User needs for location-aware mobile services

Mobile contexts of use vary a lot, and may even be continuously changing during use. The context is much more than location, but its other elements are still difficult to identify or measure. Location information is becoming an integral part of different mobile devices. Current mobile services can be enhanced with location-aware features, thus providing the user with a smooth transition towards context-aware services. Potential application fields can be found in areas such as travel information, shopping, entertainment, event information and different mobile professions. This paper studies location-aware mobile services from the user's point of view. The paper draws conclusions about key issues related to user needs, based on user interviews, laboratory and field evaluations with users, and expert evaluations of location-aware services. The user needs are presented under five main themes: topical and comprehensive contents, smooth user interaction, personal and user-generated contents, seamless service entities and privacy issues.     

Using ubiquitous computing in interactive mobile marketing

Unique features of handheld devices, including their mobility, personalization and location-awareness engender new types of applications for mobile commerce, such as mobile advertising. Mobile marketing and advertising applications deliver promotional information to consumers based on their preferences and location. In this paper, we present SMMART, a context-aware, adaptive and personalized m-commerce application designed to deliver targeted promotions to the users of mobile devices about the products they like while guarding the users’ identity and protecting them from any unsolicited messages. Promotions distributed by SMMART are personalized by performing intelligent matching of the user’s shopping interests to current promotions available at a retail site. SMMART can adapt to changing preferences of its user by inconspicuously monitoring his or her shopping habits. We describe a fully functional prototype of SMMART built for Pocket PCs running Windows CE with .NET Compact Framework. This paper also presents a study demonstrating end-user usability and economic viability of SMMART.     

Ontological user modelling and semantic rule-based reasoning for personalisation of Help-On-Demand services in pervasive environments

Existing context-aware applications are limited in their support of user personalisation. Nevertheless, the increase in the use of context-aware technologies has sparked the growth in assistive applications resulting in a need to enable adaptation to reflect the changes in user behaviours. This paper introduces a systematic approach to service personalisation for mobile users in pervasive environments and presents a service-oriented distributed system architecture. The developed approach makes use of semantic technologies for user modelling and personalisation reasoning. In the paper we characterise user behaviours and needs in pervasive environments upon which ontological user models are created with special emphasis being placed on ontological modelling of dynamic and adaptive user profiles. We develop a rule-based personalisation mechanism that exploits semantic web rule mark-up language for rule design and a combination of semantic and rule-based reasoning for personalisation. We use two case studies focusing on providing personalised travel assistance for people using Help-on-Demand services deployed on a smart-phone to contextualise the discussions within the paper. The proposed approach is implemented in a prototype system, which includes Help-on-Demand services, content management services, user models and personalisation mechanisms in addition to application specific rules. Experiments have been designed and conducted to test and evaluate the approach with initial results demonstrating the functionality of the approach.     

A middleware architecture for privacy protection

The issue of user privacy is constantly in spotlight since an ever increasing number of online services collects and processes personal information from users, in the context of personalized service provision. In fact, recent advances in mobile communications, location and sensing technologies and data processing are boosting the deployment of context-aware personalized services and the creation of smart environments; but at the same time, they pose a serious risk on individuals’ privacy rights. Although technology makes the collection of data easy, its protection against abuse is left to data protection legislation. However, the privacy requirements, other than being general and abstract terms to be regarded as legislature issues, should be brought down in technological reality and carefully accounted for in devising technical solutions. In order to limit the disclosure and avoid the misuse of personal data, this paper discusses an architectural proposal for a middleware system that will enforce protection of user privacy through technical means. The proposed architecture mediates between the users, the service providers and the law, constituting a middleware shield for individuals’ personal data.     

Personalization and Context-awareness in Social Local Search: State-of-the-art and Future Research Challenges

Location-based services (LBS) are now the platforms for aggregating relevant information about users and understanding their mobile behavior and preferences based on the location histories. The increasing availability of large amounts of spatio-temporal data brings us opportunities and challenges to automatically discover valuable knowledge. While context-aware properties quickly became the key of the success of these pervasive applications, information related to user preferences and social signals still lack of adequate capitalization. Local search in LBSs is a peculiar service where recent and current interests, the network of explicit and implicit social interactions between users can be combined for effectively performing fine-tuned and personalized recommendations of points of interest. In this article we present the various and peculiar aspects of local search in mobile scenarios. Then we explore the added value of personalization and the benefits of considering social signals, summarizing open challenges and emerging technologies.     

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.     

A web-based pervasive recommendation system for mobile tourist guides

Mobile tourist guides have attracted considerable research interest during the past decade, resulting in numerous standalone and web-based mobile applications. Particular emphasis has been given to personalization of services, typically based on travel recommender systems used to assist tourists in choosing places to visit; these systems address an important aspect of personalization and hence reduce the information burden for the user. However, existing systems fail to exploit information, behaviours, evaluations or ratings of other tourists with similar interests, which would potentially provide ground for the cooperative production of improved tourist content and travel recommendations. In this paper, we extend this notion of travel recommender systems utilizing collaborative filtering techniques while also taking into account contextual information (such as the current user’s location, time, weather conditions and places already visited by the user) for deriving improved recommendations in pervasive environments. We also propose the use of wireless sensor network (WSN) installations around tourist sites for enabling precise localization and also providing mobile users convenient and inexpensive means for uploading tourist information and ratings about points of interest (POI) via their mobile devices. We also introduce the concept of ‘context-aware rating’, whereby user ratings uploaded through WSN infrastructures are weighted higher to differentiate among users that rate POIs using the mobile tourist guide application while onsite and others using the Internet away from the POI.     

Context-aware control of user-centric virtual networks: Centralized vs distributed approaches

Recently, the Internet has been overrun by a diversity of devices, applications, technologies, and Internet users with increasingly demanding personal preferences. Due to the explosive growth of Internet usage, the quantity of context information available in networking environments is also growing rapidly, triggering research for novel architectures and protocols that are enriched or personalized based on the particular features and dynamics of such information.This article describes a virtualized architecture that splits a physical network infrastructure into a set of logical networks (or Virtual Networks – VNs) configured to meet the particular context needs of their attached users (in terms of, e.g., price, security or services’ quality). Since this architecture can be driven by volatile context needs of highly mobile users, we also present the signaling mechanisms to create, extend and remove VNs in response to user context dynamics and mobility, which can be performed in a centralized or distributed way. Further, we define and evaluate context-aware metrics to configure a VN, and discover and select VNs to assign to users or network paths.The evaluation of the proposed approach shows that distributed approaches allow the fast discovery and adaptation of VNs, at the cost of a slightly larger overhead than centralized approaches when the context dynamics are too high. We also assess the impact of considering distinct levels of knowledge distribution and user context dynamics on the design and behavior of several processes for user association and VN control. Finally, we observe that context-driven VN discovery and resource-aware path selection schemes outperform the ones that, respectively, flood the network with VN discovery requests or use shortest path-based strategies to adapt VNs.     

普适计算认证过程基于粗糙集的隐私保护策略

在普适计算环境中,用户要获得需要的服务,需要向对应的服务提供商提供一定的认证信息,而这些认证信息中往往包含有用户不希望泄漏的隐私信息。为了对这些隐私信息进行保护,本文提出了认证过程中基于粗糙集的隐私保护策略：用户将认证信息扩展成粗糙集提供给服务提供商;服务提供商根据策略从粗糙集中提取用户的真实认证信息对用户请求进行认证。该策略充分利用了粗糙集合的不确定性,能够有效地防止用户隐私泄漏。     

The ECORA framework: A hybrid architecture for context-oriented pervasive computing

An infrastructure approach to support context-aware pervasive computing is advantageous for rapid prototyping of context-aware distributed applications and beneficial for unifying modelling of context and reasoning in uncertain conditions. This paper presents the ECORA framework for context-aware computing, which is designed with a focus on reasoning about context under uncertainty and addressing issues of heterogeneity, scalability, communication and usability. The framework follows an agent-oriented hybrid approach, combining centralized reasoning services with context-aware, reasoning capable mobile software agents. The use of a centralized reasoning engine provides powerful reasoning capabilities and deploying context-aware mobile agents enables agility and robustness of components in the pervasive system. The design and implementation of the framework at different levels, as well as three case studies, are presented.     

Crowdsourcing privacy preferences in context-aware applications

Developers of context-aware applications are faced with a tough challenge: powerful privacy controls are essential to maintain user trust, but they are also hard to use and not adequate in all situations. To address this tradeoff, we present Super-Ego, a crowdsourcing framework for privacy management of location information in ubiquitous environment. We study how crowdsourcing can be used to predict the user’s privacy preferences for different location on the basis of the general user population. The crowdsourcing methods are evaluated in a 2-week user study in which we tracked the locations of 30 subjects and asked them to provide privacy preferences for the locations they had visited. Our results show that by employing simple methods for semantic analysis of locations and by profiling the user’s privacy inclination, our methods can accurately predict the privacy preferences for 80 % of the user’s locations. By employing semi-automatic decision strategies, which ask the user to decide regarding the privacy of some of the locations, the accuracy rate raises to 90 %.     

Agent-based architecture for context-aware and personalized event recommendation

In order to offer context-aware and personalized information, intelligent processing techniques are necessary. Different initiatives considering many contexts have been proposed, but users preferences need to be learned to offer contextualized and personalized services, products or information. Therefore, this paper proposes an agent-based architecture for context-aware and personalized event recommendation based on ontology and the spreading algorithm. The use of ontology allows to define the domain knowledge model, while the spreading activation algorithm learns user patterns by discovering user interests. The proposed agent-based architecture was validated with the modeling and implementation of eAgora? application, which was illustrated at the pervasive university context.     

面向移动社交网络的位置隐私保护方法

移动社交网络为人们的生活带来了极大的便利,但用户在享受这些服务带来便利的同时,个人位置隐私受到了严重威胁。首先对用户位置隐私保护需求进行了形式化描述,继而针对用户的敏感兴趣点泄露问题,提出了一种情景感知的隐私保护方法。该方法将位置信息、社交关系、个人信息引入到知识构建算法中以计算兴趣点间的相关性,并利用该相关性及时空情景实时判断发布当前位置是否会泄露用户隐私,进而实现了隐私保护与服务可用性间的平衡。最后通过仿真实验验证了该方法的有效性。     

TruBeRepec: a trust-behavior-based reputation and recommender system for mobile applications

Mobile applications are software packages that can be installed and executed in a mobile device. Which mobile application is trustworthy for a user to purchase, download, install, execute or recommend becomes a crucial issue that impacts its final success. This paper proposes TruBeRepec, a trust-behavior-based reputation and recommender system for mobile applications. We explore a model of trust behavior for mobile applications based on the result of a large-scale user survey. We further develop a number of algorithms that are used to evaluate individual user’s trust in a mobile application through trust behavior observation, generate the application’s reputation by aggregating individual trust and provide application recommendations based on the correlation of trust behaviors. We show the practical significance of TruBeRepec through simulations and analysis with regard to effectiveness, robustness, and usability, as well as privacy.     

The reflective mobile agent paradigm implemented in a smart office environment

Ubiquitous systems will integrate computers invisibly and unobtrusively in everyday objects. Data will be catched from single or multi-sensor devices and will be used for context extraction. New location-based services will be adapted to user preferences. For this the ubiquitous system needs to know user profiles, likings, and habits. As the user moves, these information must be made available at the new location of the user. Either the user carries the data on wearable or portable computers or the smart environment takes responsibility for transporting them. The amount of new devices and services makes an efficient use by centralized systems very difficult. The idea presented in this paper is that a virtual reflection of the user represented by a mobile agent accompanying in the smart environment. Mobile agents offer a possibility to encapsulate information of a person and the person’s preferences and perform location-based services of the ubiquitous system in the name of the user. Security and privacy are major concerns of such an agent system. This paper describes a ubiquitous mobile agent system named UbiMAS which has security extensions to provide high protection of agents and significant personal data. UbiMAS is applied in the smart doorplate project as part of a smart office environment.     

Privacy-Aware Autonomous Agents for Pervasive Healthcare

This article is part of a special issue on Intelligent Agents in Healthcare. Hospitals are natural candidates for pervasive computing technology, but they have significant privacy requirements. Autonomous agents can help developers design privacy-aware systems for pervasive healthcare environments. An extension of the SALSA agent framework incorporates customizable privacy mechanisms to adapt applications to a certain quality-of-privacy level, thereby harnessing the properties of autonomous agents. An implementation of a context-aware pervasive hospital application shows how this framework can help developers manage user privacy.     

Modeling context in mobile distributed systems with the UML

Context-awareness plays an important role in mobile distributed systems since it enables the adaptation of mobile devices to the users. However, one of the major challenges is the preservation of the users’ privacy. Many different approaches of modeling the context of the user exist, but the incorporation of privacy restrictions into context models, which makes the protection of privacy apparent, is missing. This paper presents the Context Modeling Profile (CMP), a lightweight UML (Unified Modeling Language) extension, as a visual language for context models in mobile distributed systems. The resulting models embody metainformation of the context, i.e. source and validity of context information, and reflect privacy restrictions. The profile provides several well-formedness rules for context models and supports the development of context-aware mobile applications through an adequate visual modeling language. A case study is used to illustrate the approach.     

Android用户隐私保护系统

随着移动互联网的快速发展,手机作为移动互联的重要终端其所受的安全威胁已不亚于传统PC。文章针对目前手机用户的隐私信息(如通讯信息、短信息、通话记录、相册、文件等)泄露这一用户最为关心的问题,在目前主流的手机操作系统Android OS下设计并实现一个用户隐私保护系统。其主要研究和解决Android系统下的恶意进程识别和隐私数据加密两个问题,通过设计黑白名单授权访问隐私数据和实时监控每个进程以及用AES、MD5等算法加密隐私数据,从而达到保护用户隐私的目的。     

Personal service areas for mobile Web applications

Location-based mobile services let wireless mobile users access Web-based information about resources in their immediate vicinities. The authors describe an algorithm that draws on context mobility elements, such as the user's travel direction and speed, to form personal service areas. Their experimental context-aware tourist information system (CATIS) leverages XML technologies and Web services to provide tourist information to mobile users based on these personal service areas and the users' preferences. Because Web service performance depends on the underlying databases, the authors also developed a layered caching scheme for storing environmental data to improve response time.     

User acceptance of location-aware mobile guides based on seven field studies

Location-awareness provides mobile users with topical and personal contents that may increase the appeal of mobile guides in different application fields. Based on the results of seven field studies, this paper identifies factors of usability, utility and user trust that affect the user acceptance of location-aware mobile guides. The results indicate that on-the-move use sets high requirements for ease-of-use and, in particular, ease of taking the services into use. The need for location-aware mobile guides turned out to be occasional, but in many of those occasional usage situations the guides proved extremely useful. The trade-off between privacy protection and effortless use requires careful consideration, even though in these studies the users turned out to be trusting towards the service providers. The results indicate that location-awareness can provide the users with easy-to-use and situationally relevant mobile guides.