System logs are a valuable source of information for monitoring and maintaining the security and stability of computer systems. Techniques based on Deep Learning and Natural Language Processing have demonstrated effectiveness in detecting abnormal behaviour from these system logs. However, existing anomaly detection approaches have limitations in terms of flexibility and practicality. Techniques that rely on log templates such as DeepLog and LogBERT fail to capture semantic information and are unable to handle variability in log content. On the other hand, classification-based approaches such as LogSy, LogRobust and HitAnomaly require time-consuming data labelling for supervised training. In this paper, a novel log anomaly detection model named LogFiT is proposed. The LogFiT model doesn’t make use of a vocabulary of log templates and it doesn’t require any labeled data as the model only requires self-supervised training. The LogFiT model uses a pretrained Bidirectional Encoder Representations from Transformers (BERT)-based language model fine-tuned to recognise the linguistic patterns of the normal log data. The LogFiT model is trained using masked sentence prediction on the normal log data only. Consequently, when presented with the new log data, the model’s top-k token prediction accuracy serves as a threshold for determining whether the new log data deviates from the normal log data. Experimental results show that LogFiT’s F1 score exceeds that of baselines on the HDFS, BGL, and Thunderbird datasets. Critically, when variability is introduced in the log data during evaluation, LogFiT retains its effectiveness compared to that of baselines.

Recent advances in wireless sensor networking (WSN) techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems. These have the potential for use in the monitoring of railway signaling systems and rail tracks. Energy efficiency is one of the most important design factors for the WSNs as the typical sensor nodes are equipped with limited power batteries. In earlier research, an energy-efficient cluster-based adaptive time-division multiple-access (TDMA) medium-access-control (MAC) protocol, named EA-TDMA, has been developed by the authors for the purpose of communication between the sensors placed in a railway wagon. This paper proposes another new protocol, named E-BMA, which achieves even better energy efficiency for low and medium traffic by minimizing the idle time during the contention period. In addition to railway applications, the EA-TDMA and E-BMA protocols are suitable for generic wireless data communication purposes. Both analytical and simulation results for the energy consumption of TDMA, EA-TDMA, BMA, and E-BMA have been presented in this paper to demonstrate the superiority of the EA-TDMA and E-BMA protocols.

The quality and bitrate modeling is essential to effectively adapt the bitrate and quality of videos when delivered to multiplatform devices over resource constraint heterogeneous networks. The recent model proposed by Wang et al. estimates the bitrate and quality of videos in terms of the frame rate and quantization parameter. However, to build an effective video adaptation framework, it is crucial to incorporate the spatial resolution in the analytical model for bitrate and perceptual quality adaptation. Hence, this paper proposes an analytical model to estimate the bitrate of videos in terms of quantization parameter, frame rate, and spatial resolution. The model can fit the measured data accurately which is evident from the high Pearson correlation. The proposed model is based on the observation that the relative reduction in bitrate due to decreasing spatial resolution is independent of the quantization parameter and frame rate. This modeling can be used for rate-constrained bit-stream adaptation scheme which selects the scalability parameters to optimize the perceptual quality for a given bandwidth constraint.

Yang proposed the concept of borrow-and-return (BR) to leverage the unused server bandwidth when a group of popular videos being broadcast with the FSFC (first segment on the first channel) broadcasting schemes in order to improve the mean waiting time (MWT) of the viewers with the help of additional receiving bandwidth available at the high-end clients. The BR model borrows the bandwidth of the videos with no new-coming viewers during a timeslot to speed up the transmission of the first segments of some of the remaining videos. In this paper, we first address the relative advantage issue among various possible BR schemes by developing a parametric generic BR (GBR) scheme controlled externally by independent borrow parameters. Later, we propose a new BR (NBR) model by incorporating an efficient transmission strategy to reduce the MWT further. Finally, an optimal NBR scheme is developed by augmenting with the optimal borrow parameters, which significantly outperforms the existing and new BR schemes in terms of overall MWT.

A controlled multicast scheme provides instantaneous service, but limited server bandwidth causes some user requests to be either delayed or rejected when insufficient free bandwidth is available. Two borrowing schemes are proposed for instantaneous video-on-demand (VOD) that reduce the user request blocking rate by borrowing bandwidth from ongoing video streams when there is insufficient free bandwidth for the server to deliver a new video stream. Both these new schemes have proved to be successful in reducing blocking rate and increasing bandwidth utilization at the expense of temporarily degrading the video quality.