compared and confirmed with the results in the database. The experiment proves that the method proposed in this paper is feasible and may greatly improve work efficiency, especially as the updating speed of online maps accelerates, an automated tile quality inspection method can greatly improve the updating speed.

A weighted hybrid indoor positioning method based on path loss exponent estimation

With the rapid development of the Internet of Things (IoT), location-based services (LBS) have gained significant attention due to their widespread applications in daily life. This paper addresses the indoor target positioning problem in wireless sensor networks (WSNs). A weighted constrained linear least squares algorithm based on path loss exponent estimation (PLE-WCLLS) with received signal strength (RSS) and angle of arrival (AoA) hybrid measurements is proposed. To address the challenges of unknown transmission power and path loss exponent (PLE), the proposed method employs a linear least squares (LLS) estimation approach based on the ranging maximum likelihood (ML) estimation model to estimate both parameters. Subsequently, a confidence weight adjustment strategy is designed to reduce positioning errors. To handle the highly non-convex and nonlinear nature of the RSS/AoA hybrid optimization model, a linearization method based on Taylor series expansion is presented. Accurate target position estimation is achieved by solving a constrained quadratic programming problem. The effectiveness of the proposed algorithm is validated through numerical simulations and experimental evaluation in a real indoor environment. Compared to traditional positioning methods, the PLE-WCLLS algorithm improves positioning accuracy by 13.2%, and it performs exceptionally well even in scenarios with fewer sensor nodes. This gives it broad application prospects in areas such as IoT device management, personnel tracking in smart buildings, and asset localization in industrial automation.

Exploiting high-precision AoA estimation method using CSI from a single WiFi station

The accuracy of estimating the angle of arrival (AoA) using wireless fidelity (WiFi) channel state information (CSI) has been a topic of intense interest in the fields of the Internet of Things, location-based services, etc. We propose a high-precision method of AoA estimation of the direct path (DP) using WiFi CSI from a single station. It contains three stages: data preprocessing, AoA-time of flight (ToF) joint estimation for all paths, and the DP's AoA estimation. Firstly, phase calibration, linear transform, and multiple-layer filtering are accordingly conducted after CSI collection in the preprocessing stage to output the denoised CSI. Then, the AoA and ToF values for all paths are simultaneously obtained utilizing a spatial smoothing multiple signal classification (MUSIC) algorithm. Finally, the density-based spatial clustering for noise applications (DBSCAN) algorithm divides all the AoA and ToF values into several clusters. The target cluster that meets the requirements of maximum counts and minimum mean ToF is subsequently selected. The weighted centroid AoA value of the target cluster is regarded as the AoA of the DP. AoA estimation experiments using different sampling packets are conducted in a small conference room with an Intel 5300 network interface card along a straight line. The proposed method could recognize the DP with a rate of 100 percent and estimate the AoA of the DP with a mean absolute error of 2° and root mean square error of 2.82° Compared with SpotFi and hierarchical clustering–logistic regression systems, the proposed method improves AoA estimation accuracy by at least 75 %. Therefore, the proposed method could achieve a high-precision estimation of the AoA of the DP in the case 26 of different short distances.

Using Location-Based Services Data to Map and Evaluate a Community Design Intervention to Increase Bicycling, Denver, Colorado.

Using Location-Based Services Data to Map and Evaluate a Community Design Intervention to Increase Bicycling, Denver, Colorado.

Inferring the Accurate Locations of Noise Records in Mobile Phone Location Data

ABSTRACTThe positioning uncertainty of mobile phone location (MPL) data greatly influences location services and crowd behavior analysis. Although many achievements have been made in controlling its main sources (signal drift and ping‐pong effects), several problems, such as single‐oscillation patterns, insufficient position optimization, and a lack of effective evaluation, remain. In this study, a set of MPL data quality optimization methods are proposed. First, the characteristics of drift records and the oscillation patterns of ping‐pong records are discussed. The quality of the MPL data is subsequently controlled with the proposed feature‐based drift‐record detection method, complex oscillation pattern‐based ping‐pong‐record detection method, and cumulative duration weighting‐based ping‐pong‐record optimization method. These methods are applied to the MPL dataset of a major operator in Nanjing city, and the optimization effect is evaluated with GPS data collected synchronously. The results show that the proposed detection and optimization methods can effectively improve the accuracy of MPL data.

Secure and Efficient k-anonymous Trajectory Privacy Protection Method based on Differential Privacy

The Location-based service scheme have already involved in every aspect of People's daily life and are increasingly used in various industries. Aiming at the problem of the security and efficiency of mobile terminal users’ trajectory privacy protection in location-based service, we propose a k-anonymous trajectory privacy protection scheme based on differential privacy. This scheme adopts differential privacy technology to add Laplace noise to the user's trajectory many times to generate 2k noise trajectory, and then according to the trajectory similarity to determine k-1 noise users whose trajectory are similar to the user trajectory, and sets them and the real user as an anonymous user group, and then uses the anonymous user group to request LBS services. Security analysis shows that the scheme satisfies the security features of anonymity, unforgeability, and anti-counterfeiting attack. The simulation results show that the scheme not only guarantees the similarity between the false trajectory and the real trajectory but also has higher execution efficiency.

Enabling Technologies and Techniques for Floor Identification

Location information has initiated a multitude of applications such as location-based services, health care, emergency response and rescue operations, and assets tracking. A plethora of techniques and technologies have been presented to ensure enhanced location accuracy, both horizontal and vertical. Despite many surveys covering horizontal localization technologies, the literature lacks a comprehensive survey incorporating up-to-date vertical localization approaches. This article provides a detailed survey of different vertical localization techniques such as path loss models, time of arrival, received signal strength, reference signal received power, fingerprinting utilized by WiFi, radio-frequency identification (RFID), global system for mobile communications (GSM), long-term evolution (LTE), barometer, inertial measurement unit (IMU) sensors, and geomagnetic field. The article primarily aims at human localization in indoor environments using smartphones in essence. Besides the localization accuracy, the presented approaches are evaluated in terms of cost, infrastructure dependence, deployment complexity, and sensitivity. We highlight the pros and cons of these approaches and outline future research directions to enhance the accuracy to meet the future needs of floor identification standards set by the Federal Communications Commission.

Managing a Resilient Multitier Architecture for Unstable IoT Networks in Location Based-Services

Managing a Resilient Multitier Architecture for Unstable IoT Networks in Location Based-Services