Multi-Layered Clustering for Power Consumption Profiling in Smart Grids

Open access publicationSmart Grids (SGs) have many advantages over traditional power grids as they enhance the way electricity is generated, distributed, and consumed by adopting advanced sensing, communication and control functionalities that depend on power consumption profiles of consumers. Clustering algorithms (e.g., centralized clustering) are used for profiling individual’s power consumption. Due to the distributed nature and ever growing size of SGs, it is predicted that massive amounts of data will be created. However, conventional clustering algorithms neither efficient enough nor scalable enough to deal with such amount of data. In addition, the cost for transferring and analyzing large amounts of data is expensive high both computationally and communicationally. This paper thus proposes a power consumption profiling model based on two levels of clustering. At the first level, local power consumption profiles are derived, which are then used by the second level in order to create a global power consumption profile. The followed approach reduces the communication and computation complexity of the proposed two level model and improves the privacy of consumers. We point out that having good knowledge of the local power profiles leads to more effective prediction model and cost-effective power pricing scheme, especially in a heterogeneous grid topology. In addition, the correlations between the local and global profiles can be used to localize/identify power consumption outliers. Simulation results illustrate that the proposed model is effective in reducing the computational complexity without much affecting its accuracy. The reduction in computational complexity is about 52% and the reduction in the communicational complexity is about 95% when compared to the centralized clustering approach

On the Sum Secrecy Rate of Multi-User Holographic MIMO Networks

The emerging concept of extremely-large holographic multiple-input multiple-output (HMIMO), beneficial from compactly and densely packed cost-efficient radiating meta-atoms, has been demonstrated for enhanced degrees of freedom even in pure line-of-sight conditions, enabling tremendous multiplexing gain for the next-generation communication systems. Most of the reported works focus on energy and spectrum efficiency, path loss analyses, and channel modeling. The extension to secure communications remains unexplored. In this paper, we theoretically characterize the secrecy capacity of the HMIMO network with multiple legitimate users and one eavesdropper while taking into consideration artificial noise and max-min fairness. We formulate the power allocation (PA) problem and address it by following successive convex approximation and Taylor expansion. We further study the effect of fixed PA coefficients, imperfect channel state information, inter-element spacing, and the number of Eve's antennas on the sum secrecy rate. Simulation results show that significant performance gain with more than 100\% increment in the high signal-to-noise ratio (SNR) regime for the two-user case is obtained by exploiting adaptive/flexible PA compared to the case with fixed PA coefficients.Comment: 7 pages, 7 figures, submitted to IEEE ICC 202

Detecting Botnets Through Log Correlation

Botnets, which consist of thousands of compromised machines, can cause a significant threat to other systems by launching Distributed Denial of Service attacks, keylogging, and backdoors. In response to this threat, new effective techniques are needed to detect the presence of botnets. In this paper, we have used an interception technique to monitor Windows Application Programming Interface system calls made by communication applications. Existing approaches for botnet detection are based on finding bot traffic patterns. Our approach does not depend on finding patterns but rather monitors the change of behaviour in the system. In addition, we will present our idea of detecting botnet based on log correlations from different hosts

Learning a deep-feature clustering model for gait-based individual identification

Gait biometrics which concern with recognizing individuals by the way they walk are of a paramount importance these days. Human gait is a candidate pathway for such identification tasks since other mechanisms can be concealed. Most common methodologies rely on analyzing 2D/3D images captured by surveillance cameras. Thus, the performance of such methods depends heavily on the quality of the images and the appearance variations of individuals. In this study, we describe how gait biometrics could be used in individuals’ identification using a deep feature learning and inertial measurement unit (IMU) technology. We propose a model that recognizes the biological and physical characteristics of individuals, such as gender, age, height, and weight, by examining high-level representations constructed during its learning process. The effectiveness of the proposed model has been demonstrated by a set of experiments with a new gait dataset generated using a shoe-type based on a gait analysis sensor system. The experimental results show that the proposed model can achieve better identification accuracy than existing models, while also demonstrating more stable predictive performance across different classes. This makes the proposed model a promising alternative to current image-based modeling

Genome sequencing of a camelpox vaccine reveals close similarity to modified Vaccinia virus Ankara (MVA)

Camelpox is a viral contagious disease of Old-World camelids sustained by Camelpox virus (CMLV). The disease is characterized by mild, local skin or severe systemic infections and may have a major economic impact due to significant losses in terms of morbidity and mortality, weight loss, and low milk yield. Prevention of camelpox is performed by vaccination. In this study, we investigated the composition of a CMLV-based, live-attenuated commercial vaccine using next-generation sequencing (NGS) technology. The results of this analysis revealed genomic sequences of Modified Vaccinia virus Ankara (MVA)

Predicting postoperative troponin in patients undergoing elective hip or knee arthroplasty: A comparison of five cardiac risk prediction tools

BACKGROUND: Elderly patients undergoing hip or knee arthroplasty are at a risk for myocardial injury after noncardiac surgery (MINS). We evaluated the ability of five common cardiac risk scores, alone or combined with baseline high-sensitivity cardiac troponin I (hs-cTnI), in predicting MINS and postoperative day 2 (POD2) hs-cTnI levels in patients undergoing elective total hip or knee arthroplasty. METHODS: This study is ancillary to the Genetics-InFormatics Trial (GIFT) of Warfarin Therapy to Prevent Deep Venous Thrombosis, which enrolled patients 65 years and older undergoing elective total hip or knee arthroplasty. The five cardiac risk scores evaluated were the atherosclerotic cardiovascular disease calculator (ASCVD), the Framingham risk score (FRS), the American College of Surgeon\u27s National Surgical Quality Improvement Program (ACS-NSQIP) calculator, the revised cardiac risk index (RCRI), and the reconstructed RCRI (R-RCRI). RESULTS: None of the scores predicted MINS in women. Among men, the ASCVD ( CONCLUSION: In elderly patients undergoing elective hip or knee arthroplasty, several of the scores modestly predicted MINS in men and correlated with POD2 hs-cTnI

Novel EEG sensor-based risk framework for the detection of insider threats in safety critical industrial infrastructure

The loss or compromise of any safety critical industrial infrastructure can seriously impact the confidentiality, integrity, or delivery of essential services. Research has shown that such threats often come from malicious insiders. To this end, survey- and electrocardiogram-based approaches were suggested to identify these insiders; however, these approaches cannot effectively detect or predict any malicious insiders. Recently, electroencephalograms (EEGs) have been suggested as a potential alternative to detect these potential threats. Threat detection using EEG would be highly reliable as it overcomes the limitations of the previous methods. This study proposes a proof of concept for a system wherein a model trained using a deep learning algorithm is employed to evaluate EEG signals to detect insider threats; this algorithm can classify different mental states based on four category risk matrices. In particular, it analyses brainwave signals using long short-term memory (LSTM) designed to remember previous mental states of each insider and compare them with the current brain state for associated risk-level classification. To evaluate the performance of the proposed system, we perform a comparative analysis using logistic regression (LR)—a predictive analysis used to describe the relationship between one dependent binary variable and one or more independent variables—on the same dataset. The experiment results suggest that LSTM can achieve a classification accuracy of more than 80% compared to LR, which yields a classification accuracy of approximately 51%

Artificial immune systems

The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self or nonself substances. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years