People started posting textual tweets on Twitter as soon as the novel coronavirus (COVID-19) emerged. Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks. Therefore, this study aimed to analyze 43 million tweets collected between March 22 and March 30, 2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis. The results indicated that unigram terms were trended more frequently than bigram and trigram terms. A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic. The high-frequency words such as "death", "test", "spread", and "lockdown" suggest that people fear of being infected, and those who got infection are afraid of death. The results also showed that people agreed to stay at home due to the fear of the spread, and they were calling for social distancing since they become aware of the COVID-19. It can be suggested that social media posts may affect human psychology and behavior. These results may help governments and health organizations to better understand the psychology of the public, and thereby, better communicate with them to prevent and manage the panic.

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Routing is a challenge in cognitive radio networks (CRNs) due to the properties of cognitive radio (CR) technology, as well as other limitations. Firstly, the CR's frequency band is considered a dynamic spectrum. Therefore, since the routing algorithms used in other types of networks rely on a fixed frequency band, they cannot be directly used in CRNs. Secondly, the dynamic spectrum access, which is enabled by CR technology, negatively affects the network performance. Thirdly, having an effective routing in CRNs needs a local and continual knowledge of its changeable environment. Lastly, the presence of adversary nodes and their malicious activities affect the route establishment process, thereby reducing the network performance. This paper addresses these limitations by combining the spectrum sensing and the spectrum management phases by proposing a novel and secure routing algorithm. Security in the proposed algorithm combines two aspects. The first aspect is measuring the nodes' behavior during the spectrum sensing phase through a parameter called belief level (BL), which refers to the nodes' reliability to correctly find and use the white spectrum channels. The second aspect is securing the routing request and reply messages by encoding them with the existing cryptography techniques. The main goal of the proposed approach is to make the available paths between any two communicating nodes secure, reduce the negative effects to the licensed users over the spectrum channels, and moderate the total cost of the used channels over the best path(s). The performance evaluation in terms of end-to-end delay, packet delivery ratio, packet loss ratio, and routing overhead show that the proposed approach outperforms multiple existing routing algorithms. Moreover, the proposed algorithm is validated and verified in terms of security functionality against any attacks.

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Spectrum sensing is the first step to overcome the spectrum scarcity problem in Cognitive Radio Networks (CRNs) wherein all unutilized subbands in the radio environment are explored for better spectrum utilization. Adversary nodes can threaten these spectrum sensing results by launching passive and active attacks that prevent legitimate nodes from using the spectrum efficiently. Securing the spectrum sensing process has become an important issue in CRNs in order to ensure reliable and secure spectrum sensing and fair management of resources. In this paper, a novel collaborative approach during spectrum sensing process is proposed. It monitors the behavior of sensing nodes and identifies the malicious and misbehaving sensing nodes. The proposed approach measures the node's sensing reliability using a value called belief level. All the sensing nodes are grouped into a specific number of clusters. In each cluster, a sensing node is selected as a cluster head that is responsible for collecting sensing-reputation reports from different cognitive nodes about each node in the same cluster. The cluster head analyzes information to monitor and judge the nodes' behavior. By simulating the proposed approach, we showed its importance and its efficiency for achieving better spectrum security by mitigating multiple passive and active attacks.

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