Context-aware Mobile Applications Research Articles

Tourism embraces blockchain towards the smart tourism era

Blockchain, also known as Distributed Ledger Technology (DLT), has attracted a considerable lot of attention from academics and industry professionals. When it comes to the tourism industry, the exploitation of blockchain technology could result in significant gains. Among the possible uses are digital identity management, tokenization of frequent flyer programs, accommodation, and transportation. After conducting a comprehensive review of the scientific literature published in this field over the past five years, we identify the most significant blockchain-enabled services and uses of this technology within the context of smart tourism. We analyze the benefits of blockchain technology for the travel industry, local economies, and tourists, laying the groundwork for future studies. Moreover, we present a novel context-aware mobile application that uses artificial intelligence and blockchain to create a hybrid cyber-smart tourist application.

Mobile Expert System: Exploring Context-Aware Machine Learning Rules for Personalized Decision-Making in Mobile Applications

Expert systems, a form of artificial intelligence (AI), are typically designed to solve many real-world problems by reasoning through knowledge, which is primarily represented as IF–THEN rules, with the information acquired from humans or domain experts. However, to assume such rules for personalized decision-making in an intelligent, context-aware mobile application is a challenging issue. The reason is that different mobile users may behave differently in various day-to-day situations, i.e., not identical, and thus the rules for personalized services must be reflected according to their symmetrical or asymmetrical behavioral activities. Therefore, our key focus is to solve this issue through adding personalized decision-making intelligence to develop powerful mobile applications to assist the end-users. To achieve our goal, in this paper, we explore on “Mobile Expert System”, where we take into account machine-learning rules as knowledge-base rather than traditional handcrafted static rules. Thus, the concept of a mobile expert system enables the computing and decision-making processes more actionable and intelligent than traditional ones in the domain of mobile analytics and applications. Our experiment section shows that the context-aware machine learning rules discovered from users’ mobile phone data can contribute in building a mobile expert system to solve a particular problem, through making personalized decisions in various context-aware test cases.

Paas EUD Tool for Developing Expert Context-Aware Mobile Applications

Context-awareness enables systems to be tailored to the needs of users and their real circumstances at certain times. A noteworthy trend in software development is that an increasing number of software systems are being developed by individuals with expert knowledge in other sectors. Because most of the current context-aware development toolkits are intended for software developers, these types of systems cannot be easily developed by non-technical consumers. The development of tools for designing context-aware frameworks by consumers who are not programming experts but are specialists in the area of implementation would result in faster adoption of such services by businesses. This paper provides a cloud-based framework for people without programming experience to create context-aware mobile applications. The platform can provide a lightweight distribution of packaged applications that allows experts to send specified information to mobile users based on their context data without overlapping between the rules of the application. An energy-efficient mobile application was developed to acquire contextual information from the user device and to create quality data accordingly. The framework adopts Platform as a Service (PaaS) and containerization to facilitate development of context-aware mobile applications by experts in various domains rather than developing a tool for each domain in isolation, while considering multitenancy.

Agent-Based Autonomic Semantic Context-Aware Platform for Smart Health Monitoring and Disease Detection

Abstract Nowadays, mobile applications have been widely used in various health and social domains. While there are large number of smart devices that connect and transmit their context data in free manner, these mobile applications have limitations regarding the increasing number of heterogeneous context data sent by devices, conducting the problem of identifying user-centric situations and providing services in real-time. Many smart mobile health systems were proposed to ensure context-aware personal health monitoring and diseases detection, while most of them are failed to ensure a right level of dynamicity and enough flexibility for assisting users everywhere, anytime, through widespread sensors and mobile devices. In fact, it becomes necessary to rethink a new way to minimize the response time combining competitive agents with semantic-based situation reasoning strategy. In this paper, we introduce a novel agent-based platform with three-layered ontology for the semantic description and parallel management of services selection approach dedicated to context-aware health mobile applications. In addition, we propose an innovative parallel services discovery and optimal selection process, which involved a set of filtered and classified semantic health multipath according to user’s context and preferences with consistent mobility of users and limited resources (low battery, processing capabilities, memory and others). Experimental results show the effectiveness of the proposed approach as it includes the semantic service information on cooperative agents. In addition, the proposed approach ensures fast response time and prolongs the continuity of the mobile application.

Paving the way to collaborative context-aware mobile applications: a case study on preventing worsening of allergy symptoms

In recent years, the evolution of smartphones and their software applications has grown exponentially; together with the advance of the Internet of Things and smart cities, it has raised huge demand for services and applications in these domains. Although the wide range of mobile applications is unquestionable, citizens already demand that applications adapt to their specific needs and situations in real time, that is, that they are context-aware. However, context-aware mobile applications are often very limited and miss out on the opportunity of benefiting from feedback provided by citizen collaboration. In order to fill this gap, this paper proposes a context-aware and collaborative software architecture and mobile application. In particular, we have implemented them in the scope of e-health, more specifically in the area of seasonal allergies, which cause allergic people to experience annoying symptoms that could be avoided by having access to pollen information in real time. Furthermore, they will also benefit from citizen collaboration through the knowledge of the symptoms other allergic people with the same allergy and in the same location are experiencing. To this end, users will be able to provide their symptoms at any time through their mobile application and the proposed architecture will constantly process that information in real time, sending notifications to users as soon as reported symptoms are seen to exceed a certain threshold. The architecture’s performance, the application’s resource consumption and a satisfaction survey of the app’s usability and usefulness have been tested; all results have been fully satisfactory.

A Big Data-Driven Hybrid Solution to the Indoor-Outdoor Detection Problem

Context-aware mobile applications are emerging as a relevant technology to improve the users' satisfaction. By being aware of the current situation of a user, a context-aware application can adapt itself to provide the most suitable services at that moment. Companies from different areas are investing in turning their applications aware of the users' context with the objective of increasing their quality-of-experience and reducing churn. Among many possible contexts, the situation of a mobile user in terms of the type of environment (i.e., indoor or outdoor) is a relevant yet difficult to obtain information. In this work, we propose the HybridIO , a solution to the Indoor-Outdoor Detection Problem that advances the state-of-the-art, since it requires less sensor data to operate and generalizes well in other domains. We validate our solution considering a large amount of real data, and the results reveal an improvement of up to 14% in precision when compared to other baselines. We also deploy the proposed solution into a real-time big data architecture that is able to enrich up to 400 records/second with the indoor-outdoor information.

Design-based research on gamified outdoor social enquiry learning with context-aware technology: integration of teacher facilitation for advancing the pedagogical effectiveness

GAMES (Gamified Authentic Mobile Enquiry in Society) is a context-aware mobile application that we developed to support students in conducting gamified social enquiry learning in outdoor environments. This paper focuses on discussing the latter part of a piece of 2-cycle Design-Based Research (DBR) conducted in the context of formal curriculum learning and teaching in Hong Kong, reporting on the work that we carried out in the second research cycle where teacher facilitation was integrated into the pedagogical implementation of GAMES for advancing its effectiveness. The entire DBR involved 9 teachers (from 9 different high schools in 3 different academic categories) and their Grade-10 classes in two consecutive school years (with a total of 555 students). Upon the different academic settings, we (researchers) collaborated with the teachers (practitioners) to derive and enact what and how they could do in order to optimise their students' social enquiry experience in the course of gamified, technology-enhanced outdoor learning. This study not only reveals the importance of teacher facilitation in the process of gamified mobile learning, but it also sheds light on how to design and implement the articulated pedagogical, administrative and technical facilitation acts in the context of formal education.

A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

With the rapid development of mobile internet technology, mobile applications (apps) have been rapidly popularized. To facilitate users' choice of apps, app recommendation is becoming a research hotspot in academia and industry. Although traditional app recommendation approaches have achieved certain results, these methods only mechanically consider the user's current context information, ignoring the impact of the user's previous related context on the user's current selection of apps. We believe this has hindered the further improvement of the recommendation effect. Based on this fact, this paper proposes a novel context-aware mobile application recommendation approach based on user behavior trajectories. We named this approach CMARA, which is the initials acronym of the proposed approach. Specifically, 1) CMARA integrates the heterogeneous information of the target users such as the user's app, time, and location, into users behavior trajectories to model the users' app usage preferences; 2) CMARA constructs the context Voronoi diagram using the users' contextual point and leverages the context Voronoi diagram to build a novel user similarity model; 3) CMARA uses the target user's current contextual information to generate an app recommendation list that meets the user's preferences. Through experiments on large-scale real-world data, we verified the effectiveness of CMARA.

Design-based research on gamified outdoor social enquiry learning with context-aware technology: integration of teacher facilitation for advancing the pedagogical effectiveness

GAMES (Gamified Authentic Mobile Enquiry in Society) is a context-aware mobile application that we developed to support students in conducting gamified social enquiry learning in outdoor environments. This paper focuses on discussing the latter part of a piece of 2-cycle Design-Based Research (DBR) conducted in the context of formal curriculum learning and teaching in Hong Kong, reporting on the work that we carried out in the second research cycle where teacher facilitation was integrated into the pedagogical implementation of GAMES for advancing its effectiveness. The entire DBR involved 9 teachers (from 9 different high schools in 3 different academic categories) and their Grade-10 classes in two consecutive school years (with a total of 555 students). Upon the different academic settings, we (researchers) collaborated with the teachers (practitioners) to derive and enact what and how they could do in order to optimise their students' social enquiry experience in the course of gamified, technology-enhanced outdoor learning. This study not only reveals the importance of teacher facilitation in the process of gamified mobile learning, but it also sheds light on how to design and implement the articulated pedagogical, administrative and technical facilitation acts in the context of formal education.

EAUHHCAMA: Extending and Adapting Usability Heuristics for Healthcare Related Context-Aware Mobile Applications

Fitness related context-aware mobile applications (FCAMA) being the fragment of healthcare informatics, use emerging technologies and provide continuous and real-time information to the end-users. This information supports the users in understanding the physical condition and the way to improve health using cues. That's why; healthcare informatics is the fast-growing domain of interest for researchers. FCAMA has collected information from inbuilt smartphone tools including accelerometer, GPS, microphone, camera, and others. FCAMA transformed the extracted data into a usable form and formulated individual plans to track and improve fitness. But, it is ambiguous whether the user interfaces of FCAMA is really effective in improving health and fitness. Although, several studies attempt to establish an effective usability evaluation mechanism, it still needs further work for comprehensively identifying usability flaws. Evaluators of FCAMA use inspection based heuristics set to identify user-related issues. In order to identify the usability issues comprehensively, it is important that heuristics are pertinent. In this paper, we propose an enhanced and adapted set of fourteen usability heuristics for FCAMA. Eleven heuristics were selected and adapted after cross-linking the usability flaws with the existing sets of heuristics. Usability flaws were captured in a user study with 850 students from the human-computer interaction (HCI) course. While we added two more against uncaptured usability flaws, to be able to adapt the heuristics set to the context awareness aspect. The FCAMA was validated using two evaluation studies conducted by students in (HCI) course, and second by usability experts. Results have justified the proposed heuristics both in terms of effectiveness and relevance.

A language for the end-user development of mobile context-aware applications

A language for the end-user development of mobile context-aware applications

A Survey of Context Simulation for Testing Mobile Context-Aware Applications

Equipped with an abundance of small-scale microelectromechanical sensors, modern mobile devices such as smartphones and smartwatches can now offer context-aware services to users in mobile environments. Although advances in mobile context-aware applications have made our everyday environments increasingly intelligent, these applications are prone to bugs that are highly difficult to reproduce and repair. Compared to conventional computer software, mobile context-aware applications often have more complex structures to process a wide variety of dynamic context data in specific scenarios. Accordingly, researchers have proposed diverse context simulation techniques to enable low-cost and effective tests instead of conducting costly and time-consuming real-world experiments. This article aims to give a comprehensive overview of the state-of-the-art context simulation methods for testing mobile context-aware applications. In particular, this article highlights the technical distinctions and commonalities in previous research conducted across multiple disciplines, particularly at the intersection of software testing, ubiquitous computing, and mobile computing. This article also discusses how each method can be implemented and deployed by testing tool developers and mobile application testers. Finally, this article identifies several unexplored issues and directions for further advancements in this field.

Recurrent neural network based scenario recognition with Multi-constellation GNSS measurements on a smartphone

As an upper layer context-aware mobile application, fast and accurate scenario recognition is essential for seamless indoor and outdoor localization and robust positioning in complex environments. With the popularity of multi-constellation smartphones, scenario recognition based on smartphone Global Navigation Satellite System (GNSS) measurements becomes desirable. In this paper, we divide the complex environments into four categories (deep indoor, shallow indoor, semi-outdoor and open outdoor) and conduct research work in two areas. Firstly, we analyze in detail the influence of multi-constellation satellite signals on scenario recognition performance based on a Hidden Markov Model (HMM) algorithm. The experimental results show that the scenario recognition accuracy is improved significantly with the increase of the number of constellations received by smartphones. Secondly, in order to solve the description degradation of the traditional model caused by scenario transitions and environmental changes around the scenario, we propose a new scenario recognition method based on Recurrent Neural Network (RNN). Considering the computational complexity and the availability of feature values, we utilize the position-independent features as the input of the RNN model, and then evaluate the performance of the model using test sets from the new places. The results indicate that our proposed algorithm has high recognition accuracy in both isolated scenarios and transition regions, with the overall accuracy of 98.65%. Especially in the scenario transitions, the recognition accuracy reaches 90.94% and in the three times of correct recognition for scenario transitions (four times in total), the maximum transition delay is only 3 s.

Context aware mobile learning application development: A systematic literature review

Context Aware Mobile Learning Applications (CAMLA) are an emerging field of research, they provide educational resources tailored to educational needs and particular circumstances of the learner. Despite the increase in the number of applications being developed, there is a lack of study that provides comprehensive knowledge on the development of CAMLA. Motivated by this shortcoming, Systematic Literature Review (SLR) was conducted to establish a body of knowledge that describes the key components of CAMLA that includes; context information extraction and representation, context adaptation and different types of applications developed. SLR was conducted on 24 papers retrieved from seven databases. The results identified different types of context amongst which learner, location and time are the most widely used in the development of CAMLA. The context data is collected using virtual and physical sensors and mostly represented as relational or ontology data. CAMLA provides different types of adaptation where adaptation to learning resource and location are very common. The results are useful to position future research activities to scientifically strengthen this field.

Considerations for Designing Context-Aware Mobile Apps for Mental Health Interventions.

This work identifies major areas of knowledge and proposes a set of relevant dimensions by area that must be taken into account in the design and delivery of context-aware mobile applications for mental health interventions. We argue that much of the related research has focused only on a few dimensions, paying little or no attention to others and, most importantly, to potential relationships between them. Our belief is that the improvement of the effectiveness of mobile interventions to support mental health necessarily implies that developers and therapists comprehensively consider the interaction between the proposed dimensions. Taking as a starting point the three areas of knowledge (Technology, Context, and Mental Health), we re-examine each area to identify relevant dimensions, discuss the relationships between them and finally draw a series of considerations. The resulting considerations can help therapists and developers to devise, design, and generate custom mobile applications in a way that increases the motivation and engagement of patients and, therefore, the effectiveness of psychological treatments.

CAP

Context-aware mobile application (App) usage prediction benefits a variety of applications such as precise bandwidth allocation, App launch acceleration, etc. Prior works have explored this topic through individual data profiles and contextual information. However, it is still a challenging problem because of the following three aspects: i. App usage behavior is usually influenced by multiple factors, especially temporal and spatial factors. ii. It is difficult to describe individuals' preferences, which are usually time-variant. iii. A single user's data is sparse on the spatial domain and only covers a limited number of locations. Prediction becomes more difficult when the user appears at a new location. This paper presents CAP, a context-aware App usage prediction algorithm that takes both contextual information (location & time) and attribution (App with type information) into consideration. We find that the relationships between App-location, App-time, and App-App type are essential to prediction and propose a heterogeneous graph embedding algorithm to map them into the common comparable latent space. In addition, we create a user profile for each user with App usage and trajectory history to describe the individual dynamic preference for personalized prediction. We evaluate the performance of our proposed CAP with two large-scale real-world datasets. Extensive evaluations demonstrate that CAP achieves 30% higher accuracy than a state-of-the-art method Personalized Ranking Metric Embedding (PRME) in terms of Accuracy@5. In terms of mean reciprocal rank (MRR), CAP achieves 1.5× higher than the straightforward baseline Sta and 2× higher than PRME. Our investigation enables a range of applications to benefit from such timely predictions, including network operators, service providers, and etc.

Improving Resource Consumption in Context- Aware Mobile Applications Through Alternative Architectural Styles

Over the last years, the Internet of Things has fostered a growing interest in context-aware mobile applications; this fact is mainly due to highly favoring information provision from multiple Internet-connected devices. To identify user context, these applications collect information from the user and his/her environment and typically filter app information, so that the user receives only the interesting and relevant information. However, such a task usually implies further resource consumption on user mobile devices, not only regarding battery usage but also in terms of network traffic. Accordingly, although context-aware applications can improve user experiences in their daily lives, they must ensure the maintenance of low-level resource consumption; otherwise, the applications are promptly replaced by less consuming ones, and therefore, removed from the mobile market. In this paper, we evaluate and discuss several architectural styles for context-aware mobile applications, as well as, providing a set of guidelines to decide on the right architecture for a particular app depending on its characteristics. The use of such guidelines when choosing the right architectural style can strongly influence the resource consumption of context-aware mobile applications. Following these guidelines, user satisfaction of a context-aware mobile application may be improved, thus guaranteeing the app success.

A scalable middleware for context-aware mobile applications

A core functionality of context-aware mobile applications is storing, indexing, and retrieving information about users, places, events and other resources. The goal of this work is to design and provide a service-oriented middleware, called Geocon, which can be used by mobile developers to implement such functionality. To represent information about users, places, events and resources of context-aware applications, Geocon defines a metadata model that can be extended to match specific application requirements. The middleware includes a geocon-service for storing, searching and selecting metadata about users, resources, events and places of interest, and a geoconclient library that allows mobile applications to interact with the service through the invocation of local methods. The paper describes the Geocon middleware and presents an experimental evaluation of its scalability on a cloud platform with a real-world mobile application.

A scalable middleware for context-aware mobile applications

A core functionality of context-aware mobile applications is storing, indexing, and retrieving information about users, places, events and other resources. The goal of this work is to design and provide a service-oriented middleware, called Geocon, which can be used by mobile developers to implement such functionality. To represent information about users, places, events and resources of context-aware applications, Geocon defines a metadata model that can be extended to match specific application requirements. The middleware includes a geocon-service for storing, searching and selecting metadata about users, resources, events and places of interest, and a geoconclient library that allows mobile applications to interact with the service through the invocation of local methods. The paper describes the Geocon middleware and presents an experimental evaluation of its scalability on a cloud platform with a real-world mobile application.

Feedback-based metric learning for activity recognition

Mobile activity recognition is an effective approach to understanding human context in real time. Existing methods based on supervised learning that require a large amount of training samples for building activity recognition models. The collection of labeled training samples is a boring process and most users are reluctant to get involved. Crowdsourcing is a simple and potential approach to collecting the training samples and building accurate activity recognizers. Since different people usually have different physical features and behavior patterns, an accurate activity recognition model cannot be constructed directly from the training samples collected by crowdsourcing. In this paper, we have proposed a Mixture Expert Model for Activity Recognition (MEMAR) based on feedback and crowdsourcing samples. The proposed model can continuously discover the difference between user activity and crowdsourcing samples. Then we update activity recognition models with the discovered differences. A mobile can correctly utilize crowdsourcing samples for recognition model construction with MEMAR and can also track and recognize mobile users’ behavior dynamics. The experiments based on a smartphone dataset verify the validity of MEMAR. We believe MEMAR provides a basis for context-aware mobile applications.