Search-and-rescue (SAR) operations in collapsed buildings requires sensing systems that are reliable, fast, and robust to cluttered environments. This paper introduces OHL-CIR, a simulation-driven framework for RF-based human detection and localization beneath rubble. By generating a large and diverse dataset of channel impulse responses (CIRs), the framework enables reproducible evaluation across multiple rubble scenarios. OHL-CIR employs a two-stage inference pipeline: binary classification for victim presence, followed by regression for 2D localization. Results show detection accuracy above 95% across all categories and sub-centimeter localization errors. A precision–stability tradeoff is observed: Gradient Boosting achieves the finest precision in structured settings, while Random Forest maintains robustness under diverse rubble conditions. CIR analyses confirm physics-consistent perturbations and density maps demonstrate how predictions can guide UAV search paths. These findings position OHL-CIR as both accurate and interpretable, with strong potential for deployment in UAV-assisted SAR and broader applications in IoT localization and future 6G systems.

In the Metaverse, real-time digital twin (DT) updates enable immersive, interactive environments by reflecting real world states. Metaverse can engage its users to share data in order to ensure the completeness of the DT. However, the limitations and heterogeneity in IoT devices' computation and transmission resources are critical challenges to synchronizing the vast volume of real-world objects with their digital replicas. In this work, we propose a novel adaptive Vision Transformer (ViT)- based semantic communication (SemCom) system to extract semantic information from raw data collected by IoT devices. The system dynamically adjusts the model size and computational complexity according to the resource constraints of individual IoT devices, enables broader participation of heterogeneous devices, enhances feature extraction capabilities, and ensures completeness and efficient DT representation. We formulate our problem as an utility-maximization optimization to manage ViT complexity under resource constraints, allowing the Metaverse Services Provider (MSP) to select high-performing IoT devices. To guide the optimization, we profile ViT models with varying architectural complexities and conduct an empirical analysis to capture the relationship between model scale and performance. To address the scalability and privacy limitations of the optimization, we propose a decentralized solution where each IoT device independently optimizes its own utility under local constraints. The MSP, in turn, selects among these devices to ensure quality and maximize its overall utility. We show that our distributed solution achieves near-optimal performance and significantly outperforms other approaches in terms of MSP utility, semantic data quality, and overall IoT utility. © 2026 IEEE.

Efficient information relay in multi-UAV networks is critical for time-sensitive applications. This paper proposes an optimized power control and bandwidth allocation strategy for joint sensing and communication in a multi-UAV system to minimize sensing and transmission delays under power and bandwidth constraints. The system comprises n UAVs, each monitoring a specific target area, and a flying communication hub that aggregates data from the UAVs and relays it to a data relief center. By dynamically managing power and bandwidth of each UAV, using convex optimization and Lagrangian duality, the system ensures efficient operation, reducing both sensing and communication delays by up to $47.2 \%$ compared to traditional fixed-resource allocation methods. The proposed approach minimizes total system delay, represented as the sum of sensing delay and communication transmission delay, improving data flow and reliability. This adaptive framework addresses critical limitations in UAV-based systems, offering a scalable and robust solution for applications requiring efficient resource utilization in dynamic and time-sensitive scenarios.

Precision agriculture applications, such as real-time crop monitoring via robots for pest and disease detection, need ultra-low latency and high-definition video streaming to enable timely decision-making and intervention. Although 5G networks have promised low latency, current streaming latency over challenging rural networks can hinder timely agricultural action. This paper suggests an adaptive solution to reduce latency in delay-sensitive precision agriculture by integrating deep reinforcement learning (DRL) with semantic communication techniques. We propose a system that replaces traditional compression methods with a modified MambaJSCC framework, focusing on the region of interest (ROI) in agricultural imagery. Our experimental results show that our method achieves significantly lower streaming latency, potentially by up to 56%, along with high-quality frame reconstruction in these agriculturally relevant regions. Our work presents a technically viable solution for enhancing real-time, delay-sensitive agricultural monitoring applications.

This paper presents an optimized multi-UAV system designed to enhance post-disaster search and rescue (SAR) operations, improving both the efficiency and effectiveness of locating individuals trapped beneath rubble or debris. Leveraging Unmanned Aerial Vehicles (UAVs) equipped with advanced sensing and communication capabilities, this framework focuses on maximizing detection accuracy in challenging disaster environments. The core of the approach involves formulating and solving a drone assignment problem to ensure optimal multiUAV coverage across multiple targets within damaged zones. By strategically assigning drones, our approach achieves superior search accuracy compared to traditional assignment methods while also significantly reducing computational complexity relative to exhaustive search techniques. Results demonstrate that the proposed framework enhances detection precision, making it a practical and efficient solution for real-world SAR missions.

The shift to virtual networks has facilitated the use of deep learning (DL) models for flexible, real-time AI services across various applications. However, to maintain inference accuracy and Quality-of-Service (QoS), these models need regular retraining as data patterns change over time, such as the appearance of new classes or changes in feature distributions. Continuous edge-assisted retraining reduces the communication cost to the cloud, however, it remains limited by network constraints. To address these issues, this paper introduces a Balanced Data Selection (BDS) algorithm, which reduces data imbalance and improves retraining accuracy within resource-constrained environments. BDS offers a low-complexity solution that scales efficiently with the size of new data. Furthermore, we introduce a continuous training framework that supports both data and class incremental learning. Experimental results indicate that our framework achieves retraining accuracy that is superior to baseline solutions, while maintaining lower complexity and effectively addressing data imbalance. This framework provides an effective approach for edge-assisted DL model retraining.

Autonomous Unmanned Aerial Vehicles (UAVs) offer substantial advantages for tasks such as surveillance, disaster management, and environmental monitoring, where human intervention can be risky. With advancements in their agility and autonomy, UAVs are becoming essential for critical tasks in combat, reconnaissance, wildfire monitoring, and disaster search and rescue. This paper addresses a key challenge in UAV path planning: efficiently visiting multiple unknown mobile targets in complex, obstacle-filled environments. We leverage the Deep Deterministic Policy Gradient (DDPG) framework to continuously control UAV movement to enable effective obstacle avoidance and sequential target visitation. Our approach allows the UAV to learn the unknown distribution of mobile targets and determine optimal paths while navigating around obstacles. With limited environment information, the agent receives rewards based on the confidence of detecting targets within its observation field. We validate the effectiveness of our method through comparison with an optimal benchmark that assumes perfect knowledge of target mobility and obstacle locations. Results indicate that increasing target numbers significantly impacts the agent's performance by requiring additional training time. Moreover, heavily cluttered environments reduce mission success rates for target visitation.

Disasters such as earthquakes, landslides, building collapses, and wars can leave people trapped in rubble, which makes rapid and accurate detection significant for successful rescue operations. Traditional search methods, such as visual inspection and thermal imaging, often struggle in complex environments where victims may be obscured. However, ensuring reliable detection remains a challenge, particularly due to visual obstruction and complex rubble scenarios. In this paper, a Frequency Modulated Continuous Wave (FMCW) radar is used to enhance victim detection. The 24 GHz FMCW radar used in this work, offers superior penetration through rubble, making it applicable for locating victims even when obstructed by collapsed structures. In our envisioned scenario, we integrate a down-looking radar with Unmanned Aerial Vehicles (UAVs) into the system. UAVs equipped with FMCW radar sensors can fly over or around collapsed structures, offering a bird’s-eye view of the scene and the ability to cover a wider area more rapidly than ground-based teams. In addition, signal processing techniques are applied to filter out the noise and retain the necessary information from the signals. This preprocessing step is essential to improve the accuracy and reliability of Machine Learning (ML) models, enabling them to better identify and interpret signs of human presence in a complex environment.

The integration of Unmanned Aerial Vehicles (UAVs) into Open Radio Access Networks (O-RAN) enhances communication in disaster management and Search and Rescue (SAR) operations by ensuring connectivity when infrastructure fails. However, SAR scenarios demand stringent security and low-latency communication, as delays or breaches can compromise mission success. While UAVs serve as mobile relays, they introduce challenges in energy consumption and resource management, necessitating intelligent allocation strategies. Existing UAV-assisted O-RAN approaches often overlook the joint optimization of security, latency, and energy efficiency in dynamic environments. This paper proposes a novel Reinforcement Learning (RL)-based framework for dynamic resource allocation in UAV relays, explicitly addressing these trade-offs. Our approach formulates an optimization problem that integrates security-aware resource allocation, latency minimization, and energy efficiency, which is solved using RL. Unlike heuristic or static methods, our framework adapts in real-time to network dynamics, ensuring robust communication. Simulations demonstrate superior performance compared to heuristic baselines, achieving enhanced security and energy efficiency while maintaining ultralow latency in SAR scenarios.

This paper introduces a novel federated learning framework termed LoRa-FL designed for training low-rank one-shot image detection models deployed on edge devices. By incorporating low-rank adaptation techniques into one-shot detection architectures, our method significantly reduces both computational and communication overhead while maintaining scalable accuracy. The proposed framework leverages federated learning to collaboratively train lightweight image recognition models, enabling rapid adaptation and efficient deployment across heterogeneous, resource-constrained devices. Experimental evaluations on the MNIST and CIFAR10 benchmark datasets, both in an independent-and-identically-distributed (IID) and non-IID setting, demonstrate that our approach achieves competitive detection performance while significantly reducing communication bandwidth and compute complexity. This makes it a promising solution for adaptively reducing the communication and compute power overheads, while not sacrificing model accuracy.

Open Radio Access Networks (O-RAN) are transforming telecommunications by shifting from centralized to distributed architectures, promoting flexibility, interoperability, and innovation through open interfaces and multi-vendor environments. However, O-RAN's reliance on cloud-based architecture and enhanced observability introduces significant security and resource management challenges. Efficient resource management is crucial for secure and reliable communication in O-RAN, within the resource-constrained environment and heterogeneity of requirements, where multiple User Equipment (UE) and O-RAN Radio Units (O-RUs) coexist. This paper develops a framework to manage these aspects, ensuring each O-RU is associated with UEs based on their communication channel qualities and computational resources, and selecting appropriate encryption algorithms to safeguard data confidentiality, integrity, and authentication. A Multi-objective Optimization Problem (MOP) is formulated to minimize latency and maximize security within resource constraints. Different approaches are proposed to relax the complexity of the problem and achieve near-optimal performance, facilitating tradeoffs between latency, security, and solution complexity. Simulation results demonstrate that the proposed approaches are close enough to the optimal solution, proving that our approach is both effective and efficient.

Generative AI (GAI) and Large Language Models (LLMs) have revolutionized natural language processing and content creation. However, their significant computational demands during inference often require cloud servers, which are currently the only viable option for handling complex multi-modal models like GPT-4. The inherent complexity of these models increases latency, posing challenges even within cloud environments. Furthermore, cloud reliance brings other challenges, including high bandwidth consumption to transfer diverse data types. Worse, in personalized GAI applications like virtual assistants, similar prompts frequently occur, causing redundant transmission and computation of replies, which further increases overhead. Accelerating the inference of multi-modal systems is, therefore, critical in artificial intelligence. In this paper, we aim to improve the inference efficiency through prompt caching; if a current prompt is semantically similar to a previous one, the system can reuse the earlier response without invoking the model again. We leverage collaborative edge computing to cache popular replies and store their request embeddings. New prompts are locally processed to extract embeddings, with their qualities determined by the resources available on edge servers. Our problem is formulated as an optimization to manage offloading decisions for GAI tasks, aiming to avoid cloud inferences and minimize latency while maximizing reply quality. Given its non-convex nature, we propose to solve it via Block Successive Upper Bound Minimization (BSUM). Reinforcement learning is employed to actively pre-cache prompts, tackling the complexity of unknown prompt popularity. Our approach demonstrates near-optimal performance, significantly outperforming cloud-only solutions.

The forthcoming 6G networks will embrace a new realm of AI-driven services that requires innovative network slicing strategies, namely slicing for AI, which involves the creation of customized network slices to meet Quality of Service (QoS) requirements of diverse AI services. This poses challenges due to time-varying dynamics of users’ behavior and mobile networks. Thus, this paper proposes an online learning framework to determine the allocation of computational and communication resources to AI services, to optimize their accuracy as one of their unique key performance indicators (KPIs), while abiding by resources, learning latency, and cost constraints. We define a problem of optimizing the total accuracy while balancing conflicting KPIs, prove its NP-hardness, and propose an online learning framework for solving it in dynamic environments. We present a basic online solution and two variations employing a pre-learning elimination method for reducing the decision space to expedite the learning. Furthermore, we propose a biased decision space subset selection by incorporating prior knowledge to enhance the learning speed without compromising performance and present two alternatives of handling the selected subset. Our results depict the efficiency of the proposed solutions in converging to the optimal decisions, while reducing decision space and improving time complexity. Additionally, our solution outperforms State-of-the-Art techniques in adapting to diverse environmental dynamics and excels under varying levels of resource availability.

Wireless networks have undergone significant advancements with the rapid progress in Software Defined Networks (SDN), Open RAN (O-RAN) and 5G technology. Among the most notable developments is the emergence of network slicing, which leverages the concept of virtual networks to separate the end-to-end network and computational resources into individual network slices per one or more services/tenants. However, determining the appropriate allocation of computational and network resources for each network slice to meet the specific requirements of each service remains a challenge, especially with continuously changing demands and varying Key Performance Indicators (KPIs) per service and the risk of insufficient or excessive resource allocation. To address these issues, this paper proposes an intelligent predictive framework based on Deep Reinforcement Learning (DRL). Our framework utilizes historical demand and KPI requirements to predict and reserve optimized network slices for multiple services in the future, considering unique constraints, dynamic pricing and under-provisioning. Using different datasets, we validated our system's effectiveness, scalability, and adaptiveness by comparing it with different baselines and state-of-the-art approaches. Our results affirm the efficiency of the proposed solution, showcasing a minimum cost reduction of 15% compared to different baselines and state-of-the-art solutions while incurring less than 2% additional resource consumption. Furthermore, our system demonstrates excellent scalability and adaptability across varying network conditions.

The paper introduces Opportunistic Federated Learning (OFL) as an approach to enhance the efficiency of distributed learning in intelligent IoT systems. OFL allows any node in the network to initiate a learning task and collaboratively use local resources. The framework enables nodes to adapt configurations based on circumstances, optimizing resource utilization. Hence, this paper proposes a reliable node selection mechanism that accommodates the dynamic nature of local data and computing resources. Incentives for participating nodes are explored through a peer-to-peer communication using the Bertrand game to determine optimal pricing strategies. Results demonstrate the Nash equilibrium of the game-based incentive mechanism in a realistic FL setup.

Today, wireless networks are becoming responsible for serving intelligent applications, such as extended reality and metaverse, holographic telepresence, autonomous transportation, and collaborative robots. Although current fifth-generation (5G) networks can provide high data rates in terms of Giga-bytes/second, they cannot cope with the high demands of the aforementioned applications, especially in terms of the size of the high-quality live videos and images that need to be communicated in real-time. Therefore, with the help of artificial intelligence (AI)-based future sixth-generation (6G) networks, the semantic communication concept can provide the services demanded by these applications. Unlike Shannon's classical information theory, semantic communication urges the use of the semantics (meaningful contents) of the data in designing more efficient data communication schemes. Hence, in this paper, we model semantic communication as an energy minimization framework in heterogeneous wireless networks with respect to delay and quality-of-service constraints. Then, we propose a sub-optimal solution to the NP-hard combinatorial mixed-integer nonlinear programming problem (MINLP) by utilizing efficient techniques such as discrete optimization variables' relaxation. In addition, AI-based autoencoder and classifier are trained and deployed to perform semantic extraction, reconstruction, and classification services. Finally, we compare our proposed sub-optimal solution with different state-of-the-art methods, and the obtained results demonstrate its superiority.

In recent years, significant strides in various domains have been fueled by the convergence of large-scale datasets and sophisticated machine learning algorithms. Nevertheless, the utilization of these datasets poses challenges, including privacy concerns, data ownership issues, and resource limitations. Cooperative data learning approaches have emerged as a solution, allowing multiple parties to collaboratively train machine learning models using their distributed data. While Federated Learning (FL) addresses the issue of privacy concerns, reluctance among data owners to share their data remains a challenge. It is imperative to provide incentives for participation in these cooperative learning settings to boost effectiveness and promote the widespread adoption of such approaches. This paper introduces an RL-ICDL-BC framework that seamlessly integrates principles of incentive design and cooperative learning, fostering effective collaboration among data owners. The framework’s primary objective is to motivate and reward participants for contributing their models while simultaneously preserving privacy and ensuring fairness in the learning process. Experimental evaluations utilizing Covid-19 datasets and diverse collaborative learning scenarios demonstrate the effectiveness of the proposed framework. The results reveal that incentivizing cooperative data learning leads to increased participation rates, improved model performance, and enhanced fairness in the learning process. Despite the challenges posed by non-iid data, the experiments yield outstanding outcomes, showcasing a Covid19 virus detection accuracy rate of approximately $99 \%$. This exceptional accuracy underscores the efficacy of our proposed approach in effectively detecting and mitigating the transmission of infectious diseases.

Locating sources of hazardous phenomena (or undesired behaviour such as satellite jamming) accurately and timely is key to emergency response operations (or operation under hostile environment). Classically, odor-based localization is performed using trained animals with a sharp sense of smell (e.g. dogs). Instead, the localization of a toxic gas leakage source (or nuclear radiation source) can be performed by robot searchers (agents) equipped with proper sensors to avoid endangering animals. Typically, the leakage distribution encounters severe randomness and sparsity induced by the diffusion physical model and the environmental aspects (e.g. varying flow currents in the medium). As a consequence, classical gradient-based search algorithms become inefficient. Infotaxis has been proposed as an effective non-gradient-based method for source localization. In this paper, we study the case of two altruistic search agents leveraging Infotaxis assuming non-ideal (erroneous) conditions for the communication channel used for cooperation. The performance of the altruistic agents is examined from accuracy and efficiency points of view. The results show that although having multiple agents improves the average search time, a severely degraded communication channel causes the rate of successfully finding the source to drop drastically, suggesting a non-cooperative approach in such scenarios.

The metaverse, the next digital frontier, demands high-performance models and quick personalization due to the dynamic nature of user tasks despite limited data availability. The frequent user customization is resource-intensive and data-heavy. Meta-learning, especially federated meta-learning (FML) known for its adaptive capabilities, is crucial for addressing the dynamics in metaverse, characterized by user heterogeneity, diverse data structures, and varied tasks. However, the diversity of tasks can compromise global training outcomes due to statistical heterogeneity. Given this, an urgent need arises for smart coalition formation that accounts for these disparities. This paper proposes a game-theoretic framework for managing FML in metaverse services, with meta-learners as workers. A blockchain-based cooperative coalition formation game is introduced, grounded on a reputation metric, the similarity of users, and their incentives. The reputation metric is derived based on our novel reputation system, which takes into account users' historical contributions and potential contributions to current tasks, by exploiting the correlations between past and new tasks. Meanwhile, the incentive mechanism is formulated as an optimization to minimize users energy cost and boost the users contribution for higher federated meta-learning efficacy. Simulations show the framework's resilience against misbehavior and its superiority over other schemes, improving service utility and worker profitability in metaverse meta-learning.

Deep Neural Networks (DNNs) have become the key technique to revolutionize the healthcare sector. However, conducting online remote inference is often impractical due to privacy constraints and latency requirements. To enable local computation, researchers have attempted network pruning with minimal accuracy loss or DNN distribution without affecting the performance. Yet, distributed inference can be inefficient due to the energy overhead and fluctuation of communication channels between participants. On the other hand, given that realistic healthcare systems use pre-trained models, local pruning and retraining relying only on the available scarce data is not possible. Even pre-pruned DNNs are limited in their ability to customize to the local load of data and device dynamics. The online pruning of DNN inferences without retraining is viable; however, it was not considered in the literature as most well-known techniques do not perform well without adjustment. In this paper, we propose a novel pruning strategy using Explainable AI (XAI) to enhance the performance of pruned DNNs without retraining, a necessity due to the scarcity and bias of local healthcare data. We combine distribution and pruning techniques to perform online distributed inference assisted by dynamic pruning when needed for highest accuracy. We use Non-Linear Integer Programming (NLP) to formulate our approach as a trade-off between resources and accuracy, and Reinforcement Learning (RL) to relax the problem and adapt to dynamic requirements. Our pruning criterion shows high performance compared to other reference techniques and ability to assist distribution by reducing resource usage while keeping high accuracy.

The metaverse, envisioned as the next digital frontier for avatar-based virtual interaction, involves high-performance models. In this dynamic environment, users’ tasks frequently shift, requiring fast model personalization despite limited data. This evolution consumes extensive resources and requires vast data volumes. To address this, meta-learning emerges as an invaluable tool for metaverse users, with federated meta-learning (FML), offering even more tailored solutions owing to its adaptive capabilities. However, the metaverse is characterized by users heterogeneity with diverse data structures, varied tasks, and uneven sample sizes, potentially undermining global training outcomes due to statistical difference. Given this, an urgent need arises for smart coalition formation that accounts for these disparities. This paper introduces a dual game-theoretic framework for metaverse services involving meta-learners as workers to manage FML. A blockchain-based cooperative coalition formation game is crafted, grounded on a reputation metric, user similarity, and incentives. We also introduce a novel reputation system based on users’ historical contributions and potential contributions to present tasks, leveraging correlations between past and new tasks. Finally, a Stackelberg game-based incentive mechanism is presented to attract reliable workers to participate in meta-learning, minimizing users’ energy costs, increasing payoffs, boosting FML efficacy, and improving metaverse utility. Results show that our dual game framework outperforms best-effort, random, and non-uniform clustering schemes -improving training performance by up to 10%, cutting completion times by as much as 30%, enhancing metaverse utility by more than 25%, and offering up to 5% boost in training efficiency over non-blockchain systems, effectively countering misbehaving users.

With the development of low-cost computer chips and wireless networks, any object or thing can become a component of the Internet of Things (IoT). As a result, these disparate things will gain digital intelligence, enabling them to connect with real-time data and be used for a wide range of valuable and practical IoT applications. However, due to the broadcast nature of wireless communication and the presence of eavesdroppers, the IoT systems pose serious threats to privacy and message integrity, particularly with mobile sensors. Recently, physical-layer security (PLS) has been proposed as a cost-effective solution that mitigates the impact of growing security threats. In this article, we propose a PLS-based hybrid nonorthogonal multiple access (NOMA)/power division multiplexing (PDM) IoT system that provides a high probability of secured transmission, a low computational complexity, and a low-power consumption. 3-D stochastic geometry tools have been used to introduce and evaluate our proposed scheme in a variety of practical scenarios where users are randomly located in a 3-D space. Furthermore, we derive the new scheme’s key agreement ratio (KAR) expression, the probability expressions of using NOMA and PDM modes. Moreover, we derive the associated secrecy outage probability (SOP), and the average ergodic capacity (AEC) expressions for the proposed scheme as well as for the pure NOMA and pure PDM schemes. In addition, we introduce and investigate the security power efficiency (SPE) metric, that serves as a new key agreement optimization metric. Numerical results are performed to validate the obtained analytical expressions and to assess the proposed scheme performances in terms of KAR, SOP, and AEC, when compared to pure NOMA and pure PDM schemes.

Although Deep Neural Networks (DNN) have become the backbone technology of several Internet of Things (IoT) applications, their execution in resource-constrained devices remains challenging. To cater for these challenges, collaborative deep inference conducted by IoT devices was introduced. However, the prevalence of DNN computation suffers from severe privacy problems, e.g. data-reverse and model leakage. Particularly, malicious participants can accurately recover the received data to access sensitive information. Furthermore, the system is composed of heterogeneous data-sources represented by different DNN models that wish to execute classifications without exposing their data and models. Though, relaying the trained models to a centralized unit managing the collaboration leads to major risks because some features can be revealed through these models, in addition to dependency and scalability problems. In this paper, we present an approach that targets the privacy of collaborative inference via controlling the amount of data assigned to different participants, to prevent them from reversing attempts. Moreover, each independent data-source requesting inference will be responsible to manage the distribution of its DNN locally. In this context, different sources are required to compete over the pervasive resources while cooperating to maintain privacy welfare. We formulate this methodology, as an integer programming problem, where we establish a trade-off between the latency of co-inference and the privacy required by heterogeneous entities. A distributed solution scheme is also developed based on the Lagrangian dual problem. Next, to relax the optimization, we shape our approach as a cooperative and competitive Multi-Agent Reinforcement Learning (MARL) that supports heterogeneous/independent agents. Our comprehensive simulations demonstrated that our method yields results on par with those of a single RL agent in terms of action performance, while maintaining the privacy of individual agents’ information. Additionally, it surpasses the Independent Q-Learning (IQL) approach, where agents operate autonomously, in safeguarding inference privacy.

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.

Clustered federated multi task learning (CFL) is introduced as an effective and efficient approach for addressing statistical challenges such as non-independent and identically distributed (non-IID) data among workers. Workers in CFL are clustered in groups based on similarity (i.e., cosine similarity) in their data distributions, in which each cluster is equipped with an efficient specialized model. However, this approach can be costly and time-consuming when implemented in hierarchical wireless networks (HWNs) due to uploading several models at every round to enable the cloud server to capture the incongruent data distribution from different edge networks. This brings about the need for novel solutions to address these challenges. To this end, this paper introduces a framework with two cloud-based model aggregation approaches, round-based and split-based, so as to minimize latency and resource consumption while attaining satisfying personalized accuracy. In the round-based scheme, the cloud aggregates the models from the edge servers after a predetermined number of rounds. As for the split-based scheme, the models are collected by the cloud only when edge servers perform the split. Extensive experiments are conducted to evaluate and compare the proposed heuristics against approaches presented in the recent literature. The numerical results and findings demonstrate that the proposed heuristics significantly conserve resources by reducing energy consumption by 60% and saving time, all while accelerating the convergence rate for cluster workers across various edge networks.

The conventional approach for Federated Learning (FL) is to train a global model by averaging local models trained on local data sets. However, given the limited computing resources at the mobile-edge nodes, unreliable models may be received from the Edge Nodes (ENs), which can lead to a significant performance degradation in the FL. Thus, this paper proposes a reliable and age sensitive FL framework that captures the dynamic nature of the local data and computing resources at each participating EN. Specifically, we formulate two optimization problems to select the optimal subset of ENs that can upload their local models in each round of the global model training, given a limited learning cost budget. The first problem aims at selecting the most reliable ENs that should cooperate to complete the FL process, while considering stationary data distributions at different ENs. The second problem aims at minimizing the average age of information experienced by each EN while selecting the most reliable ENs, given fast changing data distributions. Efficient solutions are proposed for the two problems with a worst-case linear complexity. Our Results, leveraging a real-world dataset, depict the efficiency of our solutions in obtaining a better performance compared to conventional FL approach.

The healthcare sector has undergone a significant transformation with the widespread adoption of Deep Neural Networks (DNN). However, due to privacy constraints and stringent latency requirements, online remote inference is not a viable option in healthcare scenarios. Many efforts have been conducted to enable local computation, such as network compression using pruning or DNN distribution among multiple resource-constrained devices. Yet, it is still challenging to conduct distributed inference due to the latency and energy overheads resulting from intermediate shared data. On the other hand, given that realistic healthcare systems use pre-trained models, local pruning and fine-tuning relying only on the scarce and biased data is not possible. Even pre-pruned DNNs are not efficient as they are not customized to the local load of data and the dynamics of devices. The dynamic and online pruning of DNN without fine-tuning is a promising solution; however, it was not considered in the literature as most well-known techniques do not perform well without adjustment. In this paper, driven by the data restrictions in healthcare sector, we propose a novel pruning strategy based on Explainable AI (XAI), with a target to enhance the pruned DNN performance without fine-tuning. Moreover, to maintain the highest possible accuracy, we propose to combine distribution and pruning techniques to perform online distributed inference assisted by dynamic pruning only when needed. Our experiments show the performance of our pruning criterion compared to other reference techniques, in addition to its ability to assist the distribution by reducing the shared data, while keeping high accuracy.

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.

This paper introduces a database of multi-script (Arabic and English) for indoor scene text detection, taken from different angle-of-view. This database can be used in a variety of real-world applications, such as image search, robot navigation, and assisting the visually impaired. The database contains 944 images taken with smartphones in an indoor environment at Qatar University. These images were taken from at least three angles, making the database even more challenging. To evaluate the database, an OCR method based on multiple language detection is considered. The results show that multi-language detection should be given more attention in practice. The database is publicly available11https:/www.dropbox.com/s/7s7f936y4etzsu7/QU_door_dataset%20%282%29.zip?dl=0.

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.

Localization of sources of hazardous phenomena such as gas leakages in air and oil spells in seawater is of high importance for environmental and civil protection. In this paper, we study variations of bio-inspired search and man-developed search methods that can be used to find (localize) the sources of the hazardous phenomena. We use two main quality measure factors for our comparison; namely, the success rate, which is the probability of finding the source location correctly and the number of steps which is steps (time) taken by the searcher till it declares the source found. This work aims to be the basis of future research in this important domain. The results show that the gradient-climbing and biased random walk algorithms had the highest success rate at 81.1% and 85.4% respectively, followed by the random walk algorithm at a success rate of 70.9%.

Despite its potential benefits, Federated learning (FL) is vulnerable to various types of attacks that can compromise the accuracy and security of the trained model. While several defense mechanisms have been proposed to protect FL against such attacks, attackers are continuously developing more advanced techniques to bypass these protection mechanisms.In this context, this paper proposes a novel attack mechanism that allows malicious users to optimize their crafted reports, maximizing potential damage while limiting the chances of being detected. Our proposed attack technique is a robust approach designed to bypass existing defense mechanisms in FL. Our contributions are mainly investigating the FL model attack from the attacker’s perspective, proposing a model relaxation approach to optimize a single poisoning ratio variable, and formulating a compromise between the chances of being detected and the amount of damage that the attack could cause. Additionally, we introduce three new attack designs, namely DTA, ATA, and NEA, which maximize the effect of the attack. The proposed Distance Target Attack (DTA) minimizes the distance from the target attack model, while the Accuracy Target Attack (ATA) deteriorates the accuracy of the global model. Furthermore, the Number Estimation Attack (NEA) aims to maximize the expected number of attackers that could bypass the aggregation detection mechanisms.The numerical results based on the KDD dataset confirm the ability of the proposed approach to deteriorate the global model accuracy. The experiments showed that the proposed DTA, ATA, and NEA attacks can significantly reduce the accuracy of the global model. These results demonstrate also the effectiveness and robustness of the proposed attack mechanism in compromising the accuracy and security of FL models.

The spread of infectious diseases in crowded spaces such as shopping malls, markets, and hospitals is a growing concern. In order to mitigate this risk, it is crucial to develop a method that leverages the power of distributed crowd to learn, de- tect, and alert individuals about potential health hazards. Hence, the integration of federated learning (FL), and blockchain (BC) to provide intelligent platforms that facilitate pervasive AI and trust amongst IoT devices and smart phones can play a significant role in achieving this goal. In this study, we propose a new technique named BC-FL Location-Based, which utilizes smart applications installed on IoT devices and smart phones to detect and predict imminent health risks. The technique works by using algorithms such as maximal clique to detect individuals in close proximity and sharing their health data through a blockchain network. A smart contract then triggers a node with sufficient resources to gather users' learning experiences from the blockchain, aggregate it, and run a model to determine if any of the individuals present in the area are infected. To demonstrate the effectiveness of the proposed technique, we conducted simulation experiments using Ethereum-based private blockchain network, where nodes represent individuals in different locations. We used the maximal clique algorithm to simulate the movement of individuals and compared the results of the model run on individual data versus aggregated data. Experiments showed promising results, with accuracy of detection increasing to 99% when using iid data and 90% when using non-iid data.

In the era of 5G and beyond, telecommunication networks tend to move Radio Access Network (RAN) from centralized architecture to a more distributed architecture for greater interoperability and flexibility. Open RAN (O-RAN) architecture is a paradigm shift that is proposed to enable disaggregation, virtualization, and cloudification of RAN components, possibly offered from multiple vendors, to be connected through open interfaces. Leveraging this O-RAN architecture, Deep Learning (DL) models may be running as a service close to the end users, rather than on the core network, to benefit from reduced latency and bandwidth consumption. If multiple DL models learn on the virtual edge, they will compete for the available communication and computation resources. In this paper, we introduce Optimized Resource and Model Allocation (ORMA), a framework that provides optimized resource allocation for multiple DL models learning at the edge, that aims to maximize the aggregate accuracy while respecting the limited physical resources. Distinguished from related works, ORMA optimizes the learning-related parameters, such as dataset size and number of epochs, as well as the amount of communication and computation resources allocated to each DL model to maximize the aggregate accuracy. Our results show that ORMA consistently outperforms a baseline approach that adopts a fixed, fair resource allocation (FRA) among different DL models, at different total bandwidths and CPU combinations.

Gradient descent (GD) is an iterative optimization method to minimize a differentiable cost function. A main drawback of GD is its use of the entire dataset to perform a parameter update per learning step, which is cost-prohibitive for large-scale problems. Variants of GD such as mini-batch GD and stochastic GD compute the update direction with respect to a randomly sampled selection of the training dataset per iteration. This reduces the computational burden at the cost of adding jitters to the learning direction. Alternatively, we propose an iterative optimization algorithm that performs cheap parameter updates at minimal perturbation of the GD direction. The new method computes a deterministic summary of the training dataset, and then computes the learning direction per iteration with respect to the summary. We provide a convergence analysis for the proposed method and show that the thoroughness of the summary can be tweaked to optimize the trade-off between computational complexity and convergence rate. Simulation results are illustrated numerically for single and multi-agent systems.

Recently, developing automated video surveillance systems (VSSs) has become crucial to ensure the security and safety of the population, especially during events involving large crowds, such as sporting events. While artificial intelligence (AI) smooths the path of computers to think like humans, machine learning (ML) and deep learning (DL) pave the way more, even by adding training and learning components. DL algorithms require data labeling and high-performance computers to effectively analyze and understand surveillance data recorded from fixed or mobile cameras installed in indoor or outdoor environments. However, they might not perform as expected, take much time in training, or not have enough input data to generalize well. To that end, deep transfer learning (DTL) and deep domain adaptation (DDA) have recently been proposed as promising solutions to alleviate these issues. Typically, they can (i) ease the training process, (ii) improve the generalizability of ML and DL models, and (iii) overcome data scarcity problems by transferring knowledge from one domain to another or from one task to another. Although the increasing number of articles proposed to develop DTL- and DDA-based VSSs, a thorough review that summarizes and criticizes the state-of-the-art is still missing. To that end, this paper introduces, to the best of the authors’ knowledge, the first overview of existing DTL- and DDA-based video surveillance to (i) shed light on their benefits, (ii) discuss their challenges, and (iii) highlight their future perspectives.

The rise of chronic disease patients and the pandemic pose immediate threats to healthcare expenditure and mortality rates. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, leveraging the recent advances of Internet of Things and smart sensors. Meanwhile, reinforcement learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for distinct applications and services. Thus, this article presents a comprehensive survey of the recent models and techniques of RL that have been developed/used for supporting Intelligent-healthcare (I-health) systems. It can guide the readers to deeply understand the state-of-the-art regarding the use of RL in the context of I-health. Specifically, we first present an overview of the I-health systems’ challenges, architecture, and how RL can benefit these systems. We then review the background and mathematical modeling of different RL, deep RL (DRL), and multiagent RL models. We highlight important guidelines on how to select the appropriate RL model for a given problem, and provide quantitative comparisons, showing the results of deploying key RL models in two scenarios that can be followed in monitoring applications. After that, we conduct an in-depth literature review on RL’s applications in I-health systems, covering edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and future research directions to enhance RL’s success in I-health systems, which opens the door for exploring some interesting and unsolved problems.

Remote monitoring systems analyze the environment dynamics in different smart industrial applications, such as occupational health and safety, and environmental monitoring. Specifically, in Industrial Internet of Things (IIoT) systems, the huge number of devices and the expected performance put pressure on resources, such as computational, network, and device energy. Distributed training of machine and deep learning (ML/DL) models for intelligent industrial IoT applications is very challenging for resource limited devices over heterogeneous wireless networks (HetNets). Hierarchical federated learning (HFL) performs training at multiple layers offloading the tasks to nearby multiaccess edge computing (MEC) units. In this article, we propose a novel energy-efficient HFL framework enabled by wireless energy transfer (WET) and designed for heterogeneous networks with massive multiple-input–multiple-output (MIMO) wireless backhaul. Our energy-efficiency approach is formulated as a mixed-integer nonlinear programming (MINLP) problem, where we optimize the HFL device association and manage the wireless transmitted energy. However due to its high complexity, we design a heuristic resource management algorithm, namely, H2RMA, that respects energy, channel quality, and accuracy constraints, while presenting a low-computational complexity. We also improve the energy consumption of the network using an efficient device scheduling scheme. Finally, we investigate device mobility and its impact on the HFL performance. Our extensive experiments confirm the high performance of the proposed resource management approach in HFL over HetNets, in terms of training loss and grid energy costs.

5G-and-beyond networks are designed to fulfill the communication and computation requirements of various industries, which requires not only transporting the data, but also processing them to meet/address diverse key performance indicators (KPIs). Network Function Virtualization (NFV) has emerged to enable this vision by: (i) collecting the requirements of diverse services, using graphs of Virtual Network Functions (VNFs); and (ii) mapping these requirements into network management decisions. Because of the latter, we need to efficiently allocate computing and network resources to support the desired services, and because of the former such decisions must be jointly optimized considering all KPIs associated with supported services. Thus, this paper proposes an optimized, intelligent network slicing framework to maintain a high performance of network operation by supporting diverse and heterogeneous services, while meeting new KPIs, e.g., reliability, energy consumption, and data quality. Different from the existing works, which are mainly designed considering traditional metrics like throughput and latency, we present a novel methodology and resource allocation schemes that enable high-quality selection of radio points of access, VNF placement and data routing, as well as data compression ratios, from the end users to the cloud. Our results depict the efficiency of the proposed framework in enhancing the network performance when compared to baseline approaches that consider partial network view or fair resource allocation.

Federated learning (FL) paradigms aim to amalgamate diverse data properties stored locally at each user, while preserving data privacy through sharing users’ learning experiences and iteratively aggregating their local learning models into a global one. However, the majority of FL architectures with centralized cloud do not guarantee the trust in sharing users’ models, and hence, open the door for slowing and/or contaminating the global learning experience. In this paper, we propose a decentralized Blockchain (BC)-based framework and define a comprehensive protocol for exchanging local models, in order to guarantee users’ mutual trust while sharing their local learning experiences. We then propose a technique to optimize the global learning experience using Reinforcement Learning (RL), namely RL-FL-BC, to tackle the trade-off between information age of the learning parameters, data skewness (i.e., non-iid), and BC transaction cost (i.e., Ether price). We implement the proposed framework in a realistic containerized environment to facilitate the comparative study of the RL-FL-BC technique with baselines techniques. Our results show the efficacy of the BC-based protocol to facilitate the exchange of both the models’ and the optimization parameters to guarantee users’ mutual trust, while improving global learning performance compared to baselines techniques.

People counting is one of the computer vision tasks that can be useful for crowd management. In addition, estimating the crowdedness of a surveilled scene for crowd behavior analysis is one of the prominent challenges in video surveillance systems. With the introduction of deep learning, this operation has become doable with a convincing performance. However, this task still represents a challenge for these methods. In this regard, we propose a combination of deep reinforcement learning (DRL) networks and deep learning architecture for crowd counting. DRL network used the Context-Aware Attention (CAA) module for segmenting the crowd region, Then, on the segmented results, the crowd density estimation is performed using an encoder-decoder. The proposed method is evaluated and compared with and without the segmentation parts on the existing datasets including UCF_QNRF, UCF_CC_50, ShangaiTech_(A, B), while the obtained results in terms of MAE metric achieved 84,8, 179.2, 44.6, and 8.2 respectively.

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).

Wireless enabling technologies in critical infrastructures are increasing the efficiency of communications. Most of these technologies are vulnerable to jamming attacks. Jamming attacks are among the most effective countermeasures to attack and compromise their availability. Jamming is an interfering signal that limits the intended receiver from correctly receiving the messages. Localizing a jammer deployed by the adversary in wireless sensor networks becomes difficult, if not impossible, due to the inaccessibility of the affected sensors in the network. This paper proposes an effective yet efficient jammer localization scheme where battery-free Radio-Frequency Identification (RFID) sensor tags harvest the energy from the signal emitted by a powerful jammer. We compute the distance and estimate the actual jammer location based on the power received at each energy-harvesting node. We conduct extensive simulations campaign to test and illustrate the effectiveness of the proposed scheme. Finally, we demonstrate the possibility of deploying the proposed scheme with off-shelf equipment and consuming only 0.2175 mJ,

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.

The extremely heavy global reliance on IoT devices is causing enormous amounts of data to be gathered and shared in IoT networks. Such data need to efficiently be used in training and deploying of powerful artificially intelligent models for better future event detection and decision making. However, IoT devices suffer from many limitations regarding their energy budget, computational power, and storage space. Therefore, efficient solutions have to be studied and proposed for addressing these limitations. In this paper, we propose an energy-efficient Hierarchical Federated Learning (HFL) framework with optimized client-edge association and resource allocation. This was done by formulating and solving a communication energy minimization problem that takes into consideration the data distribution of the clients and the communication latency between the clients and edges. We also implement an alternative less complex solution leveraging Reinforcement Learning (RL) that provides a fast user-edge association and resource allocation response in highly dynamic HFL networks. The proposed two solutions are compared with several state-of-the-art client-edge association techniques, leveraging MNIST dataset. Moreover, we study the trade-off between minimizing the per-round energy consumption and Kullback-Leibler Divergence (KLD) of the data distribution, and its effect on the total energy consumption.

From traffic monitoring to livestock tracking, and military reconnaissance to marine discovery, unmanned aerial vehicles (UAVs) are indispensable. Its dependence on a battery for power supply limits the flight time to visit all planned locations. Consequently, target visitation needs to be smart and minimize the mechanical energy. We propose to develop the AirEye UAV-based smart platform that can perform target visitation in the shortest time possible without knowing targets' exact locations, but with a known probabilistic distribution. We show how to integrate a UAV with the proper hardware to control it and execute commands from an on-ground command and control station. A pre-built machine learning model was modified to detect and identify targets, along with a reinforcement learning (RL) model to autonomously navigate the drone and ensure that all targets are visited while consuming minimal energy. We propose a drone energy model that can be used to estimate the total energy consumed by the drone in a complex scenario. We then use this energy model to compare the total energy consumed by the proposed RL-based technique in comparison with two other heuristic strategies, namely, random, and zigzag motion.

Mobile Edge Learning (MEL) is a learning paradigm that facilitates training of Machine Learning (ML) models over resource-constrained edge devices. MEL consists of an orchestrator, which represents the model owner of the learning task, and learners, which own the data locally. Enabling the learning process requires the model owner to motivate learners to train the ML model on their local data and allocate sufficient resources. The time limitations and the possible existence of multiple orchestrators open the doors for the resource allocation problem. As such, we model the incentive mechanism and resource allocation as a multi-round Stackelberg game, and propose a Payment-based Time Allocation (PBTA) algorithm to solve the game. In PBTA, orchestrators first determine the pricing, then the learners allocate each orchestrator a timeslot and determine the amount of data and resources for each orchestrator. Finally, we evaluate the PBTA performance and compare it against a recent state-of-the-art approach.

5G networks are designed not only to transport data, but also to process them while supporting a vast number of services with different key Performance Indicators (KPIs). Network virtualization has emerged to enable this vision, however it calls for designing efficient computing and network resource allocation schemes to support diverse services, while jointly considering all KPIs associated with these services. Thus, this paper proposes a dynamic network slicing and resource allocation framework that aims at maintaining high-level network operational performance, while fulfilling diverse services’ requirements and KPIs, e.g., availability, reliability, and data quality. Differently from the existing works, which are designed considering traditional metrics like throughput and latency, we present a novel methodology and resource allocation schemes that enable high-quality selection of radio points of access, resource allocation, and data routing from end users to the cloud. Our results depict that the proposed solutions could obtain the best trade-off between diverse services’ requirements when compared to baseline approaches that consider partial network view or fair resource allocation.

With the advent of Internet of Things (IoT) systems, privacy and integrity of messages are becoming critical issues and are threatened, especially with mobile sensors. The broadcast nature of wireless communications increase information leakage in the presence of eavesdroppers. This paper proposes a hybrid Non-Orthogonal Multiple Access (NOMA)/Orthogonal Multiple Access (OMA)- based IoT systems to improve the data transmission security of moving sensors. We derive the Key Agreement Probability (KAP) expression of the proposed scheme, and we investigate the corresponding Secrecy Outage Probability (SOP) and the Average Bit Rate (ABR), when compared to pure NOMA and pure OMA transmission schemes. Simulation results are used to validate the derived expression and to evaluate the performance of the proposed scheme in terms of KAP, SOP, and ABR.

Federated Learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. It is a promising solution for telemonitoring systems that demand intensive data collection, for detection, classification, and prediction of future events, from different locations while maintaining a strict privacy constraint. Due to privacy concerns and critical communication bottlenecks, it can become impractical to send the FL updated models to a centralized server. Thus, this paper studies the potential of hierarchical FL in Internet of Things (IoT) heterogeneous systems. In particular, we propose an optimized solution for user assignment and resource allocation over hierarchical FL architecture for IoT heterogeneous systems. This work focuses on a generic class of machine learning models that are trained using gradient-descent-based schemes while considering the practical constraints of non-uniformly distributed data across different users. We evaluate the proposed system using two real-world datasets, and we show that it outperforms state-of-the-art FL solutions. Specifically, our numerical results highlight the effectiveness of our approach and its ability to provide 4–6% increase in the classification accuracy, with respect to hierarchical FL schemes that consider distance-based user assignment. Furthermore, the proposed approach could significantly accelerate FL training and reduce communication overhead by providing 75–85% reduction in the communication rounds between edge nodes and the centralized server, for the same model accuracy.

The security and privacy of the network in Internet of Things (IoT) systems are becoming more critical as we are more dependent on smart systems. Considering that packets are exchanged between the end user and the sensing devices, it is then important to ensure the security, privacy, and integrity of the transmitted data by designing a secure and a lightweight authentication protocol for IoT systems. In this article, in order to improve the authentication and the encryption in IoT systems, we present a novel method of authentication and encryption based on elliptic curve cryptography (ECC) using random numbers generated by fuzzy logic. We evaluate our novel key generation method by using standard randomness tests, such as: frequency test, frequency test with mono block, run test, discrete Fourier transform (DFT) test, and advanced DFT test. Our results show superior performance compared to existing ECC based on shift registers. In addition, we apply some attack algorithms, such as Pollard’s$\rho $and Baby-step Giant-step, to evaluate the vulnerability of the proposed scheme.

Internet of Things (IoT) systems are becoming core building blocks for different services and applications supporting every day’s life. The heterogeneous nature of IoT devices and the complex use scenarios make it hard to build secure and private IoT systems. Physical-layer security (PLS) can lead to efficient solutions reducing the impact of the increasing security threats. In this work, we propose a new PLS-based IoT transmission scheme that offers a highly secured transmission probability, low-computational complexity, and reduced power consumption. We utilize 3-D stochastic geometry to model a more realistic IoT system and test our proposed scheme in different practical scenarios, where sensors, access points (APs), and eavesdroppers are randomly located in 3-D space. We focus on the system performance, in terms of secrecy outage probability (SOP) and secured successful transmission probability (SSTP), using tight closed-form expressions. An optimization problem is developed to deduce the optimal sensors’ transmit power, the APs’ density, and the maximum number of transmission tentative, when maximizing the SSTP. The proposed scheme outperforms the baseline retransmission scheme, in terms of SOP and SSTP based on analytical and simulation results.

We propose a framework that enables the cluster head (CH) to harvest energy from uplink transmission by Internet of Things (IoT) nodes employing data compression under nonorthogonal multiple access (NOMA) scheme. Our framework enables the CH to maximize the harvested energy while meeting constraints on outage probability, consumed energies by the transmitting IoT nodes and compression and distortion ratios. We provide necessary analysis for our framework and derive an expression for the outage probability and average harvested energy under the NOMA scheme. We formulate an optimization problem with NOMA factors, simultaneous wireless information and power transfer (SWIPT) factors, and NOMA user distances as optimization parameters. We first solve the optimization problem and find the optimized values using a grid-based search. Then, we exploit a deep reinforcement learning algorithm to solve the optimization problem more efficiently. Throughout this work, we prove the feasibility of such framework and deliver key observation that will help the CH scheduling different IoT nodes such that the harvested energy is maximized while the constraints are met.

Unmanned aerial vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems relying on direct observations obtained from fixed cameras and sensors. Furthermore, thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications. However, deep neural networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed onboard. In this article, we propose a DNN distribution methodology within UAVs to enable data classification in resource-constrained devices and avoid extra delays introduced by the server-based solutions due to data communication over air-to-ground links. The proposed method is formulated as an optimization problem that aims to minimize the latency between data collection and decision-making while considering the mobility model and the resource constraints of the UAVs as part of the air-to-air communication. We also introduce the mobility prediction to adapt our system to the dynamics of UAVs and the network variation. The simulation conducted to evaluate the performance and benchmark the proposed methods, namely, optimal UAV-based layer distribution (OULD) and OULD with mobility prediction (OULD-MP), was run in an HPC cluster. The obtained results show that our optimization solution outperforms the existing and heuristic-based approaches.

Mobile Edge Learning (MEL) is a decentralized learning paradigm that enables resource-constrained IoT devices to either learn a shared model without sharing the data, or to distribute the learning task with the data to other IoT devices and utilize their available resources. In the former case, IoT devices (a.k.a learners) need to be assigned an orchestrator to facilitate the learning and models' aggregation from different learners. Whereas in the latter case, IoT devices act as orchestrators and look for learners with available resources to distribute the learning task to. However, the coexistence of multiple learning problems in an environment with limited resources poses the learners-orchestrator assignment problem. To this end, we aim to develop an energy-efficient learner assignment and task allocation scheme, in which each orchestrator gets assigned a group of learners based on their communication channel qualities and computational resources. We formulate and solve a multi-objective optimization problem to minimize the total energy consumption and maximize the learning accuracy. To reduce the solution complexity, we also propose a lightweight heuristic algorithm that can achieve near-optimal performance. The conducted simulations show that our proposed approaches can execute multiple learning tasks efficiently and significantly reduce energy consumption compared to current state-of-art methods.

The recent pandemic along with the rapid increase in the number of patients that require continuous remote monitoring imposes several challenges to support the high quality of services (QoS) in remote health applications. Remote-health (r-health) systems typically demand intense data collection from different locations within a strict time constraint to support sustainable health services. On the contrary, the end-users with mobile devices have limited batteries that need to run for a long time, while continuously acquiring and transmitting health-related information. Thus, this paper proposes an adaptive deep reinforcement learning (DRL) framework for network selection over heteroge-neous r-health systems to enable continuous remote monitoring for patients with chronic diseases. The proposed framework allows for selecting the optimal network(s) that maximizes the accumulative reward of the patients while considering the patients' state. Moreover, it adopts an adaptive compression scheme at the patient level to further optimize the energy consumption, cost, and latency. Our results depict that the proposed framework outperforms the state-of-the-art techniques in terms of battery lifetime and reward maximization.

Training centralized machine learning (ML) models becomes infeasible in wireless networks due to the increasing number of internet of things (IoT) and mobile devices and the prevalence of the learning algorithms to adapt tasks in dynamic situations with heterogeneous networks (HetNets) and battery limited devices. Hierarchical federated learning (HFL) has been proposed as a promising learning that can preserve the data privacy of the wireless devices, tackle the communication bottlenecks in wireless networks, and improve the energy effi-ciency. We propose a novel energy-efficient HFL framework for HetNets with massive multiple-input multiple-output (MIMO) wireless backhaul enabled by wireless energy transfer (WET). We formulate a joint energy management and device association optimization problem in HFL over HetNets subject to maximal divergence constraints. Next, an optimal solution is developed, but with high complexity. To reduce the complexity, a heuristic algorithm for HFL over HetNets with energy, channel quality, and accuracy constraints, is developed in order to minimize the grid energy consumption cost and preserve the value of loss function, which captures the HFL performance. Simulation results show the efficiency of the proposed resource management approach in the HFL context in terms of grid power consumption cost and training loss.

The rapid increase in the number of patients requiring constant monitoring inspires researchers to investigate the area of mobile health (m-Health) systems for intelligent and sustainable remote healthcare applications. Extensive real-time medical data transmission using battery-constrained devices is challenging due to the dynamic network and the medical system constraints. Such requirements include end-to-end delay, bandwidth, transmission energy consumption, and application-level Quality of Services (QoS) requirements. As a result, adaptive data compression based on network and application resources before data transmission would be beneficial. A minimal distortion can be assured by applying Convolutional Auto-encoder (CAE) compression approach. This paper proposes a cross-layer framework that considers the patients' movement while compressing and transmitting EEG data over heterogeneous wireless environments. The main objective of the framework is to minimize the trade-off between the transmission energy consumption along with the distortion ratio and monetary costs. Simulation results show that an optimal trade-off between the optimization objectives is achieved considering networks and application QoS requirements for m-Health systems.

The rapid production of mobile and wearable devices along with the wireless applications boom is continuing to evolve everyday. This motivates network operators to integrate and exploit wireless spectrum across multiple radio access networks to cope with such intensive demand, while improving quality of service. However, it is crucial to develop innovative network selection techniques that consider heterogeneous networks characteristics, while meeting applications' quality requirements. Thus, this paper develops an optimal network selection with resource allocation scheme over heterogeneous networks that aims to optimize the latency, cost, and energy consumption, while accounting for data compression at the edge. Indeed, our framework could significantly enhance the performance of wireless healthcare systems by enabling data transfer from patients edge nodes to the cloud in cost-effective and energy-efficient manner, while maintaining strict Quality of Service (QoS) requirements of health applications. Our simulation results depict that our solution significantly outperforms state-of- the-art techniques in terms of energy consumption, latency, and cost.

Smart Contracts (SCs), which are software programs that run on blockchain platforms, provide appealing security guarantees characterized by decentralized, autonomous, and verifiable execution. On the other hand, Service Provisioning (SP) systems (i.e., systems that assign users to service providers in a way that maximizes the global utility) have been leveraging SCs to provide trust and transparency features. Such features are obtained by deploying the SP’s assignment criteria as an SC on the blockchain. However, deploying optimal assignment criteria as SCs does not guarantee the best performance over time since the blockchain participants join and leave flexibly, and their load varies with time, potentially deeming the initial assignment sub-optimal. Furthermore, modifying the criteria manually by a third party at every variation in the blockchain jeopardizes the autonomous and independent execution promised by SCs. Thus, in this paper, we propose the use of online learning SCs that leverage the chained data to continuously self-tune their assignment criteria and maintain maximum utility. We show that the proposed data-driven method can achieve high performance on the multi-stage assignment problem. We also compare the proposed approach to multiple assignment algorithms as well as planning methods. Results show a significant performance advantage over heuristics and better adaptability to the dynamic nature of blockchain networks compared to planning techniques.

The Internet of Medical Things (IoMT) couples the rapid growth of Internet of things (IoT) technologies with smart health systems, leveraging wireless battery-operated devices for remote health monitoring. Since 2019, a surge in the number of COVID-19 patients has increased rapidly, leading to increased strain on hospital resources and leaving some urgent patients behind. This is substantial cause to transform interactive health treatment into intelligent healthcare using edge computing and artificial intelligence (AI) techniques. However, running sophisticated AI-based edge computing techniques on IoT devices with limited battery is not sustainable. Hence, addressing the trade-off between energy-efficiency and smart AI techniques is imperative to maximize the device's lifetime. This paper proposes a Multi-Modal Reinforcement Learning (MMRL) algorithm that will help maximize the IoT device's lifetime using adaptive data compression, energy-efficient communication, and minimum latency, particularly for emergency events. The results showed a 500% longer battery life than the state-of-the-art algorithms in addition to high adaptability to different conditions.

The affordability and deployment-flexibility of Unmanned Air Vehicles (UAVs) have ignited the development of many smart applications, including surveillance, disaster management, and smart farming. Drone's energy consumption is a critical issue and it can be controlled through different factors, depending on the application. One approach is to minimize energy consumption by defining a minimal number of strategic target-coverage locations that the drone needs to traverse and efficiently plan the drone's route through these locations. In this paper, we provide solutions that efficiently allow UAVs to cover multiple targets using their cameras. These solutions identify a minimum set of strategic locations that cover the targets and plan the drone's routes across these locations. We address the problem with the objective of minimizing the total energy consumed by the drone during its mission. We model the problem as mixed-integer programming problem and provide a set of heuristics; with and without target clustering. We evaluate the system using simulations. The results indicate the significance of clustering in minimizing the number of strategic locations and saving the drone's energy. Moreover, flexibility in selecting cluster centers provides further reduction in the strategic locations and energy consumption.

Medical data exchange between diverse e-health entities can lead to a better healthcare quality, improving the response time in emergency conditions, and a more accurate control of critical medical events (e.g., national health threats or epidemics). However, exchanging large amount of information between different e-health entities is challenging in terms of security, privacy, and network loads, especially for large-scale healthcare systems. Indeed, recent solutions suffer from poor scalability, computational cost, and slow response. Thus, this article proposes medical-edge-blockchain (MEdge-Chain), a holistic framework that exploits the integration of edge computing and blockchain-based technologies to process large amounts of medical data. Specifically, the proposed framework describes a healthcare system that aims to aggregate diverse health entities in a unique national healthcare system by enabling swift, secure exchange, and storage of medical data. Moreover, we design an automated patients monitoring scheme, at the edge, which enables the remote monitoring and efficient discovery of critical medical events. Then, we integrate this scheme with a blockchain architecture to optimize medical data exchanging between diverse entities. Furthermore, we develop a blockchain-based optimization model that aims to optimize the latency and computational cost of medical data exchange between different health entities, hence providing effective and secure healthcare services. Finally, we show the effectiveness of our system in adapting to different critical events, while highlighting the benefits of the proposed intelligent health system.

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 rise in embedded and IoT device usage comes with an increase in LTE usage as well. About 70% of an estimated 18 billion IoT devices will be using cellular LTE networks for efficient connections. This introduces several challenges, such as security, latency, scalability, and quality of service, for which reason edge computing or fog computing has been introduced. The edge is capable of offloading resources to the edge to reduce workload at the cloud. Several security challenges come with multiaccess edge computing (MEC), such as location-based attacks, the man- in-the-middle attacks, and sniffing. This article proposes a software-defined perimeter (SDP) framework to supplement MEC and provide added security. The SDP is capable of protecting the cloud from the edge by only authorizing authenticated users at the edge to access services in the cloud. The SDP is implemented within a mobile-edge LTE network. Delay analysis of the implementation is performed, followed by a Denial-of-Service (DoS) attack to demonstrate the resilience of the proposed SDP. Further analyses, such as CPU usage and port scanning were performed to verify the efficiency of the proposed SDP. This analysis is followed by concluding remarks with insight into the future of the SDP in MEC.

With the emergence of smart cities, Internet of Things (IoT) devices as well as deep learning technologies have witnessed an increasing adoption. To support the requirements of such paradigm in terms of memory and computation, joint and real-time deep co-inference framework with IoT synergy was introduced. However, the distribution of Deep Neural Networks (DNN) has drawn attention to the privacy protection of sensitive data. In this context, various threats have been presented, including black-box attacks, where a malicious participant can accurately recover an arbitrary input fed into his device. In this paper, we introduce a methodology aiming to secure the sensitive data through re-thinking the distribution strategy, without adding any computation overhead. First, we examine the characteristics of the model structure that make it susceptible to privacy threats. We found that the more we divide the model feature maps into a high number of devices, the better we hide proprieties of the original image. We formulate such a methodology, namely DistPrivacy, as an optimization problem, where we establish a trade-off between the latency of co-inference, the privacy level of the data, and the limited-resources of IoT participants. Due to the NP-hardness of the problem, we introduce an online heuristic that supports heterogeneous IoT devices as well as multiple DNNs and datasets, making the pervasive system a general-purpose platform for privacy-aware and low decision-latency applications.

Smart health systems improve the existing health services by integrating information and technology into health and medical practices. However, smart healthcare systems are facing major challenges including limited network resources, energy allocation, and latency. In this paper, we leverage the dense heterogeneous network (HetNet) architecture over 5G network to enhance network capacity and provide seamless connectivity for smart health systems. The network selection and energy allocation in HetNets are important factors in this regard due to their significant impact on system performance. Inspired by the success of Deep Reinforcement Learning (DRL) in solving complicated control problems, we present a novel DRL model for energy-efficient network selection in heterogeneous health systems. The proposed model selects the set of networks to be used for data transmission with adaptive compression at the edge with an optimal energy allocation policy for all the network participants. Our experimental results show that the proposed DRL model has a good performance compared to the existing state of art techniques while meeting different users' demands in highly dynamic environments.

The number of users adopting Tor to protect their online privacy is increasing rapidly. With a limited number of volunteered relays in the network, the number of clients' connections sharing the same relays is increasing to the extent that it is starting to affect the performance. Recently, Tor's resource allocation among circuits has been studied as one cause of poor Tor network performance. In this paper, we propose two scheduling approaches that guarantee proportional fairness between circuits that are sharing the same connection. In our evaluation, we show that the average-rate-base scheduler allocates Tor's resources in an optimal fair scheme, increasing the total throughput achieved by Tor's relays. However, our second proposed approach, an optimization-based scheduler, maintains acceptable fairness while reducing the latency experienced by Tor's clients.

Leveraging video caching to collaborative Mobile Edge Computing (MEC) servers is an emerging paradigm, where cloud computing services are extended to edge networks to allocate multimedia contents close to end-users. However, despite minimizing the traffic over the content delivery networks (CDN), congestions may occur in peak hours characterized by high load demands. Involving users' devices in data offloading through Device-to-Device (D2D) connections has proved its efficiency in relieving the cellular spectrum utilization. In this paper, the Collaborative Edge network (CE) and the devices (D2D) cluster are combined to form a CE-D2D framework aiming at maximizing video caching and efficiently using cellular and backhaul bandwidths. However, since we are dealing with large sized contents, the small storage and bandwidth capacities offered by users limit the number of cached videos and restrict offloading large volume data. This makes the CE-D2D framework, so far, an incomplete solution for multimedia contents. Therefore, we propose a caching strategy to cache only the chunks of videos to be watched and instead of caching or offloading each video content by one edge node (as performed in literature), helpers (MEC and mobiles) will collaborate to store and share different chunks to optimize the storage/transmission resources usage. In this work, we model both CE and D2D frameworks as linear programs and schedule the collaboration between them constrained by resource availability. Due to the NP-hardness of the problem, we introduce an online heuristic that presents a proactive chunks caching (HLPC) and a near-optimal data offloading with polynomial complexity.

In this paper, we conduct a biological data collection and analysis study for patients undergoing routine planned intravenous antibiotic treatment. The acquired data (i.e., Electroencephalogram (EEG), temperature and blood pressure) are processed using different machine learning and deep learning models to learn the dynamic properties of brain electrical activity from this group of patients. Thus, the primary objective of our study is the safe collection of EEG data from patients receiving antibiotic therapy, in addition to analyzing the acquired data for patterns that might indicate risk of seizure. We propose two machine learning models to analyze the acquired data from these patients split into three classes: data collected before, during, and after receiving the medication. Our results show the effectiveness of our models in analyzing the acquired data, which would not possible by imitative human analysis.

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.

Smart health systems improve the efficiency of healthcare infrastructures and biomedical systems by integrating information and technology into health and medical practices. However, reliability, scalabilty and latency are among the many challenges hindering the realization of next-generation healthcare. In fact, with the exponential increases in the volume of patient data being produced and processed, many healthcare system' are being overwhelmed with the deluge of data they are facing. Many systems have been proposed to improve the system latency and scalability. However, there are concerns related to some of theses systems regarding the increasing levels of required human interaction which impact their efficiency. Recently, machine learning techniques are gaining a lot of interest in health applications as they exhibit fast processing with realtime predictions. In this paper, we propose a new healthcare system to reduce the waiting time in emergency department and improve the network scalability in any distributed system. The proposed model integrates the power of edge computing with machine learning techniques for providing a good quality of healthcare services. The machine learning algorithm is used to generate a classifier that can predict with high levels of accuracy the likelyhood of a patient to have a heart attack using his physiological signals ECG. The proposed system stores the patient data in a centralized database and generates a unique index using a new data-dependent Indexing algorithm that transforms the patient data into unique code to be sent for any medical data exchange. Multiple machine learning algorithms are studied and the best algorithm will be selected based on efficient performance result for the prediction of heart attack problem. simulation results show that the proposed model can effectively detect the abnormal heart beats with 91% using SVM algorithm. We show also that the proposed system outperforms conventional indexing algorithm systems in terms of collisions rate.

The blockchain is a storage technology and transmission of information, transparent, secure, and operating without central control. In this paper, we propose a new decentralized trust management and cooperation model where data is shared via blockchain and we explore the revenue distribution under different consensus schemes. To reduce the power calculation with respect to the control mechanism, our proposal adopts the possibility of Proof on Trust (PoT) and Proof of proof-of-stake based trust to replace the proof of work (PoW) scheme, to carry out the mining and storage of new data blocks. To detect nodes with malicious behavior to provide false system information, the trust updating algorithm is proposed..

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.

With the advancement of rich media generating devices, the proliferation of live Content Providers (CP), and the availability of convenient internet access, crowdsourced live streaming services have witnessed unexpected growth. To ensure a better Quality of Experience (QoE), higher availability, and lower costs, large live streaming CPs are migrating their services to geo-distributed cloud infrastructure. However, because of the dynamics of live broadcasting and the wide geo-distribution of viewers and broadcasters, it is still challenging to satisfy all requests with reasonable resources. To overcome this challenge, we introduce in this paper a prediction driven approach that estimates the potential number of viewers near different cloud sites at the instant of broadcasting. This online and instant prediction of distributed popularity distinguishes our work from previous efforts that provision constant resources or alter their allocation as the popularity of the content changes. Based on the derived predictions, we formulate an Integer-Linear Program (ILP) to proactively and dynamically choose the right data center to allocate exact resources and serve potential viewers, while minimizing the perceived delays. As the optimization is not adequate for online serving, we propose a real-time approach based on Reinforcement Learning (RL), namely RL-OPRA, which adaptively learns to optimize the allocation and serving decisions by interacting with the network environment. Extensive simulation and comparison with the ILP have shown that our RL-based approach is able to present optimal results compared to heuristic-based approaches.

To support multimedia applications, Mobile Edge Computing (MEC) servers offer storage and computing capacities to handle videos close to end-users. However, the high load in peak hours consumes the limited available bandwidth of existing cellular and backhaul links leading to low network performance. Hence, an elastic system model is required to maintain the high Quality of Experience (QoE) as the resource demands increase. Caching popular videos at mobile devices is considered a promising technique for content delivery. Yet, mobile users offer small capacities that are not adequate for large-sized video sharing. In this paper, we extend the collaborative caching and processing framework in edge networks (Collaborative Edge - CE) to include the users' mobile video sharing (Device-to-Device - D2D). We propose a caching strategy to cache only the chunks of videos to be watched and instead of offloading one video content by one edge node, helpers (MEC servers and users) will collaborate to store and share different chunks to optimize the storage/transmission resources usage. To only cache popular contents, we designed a D2D-aware proactive chunks caching on users’ devices based on our chunks popularity model. Next, we formulate this CE-D2D collaborative problem as a linear program. Due to the NP-hardness of the problem, we introduce a sub-optimal relaxation and an online heuristic using the proactive caching and presenting a near optimal data offloading and a profitable payment determination, with polynomial time complexity. The simulation results show that our policies and heuristics outperform other edge caching approaches by more than 10% in terms of hit ratio, average delay, and cost.

During the last decade, empowered by the technological advances of mobile devices and the revolution of wireless mobile network access, crowdsourced live streaming has become more popular. Ensuring a stable high-quality playback experience is necessary to maximize the number of viewers and profits for content providers. Additionally, because of the instability of network conditions and the heterogeneity of the end-users capabilities, transcoding the original video into multiple bitrates is required. Video transcoding is a computationally exhaustive process, where generally a single cloud instance needs to be reserved to produce one single video bitrate representation. On-demand renting of resources or inadequate resources pre-renting may cause delay of the video playback or serving the viewers with a lower quality. On the other hand, if resources provisioning is much higher than required, the extra resources will be wasted. In this paper, we introduce our resources reservation framework for geo-distributed cloud sites, to maximize the Quality of Experience (QoE) of viewers and minimize the cost to the content providers. First, we formulate an offline optimization problem to allocate transcoding resources at the viewers' proximity, while creating a trade off between the network cost and viewers QoE. Second, based on the optimizer resource allocation decisions on historical live videos, we create our time series datasets containing historical records of the optimal resources needed at each geo-distributed cloud site. Finally, we adopt machine learning to build our distributed time series forecasting models to proactively forecast the exact needed transcoding resources ahead of time at each geo-distributed cloud site.

In this paper, we investigate a multi-objective optimization framework for secure wireless health monitoring applications. In particular, we consider a legitimate link for the transmission of a vital EEG signal, threatened by a passive eavesdropping attack, that aims at wiretapping these measurements. We incorporate in our framework the practical secrecy metric, namely secrecy outage probability (SOP), which requires only the knowledge of side information regarding the eavesdropper (Ev), instead of completely having its instantaneous channel state information (CSI). To that end, we formulate an optimization problem in the form of maximizing the energy efficiency of the transmitter, while minimizing the distortion encountered at the signal resulting from the compression process prior to transmission, under realistic quality of service (QoS) constraints. The problem is shown to be nonconvex and NP-complete. Towards solving the problem, a branch and bound (BnB)-based algorithm is presented where a δ-suboptimal solution, from the global optimal one, is obtained. Numerical results are conducted to verify the system performance, where it is shown that our proposed approach outperforms similar systems deploying fixed compression policies (FCPs). We successfully meet QoS requirements while optimizing the system objectives, at all channel conditions, which cannot be attained by these FCP approaches. Interestingly, we also show that a target secrecy rate can be practically achieved with nonzero probability, even when the Ev has a better channel condition, on the average, than that for the legitimate receiver.

Driven by the tremendous technological advancement of personal devices and the prevalence of wireless mobile network accesses, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a better viewers quality of experience (QoE) is the key to maximize the audiences number and increase streaming providers' profits. This can be achieved by advocating a geo-distributed cloud infrastructure to allocate the multimedia resources as close as possible to viewers, in order to minimize the access delay and video stalls. Moreover, allocating the exact needed resources beforehand avoids over-provisioning, which may lead to significant costs by the service providers. In the contrary, under-provisioning might cause significant delays to the viewers. In this paper, we introduce a prediction driven resource allocation framework, to maximize the QoE of viewers and minimize the resource allocation cost. First, by exploiting the viewers locations available in our unique dataset, we implement a machine learning model to predict the viewers number near each geo-distributed cloud site. Second, based on the predicted results that showed to be close to the actual values, we formulate an optimization problem to proactively allocate resources at the viewers proximity. Additionally, we will present a trade-off between the video access delay and the cost of resource allocation.

In recent years, unmanned aerial vehicles (UAVs) have become increasingly accessible to the public due to their high availability with affordable prices while being equipped with better technology. However, this raises a great concern from both the cyber and physical security perspectives since UAVs can be utilized for malicious activities in order to exploit vulnerabilities by spying on private properties, critical areas or to carry dangerous objects such as explosives which makes them a great threat to the society. Drone identification is considered the first step in a multi-procedural process in securing physical infrastructure against this threat. In this paper, we present drone detection and identification methods using deep learning techniques such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Convolutional Recurrent Neural Network (CRNN). These algorithms will be utilized to exploit the unique acoustic fingerprints of the flying drones in order to detect and identify them. We propose a comparison between the performance of different neural networks based on our dataset which features audio recorded samples of drone activities. The major contribution of our work is to validate the usage of these methodologies of drone detection and identification in real life scenarios and to provide a robust comparison of the performance between different deep neural network algorithms for this application. In addition, we are releasing the dataset of drone audio clips for the research community for further analysis.

In the past few years, a rapid increase in the number of patients requiring constant monitoring, which inspires researchers to develop intelligent and sustainable remote smart healthcare services. However, the transmission of big real-time health data is a challenge since the current dynamic networks are limited by different aspects such as the bandwidth, end-to-end delay, and transmission energy. Due to this, a data reduction technique should be applied to the data before being transmitted based on the resources of the network. In this paper, we integrate efficient data reduction with wireless networking transmission to enable an adaptive compression with an acceptable distortion, while reacting to the wireless network dynamics such as channel fading and user mobility. Convolutional Auto-encoder (CAE) approach was used to implement an adaptive compression/reconstruction technique with the minimum distortion. Then, a resource allocation framework was implemented to minimize the transmission energy along with the distortion of the reconstructed signal while considering different network and applications constraints. A comparison between the results of the resource allocation framework considering both CAE and Discrete wavelet transforms (DWT) was also captured.

The advancement of technology has pushed the cloud computing capabilities to the edge networks, paving the way for network operators and multimedia service providers to leverage video caching and transcoding to the Mobile Edge computing (MEC) servers. However, because of high load demands in peak hours and the limited bandwidth, congestions may occur, leading to lower network performance. This problem is worsened by the redundancies of the same content requests and the expectation for the highest video quality, which exhausts the cellular spectrum and the backhaul links. Collaboration between edge servers, to cache and transcode videos, is proposed and proved its efficiency to store highly requested contents and relieve the load on the backhaul links. Meanwhile, Device-to-device (D2D) offloading is considered to alleviate cellular spectrum utilization. In this paper, we will extend the Collaborative Edge (CE) network to include the mobile users (D2D) caching and offloading and create a CE-D2D dual framework. Still, this framework does not present the perfect solution for exhaustive bandwidth utilization. In fact, users' mobiles have small storage capacities and very scarce bandwidth availability, which limits the number of cached videos and constraints sharing large sized contents. Additionally, existing D2D approaches are considering video requests as simple contents similar to any HTML page request. However, in realistic cases, a lower bitrate version of the content can be offered to the viewers, if the bandwidth (cellular or mobile) is unavailable. Hence, to maximize the caching efficiency, we formulate the CE-D2D framework as a linear program, where MEC servers and users' mobiles collaborate to cache and offload different chunks of the requested content constrained by cache and bandwidth availability. In this way, instead of caching and serving full popular videos, as done in previous works, we will only cache popular chunks within different helpers, which maximizes the efficiency of caching in small storage devices.

Rapid evolution of wireless medical devices and network technologies has fostered the growth of remote monitoring systems. Such new technologies enable monitoring patients' medical records anytime and anywhere without limiting patients' activities. However, critical challenges have emerged with remote monitoring systems due to the enormous amount of generated data that need to be efficiently processed and wirelessly transmitted to the service providers in time. Thus, in this paper, we leverage full-duplex capabilities for fast transmission, while tackling the trade-off between Quality of Service (QoS) requirements and consequent self-interference (SI) for efficient remote monitoring healthcare systems. The proposed framework jointly considers the residual SI resulting from simultaneous transmission and reception along with the compressibility feature of medical data in order to optimize the data transmission over wireless channels, while maintaining the application's QoS constraint. Our simulation results demonstrate the efficiency of the proposed solution in terms of minimizing the transmission power, residual self-interference, and encoding distortion.

Recently, the growing demand for rich multimedia content such as Video on Demand (VoD) has made the data transmission from content delivery networks (CDN) to end-users quite challenging. Edge networks have been proposed as an extension to CDN networks to alleviate this excessive data transfer through caching and to delegate the computation tasks to edge servers. To maximize the caching efficiency in the edge networks, different Mobile Edge Computing (MEC) servers assist each others to efficiently select which content to store and the appropriate computation tasks to process. In this paper, we adopt a collaborative caching and transcoding model for VoD in MEC networks. However, unlike other models in the literature, different chunks of the same video are not fetched and cached in the same MEC server. Instead, neighboring servers will collaborate to store and transcode different video chunks and consequently optimize the limited resources usage. Since we are dealing with chunks caching and processing, we propose to maximize the edge efficiency by studying the viewers watching pattern and designing a probabilistic model where chunks popularities are evaluated. Based on this model, popularity-aware policies, namely Proactive caching policy (PcP) and Cache replacement Policy (CrP), are introduced to cache only highest probably requested chunks. In addition to PcP and CrP, an online algorithm (PCCP) is proposed to schedule the collaborative caching and processing. The evaluation results prove that our model and policies give better performance than approaches using conventional replacement policies. This improvement reaches up to 50% in some cases.

Promoting smart and scalable remote health monitoring systems is challenging due to the enormous amount of collected data that needs to be processed and transferred given the limited network resources and battery-operated devices. Thus, the conventional cloud computing paradigm alone, is not always the most suitable solution for enabling such systems. In this context, we propose and implement a smart edge-based health system that aims at decreasing the system latency and energy consumption, while optimizing the delivery of the medical data. In particular, we formulate a multi-objective optimization framework that enables an edge node to dynamically adjust compression parameters and select the optimal radio access technology (RAT) while maintaining a trade-off between energy consumption, latency, and distortion. Furthermore, to evaluate and verify our framework, we develop an experimental testbed, where a data emulator is implemented to send EEG data to an edge node that classifies, compresses, and transfers the gathered data through the optimal RAT to the health cloud. Our experimental results show that the proposed system can offer about 30% energy savings while decreasing the delivery time to half of its value compared to a system that lacks edge processing capabilities.

The omnipresence of unmanned aerial vehicles, or drones, among civilians can lead to technical, security, and public safety issues that need to be addressed, regulated and prevented. Security agencies are in continuous search for technologies and intelligent systems that are capable of detecting drones. Unfortunately, breakthroughs in relevant technologies are hindered by the lack of open source databases for drone’s Radio Frequency (RF) signals, which are remotely sensed and stored to enable developing the most effective way for detecting and identifying these drones. This paper presents a stepping stone initiative towards the goal of building a database for the RF signals of various drones under different flight modes. We systematically collect, analyze, and record raw RF signals of different drones under different flight modes such as: off, on and connected, hovering, flying, and video recording. In addition, we design intelligent algorithms to detect and identify intruding drones using the developed RF database. Three deep neural networks (DNN) are used to detect the presence of a drone, the presence of a drone and its type, and lastly, the presence of a drone, its type, and flight mode. Performance of each DNN is validated through a 10-fold cross-validation process and evaluated using various metrics. Classification results show a general decline in performance when increasing the number of classes. Averaged accuracy has decreased from 99.7% for the first DNN (2-classes), to 84.5% for the second DNN (4-classes), and lastly, to 46.8% for the third DNN (10-classes). Nevertheless, results of the designed methods confirm the feasibility of the developed drone RF database to be used for detection and identification. The developed drone RF database along with our implementations are made publicly available for students and researchers alike.

Biometric recognition and analysis are among the most trusted features to be used by Implantable Medical Devices (IMDs). We aim to secure these devices by using these features in emergency scenarios. As patients can witness unpredictable lethal accidents, any implantable medical device should allow access to urgent medical interventions from legitimate parties. Any delay in providing immediate medical support can endanger the patient’s life. Hence, we propose in this work an authentication scheme that allows access to the implanted devices in emergency situations for only legitimate users. We have designed in the first place a scheme for authentication using Electrocardiogram instantaneous readings. Then, we joined the latter to a fixed biometric reading, which is fingerprint reading, to enable access to emergency medical teams. We have designed a scheme in a way to prevent attackers from accessing/hijacking the device even during emergency situations . This scheme has been assisted with elliptic curve cryptography to protect the wireless exchange of requested keys. The scheme relies on the instantaneous reading of the patient’s heartbeat and his/her fingerprint reading to create a secure key. This key will validate the authentication request of the new medical team. We have analyzed this scheme deeply to verify that they offer the necessary security for the patient’s life. We have tested if the wireless exchange of the key will expose the device’s privacy. We have also tested the accuracy of the authentication process to ensure a safe and a valid performance of the authentication process . The scheme has been designed with consideration to any hardware/software limitation that characterize any implantable medical device.

We investigate the model of a single primary-transceiver pair with multiple secondary-transceiver pairs. The secondary pairs can act as relays for the primary transmitter enabling access to its channel resources. Each secondary user (SU) is assumed to be a radio-frequency energy-harvester node. We formulate a framework that aims at specifying the optimal SU set that operates as relay nodes for the primary user (PU) data message. The set of the SUs is selected such that the SUs total throughput is maximized under a certain quality-of-service (QoS) requirement constraint on the PU target data rate. We propose both centralized and distributed approaches for solving the formulated optimization problems. The centralized approach is based on solving a convex optimization problem at the PU. On the other hand, the distributed approach leverages a Sackelberg game where all users interact to achieve the best relay-selection scheme and PU’s transmit power. We prove the uniqueness and Nash equilibrium of the considered Stackelberg game, and develop a game-theoretic relay and PU’s transmit power selection algorithm. We also introduce a fairness optimization-based scheme (FOBS) that aims at enhancing the fairness among the SUs under our proposed centralized approach. Our simulation results show the efficiency of our proposed schemes in terms of SUs total throughput.

Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent sensing systems. To achieve the full potential of such drones, it is necessary to develop new enhanced formulations of both common and emerging sensing scenarios. Namely, several fundamental challenges in visual sensing are yet to be solved including (1) fitting sizable targets in camera frames; (2) positioning cameras at effective viewpoints matching target poses; and (3) accounting for occlusion by elements in the environment, including other targets. In this article, we introduce Argus, an autonomous system that utilizes drones to collect target information incrementally through a two-tier architecture. To tackle the stated challenges, Argus employs a novel geometric model that captures both target shapes and coverage constraints. Recognizing drones as the scarcest resource, Argus aims to minimize the number of drones required to cover a set of targets. We prove this problem is NP-hard, and even hard to approximate, before deriving a best-possible approximation algorithm along with a competitive sampling heuristic which runs up to 100× faster according to large-scale simulations. To test Argus in action, we demonstrate and analyze its performance on a prototype implementation. Finally, we present a number of extensions to accommodate more application requirements and highlight some open problems.

Nowadays, Implantable Medical Devices (IMDs) rely mainly on wireless technology for information exchange. In spite of the many advantages wireless technology offers to patients in terms of efficiency, speed and ease; it puts the patients' health in serious danger if no proper security mechanism is deployed. The IMDs rely generally on resources that are relatively simple and sometimes require surgery to be altered. Therefore, common security mechanisms cannot be simply implemented in fear of consuming all the resources held for healthcare purposes. A certain balance between security and efficiency must be found in each IMD architecture. In this work, we try to avoid encryption algorithms to protect IMDs from Man-In-The-Middle (MITM) attacks. Encryption is generally used to protect communication confidentiality. However, this method is still a subject for replay and MITM attacks. In this work, we propose to create a signature protocol that protects IMDs from MITM attempts using less resources than common encryption/decryption algorithms. This signature algorithm is dynamic, which means that the signature output depends on a key and the same message can have different signatures if this key is different. This dynamic part will be introduced using chaotic generators.

Secure communication in medical devices is a pillar in ensuring patient's safety. However, in emergency cases, this can hinder the recovery of the patient. If an emergency team cannot give themselves access to the IMD without the user's assistance, they may be unable to offer any help. This paper introduces a security scheme for similar cases. By creating a backdoor to the IMDs, legal authentication may be performed with the IMD and gain access to it. This work presents a procedure for an emergency team to validate their actions to the IMD without the need of the patient's conscious. This is ensured using hashing function and elliptic curves for the security key generation. The seed that will be used will be the heart rhythm of the patient. The authentication process introduced will only allow access to the identified parties. An eavesdropper will be unable to interfere during emergency cases and can threaten patients' lives.

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 the last few years, the number of patients with chronic diseases requiring constant monitoring increased rapidly, which motivates researchers to develop scalable remote health applications. Nevertheless, the amount of transmitted real-time data through current dynamic networks with limited and restricted bandwidth, end-to-end delay, and transmission power; limits having an efficient transmission of the data. Motivated by the high energy consumed for transmission, applying data reduction techniques to the vital signs at the transmitter side present an efficient edge-based approach that significantly reduces the transmission energy. However, a new problem arises, which is the ability of receiving the data at the server side with an acceptable distortion rate (i.e., deformation of vital signs because of inefficient data reduction). In this paper, we introduce a Deep Learning (DL) approach based on Convolutional Auto-Encoder (CAE), to compress and reconstruct the vital signs in general and Electroencephalogram Signal (EEG) specifically with minimum distortion. The results show that using CAE provides efficient distortion rate while maximizing compression ratio. However, learning makes CAE application-specific, where each CAE model is designed specifically for a certain application.

Wireless medical devices play an important role in providing safety and privacy to patients suffering from major health issues. These light-weight devices can be worn inside or outside the patient's body and provide more convenience and reliable doctor-patient communication. However, the design, development, and usage of these devices play a critical role in present network paradigm. They are vulnerable to network threats and attacks which break the confidentiality, integrity and availability protocols in networking scenarios. Thus, it is important to have identification and authentication of only the authorized peoplewho can operate the device. This paper proposes Dynamic Time Warping (DTW) algorithm for providing trusted authentication and identification of only authorized people using ECG signal. Here, DTW algorithm is used to measure the correlation between different ECG signal records. Experiments were carried out to evaluate the proposed algorithm with a large database consisting of users of al1 ages, including abnormal ECG data and long span of time intervals between ECG recordings for evaluating the reliability of the proposed algorithm. Comparative evaluation of the proposed sy stem show ed that, it is not only efficient, but also light weight in comparison to the existing systems.

In the context of mobile Health (mHealth) applications, data are prone to several sources of contamination which would lead to false interpretation and misleading classification results. In this paper, a robust deep learning approach with low rank model is proposed to classify mHealth vital signs. Further-more, we propose using the Schatten-p norm instead of the classic nuclear norm since it has shown better recovery performance for several applications. We conduct a comprehensive study where we compare our method to the state-of-art methods and evaluate its performance with respect to the key system parameters. Our findings show indeed that combining deep network with dictionary learning model is effective for vital signs classification even in presence of 50% corruption with 8% improvement over the closest performance.

The immune system of the human body has massive potential in defending it against multiple harmful viruses and foreign bodies. All through their developmental history, human beings have been contaminated by micro-organisms. In order to restrict the nature, size, and intensity of these microbial invasions, human beings have inherent capabilities to deal with them. The human immune system is capable of protecting the body in the form of external barriers such as skin, cells, and tissues. Furthermore, it is capable of differentiating among the self and the non-self cells with the distinct properties and features that infiltrate the human body. This paper presents a case study of the human immune system in which we develop mathematical models of innate and adaptive immune system. Extensive simulations were carried out to study the effect of the foreign particles when the recovery mechanism occurs in the body. The results obtained, substantiate the reliability of the human immune mathematical model. Finally, we advocate that having a strong security and privacy around the human body can contribute in building a strong network system. For instance, the two layer immune inspired framework viz innate layer and adaptive layer can be instigated at the data layer and the control layer of Software Defined Networking respectively.

The emergence of Internet of Things (IoT) applications and rapid advances in wireless communication technologies have motivated a paradigm shift in the development of viable applications such as mobile-health (m-health). These applications boost the opportunity for ubiquitous real-time monitoring using different data types such as electroencephalography (EEG), electrocardiography (ECG), etc. However, many remote monitoring applications require continuous sensing for different signals and vital signs, which result in generating large volumes of real time data that requires to be processed, recorded, and transmitted. Thus, designing efficient transceivers is crucial to reduce transmission delay and energy through leveraging data reduction techniques. In this context, we propose an efficient data-specific transceiver design that leverages the inherent characteristics of the generated data at the physical layer to reduce transmitted data size without significant overheads. The goal is to adaptively reduce the amount of data that needs to be transmitted in order to efficiently communicate and possibly store information, while maintaining the required application quality-of-service (QoS) requirements. Our results show the excellent performance of the proposed design in terms of data reduction gain, signal distortion, low complexity, and the advantages that it exhibits with respect to state-of-the-art techniques since we could obtain about 50% compression ratio at 0% distortion and sample error rate.

With the emergence of smart grids, adopting dynamic energy pricing models has become both possible and desirable. With such a pricing dynamicity, great savings in energy costs can be achieved in telecommunication systems when energy is procured efficiently through carefully designed real-time resource schedulers. Broadly speaking, existing scheduling algorithms can be categorized into two classes: online and off-line. Off-line algorithms are not practical merely because of their need for prior knowledge of future system information. In this paper, we propose an efficient online power procurement and allocation scheduler that maximizes a long-term system utility function without the need for prior knowledge of future system information, where the system utility function is expressed in such a way that the gain coming from serving the users and the cost of the procured energy are traded off for one another. We propose an approach that allows us to derive closed-form instantaneous energy procurement and resource allocations that are functions only of the actual instantaneous system parameters. Our approach computes the optimal power procurement and users' allocation per time slot in an online fashion with very low computational complexity. Using simulations, we study the efficiency of the proposed approach under various parameters and quantify the energy costs that our approach can potentially save.

Implantable medical devices are being increasingly used to treat or monitor different medical conditions. For such purposes, wireless is the most desired communication scheme to be implemented in these devices. On the other hand, the wireless scheme increases security threats on these electronic devices, and any possibility of attack on the medical device may have lethal consequences. The patients usually have their implantable medical devices configured and monitored by their doctors. But for practical purposes, most of the time they possess a remote control for daily non-critical operations. This remote control can be considered as an open gate for attackers to target those medical devices and cause major harm. Motivated by this, we analyze in this paper the communication scheme implemented in the wireless devices, having as a starting point an Implantable Insulin Pump to develop a new protocol that can be used in the remote control-implantable device communication, and that will rely on plain text messages to avoid encryption implementation. Finally, we will analyze how the novelties introduced with this protocol can secure such a wireless link.

Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin Pump (WIP) to pass insulin into the body. A typical WIP framework comprises of an insulin pump, continuous glucose management system, blood glucose monitor, and other associated devices with all connected wireless links. This takes into consideration more granular insulin conveyance while achieving blood glucose control. WIP frameworks have progressively benefited patients, yet the multifaceted nature of the subsequent framework has posed in parallel certain security implications. This paper proposes a highly accurate yet efficient deep learning methodology to protect these vulnerable devices against fake glucose dosage. Moreover, the proposal estimates the reliability of the framework through the Bayesian network. We conduct comparative study to conclude that the proposed method outperforms the state of the art by over 15% in accuracy achieving more than 93% accuracy. Also, the proposed approach enhances the reliability of the overall system by 18% when only one wireless link is secured, and more than 90% when all wireless links are secured.

With the advancements in mobile sensors and health-care technologies, mobile health (mHealth) services are growing in demand. However, the deployment of mHealth's applications in rural and underdeveloped areas remains a major challenge, despite the investments made, largely due to unreliable communication infrastructures. In this paper, we propose delivering mHealth data in a highly disruptive wireless network using resource-limited mobile devices. We build on state-of-the-art opportunistic/DTN solutions, and propose two dynamic schemes that adapt to the level of congestion in the network. Our adaptive forwarding schemes dynamically tune data replication at forwarder nodes by engaging the most appropriate forwarding strategy at any given state, while incurring minimal overhead. In order to achieve such a goal, we propose a reactive and proactive approach to detecting or predicting congestion in the network respectively. We perform a set of data-driven simulations to compare the performance of our proposed schemes with state-of-the-art DTN forwarding algorithms. Our results show that our schemes achieve better delivery ratio for realistic mHealth applications in challenged networking environments.

Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent visual sensing systems. This potential motivated several research efforts to employ drones as standalone surveillance systems or to assist legacy deployments. However, several fundamental challenges remain unsolved including: 1) Adequate coverage of sizable targets; 2) Target orientation that render coverage effective only from certain directions; 3) Occlusion by elements in the environment, including other targets.

Deployment of Device-to-Device (D2D) communication within dense heterogeneous networks is a key solution in order to face the intense demand for high data rates and quality of the service in future 5G networks. However, this imposes challenges to develop innovative network selection mechanisms that account for both energy efficiency and user experience. In this paper, we propose a network association framework for heterogeneous systems where uplink data transfers can leverage direct device-to-infrastructure (D2I) links as well as underlay D2D connections. The proposed methodology is based on a distributed approach that optimizes the users' objectives, while accounting for the interference that underlaying D2D communication may cause, in order to enhance system performance and support reliable connectivity. Furthermore, to fully exploit the potential of D2D communication and prevent selfish behavior, a dynamic pricing strategy maximizing the profits of both source and relay nodes is proposed. Our results show that the proposed scheme provides significant performance gains and high efficiency compared to the centralized approach.

The proliferation of mobile health (mHealth) technologies in rural and disconnected areas has enabled novel communication challenges. These challenges exist due to the underdeveloped Internet infrastructure, wireless intermittent connectivity, and the large data generated by such applications that often overwhelms the weak infrastructure. In this paper, we investigate data delivery in such overloaded and sparse rural area scenarios, where patients deploy an mHealth service over a disruption-tolerant network (DTN). To quantify the feasibility and the challenges of such communication scenario, we investigate the performance of multiple opportunistic forwarding algorithms under different environments and loads. We highlight the shortcoming of most of these algorithms with regards to the lack of efficient resource management algorithms and the large overhead introduced by such state-of-the-art algorithms. Our data driven experiments highlight such limitations while comparing the performance of three classes of algorithms; flooding based, resource-aware, and controlled flooding algorithms.

Evolution of implantable medical devices for human beings has provided a radical new way for treating chronic diseases such as diabetes, cardiac arrhythmia, cochlear, gastric diseases etc. Implantable medical devices have provided a breakthrough in network transformation by enabling and accessing the technology on demand. However, with the advancement of these devices with respect to wireless communication and ability for outside caregiver to communicate wirelessly have increased its potential to impact the security, and breach in privacy of human beings. There are several vulnerable threats in wireless medical devices such as information harvesting, tracking the patient, impersonation, relaying attacks and denial of service attack. These threats violate confidentiality, integrity, availability properties of these devices. For securing implantable medical devices diverse solutions have been proposed ranging from machine learning techniques to hardware technologies. The present survey paper focusses on the challenges, threats and solutions pertaining to the privacy and safety issues of medical devices.

The emergence of mobile health (mHealth) systems has risen the challenges and concerns due to the sensitivity of the data involved in such systems. It is essential to ensure that these data are well delivered to the health monitoring center for accurate and perfect diagnosis and follow-up. Due to the wireless network constraints, these requirements become more challenging. In this paper, we propose a deep learning approach for EEG data compression in mHealth system. We show that the stacked autoencoder neural network architecture is efficient for EEG data compression. We conduct a comprehensive comparative study that demonstrates the effectiveness of our system for EEG compression in addition to preserving the total energy consumption.

The growing trend of networks densification has motivated integrating the Device-to-Device (D2D) communication with the dense heterogeneous networks in order to face the intense demand of high data rates and enhance network performance. However, this imposes challenges to develop innovative network association schemes that consider energy efficiency while meeting application quality requirements. In this context, we propose an efficient network association mechanism over D2D-enabled heterogeneous wireless networks. We consider different Quality of service (QoS) requirements, networks characteristics, and application requirements, in order to obtain an efficient- distributed solution that grasps the conflicting nature of the various objectives. The proposed methodology leverages a user-centric networks association approach over D2D-enabled heterogeneous wireless networks to enhance system performance and support reliable connectivity. Our results demonstrate the efficiency of the proposed scheme compared to the state-of-the-art techniques that ignore the potential of D2D communication.

We propose a practical, simple and hardware friendly, yet novel and very efficient, angle of arrival (AoA) estimation system. Our intuitive, two-phases cross-correlation based system requires a switched beam antenna array with a single radio frequency chain. Our system cross correlates a reference omni-directional signal with a set of received directed signals to determine the AoA. Practicality and high efficiency of our system are demonstrated through performance and complexity comparisons with multiple signal classification algorithm.

In this paper, we present a joint compression and classification approach of EEG and EMG signals using a deep learning approach. Specifically, we build our system based on the deep autoencoder architecture which is designed not only to extract discriminant features in the multimodal data representation but also to reconstruct the data from the latent representation using encoder-decoder layers. Since autoencoder can be seen as a compression approach, we extend it to handle multimodal data at the encoder layer, reconstructed and retrieved at the decoder layer. We show through experimental results, that exploiting both multimodal data intercorellation and intracorellation 1) Significantly reduces signal distortion particularly for high compression levels 2) Achieves better accuracy in classifying EEG and EMG signals recorded and labeled according to the sentiments of the volunteer.

Opportunistic spectrum access is a propitious technique to overcome the under-utilization of spectrum bands. In this work, we design an experimental test-bed for evaluating an un-slotted spectrum access scheme under real indoor environment conditions. To this end, we use the USRP software defined radio platform along with the GNURadio software that incorporates the PHY and MAC functions and modules. Our contribution is multi-fold. First, we design a MAC protocol to integrate the packet based transmission of the coexisting PU/SU network, while compensating for spectrum sensing imperfection as well as collision detection faults. Second, we evaluate the USRP-induced latency (delay) and show that it has random behavior. We work around it to obtain a fixed packet transmission time which is crucial for the channel access scheme realization and evaluation. Third, we perform helping experiments to quantify the spectrum sensing imperfection in terms of false alarm and detection probabilities. We also quantify the imperfection in collision detection. Finally, we evaluate the performance of the whole channel access scheme and compare its results to the classical sense-transmit scheme. We show that 28.5% increase in SU throughput can be achieved for the same PU packet collision rate.

Implantable Medical Devices (IMDs) are a growing industry regarding personal health care and monitoring. In addition, they provide patients with efficient treatments. In general, these devices use wireless communication technologies that may require synchronization with the medical team. Even though wireless technology offers satisfaction to the patient's daily life, it is still prone to security threats. Many malicious attacks on these devices can directly affect the patient's health in a lethal way. Using insecure wireless channels for these devices offers adversaries easy ways to steal the patient's private data and hijack these systems. This can cause damage to patients and render their devices unusable. In the aim of protecting these devices, we explore in this paper a new way to create symmetric encryption keys to encrypt the wireless communication held by the IMDs. This key generation will rely on chaotic systems to obtain synchronized Pseudo-Random keys that will be generated separately in the system. This generation is in a way that the communication channel will avoid a wireless key exchange, protecting the patient from key theft. Moreover, we will explore the performance of this generator from a cryptographic point of view, ensuring that these keys are safe to use for communication encryption.

Estimating the number of sources is a key task in many array signal processing applications. Conventional algorithms such as Akaike's information criterion (AIC) and minimum description length (MDL) suffer from underestimation and overestimation errors. In this study, the authors propose four algorithms to estimate the number of sources in white Gaussian noise. The authors’ proposed algorithms are categorised into two main categories; namely, sample correlation matrix (CorrM) based and correlation coefficient matrix (CoefM) based. Their proposed algorithms are applied on the CorrM and CoefM eigenvalues. They propose to use two decision statistics, which are the moving increment and the moving standard deviation of the estimated eigenvalues as metrics to estimate the number of sources. For their two CorrM based algorithms, the decision statistics are compared to thresholds to decide on the number of sources. They show that the conventional process to estimate the threshold is mathematically tedious with high computational complexity. Alternatively, they define two threshold formulas through linear regression fitting. For their two CoefM based algorithms, they re‐define the problem as a simple maximum value search problem. Results show that the proposed algorithms perform on par or better than AIC and MDL as well as recently modified algorithms at medium and high signal‐to‐noise ratio (SNR) levels and better at low SNR levels and low number of samples, while using a lower complexity criterion function.

Implantable medical devices (IMDs) are electronic devices implanted within human body for diagnostic, monitoring, and therapeutic purposes. It is imperative to guarantee that IMDs are completely secured since the patient's life is closely bound to the robustness and effectiveness of IMDs. Intuitively, we have to ensure that only the authorized medical personnel and IMD programmer can access the IMD. However, in recent years, several attacks have been reported which can successfully compromise a number of IMD products, e.g., stealing the sensitive health data and issuing fake commands. Up to now, there is no commonly agreed and well-recognized security standards and the protection of IMD is still an open problem. In this paper, we present a comprehensive survey of the existing literature on IMD security, with a focus on the access control schemes to prevent unauthorized access. Specifically, we first reviewed the security incidents, IMD threat model and the development of regulations for IMD security. Next, we classified existing IMD access control schemes based on architecture, type of keys used, access control channel, and logic. We also analyzed how different access control models can be adopted to secure IMD. Besides, we particularly discussed the viability of online authentication and low/zero power authentication in the IMD context.

The rapid advances in wireless communication and sensor technologies facilitate the development of viable mobile-Health applications that boost opportunity for ubiquitous real-time healthcare monitoring without constraining patients' activities. However, remote healthcare monitoring requires continuous sensing for different analog signals which results in generating large volumes of data that needs to be processed, recorded, and transmitted. Thus, developing efficient in-network data reduction techniques is substantial in such applications. In this paper, we propose an in-network approach for data reduction, which is based on fuzzy formal concept analysis. The goal is to reduce the amount of data that is transmitted, by keeping the minimal-representative data for each class of patients. Using such an approach, the sender can effectively reconfigure its transmission settings by varying the target precision level while maintaining the required application classification accuracy. Our results show the excellent performance of the proposed scheme in terms of data reduction gain and classification accuracy, and the advantages that it exhibits with respect to state-of-the-art techniques.

The spread of the wireless communication devices resulted in a remarkable growth of power consumption for telecommunication systems to satisfy the users' demands. Thus, new revolutionary solutions are needed to enhance their energy efficiency. In this paper, we propose an enhanced energy efficiency scheme for wireless systems based on sharing the energy between different cells to optimize the overall cost. The objective is to encourage cooperation between cells/micro-grids to exchange additional/needed power function of their respective throughput demand and energy availability. We propose a pricing scheme for the energy exchange and a power procurement and resource allocation strategy per operator to maximize the revenue of each operator. We derive analytic expressions for power procurement considering uniform power allocation over users. Then, we consider adaptive power allocation per user depending on channels and Quality of Service (QoS) requirements and propose a scheme to derive the global power procurement solution. Our simulation results show the feasibility of the proposed scheme allowing to enhance the energy efficiency of the hybrid powered wireless system while optimizing the revenue of the different involved operators.

Wireless EEG monitoring systems have been successfully used for seizure detection outside clinical settings. The wireless EEG sensor nodes consume a considerable amount of battery energy to acquire, encode and transmit the data to the server side. In this paper, we introduce energy-efficient monitoring systems to increase the sensors' battery lifetime. Specifically, we propose a feature extraction method that is robust to artifacts and can effectively select the most discriminant features relevant to seizures. Second, we show how to use the missing at random (MAR) method to reduce the energy required at the sensor node for data transmission without compromising the seizure detection accuracy at the server side. Finally, we show how the expectation maximization (EM) method is used at the server side to accurately substitute the missing values. The performance of the proposed scheme is compared to those of the state-of-the art methods, and is shown to achieve less power consumption without compromising the seizure detection accuracy.

Wireless electroencephalogram (EEG) sensors have been successfully applied in many medical and computer brain interface classifications. A common characteristic of wireless EEG sensors is that they are low powered devices, and hence an efficient usage of sensor energy resources is critical for any practical application. One way of minimizing energy consumption by the EEG sensors is by reducing the number of EEG channels participating in the classification process. For the purpose of classifying EEG signals, we propose a directed acyclic graph (DAG)-based channel selection algorithm. To achieve this objective, the EEG sensor channels are first realized in a complete undirected graph, where each channel is represented by a node. An edge between any two nodes indicates the collaboration between these nodes in identifying the system state; and the significance of this collaboration is quantified by a weight assigned to the edge. The complete graph is then reduced into a directed acyclic graph that encodes the knowledge of the non-increasing order of the channel ranking for each cognitive task. The channel selection algorithm utilizes this directed graph to find a maximum path such that the total weight of this path satisfies a predefined threshold. It has been demonstrated experimentally that channel utilization has been reduced by 50% in the worst case scenario for a three-state system and an EEG sensor with 14 channels; and the best classification accuracy obtained is 81%.

Smart Grid (SG)-aware cellular networks are expected to decrease their energy consumption and consequently decrease the global carbon emissions. At the same time, cellular operators are required to meet the end-user requirements in terms of throughput. In this paper we propose a novel strategy to pave the way for the cellular operators to integrate with the SG. Our strategy is based on Dynamic Spectrum Assignment (DSA) approach. We formulate the trade-off situation of the operators as a reward function. The objective is to maximize the reward while decreasing the energy consumption. We study homogeneous, spatial-heterogeneous and spatio-temporal heterogeneous types of traffic. We study the performance of the proposed strategy in a dynamic electricity pricing context. We show that by adapting the spectrum utilization properly, the cellular operator can achieve higher rewards while using less energy compared to an operator deploying classical reuse, for low and intermediate traffic loads. We show also that the proposed DSA-based strategy is capable of adapting to the system dynamics; electricity pricing as well as end-users traffic.

Unmanned Aerial Vehicles are miniature air-crafts that have proliferated in many military and civil applications. Their affordability allows for tasks to be held with not just one but a fleet of UAVs. One of the problems that arise with the use of multi-UAVs is the multi-UAV path planning and assignment problem. We propose three algorithms that aim at assigning energy efficient trajectories for a fleet of UAVs. Our optimal path planning solution (OPP) is formulated using a Mixed Integer Linear Programming model (MILP). We also propose two other heuristic solutions that are greedy in nature; namely, Greedy Least Cost (GLC) and First Detect First Reserve (FDFR). To aid with collision avoidance, we adopt the concept of space discretization, and present a more realistic view of the space a UAV occupies. The comparative study of our proposed solutions reveals insightful trade-offs between energy consumption and complexity.

Rising numbers of mobile devices and wireless access technologies motivate network operators to leverage spectrum across multiple radio access networks, in order to significantly enhance quality of service as well as network capacity. However, there is a substantial need to develop innovative network selection mechanisms that consider energy efficiency while meeting application quality requirements. In this context, this paper proposes an efficient network selection mechanism over heterogeneous wireless networks. We consider different performance aspects, as well as network characteristics and application requirements, so as to obtain an efficient solution that grasps the conflicting nature of the various objectives and addresses this ultimate tradeoff. The proposed methodology advocates a user-centric approach toward the utilization of heterogeneous wireless networks to enhance system performance and support reliable connectivity.

Many spectrum sensing techniques have been proposed to allow a secondary user (SU) to utilize a primary user's (PU) spectrum through opportunistic access. However, few of them have considered the tradeoff between accuracy and energy consumption by taking into account the selfishness of the (SUs) in a distributed network. In this work, we consider spectrum sensing as a game where the payoff is the throughput of each SU/player. Each SU chooses between two actions, parallel individual sensing and sequential cooperative sensing techniques. Using those techniques, each SU will distributively decide the existence of the PU. Due to the repetitive nature of our game, we model it using evolutionary game (EG) theory which provides a suitable model that describes the behavioral evolution of the actions taken by the SUs. We address our problem in two cases, when the players are homogeneous and heterogeneous respectively. For the sake of stability, we find the equilibria that lead to evolutionary stable strategies (ESS) by proving that our system is evolutionary asymptotically stable, in both cases, under certain conditions on the sensing time and the false alarm probability.

Energy harvesting has emerged as a promising technique which helps to increase the sustainability of wireless networks. In this paper, we consider a network with a single source, single destination and a single relay equipped with multiple antennas. We develop an optimization framework that exploits the energy harvested from the source radio frequency signals with smart antenna selection schemes at the relay node. Our main target is to minimize the source power and the antennas' circuit power jointly subject to quality of service constraints on the rate. To overcome the computational complexity of the optimization problem, we propose two special case schemes, namely Fixed Source Power Antenna Selection (FSP-AS) and All Receive Antenna Selection (AR-AS). Also, we suggest two sub-optimal heuristic schemes with low complexity and compare their performance with the optimization problem solutions numerically. The simulation results show the gain of our optimal scheme in terms of energy efficiency, which can be up to 80% as compared to solutions proposed in the literature.

In this study, the authors discuss relay selection schemes with the objective to minimise outage probability for a network consisting of a single source, multiple relays and a single destination. The relays are powered by radio frequency signals from the source. For a successful transmission, at least one of the relay nodes should be able to decode the source signals and have enough energy to relay the information to the destination. The authors assume that a relay node cannot decode information and harvest energy from the source signals simultaneously. The authors formulate an optimisation problem to minimise outage probability for the system. The relay selection scheme and the outage performance depend on the availability of the channel state information (CSI) on the source‐relay and the relay‐destination links. Based on the availability of the CSI on the relay‐destination link, the authors propose relay selection schemes for different scenarios and evaluate the performance numerically. The results show that the availability of the CSI on the relay‐destination link at the relay node helps to improve the outage performance considerably. The authors characterise the outage probability for the schemes analytically; and numerically compute the optimal number of relays which provide the optimal outage performance for a given scheme.

In this paper, we study and analyze cooperative cognitive radio networks with arbitrary number of secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data transmission demand. We consider a multi-packet transmission framework that allows multiple SUs to transmit simultaneously because of dirty-paper coding. We propose power allocation and scheduling policies that optimize the throughput for both PU and SU with minimum energy expenditure. The performance of the system is evaluated in terms of throughput and delay under different opportunistic relay selection policies. Toward this objective, we present a mathematical framework for deriving stability conditions for all queues in the system. Consequently, the throughput of both primary and secondary links is quantified. Furthermore, a moment generating function approach is employed to derive a closed-form expression for the average delay encountered by the PU packets. Results reveal that we achieve better performance in terms of throughput and delay at lower energy cost as compared with equal power allocation schemes proposed earlier in the literature. Extensive simulations are conducted to validate our theoretical findings.

Abstract The vast majority of existing secret key generation protocols exploit the inherent randomness of the wireless channel as a common source of randomness. However, independent noise added at the receivers of the legitimate nodes affects the reciprocity of the channel. In this paper, we propose a new simple technique to generate the secret key that mitigates the effect of noise. Specifically, we exploit the estimated channel to generate a secondary random process (SRP) that is common between the two legitimate nodes. We compare the estimated channel gain and phase to a preset threshold. The moving differences between the locations at which the estimated channel gain and phase exceed the threshold are the realization of our SRP. We study the properties of our generated SRP and derive a closed form expression for the probability mass function of the realizations of our SRP. We simulate an orthogonal frequency division multiplexing system and show that our proposed technique provides a drastic improvement in the key bit mismatch rate between the legitimate nodes when compared with the techniques that exploit the estimated channel gain or phase directly. In addition to that, the secret key generated through our technique is longer than that generated by conventional techniques. Moreover, we compute the conditional probabilities used to estimate the secret key capacity. Copyright © 2016 John Wiley & Sons, Ltd.

The broadcast nature of wireless communications imposes the risk of information leakage to adversarial users or unauthorized receivers. Therefore, information security between intended users remains a challenging issue. Most of the current physical layer security techniques exploit channel randomness as a common source between two legitimate nodes to extract a secret key. In this paper, we propose a new simple technique to generate the secret key. Specifically, we exploit the estimated channel to generate a secondary random process (SRP) that is common between the two legitimate nodes. We compare the estimated channel gain and phase to a preset threshold. The moving differences between the locations at which the estimated channel gain and phase exceed the threshold are the realization of our SRP. We simulate an orthogonal frequency division multiplexing (OFDM) system and show that our proposed technique provides a drastic improvement in the key bit mismatch rate (BMR) between the legitimate nodes when compared to the techniques that exploit the estimated channel gain or phase directly. In addition to that, the secret key generated through our technique is longer than that generated by conventional techniques.

Wireless powered communication network (WPCN) is an emerging area of research where energy is transferred from the access point to the mobile terminals in the downlink and information is transferred in the uplink. In the context of WPCN, we study the effects of applying different downlink/uplink scheduling schemes on the system performance in terms of achieved system throughput and fairness. In contrast to conventional wireless networks, where data scheduling determines the system sum rate and fairness behaviour, downlink energy scheduling contributes equally in WPCNs. We propose fairness based downlink energy transfer and compare different combinations of downlink and uplink scheduling schemes. Furthermore, we propose a new metric for downlink energy transfer for the special case of finite energy buffer and evaluate its effect on the achieved system throughput and fairness. Our numerical results show that a complete throughput fairness cannot be achieved as long as fairness is not employed in energy transfer in downlink regardless of the uplink scheduling scheme.

The paper cogitates about a comparative simulation for various distillation, reconciliation, and privacy amplification techniques that are used to generate secure symmetric physical layer keys. Elementary wireless model of two mobile nodes in the presence of a passive eavesdropper is used to perform the comparison process. Important modifications are proposed to some phases' techniques in order to increase the performance of the generation process as a whole. Different metrics were used for comparison in each phase, in the distillation phase, we use the Bit Mismatch Rate (BMR) for different SNR values to compare various extracted random strings of the two intended nodes. On the other hand, the messaging rate and process complexity is exploited to estimate the performance of the compared techniques in both reconciliation and privacy amplification phases. The randomness and entropy properties of the keys are verified using the NIST suite, all the generated keys are 128 bits, it is shown that the success rate of the keys passing the randomness tests depends strongly on the techniques that are used through the three generation phases.

Estimating the number of sources received by an antenna array have been well known and investigated since the starting of array signal processing. Accurate estimation of such parameter is critical in many applications that involve prior knowledge of the number of received signals. Information theoretic approaches such as Akaikes information criterion (AIC) and minimum description length (MDL) have been used extensively even though they are complex and show bad performance at some stages. In this paper, a new algorithm for estimating the number of sources is presented. This algorithm exploits the estimated eigenvalues of the auto correlation coefficient matrix rather than the auto covariance matrix, which is conventionally used, to estimate the number of sources. We propose to use either of a two simply estimated decision statistics, which are the moving increment and moving standard deviation as metric to estimate the number of sources. Then process a simple calculation of the increment or standard deviation of eigenvalues to find the number of sources at the location of the maximum value. Results showed that our proposed algorithms have a better performance in comparison to the popular and more computationally expensive AIC and MDL at low SNR values and low number of collected samples.

In the context of physical layer security, a physical layer characteristic is used as a common source of randomness to generate the secret key. Therefore an accurate estimation of this characteristic is the core for reliable secret key generation. Estimation of almost all the existing physical layer characteristic suffer dramatically at low signal to noise (SNR) levels. In this paper, we propose a novel secret key generation algorithm that is based on the estimated angle of arrival (AoA) between the two legitimate nodes. Our algorithm has an outstanding performance at very low SNR levels. Our algorithm can exploit either the Azimuth AoA to generate the secret key or both the Azimuth and Elevation angles to generate the secret key. Exploiting a second common source of randomness adds an extra degree of freedom to the performance of our algorithm. We compare the performance of our algorithm to the algorithm that uses the most commonly used characteristics of the physical layer which are channel amplitude and phase. We show that our algorithm has a very low bit mismatch rate (BMR) at very low SNR when both channel amplitude and phase based algorithm fail to achieve an acceptable BMR.

This paper presents an energy efficient and quality of service aware dynamic cell zooming algorithm for dense heterogeneous networks. The exponential growth of mobile data traffic would lead to dense deployment of small base stations and eventually higher energy consumption in Fifth Generation (5G) wireless networks. We formulate a dynamic cell zooming and base stations sleep optimization algorithm for dense heterogeneous networks as a Linear Programming (LP) problem in order to not only minimize the system power consumption but also to guarantee the quality of service to end user. This is possible by optimally zooming the coverage area of macro base stations and small cells based upon real time traffic conditions. We characterize the optimal as well as provide an approximate solution, which, however, performs very closely to the optimum. The extensive performance evaluation of our proposed dynamic cell zooming algorithm shows that our proposed algorithm can significantly decrease both system energy consumption and outage probability.

This paper studies fundamental throughput and delay tradeoffs in cognitive radio systems with cooperative secondary users. We focus on randomized cooperative policies, whereby the secondary user (SU) serves either its own queue or the primary users (PU) relayed packets queue with certain service probability. The proposed policy opens room for trading the PU delay for enhanced SU delay, and vice versa, depending on the application QoS requirements. Towards this objective, the system's stable throughput region is characterized. Furthermore, the moment generating function approach is employed and generalized for our system to derive closed-form expressions for the average packet delay for both users. The accuracy of these expressions is validated through simulations. Analytical and simulation results reveal that the service probability can steer the system into prioritizing PU's traffic at the expense of SU's QoS, or vice versa, independently from the admission probability. Alternatively, the ability of the admission probability to control the throughput and delay at the PU or the SU depends on the selected value for the service probability as well as the channel conditions. Finally, it is shown how the service and admission probabilities could be used to achieve the desired QoS level to both PU and SU.

Abstract In this paper, we address the problem of distributed interference management of femtocells that share the same frequency band with macrocells using distributed multi‐agent Q‐learning. We formulate and solve two problems representing two different Q‐learning algorithms, namely, femto‐based distributed and sub‐carrier‐based distributed power controls using Q‐learning (FBDPC‐Q and SBDPC‐Q). FBDPC‐Q is a multi‐agent algorithm that works on a global basis, for example, deals with the aggregate macrocell and femtocell capacities. Its complexity increases exponentially with the number of sub‐carriers in the system. Also, it does not take into consideration the sub‐carrier macrocell capacity as a constraint. To overcome these problems, SBDPC‐Q is proposed, which is a multi‐agent algorithm that works on a sub‐carrier basis, for example, sub‐carrier macrocell and femtocell capacities. Each of FBDPC‐Q and SBDPC‐Q works in three different learning paradigms: independent (IL), cooperative (CL), and weighted cooperative (WCL). IL is considered the simplest form for applying Q‐learning in multi‐agent scenarios, where all the femtocells learn independently. CL and WCL are the proposed schemes in which femtocells share partial information during the learning process in order to strike a balance between practical relevance and performance. We prove the convergence of the CL paradigm when used in the FBDPC‐Q algorithm. We show via simulations that the CL paradigm outperforms the IL paradigm in terms of the aggregate femtocell capacity, especially in networks with large number of femtocells and large number of power levels. In addition, we propose WCL to address the CL limitations. Finally, we evaluate the robustness and scalability of both FBDPC‐Q and SBDPC‐Q, against several typical dynamics of plausible wireless scenarios (fading, path loss, random activity of femtocells, etc.). We show that the CL paradigm is the most scalable to large number of femtocells and robust to the network dynamics compared with the IL and WCL paradigms. Copyright © 2014 John Wiley & Sons, Ltd.

Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems. Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes. In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI. The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands. In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers. Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets. The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders.

In this paper, we propose a relay selection strategy for randomly distributed multiple relays. A single relay is selected for transmitting signal from a fixed source to a fixed destination which requires minimum total transmit power. The lack of perfect channel state information (CSI) at the relay-destination link has been taken into consideration while selecting the relay. We consider the relay movement with low mobility features and evaluate the performance based on the total transmission power requirement. In addition, relay selection region is obtained to improve the overall performance of the system. Simulation results show that a similar performance in terms of total transmit power can be achieved at lower complexity if we select the relay from a specific relay selection region as compared to considering all the relays in the network.

In this paper, we study and analyze cooperative cognitive radio networks with multiple secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data demand. The proposed scheme leverages the spectral efficiency of the system via allowing two SUs to transmit simultaneously thanks to dirty-paper coding. We propose a power allocation policy that minimizes the average transmitted power at each SU. Moreover, we are concerned with enhancing the throughput of both primary and secondary links. Towards this objective, we investigate multiple opportunistic relay selection policies. We develop a mathematical framework for deriving stability conditions for the queues involved in the system using outage probabilities. Results reveal that we achieve better performance in terms of average power and throughput as compared to uniform power allocation schemes proposed earlier in literature. Extensive numerical simulations are conducted to validate our theoretical findings.

Wireless sensor and actuator networks have been extensively deployed for enhancing industrial control processes and supply-chains, and many forms of surveillance and environmental monitoring. The availability of low-cost mobile robots equipped with a variety of sensors in addition to communication and computational capabilities makes them particularly promising in target coverage tasks for ad hoc surveillance, where quick, low-cost or non-lasting visual sensing solutions are required, e.g. in border protection and disaster recovery. In this paper, we consider the problem of low complexity placement and orientation of mobile cameras to cover arbitrary targets. We tackle this problem by clustering proximal targets, while calculating/estimating the camera location/direction for each cluster separately through our cover-set coverage method. Our proposed solutions provide extremely computationally efficient heuristics with only a small increase in number of cameras used, and a small decrease in number of covered targets.

This paper presents an energy efficient relay deployment algorithm that determines the optimal location and number of relays for future wireless networks, including Long Term Evolution (LTE)-Advanced heterogeneous networks. We formulate an energy minimization problem for macro-relay heterogeneous networks as a Mixed Integer Linear Programming (MILP) problem. The proposed algorithm not only optimally connects users to either relays or eNodeBs (eNBs), but also allows eNBs to switch into inactive mode. This is possible by enabling relay-to-relay communication which forms the basis for relays to act as donors for neighboring relays instead of eNBs. Moreover, it relaxes traffic load of some eNBs in order to allow them to enter the inactive mode. We characterize the optimal as well as provide an approximate solution, which, however, performs very closely to the optimum. Our performance evaluation shows that an optimal relay deployment with relays acting as donors can significantly improve system energy efficiency.

Wireless sensor technologies can provide the leverage needed to enhance patient-caregivers collaboration through ubiquitous access and direct communication, which promotes smart and scalable vital sign monitoring of the chronically ill and elderly people live an independent life. However, the design and operation of BASNs are challenging, because of the limited power and small form factor of biomedical sensors. In this paper, an adaptive compression technique that aims at achieving low-complexity energy-efficient compression subject to time delay and distortion constraints is proposed. In particular, we analyze the processing energy consumption, then an energy consumption optimization model with constraints of distortion and time delay is proposed. Using this model, the Personal Data Aggregator (PDA) dynamically chooses the optimal compression parameters according to real-time measurements of the packet delivery ratio (PDR) or individual users. To evaluate and verify our optimization model, we develop an experimental testbed, where the EEG data is sent to the PDA that compresses the gathered data and forwards it to the server which decompresses and reconstructs the original signal. Experimental testbed and simulation results show that our adaptive compression technique can offer significant savings in the delivery time with low complexity and without affecting application accuracies.

This paper studies distributed interference management for femtocells that share the same frequency band with macrocells. We propose a multi-agent learning technique based on distributed Q-learning, called subcarrier-based distributed resource allocation using Q-learning (SBDRA-Q). SBDRA-Q operates under three different learning paradigms: Independent (IL), Cooperative (CL) and Weighted Cooperative (WCL). In the IL paradigm, all femtocells learn independently from each other. In both, CL and WCL, femtocells share partial information during the learning process in order to enhance their performance. The results show that WCL outperforms both CL and IL in terms of aggregate femtocell capacity, while slightly affecting fairness. Also, the results show that CL and WCL are more robust, when compared to IL, to new femtocells being deployed during the learning process. Finally, we show SBDRA-Q achieves higher aggregate femtocell capacity under the three learning paradigms when compared to a power allocation scheme (SBDPC-Q) that was proposed in the literature.

Cyber-Physical Systems (CPS) have the promise of presenting the next evolution in computing with potential applications that include aerospace, transportation, and various automation systems. These applications motivate advances in the different sub-fields of CPS such as mobile computing, context awareness, and computer vision. However, deploying and testing complete CPSs is known to be a complex and expensive task. In this paper, we present the design, implementation, and evaluation of Up and Away (UnA): a testbed for Cyber-Physical Systems that use Unmanned Aerial Vehicles (UAVs) as their main physical component. UnA aims to abstract the control of physical system components to reduce the complexity of UAV oriented CPS experiments. UnA provides APIs to allow for converting CPS algorithm implementations, developed typically for simulations, into physical experiments using a few simple steps. We present two scenarios of using UnA's API to bring mobile-camera-based surveillance algorithms to life, thus exhibiting the ease of use and flexibility of UnA.

Signal-to-noise ratio (SNR) estimation is an important parameter that is required in any receiver or communication systems. It can be computed either by a pilot signal data-aided approach in which the transmitted signal would be known to the receiver, or without any knowledge of the transmitted signal, which is a non-data-aided (NDA) estimation approach. In this paper, a NDA SNR estimation algorithm for QPSK signal is proposed. The proposed algorithm modifies the existing Signal-to-Variation Ratio (SVR) SNR estimation algorithm in the aim to reduce its bias and mean square error in case of negative SNR values at low number of samples of it. We first present the existing SVR algorithm and then show the mathematical derivation of the new NDA algorithm. In addition, we compare our algorithm to two baselines estimation methods, namely the M2M4 and SVR algorithms, using different test cases. Those test cases include low SNR values, extremely high SNR values and low number of samples. Results showed that our algorithm had a better performance compared to second and fourth moment estimation (M2M4) and original SVR algorithms in terms of normalized mean square error (NMSE) and bias estimation while keeping almost the same complexity as the original algorithms.

In this paper, we study and analyze fundamental throughput-delay tradeoffs in cooperative multiple access for cognitive radio systems. We focus on the class of randomized cooperative policies, whereby the secondary user (SU) serves either the queue of its own data or the queue of the primary user (PU) relayed data with certain service probabilities. The proposed policy opens room for trading the PU delay for enhanced SU delay. Towards this objective, stability conditions for the queues involved in the system are derived. Furthermore, a moment generating function approach is employed to derive closed-form expressions for the average delay encountered by the packets of both users. Results reveal that cooperation expands the stable throughput region of the system and significantly reduces the delay at both users. Moreover, we quantify the gain obtained in terms of the SU delay under the proposed policy, over conventional relaying that gives strict priority to the relay queue.

Mobile Health (mHealth) systems leverage wireless and mobile communication technologies to provide healthcare stakeholders with innovative tools and solutions that can revolutionize healthcare provisioning. Body Area Sensor Networks (BASNs) is part of the mHealth system that focuses on the acquisition by a group of biomedical sensors of vital signals. However, the design and operation of BASNs are challenging, because of the limited power and small form factor of biomedical sensors. The source encoding and data transmission are the two dominant power-consuming operations in wireless monitoring system. Therefore, in this paper, a cross-layer framework that aims at minimizing the total energy consumption subject to delay and distortion constraints is proposed. The optimal encoding and transmission energy are computed to minimize the energy consumption in a delay constrained wireless BASN. This cross-layer framework is proposed, across Application-MAC-Physical layers. At large scale networks and due to heterogeneity of wireless BASNs, centralized cross-layer optimization becomes less efficient and more complex. Therefore, a distributed cross-layer optimization has been considered in this paper. The proposed solution has close-to-optimal performance with lower complexity. Simulation results show that the distributed scheme achieves the compromise between complexity and efficiency in energy consumption compared to centralized scheme.

In this paper, we consider energy efficient multiuser scheduling. Packet loss tolerance of the applications is exploited to minimize average system energy. There is a constraint on average packet drop rate and maximum number of packets dropped successively (bursty loss). A finite buffer size is assumed. We propose a scheme which schedules the users opportunistically according to the channel conditions, packet loss constraints and buffer size parameters. We assume imperfect channel state information at the transmitter side and analyze the scheme in large user limit using stochastic optimization techniques. First, we optimize system energy for a fixed buffer size which results in a corresponding statistical guarantee on successive packet drop. Then, we determine the minimum buffer size to achieve a target (improved) energy efficiency for the same (or better) statistical guarantee. We show that buffer size can be traded effectively to achieve system energy efficiency for target statistical guarantees on packet loss parameters.

Relay technology has been included in long term evolution (LTE) cellular system through 3GPP Release 10. Relay‐based LTE cellular system is one of the most promising technologies to enhance the throughput and coverage of access network. Deployment of relays in a cell opens many issues of frequency/time domain radio resource allocation and frequency planning. In this study, the authors consider the uplink of LTE relay‐assisted network and formulate the joint scheduling and intra‐cell interference mitigation as a non‐linear optimisation problem. They take into account the projection of the gradient of user's utility function over the user's rate vector based on Lagrangian method. They propose a novel transformation of scheduling problem into combinatorial Knapsack optimisation to convert the optimisation problem into binary integer non‐linear program. Finally, they present an alternate solution through a low complexity algorithm to allocate resources based on group of resource blocks, whereas considering the contiguity constraint. Simulation results show that the proposed scheme is near optimal with a much lower complexity order.

Scheduling and spectrum allocation are tasks affecting the performance of cognitive radio wireless networks, where heterogeneity in channel availability limits the performance and poses a great challenge on protocol design. In this paper, we present a distributed algorithm for scheduling and spectrum allocation with the objective of maximizing the network's throughout subject to a delay constraint. During each time slot, the scheduling and spectrum allocation problems involve selecting a subset of links to be activated, and based on spectrum sensing outcomes, allocate the available resources to these links. This problem is addressed as an aggregate utility maximization problem. Since the throughput of any data flow is limited by the throughput of the weakest link along its end-to-end path, the utility of each flow is chosen as a function of this weakest link's throughput. The throughput and delay performance of the network are characterized using a queueing theoretic analysis, and throughput is maximized via the application of Lagrangian duality theory. The dual decomposition framework decouples the problem into a set of subproblems that can be solved locally, hence, it allows us to develop a scalable distributed algorithm. Numerical results demonstrate the fast convergence rates of the proposed algorithm, as well as significant performance gains compared to conventional design methods.

This paper studies the joint design of routing and resource allocation algorithms in cognitive radio based wireless mesh networks. The mesh nodes utilize cognitive overlay mode to share the spectrum with primary users. Prior to each transmission, mesh nodes sense the wireless medium to identify available spectrum resources. Depending on the primary user activities and traffic characteristics, the available spectrum resources will vary between mesh transmission attempts, posing a challenge that the routing and resource allocation algorithms have to deal with to guarantee timely delivery of the network traffic. To capture the channel availability dynamics, the system is analyzed from a queuing theory perspective, and the joint routing and resource allocation problem is formulated as a non-linear integer programming problem. The objective is to minimize the aggregate end-to-end delay of all the network flows. A distributed solution scheme is developed based on the Lagrangian dual problem. Numerical results demonstrate the convergence of the distributed solution procedure to the optimal solution, as well as the performance gains compared to other design methods. It is shown that the joint design scheme can accommodate double the traffic load, or achieve half the delay compared to the disjoint methods.

In a sensor network, each sensor has a limited energy supply. Therefore, it is critical to minimize the power consumed by each sensor to maximize its lifetime. Video sensor networks differ from conventional sensor networks in the fact that video compression at the sensor node consumes a significant amount of power comparable to that used for communication. This poses new challenges, and renders power efficient algorithms developed for sensor networks not suitable for video sensor networks. In this paper, we develop an algorithm for the minimization of the total consumed power by jointly optimizing the encoding power, the transmission power, and the source rate at each sensor node. Furthermore, MAC layer resource allocation is incorporated into the minimization problem. Video signal distortion due to compression, and packet losses in the wireless channel are studied as well. An efficient solution algorithm is developed using Lagrange duality to solve the minimization problem. Through numerical results, the trade-off between allocating power to the encoding process or the transmission process is characterized. Moreover, the interaction between the nodes competing for channel resources, and how this affects their power consumption and distortion levels is studied.

Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities. EEG signals capture important information pertinent to different physiological brain states. In this paper, we propose an efficient framework for evaluating the power-accuracy trade-off for EEG-based compressive sensing and classification techniques in the context of epileptic seizure detection in wireless tele-monitoring. The framework incorporates compressive sensing-based energy-efficient compression, and noisy wireless communication channel to study the effect on the application accuracy. Discrete cosine transform (DCT) and compressive sensing are used for EEG signals acquisition and compression. To obtain low-complexity energy-efficient, the best data accuracy with higher compression ratio is sought. A reconstructed algorithm derived from DCT of daubechie's wavelet 6 is used to decompose the EEG signal at different levels. DCT is combined with the best basis function neural networks for EEG signals classification. Extensive experimental work is conducted, utilizing four classification models. The obtained results show an improvement in classification accuracies and an optimal classification rate of about 95% is achieved when using NN classifier at 85% of CR in the case of no SNR value. The satisfying results demonstrate the effect of efficient compression on maximizing the sensor lifetime without affecting the application's accuracy.

Body Area Sensor Networks (BASNs) for healthcare applications have gained significant research interests recently due to the growing number of patients with chronic diseases requiring constant monitoring. Because of the limited power source and small form factors, BASNs have distinguished design and operational challenges, particularly focusing on energy optimization. In this paper, an Energy-Delay-Distortion cross-layer design that aims at minimizing the total energy consumption subject to data delay deadline and distortion threshold constraints is proposed. The optimal encoding and transmission energy are computed to minimize the total energy consumption in a delay constrained wireless body area sensor network. This cross-layer framework is proposed, across Application-MAC-Physical layers, under a constraint that all successfully received packets must have their delay smaller than their corresponding delay deadline and with maximum distortion less than the application distortion threshold. Due to the complexity of the optimal-proposed solution, sub-optimal solutions are also proposed. These solutions have close-to-optimal performance with lower complexity. In this context, there is complexity/energy-consumption trade-off, as shown in the simulation results.

Growing number of patients with chronic diseases requiring constant monitoring has created a major impetus to developing scalable Body Area Sensor Networks (BASNs) for remote health applications. In this paper, to anatomize, control, and optimize the behavior of the wireless EEG monitoring system under the energy constraint, we develop an Energy-Rate-Distortion (E-R-D) analysis framework. This framework extends the traditional distortion analysis by including the energy consumption dimension. Using the E-R-D model, an Energy-Delay-Distortion cross-layer design that aims at minimizing the total energy consumption subject to data delay deadline and distortion threshold constraints is proposed. The source encoding and data transmission are the two dominant power-consuming operations in wireless EEG monitoring system. Therefore, in the proposed cross-layer design, the optimal encoding and transmission energy are computed to minimize the energy consumption in a delay constrained wireless BASN. This cross-layer framework is proposed, across Application-MAC-Physical layers, under a constraint that all successfully received packets must have their delay smaller than their corresponding delay deadline and with maximum distortion less than the application distortion threshold. In addition to that, for efficient use of the bandwidth, a variable bandwidth allocation scheme that assigns the time-frequency slots to the sensor nodes is proposed, which results in significant energy savings over the conventional constant bandwidth allocation scheme, as shown in the simulation results.

In this paper, we address the problem of distributed interference management of cognitive femtocells that share the same frequency range with macrocells using distributed multi-agent Q-learning. We formulate and solve three problems representing three different Q-learning algorithms: namely, centralized, femto-based distributed and subcarrier-based distributed power control using Q-learning (CPC-Q, FBDPC-Q and SBDPC-Q). CPC-Q, although not of practical interest, characterizes the global optimum. Each of FBDPC-Q and SBDPC-Q works in two different learning paradigms: Independent (IL) and Cooperative (CL). The former is considered the simplest form for applying Q-learning in multi-agent scenarios, where all the femtocells learn independently. The latter is the proposed scheme in which femtocells share partial information during the learning process in order to strike a balance between practical relevance and performance. In terms of performance, the simulation results showed that the CL paradigm outperforms the IL paradigm and achieves an aggregate femtocells capacity that is very close to the optimal one. For the practical relevance issue, we evaluate the robustness and scalability of SBDPC-Q, in real time, by deploying new femtocells in the system during the learning process, where we showed that SBDPC-Q in the CL paradigm is scalable to large number of femtocells and more robust to the network dynamics compared to the IL paradigm.

We consider wireless mesh networks in which the nodes are utilizing cognitive radios and try to opportunistically gain access to spectrum resources. In such networks, the timely delivery of the traffic is a challenging task due to the licensed (primary) users' activities and their traffic characteristics. To overcome this challenge we propose an algorithm that minimizes the end-to-end delay through joint routing and spectrum resources allocation. The network is analyzed from a queueing theory perspective to capture the effects of dynamic spectrum availability on mesh network's traffic. The joint routing and resource allocation problem is formulated as a non-linear integer programming problem, for which we propose a decentralized solution based on the Lagrangian dual problem. Results demonstrate the performance of our proposed algorithm, as well as the efficiency of the decentralized implementation.

In this paper, we propose a distributed reinforcement learning (RL) technique called distributed power control using Q-learning (DPC-Q) to manage the interference caused by the femtocells on macro-users in the downlink. The DPC-Q leverages Q-Learning to identify the sub-optimal pattern of power allocation, which strives to maximize femtocell capacity, while guaranteeing macrocell capacity level in an underlay cognitive setting. We propose two different approaches for the DPC-Q algorithm: namely, independent, and cooperative. In the former, femtocells learn independently from each other, while in the latter, femtocells share some information during learning in order to enhance their performance. Simulation results show that the independent approach is capable of mitigating the interference generated by the femtocells on macro- users. Moreover, the results show that cooperation enhances the performance of the femtocells in terms fairness and aggregate femtocell capacity.

The link delivery probability in a wireless mesh network is most accurately determined only by experiment, as it depends on many factors, including environment characteristics, transmission power, distance between transmitter and receiver, channel fading, and background noise. This paper provides an insightful framework for characterizing link performance results produced at Qatar University (QU) wireless mesh test-bed under harsh environmental conditions, including heavy buildings structure, mixed indoor/outdoor architecture, weather conditions, and coexistence with high interfering wireless transmission. Indeed, we are interested in looking at the link quality behavior for indoor and outdoor environments. In the outdoor environment, the effect of different weather conditions such as high humidity and sandstorms, which are typical in the Qatari weather, will be analyzed. Regarding the indoor environment, we investigate the effect of unique cell structure, and heavy lab machinery of the engineering building on the link performance. Furthermore, we study different wireless mesh node configurations and equipments' effect on performance and network connectivity. The causes behind link instability and performance shortcoming of the QU wireless mesh network (QUMESH) are identified. Thus, the content of this paper provides an input for developing the criteria for the test-bed and performing the evaluation of new proposed adaptive mechanisms.

A primary challenge in multi casting video in a wireless LAN to multiple clients is to deal with the client diversity -- clients may have different channel characteristics and hence receive different numbers of transmissions from the AP. A promising approach to overcome this problem is to combine multi-resolution (layered) video coding with interlayer network coding. The fundamental challenge in such an approach is to determine the strategy of coding the packets across different layers that maximizes the number of decoded layers at all clients. This paper makes three contributions. (1) We first show that even for one client, the previously proposed canonical triangular scheme for inter-layer network coding can perform poorly. We show how to enhance the triangular scheme by incorporating the estimated target number of layers which significantly improves its effectiveness. (2) We show that such an enhanced triangular scheme still performs poorly for multiple clients with diverse channel characteristics, which motivates the need for searching for the optimal coding strategy. The naive way of searching for the optimal strategy is computationally prohibitive. We present several optimizations that drastically reduce the complexity of exhaustively searching for the optimal strategy, making it feasible in real time. (3) Finally, we design and evaluate an on line video delivery scheme, Percy, to be deployed at a proxy behind the AP of a wireless LAN. Our simulation results show that Percy outperforms the previous inter-layer coding heuristic by up to 22-80% with varying numbers of clients.

A primary challenge in multicasting video in a wireless LAN is to deal with the client diversity -- clients may have different channel characteristics and hence receive different numbers of transmissions from the AP. A promising approach to overcome this problem is to combine scalable video coding techniques such as MRC or MDC, which divide a video stream into multiple substreams, with inter-layer network coding. The fundamental challenge in such an approach is to determine the strategy of coding the packets across different layers that maximizes the number of decoded layers at all clients. In [7], the authors showed that inter-layer NC indeed helps the delivery of MRC coded media over the WiFi, and proposed how to efficiently search for the optimal coding strategies online.

We investigate an outage optimal adaptive resource allocation scheme for the upstream of two-hop OFDMA based decode-and-forward cooperative relay systems. The objective of this work is to design resource allocation strategy, which addresses the needs of the users minimizing their outage probability. This scheme utilizes the subchannel-pairing and proportional fairness in two-hop multiuser multirelay network to achieve the user required percentage throughput ( i.e., if the user's application can tolerate 5% outage then guaranteeing the 100% availability is actually a wastage of scarce radio resources). Using outage optimal resource allocation scheme, we achieve the complimentary fairness along with the required data rate on each node. Simulation results show that the proposed scheme achieve better throughput-fairness trade-off compared to proportional fair scheduling (PFS) and MaxMin resource allocation schemes.

Relaying is one of the key techniques considered by the 3GPP LTE-Advanced as part of 4G cellular technologies, aiming to increase the coverage and capacity of the network especially for the edge nodes. In this work, we have considered the uplink scheduling of an LTE network with the help of positioned relay nodes. We have projected the entire problem as a constrained optimization problem. As a solution, we have considered sub-gradient based approach to divide the utility maximization of all nodes, including relay nodes, in the cell into sub-problems of utility optimization of each individual node. Based on this solution we have proposed a scheduling algorithm to allocate resource blocks across all nodes. Numerical calculations and results have been shown to justify that relay nodes can potentially achieve the trade-off between throughput and fairness.

Heterogeneous multicast is an efficient communication scheme especially for multimedia applications running over multihop networks when multicast receivers in the same session require service at different rates commensurate with their capabilities. In this paper, we address the problem of resource allocation for a set of heterogeneous multicast sessions over multihop wireless networks. We propose an iterative algorithm that achieves the optimal rates for a set of heterogeneous multicast sessions such that the aggregate utility for all sessions is maximized. We present the formulation of the multicast resource allocation problem as a non-linear optimization model and highlight the cross-layer framework that can solve this problem in a distributed ad hoc network environment with asynchronous computations. Our simulations show that the algorithm achieves optimal resource utilization, guarantees fairness among multicast sessions, provides flexibility in allocating rates over different parts of the multicast sessions and adapts to changing conditions such as dynamic channel capacity and node mobility. Our results show that the proposed algorithm not only provides flexibility in allocating resources across multicast sessions, but also increases the aggregate system utility and improves the overall system throughput by almost 30% compared to homogeneous multicast.

Multicast-based data communication is an efficient communication scheme especially in multihop ad hoc networks where the MAC layer is based on one-hop broadcast from one source to multiple receivers. The problem of resource allocation for a set of homogeneous multicast sessions over multihop wireless network is addressed. An iterative algorithm is proposed that achieves the optimal rates for a set of multicast sessions such that the aggregate utility for all sessions is maximised. The authors demonstrate analytically and through simulations that the algorithm achieves optimal resource utilisation while guaranteeing fairness among multicast sessions. The algorithm in network environments with asynchronous distributed computations has been further analysed. Two implementations for the algorithm based on different network settings are presented and show that the algorithm not only converges to the optimal rates in all network settings but it also tracks network changing conditions, including mobility and dynamic channel capacity.

Multicast-based data communication is an efficient communication scheme especially in multihop ad hoc networks where the MAC layer is based on one-hop broadcast from one source to multiple receivers. In this paper, we address the problem of resource allocation for a set of homogeneous multicast sessions over multihop wireless networks. We propose an iterative algorithm that achieves the optimal rates for a set of multicast sessions such that the aggregate utility for all sessions is maximized. We demonstrate analytically and through simulations that the algorithm achieves optimal resource utilization while guaranteeing fairness amongst multicast sessions. We further analyze the algorithm in network environments with asynchronous distributed computations. We present two implementations for our algorithm based on different network settings and show that the algorithm not only converges to the optimal rates in all network settings but it also tracks network changing conditions including mobility and dynamic channel capacity.

We consider the problem of QoS-based partitioning of traffic streams for a link model with adjustable service levels. Specifically, we consider a link model with variable service levels which may be mapped to a finite number of MPLS Label- Switched-Paths (LSPs). Our target is to partition a set of traffic streams each with arbitrary local QoS-demand into a small number of classes and find the service level for each class while optimizing the residual-allocated-resources as a result of the traffic partitioning. The residual allocated resources will be measured by the service quantization overhead which is the summation of the differences between the required QoS and the offered service level for all traffic streams. We formulate the partitioning process as a Dynamic Programming problem. We then present two polynomial time algorithms to obtain the QoSbased optimal partition with bandwidth allocation. Our results indicate that using 4 or 5 service levels will accomplish the tradeoff between complexity and granularity irrespective of the distribution of the QoS requirements.

Multicast-based data communication is an efficient communication scheme especially in multihop ad hoc networks where the MAC layer is based on one-hop broadcast from one source to multiple receivers. In this paper, we discuss a framework of rate allocation for a set of homogeneous multicast sessions over multihop wireless networks. We propose a framework that facilitates the online calculation of the optimal rates for a set of multicast sessions such that the aggregate utility for all sessions is maximized. This framework is used to steer the entire network of ad hoc nodes towards the optimal point in real time using a totally distributed and asynchronous environment settings. We present a series of implementations based on different network settings and show that not only convergence to the optimal rates is attained in all these network settings but also network changing conditions such as mobility and dynamic channel capacity can be tracked in real time

We investigate the QoS-based classification of traffic streams for a multi-class link model with predetermined service levels. Specifically, we consider a link model with fixed service levels or fixed class weights which may be represented by a finite number of MPLS label-switched-paths (LSPs). Our target is to classify a set of traffic streams each with arbitrary local QoS-demand into a small number of service levels while optimizing the residual-allocated-resources as a result of the traffic classification. The residual-allocated-resources are measured by the service-quantization-overhead which is the summation of the differences between the required QoS and the offered service level for all traffic streams. We formulate the classification as a dynamic-programming problem. We then present a group of polynomial-time-algorithms to obtain the optimal classification for soft and hard QoS requirements. We also present the concept of "differentiation factor" and show the effect of this factor on minimizing the quantization-overhead

This paper presents the architecture and implementation of a multi-service streaming system for broadcast of MPEG-4 elementary streams. The proposed system promotes the use of the push channels model for information distribution. The system architecture accommodates two main layers for broadcast service management and media delivery. The broadcast service management layer uses the publisher-subscriber model for service announcement and clients' subscription. The proposed media delivery layer of the MPEG-4 media streams is based on the recommendations made by part 6 of the MPEG-4 standard, Delivery Multimedia Integration Framework (DMIF). However, the elementary specification of the standard's control plane for broadcast instance motivated the design of separate layer for broadcast service management. The paper also presents the contributive features that motivated our client/server implementation for broadcast of MPEG-4 streams including the "client random access", and the "inter-streams synchronization". These features allow clients to access the MPEG-4 media streams at any time during the presentation of the broadcast service.