Accurate estimation of dental age is crucial in forensic and clinical applications; however, traditional methods often suffer from subjectivity and observer bias. This study investigates the performance of advanced machine learning (ML) algorithms for estimating dental age based on features extracted from ConeBeam Computed Tomography (CBCT) images of mandibular canines and premolars in the Jordanian population. Eleven features encompassing volumetric measurements, morphological dimensions, and categorical dental aging stages were analyzed. Models were rigorously assessed using $\mathbf{1 0}$-fold cross-validation across male, female, and combined datasets. CatBoost and Gradient Boosting regressors consistently outperformed conventional regression methods, demonstrating superior predictive accuracy as measured by Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Standard Error of Estimate (SEE). Notably, the CatBoost regressor exhibited exceptional robustness, achieving the best overall performance with a MAE of 7.14 ± 2.11 (Male), $6.19 \pm 1.95$ (Female), and $6.64 \pm 1.15$ (combined). Additionally, the Support Vector Regressor achieved the lowest MAE for females: $6.04 \pm 2.45$. Decision Tree and Gradient Boosting models also demonstrated commendable accuracy, further emphasizing the effectiveness of ensemble-based ML approaches. These results highlight the strong potential of ML techniques to enhance objectivity, accuracy, and applicability in dental age estimation by leveraging comprehensive dental feature sets.

In federated learning (FL), achieving high model accuracy often comes at the cost of increased energy consumption, particularly when incorporating robust privacy-preserving techniques like homomorphic encryption (HE). While HE enhances security by enabling encrypted aggregation of local models—thus mitigating data leakage and inference attacks without requiring a trusted aggregator—it also introduces computational overhead that impacts energy efficiency.This paper presents a novel approach for Adaptive Encryption Federated Learning (AEFL). AEFL strategically tunes encryption complexity to achieve the best trade-off between model accuracy and energy efficiency without compromising security. When additional energy from harvested sources is available, AEFL enhances model accuracy by adjusting encryption parameters to support more intensive computations. During periods of limited energy, the system focuses on conserving resources while maintaining robust encryption to ensure data security. This adaptive approach maximizes energy efficiency without lowering the security threshold, allowing for improved model performance when energy conditions permit.Experimental results show that AEFL improves energy efficiency by over 35% and increases the pool of eligible clients for FL by approximately 18%, all while maintaining a security level above 128 bits. Importantly, these gains are achieved with minimal impact on model accuracy, demonstrating that significant energy savings are possible in FL environments without sacrificing data security or model performance.

In the rapidly advancing field of wireless communication, Unmanned Aerial Vehicles (UAVs) have become indispensable due to their extensive coverage capabilities and ability to access remote locations. Whether deployed as mobile base stations (BSs) or relays, UAVs significantly enhance network throughput and reliability. Alongside UAVs, Intelligent Reflecting Surfaces (IRS) have emerged as a cost-effective solution for improving communication quality through passive modulation arrays. Despite these advancements, the potential misuse of UAVs poses serious security risks, particularly in the form of communication eavesdropping. To address these challenges, this paper introduces a novel communication framework that integrates a UAV equipped with an adaptive IRS. The primary aim is to boost communication secrecy between BSs and multiple users, even in the presence of several UAV eavesdroppers. This objective is formulated as an optimization problem focused on maximizing the secrecy rate while considering UAV mobility constraints. To solve this non-convex problem, we propose a hybrid strategy that combines Genetic Algorithms and Gradient Descent techniques. This innovative approach efficiently determines suboptimal reflection angles and UAV trajectories for IRS-equipped UAVs, thereby enhancing the security of the communication network. This method not only addresses the complexity of the optimization but also provides a practical pathway to secure communications in environments with high eavesdropping risks.

The identification of malicious users within a large set of participants poses a significant challenge in the domains of cybersecurity, data integrity, user management, and particularly within federated learning (FL) environments. FL, a distributed machine learning approach, necessitates rigorous mechanisms for safeguarding data integrity, model accuracy by effectively managing and identifying malicious participants. Traditional methods require the sequential removal and evaluation of users to determine their impact on the system’s overall error rate or loss function, fall short in terms of efficiency and scalability, especially in FL contexts where data is distributed across multiple clients. To address these limitations, we propose the Refine and Identify Algorithm, a two-phased approach that efficiently narrows the search space for identifying malicious users by initially evaluating users in groups rather than individually and iteratively focusing on those groups with the highest potential for containing malicious users. A rigorous mathematical framework, including a proof of convergence and a detailed analysis of iteration necessities, underpins the algorithm’s efficacy. The convergence proof and analysis of iteration requirements provide a solid mathematical foundation for the proposed method’s effectiveness, paving the way for further optimization and application-specific tuning. Simulation results depict the efficiency of the proposed technique and show a significant reduction in computational resources and time required for identifying malicious users.

In the dynamic realm of wireless communication, Unmanned Aerial Vehicles (UAVs) have gained increasing prominence due to their exceptional capabilities, which include expensive coverage of large areas and access to challenging and hazardous locations. When employed as mobile base stations or relays, UAVs have shown remarkable enhancements in system throughput and reliability. In addition, Intelligent Reflecting Surfaces (IRS) present a very low cost solution that efficiently enhances wireless communication quality using passive modulation arrays. Nevertheless, the use of UAVs for malicious intents can also introduce heightened security challenges such as communication eavesdropping. In response to these challenges, we present in this paper, a communication framework that incorporates a UAV equipped with an adaptive IRS aiming to enhance the communication secrecy between the Base Station (BS) and Bob in the presence of several UAV eavesdroppers. We formulate the objective as an optimization problem that aims to maximize the secrecy rate while considering the mobility constraints. To address this complex non-convex problem, our innovative solution harnesses a hybrid approach that combines Genetic Algorithms and Gradient Descent techniques, resulting in an efficient computation of suboptimal reflection angles and UAV trajectories for IRS-equipped UAVs towards a more secure communication.

Video surveillance using drones is both convenient and efficient due to the ease of deployment and unobstructed movement of drones in many scenarios. An interesting application of drone-based video surveillance is to estimate crowd density (both pedestrians and vehicles) in public places. Deep learning using convolution neural networks (CNNs) is employed for automatic crowd counting and density estimation using images and videos. However, the performance and accuracy of such models typically depends upon the model architecture i.e., deeper CNN models improve accuracy at the cost of increased inference time. In this paper, we propose a novel crowd density estimation model for drones (DroneNet) using Self-organized Operational Neural Networks (Self-ONN). Self-ONN provides efficient learning capabilities with lower computational complexity as compared to CNN-based models. We tested our algorithm on two drone-view public datasets. Our evaluation shows that the proposed DroneNet shows superior performance on an equivalent CNN-based model.

Crowdsourced live streaming services (CLS) present significant challenges due to massive data size and dynamic user behavior. Service providers must accommodate personalized QoE requests, while managing computational burdens on edge servers. Existing CLS approaches use a single edge server for both transcoding and user service, potentially overwhelming the selected node with high computational demands. In response to these challenges, we propose the Reinforcement Learning-based-Collaborative Edge-Assisted Live Streaming (RL-CEALS) framework. This innovative approach fosters collaboration between edge servers, maintaining QoE demands and distributing computational burden cost-effectively. By sharing tasks across multiple edge servers, RL-CEALS makes smart decisions, efficiently scheduling serving and transcoding of CLS. The design aims to minimize the streaming delay, the bitrate mismatch, and the computational and bandwidth costs. Simulation results reveal substantial improvements in the performance of RL-CEALS compared to recent works and baselines, paving the way for a lower cost and higher quality of live streaming experience.

The Wireless Sensor Networks (WSN) are designed to remotely monitor and control specific physical or environmental conditions. However, due to the open nature of WSN, many threats and attacks may arise by malicious users such as jamming attacks. Several techniques have been developed for detecting such attack. However, the majority of these solutions try to decrease the impact of signal-jamming by increasing transmission power or using complicated coordinating schemes, which might be challenging in WSNs, where the sensor devices are limited in their energy and communication capabilities. In this paper, we present a new model for securing WSNs against jamming attacks based on the Colonel Blotto game where the equilibrium defines the minimization of the worst-case attack effect on the sensors communications. Then, by investigating the Nash Equilibrium (NE) of the game and for all the potential attackers, the system computes the optimal power allocation strategy to protect the network against the malicious nodes. The simulation results show that the proposed model can secure the channel communication for WSN by 55% compared to the other technique while using the same network resources.

This paper addresses the problem of coexistence between smart grid based wireless communication networks and other interfering signals. Particularly, the problem of interfering wireless local area network (WLAN) signals over smart utility networks (SUN) systems is analyzed. We develop a statistical model of the WLAN interferers, which is in turn used in predicting the capacity of SUN systems when affected by several WLAN interferers. Based on this analysis, a framework that promotes the application of an adaptive power control algorithm at the SUN transmitter is developed based on a Lagrangian optimization approach. Our proposed optimization approach involves solving a reduced-complexity algorithm which yields an optimal power allocation for each SUN packet. The results show that an adaptive power control approach results in a significant improvement of the SUN’s network capacity when compared with fixed power transmissions.

A new generation of advanced information technologies are used nowadays by healthcare systems to provide access to affordable and high-quality healthcare services. However, such services, generally require a large amount of data that needs to be stored, shared and secured efficiently. Therefore, a secure distributed data storing and sharing mechanism is proposed in this paper for healthcare systems. By using a specifically designed data splitting and encryption approach, the proposed sharing mechanism ensures that any user in the network can recuperate the data only if it gets the approval of a prefixed number of trusted nodes. This ensure that although the data is distributed among different network nodes, it becomes useless without the collection of the necessary data parts and approvals. In order to enhance the data sharing robustness to failures while minimizing the transmission delays, and satisfying the network constraints, an iterative algorithm is proposed to optimize the selection of the nodes that should participate in the data storage process. The simulation results confirm the efficiency of the proposed approaches to efficiently and securely store and share the data with the other legitimate nodes.

Federated Learning (FL) is a collaborative machine learning (ML) approach, where different nodes in a network contribute to learning the model parameters. In addition, FL provides several attractive features such as data privacy and energy efficiency. Due to its collaborative nature, model parameters among nodes should be efficiently exchanged, while considering the scarce availability of clean spectral slots. In this work, we propose low-power efficient algorithms for FL of model parameters updates. We consider mobile edge nodes connected to a leading node (LD) with practical wireless links, where uplink updates from the nodes to the LD are shared without orthogonalizing the resources. In particular, we adopt a non-orthogonal multiple access (NOMA) uplink scheme, and investigate its effect on the convergence round (CR) of the model updates. Through deriving an analytical expression of the CR, we leverage it to formulate an optimization problem to minimize the total number of communication rounds and maximize the communication fairness among the nodes. We further investigate the performance of our proposed algorithms by considering different factors, including limited per-node energy and node heterogeneity. Monte-Carlo simulations are used to verify the accuracy of our derived expression of the CR. Moreover, through comprehensive simulation, we show that our proposed schemes largely reduce the communication latency between the LD and the nodes, and improve the communication fairness among the nodes.

The usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches. Despite the abundance of such advantages, it is imperative to investigate the performance of UAV utilization while considering their design limitations. This paper investigates the deployment of UAV swarms when each UAV carries a machine learning classification task. To avoid data exchange with ground-based processing nodes, a federated learning approach is adopted between a UAV leader and the swarm members to improve the local learning model while avoiding excessive air-to-ground and ground-to-air communications. Moreover, the proposed de-ployment framework considers the stringent energy constraints of UAVs and the problem of class imbalance, where we show that considering these design parameters significantly improves the performances of the UAV swarm in terms of classification accuracy, energy consumption and availability of UAVs when compared with several baseline algorithms.

Cloud computing is a paradigm that provides multiple services over the internet with high flexibility in a cost-effective way. However, the growth of cloud-based services comes with major security issues. Recently, machine learning techniques are gaining much interest in security applications as they exhibit fast processing capabilities with real-time predictions. One major challenge in the implementation of these techniques is the available training data for each new potential attack category. In this paper, we propose a new model for secure network based on machine learning algorithms. The proposed model ensures better learning of minority classes using Generative Adversarial Network (GAN) architecture. In particular, the new model optimizes the GAN parameter including the number of inner learning steps for the discriminator to balance the training datasets. Then, the optimized GAN generates highly informative “like real” instances to be appended to the original data which improve the detection of the classes with relatively small training data. Our experimental results show that the proposed approach enhances the overall classification performance and detection accuracy even for the rarely detectable classes for both UNSW and NSL-KDD datasets. The simulation results show also that the proposed model could detect better the network attacks compared to the state-of-art techniques.

Cloud computing is attracting a lot of attention in the past few years. Although, even with its wide acceptance, cloud security is still one of the most essential concerns of cloud computing. Many systems have been proposed to protect the cloud from attacks using attack signatures. Most of them may seem effective and efficient; however, there are many drawbacks such as the attack detection performance and the system maintenance. Recently, learning-based methods for security applications have been proposed for cloud anomaly detection especially with the advents of machine learning techniques. However, most researchers do not consider the attack classification which is an important parameter for proposing an appropriate countermeasure for each attack type. In this paper, we propose a new firewall model called Secure Packet Classifier (SPC) for cloud anomalies detection and classification. The proposed model is constructed based on collaborative filtering using two machine learning algorithms to gain the advantages of both learning schemes. This strategy increases the learning performance and the system's accuracy. To generate our results, a publicly available dataset is used for training and testing the performance of the proposed SPC. Our results show that the accuracy of the SPC model increases the detection accuracy by 20% compared to the existing machine learning algorithms while keeping a high attack detection rate.

This paper proposes a secure amplify-and-forward (AF) relaying scheme in unmanned aerial vehicle (UAV) networks while accounting for the power consumption limitations of UAVs. Since a UAV's battery life is limited, we propose selecting the UAV relays based on their probability of availability, which we model to be a function of the UAV's power consumption model. Then, physical layer security is achieved by partitioning the limited available relays into information relaying UAVs and cooperative jammers while aiming to maximize the secrecy rate of the network when an eavesdropper is located in the vicinity of the destination node. Meanwhile, the information bearing relays use beamforming to enhance the information delivery to the destination, and the cooperative jamming relays use precoded artificial-noise scheme to degrade the eavesdropping links' received signal-to-noise ratios. Simulation results show significant secrecy rate enhancements when the proposed schemes are adopted compared to conventional relaying scenarios, especially when cooperative jamming is deployed.

Network security is an critical subject in any distributed network. Recently, machine learning has proven their efficiency for intrusion detection. By using a comprehensive dataset with multiple attack types, a well-trained model can be created to improve the anomaly detection performance. However, high dimensional data sets are a significant challenge for machine learning. In fact, learning algorithms considering all features in the input data, may cause over-fitting to irrelevant aspects of the data and increase the computational time caused by the process of similar features that provide redundant information, which is a critical problem especially for users with constrained resources. In this paper, we propose a new and efficient feature selection technique for intrusion detection in modern networks called Iterative Per Group Feature Selection (IPGFS). IPGFS reduces the number of features in the input data and selects the best features using the performance accuracy of the classifier. The features are sorted and selected according to their accuracy score. Both the UNSW and NSLKDD datasets are used in this paper to validate the proposed model and verify its efficiency in detecting intrusions. The simulation results show that the proposed model can reduce the number of features for the two dataset while successfully detecting intrusions with better accuracy compared to state-of-the-art techniques. Index Cloud security, feature selection, accuracy, machine learning techniques.

Recently, machine learning techniques are gaining a lot of interest in security applications as they exhibit fast processing with real-time predictions. One of the significant challenges in the implementation of these techniques is the collection of a large amount of training data for each new potential attack category, which is most of the time, unfeasible. However, learning from datasets that contain a small training data of the minority class usually produces a biased classifiers that have a higher predictive accuracy for majority class(es), but poorer predictive accuracy over the minority class. In this paper, we propose a new designed attacks weighting model to alleviate the problem of imbalanced data and enhance the accuracy of minority classes detection. In the proposed system, we combine a supervised machine learning algorithm with the node1past information. The machine learning algorithm is used to generate a classifier that differentiates between the investigated attacks. Then, the system stores these decisions in a database and exploits them for the weighted attacks classification model. Thus, for each attack class, the weight that maximizes the detection of the minority classes will be computed and the final combined decision is generated. In this work, we use the UNSW dataset to train the supervised machine learning model. The simulation results show that the proposed model can effectively detect intrusion attacks and provide better accuracy, detection rates and lower false alarm rates compared to state-of-the art techniques.1In this document we will use the words “node” to represent computing, storage, physical, and virtual machines.

The heart sound signals (Phonocardiogram – PCG) enable the earliest monitoring to detect a potential cardiovascular pathology and have recently become a crucial tool as a diagnostic test in outpatient monitoring to assess heart hemodynamic status. The need for an automated and accurate anomaly detection method for PCG has thus become imminent. To determine the state-of-the-art PCG classification algorithm, 48 international teams competed in the PhysioNet (CinC) Challenge in 2016 over the largest benchmark dataset with 3126 records with the classification outputs, normal (N), abnormal (A) and unsure – too noisy (U). In this study, our aim is to push this frontier further; however, we focus deliberately on the anomaly detection problem while assuming a reasonably high Signal-to-Noise Ratio (SNR) on the records. By using 1D Convolutional Neural Networks trained with a novel data purification approach, we aim to achieve the highest detection performance and real-time processing ability with significantly lower delay and computational complexity. The experimental results over the high-quality subset of the same benchmark dataset show that the proposed approach achieves both objectives. Furthermore, our findings reveal the fact that further improvements indeed require a personalized (patient-specific) approach to avoid major drawbacks of a global PCG classification approach.

The growth of cloud-based services is mainly supported by the core networking infrastructures of large-scale data centers, while the scalability of these services is influenced by the performance and dependability characteristics of data centers. Hence, the data center network must be agile and reconfigurable in order to respond quickly to the ever-changing application demands and service requirements. The network must also be able to interconnect the big number of nodes, and provide an efficient and fault-tolerant routing service to upper-layer applications. In response to these challenges, the research community began exploring novel interconnect topologies, namely: Flecube, DCell, Ficonn, HyperFlaNet and BCube. However, these topologies either scale too fast (grows exponentially in size), or too slow, and therefore suffer from performance bottlenecks. In this paper, we propose a novel data center topology called LaScaDa (Layered Scalable Data Center) as a new solution for building scalable and cost-effective data center networking infrastructures. The proposed topology organizes nodes in clusters of similar structure, then interconnect these clusters in a well-crafted pattern and system of coordinates for nodes to reduce the number of redundant connections between clusters, while maximizing connectivity. LaScaDa forwards packets between nodes using a new hierarchical row-based routing algorithm. The algorithm constructs the route to the source based on the modular difference between the source and destination coordinates. Furthermore, the proposed topology interconnects a large number of nodes using a small node degree. This strategy increases the number of directly connected clusters and avoids redundant connections. As a result, we get a good quality of nodes in terms of average path length (APL), bisection bandwidth, and aggregated bottleneck throughput. Experimental results show that LaScaDa has better performance than DCell, BCube, and HyperBcube in terms of scalability, while providing a good quality of service.

Data Center Networks (DCNs) are attracting immense interest from the industry, research and academia to keep pace with the increase of Internet services demands. One of the major concerns that draws the attention of researchers is the exponential growth of the energy consumption and carbon emission of the DCNs. Studies conducted to identify the causes of the increasing energy consumption have proved that the growing size of computing demand, the over-provisioning of the networking resources, the under-utilization of the infrastructure, the fault-tolerance, the high bandwidth exigence and the inefficient hardware and cooling structure are leading to considerable energy waste. Therefore, in recent years, new data center (DC) architectures are proposed where new hardware types and new technologies are implemented for the sake of energy efficiency. Other efforts are focusing on designing algorithms and strategies to enhance the utilization of the network resources. Replacing brown power by renewable energy was also one of the attractive ideas to minimize the energy costs. In this survey paper, we will present energy-related problems in data centers and review the state of the art of the research literature on energy efficient architectures, techniques, technologies, resource management, and thermal control and monitoring. Additionally, we present the challenges facing each approach and the strategies to build a green DC. This paper serves as a specification document that shows step by step how to minimize the energy consumption of different components of the system.

Algorithm complexity in machine learning problems has been a real concern especially with large-scaled systems. By increasing data dimensionality, a particular emphasis is placed on designing computationally efficient learning models. In this paper, we propose an approach to improve the complexity of a multi-classification learning problem in cloud networks. Based on the Random Forest algorithm and the highly dimensional UNSW-NB 15 dataset, a tuning of the algorithm is first performed to reduce the number of grown trees used during classification. Then, we apply an importance-based feature selection to optimize the number of predictors involved in the learning process. All of these optimizations, implemented with respect to the best performance recorded by our classifier, yield substantial improvement in terms of computational complexity both during training and prediction phases.

Advances in the design of Full-Duplex (FD) transceivers with low residual Self-Interference (SI) levels has led to their deployment in various wireless communication applications. One promising field that FD transceivers could play a major role in reshaping its dynamics is physical layer security. This paper investigates the performance of FD transceivers in the context of Military FD Radios (MFDR). Particularly, the adopted system model builds on recent investigations regarding the feasibility of deploying an MFDR in a scenario where it simultaneously jams a receiver node while intercepting the signal of a transmitter node, thus simultaneously disrupting and intercepting a tactical communication link of an opponent team. The secrecy performance of the MFDR is theoretically quantified by deriving its outage in interception probability expression. The results reveal that a well-performing SI cancellation scheme can increase the secrecy performance of an MFDR.

Cloud computing has been presented as one of the most efficient techniques for hosting and delivering services over the internet. However, even with its wide areas of application, cloud security is still a major concern of cloud computing. In order to protect the communication in such environment, many secure systems have been proposed and most of them are based on attack signatures. These systems are often not very efficient for detecting all the types of attacks. Recently, machine learning technique has been proposed. This means that if the training set does not include enough examples in a particular class, the decision may not be accurate. In this paper, we propose a new firewall scheme named Enhanced Intrusion Detection and Classification (EIDC) system for secure cloud computing environment. EIDC detects and classifies the received traffic packets using a new combination technique called most frequent decision where the nodes'11In this document we will use the words “node” and “user” interchangeably.past decisions are combined with the current decision of the machine learning algorithm to estimate the final attack category classification. This strategy increases the learning performance and the system accuracy. To generate our results, a public available dataset UNSW-NB-15 is used. Our results show that EICD improves the anomalies detection by 24% compared to complex tree.

With the significant increase of wireless network nodes and traffic load in recent years, especially in the emerging internet-of-things (IoT) and vehicular networks, the design of a fast adaptive relay selection algorithm that is able to cope with a quickly changing environment became a necessity. In particular, the problem of multiple relay selection and beamforming under individual power constraints is investigated in this paper when the amplify-and-forward protocol is used to forward the data to the destination. The proposed algorithm first performs relay selection and beamforming using iterative convex optimization. The selection decisions are stored and processed before being used by a proposed multi-agent machine-learning (ML) model to imitate with high accuracy the optimal selection decision in real time with much less computational complexity. Simulation results confirm that the performance of the proposed technique is very close to the exhaustive search (ES) and to well known algorithms but with an execution time that is thousands of times shorter than traditional techniques.

In this paper, we investigate the tradeoff between security and throughput and between security and queuing delay in wiretap time-division multiple access (TDMA) networks. We derive a simple relationship, characterized by a single key system parameter, between the stable-throughput region, where there are no perfect secrecy constraints on the data transmissions, and the secure stable-throughput region, where there are perfect secrecy constraints. We quantify the impact of the perfect secrecy constraints on the network's average queuing delay and propose a novel cross-layer security scheme for delay-limited applications. We establish an insightful link between computational security (i.e., upper-layer security implemented through cryptographic schemes) and physical-layer (information-theoretically proved) security. For the two-user case, we derive a closed-form expression for the network's minimum average queuing delay under the proposed security scheme and provide a relationship between the network's minimum queuing delay under perfect secrecy constraints and computational-only secrecy constraints. Moreover, we investigate the impact of cooperative jamming on achieving perfect secrecy, minimum network's queuing delay, and maximum throughput. We verify our theoretical findings through simulations.

Many proposed data center architectures are constructed with a huge number of network devices in order to support the increasing cloud based services. These devices are used to achieve the highest performance in case of full utilization of the network. However, the peak capacity of the network is rarely reached. As a result, many devices are set into idle state which increases the network energy consumption and lead to a non-proportionality between the consumed energy and the network load. In this paper, we present a new approach that reduces the data center energy consumption with a reduced trade off on network performance. By exploiting the correlation in time of internode communication and some topological features, the proposed approach uses the outdated traffic matrix to control the set of active communication links and ports in the network (switches ports and nodes ports). The ports activation management process is done using a proposed algorithm that guarantees network connectivity. Extensive simulations have been conducted to validate the performance of the proposed scheme in terms of average path length and energy consumption.

In this paper, we derive the maximum likelihood detector (MLD) for precoded space-frequency block coded (SFBC) systems where orthogonal frequency division multiplexing (OFDM) is incorporated. The obtained results reveal that the precoding process can be exploited to construct low complexity MLD even when the channel frequency response is not equal across each SFBC block. The derived MLD structure is similar to the conventional Alamouti linear decoder except that the decoding matrix has to be selected from four possible matrices. However, the decoding matrix selection and symbols' detection can be performed jointly, which minimizes the additional computational complexity of the derived MLD as compared to the conventional Alamouti decoder. Monte Carlo simulation results show that the MLD outperforms the suboptimal detector reported in [1] by about 5 dB at bit error rate (BER) of 10-4under various channel conditions.

In this paper, the problems of relay selection and distributed beamforming are investigated for bi-directional dual-hop amplify-and-forward frequency-division duplex cooperative wireless networks. When using individual per-relay maximum transmission power constraint, it has been proven that the relay selection and beamforming optimization problem becomes NP hard and requires exhaustive search to find the optimal solution. Therefore, we propose a computationally affordable suboptimal multiple relay selection and beamforming optimization scheme based on the ℓ1norm squared relaxation. The proposed scheme performs the selection for the two transmission directions, simultaneously, while aiming at maximizing the aggregated SNR of the two communicating nodes. Furthermore, by exploiting the previous solutions to accelerate the algorithm's convergence, our proposed algorithm converges to a suboptimal solution compared to the exhaustive search technique with much less complexity.

Data centers have an important role in supporting cloud computing services (i.e. checking social media, sending emails, video conferencing,..). Hence, data centers topologies design became more important and must be able to respond to ever changing service requirements and application demands. An ultimate challenge in this research is the design of data center network that interconnects the massive number of servers, and provides efficient and fault-tolerant routing algorithm. Several topologies such as DCell, FlatNet and ScalNet have been proposed. However, these topologies generally seek to improve the scalability without taking into consideration the energy usage neither the network nfrastructure cost which is critical parameters in data centers. Motivated by these challenges, we propose a new network topology for data center, called AdyNet. It is an adaptive, dynamic, cost effective and highly performing topology. While reducing largely the infrastructure cost and the energy consumption, AdyNet outperforms FlatNet and ScalNet in terms of Average Path Length.

In this paper, we propose a general framework that transforms the problems of designing sparse finite-impulse-response linear equalizers and nonlinear decision-feedback equalizers, for multiple antenna systems, into the problem of sparsest approximation of a vector in different dictionaries. In addition, we investigate several choices of the sparsifying dictionaries under this framework. Furthermore, the worst case coherences of these dictionaries, which determine their sparsifying effectiveness, are analytically and/or numerically evaluated. Moreover, we show how to reduce the computational complexity of the designed sparse equalizer filters by exploiting the asymptotic equivalence of Toeplitz and circulant matrices. Finally, the superiority of our proposed framework over conventional methods is demonstrated through numerical experiments.

A major performance and complexity limitation in broadband communications is the long channel delay spread which results in a highly-frequency-selective channel frequency response. Channel shortening equalizers (CSEs) are used to ensure that the cascade of a long channel impulse response (CIR) and the CSE is approximately equivalent to a target impulse response (TIR) with much shorter delay spread. In this paper, we propose a general framework that transforms the problems of design of sparse CSE and TIR finite impulse response (FIR) filters into the problem of sparsest-approximation of a vector in different dictionaries. In addition, we compare several choices of sparsifying dictionaries under this framework. Furthermore, the worst-case coherence of these dictionaries, which determines their sparsifying effectiveness, are analytically and/or numerically evaluated. Finally, the usefulness of the proposed framework for the design of sparse CSE and TIR filters is validated through numerical experiments.

In this study we address the problem of face segmentation in thumbnail images. While there have been several approaches for face detection, none performs detection in such low resolution and segmentation with pixel accuracy. In this paper, we propose convolutional segmentation networks (CSNs) that can be trained to learn segmentation of human faces. Unlike the deep classifiers such as Convolutional Neural Network (CNNs), CSNs have the unique design solely for segmentation with minimal complexity. Furthermore, we propose a self-data organization (SDO) in order to create “expert” CSNs each of which is specialized over a set of images with certain face characteristics. SDO is integrated with CSN training in an interleaved manner and it is the key for the learning with simple and compact networks rather than the deep ones. This is especially a desired property for the limited face datasets with challenging face variations and complexities. Evaluations on the benchmark dataset show that CSNs can achieve an elegant segmentation accuracy despite the limited training data size, thumbnail resolution and highly complex face modalities.

This work proposes a method to optimize the resource block allocation in the cloud radio access network for a long term evolution system. This approach consists in three steps. First, the baseband unit collects the required information about users. Then, it optimizes the resource block allocation by carrying out a genetic algorithm. After that, it sends the solution to the remote heads. The simulations results show that the performance of the proposed system is significantly higher with respect to universal frequency reuse.

According to 3GPP standards, the Single Carrier Frequency Division Multiple Access (SC-FDMA) was adopted for Uplink transmission in LTE networks. As multiple mobiles may have access to neighboring base stations simultaneously, the inter-cell interferences are inevitable with this technique and the system throughput is impacted. To mitigate the inter-cell interference and improve, therefore, the system performance, severe schemes have been proposed. However, these schemes target either adapting the power control in mobile stations (e.g. Fractional Power Control technique) or improving the Resource Block (RB) allocation (e.g. Universal Frequency Reuse technique). In this paper, we propose two new techniques to reduce the inter-cell interferences namely: (i) Dynamic Fractional Power Control and (ii) a Resource Block Allocation based on Genetic Algorithm. In addition, we advocate that, by combining these technique, the system can achieve a better throughput.

Underlay cognitive networks should follow strict interference thresholds to operate in parallel with primary networks. This constraint limits their transmission power and eventually the coverage area. Therefore, in this paper, we first design a new approach for asynchronous narrow-band interference (NBI) estimation and mitigation in orthogonal frequency-division multiplexing cognitive radio networks that does not require prior knowledge of the NBI characteristics. Our proposed approach allows the primary user to exploit the sparsity of the secondary users' interference signal to recover it and cancel it based on sparse signal recovery theory. We also propose two subcarrier selection schemes that allow the primary user to further reduce the effect of the secondary users' interference based on sparse signal recovery algorithms. We show that although the primary and secondary transmissions are performed at the same time, the performance of our proposed techniques approach the interference- free limit over practical ranges of NBI power levels.

In this paper, the performance of an orthogonal frequency division multiplexing overlay cognitive radio network with subcarrier selection schemes is investigated. We propose three subcarrier selection techniques that reduce the level of interference at the primary base station based on collected channel state information from the different network nodes. Approximated outage probability expressions are also derived and verified by simulations for the different studied techniques. In addition, we propose and investigate a new approach for asynchronous narrowband interference (NBI) estimation and mitigation in cognitive radio networks. The proposed approach does not require prior knowledge of the NBI characteristics and allows the primary user to exploit the sparsity of the secondary users interference to recover it based on sparse signal recovery theory and approach the interference-free limit over practical ranges of NBI power levels.

This paper presents PTNet, a new data center topology that is specifically designed to offer a high and parameterized scalability with just one layer architecture. Furthermore, despite its high scalability, PTNet grants a reduced latency and a high performance in terms of capacity and fault tolerance. Consequently, compared to widely known data center networks, our new topology shows better capacity, robustness, cost-effectiveness and less power consumption. Conducted experiments and theoretical analyses illustrate the performance of the novel system.

Todays data centers may contain tens of thousands of computers with progressively more specialized and expensive equipments. Thus, the research community has proposed various interconnect topologies e.g FatTree, Dcell and Bcube. However, these architectures are too complex and very expensive to construct and they suffer from high average path length (APL) and latency. Motivated by these challenges, we propose a new data center architecture called VacoNet that combines the advantages of existing architectures while avoiding their limitations. VacoNet is a reliable, high-performance, and cost effective data center topology that can improve the network performance in terms of average path length and network latency. In addition, VacoNet can reach even 50% in infrastructure cost reduction and the power consumption will be decreased with more than 50000 watt compared to all the previous architectures. Both theoretical analysis and simulation experiments are conducted to evaluate the overall performance of the proposed architecture.

Cooperative spectrum sensing (CSS) has been proposed as a solution for radio spectrum resources scarcity problem. Clustering technique is introduced to increase the performance of CSS and overcome the shadowing and fading effects, also to reduce the control channel overhead for big number of cooperative users. However, Clustering is facing two major issues which are the data falsification caused by the malicious users and the increasing delay and energy consumption especially when the cluster heads is far away from the fusion center. In this paper, we propose a cluster and forward based on the trust CSS. By dividing all the secondary users into clusters and using of the trust threshold for selecting the most trusted CHs that can send their sensing decisions to the fusion center which can save more energy consumption and delay transmission, and improve the spectrum sensing performance.

In this paper, three novel techniques are proposed and investigated for multiple relay selection in dual hop OFDM networks. These techniques are based on the exploitation of sparse signal recovery theory and on carefully-designed groupings of the subcarriers depending on the channel quality. In particular, the proposed techniques use the Orthogonal Matching Pursuit algorithm which enables them to outperform existing techniques in terms of both outage probability and computation complexity. Furthermore, a detailed performance-complexity tradeoff investigation is presented for the different studied techniques and verified by Monte Carlo simulations.

In this paper, collaborative spectrum sensing to detect random signals corrupted by Gaussian noise in the presence of Primary User Emulation Attackers (PUEAs) is studied. We consider smart PUEAs which aims at increasing the false alarm probability and constitute a PUEA network on a Cognitive Radio (CR) network by impersonating Primary Users (PUs). In addition, we propose two security schemes in which sensing nodes get assistance from the Secondary Users (SUs) using two different approaches. In the first approach, the proposed scheme requires having some knowledge about the PUEA network similar to most of the schemes available in the literature. In our second proposed scheme, information about the PUEA network is not required yielding a scheme which is robust to the strategy of attackers. In both proposed approaches, we propose an algorithm to incorporate the SUs assistance in spectrum sensing. The final collaborative decision is made through solution of an optimization problem in order to achieve the best performance and protect the CR predefined requirements. Furthermore, in order to evaluate the performance of the proposed detector at the SUs and at the employed detector in the Fusion Center (FC), the closed form expressions for detection and false alarm probabilities are computed analytically. The provided closed-form analytical results in addition to simulation results show that the proposed schemes significantly outperform the existing secure spectrum sensing schemes.

This paper proposes efficient multiple-access schemes for large wireless networks based on the transmitters' buffer state information and their transceivers' duplex transmission capability. First, we investigate the case of half-duplex nodes where a node can either transmit or receive in a given time instant. In this case, for a given frame, the transmitters send their buffer states to the destination which assigns the available time duration in the frame for data transmission among the transmitters based on their buffer state information. The network is said to be naturally sparse if the number of nonempty-queue transmitters in a given frame is much smaller than the number of users, which is the case when the arrival rates to the queues are very small and the number of users is large. If the network is not naturally sparse, we design the user requests to be sparse such that only few requests are sent to the destination. We refer to the detected nonempty-queue transmitters in a given frame as frame owners. Our design goal is to minimize the nodes' total transmit power in a given frame. In the case of unslotted-time data transmission, the optimization problem is shown to be a convex optimization program. We propose an approximate formulation to simplify the problem and obtain a closed-form expression for the assigned time durations to the nodes. The solution of the approximate optimization problem demonstrates that the time duration assigned to a node in the set of frame owners is the ratio of the square-root of the buffer occupancy of that node to the sum of the square-roots of each occupancy of all the frame owners. We then investigate the slotted-time data transmission scenario, where the time durations assigned for data transmission are slotted. In addition, we show that the full-duplex capability of a node increases the data transmission portion of the frame and enables a distributed implementation of the proposed schemes. Our numerical results demonstrate that the proposed schemes achieve higher average bits per unit power than the fixed-assignment scheme where each node is assigned a predetermined fraction of the frame duration.

We analyze the outage probability of dual-hop full-duplex decode-and-forward relaying for an orthogonal frequency division multiplexing system in the presence of I/Q imbalance. We derive accurate analytical approximations which quantify the outage probability's functional dependence on the I/Q imbalance level and the residual loopback self-interference average power level. In addition, we derive the condition at which direct transmission outperforms full-duplex decode-and-forward relay-assisted transmission in the presence of I/Q imbalance. Furthermore, we propose an opportunistic relaying approach and demonstrate its robustness against the detrimental effects of I/Q imbalance and residual loopback self-interference. Our numerical results confirm the accuracy of our analysis.

We propose a novel protocol for secondary users based on compressive sensing principles. The secondary user assigned to one of the primary frequency bands is aided by a set of nearby secondary users, which we refer to as secondary relays. We assume that each secondary relay participates in relaying the secondary packets with certain probability. In a given time slot, each secondary relay indicates its ability of decoding the secondary packet and being a relay for the secondary transmission. Our proposed protocol exploits the sparsity of the participating relays set and is efficient as it allows dynamic relay assignment. When the primary user is inactive, the relays send their states to the secondary source which, in turn, transmits its own packet if the received number of decoding relays satisfies a predefined threshold. When the primary user is active, the relays perform cooperative beamforming to achieve a cooperative diversity gain for the secondary source while completely eliminating the interference to the primary destination. We study the performance of the system from a cross-layer point of view.

We consider a cognitive setting composed of multiple primary frequency bands each of which consists of a limited-battery energy-harvesting primary user and a secondary user. The primary user is equipped with a limited-capacity energy queue to maintain the energy harvested from the environment. We propose a novel cooperative protocol for the secondary users. The secondary user assigned to one of the primary frequency bands is aided by a set of nearby secondary users, which we refer to as secondary relays. We assume that each secondary relay participates in relaying the secondary packets with a certain probability. The proposed protocol exploits the sparsity of the participating-relays set and is efficient as it allows dynamic relay assignment. When the secondary user's direct link is in outage, the received secondary packets at the relays are stored in a relaying queue for future retransmissions. The relays perform cooperative beamforming to achieve a cooperative diversity gain for the secondary source. We study two types of quality-of-service requirements for the primary user. We analyze the impact of the primary user energy queue arrival rate and capacity on the secondary user throughput. In addition, we investigate the impact of the relaying queue capacity and the number of relays participating in spectrum sensing on the secondary user throughput.

Privacy and security have an increasingly important role in wireless networks. A secure communication enables a legitimate destination to successfully retrieve information sent by a source, while it disables the eavesdropper (illegitimate destination) to interpret the intercepted information. Physical (PHY) layer security approaches for wireless communications can prevent eavesdropping without encryption. It exploits the physical characteristics of the wireless channel in order to transmit messages securely. They are typically feasible when the source-destination channel is better than the source-eavesdropper channel. Cooperative schemes are a means to improve the performance of secure wireless communications. We propose an improved relay selection scheme, based on source-eavesdropper channel SNR restriction, that will guarantee the best secrecy rate at the destination under QoS condition. Performance has been studied in terms of secrecy rate, outage probability and average error probability. Simulations shows that the proposed scheme outperforms in terms of the secrecy rate at the destination when compared to techniques in the literature.

The scarcity of radio resources coupled with the high data rate demands in the last few years, induced incremental relaying techniques one of the most promising technologies. This is simply because it allows reaping spatial diversity and saving the channel resources at the same time. In this contribution, we make use of the Decode and Forward incremental relaying technique in OFDM cooperative systems by proposing and analyzing two subcarrier mapping algorithms. Based on outage probability, diversity order and power gain analysis, we derive the optimal parameters for these algorithms to enhance the outage performance of DF cooperative system by reducing the effect of error propagation. Also, an incremental relay sharing scheme is proposed allowing multiple sources to share the same relay and results in an improved spectral efficiency, which is critical given the high cost and scarcity of RF spectrum driven by the growing big data.

In this contribution, we investigate the outage performance of OFDM-based Decode and Forward (DF), Amplify and Forward (AF) and Controlled DF (CDF) cooperative systems under IQ Imbalance (IQI). In particular, tractable and compact approximate outage probability expressions are derived and the effect of the different IQI parameters is analyzed for different SNR ranges. Furthermore, by localizing the error floor in terms of IQI and SNR for each technique, we demonstrate when it is more beneficial to invest in increasing the transmission power or in compensating the imbalance. Moreover, we prove that the IQI compensation should be concentrated in the relay for some techniques and in the destination for some others. A comparative study between AF, DF, CDF and direct link transmission techniques is also conducted for different IQI parameters, SNR ranges, transmission rates and relay's position. Hence, this work may create a paradigm for future studies of more effective adaptive IQI compensation techniques that concentrate the compensation on the right IQI, SNR ranges, transceivers depending on the used transmission technique.

The direct-conversion architecture used for Orthogonal Frequency Division Multiplexing (OFDM) systems suffers from intercarrier interference (ICI) between image subcarriers in each OFDM symbol due to I/Q imbalance between the inphase (I) and quadrature (Q) branches at the transmit and receive sides. One of the widely-used metrics to evaluate the performance degradation due to I/Q imbalance is the signal to interference plus noise ratio (SINR). The instantaneous subcarrier SINR can be represented as a ratio conditioned on a particular channel realization and the exact SINR is evaluated by averaging over the channel realizations. In the literature, it is common to approximate the SINR by taking the average of the numerator and denominator separately although the two are not independent. In this paper, we calculate the exact SINR under the assumption that each pair of interfering subcarriers experiences uncorrelated channel realizations. We show that the SINR grows as the logarithm of the input SNR when the receiver has I/Q imbalance. On the other hand, under transmit-only I/Q imbalance, there is an SINR ceiling for large input SNR. Therefore, transmitter side I/Q imbalance is more harmful, in terms of average SINR, than receiver-only I/Q imbalance. We also show that, the approximate SINR expression, commonly used in the literature, is not accurate for large input SNR values, except for the case of transmit-only I/Q imbalance.

In this paper, we start by investigating the impact of joint transmit-receive I/Q imbalance on the performance of direct-conversion beamforming OFDM transceivers. We derive a new analytical expression for the average subcarrier SINR of the I/Q imbalance-ignorant beamformer in terms of the I/Q imbalance level at both the transmit and receive sides, the size of the beamforming array, and the input SNR level. This expression motivates the need for I/Q imbalance compensation and provides valuable design insights when examining several special and limiting cases. Next, we derive the throughput-maximizing multiple beamforming transmit/receive coefficients under a general system model with an asymmetric frequency-dependent (FD) joint transmit-receive I/Q imbalance. In addition, we propose a low-complexity pilot-aided scheme for the estimation of the channel and I/Q imbalance parameters. Our simulation results demonstrate that the proposed generalized multiple beamforming scheme is highly effective in mitigating I/Q imbalance effects at practical complexity levels.

In this paper, we investigate the effects of asymmetric frequency-dependent joint transmit-receive I/Q imbalance on the performance of beamforming MIMO-OFDM systems. We derive the throughput-maximizing transmit/receive beamforming coefficients taking into account I/Q imbalance effects. In addition, we propose a low-complexity pilot-aided scheme for the estimation of the channel and I/Q imbalance parameters. Our simulation results demonstrate the effectiveness of the proposed generalized beamforming scheme in mitigating I/Q imbalance effects.

In underlay cognitive networks with regenerative relaying, secondary users operating with primary user are adhering to stringent interference constraint which limits their transmission power and coverage area. In order not to violate this interference limit, underlay network will make the use of relays to transmit signal over the secondary network. The retransmitting relay will be selected among the available secondary users; hence, relay selection becomes more challenging due to strict interference limits. Relay selection is based not only on interference limit, but also on threshold constraint which ensure the satisfactory reception of the signal by the relays and the destination and which is an important criterion since the relaying protocol used is Decode and Forward (DF). This paper proposes three new relay selection schemes based on relay to destination link and relay to primary link qualities. We derive closed form expressions of the probability density function (PDF) of the SNR at the secondary destination, the outage probability and the average bit error probability. Analytical results are validated by simulations and they provide comparative analysis of the different relay selection scheme proposed herein.

Selective OFDMA is a powerfull relaying technique with subcarrier based selection. This scheeme has a high potential to considerably improve the performance of cooperative OFDM systems. However, in practice, the implementation of this technique may suffer from multiple problems caused by the separation of the subcarriers from each other's. In this Contribution, we propose a new selection scheme denoted “Optimized Selective OFDMA” that approaches selective OFDMA performance in terms of outage probability while reducing its synchronization problems that may be caused by the subcarriers separation. The outage probability and diversity order of the proposed scheme are derived. The number of the used paths is also analyzed. We proved that for high SNR, the introduced scheme gives exactly the same performance of selective OFDMA with only one path for all the subcarriers.

In this paper, we derive an analytical expression for the subcarrier average SINR of a beamforming OFDM system in the presence of joint transmit-receive I/Q imbalance. The derived expression quantifies the impact of the I/Q imbalance level at both the transmit and receive sides, the size of the beamforming array, and the input SNR level on performance. Several special and limiting cases are investigated providing new design insights.

In this contribution, we investigate Decode and Forward (DF) OFDM Ad-hoc Routing strategy with bottleneck maximization. We derive the exact outage probability and diversity order for the different scenarios: with and without subcarrier grouping and with and without joint selection, where joint selection refers to the selection of the last two hops together. When joint selection is performed, numerical results show that a power gain can be obtained when using subcarrier grouping technique.

The standard Alamouti space-frequency block code (SFBC) suffers from performance degradation when used over highly frequency-selective channels because the channel frequency response is not necessarily flat over the Alamouti block. In this paper, we present an enhanced Alamouti space frequency block decoding scheme for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems over highly frequency selective channels. The enhanced Alamouti scheme uses the channel frequency variations in consecutive subcarriers to adapt the Alamouti decoder. Simulation results of DVB-T2 system confirm that the proposed method has substantial performance improvement in terms of bit error rate when compared to standard Alamouti decoder mainly over highly frequency-selective channels such as single frequency networks (SFN).

Delay estimation in direct-sequence code-division multiple-access (DS-CDMA) systems is necessary for accurate code synchronization and for applications such as mobile phone positioning. Multipath propagation is among the main sources of error in the DS-CDMA delay estimation process, together with multiple access interference and non-line-of-sight (NLOS) propagation. This paper provides a review of main delay estimation techniques, existing in the literature so far, which are able to cope with multipath propagation, together with our novel delay estimation techniques proposed in the context of DS-CDMA systems. The performance of all these techniques is compared through analysis and simulations, considering also their relative computational complexity and required prior information. Starting from the traditional delay locked loops (DLL) and their improved variants, we discuss several recently introduced delay estimation techniques able to cope with multipath propagation. The characterization of these methods is given in a unified framework, suited for both rectangular and root raised cosine pulse shapes. The main focus in the performance comparison of the algorithms is on the closely-spaced multipath scenario, since this situation is the most challenging for achieving diversity gain with low delay spreads and for estimating LOS component with high accuracy in positioning applications.

The problem of closely-spaced paths in DS-CDMA systems is a challenging task at the baseband receiver for applications such as mobile location and RAKE receivers. Previously, we introduced a method based on Teager-Kaiser (1990) operator for resolving paths spaced at less than one chip distance. We compare the performance of TK operator with that of the subspace based MUSIC (multiple signal classification) algorithm. In order to make a fair comparison, an extension of MUSIC algorithm to the systems with long codes is derived and the performance of both algorithms is assessed via simulations.

In this paper, we introduce an efficient and simple technique for estimating closely-spaced multipath delays in an asynchronous multiuser CDMA systems. The subchip resolution is achieved via a nonlinear quadratic operator called Teager-Kaiser operator, which exploits the structure of the cross-correlation function between the received signal and the reference code. Simulation results in the presence of multiple interfering users and Rayleigh fading multipath channels are presented. It is shown that the proposed technique is near-far resistant, and its performance in the presence of closely spaced multipaths is much better compared to the peak tracking with subtraction method. Moreover, it has the advantage of a very simple implementation, compared to other maximum likelihood approaches.

Recently, a new all-digital technique for symbol timing and carrier phase synchronization of digital receivers was introduced by the authors. The's technique is based on a polynomial approximation of the likelihood function by using the Farrow structure. In this contribution, we propose a new Tretter-based frequency estimator that is directly derived from the carrier phase estimate. The proposed estimator is simple to implement and is particularly suited for digital receiver architectures. This new Tretter-based estimator performs fairly well when compared with the Fitz and the Luise-Reggiannini frequency estimators, in the presence of substantial carrier frequency offset.

We present a novel and efficient technique for the restoration of color images which are highly corrupted with impulse noise. This is a detection-estimation based approach in which outliers are first detected using a Teager-like operator followed by a locally adaptive threshold. Center pixels whose "energy" exceeds some threshold are replaced with the local marginal median. Simulation results show the superior performance of the proposed filtering algorithm compared to the renowned vector median (VM) and generalized vector directional filter (GVDF), which are commonly used for color image restoration. Monte Carlo simulations show the edge preservation and impulse noise attenuation capabilities of the proposed technique. The efficiency of the algorithm stems from its simple arithmetic operations compared with more demanding ones, e.g. computation of distances and angles in the case of VMF and GVDF, respectively.