http://home.engineering.iastate.edu/~morris/papers/14/crn_survey_deanna.pdf

A layered approach to cognitive radio network security

Stock market data is a time-series data in which stock value varies depends on time. Prediction of the stock market is an endeavor to assess the future value of a company’s stock rate which will increase the investor’s profit. The accurate prediction of stock market analysis is still a challenging task. The proposed system predicts stock price of any company mentioned by the user for the next few days. Using the predicted stock price and datasets collected from various sources regarding a certain equity, the overall sentiment of the stock is predicted. The prediction of stock price is done by regression and candlestick pattern detection. The proposed system generates signals on the candlestick graph which allows to predict market movement to a sufficient level of accuracy so that the user is able to judge whether a stock is a ‘Buy/Sell’ and whether to short the stock or go long by delivery. The prediction accuracy of the stock exchange has analyzed and improved to 85% using machine learning algorithms.

Stock market analysis using candlestick regression and market trend prediction (CKRM)

Anomaly detection and trust authority in artificial intelligence and cloud computing

With the information and communication technologies (ICT) and Internet of Things (IoT) gradually advancing, smart homes have been able to provide home services to users. The user can enjoy a high level of comfort and improve his quality of life by using home services provided by smart devices. However, the smart home has security and privacy problems, since the user and smart devices communicate through an insecure channel. Therefore, a secure authentication protocol should be established between the user and smart devices. In 2020, Xiang and Zheng presented a situation-aware protocol for device authentication in smart grid-enabled smart home environments. However, we demonstrate that their protocol can suffer from stolen smart device, impersonation, and session key disclosure attacks and fails to provide secure mutual authentication. Therefore, we propose a secure and lightweight authentication protocol for IoT-based smart homes to resolve the security flaws of Xiang and Zheng’s protocol. We proved the security of the proposed protocol by performing informal and formal security analyses, using the real or random (ROR) model, Burrows–Abadi–Needham (BAN) logic, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. Moreover, we provide a comparison of performance and security properties between the proposed protocol and related existing protocols. We demonstrate that the proposed protocol ensures better security and lower computational costs than related protocols, and is suitable for practical IoT-based smart home environments.

A Secure and Lightweight Authentication Protocol for IoT-Based Smart Homes

Cognitive radio networks (CRNs) are an emerging paradigm for next generation wireless communication systems allowing for more efficient radio spectrum utilization. In order to harness the full potential that CRNs may offer, many challenges and problems need to be overcome and addressed. One of the critical questions is the performance of secondary networks under primary user activity constraints. In this respect, queueing assumes a primary role in characterizing the delay, throughput and other performance metrics for secondary users, which in turn has implications for resource allocation, medium access control and quality of service provisioning. This survey presents an overview and classification of the various queueing models and techniques which have been proposed in the literature in the context of CRNs. Furthermore, open problems, future research directions and further potential applications related to queueing for CRNs are identified.

Queueing Models for Cognitive Radio Networks: A Survey

Conventional Cloud Computing system possess some limitations. It is difficult for users to switch between various cloud providers due to lack of a standard architecture. There is no standard metering system for cloud computing services. For cloud providers, it is difficult to maintain performance transparency due to changing user requirements, varying loads and limited resources, especially for small and medium providers. Federation of Clouds is the possible solution for these problems. In this paradigm, various cloud providers can federate with each other to expand their business opportunities while achieving optimal resource utilization and cost effectiveness. This collaboration of clouds is fruitful to users in terms of better pricing options, availability of services and overall better Quality of Service. Unlike current Cloud Computing, Federation of Clouds requires a standard architecture to which every participating cloud provider must comply. In nutshell, Federation of Clouds opens a domain of infinite possibilities to reshape the existing world of Cloud Computing and Information Technology, in general. It will provide a level playing field for emerging small and medium level cloud providers to compete among big players. It also has the promise to provide users with better ways to access great computational power and resources.

A Survey on Cloud Federation Architecture and Challenges

In the last few decades, the Cognitive Radio (CR) paradigm has received huge interest from industry and academia. CR is a promising approach to solve the spectrum scarcity problem. Moreover, various technical issues still need to be addressed for successful deployment of CRNs, especially in the MAC layer. In this chapter, a comprehensive survey of the Medium Access Control (MAC) approaches for CRN is presented. These MAC technologies under analysis include spectrum sharing, multiple antenna techniques, cooperation, relays, distributed systems, network convergence, mobility, and network selfoptimization. Moreover, various classifications of MAC protocols are explained in this chapter on the basis of some parameters, like signaling technique, type of architecture, sharing mode, access mode, and common control channel. Additionally, some case studies of 802.11, 802.22, and Mobile Virtual Node Operator (MVNO) are also considered for the case study. The main objective of this chapter is to assist CR designers and the CR application engineers to consider the MAC layer issues and factors in the early development stage of CRNs. DOI: 10.4018/978-1-4666-2812-0.ch008

MAC Layer Protocols for Cognitive Radio Networks

The present document represents a thorough study of the making of an efficient surveillance system along with a feature of automatically informing the owner about the suspicious movement. In this moving world, normally people are suffering from the availability of time, so if any crime has happened at the site, it will take many days of searching for finding the actual presence of criminals, and thus a good chance for those burglars to flee away to protect themselves. For making the task possible, chose Python as the weapon for this battle and used different efficient techniques like COCO dataset for getting labeled and annotated images, LabelImg for making the annotation set of images, TensorFlow, object detection API for object detection and faster RCNN for training as faster RCNN has shown the highest accuracy for the COCO dataset so far. The owner can be informed in two ways: Either send a message to him via mail or phone or call at the time of suspicious image capturing. Here, both of these cases are used: For mail, the task is done via SMTP and for phone calls Twilio is used which provides us registered phone no. and can make both outbound and inbound calls. After using all the mentioned things and making the model in a way described above, it was found that faster RCNN is much more accurate than the other conventional methods. The results have been very well as RCNN show 86.7% accuracy and 100% has come out with the informing module as there simply the mail will be sent to the one whose mail is given in the code and the same is for Twilio calling.

/pdf/video-surveillance-system-with-auto-informing-feature-2360f16knu.pdf

Video Surveillance System with Auto Informing Feature

In the overlay medium access control (MAC) of cognitive radio networks, secondary user (SU) uses the spectrum portion which is assigned to primary user (PU), when PU is not using this spectrum at that time. In this article, a multiuser overlay MAC protocol is considered in which PUs and SUs are using time division multiple access and carrier sense multiple access (CSMA) MAC schemes, respectively. Firstly, new frame format and algorithm for the proposed MAC protocol are described, then the analytical modelling of this protocol is explained. In the SU network, Markov chain model along with the CSMA protocol is used for better utilization of the opportunity from the PU network (PUN). Channel utilization and probability of success are also derived and analyzed for this protocol with respect to idle time, collision (failure) time, various spectrum opportunities from PUN, number of SUs, and their fairness. This MAC scheme provides the optimal solution for channel utilization under a constraint on the probability of collisions. Numerical results are also provided to validate analysis of proposed MAC protocol and to investigate the performance behaviour of the optimal solution.

Lokesh Chouhan

Papers

A Survey on Cloud Federation Architecture and Challenges

MAC Layer Protocols for Cognitive Radio Networks

Video Surveillance System with Auto Informing Feature

Performance study of a CSMA based multiuser MAC protocol for cognitive radio networks: analysis of channel utilization and opportunity perspective

Blockchain Security for Wireless Multimedia Networks