With widely deployed WiFi network and the uniqueness feature (fingerprint) of wireless channel information, fingerprinting based WiFi positioning is currently the mainstream indoor positioning method, in which fingerprint database construction is crucial. However, for accuracy, this approach requires enough data to be sampled at many reference points, which consumes excessive efforts and time. In this paper, we collect Channel State Information (CSI) data at reference points by the method of device-free localization, then we convert collected CSI data into amplitude feature maps and extend the fingerprint database using the proposed Amplitude-Feature Deep Convolutional Generative Adversarial Network (AF-DCGAN) model. The use of AF-DCGAN accelerates convergence during the training phase, and substantially increases the diversity of the CSI amplitude feature map. The extended fingerprint database both reduces the human effort involved in fingerprint database construction and the accuracy of an indoor localization system, as demonstrated in the experiments.

Wireless sensor networks (WSNs) are currently being used for monitoring and control in smart grids. To ensure the quality of service (QoS) requirements of smart grid applications, WSNs need to provide specific reliability guarantees. Real-time link quality estimation (LQE) is essential for improving the reliability of WSN protocols. However, many state-of-the-art LQE methods produce numerical estimates that are suitable neither for describing the dynamic random features of radio links nor for determining whether the reliability satisfies the requirements of smart grid communication standards. This paper proposes a wavelet-neural-network-based LQE (WNN-LQE) algorithm that closes the gap between the QoS requirements of smart grids and the features of radio links by estimating the probability-guaranteed limits on the packet reception ratio (PRR). In our algorithm, the signal-to-noise ratio (SNR) is used as the link quality metric. The SNR is approximately decomposed into two components: a time-varying nonlinear part and a non-stationary random part. Each component is separately processed before it is input into the WNN model. The probability-guaranteed limits on the SNR are obtained from the WNN-LQE algorithm and are then transformed into estimated limits on the PRR via the mapping function between the SNR and PRR. Comparative experimental results are presented to demonstrate the validity and effectiveness of the proposed LQE algorithm.

Incipient fault detection in power distribution systems is crucial to improve the reliability of the grid. However, the nonstationary nature and the inadequacy of the training dataset due to the self-recovery of the incipient fault signal make the incipient fault detection in power distribution systems a great challenge. In this article, we focus on incipient fault detection in power distribution systems and address the above challenges. In particular, we propose an adaptive time–frequency memory (AD-TFM) cell by embedding the wavelet transform into the long short-term memory (LSTM), to extract features in time and frequency domains from the nonstationary incipient fault signals. We make scale parameters and translation parameters of the wavelet transform learnable to adapt to the dynamic input signals. Based on the stacked AD-TFM cells, we design a recurrent neural network (RNN) with the attention mechanism, named the AD-TFM-AT model, to detect incipient fault with multiresolution and multidimension analysis. In addition, we propose two data augmentation methods, namely, phase switching and temporal sliding, to effectively enlarge the training datasets. Experimental results on two open datasets show that our proposed AD-TFM-AT model and data augmentation methods achieve state-of-the-art (SOTA) performance of incipient fault detection in power distribution system. We also disclose one used dataset logged at State Grid Corporation of China to facilitate future research.

With the success of wireless sensor networks (WSNs), traditional engineering and infrastructure industries are starting to develop solutions using WSN technologies. One of the main challenges of designing and developing WSNs for industrial monitoring and control is satisfying their strict reliability requirements. In this paper, we present a network-level reliability model, namely, end-to-end data delivery reliability (E2E-DDR), for estimating and optimizing the reliability performance of WSNs. In the E2E-DDR model, a framework is presented for capturing the mapping function between the packet reception ratio, background noise, and received signal strength (RSS). We use an alpha-stable distribution to accurately represent the background noise and a modified log-normal path loss model to more realistically describe the RSS. We also report a comprehensive performance evaluation performed by applying the E2E-DDR model in a real-world case study to estimate the network-level reliability and optimize the WSN deployment parameters.

In recent years, there has been an increase in ransomware attacks worldwide. These attacks aim to lock victims’ machines or encrypt their files for ransom. These kinds of ransomware differ in their implementation and techniques, starting from how they spread, vulnerabilities they leverage, methods to hide their behaviors from antivirus software, encryption methods, and performance. The Conti ransomware is sophisticated ransomware that operates as ransomware-as-a-service. It started in 2019 and had an unprecedented human impact by targeting healthcare systems and cost $45 million. This paper analyzes the Conti ransomware source codes leaked on February 27, 2022, by an anonymous individual. We first look at the general code structure. Then, we analyze its flow, starting with its application programming interface disguise techniques, anti hook mechanisms, command-line arguments, and finally, its multithreaded encryption. We also perform a static and dynamic analysis of the latest known Conti sample in an isolated environment and compare its behavior to its source code flows.

The network-induced delays have a great impact on microgrid performance and stability. In this paper, we proposed a distributed cooperative secondary control with stochastic Markov time-varying network-induced delays, by which both output voltage/frequency restoration and active power sharing can be obtained. A new small-signal model of the microgrid control system is developed, and a small-signal model-based information Kalman observer is introduced to acquire the estimated state vector. Then, the global closed-loop stochastic system accommodated the Markov time-varying network-induced delays of the microgrid is derived, and the corresponding global stability theorem is introduced. Finally, the simulation of a microgrid test system is presented to verify the control performance. The results show that our approach has accurate regulations on voltage/frequency restoration and active power sharing in terms of load variations, plug-and-play capability, network-induced delays. In addition, the control gain parameters, network-induced delays effects on the microgrid secondary control are also verified.