Development Of Context-aware Applications Research Articles

Environmental Sound Classification using Hybrid Ensemble Model

Environmental sound classification (ESC) is the most trending research areas. The sounds in the surroundings such as screaming, air conditioners, and rain droplets can help in the development of context-aware applications. It is complex to process the envi- ronmental sounds as compared to speech and music due to the unstructured essence of environmental sounds. In the past, certain preprocessing techniques, feature extraction, and classification algorithms are used for ESC. Several researchers have applied ma- chine learning classifiers for ESC and certain ensemble classifiers are also used but the accuracy can be increased if instead of combining homogeneous classifiers, heterogeneous classifiers can be ensembled. In this paper, a hybrid ensemble classifier is used for ESC on the UrbanSound8k dataset and cepstral features Mel Frequency Cepstral Coefficients are used. Five different machine learning classifiers- Decision Tree, Support Vector Machine, Logistic Regression, K- Nearest Neighbour, and Naive Bayes are used to develop a hybrid ensemble model. The highest accuracy is obtained when all the five classifiers are combined. The proposed approach gives an accuracy of 79.4% and is compared with the benchmark results using individual classifiers and the former out- performs the latter. The results of the hybrid ensemble model on the UrbanSound8K dataset are also compared with the dataset ESC-10.

Building a COVID-Safe Navigation App Using a Meta-Model Based Context Server

Building context-aware applications is an already widely researched topic. It is our belief that context awareness has the potential to supplement the Internet of Things, when a suitable methodology including supporting tools will ease the development of context-aware applications. We believe that a meta-model based approach can be key to achieving this goal. In this paper, we present our meta-model based methodology, which allows us to define and build application-specific context models and the integration of sensor data without any programming. We describe how that methodology is applied with the implementation of a relatively simple context-aware COVID-safe navigation app. The outcome showed that programmers with no experience in context-awareness were able to understand the concepts easily and were able to effectively use it after receiving a short training. Therefore, context-awareness is able to be implemented within a short amount of time. We conclude that this can also be the case for the development of other context-aware applications, which have the same context-awareness characteristics. We have also identified further optimization potential, which we will discuss at the conclusion of this article.

Context-Aware End-User Development Review

Context-aware application development frameworks enable context management and environment adaptation to automatize people’s activities. New technologies such as 5G and the Internet of Things (IoT) increase environment context (from devices/services), making functionalities available to augment context-aware applications. The result is an increased deployment of context-aware applications to support end-users in everyday activities. However, developing applications in context-aware frameworks involve diverse technologies, so that it traditionally involves software experts. In general, context-aware applications are limited in terms of personalization for end-users. They include configurations to personalize applications, but non-software experts can only change some of these configurations. Nowadays, advances in human–computer interaction provide techniques/metaphors to approach non-software experts. One approach is end-user development (EUD)—a set of activities and development tools that considers non-software experts as application builders. In this paper, we present our analysis of existing EUD approaches for building context-aware applications. We present a literature review of 37 screened papers obtained from research databases. This review aims to identify the methods, techniques, and tools proposed to build context-aware applications. Specifically, we reviewed EUD building techniques and implementations. Building techniques include metaphors/interaction styles proposed for application specification, composition, and testing. The implementations include a specification method to integrate and process context on the target application platforms. We also present the adoption trend and challenges of context-aware end-user development.

SensoMan: Social Management of Context Sensors and Actuators for IoT

Sensor networks that collect data from the environment can be utilized in the development of context-aware applications, bringing into sight the need for data collection, management, and distribution. Boards with microcontrollers, such as Arduino and Raspberry Pi, have gained wide acceptance and are used mainly for educational and research purposes. Utilizing the information available via sensors connected to these platforms requires extended technical knowledge. In this work, we present a sensor management framework, SensoMan, that manages a collection of sensors spread in the environment connected to microcontroller boards. We present the framework’s architecture, a method for sensor data management, and a prototype system. Sensor data can also trigger the execution of actions on actuators. Thus, we further propose a rule engine as well as social connectivity following a scheme where sensors and their data can be shared among users. Our work shows that the creation of such a system is feasible and can use simple equipment (e.g., sensors, controller plugs) that can be replicated in other environments. The use of SensoMan is demonstrated via two scenarios that show its potential in combining simple tools that do not require an extended learning curve. A small-scale user study was also performed.

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.

Sensor-based and vision-based human activity recognition: A comprehensive survey

Human activity recognition (HAR) technology that analyzes data acquired from various types of sensing devices, including vision sensors and embedded sensors, has motivated the development of various context-aware applications in emerging domains, e.g., the Internet of Things (IoT) and healthcare. Even though a considerable number of HAR surveys and review articles have been conducted previously, the major/overall HAR subject has been ignored, and these studies only focus on particular HAR topics. Therefore, a comprehensive review paper that covers major subjects in HAR is imperative. This survey analyzes the latest state-of-the-art research in HAR in recent years, introduces a classification of HAR methodologies, and shows advantages and weaknesses for methods in each category. Specifically, HAR methods are classified into two main groups, which are sensor-based HAR and vision-based HAR, based on the generated data type. After that, each group is divided into subgroups that perform different procedures, including the data collection, pre-processing methods, feature engineering, and the training process. Moreover, an extensive review regarding the utilization of deep learning in HAR is also conducted. Finally, this paper discusses various challenges in the current HAR topic and offers suggestions for future research.

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

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

ContextAiDe

Mobile crowd-sensing (MCS) enables development of context-aware applications by mining relevant information from a large set of devices selected in an ad hoc manner. For example, MCS has been used for real-time monitoring such as Vehicle ad hoc Networks-based traffic updates as well as offline data mining and tagging for future use in applications with location-based services. However, MCS could be potentially used for much more demanding applications such as real-time perpetrator tracking by online mining of images from nearby mobile users. A recent example is tracking the miscreant responsible for the Boston bombing. We present a new design approach for tracking using MCS for such complex processing in real time. Since MCS applications assume an unreliable underlying computational platform, most typically sample size for recruited devices is guided by concerns such as fault tolerance and reliability of information. As the real-time requirements get stricter coupled with increasing complexity of data-mining approaches, the communication and computation overheads can impose a very tight constraint on the sample size of devices needed for realizing real-time operation. This results in trade-off in acquiring context-relevant data and resource usage incurred while the real-time operation requirements get updated dynamically. Such effects have not been properly studied and optimized to enable real-time MCS applications such as perpetrator tracking. In this article, we propose ContextAiDe architecture, a combination of API, middleware, and optimization engine. The key innovation in ContextAiDe is context-optimized recruitment for execution of computation- and communication-heavy MCS applications in edge environment. ContextAiDe uses a notion of two types of contexts, exact (hard constraints), which have to be satisfied, and preferred (soft constraints), which may be satisfied to a certain degree. By adjusting the preferred contexts, ContextAiDe can optimize the operational overheads to enable real-time operation. ContextAiDe provides an API to specify contexts requirements and the code of MCS app, offload execution environment, a middleware that enables context-optimized and a fault-tolerant distributed execution. ContextAiDe evaluation using a real-time perpetrator tracking application shows reduced energy consumption of 37.8%, decrease in data transfer of 24.8%, and 43% less time compared to existing strategy. In spite of a small increase in the minimum distance from the perpetrator, iterations of optimization tracks the perpetrator successfully. Pro-actively learning the context and using stochastic optimization strategy minimizes the performance degradation caused due to uncertainty (<20%) in usage-dependent contexts.

An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics

In large-scale context-aware applications, a central design concern is capturing, managing and acting upon location and context data. The ability to understand the collected data and define meaningful contextual events, based on one or more incoming (contextual) data streams, both for a single and multiple users, is hereby critical for applications to exhibit location- and context-aware behaviour. In this article, we describe a context-aware, data-intensive metrics platform —focusing primarily on its geospatial support—that allows exactly this: to define and execute metrics, which capture meaningful spatio-temporal and contextual events relevant for the application realm. The platform (1) supports metrics definition and execution; (2) provides facilities for real-time, in-application actions upon metrics execution results; (3) allows post-hoc analysis and visualisation of collected data and results. It hereby offers contextual and geospatial data management and analytics as a service, and allow context-aware application developers to focus on their core application logic. We explain the core platform and its ecosystem of supporting applications and tools, elaborate the most important conceptual features, and discuss implementation realised through a distributed, micro-service based cloud architecture. Finally, we highlight possible application fields, and present a real-world case study in the realm of psychological health.

An overview of a novel analysis approach for enhancing context awareness in smart environments

Abstract Context This work is part of context aware applications design and development, and smart environments in which context changes frequently. Objective The objective of the work is to facilitate the design and the development of context aware applications able to detect context changes and to predict context. Method In the paper, two analysis tasks are proposed. An analysis task for detection aiming at supporting application designers to conceive easily context aware applications able to detect context changes and an analysis task for prediction aiming at helping the application designers to conceive context aware applications able to predict context. The paper details also an analysis module that implements the functionalities of the analysis tasks. The analysis module helps the application developers to develop context aware applications. Finally, the paper introduces a case study related to smart buildings in order to show the usefulness of the analysis tasks. Results The paper shows an application scenario related to smart buildings and particularly water consumption prediction. Also the paper presents experiments related to memory consumption introduced by the use of our analysis module. Conclusions The application scenario illustrates the usefulness of the analysis approach. The overhead introduced by the analysis module is negligeable.

Crowd-Based Learning of Spatial Fields for the Internet of Things: From Harvesting of Data to Inference

The knowledge of spatial distributions of physical quantities, such as radio-frequency (RF) interference, pollution, geomagnetic field magnitude, temperature, humidity, audio, and light intensity, will foster the development of new context-aware applications. For example, knowing the distribution of RF interference might significantly improve cognitive radio systems [1], [2]. Similarly, knowing the spatial variations of the geomagnetic field could support autonomous navigation of robots (including drones) in factories and/or hazardous scenarios [3]. Other examples are related to the estimation of temperature gradients, detection of sources of RF signals, or percentages of certain chemical components. As a result, people could get personalized health-related information based on their exposure to sources of risks (e.g., chemical or pollution). We refer to these spatial distributions of physical quantities as spatial fields. All of the aforementioned examples have in common that learning the spatial fields requires a large number of sensors (agents) surveying the area [4], [5].

Taming the IoT data deluge: An innovative information-centric service model for fog computing applications

Fog Computing is a new computation paradigm, recently emerged from the convergence of IoT, WSN, mobile computing, edge computing, and Cloud Computing, which is particularly well suited for Smart City environments. Fog Computing aims at supporting the development of time-sensitive, location-, social-, and context-aware applications by using computational resources in close proximity of information producers and consumers, such as increasingly common cheap and powerful modern hardware platforms. However, realizing Fog Computing solutions for Smart Cities represents a very challenging task, because of the massive amount of data to process, the strict resource and time constraints, and the significant dynamicity and heterogeneity of computation and network resources. These formidable challenges suggest taking into consideration new information and service model solutions that explore several trade-offs between processing speed and accuracy. Along these guidelines, we designed the SPF Fog-as-a-Service platform, which proposes a new information-centric and utility-based service model and allows the definition of self-adaptive and composition-friendly services, which can execute either on edge devices or in the Cloud. In numerous evaluations, SPF proved to be a very effective platform for running Fog services on heterogeneous devices with significantly different computational capabilities while also demonstrating remarkable ease of development and management characteristics.

Cloud computing and big data: Technologies and applications

Cloud computing and big data: Technologies and applications

RETHINKING INDOOR LOCALIZATION SOLUTIONS TOWARDS THE FUTURE OF MOBILE LOCATION-BASED SERVICES

Abstract. Satellite navigation systems with GNSS-enabled devices, such as smartphones, car navigation systems, have changed the way users travel in outdoor environment. GNSS is generally not well suited for indoor location and navigation because of two reasons: First, GNSS does not provide a high level of accuracy although indoor applications need higher accuracies. Secondly, poor coverage of satellite signals for indoor environments decreases its accuracy. So rather than using GNSS satellites within closed environments, existing indoor navigation solutions rely heavily on installed sensor networks. There is a high demand for accurate positioning in wireless networks in GNSS-denied environments. However, current wireless indoor positioning systems cannot satisfy the challenging needs of indoor location-aware applications. Nevertheless, access to a user’s location indoors is increasingly important in the development of context-aware applications that increases business efficiency. In this study, how can the current wireless location sensing systems be tailored and integrated for specific applications, like smart cities/grids/buildings/cars and IoT applications, in GNSS-deprived areas.

TestAWARE

Although mobile context instrumentation frameworks have simplified the development of mobile context-aware applications, it remains challenging to test such applications. In this paper, we present TestAWARE that enables developers to systematically test context-aware applications in laboratory settings. To achieve this, TestAWARE is able to download, replay and emulate contextual data on either physical devices or emulators. To support both white -box and black-box testing, TestAWARE has been implemented as a novel structure with a mobile client and code library. In blackbox testing scenarios, developers can manage data replay through the mobile client, without writing testing scripts or modifying the source code of the targeted application. In white-box testing scenarios, developers can manage data replay and test functional/non-functional properties of the targeted application by writing testing scripts using the code library. We evaluated TestAWARE by quantifying its maximal data replay speed, and by conducting a user study with 13 developers. We show that TestAWARE can overcome data synchronisation challenges, and found that PC-based emulators can replay data significantly faster than physical smartphones and tablets. The user study highlights the usefulness of TestAWARE in the systematic testing of mobile context-aware applications in laboratory settings.

Heavyweight extension to the UML class diagram metamodel for modeling context aware systems in ubiquitous computing

PurposeThe development of context-aware applications in ubiquitous environments depends not only on the user interactions but also on several context parameters. The handling of these parameters is a fundamental problem in these systems. The key purpose of this work is to enrich the unified modeling language (UML) class diagram with new constructs to provide a universal model capable of coping with the context-awareness concerns.Design/methodology/approachThe authors provide a review of existing context handling approaches. Afterward, they relied on the UML extensibility mechanisms to propose a heavyweight extension for the UML class diagram. This generic approach allows describing the different context parameters since the modeling phase.FindingsExisting solutions for context handling apply the contextual constraints on finished applications or tend to be dependent on a specific development process. This paper presents a solution based on UML, which allows dealing with context since the modeling phase, and independently of development processes. This proposal is implemented as an eclipse editor and illustrated through a case study in the healthcare field.Originality/valueThis paper addresses the problem of context handling, and it presents a review of the foremost existing solutions. The paper also presents a heavyweight extension for the UML class diagram, which consists in enriching it with additional constructs, capable of monitoring how applications are linked to context parameters and how the values of these parameters may affect the application behavior.

A novel analysis approach for the design and the development of context-aware applications

Abstract In this paper, we propose a novel analysis approach, called ANALOG, for the design and the development of context-aware applications able to detect context change and to predict context parameter values. Our approach is described by two analysis procedures, (a) an analysis procedure for detection and (b) an analysis procedure for prediction. The proposed analysis procedures aim to offer a support for application designers allowing them to design easily context-aware applications. Each procedure is achieved by a sequence of steps performed by the designers. We describe first our analysis approach presented by the analysis procedures. Then, we give some implementation details of our approach. Afterwards, we show the usefulness of the analysis approach through presenting two execution scenarios related to a smart building case study. Finally, to illustrate the effectiveness of our approach, we present different experiments related to (i) the processing time of the analysis approach and (ii) the CPU and the memory overhead introduced by our approach.

Context Management for Supporting Context-aware Android Applications Development

<p class="0abstract">Building context-aware mobile applications is one of the most ambitious areas of research. Such applications can change their behavior according to context or perform specific tasks in specific contexts. Regardless of the application, all context-aware mobile applications share the need to retrieve and process context information. This paper presents a Context Management tool for the Android platform (ACM). ACM allows easy access to internal on-board mobile sensors and hardware features extracting corresponding raw data. Raw context is processed into higher-level more human-readable context that is provided seamlessly to the mobile applications. Different methods are used for this purpose including fuzzy classifiers. Since different mobiles have different sensors and hardware features, ACM can adapt to the mobile device by deactivating access to unavailable ones. Information regarding the available sensors and hardware features and their specifications can also be queried. Additionally, applications can request notifications regarding context change or specific context values. In addition to providing developers with supporting classes and methods, ACM is accompanied by an application that allows developers to examine its functionality and capabilities before using it. The application can be also used to examine the readings of the different sensors in different situations and thus calibrate them as needed. Additionally, it can be used to modify and personalize default interpretations of raw context values to high-level ones. ACM has been tested empirically and the results show extreme interest of context-aware mobile application developers in its promising capabilities and that it is conducive to facilitating, speeding up and triggering development of many more of such applications.</p>

A model-driven development approach for context-aware systems

The widespread usage of various types of computer devices with different platform characteristics created a need for new methods and tools to support the development of context-aware applications capable of dynamically adapting themselves to context changes. In this paper, we present a new model-based approach that addresses the development of context-aware applications from both the theoretical and practical perspectives and that supports all development phases of context-aware systems. On the one hand, we describe how our approach is applied to dynamically capture, observe the change of the context and notify the system at runtime. On the other hand, we show how our approach is used by programmers to develop a context- aware application.

An aspect-oriented model-driven approach for building adaptable context-aware service-based applications

Context-aware service-based applications development has been considered among the most studied research fields in the last decade. The objective was to accompany the rapid technology evolution of mobile computing devices by providing customized services able to interact with different contextual situations of a pervasive environment. For this purpose, many research works have advocated Model-Driven Development (MDD) for building context-aware service-based applications. However, the proposed approaches have presented specific methodologies without using development standards, which may be followed by developers. In addition, most of them have ignored the dynamic adaptation aspect at runtime that should characterize such kind of applications and no adaptation strategy was considered in their proposals. The current paper aims to propose a generic model-driven approach for context-aware service-based applications engineering with a software development methodology including a reconfiguration loop to achieve the dynamic adaptation of these applications. This approach focuses on the combination of MDD and Aspect Oriented Modelling (AOM) to take advantage of their benefits. AOM encapsulates different context-awareness logics separately in aspect models called ContextAspect that can be easily woven into the service's business logic according to the changing context over time. The proposed development methodology includes four phases (modelling, composition, transformation and adaptation) which act in conformance with the MDA technology. The main results gained by using the present approach are the possibility to combine the MDA technology with the aspect-oriented paradigm in a generic development methodology for context-aware service-based applications, and the handling of their dynamic adaptation at execution time according to the changes in the context. The development of context-aware applications is a complex, cumbersome, and time-consuming task. However, the experience reached by implementing the proposed methodology leads us to believe that the involvement of MDD and AOM is significantly beneficial to overcome some recognised shortcomings of several existing approaches and to make this task simpler, easier and faster.