ACO-GCN: A FAULT DETECTION FUSION ALGORITHM FOR WIRELESS SENSOR NETWORK NODES

Wireless Sensor Network (WSN) has become a solution for real-time monitoring environments and is widely used in various fields. A substantial number of sensors in WSNs are prone to succumb to failures due to faulty attributes, complex working environments, and their hardware, resulting in transmission error data. To resolve the existing problem of fault detection in WSN, this paper presents a WSN node fault detection method based on ant colony optimization-graph convolutional network (ACO-GCN) models, which consists of an input layer, a space-time processing layer, and an output layer. First, the users apply the random search algorithm and the search strategy of the ant colony algorithm (ACO) to find the optimal path and locate the WSN node failures to grasp the overall situation. Then, the WSN fault node information obtained by the GCN model is learned. During the data training process, where the WSN fault node is used for error prediction, the weights and thresholds of the network are further adjusted to increase the accuracy of fault diagnosis. To evaluate the performance of the ACO-GCN model, the results show that the ACO-GCN model significantly improves the fault detection rate and reduces the false alarm rate compared with the benchmark algorithms. Moreover, the proposed ACO-GCN fusion algorithm can identify fault sensors more effectively, improve the service quality of WSN and enhance the stability of the system

Optimal Power Allocation for Two-Way Decode-and-Forward OFDM Relay Networks

This paper presents a novel two-way decode-and-forward (DF) relay strategy for Orthogonal Frequency Division Multiplexing (OFDM) relay networks. This DF relay strategy employs multi-subcarrier joint channel coding to leverage frequency selective fading, and thus can achieve a higher data rate than the conventional per-subcarrier DF relay strategies. We further propose a low-complexity, optimal power allocation strategy to maximize the data rate of the proposed relay strategy. Simulation results suggest that our strategy obtains a substantial gain over the per-subcarrier DF relay strategies, and also outperforms the amplify-and-forward (AF) relay strategy in a wide signal-to-noise-ratio (SNR) region.Comment: 5 pages, 2 figures, accepted by IEEE ICC 201

Managing Virtual Team Performance: An Exploratory Study of Social Loafing and Social Comparison

This study investigates the effects of social comparison and social loafing on virtual team performance when teams engage in asynchronous ideation process. The results of the study suggest that the effects of social comparison and social loafing co-exist in virtual teams. Team members may choose to engage in different behaviors (social loafing vs. social comparison) in different team interactions. Furthermore, team members tend to elaborate on the ideas generated by co-workers. As a result, teams with less social loafing will produce richer elaboration on ideas generated

Photoconductivity of Single-crystalline Selenium Nanotubes

Photoconductivity of single-crystalline selenium nanotubes (SCSNT) under a range of illumination intensities of a 633nm laser is carried out with a novel two terminal device arrangement at room temperature. It's found that SCSNT forms Schottky barriers with the W and Au contacts, and the barrier height is a function of the light intensities. In low illumination regime below 1.46x10E-4 muWmum-2, the Au-Se-W hybrid structure exhibits sharp switch on/off behavior, and the turn-on voltages decrease with increasing illuminating intensities. In the high illumination regime above 7x10E-4 muWmum-2, the device exhibits ohmic conductance with a photoconductivity as high as 0.59Ohmcm-1, significantly higher that reported values for carbon and GaN nanotubes. This finding suggests that SCSNT is potentially a good photo-sensor material as well we a very effective solar cell material.Comment: 12pages including 5 figures, submitted to Nanotechnolog

A Foundation Course in Business Analytics:Design and Implementation at Two Universities

The current data-centric business environment has seen an increasing demand for business students with knowledge and skills in the area of business analytics. This article presents the design and implementation of a foundation business analytics (BA) course for undergraduate business students who aspire to become data-literate professionals or entry-level data analysts. The course design is built around two learning objectives and their corresponding learning outcomes and features five learning modules corresponding to a recently proposed BA pedagogical framework. The implementation of this course at two large universities is described in detail, including the timelines, topics, software tools, assignments, projects, and student feedback. Upon successful completion of the course, students are expected to be able to conduct business analytics at basic to intermediate levels using leading industry tools such as Power Pivot, Power BI, Tableau, or R