Paper Info
Title
Time-Aware Service Ranking Prediction in the Internet of Things Environment.
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.
Year
DOI
Venue
2017
10.3390/s17050974
SENSORS
Keywords
Field
DocType
time series analysis,quality of service (QoS),service ranking prediction,Internet of things (IoT)
Time series,Data mining,Pairwise comparison,Ranking,Computer science,Markov chain,Internet of Things,Quality of service,Sorting,Service selection
Journal
Volume
Issue
Citations 
17
5
7
PageRank 
References 
Authors
0.51
13
5
Name
Order
Citations
PageRank
Yuze Huang192.37
Jiwei Huang217725.99
Bo Cheng364986.91
Shuqing He491.53
Junliang Chen51051124.15