The primary aim of a cognitive radio (CR) system is to optimize spectrum usage by exploiting the existing spectrum holes. Nevertheless, the success of cognitive radio technology is significantly threatened by the primary user emulation attack (PUEA). A rogue secondary user (SU) known as the primary user emulator (PUE) impersonates a legitimate primary user (PU) in a PUEA, thereby preventing other SUs from accessing the spectrum holes. Which leads to the decrease in quality of service (QoS), connection undependability, degraded throughput, energy depletion, and the network experiences a deterioration in its overall performance. In order to alleviate the impact of PUEA on Cognitive Radio Networks (CRNs), it is necessary to detect and isolate the threat agent (PUE) from the network. In this paper, a method for finding and isolating the PUE is proposed. MATLAB simulation results showed that the presence of PUE caused a significant decrease in the throughput of SUs, from to . The throughput was highest at a false alarm (FA) probability of 0.0, indicating no PUE, and decreased as the FA probability increased. At a FA probability of 1, the throughput reached zero, indicating complete takeover of the spectrum by PUE. By isolating the PUE from the network, the other SUs can access the spectrum holes, leading to increased QoS, connection reliability, improved throughput, and efficient energy usage. The presented technique is an important step towards enhancing the security and reliability of CRNs.

There are several IoT platforms providing a variety of services for different applications. Finding the optimal fit between application and platform is challenging since it is hard to evaluate the effects of minor platform changes. Several websites offer reviews based on user ratings to guide potential users in their selection. Unfortunately, review data are subjective and sometimes conflicting – indicating that they are not objective enough for a fair judgment. Scientific papers are known to be the reliable sources of authentic information based on evidence-based research. However, literature revealed that though a lot of work has been done on theoretical comparative analysis of IoT platforms based on their features, functions, architectures, security, communication protocols, analytics, scalability, etc., empirical studies based on measurable metrics such as response time, throughput, and technical efficiency, that objectively characterize user experience seem to be lacking. In an attempt to fill this gap, this study used web analytic tools to gather data on the performance of some selected IoT cloud platforms. Descriptive and inferential statistical models were used to analyze the gathered data to provide a technical ground for the performance evaluation of the selected IoT platforms. Results showed that the platforms performed differently in the key performance metrics (KPM) used. No platform emerged best in all the KPMs. Users' choice will therefore be based on metrics that are most relevant to their applications. It is believed that this work will provide companies and other users with quantitative evidence to corroborate social media data and thereby give a better insight into the performance of IoT platforms. It will also help vendors to improve on their quality of service (QoS).

Pipeline vandalization is one of Nigeria's economy killer, since Nigeria economy to a great extent relies on oil in that capacity. Therefore, a third party damage to petroleum pipelines can be cataclysmic if undetected and prevented. This act results in budgetary misfortunes, ecological contamination and incessant death and loss of properties worth millions as an aftereffect of vandalization. Consequently, it is very paramount to protect these pipelines from vandals through intelligent monitoring systems. Several efforts have been made towards providing a reliable monitoring system for oil pipeline, however, no practically implementable solution have been achieved. Therefore, a pipeline monitoring system using multi-sensors is presented herewith. The sensor array consists of a Passive Infrared (PIR), vibration and sound sensor. An uninvolved infrared (PIR) sensor was utilized to detect intruders before they get in contact with the pipeline and for affirmation of intruders, sound and vibration sensor were set up. As the PIR recognizes an on-coming human the sound and vibration sensors affirms if the human is an intruder(s). An intrusion message containing the location of the vandals is sent to the appropriate authority by the microcontroller via a connected GSM module. Results obtained proved the system as a viable solution for detecting pipeline vandals.