In the smart grid, data communication between smart meters and utility servers should be authentic, private, have integrity while being accessible. To mitigate the risks of potential attacks, securing these two-way communications is crucial. Equally important is maintaining near real-time communication and avoiding significant delays when extra security levels are involved. Existing research on smart grids has not simultaneously tackled the issues of security, communication speed, and network scalability. In this work, we propose a novel delay-optimized blockchain solution for securing cryptographic communication between consumers and the utility in a smart grid. Our solution, based on EOS smart contracts, Edge computing, asymmetric Cryptographic functions, and Group signatures ($E E C G$), treats data communication as transactions that are asymmetrically encrypted and signed in groups before being stored on the EOS blockchain, ensuring confidentiality, privacy, availability, and low cost. The use of edge computing reduces the computational burden of smart meters, increases transaction speed, enhances data privacy, and improves scalability. Furthermore, an optimization problem for associating smart meters with edge nodes is formulated to minimize data exchange and processing delays over the blockchain, facilitating near real-time secure data access.

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.

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.

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.

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.

Owing to ample potential and versatile capabilities of artificial intelligence to solve intricate scientific problems, two regression-based artificial neural network (ANN) models are proposed to design and optimize nano-structured meta-atoms. The proposed forward predicting ANN depicts that considering the complete structural and material information of the cylindrical nano-pillar meta-atoms could predict the corresponding electromagnetic (EM) response (amplitude and phase of transmission) with a mean squared error (MSE) as low as$\mathbf{2.1}\times \mathbf{10}^{-\mathbf{3}}$. Thus, it replaces the conventional EM simulations performed using high-end commercial software's, while significantly saving time and computational resources. Inverse design deep-learning model is also presented, which is connected with the pre-trained forward model and trained in a tandem architecture to provide an optimum set of dimensions and material, given the target response as its input. Furthermore, a comparative study regarding the number of hidden layers of the ANN and the amount of training dataset size is performed for the proposed forward and tandem inverse models to analyze the effect of considering extra underlying physics related information, i.e., wavelength regime and the EM spectral information. This study reveals that considering the extra information can lead to a significant reduction in the obtained MSE. Specifically, the proposed model could achieve a decent MSE even with a smaller amount of training dataset. Hence, the use of artificial intelligence models significantly reduces the training time and computational complexity of the proposed solution.

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.

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.

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.

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.

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.

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 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.

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

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.

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

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.

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.

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

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.

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 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.

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 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.

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.

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.

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.

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.

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, a cross-layer algorithm that aims at minimizing the end-to-end transmission energy subject to a packet delay deadline constraint is proposed. The optimal transmission energy and rates, and the optimal route are computed to minimize the end-to-end total transmission energy in a delay constraint wireless mesh network. A cross-layer optimization framework is proposed under a constraint that all successfully received packets must have their end-to-end delay smaller than their corresponding delay deadline. In addition to the optimal solution, a suboptimum solution is also proposed. This solution has close-to-optimal performance with lower complexity. The simulation results show that, for the same delay constraint and bit error rate (BER), the optimum proposed algorithm has less energy consumption than routing algorithms that consider delay constraint only.

In this paper, the energy-limited wireless multihop mesh networks are considered. Minimizing the total transmission energy in the network, while satisfying the applications' delay constraints, is the target of our optimization problem. To achieve this goal, energy-efficient design should be supported across all layers of the protocol stack through a cross-layer design. This paper proposes energy-efficient joint routing, scheduling, and link adaptation strategies that minimize the total transmission energy in the network. The proposed cross-layer energy-aware algorithms allocate resources, dynamically according to channel quality and traffic load so as to minimize the overall transmission energy, while satisfying the given packets delay and bit error rate (BER) constraints. The resources considered are the transmitted power and modulation in the physical layer, scheduling in the link layer and routing in the network layer. In addition to the proposed optimal solution, suboptimum solutions are presented as well. The simulation results show that, under the same conditions, the proposed optimum algorithm has less energy consumption than routing algorithms that consider delay constraints only. Moreover, simulations show that the suboptimum algorithms have performance near to the optimum algorithm with a huge reduction in the complexity.