Aware Mobile Applications Research Articles

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.

Clustering Wi-Fi fingerprints for indoor–outdoor detection

This paper presents a method for continuous indoor-outdoor environment detection on mobile devices based solely on WiFi fingerprints. Detection of indoor outdoor switching is an important part of identifying a user's context, and it provides important information for upper layer context aware mobile applications such as recommender systems, navigation tools, etc. Moreover, future indoor positioning systems are likely to use Wi-Fi fingerprints, and therefore Wi-Fi receivers will be on most of the time. In contrast to existing research, we believe that these fingerprints should be leveraged, and they serve as the basis of the proposed method. Using various machine learning algorithms, we train a supervised classifier based on features extracted from the raw fingerprints, clusters, and cluster transition graph. The contribution of each of the features to the method is assessed. Our method assumes no prior knowledge of the environment, and a training set consisting of the data collected for just a few hours on a single device is sufficient in order to provide indoor-outdoor classification, even in an unknown location or when using new devices. We evaluate our method in an experiment involving 12 participants during their daily routine, with a total of 828 hours' worth of data collected by the participants. We report a predictive performance of the AUC (area under the curve) of 0.94 using the gradient boosting machine ensemble learning method. We show that our method can be used for other context detection tasks such as learning and recognizing a given building or room.

Data accuracy aware mobile healthcare applications

This paper proposes a new approach for online detection and isolation of inaccurate vital sign measurements in mobile healthcare applications. Our primary objective is to distinguish between inaccurate measurement and patient health degradation to reduce false medical alarms. The proposed approach couples dimensionality reduction with inaccurate data detection and isolation. On the one hand, dimension reduction is based on robust incremental Principal Component Analysis. On the other hand, multivariate anomaly detection relies on squared prediction errors and anomalous vital sign isolation is based on contribution plots. We apply our proposed approach on real medical dataset. Our simulation results prove the effectiveness of our approach in achieving good recall with a low false alarm rate compared with existing solutions. The benefit gained by our approach in terms of time and space complexities make it useful and efficient for real time mobile healthcare applications.

Socially aware mobile application integrations in heterogeneous environments

Mobile web integration technologies and wireless sensor networks provide much more opportunities for human beings to interact with heterogeneous network environment including the communication network, the internet and sensor network. This paper investigates the integrated use of socially aware computing and data mining techniques through a mobile web application. It emphasises the study of regularity of human social activities through mobile applications in order to provide mobile users with intelligent assistance and support in the real world. The research investigates the effectiveness of single-layer and multi-layer evolutionary models of socially aware computing in order for these mobile web applications to be better integrated with the human social activities. Experimental analysis of our self-developed mobile platform as proposed from this research showed the superiority of socially aware computing when applied in the mobile field.

Support Vector Machine Based Mobility Prediction Scheme in Heterogeneous Wireless Networks

To improve the intelligence of the mobile-aware applications in the heterogeneous wireless networks (HetNets), it is essential to establish an advanced mechanism to anticipate the change of the user location in every subnet for HetNets. This paper proposes a multiclass support vector machine based mobility prediction (Multi-SVMMP) scheme to estimate the future location of mobile users according to the movement history information of each user in HetNets. In the location prediction process, the regular and random user movement patterns are treated differently, which can reflect the user movements more realistically than the existing movement models in HetNets. And different forms of multiclass support vector machines are embedded in the two mobility patterns according to the different characteristics of the two mobility patterns. Moreover, the introduction of target region (TR) cuts down the energy consumption efficiently without impacting the prediction accuracy. As reported in the simulations, our Multi-SVMMP can overcome the difficulties found in the traditional methods and obtain a higher prediction accuracy and user adaptability while reducing the cost of prediction resources.

Mobile Applications for Natural Parks: Guidelines Study for the Development of a Mobile Device Application

Abstract Context aware mobile applications are being adopted as a premium solution to enhance and augment the user experience of visitors of natural parks and other public and open spaces. Under this assumption, this paper reports on a selection of existent mobile park applications featuring a set of tests that helped electing their key features. The performed evaluation also allowed setting up a proposal for design guidelines aiming the development of such a kind of applications.

Activity Theory Approach to Developing Context-Aware Mobile Learning Systems for Understanding Scientific Phenomenon and Theories

Mobile computing offers potential opportunities for students’ learning especially when it combines a sensing device such as RFID (Radio Frequency Identification). Researchers have indicated that a key feature of in-field learning supported by mobile devices and technology is context awareness, with which context and functionality provided by the learning system are adapted to the user’s situation. However, the design of context-aware mobile learning systems provides the authors with major challenges in terms of both defining use context as well as developing appropriate concepts relevant to the design of contextual information on the systems. Educators have highlighted the role of activity in science development and learning, implying that activity theory can be used to design a mobile learning environment. This paper presents the use of activity theory as a framework for describing the design of a context aware mobile learning application. A case study for the design of a mobile learning application for science learning is described using activity theory.

Enabling Spatially Aware Mobile Applications

AbstractRecently, increasing numbers of mobile phones are appearing on the market that feature advanced navigation capabilities: embedded GPS receivers for global positioning, integrated digital compasses for detecting the heading of the device, or accelerometer‐based tilt sensors will potentially enable upcoming and future mobile phones to measure their location and orientation in 3D space. In this paper, we present an application framework for building spatially aware mobile applications – applications that visualize, process or exchange geospatial information – on mobile phones equipped with such features. The core component of the framework is a novel, platform‐independent XML data exchange format for the interface between application server and mobile device that describes the geographic vicinity of the user. The format enables a variety of new mobile interaction styles and user interface types – from traditional text‐based local search and information interfaces to innovative real‐time user interfaces like Geo‐Wands and Smart Compasses.

Mobile computing with the Rover toolkit

Rover is a software toolkit that supports the construction of both mobile-transparent and mobile-aware applications. The mobile-transparent approach aims to enable existing applications to run in a mobile environment without alteration. This transparency is achieved by developing proxies for system services that hide the mobile characteristics of the environment from applications. However, to excel, applications operating in the harsh conditions of a mobile environment must often be aware of and actively adapt to those conditions. Using the programming and communication abstractions present in the Rover toolkit, applications obtain increased availability, concurrency, resource allocation efficiency, fault tolerance, consistency, and adaptation. Experimental evaluation of a suite of mobile applications demonstrates that use of the toolkit requires relatively little programming overhead, allows correct operation, substantially increases interactive performance, and dramatically reduces network utilization.

Building reliable mobile-aware applications using the Rover toolkit

This paper discusses extensions to the Rover toolkit for constructing reliable mobile-aware applications. The extensions improve upon the existing failure model, which addresses client or communication failures and guarantees reliable message delivery from clients to server, but does not address server failures (e.g., the loss of an incoming message due to server failure) (Joseph et al., 1997). Due to the unpredictable, intermittent communication connectivity typically found in mobile client environments, it is inappropriate to make clients responsible for guaranteeing request completion at servers. The extensions discussed in this paper provide both system- and language-level support for reliable operation in the form of stable logging of each message received by a server, per-application stable variables, programmer-supplied failure recovery procedures, server process failure detection, and automatic server process restart. The design and implementation of fault-tolerance support is optimized for high performance in the normal case (network connectivity provided by a high latency, low bandwidth, wireless link): measurements show a best-case overhead of less than 7% for a reliable null RPC over wired and cellular dialup links. Experimental results from both micro-benchmarks and applications, such as the Rover Web Browser proxy, show that support for reliable operation can be provided at an overhead of only a few percent of execution time during normal operation.