Context-aware Applications Research Articles

Is Privacy Regulation Slowing Down Research on Pervasive Computing?

Privacy legislation has often been identified as a roadblock for advanced context-aware applications. The feedback collected from more than 150 researchers in pervasive computing reveals a different attitude. Has pervasive computing?s privacy challenge been solved?

Implementing a semi-causal domain-specific language for context detection over binary sensors

In spite of the fact that many sensors in use today are binary (i.e. produce only values of 0 and 1), and that useful context-aware applications are built exclusively on top of them, there is currently no development approach specifically targeted to binary sensors. Dealing with notions of state and state combinators, central to binary sensors, is tedious and error-prone in current approaches. For instance, developing such applications in a general programming language requires writing code to process events, maintain state and perform state transitions on events, manage timers and/or event histories. In another paper, we introduced a domain specific language (DSL) called Allen, specifically targeted to binary sensors. Allen natively expresses states and state combinations, and detects contexts on line, on incoming streams of binary events. Expressing state combinations in Allen is natural and intuitive due to a key ingredient: semi-causal operators. That paper focused on the concept of the language and its main operators, but did not address its implementation challenges. Indeed, online evaluation of expressions containing semi-causal operators is difficult, because semi-causal sub-expressions may block waiting for future events, thus generating unknown values, besides 0 and 1. These unknown values may or may not propagate to the containing expressions, depending on the current value of the other arguments. This paper presents a compiler and runtime for the Allen language, and shows how they implement its state combining operators, based on reducing complex expressions to a core subset of operators, which are implemented natively. We define several assisted living applications both in Allen and in a general scripting language. We show that the former are much more concise in Allen, achieve more effective code reuse, and ease the checking of some domain properties.

Improving BLE Beacon Proximity Estimation Accuracy Through Bayesian Filtering

The interconnectedness of all things is continuously expanding which has allowed every individual to increase their level of interaction with their surroundings. Internet of Things (IoT) devices are used in a plethora of context-aware application, such as proximity-based services (PBSs), and location-based services (LBSs). For these systems to perform, it is essential to have reliable hardware and predict a user’s position in the area with high accuracy in order to differentiate between individuals in a small area. A variety of wireless solutions that utilize received signal strength indicators (RSSIs) have been proposed to provide PBS and LBS for indoor environments, though each solution presents its own drawbacks. In this article, Bluetooth low energy (BLE) beacons are examined in terms of their accuracy in proximity estimation. Specifically, a mobile application is developed along with three Bayesian filtering techniques to improve the BLE beacon proximity estimation accuracy. This includes a Kalman filter, a particle filter, and a nonparametric information (NI) filter. Since the RSSI is heavily influenced by the environment, experiments were conducted to examine the performance of beacons from three popular vendors in two different environments. The error is compared in terms of mean absolute error (MAE) and root mean squared error (RMSE). According to the experimental results, Bayesian filters can improve proximity estimation accuracy up to 30% in comparison with traditional filtering, when the beacon and the receiver are within 3 m.

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.

Testing tools for Android context-aware applications: a systematic mapping

ContextMobile devices, such as smartphones, have increased their capacity of information processing and sensors have been aggregated to their hardware. Such sensors allow capturing information from the environment in which they are introduced. As a result, mobile applications that use the environment and user information to provide services or perform context-based actions are increasingly common. This type of application is known as context-aware application. While software testing is an expensive activity in general, testing context-aware applications is an even more expensive and challenging activity. Thus, efforts are needed to automate testing for context-aware applications, particularly in the scope of Android, which is currently the most used operating system by smartphones.ObjectiveThis paper aims to identify and discuss the state-of-the-art tools that allow the automation of testing Android context-aware applications.MethodIn order to do so, we carried out a systematic mapping study (SMS) to find out the studies in the existing literature that describe or present Android testing tools. The discovered tools were then analyzed to identify their potential in testing Android context-aware applications.ResultA total of 68 works and 80 tools were obtained as a result of the SMS. From the identified tools, five are context-aware Android application testing tools, and five are general Android application testing tools, but support the test of the context-aware feature.ConclusionAlthough context-aware application testing tools do exist, they do not support automatic generation or execution of test cases focusing on high-level contexts. Moreover, they do not support asynchronous context variations.

CaaSSET: A Framework for Model-Driven Development of Context as a Service

Developing a stand-alone context-aware application is costly and time-consuming due to the diversity and complexity of the requirements. Context-as-a-Service (CaaS) seeks to overcome the issues by following the idea of Software-as-a-Service (SaaS). The CaaS delivery model attempts to separate context service consumers from context service providers. However, CaaS shifts the complexities of context service provisioning and management from consumers toward providers. Considering the merits of Model-Driven Development (MDD) in coping with the complexities of problems in different domains, we leverage MDD in the CaaS domain and propose the CaaSSET framework to ease the development of context services. A prerequisite for realizing this aim is to specify the CaaS domain as a meta-model. Our CaaSSET framework consists of a CaaS reference model, a context service meta-model, a graphical modeling tool, a code generation tool, and a context service management tool. Using CaaSSET, the context services are modeled in a graphical environment and transformed into executable context web services (semi-) automatically. To evaluate the applicability of the CaaSSET framework, we have modeled and developed a tourism context service as a case study. We have also compared the framework with the related work based on 44 evaluation criteria, which we have extracted from 14 context service requirements.

Context-Aware Placement of Industry 4.0 Applications in Fog Computing Environments

The fourth industrial revolution, widely known as Industry 4.0, is realizable through widespread deployment of Internet of Things (IoT) devices across the industrial ambiance. Due to communication latency and geographical distribution, Cloud-centric IoT models often fail to satisfy the Quality of Service requirements of different IoT applications assisting Industry 4.0 in real time. Therefore, Fog computing focuses on harnessing edge resources to place and execute these applications in the proximity of data sources. Since most of the Fog nodes are heterogeneous, distributed, and resource-constrained, it is challenging to place Industry 4.0-oriented applications (I4OAs) over them ensuring time-optimized service delivery. Diversified data sensing frequency of different industrial IoT devices and their data size further intensify the application placement problem. To address this issue, in this article we propose a context-aware application placement policy for Fog environments. Our policy coordinates the IoT device-level contexts with the capacity of Fog nodes and minimizes the service delivery time of various I4OAs such as image processing and robot navigation applications. It also ensures that the streams of input data flowing toward the placed applications neither congest the network nor increase the computing overhead of host Fog nodes significantly. Performance of the proposed policy is evaluated in both real-world and simulated Fog environments and compared with the existing placement policies. The experiment results show that our policy offers overall 16% improvement in service latency, network relaxation, and computing overhead management compared to other placement policies.

EGO: Optimized Sensor Selection for Multi-Context Aware Applications with an Ontology for Recognition Models

In recent years, there has been a significant growth of context-aware applications, which extract the user's context from multiple embedded sensors in smartphones and wearable sensors. However, running multiple context-aware applications simultaneously causes extensive battery drainage for mobile devices. To alleviate the energy limitation in multi-context setting, we propose EGO: an ontology-based framework for group sensor selection to achieve synergy across applications while trading off energy consumption, accuracy, and delay in context recognition. A new context recognition ontology is designed to capture context recognition models. It captures the alternative groups of sensors for each context and the parameters for context recognition models. EGO includes an adaptive sensor selection mechanism that selects the appropriate sensors based on the current user state, the available resources, and the requested accuracies by running applications. The framework provides an open architecture that allows integration with other sensor optimization modules for sensor scheduling. EGO is validated using a real test-bed implemented on an Android platform that provides accessibility to on-board mobile sensors and external sensors. Results show that EGO provides better trade-off than previous state-of-the-art methods. Furthermore, EGO provides 63 percent energy saving with comparable accuracy when compared to the most accurate group of sensors.

Context-aware rule learning from smartphone data: survey, challenges and future directions

Smartphones are considered as one of the most essential and highly personal devices of individuals in our current world. Due to the popularity of context-aware technology and recent developments in smartphones, these devices can collect and process raw contextual data about users’ surrounding environment and their corresponding behavioral activities with their phones. Thus, smartphone data analytics and building data-driven context-aware systems have gained wide attention from both academia and industry in recent days. In order to build intelligent context-aware applications on smartphones, effectively learning a set of context-aware rules from smartphone data is the key. This requires advanced data analytical techniques with high precision and intelligent decision making strategies based on contexts. In comparison to traditional approaches, machine learning based techniques provide more effective and efficient results for smartphone data analytics and corresponding context-aware rule learning. Thus, this article first makes a survey on previous work in the area of contextual smartphone data analytics and then presents a discussion of challenges and future directions for effectively learning context-aware rules from smartphone data, in order to build rule-based automated and intelligent systems.

Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors

Today’s smartphones are equipped with embedded sensors, such as accelerometers and gyroscopes, which have enabled a variety of measurements and recognition tasks. In this paper, we jointly investigate two types of recognition problems in a joint manner, e.g., human activity recognition and smartphone on-body position recognition, in order to enable more robust context-aware applications. So far, these two problems have been studied separately without considering the interactions between each other. In this study, by first applying a novel data preprocessing technique, we propose a joint recognition framework based on the multi-task learning strategy, which can reduce computational demand, better exploit complementary information between the two recognition tasks, and lead to higher recognition performance. We also extend the joint recognition framework so that additional information, such as user identification with biometric motion analysis, can be offered. We evaluate our work systematically and comprehensively on two datasets with real-world settings. Our joint recognition model achieves the promising performance of 0.9174 in terms of F 1 -score for user identification on the benchmark RealWorld Human Activity Recognition (HAR) dataset. On the other hand, in comparison with the conventional approach, the proposed joint model is shown to be able to improve human activity recognition and position recognition by 5.1 % and 9.6 % respectively.

Towards Next Generation Teaching, Learning, and Context-Aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing Enabled Smart Campuses and Universities

Smart campuses and smart universities make use of IT infrastructure that is similar to the one required by smart cities, which take advantage of Internet of Things (IoT) and cloud computing solutions to monitor and actuate on the multiple systems of a university. As a consequence, smart campuses and universities need to provide connectivity to IoT nodes and gateways, and deploy architectures that allow for offering not only a good communications range through the latest wireless and wired technologies, but also reduced energy consumption to maximize IoT node battery life. In addition, such architectures have to consider the use of technologies like blockchain, which are able to deliver accountability, transparency, cyber-security and redundancy to the processes and data managed by a university. This article reviews the state of the start on the application of the latest key technologies for the development of smart campuses and universities. After defining the essential characteristics of a smart campus/university, the latest communications architectures and technologies are detailed and the most relevant smart campus deployments are analyzed. Moreover, the use of blockchain in higher education applications is studied. Therefore, this article provides useful guidelines to the university planners, IoT vendors and developers that will be responsible for creating the next generation of smart campuses and universities.

Location Trigger System for the Application of Context-Awareness based Location services

Location Trigger System for the Application of Context-Awareness based Location services

Context-aware applications as the basis for value added creation (the case of the travel industry)

Summary: This paper seeks to focus on the idea of a user-centric context-aware digital system and its rationale in a process of tourism value creation. The purpose of the study is to offer an understanding of the context-aware applications in order to analyze their usefulness in tourism marketing. This is achieved through a literature review of existing research and the demonstration of several applications in travel and hospitality which might enrich the set of marketing tools adopted by tourism regions and enterprises.

CityTracker: Citywide Individual and Crowd Trajectory Analysis Using Hidden Markov Model

Geospatial big data analytics are changing the way that businesses operate, and have enabled various intelligent services in smart cities. User mobility plays an important role in many context-aware applications, such as location-based advertisement, traffic planning, and urban resource management. This paper proposes CityTracker, a hidden Markov model (HMM)-based framework to predict the temporal-spatial individual trajectory and analyze the representative citywide crowd mobility. After the locations are segmented into points of interests and modeled as states, HMM can find the most likely state sequence in a maximum-likelihood sense, and thus perform trajectory prediction with different orders. In addition, CityTracker can integrate the individual trajectories, achieving representative crowd mobility visualization in the target area. The proposed mechanism is evaluated on a database provided by HyXen, which contains temporal-geospatial records from thousands of smartphones in Taipei. Experimental results confirm the effectiveness of CityTracker, which achieves prediction distance errors of approximately 1.28 km and outperforms traditional probabilistic and regression-based methods. The results also show the most representative crowd mobility behaviors in the map, which is essential for citywide applications.

CHSPAM: a multi-domain model for sequential pattern discovery and monitoring in contexts histories

Context-aware applications adapt their functionalities based on users contexts. Complementarily, a context history has information about previous contexts visited by a user. Context history enables applications to explore users past behavior. Researchers have studied different ways to analyze these data. This article addresses a specific type of data analysis in contexts histories, which is the discovery and monitoring of sequential patterns. The article proposes a model, called CHSPAM, that allows the discovery of sequential patterns in contexts histories databases and keeps track of these patterns to monitor their evolution over time. There are two main contributions of this work. The first one is the use of a generic representation for stored context information on pattern recognition field, which enables the model to be used for different research domains. The second contribution is the fact that CHSPAM monitors discovered pattern evolution over time. We have build a functional prototype that allowed us to conduct experiments in two different applications. The first experiment used the model to perform pattern analysis and evaluate the prediction based on monitored sequential patterns. Prediction accuracy increased by up to 17% when compared to the use of common sequential patterns. On the second experiment, CHSPAM was used as a component of a learning object recommendation application. The application was able to recommend learning objects related to students interests based on monitored sequential patterns extracted from users session history. Usefulness for recommendations reached 84%.

What data are smartphone users willing to share with researchers?

Context-aware applications stemming from diverse fields like mobile health, recommender systems, and mobile commerce potentially benefit from knowing aspects of the user’s personality. As filling out personality questionnaires is tedious, we propose the prediction of the user’s personality from smartphone sensor and usage data. In order to collect data for researching the relationship between smartphone data and personality, we developed the Android app track your daily routine (TYDR), which tracks and records smartphone data and utilizes psychometric personality questionnaires. With TYDR, we track a larger variety of smartphone data than many other existing apps, including metadata on notifications, photos taken, and music played back by the user. Based on the development of TYDR, we introduce a general context data model consisting of four categories that focus on the user’s different types of interactions with the smartphone: physical conditions and activity, device status and usage, core functions usage, and app usage. On top of this, we developed the Privacy Model for Mobile Data Collection Applications (PM-MoDaC) specifically tailored for apps that are related to the collection of mobile data, consisting of nine proposed privacy measures. We present the implementation of all of those measures in TYDR. Our experimental evaluation is based on data collected with TYDR during a two-month period. We find evidence that our users accept our proposed privacy model. Based on data about granting TYDR all or no Android system permissions, we find evidence that younger users tend to be less willing to share their data (average age of 30 years compared to 35 years). We also observe that female users tend to be less willing to share data compared to male users. We did not find any evidence that education or personality traits are a factor related to data sharing. TYDR users score higher on the personality trait openness to experience than the average of the population, which we assume to be evidence that the type of app influences the user base it attracts in terms of average personality traits.

Indoor Positioning Based on Visible Light Communication

The emergent context-aware applications in ubiquitous computing demands for obtaining accurate location information of humans or objects in real-time. Indoor location-based services can be delivered through implementing different types of technology, among which is a recent approach that utilizes LED lighting as a medium for Visible Light Communication (VLC). The ongoing development of solid-state lighting (SSL) is resulting in the wide increase of using LED lights and thereby building the ground for a ubiquitous wireless communication network from lighting systems. Considering the recent advances in implementing Visible Light Positioning (VLP) systems, this article presents a review of VLP systems and focuses on the performance evaluation of experimental achievements on location sensing through LED lights. We have outlined the performance evaluation of different prototypes by introducing new performance metrics, their underlying principles, and their notable findings. Furthermore, the study synthesizes the fundamental characteristics of VLC-based positioning systems that need to be considered, presents several technology gaps based on the current state-of-the-art for future research endeavors, and summarizes our lessons learned towards the standardization of the performance evaluation.

8th EAI International Conference on Context-Aware Systems and Applications (ICCASA 2019) AND 5th EAI International Conference on Nature of Computation and Communication (ICTCC 2019)

8th EAI International Conference on Context-Aware Systems and Applications (ICCASA 2019) AND 5th EAI International Conference on Nature of Computation and Communication (ICTCC 2019)

A light weight smartphone based human activity recognition system with high accuracy

With the pervasive use of smartphones, which contain numerous sensors, data for modeling human activity is readily available. Human activity recognition is an important area of research because it can be used in context-aware applications. It has significant influence in many other research areas and applications including healthcare, assisted living, personal fitness, and entertainment. There has been a widespread use of machine learning techniques in wearable and smartphone based human activity recognition. Despite being an active area of research for more than a decade, most of the existing approaches require extensive computation to extract feature, train model, and recognize activities. This study presents a computationally efficient smartphone based human activity recognizer, based on dynamical systems and chaos theory. A reconstructed phase space is formed from the accelerometer sensor data using time-delay embedding. A single accelerometer axis is used to reduce memory and computational complexity. A Gaussian mixture model is learned on the reconstructed phase space. A maximum likelihood classifier uses the Gaussian mixture model to classify ten different human activities and a baseline. One public and one collected dataset were used to validate the proposed approach. Data was collected from ten subjects. The public dataset contains data from 30 subjects. Out-of-sample experimental results show that the proposed approach is able to recognize human activities from smartphones’ one-axis raw accelerometer sensor data. The proposed approach achieved 100% accuracy for individual models across all activities and datasets. The proposed research requires 3 to 7 times less amount of data than the existing approaches to classify activities. It also requires 3 to 4 times less amount of time to build reconstructed phase space compare to time and frequency domain features. A comparative evaluation is also presented to compare proposed approach with the state-of-the-art works.

EduAware: using tablet-based navigation gestures to predict learning module performance

ABSTRACT In this paper, we develop a context-aware, tablet-based learning module for adult education. Specifically, we focus on adult education in healthcare-teaching learners to perform a medical screening procedure. Based upon how learners navigate through the learning module (e.g. swipe-speed and click duration, among others), we use machine learning to predict what comprehensive test questions a user will answer correctly or incorrectly. Compared with other context aware learning applications, this is the first time tablet-based navigation gestures have been used to support learning assessment. We conducted a 21 participant user study, showing the system can predict how users respond to test questions with 87.7% accuracy without user-specific calibration data. This is compared to item response theory methods which achieve about 70% accuracy. We also investigate which attributes are most responsible for making predictions.