Quality of Service （QoS）-based service selection is the key to large-scale service-oriented Internet of Things （lOT）, due to the increasing emergence of massive services with various QoS. Current methods either have low selection accuracy or are highly time-consuming （e.g., exponential time complexity）, neither of which are desirable in large-scale lOT applications. We investigate a QoS-based service selection method to solve this problem. The main challenges are that we need to not only improve the selection accuracy but also decrease the time complexity to make them suitable for large-scale lOT applications. We address these challenges with the following three basic ideas. First, we present a lightweight description method to describe the QoS, dramatically decreasing the time complexity of service selection. Further more, based on this QoS description, we decompose the complex problem of QoS-based service selection into a simple and basic sub-problem. Finally, based on this problem decomposition, we present a QoS-based service matching algorithm, which greatly improves selection accuracy by considering the whole meaning of the predicates. The traces-driven simulations show that our method can increase the matching precision by 69% and the recall rate by 20% in comparison with current methods. Moreover, theoretical analysis illustrates that our method has polynomial time complexity, i.e., O（m^2 × n）, where m and n denote the number of predicates and services, respectively.
为了消除由于互联网异构性而导致的性能瓶颈,解决当前互联网流媒体系统所面临的服务能力不足问题,本文设计了一个以应用层流媒体技术为基础的实时流媒体传输框架,构建了由网络视频服务器(NVSs,Network Video Servers)和代理服务器(Proxy Servers)组成的视频服务组,并为多用户提供服务.提出一个基于蚁群算法的有效用户接入算法,在满足用户视频请求的基础上,实现了用户接入数目的最大化,提升了系统容量.
移动广告的分发效果对于广告商和用户来说都是相当重要的事情.目前对于高效率的广告分发特别是对用户的轨迹和预算的研究较为匮乏.为了获得有效可行的移动广告分发策略,提出了以位置为中心的移动众包网络的概念,代替传统的以用户为中心的网络和平台,其中,位置信息对于广告分发起到至关重要的作用.为此重点研究考虑有兴趣区域的覆盖(interested area coverage,IAC)策略下的移动广告用户选择问题.对于以位置为中心的研究需要考虑每个用户的时空特性,并需要有效地计算有兴趣的覆盖区域,资金预算的约束使这一问题更加难以解决.为应对上述挑战,首先,考虑到对位置敏感的移动广告应用程序时,提出了被证明是NP-hard的有预算约束的用户选择问题;其次,对问题的子模特性进行了探究,提出了一个简单而有效的具有近似比为(1-1e)的启发式算法;最后,大量的仿真结果表明,该方案使移动广告的传播效应有效提高了120%.
