"Time–Location–Frequency"–aware Internet of things service selection based on historical records. (January 2017)

• Record Type: Journal Article
• Authors: Qi, Lianyong; Dai, Peiqiang; Yu, Jiguo; Zhou, Zhili; Xu, Yanwei
• Is Part Of: International journal of distributed sensor networks. Volume 13:Number 1(2017)
• Publication Date: 2017-01
• Subjects: Internet of things service selection -- historical record -- weight -- invocation time -- invocation location -- invocation frequency; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147716688696
• Languages: English
• ISSNs: 1550-1329

Abstract

The advertised quality of an Internet of things service is not always trustable due to the exaggerated quality propagation and dynamic network environment. Therefore, it is more trustable to evaluate the Internet of things service quality based on the historical execution records of service. However, an Internet of things service often has multiple historical records whose invocation time and location are different, which makes it necessary to weigh each historical record of an identical Internet of things service. Besides, for different candidate Internet of things services, their invocation frequencies are often varied, which may also affect the final service selection decision of target user. In view of the above two challenges, a novel service selection approach "Time–Location–Frequency"–aware Service Selection Approach is put forward in this article. In Time–Location–Frequency–aware Service Selection Approach, we first weigh each historical record of an Internet of things service, based on its service invocation time and location; afterward, we weigh each candidate Internet of things service based on its invocation frequency; finally, with the derived two kinds of weights, we evaluate each candidate Internet of things service and return the quality-optimal one to the target user. At last, through a set of experiments deployed on a real service quality data set WS-DREAM, we validate the feasibility of our proposal.