Millimeter-wave (mmWave) beam selection in MIMO systems presents significant challenges in dynamic environments due to computational constraints, data heterogeneity, and privacy concerns. In this paper, we propose a novel Maturing Federated Transfer Learning (MFTL) framework that integrates radar and image data to enhance beam prediction accuracy while ensuring user data privacy. The proposed approach utilizes ResNet-50 as a pre-trained model, fine-tuned locally at distributed Antenna Units (AUs) to adapt to diverse scenarios. To mitigate the effects of data heterogeneity, we evaluate multiple aggregation strategies, with FedMedian demonstrating superior robustness compared to FedAvg and weighted averaging. Our optimized MFTL configuration, utilizing a learning rate of 0.005, a batch size of 32, and five aggregation rounds, significantly improves model performance. Experimental results on the DeepSense 6G dataset indicate that our approach achieves a Top-1 accuracy of 57.58% and a Top-5 accuracy of${9 4. 7 \%}$, outperforming state-of-the-art methods. These findings highlight the effectiveness of federated learning, transfer learning, and robust aggregation techniques in improving beam selection accuracy for next-generation mmWave communication systems.

ABSTRACT This review paper provides a systematic overview of the metaverse markets security problems and solutions. The metaverse is an emerging digital space, bridging virtual, augmented and mixed reality environments. As the metaverse evolves, issues related to customer security have emerged, which include breaches of privacy, thefts of identity and cybercrimes, all of this compounded by insecurities in the decentralised structures. Through this systematic literature review, we analyse these challenges and assess current approaches to mitigate them, including encryption, decentralised identity and related regulatory frameworks, but highlight their limited capacity for dealing with immersive virtual spaces' unique risks. The review also explores some advanced solutions adopting artificial intelligence, blockchain and privacy enhancing technologies (PETs) for securing the metaverse and enhancing its privacy. Furthermore, the review also points out the gaps in the current literature, particularly a lack of customised customer protection structures and a poor analysis of the psychological effects. It also proposes future solutions such as quantum‐resistant security and zero‐trust architecture to fortify the security. This review highlights the necessity of partnership between the industries, as well as the setting of security protocols so as to safeguard customer trust and engagement in the growing digital space.

Federated learning (FL) has gained attention for enabling efficient distributed learning while maintaining data privacy. However, the data privacy constraint reduces the transparency in the agents’ model update making the learning process vulnerable to Byzantine attacks. In this paper, a mathematical proof is provided to show that when the traditional model-combining scheme is used, the model will eventually diverge to non-useful solutions in the presence of Byzantine agents independently from their number or their contributions. A low complexity norm-control based aggregation approach is also proposed and shown to converge to the optimal and sub-optimal solutions in the absence or presence of Byzantine nodes, respectively. Monte-Carlo simulations are also conducted to verify and validate the mathematical derivations and the efficiency of the proposed approach in protecting the FL model.

Several people suffer from sudden falls, which puts them seriously at high risk if they do not get help right away. However, if people own a wearable device, they can utilize it to get them help from a nearest hospital whenever they feel tired or suffer from a sudden fall. Elderly people also tend to live alone. After a fall, it is rare for an elderly person to be able to get up or ask for assistance. Therefore, to enable patients to request assistance even in cases where they are unable to get up after a fall, an advanced fall detection system is required, with appropriate hardware and software components. Wireless sensors and Global Positioning System (GPS) gadgets are among the technologies that can be utilized in this regard. While sensors collect information about movement, a GPS device uses information from satellites to pinpoint an object’s exact location inside a given space. The goal of this paper is to describe a developed and implemented fall detection wearable wristband with lightweight security for health monitoring. In the event of an emergency, the smartphone application will communicate the user’s specific location to the nearest hospital and their emergency contacts. The proposed wristband achieved our goal by developing a system that can communicate with the mobile application. Furthermore, the model testing produced $92 \%$ overall accuracy.

Abstract This paper presents an approach for resource allocation functionality with cluster aggregation (RAFCA) for securely transmitting surveillance videos in high-speed trains (HSTs). Each train wagon is assumed to be equipped with a surveillance camera, along with a mobile relay (MR) that communicates with the cellular base station (BS) on one hand and with the indoor devices inside the train on the other. The RAFCA approach is based on a permutation process of the video frames across multiple MRs, such that parts of the video captured by the camera of a given wagon are transmitted by the MRs of all other wagons. The probability of detection by an eavesdropper is calculated in this paper and shown to be negligible, which leads to the preservation of the privacy of the passengers. Moreover, the proposed approach is shown to have no or little impact on the quality of experience (QoE) of the transmitted videos, thus preventing quality degradation.

The work assesses the efficacy of computer based remote tele-mentoring system (i.e. when the mentor and mentee are physically separated) for teaching minimally invasive surgical skills. The visual cues used for tele-mentoring comprises real-time virtual surgical instruments' motion augmented onto the operative field and remotely controlled by the mentor. In the feasibility study, the surgical task of laparoscopic intracorporeal suturing was simulated among 18 mentor-mentee pairs. Three modes of mentoring were used. Mode-I included traditional learning using pre-recorded videos (in absence of a mentor). Mode-II used traditional in-person hands-on mentoring. In Mode-III, a tele-mentoring prototype was used that connected a mentee with a remote mentor. Error count and duration were recorded for a learning stage followed by a testing stage for the three modes. The results show the error count for Mode-III reduces significantly as compared to Mode-I in the learning stage. Similarly, the error count for Mode-III also reduces significantly as compared to Mode-I in the testing stage. The errors count for Mode-III were equivalent to that of Mode-II for both learning and teaching stages. Furthermore, in Mode-III the duration reduces from learning to testing stage exhibiting the learning effect. Thus, computer based remote tele-mentoring is effective and more convenient to demonstrate surgical sub-steps consisting of tool-tissue interaction facilitating surgical skill transfer.

Telemedicine applications have recently received substantial potential and interest, especially after the COVID-19 pandemic. Remote experience will help people get their complex surgery done or transfer knowledge to local surgeons, without the need to travel abroad. Even with breakthrough improvements in internet speeds, the delay in video streaming is still a hurdle in telemedicine applications. This imposes using image compression and region of interest (ROI) techniques to reduce the data size and transmission needs. This paper proposes a Deep Reinforcement Learning (DRL) model that intelligently adapts the ROI size and non-ROI quality depending on the estimated throughput. The delay and structural similarity index measure (SSIM) comparison are used to assess the DRL model. The comparison findings and the practical application reveal that DRL is capable of reducing the delay by 13% and keeping the overall quality in an acceptable range. Since the latency has been significantly reduced, these findings are a valuable enhancement to telemedicine applications.

Asthma is a common lung illness that causes breathing difficulties. It affects people of all ages and generally begins in childhood; however, it can also appear in adults for the first time. There is presently no cure, but there are basic treatments that can help keep the symptoms under control. This paper aims to monitor asthma attacks for pediatrics under twelve years according to the following parameters: oxygen blood level, respiratory rate, and pulse rate. The resulting measures would be compared to pre-determined health standards. The respiratory rate is calculated and deduced by analyzing the result of air pressure sensor. The pulse rate and oxygen level are obtained from an oximeter. These measurements were compared with the normal health status to decide if the child has an asthma attack or not. The result is given within two minutes. The values of the three parameters and the final status of asthma are displayed on the screen. The main aim of the paper is to propose a monitoring device that can be used in a hospital or at home where parents can constantly check the status of their children in a short period of time without having to go to the hospital. The success of this device is getting accuracy higher than 85%.

The objective of the project summarized in this paper is to address both the problem of autonomous secure goods delivery to multiple destinations in one trip, while maintaining a complete log of events and transactions. This is particularly important when critical goods are delivered to remote rural areas. Moreover., the project addresses the problem in a holistic fashion., taking into account the necessity of tracking each item carried and/or delivered., and of logging the time and location of delivery., in addition to measures for securing the carried goods (protection from loss or theft)., and the delivery device itself (protection from attack., damage., or theft). The system described in this paper is composed of an autonomous robot., application., RFID reader., and RFID tags attached to the items to be delivered. The tag will be read by the reader until it is removed from the robot. After the object is removed from the robot., the reader will determine whether the GPS location matches the location to which it should be delivered; if so., the user and administrator will receive notification that the delivery was successful., and the database will be updated. Otherwise, an alert will be sent to the user's mobile app and the reader on the robot to return the item. If the item is not returned., an alert is sent., and the order is marked as mis-delivered. Later on., all mis-delivered items will be handled by the administrator who will take appropriate action.

Integrating Unmanned Aerial Vehicles (UAVs) into the 5G cellular network and O- RAN holds great potential for the UAV and communications industries. However, UAV wireless communication systems are susceptible to malicious attempts at jamming. This paper focuses explicitly on countering jamming signals that occur in a single direction, targeting UAV systems' physical layer security (PL). To address these security concerns, we utilize Online Learning (OL) methods to enhance the security of the physical layer in UAV systems. Our proposed approach involves an intrusion detection system (IDS) based on OL continuously updating its knowledge and responding to emerging real-time attack strategies to protect UAV communication networks. The primary objective of this method is to ensure the integrity, availability, and reliability of wireless Cyber-Physical Systems (CPS) operations while ensuring the safe and efficient functioning of multi-UAV swarms within the O-RAN framework. We present the performance of the OL-based IDS, supported by a mathematical analysis that demonstrates the effectiveness of the adapted solution to the problem. Moreover, while recognizing this field's inherent challenges and complexities, this research explores potential avenues for future investigation in enhancing physical layer security in UAV systems.

More than three years into the coronavirus disease 2019 (COVID-19) pandemic, it can be noted that the measures put in place for societies to manage the spread of this disease could have been better. For example, contact tracing mobile applications used to curb the spread of COVID-19 need additional enhancements to allow health care professionals to better understand the proliferation of the disease and to lessen the burden on hospitals and medical centers. In this paper, we present an intelligent solution to remotely self-monitor COVID-19 symptoms to help rapidly identify and detect suspected positives. The proposed intelligent solution is based on using a near-field communications (NFC) wristband that collects body temperature heart rate and SpO2 levels. It is connected to a dedicated mobile application to intelligently draw conclusions from the data (COVID-19 symptoms) it collects. Moreover, the application is trained to analyze cough sounds and detect the probability of infection. Results show more than 90% of detection accuracy. The proposed system can be adapted to future pandemics based on respiratory symptoms.

The gigantic increase in population, social isolation, and mobility constraint lifestyle since the COVID-19 era has resulted in challenges like remote availability of critical bio-instrumentation like magnetocardiography (MCG) and electrocardiography (ECG). This availability can only be made possible through portable hand-held bio-instrumentation systems. Unmanned aerial vehicles (UAVs) can be used to deliver these systems to remote areas. In cardiological bioinstrumentation, MCG and ECG are two major innovations-based field effect techno-scientific approaches. The MCG systems face several major challenges that have hampered their applications and utility for cardio patients and their inspection labs. In this work, the main challenges were addressed by using a novel ML-based probabilistic interpolation algorithm over a dual ECG probing system-on-chip (SoC) with IoT capabilities to generate the identical MCG signal from two ECG signals with a segmented translation of PR, QRS, ST, and QT characteristic patches at real-time. The implementation findings provided a rich resource for approximating wave-shaping filters, frequencies, mean, and variance whilst addressing redundancy.

Decision tree (DT) models are widely used in medical applications where the size of the data sets is usually small or medium. Moreover, DT ensemble models are preferred over single DT models because of their higher accuracy in spite of the need for more overhead due to using multiple trees. Several schemes have been proposed for privacy-preserving cloud-based medical diagnosis using ensemble models. However, these schemes suffer from several limitations. First, they suffer from high computation/communication overheads due to using inefficient public-key cryptosystems. Second, none of them can simultaneously protect the intellectual property of the model and preserve the privacy of the patients’ data and diagnosis results. Finally, they do not provide inherent access control for the outsourced model and micropayment, in which only the registered patients can use the model and pay for the service. In this article, we develop a lightweight and privacy-preserving cloud-based medical diagnosis scheme using ensemble models with high accuracy and acceptable overhead. Using our scheme, the model owner can control the patients who can use the model. Also, for each classification operation, patients must make a micro-payment to pay for the diagnosis service. Our analysis indicates that our scheme can protect the model’s intellectual property and diagnose diseases without leaking any sensitive information about the patients’ medical data and the diagnosis results. Our experimental results demonstrate that our scheme requires less communication/computation overhead compared to the existing schemes.

Federated learning (FL) enables an effective and private distributed learning process. However, it is vulnerable against several types of attacks, such as Byzantine behaviors. The first purpose of this work is to demonstrate mathematically that traditional arithmetic-averaging model-combining approach will ultimately diverge to an unstable solution in the presence of Byzantine agents. This article also proposes a low-complexity, decentralized Byzantine resilient training mechanism. The proposed technique identifies and isolates hostile nodes rather than just mitigating their impact on the global model. In addition, the suggested approach may be used alone or in conjunction with other protection techniques to provide an additional layer of security in the event of misdetection. The suggested solution is decentralized, allowing all participating nodes to jointly identify harmful individuals using a novel cross-check mechanism. To prevent biased assessments, the identification procedure is done blindly and is incorporated into the regular training process. A smart activation mechanism based on flag activation is also proposed to reduce the network overhead. Finally, general mathematical proofs combined with extensive experimental results applied in a healthcare electrocardiogram (ECG) monitoring scenario show that the proposed techniques are very efficient at accurately predicting heart problems.

Abstract 5G technology is intended to support three promising services with heterogeneous requirements: Ultra-Reliable and Low Latency Communication (uRLLC), enhanced Mobile Broadband (eMBB), and massive Machine Type Communication (mMTC). 6G is required to support even more challenging scenarios, including the presence of a large number of uRLLC devices, under the massive uRLLC (mURLLC) use case scenario. The presence of these services on the same network creates a challenging task of resource allocation to meet their diverse requirements. Given the critical nature of uRLLC applications, uRLLC traffic will always have the highest priority which causes a negative impact on the performance of other services. In this paper, the problem of uRLLC/eMBB resource allocation is investigated. An optimal resource allocation scheme is proposed with two scenarios including a guaranteed fairness level and minimum data rate among eMBB users. In addition, a knapsack-inspired punctured resource allocation algorithm is proposed where the users’ channel qualities of both services are considered at each time slot leading to the most suitable Resource Block (RB) selection for puncturing in a way that minimizes the negative impact on eMBB performance. The proposed solution was compared with three puncturing baseline reference algorithms and the performance was evaluated in terms of eMBB Sum throughput and Fairness level. The simulation results show that the proposed algorithm outperforms the above-mentioned reference algorithms in all evaluation metrics and is proved to be comparable to the optimal solution given its low complexity.

In a telementoring setup for open surgeries, the motion of virtual surgical instruments is superimposed onto the operative field. This assists a mentor to effectively convey to the mentee the information pertaining to the required tool-tissue interactions during the surgery. The aim of this work is to assess the effects of using a virtual reality environment at mentor's site (in contrast to conventional visualization on a two-dimensional screen) during surgical telementoring. A user study is conducted simulating motion of virtual surgical instrument in an open surgery. The results show that mentor is able to demonstrate with higher accuracy and in shorter duration, the required virtual surgical instrument motions. Thus, rendering information to the mentor in an immersive virtual reality environment assists in better understanding of the operative field and enhanced control of the virtual surgical instrument motion. This further aids in conveying accurate information pertaining to the tool-tissue interaction to the mentee.

Telementoring in surgery facilitates the transfer of surgical knowledge from the mentor to the mentee. Augmented Reality (AR) further assists this transfer by overlaying visual cues (e.g., in the form of virtual surgical instrument motion) generated by the mentor onto the operative field of the mentee. In this work, we present a benchmark for comparing such AR based surgical telementoring systems. The results compare the network performances of these systems across different types of surgery (open or minimally invasive), based on the locations of the mentor and the mentee (inter- or intra- country), and finally the underlying networking protocols (RTMP versus WebRTC).

According to the Global Organization for Stress, 80 percent of people are stressed at work and according to the American Institute of Stress, stress causes 48 percent of people to have difficulty sleeping. Relaxation reduces stress, depression, and anxiety. Electroencephalography (EEG) is used by scientists to analyze the brainwave signals that explore the emotions and the cognitive processes of the brain. In recent years, Virtual reality (VR) technology has drawn lots of attention. Thus, the use of VR as a technique of relaxation is being investigated for assisting students and workers in achieving the relaxation to help them focus on their studies and work. This work aims to design an experimental setup for acquiring the EEG signals to analyze the characteristic frequency bands of the brainwave related to relaxation, when the subject is immersed in a VR environment. Different metrics, calculated from captured brain wave signals, are analyzed, compared, and discussed in this paper.

Telemedicine is a rising technology that is gaining a lot of interest in the recent decades. Several applications of telemedicine are delay-sensitive and need to be operated in real-time. One of which is surgical tele-mentoring where a remote expert surgeon mentors local surgeons during an operation. While the advances done in telecommunications and robotics have made tele-mentoring possible in modern days, there are still many challenges that stop such telemedical applications from being completely legalized and approved as medical tools across the world. One of the main issues is the need for very high bandwidth to allow the surgery to be done accurately in real-time. Such bandwidth requirements are difficult to provide especially in rural areas with limited communications infrastructure. We propose an adaptive Region of Interest (ROI) detection and an optimization model that addresses the trade-off between the overall quality of a surgical video and the network delay. The model aims to maximize the size of the ROI, where highest video quality must be used, depending on the available network throughput, while avoiding excessive degradation of the quality of the background.

Federated Learning (FL) has attracted a lot of attention in numerous applications due to recent data privacy regulations and increased awareness about data handling issues, combined with the ever-increasing big-data sizes. This paper proposes a server-less, robust FL training mechanism that allows any set of participating data-owners to train a neural network (NN) model collaboratively without the assistance of any central node and while being resilient to Byzantine attacks. The proposed approach makes use of a dual-way update mechanism to allow each node to take a model forwarding decision towards a global collaborative decision of isolating any malicious updates. The efficiency of the proposed approach in detecting cardiac irregularities is verified using simulation results conducted based on the Physikalisch-Technische Bundesanstalt Database electro-cardiogram (PTBDB ECG) dataset.

Federated Learning (FL) is a machine learning approach that enables private and decentralized model training. Although FL has been shown to be very useful in several applications, its privacy constraints cause a lack of model update transparency which makes it vulnerable to several types of attacks. In particular, based on detailed convergence analyses, we show in this paper that when the traditional model-combining scheme is used, even a single Byzantine node that keeps sending random reports will cause the whole FL model to diverge to non-useful solutions. A low complexity model combining approach is also proposed to stabilize the FL system and make it converge to a suboptimal solution just by controlling the model norm. The Physikalisch-Technische Bundesanstalt extra-large electrocardiogram (PTB-XL ECG) dataset is used to validate the findings of this paper and show the efficiency of the proposed approach in identifying heart anomalies.

The Internet of Things (IoT) is used to interconnect a massive number of heterogeneous resource-constrained smart devices. This makes such networks exposed to various types of malicious attacks. In particular, jamming attacks are among the most common harmful attacks to IoT networks. Therefore, an anti-jamming power allocation (PA) strategy is first proposed in this article for health monitoring IoT networks by exploiting the game theory to minimize the worst case jamming effect under multichannel fading. This strategy uses an iterative algorithm based on gradient descent to identify the Nash Equilibrium (NE) of the game. An artificial neural network (ANN) model is also proposed to accelerate the convergence of the algorithm making it more suitable for IoT networks. Furthermore, novel data population (DP), extension, and balancing techniques are proposed to enhance the efficiency of the proposed strategy in combating jamming attacks even for network configurations that were never used in the training phase. In addition, time and spatial diversities are exploited using a heterogeneous iterative algorithm to enhance the security of the network.

A secure energy efficient approach is proposed to connect Internet of Things (IoT) sensors that operate with limited power resources. This is done by optimizing simultaneously the energy efficiency, the communication rate and the network security while limiting the potential data leakage and tracking the finite battery status evolution. The proposed model uses spatial diversity in addition to artificial jamming introduced by an intermediate device to forward the data from the sensors to the destination and to secure the communication links without draining the rechargeable batteries. The energy harvested by the source is also maximized without affecting the security level of the network. The outage secrecy capacity is derived to evaluate the security level. Furthermore, the system power stability is analyzed using Markov chains and statistical approaches to validate the efficiency of the proposed technique in maintaining the system in a self-sufficient mode and making it operate without the assistance of external power resources.

A jamming combating power allocation strategy is proposed to secure the data communication in IoT networks. The proposed strategy aims to minimize the worst case jamming effect on the intended transmission under multi channel fading and total power constraints by modelling the problem as a Colonel Blotto game Nash Equibrium (NE). Both Logistic Regression as well as a specifically designed algorithm are used to iteratively and rapidly obtain the equilibrium strategy. The conducted theoretical derivations and Monte Carlo simulations confirm that the proposed approach can secure the IoT network with a limited amount of power and with a number of iterations that is much reduced compared to state-of-the-art techniques.

In spite of the wide spread of practical crypto- systems and ciphers nowadays, they still lack a unique metric to measure the secrecy level they provide. Their strength is measured in an ad-hoc way by exposing them to different kinds of attacks. In addition, their ability to hold secure against these attacks is evaluated in time and computations. In this paper, we introduce an approach for calculating the equivocation of the secret key used in these ciphers. In addition, we prove that it can be used as an indicator for the work required to break the cipher. This will help in unifying the metrics used in evaluating the strength of the ciphers and in comparing them with the classical information theoretic secreacy measures.

Telemedicine is a technique that allows patients to have health-related consultations without the need to be physically present in the hospital through phone and video calling technologies. In recent years, researchers have made many contributions to reform and facilitate better telemedicine services through the use of body area networks or wireless body area networks. This paper presents a testbed where we implement a lightweight physical layer security scheme, using gray code, on an IoT-based health monitoring system to secure transmitted patient readings while preserving its clinical features. We address several existing adversarial scenarios, where an adversary can eavesdrop on the packets and infer their content using some of the existing packet inspection techniques. We prove that the introduced physical layer security scheme effectively protects the patient transmitted data, even if read by an adversary.

This paper investigates the use of unmanned aerial vehicles (UAVs)/drones for providing high data rates to mobile relays (MRs) placed on top of high speed train wagons, thus introducing the concept of Travel Hopping Enabled Resource Allocation (THEResA). The objective is to provide high data rate connectivity to train passengers in 5G+/6G networks. With the drone flying at the same train speed, highly directive beams can be formed and steered between the drone and each of the MRs. The simulation results in the paper show that this leads to high data rate connectivity, which will be reflected in the indoor links between the MRs and the train passengers inside the wagons. The drones maintain the connectivity to the cellular infrastructure by using high speed links with the cellular base stations (BSs) deployed along the rail track, e.g., through free space optics (FSO). The drones use the BS sites for recharging and resuming their operation. Therefore, a separate set of drones, or the same drones when they are not flying over trains, can be used to provide connectivity to remote rural areas. In fact, high speed trains might travel through rural areas with low population density. Although it is practical to lay fiber optic cables along the rail track, villages and small rural population agglomerations far from the railroad might not have access to the internet backhaul. UAVs can provide this connectivity in a delay tolerant fashion, by heading from the BSs/train station sites towards remote areas, collecting/transferring data from/to these areas, then returning to the sites along the rail track to recharge their batteries. This paper presents an analysis that shows the feasibility of this approach.

The rapid evolution of remote health monitoring applications is foreseen to be a crucial solution for facing an unpredictable health crisis and improving the quality of life. However, such applications come with many challenges, including: the transmission of a large amount of private medical data and the limited power budget for battery-operated devices. Thus, this article proposes an intelligent, secure, and energy-efficient (I-SEE) framework for secure and energy-efficient medical data transmission, leveraging the potential of physical-layer security. In particular, we incorporate a practical secrecy metric, namely, the secrecy outage probability (SOP), along with the adaptive compression at the edge for providing a secure solution for health monitoring applications. In the proposed framework, we first formulate an optimization problem that maximizes the energy efficiency, while maintaining quality-of-service constraints of the health application. Second, we propose a deep reinforcement learning process that obtains the optimal strategy for secure data transmission. Specifically, a multiobjective reward function is defined to optimize energy efficiency and distortion, resulting from the compression scheme. Then, a deep deterministic policy gradients (DDPGs) algorithm, named Static-DDPG is proposed to solve our problem efficiently. Third, the problem is extended to consider the battery lifetime maximization with varying channel conditions. Indeed, a Dynamic-DDPG algorithm is proposed in order to allow the edge to adapt to the environment dynamics while maximizing its battery lifetime. The conducted simulations validate the efficiency of the proposed algorithms in terms of finding the optimal policy that addresses the tradeoff between the considered conflicting objectives, along with the battery lifetime maximization

The open nature of the wireless communication medium renders it vulnerable to jamming attacks by malicious users. To detect their presence and to avoid such attacks, several techniques are present in the literature. Most of these techniques aim to reduce the effect of the jamming signals by increasing the transmission power or by using complex coordination schemes. However, the implementation of such power consuming techniques might be challenging or not feasible in limited resources Internet-of-Things (IoT) devices. Therefore, a defending strategy against jamming attacks in health monitoring IoT networks is proposed in this article. This strategy operates in orthogonal frequency-division multiplexing channels and takes into consideration the effect of slow fading channels in the strategy design. Specifically, the jamming combating problem is formulated as a Colonel Blotto game where the equilibrium defines the minimization of the worst case jamming effect on the IoT sensors communications. Then, the optimal power allocation strategy for all the potential jammer power ranges is derived by investigating the Nash equilibrium of the game. This proposed strategy is shown to be efficient in combating jamming attacks while minimizing the IoT sensors power consumption.

Electrocardiogram (ECG) is an electrical activity of the heart, which can be recorded by placing electrodes near heart or on the limbs. ECG is a vital body signal, which reflects the heart health condition. This paper presents a new wearable ECG system, which can be used for long-term rhythm monitoring with the potential of increased sensitivity to detect intermittent or subclinical arrhythmia. This study presents the design and development of a wearable pervasive healthcare monitoring system by ECG measurement systems and internet of things (IoT) platform. In this design, non-intrusive healthcare system was designed based on wireless body area network (WBAN) for wide area coverage with minimum battery power to support wireless transmission. Data were transmitted via Wi-Fi to the personalized mobile system. These were integrated into a comfortable, easy to wear, and ergonomically designed armband ECG sensor system, which can acquire an ECG signal from the upper arm of the user over a period of 72 hours.

Wirelessly connected devices play a vital role in people's daily life, especially with the significant rise in the number of devices connected to the Internet and the huge data being generated everyday. However, the open nature of the wireless channels makes them vulnerable to several threats. One of these major threats is jamming attacks which try to disrupt the reception of the useful signal by a receiver. In this paper, we propose a physical layer security anti-jamming method using massive planar antenna arrays. A receiver is assumed to perform anti-jamming against a single jammer trying to degrade the communication link between two parties. A large database of possible antenna array configurations with different radiation patterns is generated. Two methods are proposed for searching through the database. In the first, searching through the database gives the configuration with the deepest null towards the jammer, while in the second, we identify the configuration with the largest maximum to null ratio. The signal-to-interference-plus-noise-ratio is the chosen metric of performance for evaluating the chosen array configurations by both methods. Supporting simulation results validate the effectiveness of the proposed anti-jamming strategy.

The Internet of things (IoT) is one of the most exposed networks to attackers due to its widespread and its heterogeneity. In such networks, jamming attacks are widely used by malicious users to compromise the private and secure communications. Many techniques are proposed in the literature to secure the network from malicious jamming attacks. However, most of these techniques require either the implementation of complex coordination schemes or the use of high transmission power and are therefore challenging to implement in limited resources IoT networks. In this paper, a low complexity anti-jamming defending strategy using smart power allocation under limited power constraints is proposed for health monitoring IoT networks. This strategy is designed by formulating the worst case jamming effect minimization problem as a Colonel Blotto game while considering the slow channel fading effect. By analyzing the Nash Equilibrium (NE) of the game, making use of efficient and fast equilibrium approximation techniques, designing a fast numerical solving approach, training an artificial neural network (ANN) to enhance the accuracy of the estimation, an anti-jamming power allocating strategy is proposed and is shown to be effective in reducing the power consumption and in combating jamming attacks with less resources. A data population scheme is also proposed to make the proposed ANN exploit as much possible the available data to provide accurate NE estimation.

This month’s issue offers insight into efficient compression and execution of DNNs, the challenge of connecting rural areas, and the clique problem in wireless communication. which

Providing connectivity to around half of the world population living in rural or underprivileged areas is a tremendous challenge, but, at the same time, a unique opportunity. Access to the Internet would provide the population living in these areas a possibility to progress on the educational, health, environment, and business levels. In this article, a survey of technologies for providing connectivity to rural areas, which can help address this challenge, is provided. Although access/fronthaul and backhaul techniques are discussed in this article, it is noted that the major limitation for providing connectivity to rural and underprivileged areas is the cost of backhaul deployment. In addition, energy requirements and cost-efficiency of the studied technologies are analyzed. In fact, the challenges faced for deploying an electricity network, as a prerequisite for deploying communication networks, are huge in these areas, and they are granted an important share of the discussions in this article. Furthermore, typical application scenarios in rural areas are discussed, and several country-specific use cases are surveyed. The main initiatives by key international players aiming to provide rural connectivity are also described. Moreover, directions for the future evolution of rural connectivity are outlined in this article. Although there is no single solution that can solve all rural connectivity problems, building gradually on the current achievements in order to reach ubiquitous connectivity, while taking into account the particularities of each region and tailoring the solution accordingly, seems to be the most suitable path to follow.

Channel-aware encryption is investigated as a physical layer security technique in internet of things (IoT) scenarios. Clustering algorithms for grouping sensor nodes into cooperative clusters are proposed, with the purpose of decreasing energy consumption and reducing the transmission time of sensor data. Bit flipping is implemented with the clustering method in order to "encrypt" the transmitted data based on channel state information. The simulation results validate the performance of the proposed approach in terms of reducing energy consumption, reducing transmission time, and of confusing the eavesdropper from guessing the correct transmissions of sensor nodes.

This paper investigates the bit-error probability of several color shift keying (CSK) constellations in a visible light communications (VLC) scenario with a camera-based receiver. A novel VLC communication constellation set, the extended circular CSK, is presented and analyzed. Its performance is compared to the CSK constellation described in the IEEE 802.15.7 standard, in addition to other constellations that were introduced previously, namely the geometric and circular CSK constellations. The bit-error rate (BER) of the proposed extended circular CSK constellation is studied. Simulation results show that the extended circular CSK significantly outperforms the other constellations and deserves further research attention in the future.

Physical layer security has attracted research attention as a means to achieve secure communication without the need for complicated upper layer encryption techniques. The secure degrees of freedom (SDoF) of various networks in the absence of instantaneous eavesdropper channel state information is still unknown. In this work, we study the SDoF of a multiple access network composed of two transmitters and a single receiver in the presence of an eavesdropper. All parties are equipped with multiple antennas and are subject to Gaussian noise in addition to fading channel conditions. A realistic, worst case scenario, where the channel state information (CSI) for the channels between the trusted parties is known to everyone, while the trusted parties can only estimate the channel statistics (environment based) of the eavesdropper is considered. The asymptotic secure network sum capacity (aka sum SDoF) is provided utilizing a novel proposed comprehensive upperbound along with a novel achievable scheme based on exploiting jamming.

The evolution of Internet of Things (IoT) as a new application in wireless networks mandates the utilization of wireless cooperative relaying to overcome the energy limitations of IoT devices. Multi-hop relaying is a communication scheme, where packets are forwarded from source to destination through intermediate relay nodes. All these relay nodes are assumed to have buffers for temporarily storing their received packets. During each time-slot, one node can be selected among all nodes to transmit and forward a single packet to the consequent relay node towards the final destination. Based on the nature of the data and its sensitivity to the delay, different schemes can be used to control the movement of packets in the multi-hop networks. This paper presents a framework for the delay analysis of buffered multi-hop networks based on a recently proposed packet-forwarding scheme that uses the best hop for transmission. Based on the channel, the best hop, having the highest signal-to-noise ratio (SNR), is selected. This hop selection procedure produces selection diversity, which minimizes the error and outage probability. The network delay is studied analytically, based on a finite-state Markov chain model. Also, we derive analytical closed form expressions for the average queue length for each relay buffer in the network and the end-to-end network delay. Finally, we compare the delay and outage of the best hop scheme with the conventional multi-hop transmission scheme. The results show how the number of intermediate relays and the buffer size of each one can affect the network delay.

This paper introduces circular color shift keying (CSK) constellations in a visible light communications (VLC) system using a camera-based receiver with applications to vehicular communications, where vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are required to improve the safety of vehicles in high traffic density. The circular CSK constellations are shown to outperform the geometric and IEEE 802.15.7 constellations in terms of bit-error probability. Afterward, the noise at the receiver is investigated and transferred from the spatial domain to an angular noise in the International Commission on Illumination (CIE) color domain, where it is shown to behave similarly to a Gaussian distribution. Based on this noise model, a closed-form expression of the bit-error rate (BER) of circular CSK constellations is derived and shown to be accurate for BER ranges corresponding to VLC applications of practical interest. Diversity techniques are also investigated, and interesting differences are noted during the analysis compared to traditional radio frequency (RF) communication scenarios. Finally, the design of a link adaptation approach maximizing the number of bits per transmitted symbol, while meeting a target BER, is proposed. Hence, this paper presents a detailed design and analysis of a novel VLC modulation technique and demonstrates its efficiency and superiority.

Epileptic seizure detection could be detected through investigating the electroencephalography (EEG), which is deemed to be very important for IoT wearable sensor-based health systems. EEG-based classification is crucial for a wide-range of applications to analyze real-time vital signs using features concerning predefined set of data classes. The aim of this paper is to conduct a comparative study for several classification techniques and demonstrate the effect of uncertainty in the EEG data on the classification accuracy. We define a model for decomposing the EEG using various transformation such as discrete cosine transform, discrete wavelet transform into several sub-bands. After feature extraction, a comparative study to assess the classification algorithms' performance is conducted. In addition, we evaluate their overall accuracy and complexity as performance measures. For this purpose, we use the support vector machine (SVM) and the Artificial Neural Network (ANN). These are chosen as classifier models to study the performance of the obtained features. The discussion will include the evaluation of the classifiers' performance using the EEG-based epileptic seizure data in two categories, noiseless and noisy. In addition, there are some statistical features extracted to characterize the complete EEG data feeding to these two classifiers. A publically available EEG dataset is employed for both normal and epileptic seizure for automatic epileptic seizure detection as a benchmark.

In an mHealth remote patient monitoring scenario, usually control units/data aggregators receive data from the body area network (BAN) sensors then send it to the network or “cloud”. The control unit would have to transmit the measurement data to the home access point (AP) using WiFi for example, or directly to a cellular base station (BS), e.g., using the long-term evolution (LTE) technology, or both (e.g., using multi- homing to transmit over multiple radio access technologies (Multi-RATs). Fast encryption or physical layer security techniques are needed to secure the data. In fact, during normal conditions, monitoring data can be transmitted using best effort transmission. However, when real-time processing detects an emergency situation, the current monitoring data should be transmitted real-time to the appropriate medical personnel in emergency response teams. In this paper, a fast and secure approach for transmitting monitoring data over multi-RATs is proposed. The presented approach consists of benefiting of the presence of multi-RATs in order to exchange the secrecy information more efficiently while optimizing the transmission time.

In this paper, we investigate the performance of a green mobile operator collaborating with other traditional mobile operators. Its goal is to minimize its CO2emissions, maximize its profit or achieve or tradeoff between both objectives by offloading its users to neighbor networks and exploiting renewable energies. On the other hand, traditional mobile operators aim to maximize their profits by attracting the maximum number of roamed users. The problem is modeled as a two-level Stackelberg game and its equilibrium is derived. A green mobile operator level that determines how many users per each base station to offload to each neighbor network, and a non-green mobile operator level where operators focus on finding the optimal roaming price. Our simulation results show a significant saving in terms of CO2emissions compared to the non-cooperation case and that roaming decision depends essentially on the availability of renewable energy in base station sites.

Green operation of a specific category of femtocell networks, where a central controller can control the network within the premises of a campus or building, is investigated. An energy efficient approach for switching off redundant femtocell access points (FAPs) is proposed. It consists of selecting the best FAP to switch off, then moving the femto user equipments (FUEs) to other active FAPs without compromising their quality of service (QoS). Simulation results show that significant energy savings are obtained with the proposed approach, while satisfying QoS requirements.

In this paper, the problem of radio and power resource management in long term evolution heterogeneous networks (LTE HetNets) is investigated. The goal is to minimize the total power consumption of the network while satisfying the user quality of service determined by each target data rate. We study the model where one macrocell base station is placed in the cell center, and multiple small cell base stations and femtocell access points are distributed around it. The dual decomposition technique is adopted to jointly optimize the power and carrier allocation in the downlink direction in addition to the selection of turned off small cell base stations. Our numerical results investigate the performance of the proposed scheme versus different system parameters and show an important saving in terms of total power consumption.

Joint channel sensing and resource allocation in femtocell networks is investigated. An approach consisting of using jointly a sensing module in addition to a normal femto module performing resource allocation simultaneously with channel sensing is proposed. The sensed interference levels are used to define an interference transformation function that prioritizes the allocation of subcarriers based on the level of interference they are subjected to. Then, a resource allocation algorithm based on using interference transformation functions is presented. By incorporating interference information in a transformed channel gain, it permits the allocation of subcarriers subjected to little or no interference with high priority, and unfavors the allocation of the more interfered subcarriers. Simulation results show significant performance improvement when the proposed method is implemented.

A weighted mean square error (WMSE) approach to optimising digital filters is delineated. It is applied in the current work to optimising the classical Al‐Alaoui IIR differentiators to obtain new improved wideband differentiators of varying orders. These can be directly used for analog to digital conversion and in many digital signal processing applications. The weighted MSE approach is motivated by the Al‐Alaoui WMSE approach to pattern recognition. In addition, the differentiators are converted to integrators of similar orders that are further optimised. Various examples and comparisons are presented to demonstrate the viability of the proposed approach.

Abstract In this paper, the interplay between cooperative device‐to‐device (D2D) communications and green cellular communications in the long term evolution (LTE) and LTE‐advanced (LTE‐A) cellular systems is investigated. An efficient approach for grouping mobile terminals (MTs) into cooperative clusters is described. In each cluster, MTs cooperate via D2D communications to share content of common interest. In addition, an energy‐efficient approach for putting base stations in sleep mode in an LTE‐A heterogeneous network is presented. Finally, both methods are combined in order to ensure green communications for both the users' MTs and the operator's base stations. The presented techniques are investigated in the framework of orthogonal frequency division multiple access‐based state‐of‐the‐art LTE cellular networks, while taking resource allocation and intercell interference into account. Results show that the proposed approach leads to energy savings for both the operator and the MTs, while leading to enhanced quality of service for mobile users. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, the problem of both bandwidth and power allocation for two-way multiple relay systems in overlay cognitive radio (CR) setup is investigated. In the CR overlay mode, primary users (PUs) cooperate with cognitive users (CUs) for mutual benefits. In our framework, we propose that the CUs are allowed to allocate a part of the PUs spectrum to perform their cognitive transmission. In return, acting as an amplify-and-forward two-way relays, they are used to support PUs to achieve their target data rates over the remaining bandwidth. More specifically, CUs acts as relays for the PUs and gain some spectrum as long as they respect a specific power budget and primary quality-of-service constraints. In this context, we first derive closed-form expressions for optimal transmit power allocated to PUs and CUs in order to maximize the cognitive objective. Then, we employ a strong optimization tool based on particle swarm optimization algorithm to find the optimal relay amplification gains and optimal cognitive released bandwidths as well. Our numerical results illustrate the performance of our proposed algorithm for different utility metrics and analyze the impact of some system parameters on the achieved performance.

In this paper, the quality of experience (QoE) of real-time video streaming over long term evolution (LTE) networks is investigated. Network QoE metrics are proposed in order to capture the overall performance of radio resource management algorithms in terms of video quality perceived by the end users. Thus, real time video streaming over both the uplink (UL) and downlink (DL) directions is investigated, and LTE dynamic resource allocation is taken into account. Metrics corresponding to average, geometric mean, and minimum QoE in the network are measured when max C/I, proportional fair, and max-min radio resource management algorithms are implemented. Monte Carlo simulation results show that proportional fair scheduling maximizes the average network QoE both in the uplink and downlink, and that it also leads to more fairness.

Cooperative vehicle-to-vehicle (V2V) communications for real-time video streaming of scalable video coded (SVC) videos is investigated, and a novel clustering method is proposed. In the proposed method, moving vehicles are grouped into cooperative clusters to enhance the distribution of the video to the cluster members. Within each cluster, vehicles communicate over IEEE 802.11p, whereas the long term evolution (LTE) system is used to send the data over long range cellular links. The simulation results show that the collaborative V2V video streaming method significantly outperforms the non collaborative case.

This paper investigates the collaboration between multiple mobile operators to optimize the energy efficiency of cellular networks. Our framework studies the case of LTE-Advanced networks deployed in the same area and owning renewable energy generators. The objective is to reduce the CO2emissions of cellular networks via collaborative techniques and using base station sleeping strategy while respecting the network quality of service. Low complexity and practical algorithm is employed to achieve green goals during low traffic periods. Cooperation decision criteria are also established basing on derived roaming prices and profit gains of competitive mobile operators. Our numerical results show a significant save in terms of CO2compared to the non-collaboration case and that cooperative mobile operator exploiting renewables are more awarded than traditional operators.

Base station deployment in cellular networks is one of the most fundamental problems in network design. This paper proposes a novel method for the cell planning problem for the fourth generation 4G-LTE cellular networks using meta heuristic algorithms. In this approach, we aim to satisfy both coverage and cell capacity constraints simultaneously by formulating a practical optimization problem. We start by performing a typical coverage and capacity dimensioning to identify the initial required number of base stations. Afterwards, we implement a Particle Swarm Optimization algorithm or a recently- proposed Grey Wolf Optimizer to find the optimal base station locations that satisfy both problem constraints in the area of interest which can be divided into several subareas with different user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant base stations. We have also performed Monte Carlo simulations to study the performance of the proposed scheme and computed the average number of users in outage. Results show that our proposed approach respects in all cases the desired network quality of services even for large-scale dimension problems.

This paper describes a method for BS on/off switching in LTE-Advanced (LTE-A). The proposed method uses information exchanged between base stations (BSs) on the X2 interface in order to determine the BSs that should be switched on or off. It does not require centralized intelligence controlling the on/off switching in the network. Instead, it relies on always active coverage BSs that control the on/off switching of capacity BSs within their coverage area. Hence, it can be implemented locally in a distributed way. Simulation results show that the proposed switch off method can lead to significant energy savings in the network.

In this paper, the joint use of carrier aggregation (CA) and relay stations (RSs) to support machine-to-machine (M2M) traffic in 5G networks is investigated. The focus of the paper is mainly on fixed M2M devices, periodically transmitting fixed amounts of data (e.g. sensor measurements). With this type of devices, knowing the deployment density in a given area allows to plan the cellular network accordingly, by determining the number of needed RSs, and the number of wireless channels that should be used by each RS to schedule M2M transmissions. Thus, after presenting an LTE resource allocation algorithm suitable for the investigated M2M devices, numerical evaluations are presented to determine the number of M2M devices that can be served given the available bandwidth. Then, a detailed analysis is presented to determine the number of RSs required depending on the M2M device density, the use of CA, and frequency reuse.

High-speed railway system equipped with moving relay stations placed on the middle of the ceiling of each train wagon is investigated. The users inside the train are served in two hops via the 3GPP Long Term Evolution (LTE) technology. The objective of this work is to maximize the number of served users by respecting a specific quality-of-service constraint while minimizing the total power consumption of the eNodeB and the moving relays. We propose an efficient algorithm based on the Hungarian method to find the optimal resource allocation over the LTE resource blocks in order to serve the maximum number of users with the minimum power consumption. Moreover, we derive a closed-form expression for the power allocation problem. Our simulation results illustrate the performance of the proposed scheme and compare it with various previously developed algorithms as well as with the direct transmission scenario.

Energy is an important resource in wireless communications systems that has to be optimized in order to guarantee low interference in the network as well as efficient use of the available resources. One of the prominent options within the Long Term Evolution (LTE) standard is the Time Division Duplexing (TDD) option where both Uplink (UL) and Downlink (DL) share the same bandwidth and they are allocated on different times. One of the major problems in the practical implementation of TDD systems is that adjacent cells may have different loads for their UL and DL, so that the resources are allocated in a different portion between UL-DL. Such an unsynchronization among cells generates interference that downgrades the system performance. This paper calculates the optimum amount of energy that should be allocated to each entity in the system to mitigate the interference effect and to guarantee minimum Quality of Service (QoS) satisfaction at all receivers. Closed form expressions and computer simulations show the advantages of the proposed energy allocation scheme.

In this paper, the joint operation of cooperative device-to-device (D2D) communications and green cellular communications is investigated. An efficient approach for grouping mobile terminals (MTs) into cooperative clusters is described. In each cluster, MTs cooperate via D2D communications to share content of common interest. Furthermore, an energy-efficient technique for putting BSs in sleep mode in an LTE cellular network is presented. Finally, both methods are combined in order to ensure green communications for both the users' MTs and the operator's BSs. The studied methods are investigated in the framework of OFDMA-based state-of-the-art LTE cellular networks, while taking into account intercell interference and resource allocation.

In this paper, an actual deployment of a wireless sensor network is described. The purpose of the sensor network is to monitor and analyze air quality in Doha. Small scale wireless sensor stations communicate with a backend server to relay their measurements in real-time. Data stored on the server is subjected to intelligent processing and analysis in order to present it in different formats for different categories of end users. This paper describes a user friendly computation of an air quality index to disseminate the data to the general public. In addition, it describes data presentation for environmental experts using dedicated software tools, e.g. the R software system and its OpenAir package. Analysis and assessment of real measurement data is also performed in the paper.

Vehicle to vehicle (V2V) communications to enhance the downlink and uplink connectivity in vehicular networks are studied. Effective rate formulations for the uplink and downlink directions are presented and analyzed, while considering IEEE 802.11p for short range communications between vehicles, and the Long Term Evolution (LTE) for communications between the vehicles and base stations over long range cellular links. Simulation results show the importance of V2V communications, and describe the performance tradeoffs between the transmit power, number of cooperating vehicles, and LTE resource allocation.

In this study, we implement a green heuristic algorithm involving the base station sleeping strategy that aims to ensure energy saving for the radio access network of the 4GLTE (Fourth Generation Long Term Evolution) mobile networks. We propose an energy procurement model that takes into consideration the existence of multiple energy providers in the smart grid power system (e.g. fossil fuel and renewable energy sources, etc.) in addition to deployed photovoltaic panels in base station sites. Moreover, the analysis is based on the dynamic time variation of daily traffic and aims to maintain the network quality of service. Our simulation results show an important contribution in the reduction of CO2emissions that can be reached by optimal power allocation over the active base stations.

Cooperative schemes for critical content distribution over vehicular networks are presented and analyzed. The first scheme is based on unicasting from the base station, whereas the second is based on threshold based multicasting. Long Term Evolution (LTE) is used for long range communications with the base station (BS) and 802.11p is considered for inter-vehicle collaboration on the short range. A high mobility environment with correlated shadowing is adopted. Both schemes are shown to outperform non-cooperative unicasting and multicasting, respectively, when the appropriate 802.11p power class is used. The first scheme achieves the best performance among the compared methods, and a practical approximation of that scheme is shown to be close to optimal performance. © 2012 IEEE.

Relay-based multicasting for the purpose of cooperative content distribution is studied. Optimized relay selection is performed with the objective of minimizing the energy consumption or the content distribution delay within a cluster of cooperating mobiles. Two schemes are investigated. The first consists of the BS sending the data only to the relay, and the second scheme considers the scenario of threshold-based multicasting by the BS, where a relay is selected to transmit the data to the mobiles that were not able to receive the multicast data. Both schemes show significant superiority compared to the non-cooperative scenarios, in terms of energy consumption and delay reduction.

Distributed base stations are studied in the framework of LTE public safety networks. In the proposed model, users are connected to remote radio heads deployed throughout the cell and connected to a central base station. In this paper, resource allocation for the purpose of real-time video transmission, in the uplink and downlink, is investigated, and the performance of several distributed base station deployment scenarios is compared. Simulation results show that significant throughput gains can be achieved with distributed base stations. In addition, results show that the quality of service for real-time video streaming can be largely enhanced due to the reduction of loss distortion.

Uneven traffic load among the cells increases call blocking rates in some cells and causes low resource utilisation in other cells and thus degrades user satisfaction and overall performance of the cellular system. Various centralised or semi centralised Load Balancing (LB) schemes have been proposed to cope with this time persistent problem, however, a fully distributed Self Organising (SO) LB solution is still needed for the future cellular networks. To this end, we present a novel distributed LB solution based on an analytical framework developed on the principles of nature inspired SO systems. A novel concept of super-cell is proposed to decompose the problem of “system-wide blocking minimization” into the local sub-problems in order to enable a SO distributed solution. Performance of the proposed solution is evaluated through system level simulations for both macro cell and femto cell based systems. Numerical results show that the proposed solution can reduce the blocking in the system close to an Ideal Central Control (ICC) based LB solution. The added advantage of the proposed solution is that it does not require heavy signalling overheads.

ABSTRACT Cooperative ad hoc networks for the efficient distribution of content of common interest are studied in the case of fast channel variations. Mobiles are grouped into cooperative clusters for the purpose of receiving the content with optimized energy efficiency. Data are sent to mobile terminals on a long range (LR) link, and then, the terminals exchange the content by using an appropriate short range wireless technology. When channel state information is available for the LR links, unicasting is used on the LR. When accurate channel state information is not available, threshold‐based multicasting is implemented on the LR. Energy minimization is formulated as an optimization problem for each scenario, and the optimal solutions are determined in closed form in scenarios with fast channel variations. Results show significant energy savings in the proposed schemes compared with the noncooperative case and other previous related work. Furthermore, the energy minimizing solutions are shown to lead to reduced delay in the content distribution process. Practical implementation aspects of the proposed methods are also discussed. Copyright © 2012 John Wiley & Sons, Ltd.

Uplink resource allocation with intercell interference mitigation in LTE networks is investigated. A non-cooperative probabilistic interference avoidance scheme and a pricing-based cooperative power control scheme are proposed. A scheduling algorithm is presented and used with the proposed interference mitigation schemes. In the absence of power control, scheduling with the probabilistic interference avoidance scheme is shown to lead to considerable enhancements over classical reuse schemes. When cooperative power control is added, the proposed approach is shown to outperform the standard LTE power control approach.

In this paper, an application of wireless sensor networks for leakage detection in underground water pipes is considered. A mobile sensor immersed in the water pipe is assumed to communicate with relay nodes on the ground surface, relaying its measured information to a base station for processing. A communication protocol between the relay nodes and the base station is presented. The proposed protocol is based on probabilistic reservation of transmission slots based on channel knowledge. The proposed protocol can be considered as an OFDMA extension of the reservation Aloha protocol. Simulation results show the superiority of the proposed communication approach.

Opportunistic beamforming is investigated in the context of multicell LTE uplink resource allocation. A scheduling algorithm based on beam and resource block (RB) allocation with opportunistic beamforming is presented. Beams are formed in a pseudorandom fashion thus allowing users to opportunistically benefit from the antenna gain and interference rejection capabilities. Results show that enhanced performance is obtained with opportunistic beamforming.

A novel reservation Aloha scheme is presented for OFDMA systems. The proposed scheme is based on two dimensional reservation in time and frequency. The proposed approach is compared to other OFDMA extensions of reservation Aloha. The proposed scheme is shown to be superior in terms of increasing sum-rate, reducing the number of users in outage, and reducing the collision probability in the reservation phase.

Cognitive radio networks are receiving a lot of research attention lately. Most user equipment in state-of-the-art wireless communication systems are equipped with two antennas. In this paper, we take advantage of the two transmit antennas in an underlay cognitive radio network scenario. Secondary users use the two transmit antennas in order to insert a null in the direction of the primary base station, thus protecting primary users from interference on their uplink transmissions. Primary users benefit from the antennas in order to perform beam steering in direction of the primary BS, thus enhancing their uplink SINR without being concerned of the presence of the secondary users. This has the additional possible benefit of directing most primary radiation away from the secondary BS, thus reducing the interference on uplink secondary transmissions.

The weighted ergodic sum-rate maximisation problem subject to per-user power and rate constraints in the orthogonal frequency division multiple access uplink is formulated and solved. The solution is found via dual optimisation techniques, and the duality gap between the primal and dual solutions is analysed. Ergodicity allows taking advantage of the temporal dimension in addition to the frequency and multiuser dimensions. The per-user rate constraints allow an improved fairness level in the system. The achievable rate region of the weighted ergodic sum-rate maximisation problem is determined, and the effect of the per-user rate constraints on this region is studied. Offline and online iterative techniques to obtain the Lagrangian parameters are presented, and the results show that online iterations converge to the offline solution within a limited time.

Uplink scheduling in LTE systems is considered. Two low complexity heuristic algorithms for suboptimal subcarrier allocation are proposed and compared to other algorithms in the literature. Throughput and fairness analysis of the different algorithms are performed via Monte-Carlo simulations. The proposed algorithms are utility maximizing algorithms that can be used with various utility functions. Simulations show the superiority of the proposed algorithms and that promising results can be achieved by linear complexity algorithms.

Uplink ergodic sum-rate maximization in OFDMA systems is considered. The general problem is formulated as a convex optimization problem and a dual solution is presented. The problem formulation is generalized to the case of utility maximization where the utility can be any function of rate. A suboptimal scheduling algorithm with low complexity is proposed and shown to achieve close performance to the optimal solution.

Uplink scheduling in LTE systems is considered. A game theoretical formulation is derived where the scheduling problem is represented as a Nash bargaining solution. An algorithm to implement the proposed scheduling scheme is presented. Throughput and fairness analysis are performed via simulations. Results show that channel aware scheduling schemes outperform the round-robin scheme, but a tradeoff must be made between the increase of total throughput and fairness towards the different users.

Distributed uplink scheduling in OFDMA systems is considered. In the proposed scenario, mobile users have the responsibility of making their own transmission decisions. Users are grouped into categories according to their channel state on each subcarrier. Users from each category decide on their own transmission by waiting for a random delay then transmitting with a certain probability. The proposed algorithm is completely distributed and does not require scheduling effort from the base station. Although no centralized control is present in the proposed approach, promising results are achieved in terms of throughput and fairness.

The base station transmit power is a common resource shared among multiple users in the downlink of WCDMA cellular systems. The overall user capacity decreases if a certain user consumes a large fraction of the base station power. In this work, we investigate the performance gains of adaptive antenna arrays with a power allocation strategy that limits the maximum fraction of transmit power per user. Using adaptive antenna arrays at base stations is a key technique to improve the downlink capacity of WCDMA cellular systems. We consider two adaptive antenna schemes: fixed beam and steered beam. For each scheme, we study the impact on capacity of the proposed power allocation strategy. Uniform linear arrays, uniform circular arrays, and Chebyshev antenna arrays are used to form the beams. The adopted simulation environment supports various types of adaptive antenna arrays, soft/softer handover, and multiple services. Results demonstrate an increase in capacity due to the power allocation scheme, especially when combined with adaptive antenna arrays. A remarkable superiority of the Chebyshev antenna array configuration is shown.

A new method for multitask learning in a Bayesian network context is presented for multiorganism gene network estimation. When the input datasets are sparse, as is the case in microarray gene expression data, it becomes difficult to separate random correlations from actual edges in the true underlying Bayesian network. Multitask learning takes advantage of the similarity between related tasks, in order to construct a more accurate model of the underlying relationships represented by the Bayesian networks. The proposed method is tested on synthetic data to illustrate its validity. Then it is iteratively applied on real gene expression data to learn the genetic regulatory networks of two organisms with homologous genes (human and yeast).

Advanced antenna arrays at the base stations are one of the key techniques to improve the downlink capacity of WCDMA cellular systems. Traditionally, linear and circular arrays are used to form the beams. In this paper, cylindrical antenna arrays with various types of input excitations are proposed, and the beam steering adaptive antenna technique is considered. The user capacity per cell among the linear, circular, and cylindrical arrays is presented and compared in a simulation environment supporting various types of adaptive antennas, soft/softer handover, and multiple services. A notable superiority of the proposed cylindrical antenna arrays was demonstrated by the simulation results.

Advanced antenna arrays at the base stations are one of the key techniques to improve the downlink capacity of WCDMA cellular systems. Typically, uniform linear arrays and uniform circular arrays are used to form the beams. In this work, we propose the use of Chebyshev antenna arrays which are linear arrays with non-uniform input excitations. We consider two adaptive antenna schemes: fixed beam and steered beam. For each scheme, we compare the user capacity per cell among the three different types of antenna arrays (uniform linear, Chebyshev linear, and uniform circular). The adopted simulation environment supports various types of adaptive antennas, soft/softer handover, and multiple services. Results demonstrate a notable superiority of the proposed Chebyshev antenna arrays.