Abstract
While it is well understood that the Internet of things (IoT) can facilitate numerous applications (e.g., environmental supervision, forest fire prevention and Intelligent farming), it also brings a significant challenge for efficiently selecting sensors that meet users' preference and specific requirement from millions of heterogeneous sensors. In this paper, we propose an improved fast nondominated sorting algorithm for efficiently preference-based sensor selection in IoT. Specifically, this proposed method mainly includes three parts: 1) Offline constructing R-tree to search sensor resources and narrowing the size of dataset according to user's preference; 2) Using an improved fast nondominated sorting approach to get nondominated front; 3) Employing TOPSIS to characterize every sensor option of the nondominated front. In order to illustrate the usability of the model, we conduct experiments on several simulation datasets. Experimental results show that this method outperforms several baselines in terms of both response time and accuracy.
Highlights
In the Internet of things(IoT), the Internet is used as medium to control all kinds of physical devices for collecting real-time environmental data and transferring the data to middleware’s specific function modules
We propose an improved fast nondominated sorting algorithm, which gives a good balance between the low time complexity of TOPSIS algorithm and the high accuracy of the fast nondominated sorting algorithm based on Pareto optimal principle, so as to provide users with efficient sensor selections
3) EVALUATION METHODS In order to evaluate the efficiency of the model, the response time and the accuracy are selected as evaluation indexes to judge the proposed model
Summary
In the Internet of things(IoT), the Internet is used as medium to control all kinds of physical devices for collecting real-time environmental data and transferring the data to middleware’s specific function modules. Generated date contains two kinds of data, one to describe sensors and the other capture from realistic environment From this perspective, the biggest challenge for services selection in Internet of things is to discover sensor resources in large-scale heterogeneous environments and choose the best option according to users’ specific needs and constraints. 2) Applying multi-criteria decision analysis algorithm (MCDA) to characterize and sort candidate sensor resources with user’s preferences of sensor attributes, and returning results to user. The R-tree is constructed to retrieve sensor resources in two-dimension space including sensor location and type, user can obtain sensor dataset. The rest of the paper is organized as follows: Section II investigates the related work of sensor selection under the Internet of things.
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