Effective and Privacy-Preserving Estimation of the Density Distribution of LBS Users under Geo-Indistinguishability

With the widespread use of mobile devices, location-based services (LBSs), which provide useful services adjusted to users' locations, have become indispensable to our daily lives. However, along with several benefits, LBSs also create problems for users because to use LBSs, users are required to disclose their sensitive location information to the service providers. Hence, several studies have focused on protecting the location privacy of individual users when using LBSs. Geo-indistinguishability (Geo-I), which is based on the well-known differential privacy, has recently emerged as a de-facto privacy definition for the protection of location data in LBSs. However, LBS providers require aggregate statistics, such as user density distribution, for the purpose of improving their service quality, and deriving them accurately from the location dataset received from users is difficult owing to the data perturbation of Geo-I. Thus, in this study, we investigated two different approaches, the expectation-maximization (EM) algorithm and the deep learning based approaches, with the aim of precisely computing the density distribution of LBS users while preserving the privacy of location datasets. The evaluation results show that the deep learning approach significantly outperforms other alternatives at all privacy protection levels. Furthermore, when a low level of privacy protection is sufficient, the approach based on the EM algorithm shows performance results similar to those of the deep learning solution. Thus, it can be used instead of a deep learning approach, particularly when training datasets are not available.

A Temporal-Context-Aware Approach for Individual Human Mobility Inference Based on Sparse Trajectory Data

Inferring individual human mobility at a given time is not only beneficial for personalized location-based services, but also crucial for trajectory tracking of the confirmed cases in the context of the COVID-19 pandemic. However, individual generated trajectory data using mobile Apps is characterized by implicit feedback, which means only a few individual-location interactions can be observed. Existing studies based on such sparse trajectory data are not sufficient to infer individual's missing mobility in his/her historical trajectory and further predict individual's future mobility given a specific time. To address this concern, in this paper, we propose a temporal-context-aware approach that incorporates multiple factors to model the time sensitive individual-location interactions in a bottom-up way. Based on the idea of feature fusion, the driving effect of heterogeneous information such as time, space, category and sentiment on individual's mobile behavior is gradually strengthened, so that the temporal context when a check-in occurs can be accurately depicted. We leverage Bayesian Personalized Ranking (BPR) to optimize the model, where a novel negative sampling method is employed to alleviate data sparseness. Based on three real-world datasets, we evaluate the proposed approach with regard to two different tasks, namely, missing mobility inference and future mobility prediction at a given time. The empirical results encouragingly demonstrate that our approach outperforms multiple baselines in terms of two evaluation metrics, i.e., accuracy and average percentile rank. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Shop-Easy: An Online Shopping Framework Emphasizing Vocal for Local Using Location and Review Based Services

Nowadays the trend of online shopping is increasing day by day. A huge transformation and usage of online applications for shopping day-to-day essential items have been experienced during Covid-era. To further facilitate the online shopping industries, the local shopkeepers, and the customers, in this paper an efficient and organized online shopping platform is presented, which can be used by local shopkeepers to showcase their products digitally and by customers to easily manage and organize their day-to-day shopping-related tasks. With this proposed platform, the local regional shopkeepers can introduce their shops and products to nearby residents from time to time which makes their shop to be known by everyone, selling their products in an easier way and helps them in maintaining their ledger on a daily basis. Further, the proposed system will assist the customers by helping them in keeping track of their shopping lists timely and by choosing the best shop according to the location, item, and review-based notifications and reminders. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Examining the Human Activity-Intensity Change at Different Stages of the COVID-19 Pandemic across Chinese Working, Residential and Entertainment Areas.

The COVID-19 pandemic has already resulted in more than 6 million deaths worldwide as of December 2022. The COVID-19 has also been greatly affecting the activity of the human population in China and the world. It remains unclear how the human activity-intensity changes have been affected by the COVID-19 spread in China at its different stages along with the lockdown and relaxation policies. We used four days of Location-based services data from Tencent across China to capture the real-time changes in human activity intensity in three stages of COVID-19-namely, during the lockdown, at the first stage of work resuming and at the stage of total work resuming-and observed the changes in different land use categories. We applied the mean decrease Gini (MDG) approach in random forest to examine how these changes are influenced by land attributes, relying on the CART algorithm in Python. This approach was also compared with Geographically Weighted Regression (GWR). Our analysis revealed that the human activity intensity decreased by 22-35%, 9-16% and 6-15%, respectively, in relation to the normal conditions before the spread of COVID-19 during the three periods. The human activity intensity associated with commercial sites, sports facilities/gyms and tourism experienced the relatively largest contraction during the lockdown. During the relaxations of restrictions, government institutions showed a 13.89% rise in intensity at the first stage of work resuming, which was the highest rate among all the working sectors. Furthermore, the GDP and road junction density were more influenced by the change in human activity intensity for all land use categories. The bus stop density was importantly associated with mixed-use land recovery during the relaxing stages, while the coefficient of density of population in entertainment land were relatively higher at these two stages. This study aims to provide additional support to investigate the human activity changes due to the spread of COVID-19 at different stages across different sectors.

COVID-LPS: Location-Protected Services for COVID-19 Prevention

The COVID-19 pandemic has caused not only worldwide health problems but also economic damage. Numerous researchers and intuitions have attempted to visualize confirmed COVID-19 cases with maps to provide timely information to users (e.g., warnings upon entry of crowded areas) and prevent the spread of COVID-19. However, such systems are limited by their poor protection of private information because they must collect sensitive information, such as the locations of individuals. We propose a practical method of obtaining a distribution of users while anonymizing their location data that can be used in location-based services for the prevention of the spread of COVID-19. Generalization and local differential privacy are used to guarantee user and data anonymity while maintaining high data utility and accuracy. To our knowledge, COVID-LPS is not only the first COVID-19 tracing system in Taiwan but also the first system to visualize user distributions for location-based services while protecting user privacy through generalization and local differential privacy. © 2022 IEEE.

Location Based Services for Remote Education and Health Institutes Using Sum-Difference Signal Patterns

This paper presents a simple approach to providing locationbased services (LBSs) for the remotely located educational and health institutes on demand, particularly in emergency situations i.e., COVID-19, using an antenna array. The proposed approach consists of the sum and difference patterns of the signal obtained from the antenna array at the base station of a wireless network. It provides the location of the targeting institute i.e., education and health, in terms of the angle-of-arrival (AoA) which can be used to steer the radiation beam in the targeted direction in order to provide the desired services i.e., emergency wireless communication link. In this way, fast and high bandwidth-based communication and networking can be possible for the mentioned organizations. We show antenna array design and location finding results in this paper. The obtained results in terms of antenna parameters and AoA show that the proposed approach is efficient, less complicated, and can be implemented in the next-generation wireless networks i.e., AI-enabled IoT and 6G systems, particularly for the educational and health institutes. © 2022,International Journal of Interactive Mobile Technologies. All Rights Reserved.

Autonomous Navigation of an AMR using Deep Reinforcement Learning in a Warehouse Environment

Mobile robots have been used in warehouses worldwide as a means for distribution of goods and gained demand after the Covid19 labor issue. This paper proposes an Autonomous Mobile Robot (AMR) to navigate in a warehouse environment to its target location using LIDAR. The method used to solve this problem is a deep reinforcement learning algorithm called deep Q-network (DQN) to detect and avoid obstacles and reach the target location. DQN is used as it is desired for solving complex tasks. Training of the DQN algorithm is carried out in ROS Gazebo environment using LIDAR-based robot model. The LIDAR sensor detects the obstacles and the odometer sensor helps to find the distance between the target location are used as inputs for training the algorithm and optimal actions are taken based on the two inputs. A reward policy is awarded when an obstacle is avoided and reaches the target location. The results show that mobile robot can successfully navigate in an unknown environment through simulation and real life. © 2022 IEEE.

A Mean Optimization Filter to Improve Bluetooth AoA Indoor Positioning Accuracy for Ship Environments

Currently, the most effective way to reduce transmission of COVID-19 is to differentiate between close contacts. Location points of close contact are essential for differentiation. As a major mode of transportation, ships provide a vehicle for virus transmission. Timely detection location of close contacts inside a ship can prevent the spread of viruses. Location-based services can be provided for ship passengers. Bluetooth is widely available in many wearable devices. The Bluetooth 5.1 angle of arrival (AoA) indoor positioning algorithms can provide a certain indoor positioning accuracy for ship passengers. The two most essential parameters in Bluetooth 5.1 AoA indoor positioning are elevation angle and azimuth angle. Elevation and azimuth are often not accurate enough due to noise, which increases indoor positioning errors. As a result, this paper proposes a mean optimization filter for ship environments, which combines the box plot method to improve Bluetooth 5.1 AoA indoor positioning accuracy, with an RMSE of 0.34 m. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Information System Analysis and Design for Mobile-Based Homain Applications

Builders are workers with specific expertise in development. Community needs for workers with special skills to repair parts of homes remain difficult to find due to space and space constraints. In the current era of the Covid-19 pandemic, there are many people like construction workers who want to find work, and many people who need construction workers to repair parts of their homes but are not sure if construction workers are performing well or not well recruited by them. In this study, the authors propose an application to understand how construction workers can be found through an online application, and how potential clients can find suitable and trustworthy contractors. Smartphone users only need a smartphone and an internet connection to access this HoMain app. The purpose of this study is to design an online application for a mobile-based construction service ordering system to support the needs of the community and builders during construction work. With this app, people can easily order construction services based on online location. The HoMain application development method adopts the Rapid Application method, and the application development is relatively fast and efficient. © 2022 IEEE.

Towards Post-pandemic Occupancy Patterns: Investigating Building Occupancy in New York City

Urban-scale energy simulation relies on the understanding of occupants' presence in buildings and consequently in cities. Therefore, occupancy profiles (i.e., the relative number of occupants in a specific hour of the day) are usually used in the energy simulation on the city level. However, available occupancy standard profiles are incapable of considering the dynamic nature of occupancy schedules and any changes that occurred due to contextual changes (such as the dramatic increase in remote working last year). Therefore, the need for a scalable method to generate dynamic occupancy profiles for buildings is crucial. Moreover, the targeted method should allow for tracking the changes that occur in occupancy profiles due to external disruption such as pandemics. In this context, this study aims at using the emerging mobile positioning data to generate context-specific data-driven occupancy profiles for commercial and institutional buildings in New York City. The generated profiles were then compared versus ASHRAE standard profiles for each building category. Then, the occupancy profiles were clustered for each building category, using K-means clustering algorithm. Finally, the effect of COVID-19 pandemic on the peak points and shape of occupancy profiles was investigated. The results showed a significant difference between the data-driven and ASHRAE standard profiles. Additionally, a considerable variation in the shape and peak hours of the generated occupancy profile clusters was detected for some building categories. These results can be used to improve the accuracy of the urban-scale simulation models. Furthermore, they can provide a more precise evaluation of the occupant's schedules and consequently the urban scale energy consumption before field implementation of the operational strategies.

RESAT: A Utility-Aware Incentive Mechanism Based Distributed Spatial Cloaking

Distributed Spatial Cloaking () enables users to enjoy precise Location-Based Service (LBS) with location privacy-preserving. An incentive mechanism is necessary to encourage users to cooperate. However, due to the inappropriate design of incentive mechanisms, the existing works cause low user benefits and fail to encourage users, ruining the expected incentive effect. Moreover, introducing a third party to manage users’information also causes the existing works to disclose users’privacy and be unpractical. To address these issues, we propose a utility-awaRe incEntive mechanism based diStributed spATial cloaking (RESAT). By the idea of utility theory and optimization theory, RESAT devises basic and extended incentive mechanisms. The two mechanisms for assuming that all users are honest and that malicious users provide unreasonable locations. RESAT proposes an incentive mechanism-based cloaking cooperation without a third party, incorporating the developed mechanisms based on the blind signature. Theoretical analysis indicates that RESAT achieves incentive compatibility and is secure. Extensive experiments on the real dataset show that compared with the existing works, RESAT enables 1 time more users to cooperate at best while eliminating the malicious behaviors that provide unreasonable locations. The required construction time delay is limited. IEEE

LOCATION-BASED SERVICES USING WEB-GIS BY AN ANDROID PLATFORM TO IMPROVE STUDENTS’ NAVIGATION DURING COVID-19

Educational institutions seek to find optimal ways to provide educational services with the need for alternative solutions due to the requirements of the Covid-19 pandemic. The current study proposed a system that aims to identify the most critical new technologies built on Web-GIS for data analysis and associated information retrieval. It presents an algorithm to analyze the spatial information frequented by the user on the campus and determine the services that target the user based on predetermined spatial information. Provide a system based on integrating location-based services (LBS) using Web-GIS through the Android platform to help campus attendees take full advantage of services information granted to them in their whereabouts. The system employs the rule extraction algorithm to give a recommendations list (using extracted rules with confidence=100% and support= 0.7 to achieve high accuracy for the most points of interest (POI) based on the user's preferences. The proposed system evaluates the given recommendation and the application usage to produce satisfactory results. The average overall F-measure and accuracies are 94.8 % and 94.2, respectively. © 2022 Little Lion Scientific

How Advanced Technological Approaches Are Reshaping Sustainable Social Media Crisis Management and Communication: A Systematic Review

The end goal of technological advancement used in crisis response and recovery is to prevent, reduce or mitigate the impact of a crisis, thereby enhancing sustainable recovery. Advanced technological approaches such as social media, machine learning (ML), social network analysis (SNA), and big data are vital to a sustainable crisis management decisions and communication. This study selects 28 articles via a systematic process that focuses on ML, SNA, and related technological tools to understand how these tools are shaping crisis management and decision making. The analysis shows the significance of these tools in advancing sustainable crisis management to support decision making, information management, communication, collaboration and cooperation, location-based services, community resilience, situational awareness, and social position. Moreover, the findings noted that managing diverse outreach information and communication is increasingly essential. In addition, the study indicates why big data and language, cross-platform support, and dataset lacking are emerging concerns for sustainable crisis management. Finally, the study contributes to how advanced technological solutions effectively affect crisis response, communication, decision making, and overall crisis management.

PBeL—A Novel Problem-Based (e-)Learning for Geomatics Students

Due to the COVID-19 pandemic, distance learning had to be increasingly implemented at universities, and more e-learning formats had to be applied. The LBS2ITS project carried out under the lead of the Department of Geodesy and Geoinformation at TU Wien (TUW), Austria, came at the right time for these tasks. Education in Location-Based Services (LBS) is put to a new level including interactive e-learning and Problem-Based Learning (PBL) pedagogy. In the courses modernization, special attention is paid to the development and/or update of the courses to be implemented with these two pedagogic forms. Thus, teaching with an emphasis on learning outcomes is a central theme in the LBS2ITS project. To achieve this goal, the active verbs used in updated Bloom’s taxonomy for teaching on learning outcomes, i.e., remembering, understanding, applying, analyzing, evaluating, and creating, are applied to achieve the six levels of thinking and the active nature of learning. LBS2ITS will build a fully immersive and integrated LBS teaching and learning experience with the LBS application of Intelligent Transportation Systems (ITS) in mind. The outcome will be an innovative digital learning environment supporting synthetic and real-world PBL learning experiences. In the course of the project, a workshop for introduction of these new developments was held. This paper provides an insight into the results and experiences from this workshop. As e-learning and PBL must be combined and integrated nowadays, the new term PBeL (Problem-Based e-Learning) is proposed and introduced in this paper. The development of this approach and background information on the theory and the LBS2ITS project are presented.

EFFECT ASSESSMENT OF LARGE-SCALE EVENTS VIA SPATIOTEMPORAL APPROACH

Together with rapid development of location-based services and big-data platforms especially in urban areas, huge amount of spatiotemporal data are collected without properly used;on the other hand, state-of-the-art quantitative policy effect assessment techniques usually require panel data as input. To solve both issues, this paper follows the following approach: obtaining panel data by aggregating spatiotemporal data and feeding them to the effect assessment module. With the help of high-performance computing techniques which are able to deal with huge amount of data, we build framework Aggr-analysis which applies clustering algorithms to shrink the raw data set and find associations between different data sets via co-location analysis. Finally, we prove the effectiveness by an example: analysis of resident activities during the COVID-19 Pandemic. We apply Aggr-analysis to process the share-bike usage data and POI (Point Of Interest) data in Beijing, then obtain the panel data required by DID (Difference-in-Differences) method. Supplemented with environmental data, we conclude the net effect of the COVID-19 breakout on society and economy - the pandemic has reduced the overall resident mobility by 64.8% within two months.

A Survey of Real-time Health Care Tracking System for Post Covid Patients

The outbreak of Covid-19 throughout the world caught the whole population unprepared And one of the most common problems includes the availability of medicines. People in need weren't able to get their medical supplies even after visiting number of shops. Hence a location service that helps find the nearest medical store that will display required medicines can solve this problem. This service is widely sourced for everyone to use. The data from the medical stores displays the list of medicines and their availability. A flexible reservation system is implemented to book medicines for immediate purchase and can be collected within few minutes. A location reminder feature is provided in cases where the medicines with less availability are in your vicinity. In this paper, main research on review topics that deals with reservation systems, location finder, online monitoring of medicinal drugs, algorithms to find the shortest distance between the source and the destination is summarized. © 2022 IEEE.

Preserving Location Privacy in the IoT against Advanced Attacks using Deep Learning

Location-based services (LBSs) have received a significant amount of recent attention from the research community due to their valuable benefits in various aspects of society. In addition, the dependency on LBS in the performance of daily tasks has increased dramatically, especially after the spread of the COVID-19 pandemic. LBS users use their real location to build LBS queries to take benefits. This makes location privacy vulnerable to attacks. The privacy issue is accentuated if the attacker is an LBS provider since all information about users is accessible. Moreover, the attacker can apply advanced attacks, such as map matching and semantic location attacks. In response to these issues, this work employs artificial intelligence to build a robust defense against advanced location privacy attacks. The key idea behind protecting the location privacy of LBS users is to generate smart dummy locations. Smart dummy locations are false locations with the same query probability as the real location, but they are far from both the real location and each other. Relying on the previous two conditions, the deep-learning-based intelligent finder ensures a high level of location privacy protection against advanced attacks. The attacker cannot recognize the dummies from the real location and cannot isolate the real location by a filtering process. In terms of entropy (the privacy protection metric), accuracy (the deep learning metric), and total execution time (the performance metric) and compared to the well-known DDA and BDA systems, the proposed system shows better results, where entropy = 15.9, accuracy = 9.9, and total execution time = 17 sec. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

THE SIXTH INTERNATIONAL CONFERENCE ON SMART CITY APPLICATIONS: PREFACE

This Conference Proceedings volume contains the written versions of the contributions presented during the 6th International Conference on Smart City Applications.The event had been planned to organized in Safranbolu Campus of Karabuk University, Turkey. Then, it has been converted to the online conference because of the Covid-19 situation. It took place with the motto of “Virtual Safranbolu” by inspiring historical UNESCO Heritage city Safranbolu, on October 27–29, 2021. The conference provided a setting for discussing recent developments in a wide variety of topics including Geo-Smart Information Systems, Smart Cities, 3D City Modeling and Visualization, Smart Building and Home Automation, Smart Environment and Smart Agriculture, Location Based Services, GeoInformation for Mobile, Wearable Technologies and Wireless Sensor Networks, Building Information Modeling, Virtual and Augmented Reality, Big Data and Urban Data Analytics, Smart Healthcare, Smart Economy and Digital Business, Smart Education and Intelligent Learning System, and etc.The event has been a good opportunity for the more than 400 participants coming from 43 countries of the world to present and discuss topics in their respective research areas. In addition, five keynote speakers presented latest achievements on their fields;Domingos Santos “Smart Cities Strategies: Critical Sucess Factors”, Mohsen Kalantari Soltanieh “Smart buildings to Smart cities – The role of BIM and GIS integration”, Ksentini Adlen, “Zero Touch Management and Orchestration of Network Slices in 5G and Beyond Networks”, Bakr M.Aly Ahmed, “Smart Sustainable Urbanism”, Yusuf Arayıcı, “Design for Energy:Prosumer Buildings”.The 86 papers that were selected as a result of review process and presented during the conference were accepted for the final publication in the ISPRS Archives.We would like to thank all participants, organizing and scientific committee members, and session chairs for their contributions to the conference program and these Proceedings.