基于活动的情境感知系统交互设计

情境感知是移动和普适计算研究中一个重要的组成部分。现有研究大多从技术的角度进行情境感知系统的设计和实现。由于缺少对人的情境感知机制的认识,导致系统的情境感知行为和人的期望之间存在着很大的差距。在活动理论和人的情境感知理论的基础上,提出了基于活动的情境感知模型,用于描述特定活动中人的情境感知过程;并在该模型的基础上,提出了基于活动的情境感知系统交互设计方法。通过基于活动的情境感知购物助手的交互设计过程,验证了基于活动的情境感知模型在情境感知系统交互设计中的应用。     

EXEHDA-HM: A compositional approach to explore contextual information on hybrid models

The current proposals of hybrid context modeling bring new challenges, an important one is how applications can access and process data stored on these models. Thinking about that, this paper proposes a solution to deal with this challenge through a compositional approach that explores the context information on hybrid models, called EXEHDA-HM. The proposed approach stands out by the design of a repository that supports three database models and by the compositional processing strategy based on rules. In our proposal, the applications can combine data stored on different bases in a single rule, which could enhance the identification of contextual situations. For the evaluation we designed and implemented some case studies on information security area, exploring the hybrid repository composed of relational, non-relational, and triple storage models. Our results demonstrate that was possible to identify richer situations with the data composition across more than one model and there are situations that can only be found through this composition.     

Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues

In wireless networks, context awareness and intelligence are capabilities that enable each host to observe, learn, and respond to its complex and dynamic operating environment in an efficient manner. These capabilities contrast with traditional approaches where each host adheres to a predefined set of rules, and responds accordingly. In recent years, context awareness and intelligence have gained tremendous popularity due to the substantial network-wide performance enhancement they have to offer. In this article, we advocate the use of reinforcement learning (RL) to achieve context awareness and intelligence. The RL approach has been applied in a variety of schemes such as routing, resource management and dynamic channel selection in wireless networks. Examples of wireless networks are mobile ad hoc networks, wireless sensor networks, cellular networks and cognitive radio networks. This article presents an overview of classical RL and three extensions, including events, rules and agent interaction and coordination, to wireless networks. We discuss how several wireless network schemes have been approached using RL to provide network performance enhancement, and also open issues associated with this approach. Throughout the paper, discussions are presented in a tutorial manner, and are related to existing work in order to establish a foundation for further research in this field, specifically, for the improvement of the RL approach in the context of wireless networking, for the improvement of the RL approach through the use of the extensions in existing schemes, as well as for the design and implementation of RL in new schemes.     

Automated context aggregation and file annotation for PAN-based computing

This paper presents a method for automatically annotating files created on portable devices with contextual metadata. We achieve this through the combination of two system components. One is a context dissemination mechanism which allows devices in a personal area network (PAN) to maintain a shared aggregate contextual perception. The other is a storage management system that uses such context information to automatically decorate files created on personal devices with annotations. As a result, the user is able to flexibly browse and lookup files that were generated on the move, based on the contextual situation at the time of their creation. What is equally important is that the user is relieved from the cumbersome task of having to manually provide annotations in an explicit fashion. This is especially valuable when generating files on the move, using U/I-restricted portable devices.

移动云计算中的上下文相关身份认证

上下文相关信息,例如可操作的、用户个人的、空间的、环境的信息能够为用户提供他们确实需要而没有明确表示的服务。利用上下文内容相关的方法分析移动设备提供的用户通话记录、日程表、GPS和用户安装的应用。在收集这几方面信息之后,加以考虑时间和地点的因素,使整个认证过程更有可靠性。提出在移动云计算背景下的上下文相关身份认证框架,并详细描述框架的各个组件及决策器的算法模型和通信协议部分,使用户能够更加方便、安全地使用移动云。     

A context awareness framework for cross-platform distributed applications

With the introduction of interconnected cross-platform middleware, a new area of opportunities for ubiquitous/pervasive computing has emerged. Context aware applications can be enhanced to practically and realistically incorporate multiple facets of human–machine interactions in everyday life that are not limited to a device-centered model for deducing context. In this paper, we propose that they can rather extend this model to a human-centered, device and platform independent model, based on a personal distributed application and data cloud ecosystem. For this to be achieved, webinos, a set of web runtime extensions that enable web applications and services to be used and shared consistently and securely over a broad spectrum of converged and connected devices, is used to provide this ecosystem. The webinos Context Awareness Framework described here is accessible to each webinos-enabled application. After strict policy enforcement, it can collect contextual information, either via an automatic mechanism that intercepts native calls made by webinos applications through the various webinos APIs, via an automatic polling mechanism to these APIs, or via custom, application-specific context schema extensions. It can then distribute the contextual information from its own personal cloud storage mechanism, in the form of simple, manageable and intuitive Context Objects, to and from all webinos-enabled devices owned by the same user, or even other, authorized users.     

移动情境感知及其交互研究

结合情境信息的移动应用和交互研究是普适计算领域中非常重要的内容.综合移动情境感知相关研究的发展,从人机交互研究的角度,提出了基于用户、环境和任务的情境信息分类方法.根据情境信息的数据获取、数据表示、系统架构、数据处理、服务应用和系统评价等六个方面,分析了移动情境感知应用研究的关键问题,总结了移动情境感知对人机交互研究中的研究方法、数据收集、用户控制感及交互方式等产生的影响.最后提出了当前移动情境感知及其交互研究中存在的问题和可能的研究方向.     

基于二次特征的模糊分类算法在普适计算上下文变化感知中的研究*

为了简化普适计算环境原始特征的复杂度,方便该环境下的知识推理,采用核典型相关分析方法进行原始特征的二次提取,得到简约而重要的二次特征。在此二次特征的基础上施以模糊K近邻法计算待测模式相对于各类中心的隶属度,并按最大隶属度原则确定该模式所属上下文场景类。本算法致力于在不显著损失分类准确度的情况下尽可能地降低分类复杂度,从而解决普适计算环境中上下文变化感知的问题。     

Acoustic environment as an indicator of social and physical context

Acoustic environments provide many valuable cues for context-aware computing applications. From the acoustic environment we can infer the types of activity, communication modes and other actors involved in the activity. Environmental or background noise can be classified with a high degree of accuracy using recordings from microphones commonly found in PDAs and other consumer devices. We describe an acoustic environment recognition system incorporating an adaptive learning mechanism and its use in a noise tracker. We show how this information is exploited in a mobile context framework. To illustrate our approach we describe a context-aware multimodal weather forecasting service, which accepts spoken or written queries and presents forecast information in several forms, including email, voice and sign languages.

There is more to context than location

Context is a key issue in interaction between human and computer, describing the surrounding facts that add meaning. In mobile computing location is usually used to approximate context and to implement context-aware applications. We propose that ultra-mobile computing, characterized by devices that are operational and operated while on the move (e.g. PDAs, mobile phones, wearable computers), can significantly benefit from a wider notion of context. To structure the field we introduce a working model for context, discuss mechanisms to acquire context beyond location, and application of context-awareness in ultra-mobile computing. We investigate the utility of sensors for context-awareness and present two prototypical implementations — a light-sensitive display and an orientation-aware PDA interface. The concept is then extended to a model for sensor fusion to enable more sophisticated context recognition. Based on an implementation of the model an experiment is described and the feasibility of the approach is demonstrated. Further, we explore fusion of sensors for acquisition of information on more sophisticated contexts.     

ML aided context feature extraction for cognitive radio

This paper addresses the estimation of different context features of a primary user network, such as transmitters’ positions, antenna patterns and directions, and propagation model characteristics. It is based on radio signal strength measurements obtained by a sensor network without any prior knowledge about the configuration of the primary transmitters in terms of antenna types or propagation model. A Maximum Likelihood Aided Context Feature Extraction (MLACFE) method is introduced based on applying image processing and a Maximum Likelihood estimation algorithm over the set of measurements to identify the existing transmitters in the scenario and their parameters. The proposed method can provide a quite similar performance than a classical ML method, in terms of average estimation errors while at the same time reducing the computation time in about three orders of magnitude, for the considered case study.     

Implicit human computer interaction through context

In this paper the term implicit human-computer interaction is defined. It is discussed how the availability of processing power and advanced sensing technology can enable a shift in HCI from explicit interaction, such as direct manipulation GUIs, towards a more implicit interaction based on situational context. In the paper, an algorithm is given based on a number of questions to identify applications that can facilitate implicit interaction. An XML-based language to describe implicit HCI is proposed. The language uses contextual variables that can be grouped using different types of semantics as well as actions that are called by triggers. The term of perception is discussed and four basic approaches are identified that are useful when building context-aware applications. Two examples, a wearable context awareness component and a sensor-board, show how sensor-based perception can be implemented. It is also discussed how situational context can be exploited to improve input and output of mobile devices.     

Sensor planning with Bayesian decision theory

In this paper ongoing work on an approach for planning sensing actions and controlling intelligent, purposive robotic systems is presented. The method uses Bayesian decision analysis (BDA) for deciding what sensing actions should be performed. This offers a probabilistic framework that provides a more dynamic and modular behaviour than traditional rule based planners. Experiments show that the Bayesian sensor planning strategy is capable of controlling an autonomous mobile robot operating in partly known environments.     

Ontology-based approach for context modeling in enterprise applications

Today, enterprise applications provide large amounts of data and finding the right information on time for a given purpose is often a challenge. In these environments, users do not know what information is important, why it is important and finally, how to find this important information. Therefore, an enterprise application has to decide which information is relevant in certain a situation for certain a user. In order to accomplish that, the context of the information must be taken to account. Moreover, this application must be able to capture the context of the application user as well as the overall business context which describes the situation in which information is relevant. In this paper we propose an ontology-based context model which captures the general concepts about user and business context. Also, we discuss the challenges for context reasoning and interpreting and we present a case study to demonstrate the benefits of the developed concepts.     

Using tracked mobile sensors to make maps of environmental effects

We present the results of a study of environmental carbon monoxide pollution that uses a set of tracked, mobile pollution sensors. The motivating concept is that we will be able to map pollution and other properties of the real world at a fine scale if we can deploy a large set of sensors with members of the general public who would carry them as they go about their normal everyday activities. To prove the viability of this concept we have to demonstrate that data gathered in an ad hoc manner is reliable enough in order to allow us to build interesting geo-temporal maps. We present a trial using a small number of global positioning system-tracked CO sensors. From analysis of raw GPS logs we find some well-known spatial and temporal properties of CO. Further, by processing the GPS logs we can find fine-grained variations in pollution readings such as when crossing roads. We then discuss the space of possibilities that may be enabled by tracking sensors around the urban environment—both in getting at personal experience of properties of the environment and in making summative maps to predict future conditions. Although we present a study of CO, the techniques will be applicable to other environmental properties such as radio signal strength, noise, temperature, humidity and so on.     

A two-tier test approach to developing location-aware mobile learning systems for natural science courses

The advancement of wireless and mobile technologies has enabled students to learn in an environment that combines learning resources from both the real world and the digital world. Although such an approach has been recognized as being innovative and important, several problems have been revealed in practical learning activities. One major problem is owing to the lack of proper learning strategies or tools for assisting the students to acquire knowledge in such a complex learning scenario. Students might feel excited or engaged when using the mobile devices to learn in the real context; nevertheless, their learning achievements could be disappointing. To deal with this problem, this study presents a mobile learning system that employs Radio Frequency Identification (RFID) technology to detect and examine real-world learning behaviors of students. This study also utilizes each student’s responses from a two-tier test (i.e., multiple-choice questions in a two-level format) to provide personalized learning guidance (called two-tier test guiding, T³G). The experimental results from a natural science course of an elementary school show that this innovative approach is able to improve the learning achievements of students as well as enhance their learning motivation.     

Toward personalized and adaptive QoS assessments via context awareness

Quality of Service (QoS) properties play an important role in distinguishing between functionally equivalent services and accommodating the different expectations of users. However, the subjective nature of some properties and the dynamic and unreliable nature of service environments may result in cases where the quality values advertised by the service provider are either missing or untrustworthy. To tackle this, a number of QoS estimation approaches have been proposed, using the observation history available on a service to predict its performance. Although the context underlying such previous observations (and corresponding to both user and service related factors) could provide an important source of information for the QoS estimation process, it has only been used to a limited extent by existing approaches. In response, we propose a context‐aware quality learning model, realized via a learning‐enabled service agent, exploiting the contextual characteristics of the domain to provide more personalized, accurate, and relevant quality estimations for the situation at hand. The experiments conducted demonstrate the effectiveness of the proposed approach, showing promising results (in terms of prediction accuracy) in different types of changing service environments.     

A lightweight and distributed middleware to provide presence awareness in mobile ubiquitous systems

Several researchers have identified the need to count on presence awareness in ubiquitous systems that support mobile activities, particularly when these systems are used to perform loosely-coupled mobile work. In such a work style, mobile users conduct face-to-face on-demand interactions, therefore counting on awareness information about the position and availability of potential collaborators becomes mandatory for these applications. Most proposed solutions that provide user presence awareness involve centralized components, have reusability limitations, or simply address a part of that service. This article presents a lightweight and fully distributed middleware named Moware, which allows developers to embed presence awareness services in mobile ubiquitous systems in a simple way. The article also describes the Moware architecture, its main components and strategies used to deal with several aspects of the presence awareness support. These design strategies can be reused by software designers to provide presence awareness capabilities into middleware and specific software applications. Moware services were embedded in a mobile ubiquitous system that supports inspectors during the construction inspection process. The preliminary results indicate that the middleware was easy to use for developers, and its services were useful for the end-users.     

A comprehensive approach to model and use context for adapting applications in pervasive environments

With an increasing diversity of pervasive computing devices integrated in our surroundings and an increasing mobility of users, it will be important for computer systems and applications to be context-aware. Lots of works have already been done in this direction on how to capture context data and how to carry it to the application. Among the remaining challenges are to create the intelligence to analyze the context information and deduce the meaning out of it, and to integrate it into adaptable applications. Our work focuses on these challenges by defining generic context storage and processing model and by studying its impact on the application core. We propose a reusable context ontology model that is based on two levels: a generic level and a domain specific level. We propose a generic adaptation framework to guarantee adaptation of applications to the context in a pervasive computing environment. We also introduce a comprehensive adaptation approach that involves content adaptation and presentation adaptation inline with the adaptation of the core services of applications. Our case study shows that the context model and the application adaptation strategies provide promising service architecture.     

Social functions of location in mobile telephony

Location appears to be one of the most important aspects of context in mobile communication. It is a complex piece of information involving several levels of detail. Location intertwines with other relevant aspects of context: the parties’ present activity, relative time and identities. The analysis of mobile conversations provides insights into the functions of “location” for mobile users. Most mobile calls involve a sequence in which location is reported. Location is made relevant by the parties’ activities. Location telling takes place in five different activity contexts during mobile calls. Location may be an index of interactional availability, a precursor for mutual activity, part of an ongoing activity, or it may bear emergent relevance for the activity or be presented as a social fact. Typically, joint activities make relevant spatio-temporal location such as distance in minutes from the meeting point via the vehicle used. For users, location does not appear to be relevant in purely geographical terms.