Everything is going digital around us! Disruptive technologies and their disruptive use models are creating a new world and a different future. Over the last few years, the creation and utilization of technologies has played a critical role to improve the life quality of people, advance economies, and open new horizons for individuals, businesses, and even nations around the world. This is the bright side! On the other side, the world is experiencing nowadays exceptional technology disruption that has triggered an enormous change; a change that is behind the classical control of the

Mobile operators are facing strong challenges regarding the environmental impact of their operations. The International Telecommunications Union (ITU) considered energy consumption as the prime source of Carbon dioxide (CO2) emissions for the whole Information and Communications Technology (ICT) sectors. ITU encouraged all ICT sectors to commit their efforts in order to control their energy consumption and consider renewable energy sources. By the year 2050, CO2 emissions should be fully neutralized as per the Paris climate agreement. The energy consumption of the Radio Access Network (RAN)

accurate prediction of sales data is an important and challenging problem in the retail industry. However, due to the diversity of data types, it is difficult for traditional time series prediction models to generate good prediction results for different types of data. This research develops a cognitive analytic framework to help transform retailers from the reactive environment to automated continuously learning environment utilizing the power of AI technology in predicting future sales with high accuracy. It proposes a multi-stage prediction framework based on the integration of Statistical

Big Data Stream processing engines such as Apache Flink use windowing techniques to handle unbounded streams of events. Gathering all pertinent input within a window is crucial for event-time windowing since it affects how accurate results are. A significant part of this process is played by watermarks, which are unique timestamps that show the passage of events in time. However, the current watermark generation method in Apache Flink, which works at the level of the input stream, tends to favor faster sub-streams, resulting in dropped events from slower sub-streams. In our analysis, we found

The demand for electric vehicles (EVs) is growing rapidly. This requires an ecosystem that meets the user’s needs while preserving security. The rich data obtained from electric vehicle stations are powered by the Internet of Things (IoT) ecosystem. This is achieved through us of electric vehicle charging station management systems (EVCSMSs). However, the risks associated with cyber-attacks on IoT systems are also increasing at the same pace. To help in finding malicious traffic, intrusion detection systems (IDSs) play a vital role in traditional IT systems. This paper proposes a classifier

The fast expansion of the Internet of Things (IoT) in the technology and communication industries necessitates a continuously updated cyber-security mechanism to keep protecting the systems' users from any possible attack that might target their data and privacy. Botnets pose a severe risk to the IoT, they use malicious nodes in order to compromise other nodes inside the network to launch several types of attacks causing service disruption. Examples of these attacks are Denial of Service (DoS), Distributed Denial of Service (DDoS), Service Scan, and OS Fingerprint. DoS and DDoS attacks are the

Active Directory Domain is a Microsoft service that allows and facilitates the centralized administration of all workstations and servers in any environment. Due to the wide use and adoption of this service, it has become a target for many attackers. Active Directory attacks have evolved through years. The attacks target different functions and features provided by Active Directory. In this paper, we provide insights on the criticality, impact, and detection of Active Directory attacks. We review the different Active Directory attacks. We introduce the steps of the Active Directory attack and

Wireless sensor networks (WSNs) have garnered much attention in the last decades. Nowadays, the network contains sensors that have been expanded into a more extensive network than the internet. Cost is one of the issues of WSNs, and this cost may be in the form of bandwidth, computational cost, deployment cost, or sensors’ battery (sensor life). This paper proposes a dual-level sensor selection (DLSS) model used to reduce the number of sensors forming WSNs. The sensor reduction process is performed at two consecutive levels. First, a combination of the Fisher score method and ANOVA test at the

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm (PLTVACIW-PSO). Its designed has introduced the benefits of Parallel computing into the combined power of TVAC (Time-Variant Acceleration Coefficients) and IW (Inertial Weight). Proposed algorithm has been tested against linear, non-linear, traditional, and multiswarm based optimization algorithms. An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO. Phase I uses 12 recognized Standard Benchmarks methods to evaluate the

Foresight projects are expected to provide realistic scenarios for different future scenarios, which provides a better information base for relevant strategies. However, these expectations often turn out to be at least difficult to fulfill due to the uncertainty of the external environment and cognitive biases. Therefore, the idea of assessing each stage of Foresight is gaining relevance, which is of particular importance in the energy sector, which affects a variety of areas of life. This article analyzes the results of the Egyptian energy foresight study, Egypt LEAPS, in terms of process