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The efficient compression and classification of medical signals, particularly electroencephalography (EEG) and electrocardiography (ECG) signals in wireless body area network (WBAN) systems, are crucial for real-time monitoring and diagnosis. This paper addresses the challenges of compressive sensing and classification in WBAN systems for EEG and ECG signals. To tackle the challenges of the compression process, a sequential approach is proposed. The first step involves compressing the EEG and ECG signals using the optimized Walsh-Hadamard transform (OWHT). This transform allows for efficient representation of the signals, while preserving their essential characteristics. However, the presence of noise can impact the quality of the compressed signals. To mitigate this effect, the signals are subsequently recovered using the Sparse Group Lasso 1 (SPGL1) algorithm and OWHT, which take into account the noise characteristics during the recovery process. To evaluate the performance of the proposed compressive sensing algorithm, two metrics are employed: mean squared error (MSE) and maximum correntropy criterion (MCC). These metrics provide insights into the accuracy and reliability of the recovered signals at different signal-to-sample ratios (SSRs). The results of the evaluation demonstrate the effectiveness of the proposed algorithm in accurately reconstructing the EEG and ECG signals, while effectively managing the noise interference. Furthermore, to enhance the classification accuracy in the presence of signal compression, a local binary pattern (LBP) tehnique is applied. This technique extracts discriminative features from the compressed signals. These features are then fed into a classification algorithm based on residual learning. This classification algorithm is trained from scratch and specifically designed to work with the compressed signals. The experimental results showcase the high accuracy achieved by the proposed approach in classifying the compressed EEG and ECG signals without the need for signal recovery. The findings of this study highlight the potential of the proposed approach in achieving efficient and accurate medical signal analysis in WBAN systems. By eliminating the computational burden of signal recovery and leveraging the advantages of compressive sensing, the proposed approach offers a promising solution for real-time monitoring and diagnosis, ultimately improving the overall efficiency and effectiveness of healthcare systems.

Query by Image Content (QBIC), subsequently known as Content-Based Image Retrieval (CBIR), offers an advantageous solution in a variety of applications, including medical, meteorological, search by image, and other applications. Such CBIR systems primarily use similarity matching algorithms to compare image content to get matched images from datasets. They essentially measure the spatial distance between extracted visual features from a query image and its similar versions in the dataset. One of the most challenging query retrieval problems is Facial Sketched-Real Image Retrieval (FSRIR), which is based on content similarity matching. These facial retrieval systems are employed in a variety of contexts, including criminal justice. The difficulties of retrieving such sorts come from the composition of the human face and its distinctive parts. In addition, the comparison between these types of images is made within two different domains. Besides, to our knowledge, there is a few large-scale facial datasets that can be used to assess the performance of the retrieval systems. The success of the retrieval process is governed by the method used to estimate similarity and the efficient representation of compared images. However, by effectively representing visual features, the main challenge-posing component of such systems might be resolved. Hence, this paper has several contributions that fill the research gap in content-based similarity matching and retrieval. The first contribution is extending the Chinese University Face Sketch (CUFS) dataset by including augmented images, introducing to the community a novel dataset named Extended Sketched-Real Image Retrieval (ESRIR). The CUFS dataset has been extended from 100 images to include 53,000 facial sketches and 53,000 real facial images. The paper second contribution is presenting three new systems for sketched-real image retrieval based on convolutional autoencoder, InfoGAN, and Vision Transformer (ViT) unsupervised models for large datasets. Furthermore, to meet the subjective demands of the users due to the prevalence of multiple query formats, the third contribution of the paper is to train and assess the performance of the proposed models on two additional facial datasets of different image types. Recently, the majority of people have preferred searching for brand logo images, but it may be tricky to separate certain brand logo features their alternatives and even from other features in an image. Thus, the fourth contribution is to compare logo image retrieval performance based on visual features derived from each of the three suggested retrieval systems. The paper also presents cloud-based energy and computational complexity saving approaches on large-scale datasets. Due to the ubiquity of touchscreen devices, users often make drawings based on their fantasies for certain object image searches. Thus, the proposed models are tested and assessed on a tough dataset of doodle-scratched human artworks. They are also studied on a multi-category dataset to cover practically all possible image types and situations. The results are compared with those of the most recent algorithms found in the literature. The results show that the proposed systems outperform the recent counterparts.

There exist several performance criteria for cryptographically-strong substitution boxes (S-boxes), which are often conflicting with each other. Constructing S-boxes that satisfy multiple criteria with optimal tradeoffs is one of the challenging tasks for cryptographers. In practice, the existing S-box design algorithms are used to optimize performance according to a single performance criterion, mainly the nonlinearity, which usually results in weak scores for other equally-significant criteria. To overcome this problem, a multi-objective optimization-based method is presented in this paper. In this method, $8 \times 8$ S-boxes are constructed satisfying multiple criteria of balancedness, high nonlinearity, low differential uniformity, and low auto-correlation. Multiple objectives are fulfilled by applying the chaos-assisted non-dominated sorting genetic algorithm-II to introduce the S-boxes. The performance assessment of the proposed method and the comparative analysis with available optimization tools and other state-of-the-art algorithms demonstrate its proficiency in generating significantly-better S-box solutions with good Pareto-optimal security features. Eventually, the S-boxes with minimum nonlinearity (NL) of 110, differential uniformity (DU) as low as 8, and auto-correlation function (ACF) as low as 80 are obtained after the optimization. Furthermore, the obtained Pareto-optimal S-box is utilized to put forward a medical image encryption algorithm for secure telemedicine services. The suggested encryption algorithm uses an S-box to perform the required permutation and diffusion of images. The encryption performance assessment and comparison analyses validate its effectiveness for securing medical imagery data in telemedicine networks.

Precision agriculture is a challenging task to achieve. Several studies have been conducted to forecast agricultural yields using machine learning algorithms (MLA), but few studies have used ensemble machine learning algorithms (EMLA). In the current study, we use a dataset generated by a computer simulation program, and meteorological data obtained over 30 years from Maine, United States (USA). The primary goal of this research is to increase the forecast accuracy of the best characteristics for overcoming hunger challenges. We adopted stacking regression (SR) and cascading regression (CR) with a novel combination of MLA based on the wild blueberry dataset. We used features that indicated the best regulation for wild blueberry agroecosystems. Four feature engineering selection techniques are applied, namely variance inflation factor (VIF), sequential forward feature selection (SFFS), sequential backward elimination feature selection (SBEFS), and extreme gradient boosting based on feature importance (XFI). We applied Bayesian optimization on popular MLA to obtain the best hyperparameters to achieve accurate wild blueberry yield prediction. The SR used a two-layer structure: level-0 containing light gradient boosting machine (LGBM), gradient boost regression (GBR) and extreme gradient boosting (XGBoost), and level-1 providing the output prediction using a Ridge. The CR topology is the same MLA used in SR, but in a series form that takes the new prediction as a feeder to each MLA and removes the previous prediction in each stage. We assessed the CR, and SR with outcomes according to the root mean square error (RMSE) and coefficient of determination ( $R^{2}$ ). In the results, the proposed SR showed the best performance with $R^{2}$ of 0.984 and RMSE of 179.898 compared with another study that reported $R^{2}$ of 0.938 and RMSE of 343.026 on the seven features selected by XFI. The SR achieved the highest $R^{2}$ of 0.985 on all features and the features that were selected by the SBEFS. Our SR outperformed CR, and another study on wild blueberry yield prediction.

Multimedia Super-Resolution (SR) reconstruction is an essential and mandatory process for different visualization functions. Recently, several schemes have been suggested for single- and multi-image SR reconstruction. This work presents an effective SR reconstruction scheme for visual quality and resolution enhancement of 3D Video (3DV) sequences. The idea behind the proposed 3DV SR reconstruction scheme is the utilization of a recursive Bayesian algorithm for improving the resolution of the degraded 3DV sequences with down-sampling, blurring, and noise effects. In addition, a significant stage of histogram matching based on a visual image with a better-distributed histogram is employed. The main aim of employing the histogram matching stage for enhancing the 3DV sequence is to introduce a dynamic range modification of each 3DV frame. Hence, it presents a 3DV sequence with an enhanced distribution of intensities. This improves the whole performance efficiency of the suggested scheme. The performance of the proposed SR reconstruction scheme is compared with that of the conventional bicubic interpolation scheme. Comparisons with recent and related SR reconstruction schemes are also introduced. Simulation results reveal that the proposed scheme achieves superior outcomes in terms of Structural Similarity (SSIM) index, local contrast, average gradient, Mean Square Error (MSE), edge intensity, entropy, and Peak Signal-to-Noise Ratio (PSNR) of the resulting 3DV frames.

A hybrid multi-state orbital angular momentum-multi pulse-position modulation ( $N$ OAM-MPPM) scheme over gamma-gamma free-space optical ( ${\Gamma \Gamma }$ -FSO) channel is studied in this paper. In our study, all atmospheric and pointing error impacts are taken into account. Expressions for the parameters of ${\Gamma \Gamma }$ -FSO-pointing error channel are derived. In addition, approximate-tight upper bounds on the bit-error rates (BERs) of $N$ OAM and $N$ OAM-MPPM techniques are developed over ${\Gamma \Gamma }$ -FSO-pointing error channels, considering the influences of beam divergence and pointing error (PE). The ${\Gamma \Gamma }$ -FSO-PE channel parameters and the BER expressions are evaluated numerically and verified by simulation. It turned out that the analytical results are nearly the same as those obtained from simulation under different turbulence scenarios and OAM modes. The results demonstrate that under variable turbulence conditions, the $N$ OAM-MPPM technique outperforms both ordinary $N$ OAM and MPPM systems. Furthermore, different deep learning (DL) techniques, namely random forest (RF), convolution neural network (CNN), and auto-encoder (AE), are employed to get the optimum classification accuracy using different datasets of $N$ OAM-MPPM over ${\Gamma \Gamma }$ -PE channel model. Finally, the results indicate that AE has the best performance metrics compared to other models using different datasets.

Cancelable biometric recognition techniques play a vital role in the privacy and security of remote surveillance systems to keep the genuine users’ confidential data safe and away from intruders. This research work presents an efficient cancelable biometric recognition framework that exploits an irreversible hybrid encryption algorithm. It incorporates Deoxyribonucleic, Ribonucleic Acid sequence (DNA and RNA) encryption technique, and an evolutionary optimization technique, namely Genetic Algorithms (GAs). These techniques are employed to create completely deformed templates from their original ones. Hence, the main contribution is introducing a novel biometric security framework that achieves unique randomness characteristics using RNA and DNA sequences and the evolutionary GA technique. The proposed framework produces entirely deformed biometric templates by ciphering the main discriminative features of the biometric traits of the authorized clients. It is firstly initialized by creating several encrypted biometric images for the original users with the logistic map. After that, the initially encrypted images are transformed into vectors of a binary array. Then, they are converted to their corresponding introns, and exons, and consequently, their relevant codons are stored in the cloud database. These relevant codons are replaced by new ones after generating encrypted RNA lists. The utilized encryption key for each template is extracted from the original biometric image through excessive permutations between pixels. The GA optimization technique is applied to select the most convenient biometric features. Finally, after employing the GA-based cross-over and mutation operations, the chosen features are used to generate the cancelable biometric traits. To assess the proposed framework, six different biometric databases are considered. These databases are Olivetti Research Laboratory (ORL) Faces (gray), CASIA v.5 Faces (color), UPOL Iris (gray), Indian Institute of Technology Delhi (IIT Delhi) Ear (color and gray), Fingerprint, and CASIA Palmprint (color and gray). The security performance of the proposed encryption algorithm is compared to those of recent studies in this field, such as Optical Scanning Holography (OSH) and Double Random Phase Encoding (DRPE). The simulation results prove the superior performance of the proposed framework in terms of all adopted evaluation metrics. The proposed framework provides high Area under the Receiver Operating Characteristic (AROC) curve that reaches 0.9990, low False Acceptance Rate (FAR) of 0.0015, more uniform histograms, high correlation values for genuine users, and completely hidden biometric features. In addition, from the security perspective, the proposed framework achieves good entropy, Unified Average Changing Intensity (UACI), and Number of Pixels Change Rate (NPCR) values that reach 7.9960, 33.55%, and 99.65%, respectively.

This paper proposed a cross-domain self-authentication scheme to address the “information isolated island” problem of users’ identities storage in servers and the “redundant registration problem” of users’ identities for Autonomous Valet Parking (AVP). This scheme adopts a decentralized anonymous authentication method to relieve the authentication center’s service load. Users are segregated into two categories to increase authentication efficiency: inexperienced and regular users. For the former, the paper explores a self-authentication mechanism based on verification parameters. Then, its valid personal information, pseudonym and public key, were stored in a consortium blockchain (PseIDChain) as the transaction records so that they can be securely shared among servers located in different domains. For the latter (regular users), an efficient authentication mechanism, searching users’ personal information on PseIDChain by the smart contract, was proposed. Security proof and simulation results show that the designed scheme has superior security to the existing schemes. Its authentication efficiency is 80.29% and 50.45% higher than the traditional anonymous and batch authentication schemes.

Metric learning has significantly improved machine learning applications such as face re-identification and image classification using K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers. However, to the best of our knowledge, it has not been investigated yet, especially for the multimodal biometric recognition problem in immigration, forensic and surveillance applications with uncontrolled ear datasets. Therefore, it is interesting and very attractive to propose a novel framework for multimodal biometric recognition based on Learning Distance Metric (LDM) via kernel SVM. This paper considers metric learning for SVM by investigating a hybrid Learning Distance Metric and Directed Acyclic Graph SVM (LDM-DAGSVM) model for multimodal biometric recognition, where LDM and DAGSVM are two emerging techniques in dealing with classification problems. Different from existing multimodal biometric recognition methods, the proposed approach aims to learn Mahalanobis distance metric via kernel SVM to maximize the inter-class variations and minimize the intra-class variations, simultaneously. Experimental results on the uncontrolled datasets such as AR face and AWE ear datasets show that the proposed approach achieves competitive performance compared with models working on individual modalities and overperforms the state-of-the-art multimodal methods. The proposed model achieves five-fold classification accuracy around 99.85 % for the face and ear images.

Vehicles are equipped with Electronic Control Units (ECUs) to increase their overall system functionality and connectivity. However, the rising connectivity exposes a defenseless internal Controller Area Network (CAN) to cyberattacks. An Intrusion Detection System (IDS) is a supervisory module, proposed for identifying CAN network malicious messages, without modifying legacy ECUs and causing high traffic overhead. The traditional IDS approaches rely on time and frequency thresholding, leading to high false alarm rates, whereas state-of-the-art solutions may suffer from vehicle dependency. This paper presents a wavelet-based approach to locating the behavior change in the CAN traffic by analyzing the CAN network’s transmission pattern. The proposed Wavelet-based Intrusion Detection System (WINDS) is tested on various attack scenarios, using real vehicle traffic from two independent research centers, while being expanded toward more comprehensive attack scenarios using synthetic attacks. The technique is evaluated and compared against the state-of-the-art solutions and the baseline frequency method. Experimental results show that WINDS offers a vehicle-independent solution applicable for various vehicles through a unique approach while generating low false alarms.

Differential equations are commonly used to model several engineering, science, and biological applications. Unfortunately, finding analytical solutions for solving higher-order Ordinary Differential Equations (ODEs) is a challenge. Numerical methods represent a leading candidate for solving such ODEs. This work presents an innovated adaptive technique that uses polynomials to solve linear or nonlinear third-order ODEs. The proposed technique adapts the coefficients of the polynomial to obtain an explicit analytical solution. A signed least mean square algorithm is exploited to enhance the adaptation process and decrease both computational requirements and time. The efficiency of the proposed Adaptive Polynomial Method (APM) is illustrated through six well-known examples. The proposed technique is compared with recent analytical and numerical methods to validate its effectiveness in terms of Mean Square Error (MSE) and computation time. An application in a thin film flow system is modeled to a third-order ODE. The proposed technique is compared with recent numerical and analytical methods in solving the thin film flow equation, and it achieves better results. Furthermore, the proposed technique provides an analytical solution with an increased dynamic range and much lower computational time than those of the conventional numerical methods.

3D Stacked Integrated Circuits (SICs) offer a promising way to cope with the technology scaling; however, the test access requirements are highly complicated due to increased transistor density and a limited number of test channels. Moreover, although the vertical interconnects in 3D SIC are capable of high-speed data transfer, the overall test speed is restricted by scan-chains that are not optimized for timing. Reduced Pin-Count Testing (RPCT) has been effectively used under these scenarios. In particular, Time Division Multiplexing (TDM) allows full utilization of interconnect bandwidth while providing low scan frequencies supported by the scan chains. However, these methods rely on Uni-Directional Signaling (UDS), in which a chip terminal (pin or a TSV) can either be used to transmit or receive data at a given time. This requires that at least two chip terminals are available at every die interface (Tester-Die or Die-Die) to form a single test channel. In this paper, we propose Simultaneous Bi-Directional Signaling (SBS), which allows a chip terminal to be used simultaneously to send and receive data, thus forming a test channel using one pin instead of two. We demonstrate how SBS can be used in conjunction with TDM to achieve reduced pin count testing while using only half the number of pins compared to conventional TDM based methods, consuming only 22.6% additional power. Alternatively, the advantage could be manifested as a test time reduction by utilizing all available test channels, allowing more parallelism and test time reduction down to half compared to UDS-based TDM. Experiments using 45nm technology suggest that the proposed method can operate at up to 1.2 GHz test clock for a stack of 3-dies, whereas for higher frequencies, a binary-weighted transmitter is proposed capable of up to 2.46 GHz test clock.

Currently, secure multimedia applications are becoming a very hot research topic, specifically over the Internet and wireless communication networks due to their rapid progress. Several researchers have implemented various chaotic image and video encryption algorithms to achieve data stability and communication security. This paper presents a novel bit-level video frame cryptosystem that is dependent on the piecewise linear chaotic maps (PWLCMs). It is implemented for orbital angular momentum (OAM) modulation over different turbulence channels. Firstly, the mathematical model for the bit error rate (BER) of OAM modulation is derived over the gamma-gamma turbulence channel. After that, a comparison between the theoretical results from Mathematica and the simulation results from MATLAB for different turbulence strengths, signal-to-noise ratios (SNRs), and propagation distance values is presented to assure that there is a perfect match. The proposed video cryptosystem is checked using entropy analysis, histogram testing, attack analysis, time analysis, correlation testing, differential analysis, and other quality and security evaluation metrics. The simulation results and the performance analysis confirm that the proposed cryptosystem is reliable and secure for video frame encryption, and communication with different turbulence conditions in free space.

Smart cities and their applications have become attractive research fields birthing numerous technologies. Fifth generation (5G) networks are important components of smart cities, where intelligent access control is deployed for identity authentication, online banking, and cyber security. To assure secure transactions and to protect user’s identities against cybersecurity threats, strong authentication techniques should be used. The prevalence of biometrics, such as fingerprints, in authentication and identification makes the need to safeguard them important across different areas of smart applications. Our study presents a system to detect alterations to biometric modalities to discriminate pristine, adulterated, and fake biometrics in 5G-based smart cities. Specifically, we use deep learning models based on convolutional neural networks (CNN) and a hybrid model that combines CNN with convolutional long-short term memory (ConvLSTM) to compute a three-tier probability that a biometric has been tempered. Simulation-based experiments indicate that the alteration detection accuracy matches those recorded in advanced methods with superior performance in terms of detecting central rotation alteration to fingerprints. This makes the proposed system a veritable solution for different biometric authentication applications in secure smart cities.

Orbital angular momentum-shift keying (OAM-SK), which is the rapid switching of OAM modes, is vital but seriously impeded by the deficiency of OAM demodulation techniques, particularly when videos are transmitted over the system. Thus, in this paper, 3D chaotic interleaved multi-coded video frames (VFs) are conveyed via an N-OAM-SK free-space optical (FSO) communication system to enhance the reliability and efficiency of video communication. To tackle the defects of the OAM-SK-FSO mechanism, two efficient deep learning (DL) techniques, namely convolution recurrent neural network (CRNN) and 3D convolution neural network (3DCNN) are used to decode OAM modes with a low bit error rate (BER). Moreover, a graphics processing unit (GPU) is used to accelerate the training process with slight power consumption. The utilized datasets for OAM states are generated by applying different scenarios using a trial-and-error method. The simulation results imply that LDPC-coded VFs achieve the largest peak signal-to-noise ratios (PSNRs) and the lowest BERs using the 16-OAM-SK model. Both 3DCNN and CRNN techniques have nearly the same performance, but this performance deteriorates in the case of larger dataset classes. Moreover, the GPU accelerates the performance by almost 67.6% and 36.9% for the CRNN and 3DCNN techniques, respectively. These two DL techniques are more effective in evaluating the classification accuracy than the other traditional techniques by almost 10 – 20%.

The track of medical imaging has witnessed several advancements in the last years. Several medical imaging modalities have appeared in the last decades including X-ray, Computed Tomography (CT), Magnetic Resonance (MR), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT) and ultrasound imaging. Generally, medical images are used for the diagnosis purpose. Each type of acquired images has some merits and limitations. To maximize medical images utilization for the purpose of diagnosis, medical imaging fusion trend has appeared as a hot research field. Different medical imaging modalities are fused to obtain new images with complementary information. This paper presents a survey study of medical imaging modalities and their characteristics. In addition, different medical image fusion approaches and their appropriate quality metrics are presented. The main aim of this comprehensive survey analysis is to contribute in the advancement of medical image approaches that can help for better diagnosis of different diseases.

The ageing phenomenon of negative bias temperature instability (NBTI) continues to challenge the dynamic thermal management of modern FPGAs. Increased transistor density leads to thermal accumulation and propagates higher and non-uniform temperature variations across the FPGA. This aggravates the impact of NBTI on key PMOS transistor parameters such as threshold voltage and drain current. Where it ages the transistors, with a successive reduction in FPGA lifetime and reliability, it also challenges its security. The ingress of threshold voltage-triggered hardware Trojan, a stealthy and malicious electronic circuit, in the modern FPGA, is one such potential threat that could exploit NBTI and severely affect its performance. The development of an effective and efficient countermeasure against it is, therefore, highly critical. Accordingly, we present a comprehensive FPGA security scheme, comprising novel elements of hardware Trojan infection, detection, and mitigation, to protect FPGA applications against the hardware Trojan. Built around the threat model of a naval warship's integrated self-protection system (ISPS), we propose a threshold voltage-triggered hardware Trojan that operates in a threshold voltage region of 0.45V to 0.998V, consuming ultra-low power (10.5nW), and remaining stealthy with an area overhead as low as 1.5% for a 28 nm technology node. The hardware Trojan detection sub-scheme provides a unique lightweight threshold voltage-aware sensor with a detection sensitivity of 0.251mV/nA. With fixed and dynamic ring oscillator-based sensor segments, the precise measurement of frequency and delay variations in response to shifts in the threshold voltage of a PMOS transistor is also proposed. Finally, the FPGA security scheme is reinforced with an online transistor dynamic scaling (OTDS) to mitigate the impact of hardware Trojan through run-time tolerant circuitry capable of identifying critical gates with worst-case drain current degradation.

The need for cybersecurity increases to protect the exchange of information for improving the data privacy. This paper presents an investigation of the encryption efficiency of the chaotic-based image block ciphering in the spatial and Fractional Fourier Transform (FrFT) domains. The main aim of this investigation is to examine the efficiency of different chaotic maps, while considering the parameters of the FrFT as additional keys for encryption and achieving reliable cybersecurity for robust image communication. In this paper, Cat, Baker, and Logistic map confusion approaches are applied in the spatial and FrFT domains to study and analyze the cybersecurity and ciphering efficiency of chaos-based image cryptosystems. The confusion features of the chaotic maps in spatial and FrFT domains are investigated using information entropy, differential analysis, histograms, visual observation, attack analysis, impact of noise, and encryption quality tests. Simulation results prove that the chaotic-based image encryption in the FrFT domain increases the efficiency of the confusion process and achieves a high nonlinear relation between the plainimage and the cipherimage in a symmetric ciphering approach. Moreover, the results demonstrate that the Cat-FrFT scheme is more susceptible to channel noise attacks than the Baker-FrFT and the Logistic-FrFT schemes. Hence, they can be implemented efficiently in the scenarios of noisy channels due to their high robustness to channel noise.

The increasing growth in the demand for cloud computing services, due to the increasing digital transformation and the high elasticity of the cloud, requires more efforts to improve the electrical energy efficiency of cloud data centers. In this paper, an energy-efficient hybrid (EEH) framework for improving the efficiency of consuming electrical energy in data centers is proposed and evaluated. The proposed framework is based on both the request scheduling and servers consolidation approaches rather than depending only on one approach as in the existing related works. The EEH framework sorts the customers' requests (tasks) according to their time and power needs before performing the scheduling. It has a scheduling algorithm that considers power consumption when taking its scheduling decisions. It also has a consolidation algorithm that determines the underloaded servers to be slept or hibernated, the overloaded servers, the virtual machines to be migrated and the servers that will receive migrated virtual machines. In addition, the EEH framework includes a migration algorithm for transferring migrated virtual machines to new servers. Results of simulation experiments indicate the superiority of the EEH framework to the utilization of one approach only to reduce power consumption in terms of power usage effectiveness (PUE), data center energy productivity (DCEP), average execution time, throughput and cost saving.

Recently, massive research works have been accomplished for augmenting privacy and security requirements for cybersecurity applications in wireless communication networks. This is attributed to the fact that conventional security processes are not appropriate for robust, efficient, and reliable multimedia streaming over unsecure media. Therefore, this paper presents an efficient color image cryptosystem based on RC6 with different modes of operation. The proposed cryptosystem is composed of two phases: encryption and decryption. The encryption phase starts by decomposing the color plainimage with few details into its RGB components, which in turn, are segmented into 128-bit blocks. These blocks are then enciphered using RC6 with an appropriate mode of operation. After that, the corresponding enciphered blocks of RGB components are multiplexed for constructing the final cipherimage. This scenario is reversed in the decryption phase. The performance of the proposed cryptosystem is gauged via simulation using a set of encryption quality metrics. The simulation results reveal that the proposed cryptosystem with cipher block chaining (CBC), cipher feedback (CFB), and output feedback (OFB) modes can efficiently and effectively hide all information of the color images with few details even in the presence of some input blocks with similar data. On the other hand, the results show that the electronic codebook (ECB) mode is not effective at all in hiding all details of images. Finally, the obtained results ensure the applicability of the proposed cryptosystem and its efficiency in encrypting images in terms of security, encryption quality, and noise immunity.

Multimedia cybersecurity is a prevalent research topic in the digital world due to the rapid progress of digital multimedia and Internet applications. Watermarking, encryption, and steganography schemes are employed to attain multimedia data confidentiality and robustness. However, these schemes are externally applied on trusted computers, and there has been a lack of similar schemes that can be effectively and efficiently enabled through an untrusted transmission medium. In this work, a self-embedding-based High-Efficiency Video Coding (HEVC) transmission and integrity verification framework is presented. This framework is robust and reliable for verifying the integrity of HEVC frames transmitted through insecure communication channels. Firstly, the transmitted HEVC frames are divided into a number of blocks with a certain block size. After that, a discrete transform is used for self-embedding of watermarks from each block into another one depending on a predefined mechanism. The Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Fourier Transform (DFT) are tested for this task. The watermarked HEVC frames are transmitted through a wireless communication channel, and hence they become subject to different attacks and corruptions. At the receiver side, the secret watermarks in each block are sensed with a correlation-based method to discover dubious counterfeit operations. To verify the reliability of the suggested transmission framework and its ability to achieve high protection and robust content verification of the transmitted HEVC frames over insecure communication channels, different HEVC analyses and comparisons are performed. Simulation results demonstrate the suitability of the suggested transmission framework for different multimedia cybersecurity applications. Furthermore, the comparative analysis shows that the DFT is an efficient discrete transform that can be employed with the proposed transmission framework to guarantee a higher HEVC frame integrity. It allows higher sensitivity to simple modifications in the transmitted watermarked HEVC frames. This makes the suggested cybersecurity framework applicable, secure, and appropriate for multimedia integrity verification purposes.

With the widespread use of IoT applications and the increasing trend in the number of connected smart devices, the concept of routing has become very challenging. In this regard, the IPv6 Routing Protocol for Low-power and Lossy Networks (PRL) was standardized to be adopted in IoT networks. Nevertheless, while mobile IoT domains have gained significant popularity in recent years, since RPL was fundamentally designed for stationary IoT applications, it could not well adjust with the dynamic fluctuations in mobile applications. While there have been a number of studies on tuning RPL for mobile IoT applications, but still there is a high demand for more efforts to reach a standard version of this protocol for such applications. Accordingly, in this survey, we try to conduct a precise and comprehensive experimental study on the impact of various mobility models on the performance of a mobility-aware RPL to help this process. In this regard, a complete and scrutinized survey of the mobility models has been presented to be able to fairly justify and compare the outcome results. A significant set of evaluations has been conducted via precise IoT simulation tools to monitor and compare the performance of the network and its IoT devices in mobile RPL-based IoT applications under the presence of different mobility models from different perspectives including power consumption, reliability, latency, and control packet overhead. This will pave the way for researchers in both academia and industry to be able to compare the impact of various mobility models on the functionality of RPL, and consequently to design and implement application-specific and even a standard version of this protocol, which is capable of being employed in mobile IoT applications.

Epilepsy is one of the most serious disorders that affect patients' daily lives. When seizures occur, patients cannot control their behaviors, which can lead to serious injuries. With the great advances in recording both electroencephalogram (EEG) and magnetoencephalography (MEG) signals, it has become possible to analyze these signals in an automated manner for information extraction to help in seizure detection and prediction. Both EEG and MEG recordings of epilepsy patients contain spikes that can be used for the localization of epileptogenic zones, efficient onset detection, and even, in some cases, prediction. In this paper, we consider the characteristics of EEG and MEG spikes, present a discussion of the importance of spike detection in both signal modalities, and provide a review of spike detection algorithms. Since EEG signals have been widely used for decades, most of the algorithms presented in this paper cover the EEG spike detection methods. Few works in the literature are dedicated to MEG spike detection. Nevertheless, we assert that with some modifications, a considerable number of EEG spike detection algorithms can be applied to MEG signals. We classify the spike detection algorithms according to the domain used for processing the signal. Finally, we conclude with future research directions and open problems in this area.