Nqa

Abstract

Radio frequency identification (RFID) systems, as one of the key components in the Internet of Things (IoT), have attracted much attention in the domains of industry and academia. In practice, the performance of RFID systems rather relies on the effectiveness and efficiency of anti-collision algorithms. A large body of studies have recently focused on the anti-collision algorithms, such as the Q-algorithm ( QA ), which has been successfully utilized in EPCglobal Class-1 Generation-2 protocol. However, the performance of those anti-collision algorithms needs to be further improved. Observe that fully exploiting the pre-processing time can improve the efficiency of the QA algorithm. With an objective of improving the performance for anti-collision, we propose a Nested Q-algorithm ( NQA ), which makes full use of such pre-processing time and incorporates the advantages of both Binary Tree ( BT ) algorithm and QA algorithm. Specifically, based on the expected number of collision tags, the NQA algorithm can adaptively select either BT or QA to identify collision tags. Extensive simulation results validate the efficiency and effectiveness of our proposed NQA (i.e., less running time for processing the same number of active tags) when compared to the existing algorithms.

Link prediction in paper citation network to construct paper correlation graph

“…Secondly, since the process of obtaining data may involve some privacy issues [23][24][25][26][27][28][29][30][31][32][33], our work will further consider the privacy-preservation effects when making link prediction. Finally, more complex multi-dimensional or multi-criterion application scenarios [34][35][36][37][38][39][40][41][42][43][44] should be considered in the future to make our proposal more comprehensive.…”

Time series classification based on statistical features

“…Some of the interesting problems are how to adjust subsequence length/ step length and regularize a network and how to use LSTM-FCN, ALSTM-FCN [9], hierarchies of feature [19], or time aware [20,21] to improve the performance of time series classification. In addition, we will try to apply the method of classification service in distributed cloud/fog environment [22][23][24][25][26][27][28][29][30][31][32] in the future.…”

Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph

“…Next, tree growth operation is performed on Figure 7(c). For example, trees 13 , the new tree contains not only same query keywords k 2 but also more nodes. erefore, five required trees are both retained in Figure 7(d).…”

“…(1) We propose a novel keyword-driven and popularityaware paper recommendation approach, which efficiently recommends a set of satisfactory papers. (2) We build an undirected paper citation graph [13], and the users' keyword query problem is regarded as the Steiner tree problem. Finally, we employ papers' popularity to find optimal solutions.…”