The rapid evolution of beyond fifth-generation (B5G) and sixth-generation (6G) networks is reshaping mobile edge computing (MEC) to support large-scale, heterogeneous Internet of Things (IoT) deployments and complex cyber-physical systems (CPS). Conventional data-driven intelligence in MEC traditionally relies on centralized learning paradigms that often fail to meet the privacy, latency, scalability, and adaptability requirements in distributed and resource-constrained environments. To address these shortcomings, the objective of our work in this paper is to investigate the paradigm shift toward distributed learning and demonstrate how its co-design with emerging communication and system-level technologies can enable scalable and trustworthy intelligence for next-generation IoT and CPS. Building on this objective, we conduct a systematic survey of recent studies and analyze twelve key enabling technologies, including concept drift adaptation, transformers, TinyML, blockchain, integrated sensing and communication (ISAC), digital twins, explainable AI, federated learning and unlearning, adversarial ML, meta-learning, and multi-armed bandits. The surveyed literature is organized using a unified taxonomy and an integrated conceptual pipeline, which clarifies how these enablers interact across sensing, communication, computation, trust, and adaptation layers of IoT and CPSs. The main outcomes of this study include: (i) a comprehensive taxonomy characterizing enabling technologies for distributed edge intelligence, (ii) a comparative synthesis of representative works highlighting common architectural patterns and evaluation practices, and (iii) the identification of research gaps, critical trade-offs, and open challenges, particularly related to model robustness, energy efficiency, data heterogeneity, and secure real-time inference. Overall, this survey establishes a structured foundation and forward-looking roadmap for designing scalable, privacy-preserving, and intelligent distributed learning systems in future B5G- and 6G-enabled IoT and CPS environments.

Most existing image-based parking space detection and classification methods assume that all training data reside in a single, centralized location—an unrealistic scenario that yields models unable to generalize to new parking lots. Furthermore, privacy concerns prevent lot owners from sharing raw images, limiting collaboration. To overcome these challenges, we present ParkFL, the first federated learning framework for parking space detection and classification that trains models across distributed client sites without exchanging raw image data. Built on NVFlare, ParkFL demonstrates model-agnosticism through evaluation on two deep-learning architectures. We benchmark four federated algorithms—FedAvg, FedProx, FedOpt, and SCAFFOLD—using real-world datasets. Despite training on non-centralized, heterogeneous data, ParkFL achieves 99.5% mAP, matching the accuracy of state-of-the-art centralized models on the same parking lots. When evaluated on images from different parking lots, ParkFL significantly outperforms models trained solely on individual-site data, which achieve 25.6% mAP, even though ParkFL never accesses raw images from other sites. Communication overhead remains below 3% of total training time, demonstrating a scalable, privacy-preserving solution with nearly state-of-the-art performance. We release ParkFL code at https://github.com/ahmedmbakr/ParkFL.

While millimeter-wave (mmWave) communication in emerging Sixth Generation (6G) networks offers high bandwidth for the Internet of Things (IoT), it is highly susceptible to blockages, necessitating intelligent relay positioning. Current static relay selection methods are typically unable to adapt to dynamic blockage conditions in IoT deployments, leading to frequent connectivity outages. In this paper, we address this by introducing Hierarchical Mobile Adaptive Relay Control (H-MARC), a reinforcement-learning framework for intelligent mobile relay positioning in IoT networks. H-MARC decomposes relay positioning into strategic long-term planning and tactical real-time control using Twin Delayed Deep Deterministic (TD3) Policy Gradient algorithms. We further present a context-aware extension of H-MARC, referred to as H-MARC-C, by exploiting WiFi context information for predictive blockage detection through cross-band correlation analysis. Computer-based simulations demonstrate that H-MARC achieves 4.2 bits/s/Hz spectral efficiency with 87% connection reliability, while H-MARC-C attains 4.8 bits/s/Hz with 93% reliability representing 35% and 55% improvements over static methods. The framework reduces blockage adaptation time from 2.3s to 0.3s and achieves 80% higher energy efficiency (4.2×106bits/J) compared to reactive approaches, with 24% faster convergence than flat RL (reinforcement learning) baselines, enabling ultra-reliable communications for demanding IoT applications including industrial automation and smart cities.

The evolution toward 6G wireless networks demands ultra-dense cell-free massive MIMO (Multiple-Input Multiple-Output) systems that can deliver unprecedented connectivity while preserving data privacy and enabling rapid adaptation to dynamic conditions. Current resource allocation approaches rely on centralized deep reinforcement learning frameworks that create scalability bottlenecks, require extensive training periods, and violate emerging privacy regulations through global data aggregation. This paper introduces a Privacy-Preserving Federated Meta-Learning (PP-FML) framework that addresses these fundamental limitations through distributed intelligence and instant adaptation mechanisms. The proposed approach enables each access point to learn optimal resource allocation policies locally while collaboratively improving system-wide performance through cryptographically secure gradient sharing. The meta-learning component provides few-shot adaptation capabilities, allowing networks to respond to new conditions within minutes rather than hours. Comprehensive performance evaluation demonstrates that PP-FML achieves 42.3% sum rate improvement, 28.8% better energy efficiency (15.2 bits/Hz/J), sub-minute adaptation latency (51 seconds), and strong privacy guarantees epsilon 1.0 differential privacy compared to centralized approaches while maintaining complete data privacy and enabling rapid adaptation to changing network conditions. The framework scales linearly to ultra-dense deployments exceeding 300 access points per square kilometer with constant per-node computational complexity, making it suitable for practical 6G network deployment with heterogeneous device populations including mobile users and diverse applications.

The emergence of 6G-enabled Vehicle-to-Everything (V2X) networks has created unprecedented demand for ultra-reliable, low-latency spectrum allocation across heterogeneous entities including vehicles, IoT devices, and industrial systems. Current spectrum allocation methods suffer from exponential computational complexity, extensive information sharing requirements, and poor scalability in dense networks. This paper proposes AMADRL (Attention-based Multi-Agent Deep Reinforcement Learning), a novel framework employing dual critic networks with multi-head self-attention mechanisms for intelligent spectrum allocation. The dual critic architecture resolves individual-collective optimization conflicts through local critics for independent entity optimization and a global critic with attention-based coordination. Our approach significantly reduces information sharing requirements while handling heterogeneous QoS demands across diverse entity types. Comprehensive experimental evaluation comparing AMADRL against state-of-the-art baselines including MADDPG, MAAC, QMIX, attention-based methods (A-DDPG, MHA-DQN), and game-theoretic approaches reveals that AMADRL achieves superior performance across multiple metrics including spectrum utilization efficiency, interference mitigation, and network scalability, while preserving user privacy and satisfying strict latency constraints required by safety-critical and industrial use cases.

Unmanned aerial vehicles (UAV)-mounted simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) systems can provide full-dimensional coverage and flexible deployment opportunities in future 6G-enabled IoT networks. However, practical imperfections such as jittering and airflow of UAV could affect the phase shift of STAR-RIS, and consequently degrade network security. In this respect, this paper investigates the impact of phase shift errors on the secrecy performance of UAV-mounted STAR-RIS-assisted IoT systems. More specifically, we consider a UAV-mounted STAR-RIS-assisted non-orthogonal multiple access (NOMA) system where IoT devices are grouped into two groups: one group on each side of the STAR-RIS. The nodes in each group are considered as potential Malicious nodes for the ones on the other side. By modeling phase estimation errors using a von Mises distribution, an analytical closed-form expressions for the ergodic secrecy rates under imperfect phase adjustment are derived. An optimization problem to maximize the weighted sum secrecy rate (WSSR) by optimizing the UAV placement is formulated and is then solved using a linear grid-based algorithm. Monte Carlo simulations are provided to validate the analytical derivations. The impact of phase estimation errors on system’s secrecy performance is analyzed, providing critical insights for the practical realisation of STAR-RIS deployments for secure UAV-enabled IoT networks.

Healthcare is revolutionized by the integration of the Internet of Medical Things (IoMT) and artificial intelligence (AI), enabling real-time patient monitoring, advanced predictive analytics, and personalized treatment plans. However, the existing AI healthcare models are typically trained offline on static datasets, limiting their adaptability to the dynamic nature of health data. This may result in compromising models' accuracy and healthcare decision-making, rendering them obsolete. Moreover, attackers may exploit concept drift by injecting frequent data shifts, which can exhaust healthcare institutions' resources. To address this research gap, we propose a novel adaptive, secure, and efficient concept drift detection framework for healthcare. First, a robust deep learning (DL) model is devised to leverage its high-confidence probability to detect data drift efficiently without relying on labeled data. Then, we propose a customized consortium blockchain network that leverages group signatures to ensure anonymity and unlinkability of patients' health data. It also utilizes a dualledger structure, facilitating a unified drift detection model and enabling authenticated, drift-specific data sharing among medical centers. This design protects against data tampering and falsely claiming drift incidents. Our experiments, conducted on a real health monitoring dataset, show that our concept drift detection approach achieves comparable drift detection performance to the existing methods while reducing the computational time by 52.35%, and achieving an accuracy of 98.43 with our offline model and a 95% accuracy with the online adaptive model.

With the rise of Software-Defined Networking (SDN) for managing traffic and ensuring seamless operations across interconnected devices, challenges arise when SDN controllers share infrastructure with deep learning (DL) workloads. Resource contention between DL training and SDN operations, especially in latency-sensitive IoT environments, can degrade SDN's responsiveness and compromise network performance. Federated Learning (FL) helps address some of these concerns by decentralizing DL training to edge devices, thus reducing data transmission costs and enhancing privacy. Yet, the computational demands of DL training can still interfere with SDN's performance, especially under the continuous data streams characteristic of IoT systems. To mitigate this issue, we propose REDUS (Resampling for Efficient Data Utilization in Smart-Networks), a resampling technique that optimizes DL training by prioritizing misclassified samples and excluding redundant data, inspired by AdaBoost. REDUS reduces the number of training samples per epoch, thereby conserving computational resources, reducing energy consumption, and accelerating convergence without significantly impacting accuracy. Applied within an FL setup, REDUS enhances the efficiency of model training on resource-limited edge devices while maintaining network performance. In this paper, REDUS is evaluated on the CICIoT2023 dataset for IoT attack detection, showing a training time reduction of up to 72.6% with a minimal accuracy loss of only 1.62%, offering a scalable and practical solution for intelligent networks.

Network slicing enables multiple virtual networks on shared 6G infrastructure, but dynamic resource allocation across Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC) services remains challenging. We present a hybrid Convolutional Neural Network-Long Short-Term Memory Architecture (CNN-LSTM) framework with service-specific utility functions that optimize resources while ensuring Quality of Service (QoS) guarantees under dynamic conditions. Our approach integrates spatial pattern recognition with temporal prediction, incorporating constraint measurement and lightweight optimization. Experimental results on a testbed with 100 base stations and 10,000 users demonstrate superior performance over state-of-the-art methods. The framework achieves significant improvements in resource utilization, QoS satisfaction, and energy efficiency with real-time inference capability. Convergence analysis validates system stability, confirming practical deployment feasibility for latency-critical 6G applications.

Electromagnetic interference (EMI) still poses a serious threat to reliable wireless communication, particularly in crowded and hostile electromagnetic environments. While space-time block coding (STBC) and other conventional diversity techniques have been extensively employed to mitigate multipath fading, their ability to mitigate EMI is inherently limited, especially when interference uniformly affects every component of the antenna. The spectral efficiency of Fluid Antenna (FA)-enabled Multiple-Input Multiple-Output (MIMO) systems can be significantly enhanced by employing Index Modulation (IM). However, current FA-enabled IM (FAIM)-aided MIMO systems suffer from considerable performance degradation due to strong spatial correlation in the wireless channel, which is caused by the dense port distribution of the FA. In this paper, we propose an effective approach that integrates a Fluid Antenna System (FAS) with STBC to enhance system robustness in the presence of EMI. Simulation results show that integrating an FAS into a dual-antenna receiver employing STBC significantly improves spectral efficiency and reliability under EMI. At low signal-to-noise ratios (SNRs), the presence of EMI reduces Bit Error Rate (BER) performance by more than 12% when compared to the traditional arrangement without EMI. In contrast to the system affected by EMI without FAS, the BER curve closely resembles the scenario without EMI when FAS selection is used, resulting in a ~10% BER reduction at Eb/N0= 6 dB. Similarly, the achievable rate bridges the performance gap caused by EMI by improving by more than 1.5 bps/Hz over the SNR range. These findings confirm that FAS is effective in reducing EMI and improving communication in unfriendly settings.

The dynamic nature of user mobility and density in indoor WiGig networks poses a significant challenge to seamless handover, particularly in the 60 GHz band, where small and closely clustered channel gain values hinder effective decision-making. To address this, we propose a generalizable deep reinforcement learning (DRL)-based handover that integrates a novel reward function designed to amplify channel gain differentials, thereby improving the convergence speed and decision accuracy of learning agents. We investigate the performance of state-of-the-art DRL algorithms—Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C)—enhanced through advanced hyperparameter-tuning techniques, including grid search, random search, optuna, and hyperopt. Among these, DQN combined with grid search yields the best overall performance, surpassing A2C by 48% in average rolling reward and achieving 32% faster convergence than PPO.To assess generalization, we evaluate the merged DQN agent trained across varying user density scenarios (1–8 users) against expert agents specialized for individual densities and a high-density-trained agent tested across all scenarios. Our results reveal that the merged agent exhibits robust and consistent performance across all densities, indicating strong generalization capability. In contrast, the high-density agent suffers performance degradation of up to 13% when exposed to unseen scenarios, underscoring its limited adaptability. While expert agents perform optimally within their specific environments, their deployment complexity renders them impractical for real-time systems. These findings highlight the importance of training DRL agents across diverse scenarios to achieve scalable and generalizable handover solutions in dense and dynamic WiGig networks.

The escalating integration of deep neural networks (DNNs) in autonomous vehicles underscores the urgency of fortifying them against adversarial attacks. This paper presents a novel approach to enhance the robustness of convolutional neural networks (CNNs) in self-driving cars through a combination of adversarial mitigation techniques: they are randomization, image padding, and, most uniquely, the addition of random Gaussian noise after convolution layers. Our specialized neural network demonstrates consistent steering control under various attack scenarios, avoiding the over-steering or under-steering issues observed in standard models. As 6 G networks emerge with their ultra-reliable low-latency communication capabilities, our research contributes to the security foundation necessary for autonomous vehicles in this coming era, where resilience against adversarial manipulation will be crucial for maintaining safety in increasingly connected transportation ecosystems. Our open-sourced model provides a benchmark for real-time attackresistant systems applicable to 6G-enabled autonomous driving technologies.

Autonomous vehicles generate massive sensor data daily but operate as isolated intelligence units due to privacy constraints and network limitations. Current centralized machine learning approaches face critical barriers including compliance issues, high bandwidth costs, and latency constraints preventing real-time safety decisions. While Federated Learning (FL) enables collaborative training without raw data sharing and 6G networks promise ultra-low latency, a fundamental mismatch exists between FL's dynamic computational demands and 6G's static resource allocation mechanisms. This paper presents Predictive FL-Aware Network Slicing (PFANS), a novel framework that integrates real-time convergence modeling with proactive 6 G slice reconfiguration for autonomous vehicle networks. PFANS predicts FL computational demands multiple training rounds in advance and automatically reconfigures network slices before bottlenecks occur. Experimental results demonstrate superior resource utilization efficiency, significantly faster convergence compared to baseline approaches, and excellent handover success rates with minimal context migration times. The framework achieves state-of-theart prediction accuracy while introducing negligible network overhead, establishing effective adaptive resource management for next-generation vehicular networks.

Unmanned Aerial Vehicles (UAVs) play a crucial role in various applications, including detecting environmental hazards, e.g., wildfire smoke detection. However, the limited computational capabilities and battery life of UAVs present barriers to deploying complex artificial intelligence (AI) models onboard. To address this challenge, we propose a novel hybrid deep learning framework for UAVs to carry out light-weight yet efficient smoke detection. The framework combines a lightweight model for initial image assessment and a depth-wise model for selective processing of uncertain cases. Bayesian optimization is employed to determine the optimal threshold values for activating the depth-wise model, striking a balance between accuracy and computational efficiency. The proposed approach eliminates the need for cloud server connectivity, enabling onboard decision-making. Experimental results demonstrate that the hybrid framework achieves significant reductions in processing time and the number of calls to the depth-wise model while maintaining high accuracy. The framework’s adaptability and robustness make it suitable for real-time smoke detection applications in resource-constrained environments.

Cell-free networks have emerged as a new paradigm for beyond-5G networks, offering uniform coverage and improved control over interference. However, scalability poses a challenge in full cell-free networks, where all access points (APs) serve all users. This challenge is addressed by user-centric clustering, where each user is served by a subset of APs, reducing complexity while maintaining coverage. In this paper, we provide an analysis of the relation between the user-centric clustering and pilot assignment problems in cell-free networks, and introduce a formulation which decouples both problems enabling each to be solved independently. We present a general problem formulation for the user-centric clustering problem, allowing the use of diverse per-user and network-wide performance metrics. Specifically, we focus on one instance of this framework, utilizing per-user spectral efficiency and network-wide sum spectral efficiency (SE) as metrics. Additionally, we formulate the pilot assignment problem to minimize overall channel estimation error while considering the user-centric clusters in evaluating the desirability of pilot assignments, which leads to better performing solutions. Both problems are classified as binary nonlinear programs that are at least NP-hard. To solve these optimization problems, our proposed methodology employs sample average approximation coupled with surrogate optimization for the user-centric clustering problem and utilizes the genetic algorithm for the pilot assignment problem. Numerical experiments demonstrate that the optimized solutions surpass baseline solutions, leading to significant improvements in spectral efficiency.

The convergence of Intelligent Reflecting Surfaces (IRS) and Terahertz (THz) communications represents a transformative advancement for sixth-generation (6G) wireless networks, yet presents unprecedented challenges in system optimization. This paper addresses the critical challenge of joint optimization between IRS phase shifts and THz resource allocation in dynamic Internet of Things (IoT) environments, focusing on real-time adaptation to rapidly changing channel conditions. We propose a novel Adaptive Online Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework that leverages dynamic experience weighting to automatically adjust learning based on detected environmental changes. Our approach incorporates a multi-resolution buffer structure that balances recent observations with historical patterns, enabling both rapid adaptation and long-term optimization while considering the unique characteristics of THz-band propagation and IRS reflection patterns. The framework employs explicit coordination protocols between IRS controllers and resource managers, significantly improving convergence in non-stationary environments. Comprehensive simulations using realistic THz channel models and practical IRS configurations demonstrate that our proposed framework achieves a 45% improvement in system throughput, a 38% reduction in end-to-end latency, and a 30% enhancement in energy efficiency compared to conventional optimization approaches. More significantly, our solution demonstrates unprecedented adaptation capabilities, recovering 90% of optimal performance within 5 ms after abrupt environmental changes a critical requirement for future 6G networks. The framework maintains robust performance under diverse conditions, including high user mobility scenarios and adverse atmospheric conditions, while exhibiting linear computational scaling with increasing IRS elements (tested up to 512 elements). These results establish the viability of Adaptive Online MADDPG-based joint IRS-THz optimization for practical 6G deployments, particularly in dynamic IoT environments where traditional communication approaches face significant limitations.

Cloud-based federated learning (FL) services have received increasing attention due to their ability to enable collaborative global model training without the need to collect local data from participants. To generate a global model, local models are trained on participants' local data and only model parameters are sent to an aggregator server. Nonetheless, revealing model parameters can still reveal training data via launching attacks, e.g., inference and membership. Hence, to protect model parameters, a secure global model aggregation scheme is needed to protect these parameters from unauthorized access. Existing solutions to this issue, which are based on homomorphic encryption and secure multi-party computation, tend to have large overheads and slow down training times. Functional encryption (FE) has been proposed as a solution for resolving privacy-preservation issues in FL, but current solutions suffer from high overhead and lack of security such as leaking master private key. To address these issues, this paper proposes a privacy-protecting, efficient, and decentralized FL framework, called FedSafe, based on FE without the need for a trusted key distribution center (KDC). The proposed scheme allows the participants to communicate with an aggregator to construct a global model without disclosing or learning their local models' parameters or the training data, thereby safeguarding their privacy. Through rigorous testing with real-world data, it is demonstrated that FedSafe outperforms the state-of-the-art privacy-protecting FL schemes in terms of security, scalability, and communication and computation overhead. Unlike existing approaches, this is accomplished without depending on any trusted KDC.

This paper explores secret key generation in 5G and beyond LiFi networks using visible light in the downlink and infrared in the uplink. Unlike the existing works, we focus on a realistic indoor environment with multi-user mobility. Given inaccuracies in high-frequency channel models, we introduce the first deep learning model that combines the channel probing and quantization phases to generate initial secret keys with a minimal key disagreement rate (KDR) of 16% between the uplink and downlink, leading to a key generation rate (KGR) of 79 bits/s after information reconciliation. We show that LiFi channel statistics suffer from concept drifts with user density changes in the room. This increases the KDR by 28% - 44% and the generated keys fail to pass the NIST randomness tests. As a countermeasure, we introduce a voting ensemble model that mitigates concept drifts, maintaining a stable 16% KDR, 79 bits/s KGR, and passing NIST tests, despite the varying user densities.

Artificial Intelligence (AI) applications on Internet of Things (IoT) networks often involve relaying generated data to a server for deep learning training, which poses security risks to users' data. Federated Learning (FL) offers a distributed model training paradigm in which local data are kept at the edge and locally trained models are exchanged and aggregated by a server over several rounds to produce a global model. While successful, standard FL algorithms do not support heterogeneous local model design, an essential requirement, especially for resource-limited edge devices. Recently, Knowledge Distillation-based FL algorithms have provided model-agnostic FL to enable clients to independently design their local model and share soft labels instead of model parameters. KD-based FL algorithms are computationally expensive due to additional distillation training. We propose Federated Learning with Selective Knowledge Distillation (FedSKD) to address the limitations of system heterogeneity; and computation and communication demands. We evaluate different aspects of the proposed algorithm relative to baseline FL algorithms. Results show that FedSKD incurs significantly less per-round computation time and communication overhead relative to the considered model-based and KD-based FL algorithms.

Load forecasting (LF) in smart grids is beneficial not only in mitigating equipment failures and power outages but also in facilitating effective power dispatching and infrastructure planning. To predict future loads accurately, the consumers' fine-grained energy consumption readings are fed into machine-learning (ML) models. However, revealing these readings enables adversaries to deduce confidential information about consumers, including details about their lifestyle, and hence their privacy is violated. To address this privacy issue, the existing works only focus on using federated learning (FL)-based approaches to train and obtain an accurate global LF model. Nevertheless, addressing the privacy violation problem during the LF process (in the deployment phase) after obtaining the global model for AMI networks has not been well investigated yet. Therefore, this paper proposes a novel, efficient, and decentralized approach that enhances the precision of LF while safeguarding the privacy of consumers. The proposed scheme incorporates inner product functional encryption (IPFE) to allow smart meters (SMs) to encrypt their readings with no need for a trusted key distribution center (KDC) while allowing LF without divulging or acquiring knowledge of the consumers' readings to protect their privacy. In addition, a hybrid deep learning approach is developed to construct an LF model that can yield precise forecasts. To show the feasibility of the proposed scheme, the performance of our scheme was assessed on a real energy consumption readings dataset, and the results demonstrate proficiency in LF while providing robustness and privacy preservation with reasonable communication efficiency.

This paper explores the vulnerability of wireless secret key generation (WSKG) to eavesdropping in a dynamic indoor light-fidelity (LiFi) network. It analyzes the channel impulse response (CIR) similarities of two moving user equipments (UEs) across scenarios with two, four, and eight UEs. We observe that as the number of UEs increases, the similarity in CIR also rises, due to the proximal movement patterns among UEs. Specifically, the similarity rate peaks at 70% when eight UEs enter the room; it then drops to 24% during the wandering phase and rises again to 80% as UEs exit the room. Consequently, an eavesdropper among the eight UEs is able to generate 27% of a legitimate UE’s secret key, it significantly reduces the key’s complexity, decreasing the number of possible keys that need to be tested to break the encryption and making it easier to predict the remainder of the key. To mitigate this issue, we introduce a novel approach that utilizes a generative adversarial network (GAN) to artificially manipulate the CIR, thereby reducing the effectiveness of eavesdropping by adding noise into the observed CIR. This method effectively reduces the CIR similarity to a negligible 1%, thus ensuring the integrity of WSKG against eavesdropping threats.

The adoption of Internet of Things (IoT) and monitoring devices in 5G and beyond networks has been widespread. Unmanned aerial vehicles (UAVs) have shown success in connecting rural and remote areas due to the high cost of deploying infrastructures like cellular network base stations and optical fiber connections in vast landscapes with sparse populations. The constrained energy of UAVs results in limited coverage area and flight time, which in turn reduces the potential of UAVs to provide task-oriented wireless communication links. In this article, we explore path optimization and transmission organization algorithms to minimize flight time and extend the range of UAVs performing collaborative federated learning (FL) among geographically dispersed nodes communicating through wireless connections offered by UAVs coupled with device-to-device (D2D) networks. The UAV orchestrates FL between spatially scattered homes via long-range radio wireless communication. We formulate the drone path optimization as a traveling salesman problem (TSP) and employ self-organizing maps (SOM) for path planning. Additionally, knowledge distillation (KD)-based FL is used to reduce communication overhead for the resource-constrained UAV-IoT system. Experimental results demonstrate SOM’s ability to represent the topological structure of nodes and produce a cost-efficient Hamiltonian cycle, from which the drone path is derived. Our results demonstrate the communication efficiency and utility of KD-based FL compared to model-based FL methods. The proposed hybrid solution enables energy-constrained UAVs to perform FL over large areas leveraging a shared data set for KD and a SOM-based path optimization algorithm.

This paper addresses the user-centric clustering and pilot assignment problems in cell-free networks, recognizing the need to solve both problems simultaneously. The motivation of this research stems from the absence of benchmarks, general formulations, and the reliance on subjectively designed objective functions and heuristic algorithms prevalent in existing literature. To tackle these challenges, we formulate stochastic non-linear binary integer programs for both the user-centric clustering and pilot assignment problems. We specifically design the pilot assignment formulation to incorporate user-centric clusters when evaluating the desirability of pilot assignments, resulting in improved efficiency. To solve the problems, the proposed methodology employs sample average approximation coupled with surrogate optimization for the user-centric clustering problem and the genetic algorithm for the pilot assignment problem. Numerical experiments demonstrate that the optimized solutions outperform baseline solutions, leading to significant gains in spectral efficiency.

Federated learning is a promising approach for training models on distributed data, driven by increasing demand in various industries. However, federated learning framework faces several key challenges, including communication bottlenecks and client data heterogeneity. Personalized asynchronous federated learning addresses these challenges by customizing the model for individual users based on their local data while trading model updates asynchronously. In this paper, we propose the Personal-ized Moreau Envelopes-based Asynchronous Federated Learning (APFedMe). Our approach combines the strengths of Moreau En-velopes to handle optimization problems and asynchronous weight updates to improve communication efficiency while mitigating heterogeneity data challenges through a personalized learning environment. We evaluate our approach on several datasets and compare it with the baseline PFedMe method. Our experiments demonstrate that the proposed APFedMe outperforms other meth-ods in terms of convergence speed and communication efficiency. Overall, our work contributes to developing more effective and efficient federated learning methods that can be applied in various real-world scenarios.

Medical image segmentation is a crucial component of computer-aided diagnosis (CAD) systems, as it aids in identifying important areas in medical images. In order to achieve optimal segmentation results, it is important to preserve the resolution of the input image. The dilated convolution module was introduced to maintain resolution across layers of a deep convolutional neural network by increasing the receptive field exponentially while keeping the parameters increase linearly. However, one drawback of using dilated convolution is that it can result in local spatial resolution loss by increasing the sparsity of the kernel in checkboard patterns. This work proposes a double-dilated convolution module to maintain local spatial resolution in medical image segmentation tasks while having a large receptive field. The module is applied to tumor segmentation in breast cancer mammograms using the state-of-art Deeplabv3+ network. The study also evaluates the developed models with the Gradient weighted Class Activation Map (Grad-CAM) and compares the performance of lesion segmentation networks on mammogram screenings from the INBreast dataset before and after using the proposed dilation module. The results show that the proposed module effectively improves the segmentation performance.

Federated Learning (FL) has emerged as a powerful approach to facilitate the construction of centralized models without compromising the data privacy of multiple participants. However, conventional FL methodologies do not address system heterogeneity where each participant needs to independently design its own model, a prevalent requirement in Internet of Things (IoT) applications due to the heterogeneous nature of tasks and data. Knowledge Distillation-based FL algorithms tackle this limitation by exchanging soft labels instead of model weights, thus giving each client the ability to independently design its local model architecture. While FL is inherently private, studies have indicated that exploiting gradients for a few iterations can reveal sensitive training data. To protect against privacy attacks, FL algorithms employ Differential Privacy (DP) to guarantee privacy protection, which can be applied using Local Differential Privacy (LDP). In this paper, we elaborate on preserving clients' training data privacy in KD (Knowledge Distillation)-based FL using DP, providing both privacy and flexibility. We provide theoretical analysis to extend the privacy guarantee to exchanged updates. Experimental analysis is performed utilizing Human Activity Recognition (HAR) datasets with different modalities. The results obtained demonstrate the capacity of KD-based FL to maintain a robust utility-privacy balance. Furthermore, for the same DP protection level, the utility of models trained on images was severely reduced across all FL algorithms. This suggests that the modality and complexity of a dataset are important factors for shaping the utility-privacy tradeoff of DP.

As vehicular communication networks embrace metaverse beyond 5G/6G systems, the rich content update via the least interfered subchannel of the optimal frequency band in a hybrid band vehicle to everything (V2X) setting emerges as a challenging optimization problem. We model this problem as a tradeoff between multi-band VR/AR devices attempting to perform metaverse scenes and environmental updates to metaverse roadside units (MRSUs) while minimizing energy consumption. Due to the computational hardness of this optimization, we formulate an opportunistic band selection problem using a multi-armed bandit (MAB) that provides a good quality solution in real-time without computationally burdening the already stretched augmented/virtual reality (AR/VR) units acting as transmitting nodes. The opportunistic use of scheduling rich content updates at traffic signals and stand-still scenarios maps well with the formulated bandit problem. We propose a Dual-Objective Minimax Optimal Stochastic Strategy (DOMOSS) as a natural solution to this problem. Through extensive computer-based simulations, we demonstrate the effectiveness of our proposal in contrast to baselines and comparable solutions. We also verify the quality of our solution and the convergence of the proposed strategy.

Emerging Internet of Things (IoT) applications, such as sensor-based Human Activity Recognition (HAR) systems, require efficient machine learning solutions due to their resource-constrained nature which raises the need to design heterogeneous model architectures. Federated Learning (FL) has been used to train distributed deep learning models. However, standard federated learning (fedAvg) does not allow the training of heterogeneous models. Our work addresses the model and statistical heterogeneities of distributed HAR systems. We propose a Federated Learning via Augmented Knowledge Distillation (FedAKD) algorithm for heterogeneous HAR systems and evaluate it on a self-collected sensor-based HAR dataset. Then, Kullback-Leibler (KL) divergence loss is compared with Mean Squared Error (MSE) loss for the Knowledge Distillation (KD) mechanism. Our experiments demonstrate that MSE contributes to a better KD loss than KL. Experiments show that FedAKD is communication-efficient compared with model-dependent FL algorithms and outperforms other KD-based FL methods under the i.i.d. and non-i.i.d. scenarios.

Fifth Generation (5G) networks operating on mmWave frequency bands are anticipated to provide an ultrahigh capacity with low latency to serve mobile users requiring high-end cellular services and emerging metaverse applications. Managing and coordinating the high data rate and throughput among the mmWave 5G Base Stations (BSs) is a challenging task, and it requires intelligent network traffic analysis. While BSs coordination has been traditionally treated as a centralized task, this involves higher latency that may adversely impact the user’s Quality of Service (QoS). In this paper, we address this issue by considering the need for distributed coordination among BSs to maximize spectral efficiency and improve the data rate provided to their users via embedded AI. We present Peer-Coordinated Sequential Split Learning dubbed PC-SSL, which is a distributed learning approach whereby multiple 5G BSs collaborate to train and update deep learning models without disclosing their associated mobile users data, i.e., without privacy leakage. Our proposed PC-SSL minimizes the data transmitted between the client BSs and a server by processing data locally on the clients. This results in low latency and computation overhead in making handoff decisions and other networking operations. We evaluate the performance of our proposed PC-SSL in the mmWave 5G throughput prediction use-case based on a real dataset. The results demonstrate that our proposal outperforms conventional approaches and achieves a comparable performance to centralized, vanilla split learning.

As 5G and beyond networks continue to proliferate, intelligent monitoring systems are becoming increasingly prevalent. However, geographically isolated regions with sparse populations still face difficulties in accessing these technologies due to infrastructure deployment challenges. Additionally, the high cost and unreliability of satellite Internet services make them less appealing. This paper studies the challenges of drone-aided networks and presents a communication-efficient Federated Learning (FL) system on a drone-aided Internet of Things (IoT) network to facilitate health analysis in rural areas over LoRa wireless links. The proposed approach consists of two primary components. Firstly, optimizing the drone’s trajectory is theoretically formulated as a modified version of the Traveling Salesman Problem (TSP), with the Self-Organizing Map (SOM) algorithm employed for effective route planning. Secondly, the Knowledge Distillation (KD)-based FL algorithm is utilized to reduce communication overhead by leveraging soft labels. The quality of drone routes generated by the SOM is evaluated on multi-scale maps with pre-determined optimal paths. The experiments reveal SOM’s ability to accurately represent node topologies and yield cost-effective Hamiltonian cycles. The KD-based FL proves to be more efficient in terms of communication than FedAvg as the former exchanges soft labels while the latter exchanges model weights, thus reducing drone waiting time and battery consumption. We showcase the performance of our KD-based FL algorithm using Human Activity Recognition (HAR) datasets, illustrating a communication-efficient alternative for distributed learning, offering competitive performance leveraging a shared dataset for knowledge transfer among IoT devices.

Recently emerging WiGig systems experience limited coverage and signal strength fluctuations due to strict line-of-sight (LoS) connectivity requirements. In this paper, we address these shortcomings of WiGig communication by exploiting two emerging technologies in tandem, namely the reconfigurable intelligent surface (RIS) and unmanned aerial vehicles (UAVs). In ultra-dense traffic sites (referred to as hotspots) where WiGig nodes or User Devices (UDs) experience complex propagation and non-line-of-sight (non-LoS) environment, we envision the deployment of a UAV-mounted RIS system to complement the WiGig base station (WGBS) to deliver services to the UDs. However, commercially available UAVs have limited energy (i.e., constrained flight time). Therefore, the trajectory of our considered UAV needs to be locally estimated to enable it to serve multiple hotspots while minimizing its energy consumption within the WGBS coverage boundaries. Since this tradeoff problem is computationally expensive for the resource-constrained UAV, we argue that sequential learning can be a lightweight yet effective solution to locally solve the problem with a low impact on the available energy on the UAV. We formally formulate this problem as a contextual multi-armed bandit (CMAB) game. Then, we develop the linear randomized upper confidence bound (Lin-RUCB) algorithm to solve the problem effectively. We regard the UAV as the bandit learner, which attempts to maximize its attainable rate (i.e., the reward) by serving distinct hotspots in its trajectory that we treat as the arms of the considered bandit. The context is defined as the hotspots’ locations provided using GPS (global positioning system) service and the reward history of each hotspot. Our proposal accounts for the energy expenditure of the UAV in moving from one hotspot to another within its battery charge lifetime. We evaluate the performance of our proposal via extensive simulations that exhibit the superiority of our proposed Lin-RUCB algorithm over benchmarking methods.

General Purpose Vision System (GPVS) is a task-agnostic vision-language system that inputs an image and a question from which the system recognizes the tasks to be performed and outputs bounding boxes, confidence scores, and text outputs to answer the question. While much attention to GPVS has been recently given in the computer vision field, its medical field applications are still in their infancy. This paper presents MED-GPVS, a customized deep learning-based GPVS on biomedical images to perform various vision tasks, such as object detection and visual question answering, on medical images to facilitate precision medicine/e-health services. Our envisioned MED-GPVS takes an image and a natural language text as inputs, and then outputs bounding boxes, confidence scores, and generates a caption (i.e., the answer to the posed query). For example, if a medical image of a patient’s abdomen is presented to MED-GPVS followed by the question: "does the picture contain stomach?", MED-GPVS should ideally provide the answer "yes" along with a prediction box and prediction score on the image. We utilize the multilingual SLAKE dataset, which was annotated by expert physicians with a full semantic label, to validate the performance of MED-GPVS under various scenarios involving different biomedical image-based diagnoses. For the visual question answering (VQA) task, MED-GPVS demonstrates encouraging performance with significantly high accuracy of 82.41%.

During the recent years, online users, particularly in social networks, have witnessed an upsurge in racism, sexism, and other types of aggressive and cyberbully content, which are often manifested through offensive, abusive, or hateful speech and harassment. This can lead to severe physical and psychological stress in young children and adolescents, leading to even suicides and negatively affecting social policies. Therefore, there is a significant need to identify and regulate harassing content posted on the Internet in a smart, automated, and accurate manner. With this aim, in this paper, we design and develop a hierarchical framework comprising machine learning algorithms in order of higher computational complexity to adaptatively switch among them for efficiently detecting hateful and abusive content. We combine simple machine learning models such as Naive Bayes/Logistic Regression classifiers with customized calibration and Expectation-Maximization (EM) algorithms, and compare them with the much stronger deep learning techniques. Our proposed hierarchical framework demonstrates a significant improvement of the automated detection of abusive contents in social networks with a relatively small twitter dataset in contrast with the deep learning-based counterpart, namely the Bidirectional Encoder Representations from Transformers (BERT) model, training of which typically requires a much higher volume of labeled documents to detect abusive comments.

The recent COVID-19 (novel coronavirus disease) pandemic induced a deep polarization among regional as well as global communities. The sentiments regarding the pandemic and its impact on lifestyle and economy, often expressed via social networks, are regarded as critical metrics for capturing such polarization and formulating appropriate intervention by the relevant authorities. While there exist a myriad of Natural Language Processing (NLP) models for mining social media data, we demonstrate the shortcomings of the individual models in this paper, and explore how to improve the COVID-19 sentiment analysis in social media network data via two hybrid predictive models based on a Long-Short-Term-Memory (LSTM)-based autoencoder and a Convolutional Neural Network (CNN) model coupled with a bi-directional LSTM. Through extensive experiments on the recently acquired Twitter dataset, we compare the COVID-19 sentiments exhibited in the USA and Canada using our proposed hybrid predictive models and demonstrate their superiority over individual Artificial Intelligence (AI) models.

To handle the exponentially growing service expectations from mobile users and circumvent the band switching slow rate, device-to-device (D2D) communication is receiving much research attention in the Internet of Things (IoT). While the emerging D2D nodes can support heterogeneous frequency bands [radio frequency (RF) including 2.4 GHz/5 GHz wireless local area network (WLAN), 38-GHz millimeter wave (mmWave), and visible light communication (VLC)], the physical constraints (e.g., blocking) require the user devices to dynamically switch between the bands in order to avoid the loss of connectivity and throughput degradation. In this article, we investigate an effective online link selection in hybrid RF-VLC scenarios for direct user data handling. First, we model the multiband selection issue as a multiarmed bandit (MAB) problem. The source/relay node acts as a player who gambles to maximize its long-term feedback/reward via selecting suitable arms, i.e., available bands (WLAN, mmWave, or VLC). Then, we propose an online, energy-aware band selection (EABS) methodology by leveraging three theoretically guaranteed MAB techniques [upper confidence bound (UCB), Thompson sampling (TS), and minimax optimal stochastic strategy (MOSS)] to derive optimal band selection policies. Based on these adopted policies, we propose three algorithms, namely, EABS-UCB, EABS-TS, and EABS-MOSS, to implement the EABS strategy, respectively. Extensive simulations demonstrate our proposed algorithms’ superior performance compared to the traditional link selection schemes regarding energy efficiency, average throughput, and convergence rate. In particular, EABS-MOSS emerges as the best algorithm as it exhibits near-optimal performance due to its flexibility to both stochastic and adversarial environments.

While hybrid-band wireless systems recently gained prominence to achieve high capacity, selecting the best channel in these systems in real-time is still a formidable research challenge that requires further investigations. In this paper, we address this challenge in terms of an optimization problem, which is reformu-lated as a stochastic multi-armed bandit (MAB). Then, we introduce online learning-based solutions to solve the MAB problem for the multi-band/channel selection (MBS). Improved variants of the upper confidence bound (UCB) scheme are investigated and modified to be energy-aware. Hence, we propose Energy-Aware Randomized UCB-MBS (EA-RUCB-MBS) and Energy-Aware Kullback-Leibler UCB-MBS (EA-KLUCB-MBS) methods, which demonstrate near-optimal results. Also, EA-KLUCB-MBS exhibits the fastest convergence, while the convergence of EA-RUCB-MBS is similar to that of the original UCB. Based on extensive simulation results, we evaluate the performance of our proposed algorithms against benchmark MBS schemes including UCB and Thompson sampling (TS).

Continuous low-rate monitoring is an important IoT application, which requires high-fidelity in observing signals with low frequency. However, most sensors exhibit noise that is inversely-proportional to spectral frequency (1/f noise). Because both the relevant signal and noise share the same spectral properties, standard linear filtering techniques cannot be used. We are looking into a special application for remote healthcare of the magnetic field sensing of cardiac activity, magnetocardiography (MCG). For such an application, we need to develop a noise separation method, that is also resource-efficient. Previously, we demonstrated AI-based removal of 1/f noise in MCG by a convolutional neural network coupled with gated recurrent units. However, it needs a large amount of data for training, requiring significant training time and computational power. In this work, we employ reservoir computing (RC) for noise-removal, while being conservative in computing resources.

To combat the novel coronavirus (COVID-19) spread, the adoption of technologies including the Internet of Things (IoT) and deep learning is on the rise. However, the seamless integration of IoT devices and deep learning models for radiograph detection to identify the presence of glass opacities and other features in the lung is yet to be envisioned. Moreover, the privacy issue of the collected radiograph data and other health data of the patients has also arisen much concern. To address these challenges, in this paper, we envision a federated learning model for COVID-19 prediction from radiograph images acquired by an X-ray device within a mobile and deployable screening resource booth node (RBN). Our envisioned model permits the privacy-preservation of the acquired radiograph by performing localized learning. We further customize the proposed federated learning model by asynchronously updating the shallow and deep model parameters so that precious communication bandwidth can be spared. Based on a real dataset, the effectiveness of our envisioned approach is demonstrated and compared with baseline methods.

Recently, the problem of optimal resource scheduling for the increasingly growing number of smart vehicles has emerged as a daunting research challenge. While Over the Air (OTA) software updates are critical for the safe routing/driving of smart vehicles, they consume precious network resources if not planned or scheduled appropriately. In this paper, we consider a smart fog computing network to serve the smart vehicles with OTA updates and present two optimal resource scheduling problems SP1and SP2. The objective of SP1is to minimize the maximum waiting time of any smart vehicle and the objective of SP2aims to minimize the maximum transmission time of any channel. These problems cannot always ascertain optimal solutions in polynomial time. We first propose a random algorithm and a greedy algorithm for these problems and find that these two algorithms may not perform adequately in many cases. Hence, we propose a local search algorithm which takes any feasible solution of a scheduling problem as input and improves that solution. Computer-based simulations demonstrate the effectiveness of our proposed algorithms.

Multihop device-to-device (D2D)-enabled relay networks are envisaged to be utilized by the Internet of Things (IoT) and massive machine-type communication (mMTC) traffic for the purpose of offloading data in beyond fifth-generation (B5G) networks. The emerging challenge of spectrum scarcity and overloading of cellular base stations can be addressed using such relay nodes in terms of spectrum and energy efficiency. In order to improve spectral efficiency, in this article, we intend to employ several frequency bands concurrently in the relay node rather than the traditional concept of specifying one channel on a specific band at a time. A deep learning-based predictive channel selection method is leveraged to unravel the potential challenges associated with the dynamic channel conditions in the multiband relay networks. For predicting the most appropriate channel based on its quality, signal-to-interference-plus-noise-ratio (SINR) is adopted as the metric, which is predicted by the proposed convolutional neural network (CNN) model. The best modulation and coding rates of the predicted band are attained in order to transmit the packets received from the source or previous relay node to the successive relay node/destination. Two proactive channel assignment strategies, referred to as controlled and smart prediction schemes, are employed to exhibit the performance of the shallow and deep-CNN models. The proposed model is evaluated on multiple publicly available data sets from diverse network systems and compared with several machine/deep learning methods. Our proposal leads to encouraging results for proactively predicting the conditions of the channels and choosing the most suitable ones in multiband relay systems.

As we are about to embark upon the highly hyped “Society 5.0”, powered by the Internet of Things (IoT), traditional ways to monitor human heart signals for tracking cardio-vascular conditions are challenging, particularly in remote healthcare settings. On the merits of low power consumption, portability, and non-intrusiveness, there are no suitable IoT solutions that can provide information comparable to the conventional Electrocardiography (ECG). In this paper, we propose an IoT device utilizing a spintronic-technology-based ultra-sensitive Magnetic Tunnel Junction (MTJ) sensor that measures the magnetic fields produced by cardio-vascular electromagnetic activity, i.e. Magentocardiography (MCG). We treat the low-frequency noise generated by the sensor, which is also a challenge for most other sensors dealing with low-frequency bio-magnetic signals. Instead of relying on generic signal processing techniques such as moving average, we employ deep-learning training on bio-magnetic signals. Using an existing dataset of ECG records, MCG signals are synthesized. A unique deep learning model, composed of a one-dimensional convolution layer, Gated Recurrent Unit (GRU) layer, and a fully-connected neural layer, is trained using the labeled data moving through a striding window, which is able to smartly capture and eliminate the noise features. Simulation results are reported to evaluate the effectiveness of the proposed method that demonstrates encouraging performance.

Preserving the customers' privacy, while collecting their power consumption for monitoring and billing, is a prime concern in an Advanced Metering Infrastructure (AMI) network of the Smart Grid (SG). In this paper, we address this concern by formally formulating the data aggregation privacy problem, and propose a uniquely crafted Privacy-Preserving Monitoring and Billing scheme using Functional Encryption, referred to as PMBFE. Our proposed PMBFE fulfills four key objectives: (i) data aggregation for billing, (ii) dynamic pricing flexibility, (iii) load monitoring with customers' privacy preservation; and (iv) analysis on how the adopted functional encryption is able to jointly perform data aggregation efficiently and guarantee privacy-preservation. Our envisioned PMBFE approach is evaluated with extensive computer-based simulations. In contrast with the widely employed homomorphic-based encryption in AMI networks, our proposed PMBFE demonstrates significant performance improvement in terms of both communication and computation overheads while guaranteeing user-data privacy. Furthermore, the conducted security analysis exhibits the robustness of our proposal against collusion and eavesdropping attacks.

Traditionally, the Internet of Things (IoT) devices, deployed on the ultra-edge of the network, lack computation, and energy resources. In this paper, we press on the need to go beyond the realms of traditional edge computing (e.g., limited to user-smartphones) and investigate how to incorporate intelligence into the ultra-edge IoT sensors. Among numerous use-cases, we select a mobile Health (mHealth) scenario where we conceptualize a smart IoT sensor to collect and intelligently process single-channel Electrocardiogram (ECG) signals to detect arrhythmia, a heart-condition often associated with morbidity and even mortality. The arrhythmia detection can be regarded as a non-linear Delay Differential Equation (DDE) time-series analysis problem, and the conventional solutions to this problem are not suitable for integration with IoT sensors due to rigorous pre-processing steps. As a solution, a Convolutional Neural Network (CNN)-based, lightweight Arrhythmia classification system is proposed in the paper without the need for noise-filtering and feature extraction steps. Four classes of the heartbeats are considered to comply with the ANSI/AAMI EC57:1998 standard. The proposed system's performances and generalization potential are assessed using three datasets from PhysioNet trained on a deep learning workstation and then transferred to virtualized micro-controllers connected to IoT sensors. The proposed deep learning model exhibits encouraging performance (accuracy 95.27%) in heartbeat classification. Experimental and numerical results demonstrate that the proposed deep learning technique outperforms conventional DDE-based optimization techniques and machine learning techniques such as K-Nearest Neighbor (KNN), and random forest (RF).

Deep learning has emerged as a popular Artificial Intelligence (AI) technique to make conventional cyber physical systems become intelligent and sustainable. Recently, deep learning has been widely used in the network domain. With the aid of powerful deep neural networks, the communication network can carry out packets forwarding actions intelligently to avoid possible failure and congestion. However, with the high computing cost and process limitation in only the static network scenario, the existing deep learning based network traffic control algorithms cannot satisfy the sustainable requirement of next generation large scale dynamic network. To conquer the existing problems, a novel spatial-temporal value network aided deep learning based intelligent traffic control algorithm referred as ST-DeLTA is proposed in this paper. In ST-DeLTA, the value matrix and spatial temporal training model (ST model) are employed to intelligently extract the spatial as well as temporal features of traffic patterns and make adaptive packets forwarding decision in large scale and dynamic networks. The mathematical analysis gives the computing cost reduction of our proposal, and the computer simulation demonstrates that our proposal has significantly better training and network performance compared with traditional algorithms in terms of training accuracy, transmission throughput, and average packets loss rate.

Disasters, such as earthquakes, typhoons, and tsunamis, usually cause extreme damages to the communication infrastructures, which results in a heavy recovery workload and seriously affects people's life. The disaster recovery networks play a critical role to reduce the loss caused by the disasters. However, the suddenly varying traffic demand and limited resources after disasters may lead to the repetitive reconfigurations for running the existing packet forwarding strategies, such as the shortest path algorithms. To handle this problem, it is necessary to adopt the deep learning technique to develop a disaster-resilient solution. In this paper, we utilize the deep reinforcement learning technique to propose a self-adaptive routing method for the Movable and Deployable Resource Unit (MDRU) based backbone network. Compared with existing deep learning based routing strategy, our proposal can adapt to the sudden network errors. Moreover, we also analyze the deployment manner and consider a centralized control structure to significantly balance the traffic.

Recently, the rapid advancement of high computing platforms has accelerated the development and applications of artificial intelligence techniques. Deep learning, which has been regarded as the next paradigm to revolutionize users' experiences, has attracted networking researchers' interests to relieve the burden due to the exponentially growing traffic and increasing complexities. Various intelligent packet transmission strategies have been proposed to tackle different network problems. However, most of the existing research just focuses on the network related improvements and neglects the analysis about the computation consumptions. In this paper, we propose a Value Iteration Architecture based Deep Learning (VIADL) method to conduct routing design to address the limitations of existing deep learning based routing algorithms in dynamic networks. Besides the network performance analysis, we also study the complexity of our proposal as well as the resource consumptions in different deployment manners. Moreover, we adopt the Heterogeneous Computing Platform (HCP) to conduct the training and running of the proposed VIADL since the theoretical analysis demonstrates the significant reduction of the time complexity with the multiple GPUs in HCPs. Furthermore, simulation results demonstrate that compared with the existing deep learning based method, our proposal can guarantee more stable network performance when network topology changes.

The fast increasing chip processing capacities driven by the Moore's Law have encouraged the academia and industry to consider more about general hardware architectures since they allow the repeated use for multiple purposes through the installations of applications. Some techniques utilizing the general hardware architectures have been developed to improve the flexibility of computer networks, such as the Software Defined Networking (SDN) and the Network Functions Virtualization (NFV). For these networks, the applications are required to be computation/communication-efficient since the installed applications share the hardware. In this paper, we study the resource-limited disaster recovery networks constructed by the Movable and Deployable Resource Units (MDRUs) which consist of various general computation platforms. We propose an efficient safety confirmation method through the photo sharing by the survivors. In the proposal, the Absorbing Markov Chain is utilized to model the safety confirmation process, transition matrix of which can be adopted to choose the suitable photo size for optimizing the traffic overhead and buffer consumption. Through periodical update of the photo database, unnecessary packet transmissions can be further avoided with reasonable sacrifice of the computation overhead. To expedite the computation, the GPU-accelerated MDRU is considered to conduct the matrix calculations in a parallel fashion.

The high-speed transmission has become extremely important with the rapid growth of network traffic in wireless networks. Because the available bandwidth of wireless channels are limited, Partially Overlapping Channels (POCs) are widely used in wireless networks to maximize the utilization of channel resources. However, with the traffic patterns of wireless networks becoming huge and dynamic, conventional POC assignment algorithms only designed for constantly generated network traffic are not suitable for the new generation wireless networks. Therefore, in this article, a joint deep Covolutional Neural Network (CNN) and activity vector based intelligent channel assignment algorithm is proposed, which is referred to as CNNAV. With the proposed CNNV approach, the network can learn from the historical traffic patterns and intelligently assign POCs to wireless links. The simulation result shows that, the network performance of our proposal in terms of both packets loss rate and network throughput are better than conventional POC assignment algorithms.

As the age of the Internet of Things (IoT) continues to flourish, the concept of smart healthcare has taken an unprecedented turn due to interdisciplinary thrusts. To carry the big healthcare data emanating from the plethora of bio-sensors and machines in the IoT sensing plane to the central cloud, next generation high-speed delivery networks are essential. On the other hand, once the IoT data are delivered to the cloud, the massive IoT healthcare data are processed and analyzed employing the state-of-the-art analytics tools such as deep machine learning and so forth. However, given the explosion of big data (from various sources in addition to the healthcare data), the delivery network as well the cloud may experience network and computational congestion, respectively. This may impact the realtime analytics of the healthcare data, e.g., critical for in-house patients and senior citizens aging at home. To address this issue, the emerging IoT edge analytics concept can be regarded as a promising solution to process the big healthcare data close to the source. For large-scale IoT deployments, this functionality is critical because of the sheer volumes of Data being generated. In this paper, we propose a deep learning based IoT edge analytics approach to support intelligent healthcare for residential users. The performance of the proposal is validated using computer-based simulation for online training of a real dataset. The reported results of our proposal exhibit encouraging performance in terms of low loss rate, high accuracy, and low execution time to support near real-time actionable decision making on the healthcare data.

Due to the fast increase of sensing data and quick response requirement in the Internet of Things (IoT) delivery network, the high speed transmission has emerged as an important issue. Assigning suitable channels in the wireless IoT delivery network is a basic guarantee of high speed transmission. However, the high dynamics of traffic load (TL) make the conventional fixed channel assignment algorithm ineffective. Recently, the software defined networking-based IoT (SDN-IoT) is proposed to improve the transmission quality. Besides this, the intelligent technique of deep learning is widely researched in high computational SDN. Hence, we first propose a novel deep learning-based TL prediction algorithm to forecast future TL and congestion in network. Then, a deep learning-based partially channel assignment algorithm is proposed to intelligently allocate channels to each link in the SDN-IoT network. Finally, we consider a deep learning-based prediction and partially overlapping channel assignment to propose a novel intelligent channel assignment algorithm, which can intelligently avoid potential congestion and quickly assign suitable channels in SDN-IoT. The simulation result demonstrates that our proposal significantly outperforms conventional channel assignment algorithms.

Recently, network operators are confronting the challenge of exploding traffic and more complex network environments due to the increasing number of access terminals having various requirements for delay and package loss rate. However, traditional routing methods based on the maximum or minimum single metric value aim at improving the network quality of only one aspect, which makes them become incapable to deal with the increasingly complicated network traffic. Considering the improvement of deep learning techniques in recent years, in this paper, we propose a smart packet routing strategy with Tensor-based Deep Belief Architectures (TDBAs) that considers multiple parameters of network traffic. For better modeling the data in TDBAs, we use the tensors to represent the units in every layer as well as the weights and biases. The proposed TDBAs can be trained to predict the whole paths for every edge router. Simulation results demonstrate that our proposal outperforms the conventional Open Shortest Path First (OSPF) protocol in terms of overall packet loss rate and average delay per hop.

Recent years, Software Defined Routers (SDRs) (programmable routers) have emerged as a viable solution to provide a cost-effective packet processing platform with easy extensibility and programmability. Multi-core platforms significantly promote SDRs' parallel computing capacities, enabling them to adopt artificial intelligent techniques, i.e., deep learning, to manage routing paths. In this paper, we explore new opportunities in packet processing with deep learning to inexpensively shift the computing needs from rule-based route computation to deep learning based route estimation for high-throughput packet processing. Even though deep learning techniques have been extensively exploited in various computing areas, researchers have, to date, not been able to effectively utilize deep learning based route computation for high-speed core networks. We envision a supervised deep learning system to construct the routing tables and show how the proposed method can be integrated with programmable routers using both Central Processing Units (CPUs) and Graphics Processing Units (GPUs). We demonstrate how our uniquely characterized input and output traffic patterns can enhance the route computation of the deep learning based SDRs through both analysis and extensive computer simulations. In particular, the simulation results demonstrate that our proposal outperforms the benchmark method in terms of delay, throughput, and signaling overhead.

Recently, numerous real-time, data-rich, and differentiated applications and services have appeared in the next-generation Heterogeneous Networks. As a result, the number of potentially “untrusted” connections to the mobile operator's core network is expected to dramatically increase. Therefore, the operators must provide adequate security, without significantly affecting the quality of service (QoS). Hence, joint consideration of QoS and security is a critical research issue. However, due to their difficult-to-model conflicting objectives, existing research works have often dealt with them separately. In this paper, we address this problem, formally formulate it, and envision GT-QoSec, a game-theoretic joint optimization of QoS and security. Using GT-QoSec, the mobile user equipment (UE) and their servicing base stations (eNBs) play games with each other. Thus, the UE obtains a balanced set of QoS and security levels while the eNBs maximize their bandwidth utilization. Extensive analysis and simulation results are presented to evaluate the performance of GT-QoSec in contrast with several conventional methods.

Advertising among vehicles has become popular with the proliferation of vehicular ad-hoc networks (VANETs). Since the price of the advertisements broadcast in such networks decay over time, distributing advertisements with a high price value to more private vehicles can generate more revenues to the sellers. In this paper, we consider a bus-based priced advertising scenario in a VANET, in which the buses act as the sources of advertisements and broadcast advertisements to private vehicles running within their communication range. Meanwhile, in the area where no bus exists, private vehicles share their advertising segments. The manner in which the buses and the private vehicles distribute and share advertisements in the network so as to draw the largest benefit is addressed in our formulated problem. To solve this problem, a bus-based priced advertisement dissemination scheme dubbed Bus-Ads is proposed by using coalition formation game. First, a bus-broadcast method is presented to enable each bus to distribute the priced advertising segments with the largest potential benefit to surrounding private vehicles. Second, we apply coalition formation game to guide private vehicles to construct broadcast coalitions for efficient advertisement sharing. Simulation results demonstrate that our proposed Bus-Ads method can achieve about twice the total benefits compared with that of the non-coalition-based approach.

Data collected from the mobile Internet have the potential knowledge to provide important human mobility patterns. Understanding human mobility patterns is important to many location-based services, and could be used to predict users' behavior. In this paper, we concentrate on the issue of discovering human mobility patterns on both global and individual levels based on hotspots. We study the human mobility trajectories during 22 days for 3474 individuals collected at the core of a metropolitan Long Term Evolution (LTE) network in China. We employ a parameter-free method to detect hotspots, and demonstrate the effectiveness of our mobility pattern discovery algorithm by using the hotspots identified on both global and individual levels. We analyze the occurrence time distribution of these patterns and find that the global mobility patterns have higher occurrence probability in the morning, which indicates that people in a city tend to share the common commuting routes. For individual mobility patterns, there exists a strong spatiotemporal correlation property, implying that the individual mobility patterns have their own typical occurrence time depending on the pattern's context.

Recently, mobile Internet gained a strong momentum of development, which has led to increasing demand on mobile network traffic characterization and modeling. A good model of mobile network traffic can be used to make accurate prediction regarding various performance metrics. Based on the network trace collected from network backbone, our paper studies mobile network traffic characteristics in terms of the flow arrival numbers and flow connection duration. Basically, we employ the Poisson regression from Generalized Linear Model with time window clustering so as to approximate a time-dependent Poisson Process to the flow arrival process. Our analytical results demonstrate the accuracy of the adopted approach. In addition, through approximating the Phase Type distribution to the heavy-tailed distribution, our paper also models the flow connection duration. The obtained results can help us get a comprehensive understanding of the network performance, in accordance with which the resource usage may be optimized, e.g., we can expand network bandwidth or increase the buffer size when the network arrival is high.

Recent technological advances in electronics, sensors, and communications have accelerated the widespread deployment of Unmanned Aircraft System (UAS)-aided applications. Nevertheless, networks composed of multiple UAS and ground stations, referred to as UAS-aided communications networks, have yet to receive sufficient research attention. In this paper, we address a fundamental research challenge stunting such networks, which is how to fairly maximize the energy efficiency (throughput per energy) in networks comprising adaptive modulation-capable ground nodes. For the mobility pattern intrinsic to the UASs, we demonstrate how adaptive modulation is affected. Furthermore, we formulate the problem of maximizing fair energy efficiency as a potential game that is played between the multiple ground nodes and substantiate its stability, optimality, and convergence. Based on the formulated potential game, a data collection method is proposed to maximize the energy efficiency with a fairness constraint. Additionally, we analyze the Price of Anarchy of our proposed game-theoretic data collection method. Extensive simulations exhibit the effectiveness of our proposal under varying environments.

The design of a wireless network is often critically affected by issues such as determining the optimal density of Access Points (APs) and the optimal channel assignment by exploiting partially overlapped channels (POCs) for significantly improving the network performance in terms of maximizing the overall network capacity. Contemporary research works have traditionally dealt with these two problems in an isolated manner though they should be considered within the same problem formulation. Furthermore, even though deployment of additional APs can improve the network capacity in case there are a few APs in a given area, the APs cannot be indefinitely added to the wireless network. This means that there is an upper bound to the network capacity maximization with respect to the number of APs. In fact, the network capacity starts to dramatically decrease when the number of deployed APs becomes excessive. This performance decrease can be accredited to the substantial interference among the high number of deployed APs. In order to address this challenge, in this paper, we propose an approach to jointly optimize the number of APs and POCs assignment. Our proposal derives the existence of the optimal density of APs with POCs, and models the POC assignment to the deployed APs from a novel perspective. Computer-based simulations are conducted to demonstrate the effectiveness of our proposal.

Recent technological advances in electronics, sensors, and communications devices have facilitated the proliferation of Unmanned Aircraft System (UAS)-aided applications. However, the UAS-aided communications networks are yet to receive sufficient research endeavor. In this paper, we address one of the most important research challenges pertaining to UAS-aided networks comprising adaptive modulation-capable nodes, namely how to fairly maximize the energy efficiency (throughput per energy). For the mobility pattern innate to the UAS, we demonstrate how the adaptive modulation behaves. Furthermore, we formulate the problem as a potential game that is played between the UAS and the network-nodes, and prove its stability, optimality, and convergence. Based upon the potential game, a data collection method is envisioned to maximize the energy efficiency with the fairness constraint. Additionally, we analyze the Price of Anarchy (PoA) of our proposed game. Extensive simulations exhibit the effectiveness of our proposal under varying environments.

Smart micro-grids can produce “renewable” energy and store them in power storage devices. Power loss, however, is a significant problem in power exchange among the micro-grids and between the macro-station and individual micro-grids. To optimally reduce the total power losses in such a power grid system, in this paper, a greedy coalition formation algorithm is proposed, which allows the macro-station to coordinate mutual power exchange among the micro-grids and between each micro-grid and the macro-station. Our algorithm optimizes the total power losses across the entire power grid, including the cost of charging and discharging power storage devices and power losses due to power transfers. The algorithm creates exchange pairs among the micro-grids, giving priority to pairs with higher power loss reduction per exchanged power unit. Through computer-based simulations, we demonstrate that the proposed approach significantly reduces the average power loss compared with the conventional noncooperative method. The simulations also demonstrate that the communications overhead of our proposal (due to negotiations aimed at forming coalitions) does not significantly affect the available communication resource.

Due to the increasing popularity of multimedia streaming applications and services in recent years, the issue of trusted video delivery to prevent undesirable content-leakage has, indeed, become critical. While preserving user privacy, conventional systems have addressed this issue by proposing methods based on the observation of streamed traffic throughout the network. These conventional systems maintain a high detection accuracy while coping with some of the traffic variation in the network (e.g., network delay and packet loss), however, their detection performance substantially degrades owing to the significant variation of video lengths. In this paper, we focus on overcoming this issue by proposing a novel content-leakage detection scheme that is robust to the variation of the video length. By comparing videos of different lengths, we determine a relation between the length of videos to be compared and the similarity between the compared videos. Therefore, we enhance the detection performance of the proposed scheme even in an environment subjected to variation in length of video. Through a testbed experiment, the effectiveness of our proposed scheme is evaluated in terms of variation of video length, delay variation, and packet loss.

In recent years, research attention on smart grid comprising distributed power generators has increased. To produce electricity in the smart grid, many micro grids (MGs) may exploit various renewable energy resources. Because the production capacity of renewable resources cannot be controlled, the MGs often require the power plants to provide power for them. However, the power loss between each MG and the power plant is larger than that among the MGs. To alleviate this power loss, we propose a game theoretic coalition formulation strategy for the MGs dubbed GT-CFS. Our proposed GT-CFS allows the MGs (belonging to the same macro station (MS)) to autonomously cooperate and self-organize into a partition composed of disjoint MG coalitions. Also, GT-CFS enables the MGs, in a distributed manner, to decide whether they will remain in the coalitions or not upon environmental changes, e.g., the variation of the power demand of the MGs. Within every coalition, MGs coordinate the power transfer among themselves as well as with the MS, in a fashion to optimize a utility function, which captures the total losses over the power distribution lines. MGs in the same coalition will distribute the extra profits (i.e., payoff) produced from forming coalitions by their “Shapley value.” Through computer simulations, we demonstrate that the proposed GT-CFS reduces the average power loss per MG significantly in contrast with the conventional noncooperative approach.

In order to optimize energy consumption in smart grid, demand side management has gained a lot of attention recently. While existing research works attempt to optimize energy consumption either from the view point of the power company or that of users, we investigate whether it is possible to consider both parties' interests at the same time. In this paper, we propose a novel energy price model, which is a function of the total energy consumption in the considered system. In addition, a new objective function, to optimize the difference between the value and cost of energy, is proposed. The power company sends the energy price parameter and the latest consumption summary vector information to the users sequentially. Upon receiving these information, a user can optimize his own schedule and report it to the company. The company then updates its energy price parameter before communicating with the next customers. A two-step centralized game is proposed that models this interaction between the power company and its consumers. The game aims at reducing the system peak-to-average power ratio by simultaneously optimizing users' energy schedules and lowering the overall energy consumption in the system. Through simulation results the performance of the proposed game-based demand side management technique is evaluated.

Smart grid has emerged as a promising technology for enabling bi-directional communication between the power company and its users to facilitate intelligent, robust, and resilient next generation power grid systems. Through this technology, both the power company and its subscribers can be equally benefited, not only from economic point of view, but also in terms of environment-friendly quality of power usage. One important challenge for the smart grid designers is the demand side management, which can lead to avoiding the peak hours and reducing the cost for the consumers. In this paper, we address the power balancing challenge for the smart grid and discuss different solutions including game theoretic methods and demand control policies. Also, we present our novel demand control policy for achieving an effective management of the power consumption. Computer simulations demonstrate the effectiveness of the proposed policy compared to existing ones.

The concept of Quality of Service (QoS) offers different service levels to the network users. Through Service Level Specifications (SLSs), the users in a wireless network, which supports QoS, are able to express, at run-time, their expected service requirements through well defined parameters. Conventional QoS parameters, such as throughput, delay, jitter, packet loss rates, and so forth, are used for reliably ensuring a certain service level with respect to reliability and/or performance. However, most existing researches have ignored tunable security as a Quality of Service (QoS) parameter. The biggest challenge of integrating QoS and security parameters consists in their contrasting goals. This paper presents an idea to permit the users of an IEEE 802.11 Wireless Local Area Network (WLAN) to specify their security and QoS requirements in their Service Level Specifications (SLSs). Then, a game theoretic approach is presented so that the system can reach Service Level Agreement (SLA) with the users to ascertain a balanced set of security and QoS parameters for the users. The effectiveness of the proposed approach is verified through computer simulations.

The Wireless Mesh Network (WMN) has already been recognized as a promising broadband access network technology from both academic and commercial perspective. In order to improve the performance of WMNs, extensive research efforts have been dedicated towards finding means to increase the number of simultaneous transmissions in the network while avoiding signal interference among radios. In case of WMNs based on IEEE 802.11 b/g standards, most recent research works have relied upon the usage of orthogonal channels for solving the Channel Assignment (CA) problem. In this paper, we explore the possibility of exploiting Partially Overlapped Channels (POCs) by introducing a novel game theoretic distributed CA algorithm. Our proposed algorithm outperforms both the conventional orthogonal channel approach and the recent heuristic CA algorithms using POC. The proposed algorithm is shown to achieve near-optimal performance in the average case. In addition, the upper bound Price of Anarchy for Multi-Radio Multi-Channel (MRMC) networks is derived to evaluate the effectiveness of the proposed approach.

The Wireless Mesh Network (WMN) has already been recognized as a promising technology as broadband access network from both academic and industry points of view. In order to improve its performance, research has been carried on how to increase the number of simultaneous transmissions in the network while avoiding signal interference among radios. Considering WMNs based upon IEEE 802.11 b/g standards, lately most of researchers have been relying on the usage of orthogonal channels for solving the Channel Assignment (CA) problem. However, in this paper, we introduce a novel CA algorithm exploiting partially overlapped channels (POC) that overcome the common orthogonal channel approach and also a recent proposed CA algorithm using POC.

The unbridled growth of the Internet and the network-based applications has contributed to enormous security leaks. Even the cryptographic protocols, which are used to provide secure communication, are often targeted by diverse attacks. Intrusion detection systems (IDSs) are often employed to monitor network traffic and host activities that may lead to unauthorized accesses and attacks against vulnerable services. Most of the conventional misuse-based and anomaly-based IDSs are ineffective against attacks targeted at encrypted protocols since they heavily rely on inspecting the payload contents. To combat against attacks on encrypted protocols, we propose an anomaly-based detection system by using strategically distributed monitoring stubs (MSs). We have categorized various attacks against cryptographic protocols. The MSs, by sniffing the encrypted traffic, extract features for detecting these attacks and construct normal usage behavior profiles. Upon detecting suspicious activities due to the deviations from these normal profiles, the MSs notify the victim servers, which may then take necessary actions. In addition to detecting attacks, the MSs can also trace back the originating network of the attack. We call our unique approach DTRAB since it focuses on both Detection and TRAceBack in the MS level. The effectiveness of the proposed detection and traceback methods are verified through extensive simulations and Internet datasets.

Owing to the diverse geographical distributions of users, multi-layered satellite networks tend to exhibit high variances causing traffic concentrations at particular satellites to increase drastically. This results in high packet drop rates and severe degradation of Quality of Service (QoS). The Explicit Load Balancing (ELB) scheme was developed to address these issues in Low Earth Orbit (LEO) satellite networks by having the satellites, which experience heavy traffic, redirect a portion of the traffic via alternative paths. To cope with network congestion (over a single layer) and for better traffic distribution, multi layer satellites were proposed. In this paper, we propose an efficient traffic distribution scheme for multi-layered satellite networks based on ELB in which we extend the range for exchanging the traffic-load information for achieving further reductions in packet drop rates. We also present an enhanced technique for efficiently computing the detouring ratio. The effectiveness of the envisioned approach is validated via simulations.

In the modern era of Internet, providing Quality of Service (QoS) is a challenging issue, particularly in resource-constrained wireless networks with delay-sensitive multimedia traffic. Real-time and multimedia services are now available to end-users over wired networks, Wireless Local Area Networks (WLANs), and Wireless Personal Area Networks (WPANs). While the usual trend is to provide the best possible QoS for these services, it is also imperative to deploy security requirements along with the QoS parameters. In this paper, we argue that the existing approaches for including security parameters (such as encryption/decryption key lengths) with QoS parameters (e.g., end-to-end delay requirements) lead to further security risks and consequently fail to provide an adequate solution. Through simulations, we point out the pitfalls of integrating delay and security support in the contemporary approaches. We also envision QoS2, a framework integrating both quality of security and QoS, in order to provide possible solutions for solving these problems. We also demonstrate via simulation the effectiveness and strength of our adopted approach.

Recent advances in wireless technology, sensors and portable devices offer interesting opportunities to enable ubiquitous assistance to individuals in need of prompt help. Providing healthcare services to mobile users, such as, patients, elders, or potential drug abusers, is a rather challenging task. Novel middleware-level supports are required to integrate sensor infrastructures capable of detecting changes in the monitored subjects' health conditions and of alerting medical personnel, and the victim's relatives and friends in case of emergency situations. Along this line, the paper envisions a context-aware middleware-level solution dubbed Pervasive Environment for Affective Healthcare (PEACH). PEACH integrates together various sensors in a Wireless Body Area Network (WBAN) to detect alterations of monitored subjects' affective and physical conditions, aggregate the sensed information, and also detect potentially dangerous situations for the monitored subject. Finally, PEACH aims at providing outdoor assistance to the victim/patient by quickly promoting the formation of ad hoc rescue groups comprising nearby volunteers. Through encouraging results obtained from both simulations and a practical drug-rehabilitation application testbed, the effectiveness of the envisioned PEACH framework is verified.

Fast handover management in mobile IPv6 environments has been a research subject for a long time. Exploiting the cooperative diversity paradigm in partner-based hierarchical MIPv6 (PHMIPv6) promises an acceleration of the handoff management operation by relaying some signaling over a selected partner node prior to the actual handover to the new access point. For this purpose, a suitable partner node, that stays in communication range for sufficient time until the signaling in the pre-handoff phase is finalized, should be selected. PHMIPv6 proposes to select the node with the highest signal strength as the partner node. In this paper, we show that using the link expiration time (LET) metric to select the partner node can significantly improve handovers in mobile IP (MIP) networks. The basis of this new metric is the relative position and the relative speed of the mobile node to the potential partner nodes. A set of simulations is conducted to evaluate the performance of the proposed scheme and encouraging results are obtained.

Attacks against encrypted protocols are becoming increasingly popular. They pose a serious challenge to the conventional intrusion detection systems (IDSs) which heavily rely on inspecting the network packet fields and are consequently unable to monitor encrypted sessions. IDSs can be broadly categorized into two types: signature-based and anomaly-based IDSs. The signature-based IDSs rely on previous attack signatures but are often ineffective against new attacks. On the other hand, anomaly-based detection systems depend on detecting the change in the protocol behavior caused by an attack. The latter can be employed to detect novel attacks, and therefore are often preferred over their signature-based counterpart. In this paper, we envision an anomaly-based IDS which can detect attacks against popular encrypted protocols, such as SSH and SSL. The proposed system creates a normal behavior profile and uses non-parametric Cusum algorithm to detect deviation from the normal profile. Upon detecting an anomaly, the proposed mechanism generates an alert, sets a delay to the protocol response, and traces back the attacker. The effectiveness of the proposed detection scheme is verified via simulations.

Attacks against encrypted protocols have become increasingly popular and sophisticated. Such attacks are often undetectable by the traditional Intrusion Detection Systems (IDSs). Additionally, the encrypted attack-traffic makes tracing the source of the attack substantially more difficult. In this paper, we address these issues and devise a mechanism to trace back attackers against encrypted protocols. In our efforts to combat attacks against cryptographic protocols, we have integrated a traceback mechanism at the monitoring stubs (MSs), which were introduced in one of our previous works. While we previously focused on strategically placing monitoring stubs to detect attacks against encrypted protocols, in this work we aim at equipping MSs with a traceback feature. In our approach, when a given MS detects an attack, it starts tracing back to the root of the attack. The traceback mechanism relies on monitoring the extracted features at different MSs, i.e., in different points of the target network. At each MS, the monitored features over time provide a pattern which is compared or correlated with the monitored patterns at the neighboring MSs. A high correlation value in the patterns observed by two adjacent MSs indicates that the attack traffic propagated through the network elements covered by these MSs. Based on these correlation values and a prior knowledge of the network topology, the system can then construct a path back to the attacking hosts. The effectiveness of the proposed traceback scheme is verified by simulations.