基于情境感知的户外运动类应用设计策略研究

目的 探索情境感知相关理论在户外运动类APP中的应用,以期改善目前市场上户外运动类应用中安全保障性不强、同质化严重、个性化与专属性不强等问题,从而全方位优化用户体验及提升用户户外运动的安全感,并为今后户外运动类应用的设计开发提供参考依据。方法 首先对户外运动及其应用的实践和理论现状进行分析,其次在梳理情境感知理论的基础上,分析户外运动类应用中的用户、环境、社交和设备4类情境信息及情境感知介入户外运动类应用的技术支撑。通过桌面调研、问卷调查和用户访谈3种方法洞察出用户需求,最后总结出基于上述4类情境信息的针对性设计策略。结论 情境感知理论的运用有助于在设计应用时深度理解和挖掘用户需求,在更大程度上保障用户安全的同时向用户提供更具智能化、个性化、情感化的服务。     

基于上下文学习的电力物联网接入控制方法

针对6G时代电力物联网海量终端接入冲突严重、队列积压大、能量效率低等问题,提出了一种基于上下文学习的接入控制算法。所提算法基于强化学习和快速上行链路授权技术,考虑终端活跃与休眠2种状态,优化目标为在终端接入服务质量需求的长期约束下最大化网络总能量效率。利用李雅普诺夫优化对长期优化目标与约束进行解耦,将长期优化问题转化为单时隙独立的确定性子问题,并利用基于终端状态感知的上置信界算法进行求解。仿真结果表明,所提算法能够在满足终端接入服务质量需求的同时,有效提高网络总能量效率。相较于传统快速上行链路授权算法,所提算法可提高平均能量效率48.11%,提高满足接入服务质量需求的终端比例54.95%,降低平均队列积压83.83%。     

基于上下文项目评分分裂的协同过滤推荐

上下文感知推荐系统的主要任务是利用上下文信息进一步提高推荐系统的推荐精度和用户满意度。提出了一种基于上下文项目评分分裂的推荐方法。该方法首先依据项目分裂判别标准对多维度上下文信息下的项目进行分裂,然后根据分裂结果并通过上下文维度进行聚类。在此基础上,利用协同过滤推荐算法进行未知评分预测。最后,面向不同的项目分裂标准,在LDOS-CoMoDa数据集上进行仿真对比实验。实验结果表明,相对于其他推荐算法,该方法有效提升了推荐精度,达到了提高推荐质量效果的目的。     

用户协作模式下自适应情境的LBS隐私保护方法

针对当前LBS(located-based service)应用中位置隐私保护方法难以实现隐私保护度与服务质量平衡的问题,提出了一种基于用户协作的自适应情境的位置隐私保护方法:移动用户消费LBS服务时,需从服务提供商处实时下载“粗糙化”的路网数据,并以此为基础,通过情境感知自主模糊个人真实位置,然后选择具有互助意愿的代理用户进行自适应情境的增量近邻查询.为了提高系统的服务质量,引入通讯成本估算方法确定查询点位置,并采用Voronoi图技术过滤代理用户的查询结果,从而减少系统通讯开销.不仅从理论上分析了该方法的隐私保护度与自适应性,而且对方法性能进行了实验与比较,实验结果表明:该方法较SpaceTwist算法具有更高的性能优势,实现了隐私保护度与服务质量的平衡.     

基于推理的上下文感知RBAC模型设计和实现

为了解决现有基于角色的访问控制（RBAC）模型缺乏对上下文约束条件的动态调整和生成,无法根据调整后的感知条件对用户权限实时进行调节的问题,在融合上下文感知RBAC模型和基于推理的RBAC模型基础上,提出了基于推理的上下文感知RBAC扩展模型.该扩展模型采用逻辑推理方法实现了上下文约束条件的动态调整,启用相应的公共感知器和自定义感知器感知约束条件的属性值;利用感知获得的动态上下文值进行角色和权限规则的推理,实现了访问主体对客体控制权限的实时更新.应用实例表明,该模型能提高分布式环境中用户动态访问控制的灵活性,并降低实时访问控制管理的复杂度．     

景区智能空间下的实时景点推荐技术

针对景区景点推荐不能满足游客个性化及实时性的要求,在景区智能空间条件下提出一种实时景点推荐技术,该技术通过上下文感知来获取动态信息,结合景点实时个性化推荐算法为游客提供景点推荐服务.通过实验说明所提出的实时景点推荐技术的可行性.     

A Hybrid Modular Context-aware Services Adaptation for a Smart Living Room

Smart spaces have attracted considerable amount of interest over the past few years. The introduction of sensor networks, powerful electronics and communication infrastructures have helped a lot in the realization of smart homes. The main objective of smart homes is the automation of tasks that might be complex or tedious for inhabitants by distracting them from concentrating on setting and configuring home appliances. Such automation could improve comfort, energy savings, security, and tremendous benefits for elderly persons living alone or persons with disabilities. Context awareness is a key enabling feature for development of smart homes. It allows the automation task to be done proactively according to the inhabitant’s current context and in an unobtrusive and seamlessly manner. Although there are several works conducted for the development of smart homes with various technologies, in most cases, robust. However, the context-awareness aspect of services adaptation was not based on clear steps for context elements extraction (resp. clear definition of context). In this paper, we use the divide and conquer approach to master the complexity of automation task by proposing a hybrid modular system for context-aware services adaptation in a smart living room. We propose to use for the context-aware adaptation three techniques of machine learning, namely Naïve Bayes, fuzzy logic and case-based reasoning techniques according to their convenience.     

智能空间的情景感知冲突研究

情景感知是智能空间中的关键技术,是用户在智能空间中获得服务的依据。由于智能空间中提供服务的资源是有限的,多个用户在同一时间内争夺资源服务时,就造成了情景冲突。根据模糊集和系统学,建立了情景冲突的数学模型,并将解决情景冲突问题可转化为一个多元函数求条件极值的问题。实例分析表明,这个方法能简明地描述情景冲突问题,并能有效协调解决情景冲突。这方法具有较好的描述情景冲突能力,适合于在计算机实现。     

A CAWL handler for context-aware composite workflow services

In distributed computing environment,workflow technologies have been continuously developed.Recently,there is an attempt to apply these technologies to context-aware services in ubiquitous computing environment.The middleware,which offers services in such environments,should support the automation services suited for the user using various types of situational information around the user.In this paper,based on context-aware workflow language （CAWL）,we propose a CAWL based composite workflow handler for supporting composite workflow services,which can integrate more than two service flows and handle them.The test results shows that the proposed CAWL handler can provide the user with the composite workflow services to cope with various demands on a basis of a scenario document founded on CAWL.     

面向移动云计算的自适应服务选择方法研究

Despite the rapid advances in mobile tech-nology, many constraints still prevent mobile de-vices from running resource-demanding applica-tions in mobile environments. Cloud computing with flexibility, stability and scalability enables ac-cess to unlimited resources for mobile devices, so more studies have focused on cloud computing- based mobile services. Due to the stability of wire-less networks, changes of Quality of Service (QoS) level and user' real-time preferences, it is becoming challenging to determine how to adaptively choose the “appropriate”service in mobile cloud compu-ting environments. In this paper, we present an a-daptive service selection method. This method first extracts user preferences from a service's evaluation and calculates the similarity of the service with the weighted Euclidean distance. Then, they are com-bined with user context data and the most suitable service is recommended to the user. In addition, we apply the fuzzy cognitive maps-based model to the adaptive policy, which improves the efficiency and performance of the algorithm. Finally, the experi-ment and simulation demonstrate that our approach is effective.