A survey on the use of machine learning methods in context-aware middlewares for human activity recognition

Human activity recognition (HAR) essentially uses (past) sensor data or complex context information for inferring the activities a user performs in his daily tasks. HAR has been extensively studied using different paradigms, such as different reasoning mechanisms, including probabilistic, rule-based, statistical, logical reasoning, or the machine learning (ML) paradigm, to construct inference models to recognize or predict user activities. ML for HAR allows that activities can be recognized and even anticipated through the analysis of collected data from different sensors, with greater accuracy than the other paradigms. On the other hand, context-aware middlewares (CAMs) can efficiently integrate a large number of different devices and sensors. Moreover, they provide a programmable and auto-configurable infrastructure for streamline the design and construction of software solutions in scenarios where lots of sensors and data are their bases, such as ambient intelligence, smart cities, and e-health domains. In this way, the full integration of ML capabilities as services in CAMs can advance the development of software solutions in these domains when ML is necessary, specially for HAR, which is the basis for many scenarios in these domains. In this work, we present a survey for identifying the state-of-the-art in using ML for HAR in CAMs through a systematic literature review (SLR). In our SLR, we worked to answer four research questions: (i) what are the different types of context reasoners available in CAMs; (ii) what are the ML algorithms and methods used for generating models for context reasoning; (iii) which CAMs support data processing in real time; and (iv) what are the HAR scenarios usually tackled by the research works. In our analysis, we observed that, although ML offers viable approaches to construct inference models for HAR using different ML approaches, including batch learning, adaptive learning and data stream learning, there are yet some gaps and research challenges to be tackled, specially on the use of data stream learning considering concept drift on data, mechanisms for adapting the inference models, and further considering all of this as services in CAMs, specially for HAR.

     

A context-aware mobile application framework using audio watermarking

Multimedia Systems - In this paper, we propose a proximity-based indoor positioning system which is capable of monitoring mobile device user’s indoor locations where the commonly used GPS...     

A framework for context-aware self-adaptive mobile applications SPL

Mobile Applications are rapidly emerging as a convenient medium for using a variety of services. Over time and with the high penetration of smartphones in society, self-adaptation has become an essential capability required by mobile application users. In an ideal scenario, an application is required to adjust its behavior according to the current context of its use. This raises the challenge in mobile computing towards the design and development of applications that sense and react to contextual changes to provide a value-added user experience. In its general sense, context information can relate to the environment, the user, or the device status. In this paper, we propose a novel framework for building context aware and adaptive mobile applications. Based on feature modeling and Software Product Lines (SPL) concepts, this framework guides the modeling of adaptability at design time and supports context awareness and adaptability at runtime. In the core of the approach, is a feature meta-model that incorporates, in addition to SPL concepts, application feature priorities to drive the adaptability. A tool, based on that feature model, is presented to model the mobile application features and to derive the SPL members. A mobile framework, built on top of OSGI framework to dynamically adapt the application at runtime is also described.     

A new model for context-aware transactions in mobile services

With the ubiquity of handheld devices (such as smart phones and PDAs) and the availability of a wide range of mobile services (such as mobile banking, road traffic updates, and weather forecast), people can nowadays access information and conduct online transactions virtually anywhere and anytime. In such flexible, dynamic but less reliable environment, transaction management technology is believed to provide service reliability and data consistency. Indeed, in mobile and ubiquitous environments where devices as well as services can seamlessly join and leave the ubiquitous network; transaction management can be very helpful during the recovery of services from failure. Current transaction models and commit protocols do not take into account context information. However, in mobile environments, it is imperative to consider context information in the commit of a transaction—i.e., a transaction can be successfully completed if it meets the required context. In this paper, we propose a new model for context-aware transactions and their performance management in mobile environments. Unlike conventional transactions, context-aware transactions adapt to the required context. By context, we mean the service’s context as well as the users’ context that includes users’ needs and preferences. This paper designs and develops the proposed transaction model and evaluates its performance in terms of time and message complexities as well as transaction’s throughput.     

A pluggable middleware architecture for developing context-aware mobile applications

The proliferation of powerful smartphone devices provides a great opportunity for context-aware mobile applications becoming mainstream. However, we argue that conventional software development techniques suffer because of the added complexity required for collecting and managing context information. This paper presents a component-based middleware architecture which facilitates the development and deployment of context-aware applications via reusable components. The main contribution of this approach is the combination of a development methodology with the middleware architecture, which together bring significant value to developers of context-aware applications. Further contributions include the following: The methodology utilizes separation of concerns, thus decreasing the developmental cost and improving the productivity. The design and implementation of context-aware applications are also eased via the use of reusable components, called context plug-ins. Finally, the middleware architecture facilitates the deployment and management of the context plug-ins in a resource-aware manner. The proposed methodology and middleware architecture are evaluated both quantitatively and qualitatively.     

Programming mobile context-aware applications with TOTAM

In tuple space approaches to context-aware mobile systems, the notion of context is defined by the presence or absence of certain tuples in the tuple space. Existing approaches define such presence either by collocation of devices holding the tuples or by replication of tuples across all devices. We show that both approaches can lead to an erroneous perception of context. Collocation ties the perception of context to network connectivity which does not always yield the expected result. Tuple replication can cause that a certain context is perceived even if the device has left the context a long time ago. We propose a tuple space approach in which tuples themselves carry a predicate that determines whether they are in the right context or not. We present a practical API for our approach and show its use by means of the implementation of various mobile applications. Benchmarks show that our approach can lead to a significant increase in performance compared to other approaches.     

Modular framework support for context-aware mobile cinema

In this paper we describe a novel approach for interactive cinema based on context-aware narration using handheld computers. The paper describes both the artistic approach and the ubiquitous computing framework developed to realize the scenario. This framework has been used in various projects, including the described video production course at the ISNM, where five interactive cinema concepts have been developed and shown during a public demonstration. In our approach, a new type of user experience has been established by placing the viewer inside the movie’s physical locations during playback. Moreover, the developed ubiquitous computing framework provides a foundation for future work in the area of ad-hoc, service-oriented Ubicomp scenarios.     

Uncertainty handling in rule-based mobile context-aware systems

Uncertainty handling is one of the most important aspects of modelling of context-aware systems. It has direct impact on the adaptability, understood as an ability of the system to adjust to changing environmental conditions or hardware configuration (missing data), changing user habits (ambiguous concepts), or imperfect information (low quality sensors). In mobile context-aware systems, data is most often acquired from device’s hardware sensors (like GPS, accelerometer), virtual sensors (like activity recognition sensor provided by the Google API) or directly from the user. Uncertainty of such data is inevitable, and therefore it is obligatory to provide mechanisms for modelling and processing it. In this paper, we propose three complementary methods for dealing with most common uncertainty types present in mobile context-aware systems. We combine modified certainty factors algebra, probabilistic interpretation of rule-based model, and time-parametrised operators into a comprehensive toolkit for modelling and building robust mobile context-aware systems. Presented approach was implemented and evaluated on the practical use-case.     

A survey on mobile affective computing

The spontaneous recognition of emotional states and personality traits of individuals has been puzzling researchers for years whereas pertinent studies demonstrating the progress in the field, despite their diversity, are still encouraging. This work surveys the most well-known research studies and the state-of-the-art on affect recognition domain based on smartphone acquired data, namely smartphone embedded sensors and smartphone usage. Inevitably, supplementary modalities employed in many eminent studies are also reported here for the sake of completeness. Nevertheless, the intention of the survey is threefold; firstly to document all the to-date relevant literature on affect recognition through smartphone modalities, secondly to argue for the full potential of smartphone use in the inference of affect, and thirdly to demonstrate the current research trends towards mobile affective computing.     

A cross-layer middleware for context-aware cooperative application on mobile ad hoc peer-to-peer network

Mobile ad hoc peer-to-peer (P2P) applications become popular for providing the file sharing, voice communicating, and video streaming services due to entertainments and disaster recovery. However, both the topology of wireless network and the overlay of P2P network are dynamic, so the middleware is proposed to integrate such architectures of service-oriented applications. Therefore, we propose context-aware cooperative application (CACA) to overcome the frequent churn and high mobility problems. CACA proposes a cross-layer middleware to integrate DHT-based lookup, anycast query, and P2P delivery via the IPv6 routing header. Through anycast query, the response delay can be shortened and the query duplication can be minimized. Via IPv6 routing header, the delivery efficiency can be improved. Through the cross-layer design, the finger table in overlay layer is combined with the routing table in network layer to heighten proximity. The simulation results demonstrate that CACA has the outstanding performances of short download delay, high playback continuity, and low signaling overhead in mobile ad hoc network.     

Dealing with variability in context-aware mobile software

Mobile context-aware software pose a set of challenging requirements to developers as these applications exhibit novel features, such as handling varied sensing devices and dynamically adapting to the user’s context (e.g. his or her location), and evolve quickly according to technological advances.In this paper, we discuss how to handle variability both across different domains and during the evolution of a single application. We present a set of design structures for solving different problems related with mobility (such as location sensing, behaviour adaptation, etc.), together with the design rationale underlying them, and show how these sound micro-architectural constructs impact on variability. Our presentation is illustrated with case studies in different domains.     

A cloud-based platform to develop context-aware mobile applications by domain experts

Context-awareness enables the personalization of computer systems according to the users' needs and their particular situation at a given time. The personalization capabilities are usually implemented by programmers due to the complex processes that are involved. However, an important trend in software development is that more and more software systems are being implemented not only by programmers but also by people with expertise in other domains. Since most of the existing context-aware development toolkits are designed for programmers, non-technical users cannot develop these kinds of systems. The design of tools to create context-aware systems by users that do not have programming skills but are experts in the domain where the system is going to be deployed, will contribute to speed up the adoption of these kinds of services by the society. This paper presents a cloud-based platform to ease the development of context-aware mobile applications by people without programming skills. The platform has been designed to be used in a tourism domain. This way, tourism experts can send tourist information to mobile users according to their context data (indoor/outdoor location, language, and date and time range). An energy-efficient mobile app has been developed in order to obtain context data from the user's device and to receive personalized information in real time based on these data. The architecture and implementation details of the system are presented and the evaluation of the platform by tourism domain experts is discussed.     

A user-centric evaluation of context-aware recommendations for a mobile news service

Traditional recommender systems provide personal suggestions based on the user’s preferences, without taking into account any additional contextual information, such as time or device type. The added value of contextual information for the recommendation process is highly dependent on the application domain, the type of contextual information, and variations in users’ usage behavior in different contextual situations. This paper investigates whether users utilize a mobile news service in different contextual situations and whether the context has an influence on their consumption behavior. Furthermore, the importance of context for the recommendation process is investigated by comparing the user satisfaction with recommendations based on an explicit static profile, content-based recommendations using the actual user behavior but ignoring the context, and context-aware content-based recommendations incorporating user behavior as well as context. Considering the recommendations based on the static profile as a reference condition, the results indicate a significant improvement for recommendations that are based on the actual user behavior. This improvement is due to the discrepancy between explicitly stated preferences (initial profile) and the actual consumption behavior of the user. The context-aware content-based recommendations did not significantly outperform the content-based recommendations in our user study. Context-aware content-based recommendations may induce a higher user satisfaction after a longer period of service operation, enabling the recommender to overcome the cold-start problem and distinguish user preferences in various contextual situations.     

基于蓝牙情境感知技术的移动交友系统设计

针对现阶段大龄青年交友难题,提出了基于蓝牙情境感知技术的交友服务设计方案,帮助用户随时随地交友。这种交友方式根据用户的预先设置,自动搜索周围符合条件的人,并征求用户是否与之取得联系。整个设计过程采取了以用户为中心的设计理念,结合用户需求与特点进行设计。通过与用户进行访谈,了解用户需求及建议,并以此为标准指导设计与开发,尤其在用户强调的隐私、安全等方面做了充分考虑。采用Java语言开发了系统原型用以体现设计理念,并证明了该技术的可行性。     

An application framework for mobile,context-aware trails

In this paper we describe the design, implementation and evaluation of a software framework that supports the development of mobile, context-aware trails-based applications. A trail is a contextually scheduled collection of activities and represents a generic model that can be used to satisfy the activity management requirements of a wide range of context-based time management applications. Trails overcome limitations with traditional time management techniques based on static to-do lists by dynamically reordering activities based on emergent context.