Cooperative Routing in Multi-Radio Multi-Hop Wireless Network

There are many recent interests on cooperative communication (CC) in wireless networks. Despite the large capacity gain of CC in small wireless networks, CC can result in severe interference in large networks and even degraded throughput. The aim of this chapter is to concurrently exploit multi-radio and multi-channel (MRMC) and CC technique to combat co-channel interference and improve the performance of multi-hop wireless network. Our proposed solution concurrently considers cooperative routing, channel assignment, and relay selection and takes advantage of both MRMC technique and spatial diversity to improve the throughput. We propose two important metrics, contention-aware channel utilization routing metric (CACU) to capture the interference cost from both direct and cooperative transmission, and traffic aware channel condition metric (TACC) to evaluate the channel load condition. Based on these metrics, we propose three algorithms for interference-aware cooperative routing, local channel adjustment, and local path and relay adaptation, respectively, to ensure high-performance communications in dynamic wireless networks. Our algorithms are fully distributed and can effectively mitigate co-channel interference and achieve cooperative diversity gain. To our best knowledge, this is the first distributed solution that supports CC in MRMC networks. Our performance studies demonstrate that our algorithms can significantly increase the aggregate throughput

LogUAD: Log unsupervised anomaly detection based on word2Vec

System logs record detailed information about system operation and are important for analyzing the system\u27s operational status and performance. Rapid and accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more and more complex, and the number of system logs gradually increases, which brings challenges to analyze system logs. Some recent studies show that logs can be unstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a long time to train models. Therefore, to reduce the computational cost and avoid log instability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takes original log messages as input to avoid the noise. LogUAD uses Word2Vec to generate word vectors and generates weighted log sequence feature vectors with TF-IDF to handle the evolution of log statements. At last, a computationally efficient unsupervised clustering is exploited to detect the anomaly. We conducted extensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25% compared to LogCluster

LogEvent2vec : LogEvent-to-vector based anomaly detection for large-scale logs in internet of things

Funding: This work was funded by the National Natural Science Foundation of China (Nos. 61802030), the Research Foundation of Education Bureau of Hunan Province, China (No. 19B005), and the International Cooperative Project for “Double First-Class”, CSUST (No. 2018IC24), the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education (No. JZNY201905), the Open Research Fund of the Hunan Provincial Key Laboratory of Network Investigational Technology (No. 2018WLZC003). This work was funded by the Researchers Supporting Project No. (RSP-2019/102) King Saud University, Riyadh, Saudi Arabia. Acknowledgments: We thank Researchers Supporting Project No. (RSP-2019/102) King Saud University, Riyadh, Saudi Arabia, for funding this research. We thank Francesco Cauteruccio for proofreading this paper.Peer reviewedPublisher PD