Rasha Kashef

Eleonora Achiluzzi, Menglu Li, Md Fahd Al Georgy et al.

The Internet of Things (IoT) is a system that connects physical computing devices, sensors, software, and other technologies. Data can be collected, transferred, and exchanged with other devices over the network without requiring human interactions. One challenge the development of IoT faces is the existence of anomaly data in the network. Therefore, research on anomaly detection in the IoT environment has become popular and necessary in recent years. This survey provides an overview to understand the current progress of the different anomaly detection algorithms and how they can be applied in the context of the Internet of Things. In this survey, we categorize the widely used anomaly detection machine learning and deep learning techniques in IoT into three types: clustering-based, classification-based, and deep learning based. For each category, we introduce some state-of-the-art anomaly detection methods and evaluate the advantages and limitations of each technique.

Saad Emshagin, Wayes Koroni Halim, Rasha Kashef

With the evolution of power systems as it is becoming more intelligent and interactive system while increasing in flexibility with a larger penetration of renewable energy sources, demand prediction on a short-term resolution will inevitably become more and more crucial in designing and managing the future grid, especially when it comes to an individual household level. Projecting the demand for electricity for a single energy user, as opposed to the aggregated power consumption of residential load on a wide scale, is difficult because of a considerable number of volatile and uncertain factors. This paper proposes a customized GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) architecture to address this challenging problem. LSTM and GRU are comparatively newer and among the most well-adopted deep learning approaches. The electricity consumption datasets were obtained from individual household smart meters. The comparison shows that the LSTM model performs better for home-level forecasting than alternative prediction techniques-GRU in this case. To compare the NN-based models with contrast to the conventional statistical technique-based model, ARIMA based model was also developed and benchmarked with LSTM and GRU model outcomes in this study to show the performance of the proposed model on the collected time series data.

Mahta Taghi Zadeh, Rasha Kashef

This study aims to assist Company leaders and management team in providing more accurate time and cost estimations for future software projects. This paper focuses on analyzing the relationship between project complexity and cost and time overrun. The sample data of around 50 projects are collected from the Changepoint database. The two research hypotheses were defined and tested using statistical techniques. The quantitative analysis includes descriptive analysis and regression modelling. Experimental results show a strong positive linear relationship between project complexity and cost and time overrun. Based on the study outcome, there is a need to focus on the planning phase of complex projects and allocate more time and budget to estimate the completion date and the initial budget of this type of project more accurately.

Abbas Saleminezhadl, Manuel Remmele, Ravikumar Chaudhari et al.

The Internet of Things (IoT) is revolutionizing human life with the idea of interconnecting everyday used devices (Things) and making them smart. By establishing a communication network between devices, the IoT system aids in automating tasks and making them efficient and powerful. The sensors and the physical world, connected over a network, involve a massive amount of data. The data collection and sharing possess a critical threat of being stolen and manipulated over the network. These inadequate data security and privacy issues in IoT systems raise concerns about maintaining authentication of IoT data. Blockchain, a tempter-resistant ledger, has emerged as a viable alternative to provide security features. Blockchain technologies with decentralized structures can help resolve IoT structure issues and protect against a single point of failure. While providing robust security features, Blockchain also bears various critical challenges in the IoT environment to adapt. This paper presents a survey on state-of-the-art Blockchain technologies focusing on IoT applications. With Blockchain protocols and data structures, the IoT applications are outlined, along with possible advancements and modifications.