Prospects and problems of cognitive radio network architectures in wireless sensor networks

Cognitive radio is a technique proposed by Mitola which provides a way to efficiently use precious radio spectrum resources [1, 2]. A cognitive radio recognizes, analyzes, and learns the situations of the radio spectrum and then employs various strategies to maximize spectrum usage. The concept of cognitive radio is to detect and ascertain \vhich aspect of the spectrum is presently unused, and then perform data transmission at the newly discovered frequency band. An alternative cognitive radio scheme is to transmit signals on top of existing transmissions as long as the interference temperature measure is lower than threshold. Spectrum sharing, spectrum sensing, and spectrum management are the stages of a cognitive radio. Spectrum sensing gathers up-to-date spectrum usage data, spectrum management determines the optimal spectrum access timing and scheme, and spectrum sharing ensures that users are served in a fair and timely mann

Investigation of techniques for combating malicious objects in unreliable wireless sensor networks

Unreliable net\vorks can take many different forms such as in the general case an ad hoc network or more specifical1y as a wireless sensor networks (WSNs). Some of the fields you can [md these types of networks used in would be the telecommunications industry with mobile phones, biological research for monitoring animals in the wild and military applications to monitor soldiers. Networks in these areas are handling an increasing amount of data. This data is very valuable and therefore a source of concern in making sure that none of it is lost or damaged. From a security standpoint there are many ways that an attack on these types of networks can be implemented. Some attacks are rather difficult to execute and would require knowledge of the particular network that is being attacked to be effective. One of the most effective attack methods would be for the attacker to inject its own data into the network either with the simplest goal of consuming network resources or having some other purpose such as capturing or corrupting the data stored in the network. Thinking in terms of biological systems the data that these attackers inject into the network is similar to a virus entering a human body. In the medical field medicine can be used to help cure a person by targeting this virus, in much the same way this chapter considers introducing a special type of anti-virus to the network to remove this data inserted by an attacker. This chapter considers leveraging the properties of unreliable network combined with a recommended approach employing an anti-virus to remove the virus from the network effectivel

Towards Enhancing Keyframe Extraction Strategy for Summarizing Surveillance Video: An Implementation Study

The large amounts of surveillance video data are recorded, containing many redundant video frames, which makes video browsing and retrieval difficult, thus increasing bandwidth utilization, storage capacity, and time consumed. To ensure the reduction in bandwidth utilization and storage capacity to the barest minimum, keyframe extraction strategies have been developed. These strategies are implemented to extract unique keyframes whilst removing redundancies. Despite the achieved improvement in keyframe extraction processes, there still exist a significant number of redundant frames in summarized videos. With a view to addressing this issue, the current paper proposes an enhanced keyframe extraction strategy using k-means clustering and a statistical approach. Surveillance footage, movie clips, advertisements, and sports videos from a benchmark database as well as Compeng IP surveillance videos were used to evaluate the performance of the proposed method. In terms of compression ratio, the results showed that the proposed scheme outperformed existing schemes by 2.82%. This implies that the proposed scheme further removed redundant frames whiles retaining video quality. In terms of video playtime, there was an average reduction of 27.32%, thus making video content retrieval less cumbersome when compared with existing schemes. Implementation was done using MATLAB R2020b

Practical applications and design challenges of wireless heterogeneous sensor networks

Heterogeneous, {hetero + genos 'type', from Greek), is defined in Oxford Advanced Learner's Dictionary as "consisting of many different kinds of people or things" and defined in Longman Dictionary as "consisting of parts or members that are very different from each other." The design of interconnected nodes may be heterogeneous or homogeneous with the aim of catering for the design demands and purpose of various wireless applications. The heterogeneity or homogeneity of the interconnected nodes designed is with respect to their ability to sense events, transmit desired sensed data, computing and processing user queries, managing of energy resources and minimizing the complexity of the hardware design. The participating nodes in heterogeneous networks may be different in many aspects. They could have ditferent transmission radius, various kind of sensing units, different hardware power, and dit1erent power supply. Nodes with lesser energy resources serve as sensing nodes to collect physical information while nodes with more energy resources serve as data sinks. Heterogeneity could be viewed either in terms of capability or functionality of sensor nodes. In homogeneous networks, all the participating and active nodes are alike in nature and the same transmit power level is used for their operation. These alike nodes are inherently built with the same sensing units to track a single event [5, 9, 10]. Cluster heads and cluster members have different tasks in clustered sensor networks in the course of data delivering to the base station. An example is a tiered sensor network architecture where 802.11 mesh network comprise of high-end nodes, such as Intel XScale nodes which are deployed on a plain WSN fiel

Information and communication privacy in wireless sensor networks

This chapter places special emphasis on sensor networks applications in a battlefield.When a sensor network is used to monitor soldiers' movements in a battlefield, information about the soIdiers' whereabouts is sent back to the base station and accessed by commanders in the headquarters as shown in Figure 33.1. Before the fight, planes fly over the battlefield and deploy the sensors. These sensors organize themselves into a network and transmit data collected from the battlefield and send them back to the base statio

Towards Enhancing Keyframe Extraction Strategy for Summarizing Surveillance Video: An Implementation Study

The large amounts of surveillance video data are recorded, containing many redundant video frames, which makes video browsing and retrieval difficult, thus increasing bandwidth utilization, storage capacity, and time consumed. To ensure the reduction in bandwidth utilization and storage capacity to the barest minimum, keyframe extraction strategies have been developed. These strategies are implemented to extract unique keyframes whilst removing redundancies. Despite the achieved improvement in keyframe extraction processes, there still exist a significant number of redundant frames in summarized videos. With a view to addressing this issue, the current paper proposes an enhanced keyframe extraction strategy using k-means clustering and a statistical approach. Surveillance footage, movie clips, advertisements, and sports videos from a benchmark database as well as Compeng IP surveillance videos were used to evaluate the performance of the proposed method. In terms of compression ratio, the results showed that the proposed scheme outperformed existing schemes by 2.82%. This implies that the proposed scheme further removed redundant frames whiles retaining video quality. In terms of video playtime, there was an average reduction of 27.32%, thus making video content retrieval less cumbersome when compared with existing schemes. Implementation was done using MATLAB R2020b