Huang Yuze, Huang Jiwei, Cheng Bo, He Shuqing, Chen Junliang
State Key Libratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2017 Apr 27;17(5):974. doi: 10.3390/s17050974.
With the rapid development of the Internet of things (IoT), building IoT systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedures of building IoT systems, QoS-aware service selection is an important concern, which requires the ranking of a set of functionally similar services according to their QoS values. In reality, however, it is quite expensive and even impractical to evaluate all geographically-dispersed IoT services at a single client to obtain such a ranking. Nevertheless, distributed measurement and ranking aggregation have to deal with the high dynamics of QoS values and the inconsistency of partial rankings. To address these challenges, we propose a time-aware service ranking prediction approach named TSRPred for obtaining the global ranking from the collection of partial rankings. Specifically, a pairwise comparison model is constructed to describe the relationships between different services, where the partial rankings are obtained by time series forecasting on QoS values. The comparisons of IoT services are formulated by random walks, and thus, the global ranking can be obtained by sorting the steady-state probabilities of the underlying Markov chain. Finally, the efficacy of TSRPred is validated by simulation experiments based on large-scale real-world datasets.
随着物联网（IoT）的快速发展，构建具有高服务质量（QoS）的物联网系统已成为学术界和工业界的迫切需求。在构建物联网系统的过程中，QoS感知服务选择是一个重要问题，这需要根据一组功能相似的服务的QoS值对它们进行排序。然而，在现实中，在单个客户端评估所有地理上分散的物联网服务以获得这样的排名是相当昂贵的，甚至是不切实际的。尽管如此，分布式测量和排名聚合必须应对QoS值的高动态性和部分排名的不一致性。为了应对这些挑战，我们提出了一种名为TSRPred的时间感知服务排名预测方法，用于从部分排名的集合中获得全局排名。具体来说，构建了一个成对比较模型来描述不同服务之间的关系，其中部分排名是通过对QoS值进行时间序列预测获得的。物联网服务的比较通过随机游走进行，因此，可以通过对底层马尔可夫链的稳态概率进行排序来获得全局排名。最后，通过基于大规模真实世界数据集的模拟实验验证了TSRPred的有效性。
