The frequency of cyber-attacks on the Internet of Things (IoT) networks has significantly increased in recent years. Anomaly-based network intrusion detection systems (NIDSs) offer an additional layer of network protection by detecting and reporting the infamous zero-day attacks. However, the efficiency of real-time detection systems relies on several factors, including the number of features utilized to make a prediction. Thus, minimizing them is crucial as it implies faster prediction and lower storage space. This paper utilizes recursive feature elimination with cross-validation using a decision tree model as an estimator (DT-RFECV) to select an optimal subset of 15 of UNSW-NB15’s 42 features and evaluates them using several ML classifiers, including tree-based ones, such as random forest. The proposed NIDS exhibits an accurate prediction model for network flow with a binary classification accuracy of 95.30% compared to 95.56% when using the entire feature set. The reported scores are comparable to those attained by the state-of-the-art systems despite decreasing the number of utilized features by about 65%.

The past few years have witnessed a substantial increase in cyberattacks on Internet of Things (IoT) devices and their networks. Such attacks pose a significant threat to organizational security and user privacy. Utilizing Machine Learning (ML) in Intrusion Detection Systems (NIDS) has proven advantageous in countering novel zero-day attacks. However, the performance of such systems relies on several factors, one of which is prediction time. Processing speed in anomaly-based NIDS depends on a few elements, including the number of features fed to the ML model. NetFlow, a networking industry-standard protocol, offers many features that can be used to predict malicious attacks accurately. This paper examines NetFlow features and assesses their suitability in classifying network traffic. Our paper presents a model that detects attacks with (98–100%) accuracy using as few as 13 features. This study was conducted using a large dataset of over 16 million records released in 2021.

Air quality has been negatively affected by industrial activities, which have caused imbalances in nature. The issue of air pollution has become a big concern for many people, especially those living in industrial areas. Air pollution levels can be measured using smart sensors. Additionally, Internet of Things (IoT) technology can be integrated to remotely detect pollution without any human interaction. The data gathered by such a system can be transmitted instantly to a web-based application to facilitate monitoring real time data and allow immediate risk management. In this paper, we describe an entire Internet of Things (IoT) system that monitors air pollution by collecting real-time data in specific locations. This data is analyzed and measured against a predetermined threshold. The collected data is sent to the concerned official organization to notify them in case of any violation so that they can take the necessary measures. Furthermore, if the value of the measured pollutants exceeds the threshold, an alarm system is triggered taking several actions to warn the surrounding people.

Climate change and global warming are attributed to the increased levels of greenhouse Gases in the atmosphere. Miniature low-cost, lightweight instruments on-board low-cost nanosatellite platforms such as CubeSats could be used to provide precise measurements of greenhouse gases levels. CubeSats, which usually have a narrow field of view, cost a fraction of what more expensive satellites with wide swaths cost. MeznSat is a 3U CubeSat that will carry a shortwave infrared (SWIR) micro-spectrometer as its primary payload, with the aim of deriving greenhouse gas concentrations in the atmosphere by making observations in the 1000–1650 nm wavelength region. The satellite, which is planned for launch in March 2020, is the result of a collaborative project between Khalifa University of Science and Technology (KUST) and the American University of Ras Al-Khaimah (AURAK) with a fund from the United Arab Emirates Space Agency (UAE-SA). The primary payload, Argus 2000, is a miniature, low-cost, space-qualified spectrometer that operates in the shortwave infrared (SWIR) bands. Argus 2000 is a ruggedized unit with a mass of less than 230 g and power consumption of less than 1 W. Also, the Argus 2000 has 0.15 degrees viewing angle and 15 mm fore-optics. The secondary payload will consist of a high definition (HD) camera that will allow post-processing to achieve the high geolocation accuracy required for the SWIR spectrometer data. The RGB combination of visible and SWIR bands setup makes MeznSat a unique CubeSat mission that will generate an interesting dataset to explore atmospheric correction algorithms, which employ SWIR data to process visible channels. This paper describes the mission feasibility, mission analysis, design, and status of MeznSat.

Continuous advancements in Information and Communication Technology and the emergence of the Big Data era have altered how traditional power systems function. Such developments have led to increased reliability and efficiency, in turn contributing to operational, economic, and environmental improvements and leading to the development of a new technique known as Demand Side Management or DSM. In essence, DSM is a management activity that encourages users to optimize their electricity consumption by controlling the operation of their electrical appliances to reduce utility bills and their use during peak times. While users may save money on electricity costs by rescheduling their power consumption, they may also experience inconvenience due to the inflexibility of getting power on demand. Hence, several challenges must be considered to achieve a successful DSM. In this work, we analyze the power scheduling techniques in Smart Houses as proposed in most cited papers. We then examine the advantages and drawbacks of such methods and compare their contributions based on operational, economic, and environmental aspects.