User Location Research Articles

TeleAware Robot: Designing Awareness-augmented Telepresence Robot for Remote Collaborative Locomotion

Telepresence robots can be used to support users to navigate an environment remotely and share the visiting experience with their social partners. Although such systems allow users to see and hear the remote environment and communicate with their partners via live video feed, this does not provide enough awareness of the environment and their remote partner's activities. In this paper, we introduce an awareness framework for collaborative locomotion in scenarios of onsite and remote users visiting a place together. From an observational study of small groups of people visiting exhibitions, we derived four design goals for enhancing the environmental and social awareness between social partners, and developed a set of awareness-enhancing techniques to add to a standard telepresence robot - named TeleAware robot. Through a controlled experiment simulating a guided exhibition visiting task, TeleAware robot showed the ability to lower the workload, facilitate closer social proximity, and improve mutual awareness and social presence compared with the standard one. We discuss the impact of mobility and roles of local and remote users, and provide insights for the future design of awareness-enhancing telepresence robot systems that facilitate collaborative locomotion.

Privacy-preserving Cross-domain Recommendation with Federated Graph Learning

As people inevitably interact with items across multiple domains or various platforms, cross-domain recommendation (CDR) has gained increasing attention. However, the rising privacy concerns limit the practical applications of existing CDR models, since they assume that full or partial data are accessible among different domains. Recent studies on privacy-aware CDR models neglect the heterogeneity from multiple-domain data and fail to achieve consistent improvements in cross-domain recommendation; thus, it remains a challenging task to conduct effective CDR in a privacy-preserving way. In this article, we propose a novel, as far as we know, federated graph learning approach for Privacy-Preserving Cross-Domain Recommendation (PPCDR) to capture users’ preferences based on distributed multi-domain data and improve recommendation performance for all domains without privacy leakage. The main idea of PPCDR is to model both global preference among multiple domains and local preference at a specific domain for a given user, which characterizes the user’s shared and domain-specific tastes toward the items for interaction. Specifically, in the private update process of PPCDR, we design a graph transfer module for each domain to fuse global and local user preferences and update them based on local domain data. In the federated update process, through applying the local differential privacy technique for privacy-preserving, we collaboratively learn global user preferences based on multi-domain data and adapt these global preferences to heterogeneous domain data via personalized aggregation. In this way, PPCDR can effectively approximate the multi-domain training process that directly shares local interaction data in a privacy-preserving way. Extensive experiments on three CDR datasets demonstrate that PPCDR consistently outperforms competitive single- and cross-domain baselines and effectively protects domain privacy.

Higher-order embedded learning for heterogeneous information networks and adaptive POI recommendation

Traditional POI recommendation results tend to be homogeneous due to data sparsity, and cannot adapt to the dynamic switching of user locations. Regarding the issues, we propose an adaptive Point of Interest (POI) recommendation algorithm that integrates the user social attributes and POI natural attributes. On the one hand, to fully utilize the complex node interactions and semantic information of heterogeneous networks, an effective higher-order embedding learning framework based on multi-head attention mechanism and multi-task shared features, called UI-HEHo, is presented. It divides hierarchical embedding learning into inter-order and intra-order aggregation to learn richer and more comprehensive representations. Based on UI-HEHo framework, a POI-user social attribute model is constructed to obtain the relational distance between interest points. On the other hand, a POI-natural attributes model based on user preference activity range prediction is constructed to further draw the influence between interest points. Furthermore, a tensor-based dynamic update scheme is designed to reduce the high cost of model updates. Finally, the Radviz graph is utilized to obtain the final recommendation results. Experimental results demonstrate our proposed approach significantly improves the POI recommendation performance, especially achieves 20.7% and 23.8% improvement on the Foursquare and Yelp datasets in terms of the newly defined diversity and novelty metrics.

Development of an Android Application for Smart Parking System

Now-a-days, vehicle parking has become a major problem in urban areas with the shortage of parking spaces. It is very difficult and frustrating to find a parking space in most metropolitan areas, especially during the rush hours to solve this problem. The project entitled smart parking system using android application, the major motivation of this project is to reduce the traffic congestion in roads, multi storied buildings and malls due to unavailability of parking spaces. The proposed application provides an easy way for reservation of parking slot. In this application user can view various parking areas and also view whether space is available or not. The project displays the nearest empty slot if present with respect to user location. This system gives a further feature of cancelling the bookings. Users may even make price online primarily based totally at the time taken for the reserved area the quantity might be calculated and the person can make charge. This project provides a good insight into the guidance, monitoring and reservations components of the smart parking and directions to the future development.

A privacy-preserving location data collection framework for intelligent systems in edge computing

With the rise of smart city applications, the accessibility of users’ location data by smart devices has increased significantly. However, this poses a privacy concern as attackers can deduce personal information from the raw location data. In this paper, we propose a framework to collect user location data while ensuring local differential privacy (LDP) in the last-mile delivery system of Unmanned Aerial Vehicles (UAVs) within an edge computing environment. Firstly, we obtain the user location distribution Quad-tree by employing a region partitioning method based on Quad-tree retrieval in the specified data collection area. Next, the user location matrix is retrieved from the obtained Quad-tree, and we perturb the user location data using an LDP perturbation scheme on the location matrix. Finally, the collected data is aggregated using blockchain to evaluate the utility of the dataset from various regions. Furthermore, to validate the effectiveness of our framework in a real-world scenario, we conduct extensive simulations using datasets from multiple cities with varying urban densities and mobility patterns. These simulations not only demonstrate the scalability of our approach but also showcase its adaptability to different urban environments and delivery demands. Finally, our research opens new avenues for future work, including the exploration of more sophisticated LDP mechanisms that can offer higher levels of privacy without significantly compromising the quality of service. Additionally, the integration of emerging technologies such as 5G and beyond in the edge computing environment could further enhance the efficiency and reliability of UAV-based delivery systems, while also offering new challenges and opportunities for privacy-preserving data collection and analysis.

Incorporating a Hands-On Device-Based Activity in a Human Factors Biomedical Engineering Course in Sub-Saharan Africa

A challenge in building the biomedical engineering human factors course at Malawi University of Business and Applied Sciences was integrating meaningful direct experiences with medical products. The instructor also noticed a significant gap between the topics in the course and their surrounding clinical context, a low-income setting. Recognizing that devices should be designed and evaluated in the context of the local users’ needs and situations, new hands-on modules were created and implemented in this BME human factors course. Students were asked to critically evaluate and make recommendations to improve the human factors aspects of the software and hardware of the IMPALA, a vital signs monitoring device developed for use in Malawi. Engaging with this medical device, students observed and understood many issues discussed in human factors, including the design of ports, controls, and other user interfaces. The collaboration between the course and the IMPALA project harnessed the local expertise of students to improve the design of a new patient monitoring system. Thus, the IMPALA project itself benefited from this collaboration. Second, students greatly benefited from applying the class concepts to the IMPALA. Students were engaged far more during the interactive components than during the lecture components. Many students successfully translated their knowledge on human factors to their final-year design project.

CMFog[formula omitted]: Proactive content migration for multi-level fog computing

The popularization of Fog Computing has provided the foundation for a computational environment better suited to applications demanding low communication latency. However, Fog environments has limited resources and restricted coverage areas, besides the user mobility that needs continuous migrations to maintain accessible and nearby content. To enable applications to harness the low latency offered by Fog, it is crucial to develop migration strategies capable of addressing the complexities of the Fog environment while ensuring content availability regardless of user location. This work proposes CMFogV, a proactive content migration strategy that leverages mobility prediction in a multi-level fog. Our results show that CMFogV is able to provide enhanced flexibility in the migration decision process across a wide diversity of scenario.

Optimal location of base stations for cellular mobile network considering changes in users locations

In the past few decades, the demand for cellular networks has grown tremendously due to the growing demand for data. However, the vast majority of the users are dynamic since they use their smart phones and tablets to connect to these networks when they move to one location to another to attend sport games or other events for example. The location of these events might not cover the large demand. In this paper, we address the classical problem of locating base stations for a mobile cellular network to serve mobile users in a given geographical area considering the users' movements within the network. We developed a mixed integer programming model to provide the optimal location of base stations at different time periods with the network's minimum total cost (i.e., installation and operation of base stations). This paper considers the COST-231-Walfisch-Ikegami propagation model for the path loss calculation with a non-line-of-site situation. To demonstrate the capability of the proposed model, it is experimented with a real-world problem on the Red Sea coast of Saudi Arabia. The model was capable of finding the optimal base station locations with minimum installation and operational costs considering the capacity and quality of service constraints.

Genomic, climatic, and cultural diversity of maize landraces from the Himalayan Kingdom of Bhutan

Societal Impact StatementBhutan is an ancient kingdom in the Himalayan range and one of the most rugged, geodiverse, and mountainous agricultural countries in the world. Historically secluded and geographically isolated, Bhutan is a hotspot for Himalayan agrobiodiversity where small‐scale agriculture supports the livelihoods of a large share of the resident population. Here, Bhutanese maize agrobiodiversity is explored to unlock its adaptation potential using genomics and participatory variety selection in combination with climate research. We show that Bhutanese traditional farmers maintain a wealth of diversity that may support the sustainable intensification of maize cropping in the Himalayas and beyond.Summary Bhutan, an ancient kingdom enshrouded in the Himalayas, hosts largely untapped agrobiodiversity that may harness genetic variation useful for adaptation to local climates and user needs. Here, we genotyped‐by‐sequencing 351 pooled samples of local maize (Zea mays L.) landraces, the entire collection of the Bhutan National Gene Bank, comparing their genomic diversity with maize from other countries in the Himalayan range. We reconstructed the adaptation of Bhutanese maize to historical and projected climates, identifying areas of future maladaptation. We then run a common garden experiment involving local smallholder farmers in a participatory evaluation of landraces' performance, aiming at the identification of quantitative trait nucleotides (QTNs) contributing to adaptation, performance, and farmers' choice. We found that Bhutanese maize agrobiodiversity is unique in the Himalayan range, and a locus on Chromosome 5 supports the differentiation of three distinct genetic clusters. We found that a portion of current genomic diversity can be associated with the Bhutanese landscape and that maize cultivation in the southwest of the country may be negatively impacted by projected climates. We also found that Bhutanese maize agrobiodiversity is large and may contribute to adaptation and improvement. A genome‐wide association study identified 117 QTNs for climatic adaptation, agronomic performance, and farmers' preferences. Our results show that Bhutanese maize landraces are a unique source of genetic agrobiodiversity for local adaptation. We found that the integration of genomics, climate science, and participatory methods can speed up the identification of genetic factors contributing to the sustainable intensification of maize cultivation in the Himalayas and beyond.

Navigating Event Landscapes: A Study of GPS-Based Event Discovery Systems

Abstract: This paper proposes a system for locating events that are around the user by using the Global Positioning System. Globally, GPS has transformed location-based services and navigation, radically changing our understanding of and use for geographic positioning. These days, GPS is everywhere. It's been incorporated into cars, wearable technology, cellphones, and a host of other products. To identify new trends of substantial correlations between geographic regions and events, the proposed system examines the correlation between user locations and event locations in our research. In our research, we aim to explore the evolving landscape of event discovery and participation by harnessing the ubiquitous presence of the Global Positioning System (GPS). With GPS technology seamlessly integrated into various devices and applications, including cars, wearable technology, and smartphones, users have come to rely on its accuracy and accessibility for navigating their surroundings and accessing location-based services. By leveraging the wealth of location data provided by GPS, our proposed system seeks to identify emerging trends and patterns of substantial correlations between user locations and event locations. Through in-depth analysis and correlation studies, we aim to uncover insights that can inform the development of more effective and personalized event discovery solutions, ultimately enhancing the user experience and engagement with local events and activities.

Indoor Navigation in Facilities with Repetitive Structures.

Most facilities are structured in a repetitive manner. In this paper, we propose an algorithm and its partial implementation for a cellular guide in such facilities without GPS use. The complete system is based on iBeacons-like components, which operate on BLE technology, and their integration into a navigation application. We assume that the user's location is determined with sufficient accuracy. Our main goal revolves around leveraging the repetitive structure of the given facility to optimize navigation in terms of storage requirements, energy efficiency in the cellular device, algorithmic complexity, and other aspects. To the best of our knowledge, there is no prior experience in addressing this specific aim. In order to provide high performance in real time, we rely on optimal saving and the use of pre-calculated and stored navigation sub-routes. Our implementation seamlessly integrates iBeacon communications, a pre-defined indoor map, diverse data structures for efficient information storage, and a user interface, all working cohesively under a single supervision. Each module can be considered, developed, and improved independently. The approach is mainly directed to places, such as passenger ships, hotels, colleges, and so on. Because of the fact that there are "replicated" parts on different floors, stored once and used for multiple routes, we reduce the amount of information that must be stored, thus helping to reduce memory usage and as a result, yielding a better running time and energy consumption.

Tabib: Chatbot for Healthcare Automation with Audio Assistance using Artificial Intelligence

To live a happy and healthy life, people must prioritize their health care. It's not always easy to contact a doctor when you have a health issue, though. The idea is to build a chatbot for healthcare that functions as a virtual assistant using artificial intelligence. The healthcare chatbot system's medical database will be trained using neural networks and natural language processing. The purpose of the healthcare chatbot was to increase access to medical information while reducing overall healthcare costs. There are certain chatbots that function as medical encyclopedias; educating patients about their conditions and helping them take better care of themselves. In the proposed project, a chatbot system converses with patients about their health issues via voice-to-voice and text- to-text and provides a personalized diagnosis based on their symptoms. In addition to text assistance, this chatbot also offers an audio assistance feature that makes it easier for disabled (visually impaired) patients to access the system. This feature eliminates the restrictions that visually impaired patients face with the currently available text-enabled healthcare chat bots. We used the user's current location to locate the specialists nearby and present the information about them. Users can schedule appointments with various physicians in their area. As a result, people will be aware of their health status, receive rapid treatment, and be informed about the best medicines.

The Impact of Service Quality, Prices and Consumer Trust on Interest In Goods Deliveri Services (Case Study of Local Expedition Company Service Users in Pontianak City)

This research aims to determine service quality, price and costumer trust in interest in goods delivery services at local expedition companies in the city of Pontianak. The population in this study were all existing users of local expedition company services and the sample was determined using the Purposive Sampling method. The form of research is a form of causality research with data collection techniques using questionnaires. The analytical method used in this research is the rating scale method which is processed on a computer using the SPSS version 25 program. Data testing is carried out using validity tests, reliability tests, classical assumption tests (normality, multicollinearity, heteroscedasticity and autocorrelation), correlation tests, coefficient tests determination, multiple linear regression test, F test and t test. Based on the test results (Test F), it shows that the variables of service quality, price and customer trust influence consumer buying interest. Partial test results (t test) show that service quality does not influence costumer buying interest, but price and costumer trust influence interest in using the company's goods delivery services.

Individual and community empowerment improve resource users' perceptions of community-based conservation effectiveness in Kenya and Tanzania.

Community-based conservation has been increasingly recognized as critical to achieve both conservation and socio-economic development goals worldwide. However, the long-term sustainability of community-based conservation programs is dependent on a broadly shared perception among community members that management actions are achieving their stated goals. Thus, understanding the underlying factors driving differences in perceptions of management effectiveness can help managers prioritize the processes and outcomes most valued by resource users and thereby promote sustained support for conservation efforts. Here, we utilize large-scale interview survey data and machine learning to identify the factors most strongly associated with differences in perceived management effectiveness between resource users engaged in marine community-based conservation programs in Kenya and Tanzania. Perceptions of management effectiveness were generally favorable in both countries, and the most important predictors of positive perceptions were associated with community and individual empowerment in resource management and use, but within disparate focal domains. Improved perceptions of management effectiveness in Kenya were closely related to increases in women's empowerment in community-based conservation programs, while inclusionary and transparent governance structures were the most important factors driving improved perceptions in Tanzania. Additionally, the strongest predictors of differences between individuals in both countries often interacted synergistically to produce even higher rates of perceived effectiveness. These findings can help future initiatives in the region tailor management to match community-level priorities and emphasize the need for community-based conservation programs to understand local context to ensure that metrics of "success" are aligned with the needs and desires of local resource users.

Threat Detection and Tracking System

This device provides a platform to ensure user safety by generating the user’s location and alerting authorities regarding user’s location. In today’s era safety is a matter of prime concern as the crimes are increasing and it has become dangerous to roam freely alone especially in deserted streets and thus, we have designed this project which work on principle of a person’s safety by taking into account their location monitored by fingerprint sensor also there is a GPS module that determines the location coordinates and GSM module that is use to send emergency message in pre-stored contacts. Detection of the input from the user is performed with the help of a fingerprint sensor at every minute, a distress signal is sent to authorized numbers when detection does not occur within time. The GPS track the location of user and send that information to authorized number and GSM is responsible for alerting authorized personnel by sending SMS on the mobile number with the location information of the user. Key word: Threat Detection, Tracking, Safety, GPS module, GSM module, etc.

An IOT Navigating Device for Blind People

Blindness involves the loss of vision. Mobility and independence have long been challenges for blind and visually impaired people. This project proposes BlinDar, a smart Electronic Travel Aid (ETA) to improve their lives. Equipped with Internet of Things technology, BlinDar aims to help the visually impaired navigate indoor and outdoor spaces freely. This highly efficient, reliable, fast-responding, lightweight, low-power, and cost-effective device uses ultrasonic sensors to detect obstacles and potholes within 2 meters. GPS and an ESP32 Wi-Fi module share the user's location with the cloud. An MQ2 gas sensor detects fires along the path, while an RF transmitter/receiver helps locate a misplaced walking stick. The Arduino Mega2560 microcontroller, with its 54 digital I/O pins, provides easy interfacing between all components. . KeyWords: Blindness, Smart Stick, BlinDar, GPS, ESP8266, Internet of Things, RF Tx/Rx, MQ2.

Microcontroller Based Women Safety Tracker Device

This paper presents the design and implementation of a microcontroller-based women safety tracker device aimed at enhancing personal safety for women. In recent times, there has been an increasing concern for women's safety, especially in urban areas. To address this issue, our device integrates various sensors and communication modules to provide tracking and alert functionalities. The core of the device is a microcontroller unit that coordinates the operation of different components. It interfaces with sensors such as GPS, Pulse sensor, MEMs sensor to track the user's location and detect any sudden movements or falls. These sensors continuously monitor the user's activities and send data to the microcontroller for processing. In case of an emergency the device alert GSM/GPS communication for sending SMS alerts with the user's location coordinates. The device is designed to be compact, lightweight, and discreet, allowing users to carry it comfortably in various situations. Overall, the proposed women safety tracker device provides a reliable and efficient solution for enhancing personal safety.

Edge caching and computing of video chunks in multi-tier wireless networks

This paper proposes a video caching and transcoding strategy for delay-constrained content delivery in multi-tier wireless networks. Particularly for multimedia services whose content can be encoded into multiple quality versions and consists of multiple chunks, we present an approach of caching chunks of an identical file separately in different network layers with different qualities. Given this caching policy, a complete file can be retrieved by receiving its chunks from different network tiers; therefore, a video transcoding and delivery method is jointly designed with caching to maximize the expected content quality with a strict delay constraint. Motivated by that caching is proactively conducted in advance of many user requests, the joint optimization problem is decomposed into caching and transcoding subproblems. The optimal transcoding policy and its transcoded qualities at different network tiers are analytically found by linear programming for a given cache state. Using the closed-form transcoded qualities, the caching policy is optimized to maximize the expected content quality with respect to randomness of user locations and channel gains. Due to the difficulty of expectation, the caching policy of each network layer is independently obtained by water-filling first, and an alternating optimization method is used to find the joint optimal caching policy satisfying the cache size constraints. Simulation results show that the proposed scheme succeeds in delivering content of 5% to 10% higher expected quality to users than comparison schemes which do not use partitioning for caching. Simultaneously, our approach meets the delay requirement and effectively manages the trade-offs arising from the varying storage and computational capacities within hierarchies, as well as video diversity.

Micro Controller Based Women Safety Tracker Device

This paper presents the design and implementation of a microcontroller-based women safety tracker device aimed at enhancing personal safety for women. In recent times, there has been an increasing concern for women's safety, especially in urban areas. To address this issue, our device integrates various sensors and communication modules to provide tracking and alert functionalities. The core of the device is a microcontroller unit that coordinates the operation of different components. It interfaces with sensors such as GPS, Pulse sensor, MEMs sensor to track the user's location and detect any sudden movements or falls. These sensors continuously monitor the user's activities and send data to the microcontroller for processing. In case of an emergency the device alert GSM/GPS communication for sending SMS alerts with the user's location coordinates. The device is designed to be compact, lightweight, and discreet, allowing users to carry it comfortably in various situations. Overall, the proposed women safety tracker device provides a reliable and efficient solution for enhancing personal safety. Keywords- Microcontroller,, Tracker device, Sensors, GPS

Evolution Process of Urban Industrial Land Redevelopment in China: A Perspective of Original Land Users

The crucial role of urban industrial land redevelopment in sustainable urban renewal has garnered widespread attention. While some scholars have explored the interest game among stakeholders in industrial land redevelopment, they primarily focus on the government-led model. Moreover, there remains a research gap concerning the impact of government intervention on the redevelopment of industrial land. This article utilizes evolutionary game theory to investigate the interest game between local governments and original land users in the model of urban industrial land redevelopment dominated by original land users. We establish evolutionary game models considering incentives and the combination of incentives and regulations, explore the interest balance strategy, and examine the impact of positive incentives and mandatory regulations on industrial land redevelopment. Furthermore, we employ a numerical simulation to unveil the impact of initial strategies and parameter adjustments on game strategy. The research results are as follows: (1) Under the original land user-led redevelopment model, only two evolutionary stability strategies exist: either the original land users implement industrial land redevelopment with positive responses from local governments, or neither party advances the process. (2) Government intervention is pivotal in facilitating the redevelopment of inefficient industrial land as economic subsidies and punitive measures motivate more participants to adopt proactive strategies. (3) The increase in government support positively correlates with the likelihood of industrial land redevelopment implementation by original land users. (4) The interests and costs of original land users emerge as crucial parameters influencing strategic decisions. This study enriches the understanding of the interests of core participants in industrial land redevelopment and provides valuable insights for sustainable urban renewal.