Compressed Sensing based Dynamic PSD Map Construction in Cognitive Radio Networks

In the context of spectrum sensing in cognitive radio networks, collaborative spectrum sensing has been proposed as a way to overcome multipath and shadowing, and hence increasing the reliability of the sensing. Due to the high amount of information to be transmitted, a dynamic compressive sensing approach is proposed to map the PSD estimate to a sparse domain which is then transmitted to the fusion center. In this regard, CRs send a compressed version of their estimated PSD to the fusion center, whose job is to reconstruct the PSD estimates of the CRs, fuse them, and make a global decision on the availability of the spectrum in space and frequency domains at a given time. The proposed compressive sensing based method considers the dynamic nature of the PSD map, and uses this dynamicity in order to decrease the amount of data needed to be transmitted between CR sensors’ and the fusion center. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 20 % of full data transmission between sensors and master node. Also, simulation results show the robustness of the proposed method against the channel variations, diverse compression ratios and processing times in comparison with static methods

Complexity Reduction in Beamforming of Uniform Array Antennas for MIMO Radars

Covariance matrix design and beamforming in multiple-input multiple-output (MIMO) radar systems have always been a time-consuming task with a substantial number of unknown variables in the optimization problem to be solved. Based on the radar and target conditions, beamforming can be a dynamic process and in real-time scenarios, it is critical to have a fast beamforming. In this paper, we propose a beampattern matching design technique that is much faster compared to the well-known traditional semidefinite quadratic programming (SQP) counterpart. We show how to calculate the covariance matrix of the probing transmitted signal to obtain the MIMO radar desired beampattern, using a facilitator library. While the proposed technique inherently satisfies the required practical constraints in covariance matrix design, it significantly reduces the number of unknown variables used in the minimum square error (MSE) optimization problem, and therefore reduces the computational complexity considerably. Simulation results show the superiority of the proposed technique in terms of complexity and speed, compared with existing methods. This superiority is enhanced by increasing the number of antennas

The Impact of GDPR Infringement Fines on the Market Value of Firms

Previous studies have shown (varying degrees of) evidence of a negative impact of data breach announcements on the share price of publicly listed companies. Following on from this research, further studies have been carried out in assessing the economic impact of the introduction of legislation in this area to encourage firms to invest in cyber security and protect the privacy of data subjects. Existing research has been predominantly US-centric. This paper looks at the impact of the General Data Protection Regulation (GDPR) infringement fine announcements on the market value of mostly European publicly listed companies with a view to reinforcing the importance of data privacy compliance, thereby informing cyber security investment strategies for organisations. Using event study techniques, a dataset of 25 GDPR fine announcement events was analysed, and statistically significant cumulative abnormal returns (CAR) of around-1% on average up to three days after the event were identified. In almost all cases, this negative economic impact on market value far outweighed the monetary value of the fine itself, and relatively minor fines could result in major market valuation losses for companies, even those having large market capitalisations. A further dataset of four announcements where sizeable GDPR fines were subsequently appealed was also analysed and although positive returns for successful appeals were observed (and the reverse), they could not be shown to be statistically significant-perhaps due, at least in part, to COVID-19 related market volatility at that time. This research would be of benefit to business management, practitioners of cyber security, investors and shareholders as well as researchers in cyber security or related fields (pointers to future research are given). Data protection authorities may also find this work of interest

The Impact of Data Breach Announcements on Company Value in European Markets

Recent research on the economic impact of data breach announcements on publicly listed companies was found to be sparse, with the majority of existing studies having a strong US bias. Here, a dataset of 45 data breach disclosures between 2017 and 2019 relevant to European publicly listed companies was hand-gathered (from various sources) and detailed analyses of share price impact carried out using event study techniques with the aim of supporting business cases for firms to invest in cyber security. Differences from existing studies (in particular, the US market) are highlighted and discussed along with pointers to future research in this area. Although some evidence of negative cumulative abnormal returns (CAR) in the days surrounding the announcement were observed, along with one extreme case leading to insolvency, the results were not statistically significant overall with the notable exception of the Spanish market, which appeared to be more sensitive to data breaches, reacting rapidly. Therefore, justification for cyber security investment purely based on the market value effect of a data breach disclosure would be challenging. Other factors would need to be taken into consideration such as risk appetite, industry sector and nature of the information compromised as well as relevant legislation. Certain other observations were noted such as the lack of a comprehensive breach database for Europe (unlike US) and the effect of the introduction of the General Data Protection Regulation (GDPR). This research would be of benefit to business management, practitioners of cyber security, investors and shareholders as well as researchers in cyber security or related fields

The Impact of CISO Appointment Announcements on the Market Value of Firms

Previous studies concerning the economic impact of security events on publicly listed companies have focussed on the negative effect of data breaches and cyberattacks with a view to encouraging firms to improve their cyber security posture to avoid such incidents. This paper is an initial study on the impact of investment in human capital related to security, specifically appointments of chief information security officers (CISO), chief security officers (CSO) or similar overall head of security roles. Using event study techniques, a dataset of 37 CISO type appointment announcements spanning multiple world markets between 2012 and 2019 was analysed and statistically significant (at the 5% level) positive cumulative abnormal returns (CAR) of around 0.8% on average were observed over the three-day period before, during and after the announcement. Furthermore, this positive CAR was found to be highest, at nearly 1.8% on average, within the financial services sector and showing statistical significance at the 1% level. In addition to the industry sector, other characteristics were investigated such as job title, reporting structure, comparison of internal versus external appointments, gender and variations between markets. Although these findings were not as conclusive they are, nevertheless, good pointers for future research in this area. This overall positive market reaction to CISO related announcements is a strong case for publicly listed firms to be transparent in such appointments and to, perhaps, review where that function sits within their organisation to ensure it delivers the greatest benefits. As 24% of the firms analysed were listed outside the US, this study also begins to counter the strong US bias seen in similar and related studies. This research is expected to be of interest to business management, cyber security practitioners, investors and shareholders as well as researchers in cyber security or related fields

Joint Optimization of Power and Location in Full-Duplex UAV Enabled Systems

Unmanned aerial vehicles (UAVs) can be used as aerial base stations (BSs) for future small cells. They can increase the spectral efficiency of the small cells due to their higher probability to have line-of-sight (LOS) connections and their mobility as a BS. In this article, in order to show the effectiveness of using full-duplex (FD) technology in UAV networks, we consider a UAV equipped with FD technology (FD-UAV) with imperfect self-interference cancelation as an aerial BS that serves both uplink (UL) and downlink (DL) users simultaneously in a small cell network. We aim to maximize DL sum-rate, whilst prescribing a certain quality of service for UL users, by optimizing the location of FD-UAV and available resources. The problem is nonconvex; so we propose an iterative method by exploiting the difference of convex functions programming to jointly optimize transmission power of users, FD-UAV location, and FD-UAV transmission power. Simulation results are illustrated to show the effectiveness of the proposed method for FD-UAV in comparison with ground BS, in both FD and half-duplex modes

Throughput Improvement by Mode Selection in Hybrid Duplex Wireless Networks

Hybrid duplex wireless networks, use half duplex (HD) as well as full duplex (FD) modes to utilize the advantages of both technologies. This paper tries to determine the proportion of the network nodes that should be in HD or FD modes in such networks, to maximize the overall throughput of all FD and HD nodes. Here, by assuming imperfect self-interference cancellation (SIC) and using ALOHA protocol, the local optimum densities of FD, HD and idle nodes are obtained in a given time slot, using Karush–Kuhn–Tucker (KKT) conditions as well as stochastic geometry tool. We also obtain the sub-optimal value of the signal-to-interference ratio (SIR) threshold constrained by fixed node densities, using the steepest descent method in order to maximize the network throughput. The results show that in such networks, the proposed hybrid duplex mode selection scheme improves the level of throughput. The results also indicate the effect of imperfect SIC on reducing the throughput. Moreover, it is demonstrated that by choosing an optimal SIR threshold for mode selection process, the achievable throughput in such networks can increase by around 5%

A CSI-Based Human Activity Recognition Using Deep Learning

The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics ofWiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities

Resource Allocation in Full-Duplex UAV Enabled Multi Small Cell Networks

Flying platforms such as Unmanned Aerial Vehicles (UAVs) are a promising solution for future small cell networks. UAVs can be used as aerial Base Stations (BSs) to enhance coverage, capacity and reliability of wireless networks. Also, with recent advances of Self Interference Cancellation (SIC) techniques in Full-Duplex (FD) systems, practical implementation of FD BSs is feasible. In this paper, we investigate the problem of resource allocation for multi-small cell networks with FD-UAVs as aerial BSs with imperfect SIC. We consider three different scenarios: a) maximizing the DL sum-rate, b) maximizing the UL sum-rate, and finally c) maximizing the sum of UL and DL sum-rates. The aforementioned problems result in non-convex optimization problems, therefore, successive convex approximation algorithms are developed by leveraging D.C. (Difference of Convex functions) programming to find sub-optimal solutions. Simulation results illustrated validity and effectiveness of the proposed radio resource management algorithms in comparison with ground BSs, in both FD mode and its half-duplex (HD) counterpart. The results also indicate those situations where using aerial BS is advantageous over ground BS and reveal how FD transmission enhances the network performance in comparison with HD one