Abstract
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.
Highlights
The Internet of things (IoT) is an infrastructure that interconnects uniquely identifiable sensors through the Internet [1]
With the random walk model presented above, in order to obtain the global ranking from the collection of partial rankings, the random walk can be regarded as a discrete-time Markov chain (DTMC)
We propose a time-aware service ranking prediction approach for obtaining the global service ranking from partial rankings
Summary
The Internet of things (IoT) is an infrastructure that interconnects uniquely identifiable sensors through the Internet [1]. How to obtain the global service ranking by studying the temporal dynamic changes of QoS is an important and unexplored problem for IoT service ranking. To fill this gap, we present a time-aware service ranking prediction approach named TSRPred. (1) The time-aware service ranking prediction approach is proposed to obtain the global ranking, which can obtain the service ranking by studying the temporal dynamic changes of QoS. (2) During the process of our approach, the temporal dynamic changes of QoS attributes are studied by time series forecasting method, which can forecast the future values and dynamic trends using fitted models.
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