Large Language Models (LLMs) inference-time privacy focuses mainly on protecting sensitive information within the prompt. However, enabling end users to repudiate the submission of exact prompts, to avoid potential legal consequences, remains an unexplored research direction. To address this gap, we propose Repudiation for Good (R4G), a privacy-preserving approach that enables users to deny submitting exact prompts while allowing cloud-based LLM providers to receive sufficient semantic information but preventing them from proving with certainty which specific prompt was submitted. R4G is a paradigm shift in LLM inference-time privacy. Rather than encrypting exact prompts, which eventually reveal their precise content upon decryption, R4G transforms prompts into an intentionally ambiguous semantic representation through LLM embedding. This transformation creates a many-to-one mapping where multiple lexically distinct prompts collapse into a similar embedding space. The key insight is that while traditional privacy approaches aim to hide information temporarily (through encryption) or completely (through anonymization), our approach provides a probabilistic association between the prompt and its embedding, creating a deniability space, where users can legitimately deny submitting exact prompts that might carry legal, social, or professional consequences. We conducted experiments to assess the efficacy of R4G by measuring the semantic similarity between the original and approximated prompts. The results show that R4G achieved an average cosine similarity score of approximately 50%, effectively striking a balance between utility and privacy.

Graph neural networks (GNNs) have recently garnered significant attention for use in network intrusion detection systems (NIDS), owing to their ability to model network traffic as graphs and capture complex dependencies between flows. However, existing GNN-based methods face critical limitations: their reliance on labeled data, often scarce or noisy in practice, and their inability to address multi-scale threats, such as localized node anomalies (e.g., port scanning), coordinated subnet-work attacks (e.g., botnets), and global network-wide campaigns (e.g., DDoS attacks). To bridge this gap, we propose Multi-Scale Graph Contrastive Representation Learning (MGCRL), a semi-supervised framework that hierarchically integrates three perspectives to model network intrusions. At the node level, MGCRL constructs semantic subnetworks around individual traffic flows to capture fine-grained behavioral deviations. For subnetwork-level threats, it employs substructure-aware pooling to identify coordinated anomalies, such as clusters of devices exhibiting synchronized malicious activity. Finally, at the global level, MGCRL derives representations that reflect the holistic state of the network, enabling detection of large-scale threats, such as distributed malware propagation. MGCRL couples a shared GNN encoder with a multi-level contrastive loss to align multi-scale representations while largely eliminating label dependence. It learns discriminative features from unlabeled traffic, sharpens decision boundaries with minimal supervision, and exposes anomalies that surface in a hierarchical network context by contrasting related and unrelated nodes at each scale. Extensive experiments on three benchmark datasets for multi-class classification show that MGCRL consistently outperforms SOTA methods, particularly under severe label scarcity and class imbalance.

This paper presents FFS-ORAN-IDS, a federated few-shot intrusion-detection framework that secures streaming traffic in Open Radio Access Networks (O-RAN) while respecting their stringent latency and resource constraints. The framework addresses the twin challenges of scarce attack labels and heterogeneous data, where naïve pseudo-label injection without sufficient confidence propagates errors, large-scale labeling of streaming traffic is impractical, and inherent uncertainty often requires costly human intervention. FFS-ORAN-IDS combines three coordinated x-functional blocks: a confidence-adaptive curriculum that releases pseudo-labels only when local TabTransformers are reliable, a diversity filter that retains the most informative uncertain packets, and a token-budgeted large-language-model (LLM) oracle that verifies the remaining hard samples. A mixed-integer optimization jointly governs curriculum pacing, sampling size, and Oracle LLM calls so that each federated round minimizes detection loss, propagation error, and LLM token cost under per-round resource caps. We train FFS-ORAN-IDS in two stages: an initial few-shot phase that fits the TabTransformer on the scarce ground-truth packets, followed by iterative rounds that refine the model with oracle-verified pseudo-labels. Experimental evaluation on the CIC-IDS 2018 benchmark shows that the proposed framework improves detection accuracy by 6%, reduces label-error propagation by 20%, and lowers energy consumption by 40% in the most label-constrained scenarios.

In the dynamic vehicular metaverse, delivering a seamless user experience (UX) and effective human-machine interaction (HMI) is challenging due to vehicle mobility and varying resource needs. This paper introduces an adaptive resource allocation and twin migration framework using Multi-Agent Deep Reinforcement Learning (MADRL) for a multi-tier vehicular metaverse. The framework enables cooperative agents to dynamically allocate resources and migrate vehicle twins across vehicle, edge, and cloud layers, ensuring seamless UX and efficient HMI. The joint resource allocation and twin migration optimization problem is modeled as MDP and a hierarchical multi-agent deep deterministic policy gradient-with QMIX (MADDPG-Q) strategy is adopted to solve it, reducing latency and optimizing resource use. Moreover, the proposed framework is designed to be context-aware, adjusting HMI based on real-time conditions, and enhancing interaction quality. Simulation results show significant improvements in UX, latency reduction, and resource efficiency.

Unmanned aerial vehicles (UAVs) are proven useful for enabling wireless power transfer (WPT), resource offloading, and data collection from ground IoT devices in post-disaster scenarios where conventional communication infrastructure is compromised. As 6G networks emerge, offering ultra-reliable low-latency communication and enhanced energy efficiency, UAVs are poised to play a critical role in extending 6G features to challenging environments. The key challenges in this context include limited UAV flight duration, energy constraints, limited resources, and the reliability of data collection, all of which impact the effectiveness of UAV operations. Motivated by the need for efficient resource allocation and reliable data collection, we propose a solution using UAV swarms combined with reconfigurable intelligent surfaces (RIS) to optimize energy harvesting for IoT devices and enhance communication quality. We formulate the problem of resource optimization, UAVs-RIS trajectory planning, and RIS configuration as a mixed integer nonlinear programming optimization problem and solve it in a dynamic condition by transforming it into a Markov decision process and utilizing a deep reinforcement learning (DRL) approach based on proximal policy optimization (PPO) algorithm to solve it. Simulation results demonstrate that our framework outperforms traditional approaches, including the Actor-Critic (AC) algorithm and a greedy solution, achieving superior performance in energy harvesting efficiency, data collection, and communication reliability.

Deploying transformer models in Personalized Federated Learning (PFL) over wireless networks is challenging due to their large size, which leads to high communication overhead, increased latency, and excessive energy consumption. Traditional pruning and sparsification methods, designed mainly for conventional deep learning architectures, are ineffective for transformers and can cause divergence or degrade performance—especially when applied to self-attention layers or through direct federated averaging. To address these challenges, we propose a novel dual approach called PFL-TPS (PFL with Transformer Pruning and Sparsification). Our approach efficiently reduces communication and computation costs while maintaining model performance, making it suitable for resource-constrained wireless networks. Specifically, we apply adaptive pruning with trainable thresholds to the transformer's Feed-Forward Layers (FFLs), and only these trainable thresholds are shared with the server, resulting in minimal uploaded data. For the Self-Attention Layers (SALs), instead of transmitting bandwidth-intensive model parameters, we employ a server-side hypernetwork that generates personalized parameters based on device-specific embedding vectors sent by the devices, significantly reducing communication overhead and maintaining personalization. Extensive experiments show that PFL-TPS reduces energy consumption by up to 50%, decreases training time by 60.44%, and improves model accuracy by 49.87% compared to baselines in wireless networks.

Spatiotemporal graph neural networks (STGNNs) have proven especially effective in traffic prediction tasks by modeling sensors or regions as nodes, with distances and correlations as edges. Their ability to capture complex spatiotemporal dependencies in road networks drives key applications in public safety, urban planning, and intelligent transportation. However, existing STGNNs often rely on manually designed architectures and typically process multiview graphs separately, requiring specialized expertise, thus limiting flexibility and overlooking intricate spatiotemporal interdependencies. To address these challenges, we propose an Attention-based Dynamic Multiview Spatiotemporal Graph Neural Architecture Search (ADMGNAS) framework, comprising three interconnected components. First, we introduce a unified Attention-based Dynamic Multiview Spatiotemporal Graph (ADMSTG) architecture that integrates spatiotemporal and view-aware attention mechanisms, enabling the effective capture of complex cross-view relationships. Building upon this architecture, we then develop a dedicated Multiview Attention Spatiotemporal (MVAS) search space, which systematically automates the selection of optimal attention operations. Then, a specialized differentiable search algorithm efficiently explores the MVAS space to dynamically identify architecture variations specifically tailored to dynamic multiview spatiotemporal graphs. Extensive experiments on benchmark datasets show that ADMGNAS consistently outperforms SOTA methods, proving its effectiveness and adaptability.

Deploying large language models (LLMs) at the wireless edge is a promising solution to meet the low-latency, high-computation demands of next-generation AI applications. Although the existing literature has introduced approaches to enable distributed LLM inference, these methods largely overlook the distinct computational and communication characteristics of the two-phase LLM inference process—the pre-fill and decode phases. This oversight leads to suboptimal performance and limits scalability in real-world deployments. To address these issues, we propose a novel collaborative inference framework that strategically minimizes inference latency by optimally distributing computational loads across edge devices, the edge server, and the cloud. Our approach introduces a hybrid framework that combines head-wise parallel processing with layer-wise partitioning of LLM models, supported by a dual-phase optimization strategy. In the pre-fill phase, we optimize assigning attention heads to selected edge devices for parallel computation and efficient resource use. We then optimize for minimal latency by selecting participants, determining head assignments per device, and allocating bandwidth while meeting all constraints. In the de-code phase, our framework adaptively decides whether to execute computations locally on the edge server, offload them to the cloud, or redistribute tasks among edge devices, optimizing this decision based on the remaining latency budget and the sequential nature of the decode phase. The simulation results demonstrate that the proposed framework significantly outperforms the baseline methods, achieving a 56% reduction in inference latency, 40% improvement in bandwidth efficiency and 35% improvement in resource utilization.

Smart electric vehicle charging stations (EVCSs) are crucial in advancing sustainable transportation by scheduling charging based on user preferences and grid constraints. However, their reliance on digital communication makes them vulnerable to attacks that can shift the EV aggregator’s load profile to different times, leading to substantial financial losses. They can also alter charging times in ways that degrade battery health and overburden the grid. Although the existing literature has explored various strategies to mitigate these risks, most prior work has focused on simple, handcrafted charge manipulation attacks (CMAs). This makes them often fall short when confronted with artificially intelligent methods to remain undetected. To address these limitations, we propose a novel framework that both generates and defends against highly evasive CMAs. First, we utilize deep reinforcement learning (DRL) to craft advanced, stealthy attacks capable of bypassing intrusion detection systems (IDS). Second, we introduce an IDS built on LSTM variational autoencoders, which captures the nuanced temporal dependencies of smart CMAs, as well as intricate patterns. This enables our IDS to significantly enhance the detection and mitigation of complex threats. We conduct extensive simulations using real-world datasets, which reveal critical security gaps in existing benchmark approaches while highlighting the strong performance of our proposed framework. Notably, our IDS achieves detection accuracies of 0.97, 0.96, and 0.96 across different scenarios, even against highly evasive CMAs.

ABSTRACT This paper presents a new data clustering technique aimed at enhancing the performance of the trainable path‐cost algorithm and reducing the computational complexity of data clustering models. The proposed method facilitates the discovery of natural groupings and behaviours, which is crucial for effective coordination in complex environments. It identifies natural groupings within a set of features and detects the best clusters with similar behaviour in the data, overcoming the limitations of traditional state‐of‐the‐art methods. The algorithm utilises a density peak clustering method to determine cluster centers and then extracts features from paths passing through these peak points (centers). These features are used to train the support vector machine (SVM) to predict the labels of other points. The proposed algorithm is enhanced using two key concepts: first, it employs Q‐Generalised Extreme Value (Q‐GEV) under power normalisation instead of traditional generalised extreme value distributions, thereby increasing modelling flexibility; second, it utilises the random vector functional link (RVFL) network rather than the SVM, which helps avoid overfitting and improves label prediction accuracy. The effectiveness of the proposed clustering algorithm is evaluated through various experiments, including those on UCI benchmark datasets and real‐world data, demonstrating significant improvements across multiple performance metrics, including F1 measure, Jaccard index, purity, and accuracy, highlighting its capability in accurately identifying paths between similar clusters. Its average F1 measure, Jaccard index, purity, and accuracy is measured 76.87%, 56.29%, 80.29%, and 79.64%, respectively.

Abstract Semantic understanding of roadways is a key enabling factor for safe autonomous driving. However, existing autonomous driving datasets provide well-structured urban roads while ignoring unstructured roadways containing distress, potholes, water puddles, and various kinds of road patches i.e., earthen, gravel etc. To this end, we introduce Road Region Segmentation dataset (R2S100K)—a large-scale dataset and benchmark for training and evaluation of road segmentation in aforementioned challenging unstructured roadways. R2S100K comprises 100K images extracted from a large and diverse set of video sequences covering more than 1000 km of roadways. Out of these 100K privacy respecting images, 14,000 images have fine pixel-labeling of road regions, with 86,000 unlabeled images that can be leveraged through semi-supervised learning methods. Alongside, we present an Efficient Data Sampling based self-training framework to improve learning by leveraging unlabeled data. Our experimental results demonstrate that the proposed method significantly improves learning methods in generalizability and reduces the labeling cost for semantic segmentation tasks. Our benchmark will be publicly available to facilitate future research at https://r2s100k.github.io/ .

With its significant security potential, the quantum internet is poised to revolutionize technologies like cryptography and communications. Although it boasts enhanced security over traditional networks, the quantum internet still encounters unique security challenges essential for safeguarding its Confidentiality, Integrity, and Availability (CIA). This study explores these challenges by analyzing the vulnerabilities and the corresponding mitigation strategies across different layers of the quantum internet, including physical, link, network, and application layers. We assess the severity of potential attacks, evaluate the expected effectiveness of mitigation strategies, and identify vulnerabilities within diverse network configurations, integrating both classical and quantum approaches. Our research highlights the dynamic nature of these security issues and emphasizes the necessity for adaptive security measures. The findings underline the need for ongoing research into the security dimension of the quantum internet to ensure its robustness, encourage its adoption, and maximize its impact on society.

Clustered Federated Multi-task Learning (CFL) has emerged as a promising technique to address statistical challenges, particularly with non-independent and identically distributed (non-IID) data across users. However, existing CFL studies entirely rely on the impractical assumption that devices possess access to accurate ground-truth labels. This assumption becomes specifically problematic in hierarchical wireless networks (HWNs), with vast unlabeled data and dual-level model aggregation, not only leading to slowing down convergence speeds and extending processing times but also resulting in increased resource consumption. To this end, we propose Clustered Federated Semi-Supervised Learning (CFSL), a novel framework tailored for more realistic scenarios in HWNs. We leverage specialized models resulting from device clustering and present two prediction model schemes, the best-performing specialized model and the weighted-averaging ensemble model, to correctly label unlabeled, unseen data. For the best-performing specialized model scheme, a specialized model excelling in label prediction for a specific device is assigned to correctly label the unlabeled data, even when the data originates from other environments, while the weighted-averaging ensemble model combines all specialized models into a unified model, capturing more details from broader data distributions across edge networks. The CFSL also introduces two novel prediction time schemes, split-based and stopping-based, for accurately timing the labeling process, alongside two strategic device selection schemes, greedy and round-robin, upon reaching each cluster’s stopping point. Extensive testing validates CFSL’s superiority over existing models in labeling and testing accuracies and resource efficiency, achieving up to 51% energy savings.

Recently, unmanned aerial vehicles (UAVs) have demonstrated exemplary performance in various scenarios, such as search and rescue, smart city services, and disaster response applications. UAVs can facilitate wireless power transfer (WPT), resource offloading, and data collection from ground IoT devices. However, employing UAVs for such applications poses several challenges, including limited flight duration, constrained energy resources, and the age of information of the data collected. To address these challenges, we employ a UAV swarm to maximize energy harvesting (EH) and data rates for IoT devices by optimizing UAV paths and integrating reconfigurable intelligent surfaces (RIS) technology. We tackle critical constraints, including UAV energy consumption, flight duration, and data collection deadlines, by formulating an optimization problem to find optimal UAV paths and RIS phase shifts. Given the complexity of the problem, its combinatorial nature, and the challenges of obtaining an optimal solution through conventional optimization methods, we decompose the problem into two sub-problems, employing deep reinforcement learning (DRL) to optimize EH and particle swarm optimization (PSO) to optimize RIS phase shifts. Our extensive simulations show that the proposed solution outperforms competitive algorithms, including Brute-Force-PSO, AC-PSO, and PPO-PSO algorithms, providing a robust solution for modern IoT applications.

The reciprocity nature of a wireless channel has been leveraged to generate and establish secret keys between two devices communicating over the channel. This is typically done by having the two devices exchange probing signals to measure their channel state information (CSI), which each then uses to derive secret keys. While extensive research on secret key generation primarily focuses on theoretical and simulation analyses, the practical implementation and adoption of key generation encounter obstacles arising from hardware constraints and varying channel conditions. To address this research gap, we experimentally investigate the channel measurements by collecting CSI data using resource-constrained devices. Our experiments reveal a significant degradation in the correlation between the collected CSI from two low-cost devices limiting the key generation performance. Through our experimental investigations, we first demonstrate that wavelet coherence provides insights on the reciprocity of the channel measurements on both time and frequency. We then propose a new wavelet coherence-based CSI data reconstruction technique that uses wavelet coherence and time-lagged cross-correlation to reconstruct CSI data that is consistent between the two participating devices, resulting in significant improvement in the channel measurements. Additionally, we propose a secret-key generation scheme that exploits the proposed wavelet coherence-based CSI data reconstruction, yielding a significant reduction in the bit error rates and an increase in the key generation rates.

Recently, Unmanned Aerial Vehicles (UAVs) have attracted the attention of researchers in academia and industry for providing wireless services to ground users in diverse scenarios like festivals, large sporting events, natural and man-made disasters due to their advantages in terms of versatility and maneuverability. However, the limited resources of UAV s (e.g., energy budget and different service requirements) can pose challenges for adopting UAV s for such applications. Our system model considers a UAV swarm that navigates an area, providing wireless communication to ground users with RIS support to improve the coverage of the UAV s. In this work, we introduce an optimization model with the aim of maximizing the throughput and UAVs coverage through optimal path planning of UAVs and multi-RIS phase configurations. The formulated optimization is challenging to solve using standard linear programming techniques, limiting its applicability in real-time decision-making. Therefore, we introduce a two-step solution using deep reinforcement learning and particle swarm optimization. We conduct extensive simulations and compare our approach to two competitive solutions presented in the recent literature. Our simulation results demonstrate that our adopted approach is 20 % better than the brute-force approach and 30% better than the baseline solution in terms of QoS.

The increasing popularity of electric vehicles (EVs) necessitates robust defenses against sophisticated cyber threats. A significant challenge arises when EVs intentionally provide false information to gain higher charging priority, potentially causing grid instability. While various approaches have been proposed in existing literature to address this issue, they often overlook the possibility of attackers using advanced techniques like deep reinforcement learning (DRL) or other complex deep learning methods to achieve such attacks. In response to this, this paper introduces a hierarchical adversarial framework using DRL (HADRL), which effectively detects stealthy cyberattacks on EV charging stations, especially those leading to denial of charging. Our approach includes a dual approach, where the first scheme leverages DRL to develop advanced and stealthy attack methods that can bypass basic intrusion detection systems (IDS). Second, we implement a DRL-based scheme within the IDS at EV charging stations, aiming to detect and counter these sophisticated attacks. This scheme is trained with datasets created from the first scheme, resulting in a robust and efficient IDS. We evaluated the effectiveness of our framework against the recent literature approaches, and the results show that our IDS can accurately detect deceptive EVs with a low false alarm rate, even when confronted with attacks not represented in the training dataset.

Clustered Federated Multi-task Learning (CFL) has gained considerable attention as an effective strategy for over-coming statistical challenges, particularly when dealing with non-independent and identically distributed (non-IID) data across multiple users. However, much of the existing research on CFL operates under the unrealistic premise that devices have access to accurate ground-truth labels. This assumption becomes especially problematic in, especially hierarchical wireless networks (HWNs), where edge networks contain a large amount of unlabeled data, resulting in slower convergence rates and increased processing times-particularly when dealing with two layers of model aggregation. To address these issues, we introduce a novel frame-work-Clustered Federated Semi-Supervised Learning (CFSL), designed for more realistic HWN scenarios. Our approach leverages a best-performing specialized model algorithm, wherein each device is assigned a specialized model that is highly adept at generating accurate pseudo-labels for unlabeled data, even when the data stems from diverse environments. We validate the efficacy of CFSL through extensive experiments, comparing it with existing methods highlighted in recent literature. Our numerical results demonstrate that CFSL significantly improves upon key metrics such as testing accuracy, labeling accuracy, and labeling latency under varying proportions of labeled and unlabeled data while also accommodating the non-IID nature of the data and the unique characteristics of wireless edge networks.

Our study addresses the challenge of imbalanced regression data in Machine Learning (ML) by introducing tailored methods for different data structures. We adapt K-Nearest Neighbor Oversampling-Regression (KNNOR-Reg), originally for imbalanced classification, to address imbalanced regression in low population datasets, evolving to KNNOR-Deep Regression (KNNOR-DeepReg) for high-population datasets. For tabular data, we also present the Auto-Inflater neural network, utilizing an exponential loss function for Autoencoders. For image datasets, we employ Multi-Level Autoencoders, consisting of Convolutional and Fully Connected Autoencoders. For such high-dimension data our approach outperforms the Synthetic Minority Oversampling Technique for Regression (SMOTER) algorithm for the IMDB-WIKI and AgeDB image datasets. For tabular data we conducted a comprehensive experiment using various models trained on both augmented and non-augmented datasets, followed by performance comparisons on test data. The outcomes revealed a positive impact of data augmentation, with a success rate of 83.75% for Light Gradient Boosting Method (LightGBM) and 71.57% for the 18 other regressors employed in the study. This success rate is determined by the frequency of instances where models performed better when augmented data was used compared to instances with no augmentation. Access to the comparative code can be found in GitHub.

This article outlines the architecture of autonomous driving and related complementary frameworks from the perspective of human comfort. The technical elements for measuring autonomous vehicle (AV) user comfort and psychoanalysis are listed here. At the same time, this article introduces the technology related to the structure of automatic driving and the reaction time of automatic driving. We also discuss the technical details related to the automatic driving comfort system, the response time of the AV driver, the comfort level of the AV, motion sickness, and related optimization technologies. The function of the sensor is affected by various factors. Since the sensor of automatic driving mainly senses the environment around a vehicle, including “the weather” which introduces the challenges and limitations of second-hand sensors in AVs under different weather conditions. The comfort and safety of autonomous driving are also factors that affect the development of autonomous driving technologies. This article further analyzes the impact of autonomous driving on the user’s physical and psychological states and how the comfort factors of AVs affect the automotive market. Also, part of our focus is on the benefits and shortcomings of autonomous driving. The goal is to present an exhaustive overview of the most relevant technical matters to help researchers and application developers comprehend the different comfort factors and systems of autonomous driving. Finally, we provide detailed automated driving comfort use cases to illustrate the comfort-related issues of autonomous driving. Then, we provide implications and insights for the future of autonomous driving.

Recently, unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) networks are considered to provide effective and efficient solutions for disaster management. However, the limited size of end-user devices comes with the limitation of battery lives and computational capacities. Therefore, offloading, energy consumption, and computational efficiency are significant challenges for uninterrupted communication in UAV-assisted MEC networks. This article considers a UAV-assisted MEC network with energy harvesting (EH). To achieve this, we mathematically formulate a mixed-integer nonlinear programming problem to maximize the computational efficiency of UAV-assisted MEC networks with EH under disaster situations. A power-splitting architecture splits the source power for communication and EH. We jointly optimize user association, transmission power of user equipment (UE), task offloading time, and UAV’s optimal location. To solve this optimization problem, we divide it into three stages. In the first stage, we adopt$k$-means clustering to determine the optimal locations of the UAVs. In the second stage, we determine user association. In the third stage, we determine the optimal power of UE and offloading time using the optimal UAV location from the first stage and the user association indicator from the second stage, followed by linearization and the use of the interior-point method to solve the resulting linear optimization problem. Simulation results for offloading, no-offloading, offloading-EH, and no-offloading-EH scenarios are presented with a varying number of UAVs and UEs. The results show the proposed EH solution’s effectiveness in offloading scenarios compared to no-offloading scenarios in terms of computational efficiency, bits computed, and energy consumption.

In contrast to methods relying on a centralized training, emerging Internet of Things (IoT) applications can employ federated learning (FL) to train a variety of models for performance improvement and improved privacy preservation. FL calls for the distributed training of local models at end-devices, which uses a lot of processing power (i.e., CPU cycles/sec). Most end-devices have computing power limitations, such as IoT temperature sensors. One solution for this problem is split FL. However, split FL has its problems, including a single point of failure, issues with fairness, and a poor convergence rate. We provide a novel framework, called hierarchical split FL (HSFL), to overcome these issues. On grouping, our HSFL framework is built. Partial models are constructed within each group at the devices, with the remaining work done at the edge servers. Each group then performs local aggregation at the edge following the computation of local models. End devices are given access to such an edge aggregated model so they can update their models. For each group, a unique edge aggregated HSFL model is produced by this procedure after a set number of rounds. Shared among edge servers, these edge aggregated HSFL models are then aggregated to produce a global model. Additionally, we propose an optimization problem that takes into account the relative local accuracy (RLA) of devices, transmission latency, transmission energy, and edge servers’ compute latency in order to reduce the cost of HSFL. The formulated problem is a mixed-integer nonlinear programming (MINLP) problem and cannot be solved easily. To tackle this challenge, we perform decomposition of the formulated problem to yield subproblems. These subproblems are edge computing resource allocation problem and joint RLA minimization, wireless resource allocation, task offloading, and transmit power allocation subproblem. Due to the convex nature of edge computing, resource allocation is done so utilizing a convex optimizer, as opposed to a block successive upper-bound minimization (BSUM)-based approach for joint RLA minimization, resource allocation, job offloading, and transmit power allocation. Finally, we present the performance evaluation findings for the proposed HSFL scheme.

Recent works have shown that deep learning (DL) models can effectively learn city-wide crowd-flow patterns, which can be used for more effective urban planning and smart city management. However, DL models have been known to perform poorly on inconspicuous adversarial perturbations. Although many works have studied these adversarial perturbations in general, the adversarial vulnerabilities of deep CFP models in particular have remained largely unexplored. In this paper, we perform a rigorous analysis of the adversarial vulnerabilities of DL-based CFP models under multiple threat settings, making three-fold contributions; 1) we propose CaV-detect by formally identifying two novel properties—ConsistencyandValidity—of the CFP inputs that enable thedetection of standard adversarial inputs with 0% false acceptance rate (FAR); 2) we leverage universal adversarial perturbations and an adaptive adversarial loss to present adaptive adversarial attacks to evade CaV-detect defense; 3) we propose CVP, aConsistent,Valid andPhysically-realizable adversarial attack, that explicitly inducts the consistency and validity priors in the perturbation generation mechanism. We find out that although the crowd-flow models are vulnerable to adversarial perturbations, it is extremely challenging to simulate these perturbations in physical settings, notably when CaV-detect is in place. We also show that CVP attack considerably outperforms the adaptively modified standard attacks in FAR and adversarial loss metrics. We conclude with useful insights emerging from our work and highlight promising future research directions.

In the past decade, Unmanned Aerial Vehicles (UAVs) have grabbed the attention of researchers in academia and industry for their potential use in critical emergency applications, such as providing wireless services to ground users and collecting data from areas affected by disasters, due to their advantages in terms of maneuverability and movement flexibility. The UAVs' limited resources, energy budget, and strict mission completion time have posed challenges in adopting UAVs for these applications. Our system model considers a UAV swarm that navigates an area collecting data from ground IoT devices focusing on providing better service for strategic locations and allowing UAVs to join and leave the swarm (e.g., for recharging) in a dynamic way. In this work, we introduce an optimization model with the aim of minimizing the total energy consumption and provide the optimal path planning of UAVs under the constraints of minimum completion time and transmit power. The formulated optimization is NP-hard making it not applicable for real-time decision making. Therefore, we introduce a light-weight meta-reinforcement learning solution that can also cope with sudden changes in the environment through fast convergence. We conduct extensive simulations and compare our approach to three state-of-the-art learning models. Our simulation results prove that our introduced approach is better than the three state-of-the-art algorithms in providing coverage to strategic locations with fast convergence.

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

Drone-enabled vehicular ad-hoc network (VANET) is a promising solution for safe driving as it improves traffic efficiency and reliability by timely sharing road events and traffic information. However, there is an urgent need to tackle security, privacy, and computational delay related issues. In this paper, we propose a blockchain-based privacy preservation scheme using steganography to overcome the aforementioned issues in drone-enabled VANETs. The proposed scheme is based on a decentralized key management mechanism that combines lightweight authentication and key agreement. Data redundancy is avoided with the help of interplanetary file system (IPFS) which stores traffic event related data in blockchain through the smart contract. We further modified consensus algorithm using the practical byzantine fault tolerates algorithm and steganography to achieve better efficiency. Moreover, trust management is achieved by combining blockchain, IPFS, and steganography, which also enables the distributed storage and quick access to data for drone-enabled VANETs. We evaluate the performance of the proposed scheme in terms of response time, and computational time against incentive-based scheme.

The majority of streaming Internet of Things (IoT) applications use machine learning models to identify and classify streaming inputs before forwarding them for further processing. These streaming IoT systems, however, are vulnerable to poisoning and adversarial attacks. An adversary deliberately modifies the input by adding a small perturbation during the communication to fool the class label into producing an arbitrary or specific output. The increasing number of well-developed, imperceptible attacks necessitates more sophisticated countermeasures. To this end, this paper underlines this problem and proposes a new scheme based on committee-based machine learning models: some have experience with only benign inputs, and others with benign and adversarial inputs. Then, the probabilities of the outputs of these pairs' models are utilized. The KL-divergence after that is applied to identify, detect, and mitigate such streaming attacks. Specifically, we use the uncertainty measures between the output of mitigation and non-mitigation ML models as a proxy to identify adversely attacked inputs. We use traffic sign classification in autonomous vehicle technology as a streaming IoT application. Our experiments demonstrate that the proposed approach can detect and mitigate adversarial attacks with high confidence for the white-box attack.

Protecting the privacy of personal information, including emotions, is essential, and organizations must comply with relevant regulations to ensure privacy. Unfortunately, some organizations do not respect these regulations, or they lack transparency, leaving human privacy at risk. These privacy violations often occur when unauthorized organizations misuse machine learning (ML) technology, such as facial expression recognition (FER) systems. Therefore, researchers and practitioners must take action and use ML technology for social good to protect human privacy. One emerging research area that can help address privacy violations is the use of adversarial ML for social good. Evasion attacks, which are used to fool ML systems, can be repurposed to prevent misused ML technology, such as ML-based FER, from recognizing true emotions. By leveraging adversarial ML for social good, we can prevent organizations from violating human privacy by misusing ML technology, particularly FER systems, and protect individuals' personal and emotional privacy. In this work, we propose an approach called Chaining of Adversarial ML Attacks (CAA) to create a robust attack that fools misused technology and prevents it from detecting true emotions. To validate our proposed approach, we conduct extensive experiments using various evaluation metrics and baselines. Our results show that CAA significantly contributes to emotional privacy preservation, with the fool rate of emotions increasing proportionally to the chaining length. In our experiments, the fool rate increases by 48% in each subsequent chaining stage of the chaining targeted attacks (CTA) while keeping the perturbations imperceptible ($\epsilon = 0.0001$).

With the growing number of vehicles and increasing traffic complexity, the development of Intelligent Transportation Systems (ITS) has become essential in managing traffic flow, reducing congestion, and improving road safety. As a result, numerous research initiatives and projects have been launched in recent years in the Middle East and North African (MENA) countries. This paper presents a comprehensive review of the ITS projects funded in this region. We then evaluate these projects based on three criteria: security and data privacy, data analysis, and sensing techniques. The main objective is to identify common threads among these projects and explore potential opportunities for developing an open-source platform that can benefit the research community in the MENA region with similar research objectives.

The use of artificial intelligence (AI) at the edge is transforming every aspect of the lives of human beings from scheduling daily activities to personalized shopping recommendations. Since the success of AI is to be measured ultimately in terms of how it benefits human beings, and that the data driving the deep learning-based edge AI algorithms are intricately and intimately tied to humans, it is important to look at these AI technologies through a human-centric lens. However, despite the significant impact of AI design on human interests, the security and trustworthiness of edge AI applications are not foolproof and ethicalneither foolproof nor ethical; Moreover, social norms are often ignored duringin the design, implementation, and deployment of edge AI systems. In this paper, we make the following two contributions: Firstly, we analyze the application of edge AI through a human-centric perspective. More specifically, we present a pipeline to develop human-centric embedded machine learning (HC-EML) applications leveraging a generic human-centric AI (HCAI) framework. Alongside, we also analyzediscuss the privacy, trustworthiness, robustness, and security aspects of HC-EML applications with an insider look at their challenges and possible solutions along the way. Secondly, to illustrate the gravity of these issues, we present a case study on the task of human facial emotion recognition (FER) based on AffectNet dataset, where we analyze the effects of widely used input quantization on the security, robustness, fairness, and trustworthiness of an EML model. We find that input quantization partially degrades the efficacy of adversarial and backdoor attacks at the cost of a slight decrease in accuracy over clean inputs. By analyzing the explanations generated by SHAP, we identify that the decision of a FER model is largely influenced by features such as eyes, alar crease, lips, and jaws. Additionally, we note that input quantization is notably biased against the dark skin faces, and hypothesize that low-contrast features of dark skin faces may be responsible for the observed trends. We conclude with precautionary remarks and guidelines for future researchers.

The growing use of media has led to the development of several machine learning (ML) and natural language processing (NLP) tools to process the unprecedented amount of social media content to make actionable decisions. However, these ML and NLP algorithms have been widely shown to be vulnerable to adversarial attacks. These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing. In this article, we provide a comprehensive review of the main approaches for adversarial attacks and defenses in the context of social media applications with a particular focus on key challenges and future research directions. In detail, we cover literature on six key applications: 1) rumors detection; 2) satires detection; 3) clickbaits and spams identification; 4) hate speech detection; 5) misinformation detection; and 6) sentiment analysis. We then highlight the concurrent and anticipated future research questions and provide recommendations and directions for future work.

Clustered federated Multitask learning is introduced as an efficient technique when data is unbalanced and distributed amongst clients in a non-independent and identically distributed manner. While a similarity metric can provide client groups with specialized models according to their data distribution, this process can be time-consuming because the server needs to capture all data distribution first from all clients to perform the correct clustering. Due to resource and time constraints at the network edge, only a fraction of devices is selected every round, necessitating the need for an efficient scheduling technique to address these issues. Thus, this paper introduces a two-phased client selection and scheduling approach to improve the convergence speed while capturing all data distributions. This approach ensures correct clustering and fairness between clients by leveraging bandwidth reuse for participants spent a longer time training their models and exploiting the heterogeneity in the devices to schedule the participants according to their delay. The server then performs the clustering depending on predetermined thresholds and stopping criteria. When a specified cluster approximates a stopping point, the server employs a greedy selection for that cluster by picking the devices with lower delay and better resources. The convergence analysis is provided, showing the relationship between the proposed scheduling approach and the convergence rate of the specialized models to obtain convergence bounds under non-i.i.d. data distribution. We carry out extensive simulations, and the results demonstrate that the proposed algorithms reduce training time and improve the convergence speed by up to 50% while equipping every user with a customized model tailored to its data distribution.

Deep neural networks have shown vulnerability to well-designed inputs called adversarial examples. Researchers in industry and academia have proposed many adversarial example defense techniques. However, they offer partial but not full robustness. Thus, complementing them with another layer of protection is a must, especially for mission-critical applications. This article proposes a novel online selection and relabeling algorithm (OSRA) that opportunistically utilizes a limited number of crowdsourced workers to maximize the machine learning (ML) system's robustness. The OSRA strives to use crowdsourced workers effectively by selecting the most suspicious inputs and moving them to the crowdsourced workers to be validated and corrected. As a result, the impact of adversarial examples gets reduced, and accordingly, the ML system becomes more robust. We also proposed a heuristic threshold selection method that contributes to enhancing the prediction system's reliability. We empirically validated our proposed algorithm and found that it can efficiently and optimally utilize the allocated budget for crowdsourcing. It is also effectively integrated with a state-of-the-art black box defense technique, resulting in a more robust system. Simulation results show that the OSRA can outperform a random selection algorithm by 60% and achieve comparable performance to an optimal offline selection benchmark. They also show that OSRA's performance has a positive correlation with system robustness.

Federated edge learning (FEEL) is a rapidly growing distributed learning technique for next-generation wireless edge systems. Smart systems across various application domains face challenges, such as data heterogeneity, limited wireless resources, and device heterogeneity, which necessitate intelligent participant selection schemes that accelerate convergence rates. Consequently, this article presents joint participant selection and bandwidth allocation schemes to address these challenges. First, we formulate an optimization problem that considers communication and computation latencies, as well as imbalanced data distribution, while meeting round deadlines and bandwidth constraints. To address the combinatorial problems of participant selection, we employ a relaxation method followed by a proposed priority selection algorithm to select near-optimal participants. The proposed algorithm initially prioritizes participants with larger datasets, effective channel states, and better CPU speeds. To address data heterogeneity, we propose a randomized deadline-controlling algorithm that diversifies updates by allowing the edge server to include different participants with fewer data samples in training rounds. The proposed algorithms offer near-optimal performance compared to the brute-force method. Experiments demonstrate that our proposed scheme accelerates the convergence rate by up to 55% under extensive non-IID settings compared to benchmarks. Furthermore, the deadline-controlling algorithm improves performance at high levels of data heterogeneity, resulting in faster FEEL systems.

In recent years, Federated Edge Learning has gained interest from both industry and academia for deployment at the wireless network edge. However, some resource-restricted edge devices (EDs) bear more computation and communication loads due to the heterogeneity of data and resources. Several approaches have been proposed in the literature to reduce energy costs by scheduling only a few EDs to complete training tasks based on their energy budgets. Nevertheless, from a practical perspective, the incongruent data distribution cannot be captured, resulting in a biased model for EDs that are frequently selected. Furthermore, the frequently scheduled devices deplete their energy quickly, making them inaccessible. Thus, this paper proposes a novel scheduling policy based on the historical participation of each ED that ensures an unbiased model while balancing learning tasks so that all EDs consume equivalent energy at the end of the training. We formulate an optimization problem based on Jain's fairness index, followed by tractable algorithms to solve this problem. Extensive experiments have been conducted, and the results show that the proposed algorithm balances the energy consumption among EDs and accelerates the convergence rate while achieving satisfactory performance.

Federated Learning (FL) is one of the hot research topics, and it utilizes Machine Learning (ML) in a distributed manner without directly accessing private data on clients. How-ever, FL faces many challenges, including the difficulty to obtain high accuracy, high communication cost between clients and the server, and security attacks related to adversarial ML. To tackle these three challenges, we propose an FL algorithm inspired by evolutionary techniques. The proposed algorithm groups clients randomly in many clusters, each with a model selected randomly to explore the performance of different models. The clusters are then trained in a repetitive process where the worst performing cluster is removed in each iteration until one cluster remains. In each iteration, some clients are expelled from clusters either due to using poisoned data or low performance. The surviving clients are exploited in the next iteration. The remaining cluster with surviving clients is then used for training the best FL model (i.e., remaining FL model). Communication cost is reduced since fewer clients are used in the final training of the FL model. To evaluate the performance of the proposed algorithm, we conduct a number of experiments using FEMNIST dataset and compare the result against the random FL algorithm. The experimental results show that the proposed algorithm outperforms the baseline algorithm in terms of accuracy, communication cost, and security.

While the technique of Deep Neural Networks (DNNs) has been instrumental in achieving state-of-the-art results for various Natural Language Processing (NLP) tasks, recent works have shown that the decisions made by DNNs cannot always be trusted. Recently Explainable Artificial Intelligence (XAI) methods have been proposed as a method for increasing DNN’s reliability and trustworthiness. These XAI methods are however open to attack and can be manipulated in both white-box (gradient-based) and black-box (perturbation-based) scenarios. Exploring novel techniques to attack and robustify these XAI methods is crucial to fully understand these vulnerabilities. In this work, we propose Tamp-X—a novel attack which tampers the activations of robust NLP classifiers forcing the state-of-the-art white-box and black-box XAI methods to generate misrepresented explanations. To the best of our knowledge, in current NLP literature, we are the first to attack both the white-box and the black-box XAI methods simultaneously. We quantify the reliability of explanations based on three different metrics—the descriptive accuracy, the cosine similarity, and the Lp norms of the explanation vectors. Through extensive experimentation, we show that the explanations generated for the tampered classifiers are not reliable, and significantly disagree with those generated for the untampered classifiers despite that the output decisions of tampered and untampered classifiers are almost always the same. Additionally, we study the adversarial robustness of the tampered NLP classifiers, and find out that the tampered classifiers which are harder to explain for the XAI methods, are also harder to attack by the adversarial attackers.

Federated learning (FL) is a promising paradigm for future sixth-generation wireless systems to underpin network edge intelligence for smart cities applications. However, most of the data collected by the Internet of Things devices in such applications is unlabeled, necessitating the use of semi-supervised learning. Existing studies have introduced solutions to run semi-supervised FL; however, they overlooked the inherent critical impacts of the wireless characteristics at the network edge. We fill this gap by proposing novel solutions to run semi-supervised FL over wireless network edge, considering the limited computation and communication resources and deadline constraints and realizing that unlabeled data can be automatically labeled during the training rounds to improve the performance of the global model. The problem is first formulated as an optimization problem followed by a two-phase solution. In the first phase, we propose a bisection-based algorithm to find the transmit power and local processing speed that optimally fit the new injected labeled data. In the second phase, we propose three algorithms to control the local updates and injected samples that meet the deadline constraint. We analyze the performance of each algorithm concerning the tradeoffs between learning performance, training time, and total energy consumption. Targeting two applications in smart cities, human activity recognition and object detection, we conduct extensive simulations using realistic federated data sets under nonindependent and identically distributed settings. Numerical results show that the proposed algorithms effectively utilize unlabeled samples while accounting for the characteristics of wireless edge networks in smart cities.

The increasing need for economic, safe, and sustainable smart manufacturing combined with novel technological enablers has paved the way for artificial intelligence (AI) and big data in industries. This implies a substantial integration of AI, Industrial Internet of Things (IIoT), Robotics, big data, Blockchain, and 5G communications in support of smart manufacturing and the dynamical processes in modern industries. In this article, we provide a comprehensive overview of different aspects of AI and big data in Industry 4.0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective toward deployment of Industry 5.0. In detail, we highlight and analyze how the duo of AI and big data is helping in different applications of Industry 4.0. We also highlight key challenges in a successful deployment of AI and big data solutions in smart industrial applications with a particular emphasis on data-related issues, such as availability, bias, auditing, management, interpretability, communication, and different adversarial attacks and security issues. Finally, we explore the significance of AI and big data toward Industry 4.0 applications through panoramic reviews and discussions. This work is expected to provide a baseline for future research in the domain.

Clustered Federated Multitask Learning (CFL) was introduced as an efficient scheme to obtain reliable specialized models when data is imbalanced and distributed in a non-i.i.d. (non-independent and identically distributed) fashion amongst clients. While a similarity measure metric, like the cosine similarity, can be used to endow groups of the client with a specialized model, this process can be arduous as the server should involve all clients in each of the federated learning rounds. Therefore, it is imperative that a subset of clients is selected periodically due to the limited bandwidth and latency constraints at the network edge. To this end, this paper proposes a new client selection algorithm that aims to accelerate the convergence rate for obtaining specialized machine learning models that achieve high test accuracies for all client groups. Specifically, we introduce a client selection approach that leverages the devices' heterogeneity to schedule the clients based on their round latency and exploits the bandwidth reuse for clients that consume more time to update the model. Then, the server performs model averaging and clusters the clients based on predefined thresholds. When a specific cluster reaches a stationary point, the proposed algorithm uses a greedy scheduling algorithm for that group by selecting the clients with less latency to update the model. Extensive experiments show that the proposed approach lowers the training time and accelerates the convergence rate by up to 50% while imbuing each client with a specialized model that is fit for its local data distribution.

In this paper, we make use of systems thinking insights to study education during and beyond COVID-19. Systems thinking is a rich discipline that studies nonlinear models of social complex adaptive systems that has many insights and tools that are relevant for modelling and understanding how interactions unfold in educational systems. An important insight of systems thinking is that the root cause of chronic complex problems often lay in the underlying systemic structure. Using insights from systems thinking to study learning/education has many benefits, including: (1) support for rigorous big-picture thinking; (2) anticipating and managing unintended consequences; (3) understanding dysfunctional learning systems using systems archetypes - which are systemic structures that, experts have noticed, typically lead to a performance rut; and finally (4) identification of high-leverage interventions that lead to long-lasting benefits without being neutralized by the system. In the paper, we have modelled COVID-19 pandemic effects on students learning in a novel way by using system thinking tools (Causal Loop Diagrams and Stock and Flow Diagrams), which help us to understand the complex interconnections of students performance, learning and management reforms. We demonstrate that successful student learning during and beyond COVID-19 requires not only a focus on lectures and curriculum reforms but also on motivating students, instilling a growth mindset, and developing strategies to track and minimize online distractions.

Machine learning (ML), and deep learning (DL) in particular, play a vital role in providing smart services to the industry. These techniques, however, suffer from privacy and security concerns since data are collected from clients and then stored and processed at a central location. Federated learning (FL), an architecture in which model parameters are exchanged instead of client data, has been proposed as a solution to these concerns. Nevertheless, FL trains a global model by communicating with clients over communication rounds, which introduces more traffic on the network and increases the convergence time to the target accuracy. In this work, we solve the problem of optimizing accuracy in stateful FL with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process. Next, we propose an online stateful FL heuristic to find the best candidate clients. Additionally, we propose an IoT client alarm application that utilizes the proposed heuristic in training a stateful FL global model based on IoT device-type classification to alert clients about unauthorized IoT devices in their environment. To test the efficiency of the proposed online heuristic, we conduct several experiments using a real data set and compare the results against state-of-the-art algorithms. Our results indicate that the proposed heuristic outperforms the online random algorithm with up to 27% gain in accuracy. Additionally, the performance of the proposed online heuristic is comparable to the performance of the best offline algorithm.

With machine learning (ML) services now used in a number of mission-critical human-facing domains, ensuring the integrity and trustworthiness of ML models becomes all important. In this work, we consider the paradigm where cloud service providers collect big data from resource-constrained devices for building ML-based prediction models that are then sent back to be run locally on the intermittently connected resource-constrained devices. Our proposed solution comprises an intelligent polynomial-time heuristic that maximizes the level of trust of ML models by selecting and switching between a subset of the ML models from a superset of models in order to maximize the trustworthiness while respecting the given reconfiguration budget/rate and reducing the cloud communication overhead. We evaluate the performance of our proposed heuristic using two case studies. First, we consider Industrial IoT (IIoT) services, and as a proxy for this setting, we use the turbofan engine degradation simulation data set to predict the remaining useful life of an engine. Our results in this setting show that the trust level of the selected models is 0.49%-3.17% less compared to the results obtained using integer linear programming (ILP). Second, we consider smart cities services, and as a proxy of this setting, we use an experimental transportation data set to predict the number of cars. Our results show that the selected model's trust level is 0.7%-2.53% less compared to the results obtained using ILP. We also show that our proposed heuristic achieves an optimal competitive ratio in a polynomial-time approximation scheme for the problem.

The Internet of Things (IoT) revolution and the development of smart communities have resulted in increased demand for bandwidth due to the rise in network traffic. Instead of investing in expensive communications infrastructure, some researchers have proposed leveraging Vehicular Ad-Hoc Networks (VANETs) as the data communications infrastructure. However VANETs are not cheap since they require the deployment of expensive Road Side Units (RSU)s across smart communities. In this research, we propose an infrastructure-less system that opportunistically utilizes vehicles to serve as Local Community Brokers (LCBs) that effectively substitute RSUs for managing communications between smart devices and the cloud in support of smart community applications. We propose an opportunistic algorithm that strives to select vehicles in order to maximize the LCBs' service time. The proposed opportunistic algorithm utilizes an ensemble of online selection algorithms by running all of them together in passive mode and selecting the one that has performed the best in recent history. We evaluate our proposed algorithm using a dataset comprising real taxi traces from the city of Shanghai in China and compare our algorithm against a baseline of 9 Threshold Based Online (TBO) algorithms. A number of experiments are conducted and our results indicate that the proposed algorithm achieves up to 87 % more service time with up to 10% fewer vehicle selections compared to the best-performing existing TBO online algorithm.

As we move into the third decade of the 21st century, the 2020s, the unprecedented rate of technological disruption and the short-lived nature of the specifics of engineering state-of-the-art require us to carefully evaluate what it takes to be an effective engineer and what this entails for engineering education and their lifelong learning. While it is true that certain basics of engineering will not change, there will be an increased premium for some skills (such as lifelong learning, meta-learning, collaboration, creativity, critical thinking, communication skills, and cultural/global literacy). 21st-century skills are, as such, timeless skills: it is paradoxically the volatile nature of the modern world that has forced us from ephemeral vocational fads back to these permanently valuable skills. In this full research-to-practice paper, after reporting on the skills that policy think tanks and thought leaders deem necessary for the 21st century, we provide a synthesis in which we describe the pulls and pushes that learners and educators will face in the turbulent times of 2020 and beyond, and how they can thrive in the uncertain future through holistic well-rounded engineering education.

Most of the research on Federated Learning (FL) has focused on analyzing global optimization, privacy, and communication, with limited attention focusing on analyzing the critical matter of performing efficient local training and inference at the edge devices. One of the main challenges for successful and efficient training and inference on edge devices is the careful selection of parameters to build local Machine Learning (ML) models. To this aim, we propose a Particle Swarm optimization (PSO)-based technique to optimize the hyperparameter settings for the local ML models in an FL environment. We evaluate the performance of our proposed technique using two case studies. First, we consider smart city services, and use an experimental transportation dataset for traffic prediction as a proxy for this setting. Second, we consider Industrial IoT(IIoT) services, and use the real-time telemetry dataset to predict the probability that a machine will fail shortly due to component failures. Our experiments indicate that PSO provides an efficient approach for tuning the hyperparameters of deep Long short-term memory (LSTM) models when compared to the grid search method. Our experiments illustrate that the number of client-server communication rounds to explore the landscape of configurations to find the near-optimal parameters are greatly reduced (roughly by two orders of magnitude needing only 2%-4% of the rounds compared to state of the art non-PSO-based approaches). We also demonstrate that utilizing the proposed PSO-based technique to find the near-optimal configurations for FL and centralized learning models does not adversely affect the accuracy of the models.

Privacy concerns are considered one of the main challenges in smart cities as sharing sensitive data induces threatening problems in people's lives. Federated learning has emerged as an effective technique to avoid privacy infringement as well as increase the utilization of the data. However, there is a scarcity in the amount of labeled data and an abundance of unlabeled data collected in smart cities; hence there is a necessity to utilize semi-supervised learning. In this paper, we present the primary design aspects for enabling federated learning at the edge networks taking into account the problem of unlabeled data. We propose a semi-supervised federated edge learning method called FedSem that exploits unlabeled data in real-time. FedSem algorithm is divided into two phases. The first phase trains a global model using only the labeled data. In the second phase, Fedsem injects unlabeled data into the learning process using the pseudo labeling technique and the model developed in the first phase to improve the learning performance. We carried out several experiments using the traffic signs dataset as a case study. Our results show that FedSem can achieve accuracy by up to 8% by utilizing the unlabeled data in the learning process.

To fully realize the potential of vehicular networks, several obstacles and challenges need to be addressed. Chief among the obstacles are strict QoS requirements of applications and differentiated service requirements in different situations. Although DSRC and WAVE have been adopted as the de facto standards, they do not address all the problems and there is room for improvements. In this study, we propose a generic prioritization and resource management algorithm that can be used to prioritize processing of received packets in vehicular networks. We formulate the generic severity-based prioritized packet processing problem as Penalized Multiple Knapsack Problem (PMKP) and prove that it is an NP-Hard problem. We thus develop a real-time heuristic that utilizes a relaxed version of the formulation. The relaxed formulation executes in polynomial time and guarantees a minimum delay per severity-level while respecting the processing rate constraint. To measure the performance of the proposed heuristic, real traffic data is used in a small-scale experiment. The proposed heuristic is tested against the PMKP solution and results show a small degradation of up to 4 percent in profit for the heuristic compared to the PMKP solution. Also, the proposed heuristic is tested against a non-prioritized processing algorithm that works using first come first served policy. Results show that the proposed heuristic gains 9 to 67 percent more profit than the non-prioritized processing algorithm in moderate and high congestion scenarios.

With the exponential growth of data and the high demand for the analysis of large datasets, the MapReduce framework has been widely utilized to process data in a timely, cost-effective manner. It is well-known that the performance of MapReduce is limited by its default configuration parameters, and there are a few research studies that have focused on finding the optimal configurations to improve the performance of the MapReduce framework. Recently, machine learning based approaches have been receiving more attention to be utilized to auto configure the MapReduce parameters to account for the dynamic nature of the applications. In this article, we propose and develop a reinforcement learning (RL)-based scheme, named RL-MRCONF, to automatically configure the MapReduce parameters. Specifically, we explore and experiment with two variations of RL-MRCONF; one variation is based on the traditional RL algorithm and the second is based on the deep RL algorithm. Results obtained from simulations show that the RL-MRCONF has the ability to successfully and effectively auto-configure the MapReduce parameters dynamically according to changes in job types and computing resources. Moreover, simulation results show our proposed RL-MRCONF scheme outperforms the traditional RL-based implementation. Using datasets provided by MR-Perf, simulation results show that our proposed scheme provides around 50% performance improvement in terms of execution time when compared with MapReduce using default settings.

Intrusion detection systems (IDSs) are an essential cog of the network security suite that can defend the network from malicious intrusions and anomalous traffic. Many machine learning (ML)-based IDSs have been proposed in the literature for the detection of malicious network traffic. However, recent works have shown that ML models are vulnerable to adversarial perturbations through which an adversary can cause IDSs to malfunction by introducing a small impracticable perturbation in the network traffic. In this paper, we propose an adversarial ML attack using generative adversarial networks (GANs) that can successfully evade an ML-based IDS. We also show that GANs can be used to inoculate the IDS and make it more robust to adversarial perturbations.

Deep machine learning techniques have shown promising results in network traffic classification, however, the robustness of these techniques under adversarial threats is still in question. Deep machine learning models are found vulnerable to small carefully crafted adversarial perturbations posing a major question on the performance of deep machine learning techniques. In this paper, we propose a black-box adversarial attack on network traffic classification. The proposed attack successfully evades deep machine learning-based classifiers which highlights the potential security threat of using deep machine learning techniques to realize autonomous networks.

Interest in smart cities is rapidly rising due to the global rise in urbanization and the wide-scale instrumentation of modern cities. Due to the considerable infrastructural cost of setting up smart cities and smart communities, researchers are exploring the use of existing vehicles on the roads as "message ferries" for transporting data for smart community applications to avoid the cost of installing new communication infrastructure. In this paper, we propose an opportunistic data ferry selection algorithm that strives to select vehicles that can minimize the overall delay for data delivery from a source to a given destination. Our proposed opportunistic algorithm utilizes an ensemble of online hiring algorithms, which are run together in passive mode, to select the online hiring algorithm that has performed the best in recent history. The proposed ensemble- based algorithm is evaluated empirically using real-world traces from taxies plying routes in Shanghai, China, and its performance is compared against a baseline of four state-of-the-art online hiring algorithms. A number of experiments are conducted and our results indicate that the proposed algorithm can reduce the overall delay compared to the baseline by an impressive 13% to 258%.

In vehicular ad-hoc networks, due to high mobility, vehicles usually communicate for short periods of time with several neighboring vehicles and are required to process data fast; sometimes in the order of few milliseconds. This urgency of data processing is further heightened in safety-critical scenarios that involve many vehicles. Such scenarios require data to be prioritized and processed with minimum delay. While packet scheduling has been extensively studied, these studies focus on channel scheduling, our work focuses on processing received packets by a vehicle in dense scenarios. In this paper, we formulate the prioritized data processing problem as an integer linear program given a prior knowledge of the request sequence and prove that it is NP-complete. Due to the difficulty of predicting the traffic patterns and obtaining the request sequence in advance, we propose an online algorithm that does not require the prior knowledge of the request sequence and achieves an O(1) competitive ratio. The proposed online algorithm strives to accept higher severity packets for processing in order to maximize the cumulative severity given vehicular communications/computation capacity constraints. Using real traffic traces, we evaluate the performance of the online algorithm against three online algorithms, in which two of them use an exponentially weighted moving average-based threshold while the other one accepts requests as capacity permits. Our evaluation shows that our algorithm achieves up to 492% more cumulative severity compared to the three other baseline algorithms.

Leaking data using covert timing channels is considered as a critical threat in network communication. These type of channels use the time between inter-arrival packets to pass information between different processes, making it a very critical to design techniques to eliminate and mitigate such channels; hence, ensuring a more secure communication environment. This paper proposes a new online streaming approach to the mitigation of covert timing channels. The new approach eliminates covert timing channels while having a little impact on the overall Quality of Service (QoS). A classification-based strategy was used to test the performance of the proposed mitigation model. In fact, we showed that the classification accuracy of a classifier model trained and tested on a mitigated flow fell down to 50% compared to the same classifier model when trained and tested on the same flow before mitigation indicating that the classifier is no longer able to distinguish between covert and overt channels.

Smart services are an important element of the smart cities and the Internet of Things (IoT) ecosystems where the intelligence behind the services is obtained and improved through the sensory data. Providing a large amount of training data is not always feasible; therefore, we need to consider alternative ways that incorporate unlabeled data as well. In recent years, deep reinforcement learning (DRL) has gained great success in several application domains. It is an applicable method for IoT and smart city scenarios where auto-generated data can be partially labeled by users' feedback for training purposes. In this paper, we propose a semisupervised DRL model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent. The model utilizes variational autoencoders as the inference engine for generalizing optimal policies. To the best of our knowledge, the proposed model is the first investigation that extends DRL to the semisupervised paradigm. As a case study of smart city applications, we focus on smart buildings and apply the proposed model to the problem of indoor localization based on Bluetooth low energy signal strength. Indoor localization is the main component of smart city services since people spend significant time in indoor environments. Our model learns the best action policies that lead to a close estimation of the target locations with an improvement of 23% in terms of distance to the target and at least 67% more received rewards compared to the supervised DRL model.

Modern information technology services largely depend on cloud infrastructures to provide their services. These cloud infrastructures are built on top of Datacenter Networks (DCNs) constructed with high-speed links, fast switching gear, and redundancy to offer better flexibility and resiliency. In this environment, network traffic includes long-lived (elephant) and short-lived (mice) flows with partitioned/aggregated traffic patterns. Although SDN-based approaches can efficiently allocate networking resources for such flows, the overhead due to network reconfiguration can be significant. With limited capacity of Ternary Content-Addressable Memory (TCAM) deployed in an OpenFlow enabled switch, it is crucial to determine which forwarding rules should remain in the flow table and which rules should be processed by the SDN controller in case of a table-miss on the SDN switch. This is needed in order to obtain the flow entries that satisfy the goal of reducing the long-term control plane overhead introduced between the controller and the switches. To achieve this goal, we propose a machine learning technique that utilizes two variations of Reinforcement Learning (RL) algorithms—the first of which is a traditional RL-based algorithm, while the other is deep reinforcement learning-based. Emulation results using the RL algorithm show around 60% improvement in reducing the long-term control plane overhead and around 14% improvement in the table-hit ratio compared to the Multiple Bloom Filters (MBF) method, given a fixed size flow table of 4KB.

As wireless devices continue to proliferate, spectrum management is essential to a healthy and functioning digital ecosystem. Here we present an evolutionary analysis of how interbrand relationships can be expected to evolve in the cognitive radio domain over long time scales. We find that a range of trajectories are possible, and that the eventual outcomes depend on a variety of system parameters including the number of users and transmission band switching costs. Starting from previous bio-socially inspired fair spectrum sharing protocols, we put forward an extended model of secondary user etiquette that allows for a range of inter-group dynamics to arise in the natural course of competition over and co-use of spectrum resources. We show that as populations grow, increases in transmission switching costs lead to evolutionary pressures toward increasing antagonism between brands, and that in such scenarios devices tend to segregate by brand across bands. Understanding the drivers behind emerging inter-brand dynamics from an evolutionary perspective is an important input to the long term view of the successful application of distributed spectrum access and cognitive radio.

Covert timing channels provide a mechanism to transmit unauthorized information across different processes. Applications that generate large datasets allow this information to be easily hidden within the big data, making it difficult to detect. In this paper, we introduce the application of big data analysis techniques, specifically MapReduce, in the process of speeding up the performance of covert time channels detection. The hierarchal entropy algorithm (HEA) is utilized to reveal a “needle” of covert timing channels from a huge “haystack” of inter-arrival times. A real indexed inter-arrival dataset of approximately 1.4 gigabyte is generated between two different machines and injected by 615 bytes of covert timing message. The HEA with MapReduce was able to uncover around 7*10−6of hidden covert message from this huge amount of data in a significantly shorter time as compared to the classical sequential HEA.

Deep learning has been successfully applied in several fields such as machine translation, manufacturing, and pattern recognition. However, successful application of deep learning depends upon appropriately setting its parameters to achieve high-quality results. The number of hidden layers and the number of neurons in each layer of a deep machine learning network are two key parameters, which have main influence on the performance of the algorithm. Manual parameter setting and grid search approaches somewhat ease the users' tasks in setting these important parameters. Nonetheless, these two techniques can be very time-consuming. In this paper, we show that the Particle swarm optimization (PSO) technique holds great potential to optimize parameter settings and thus saves valuable computational resources during the tuning process of deep learning models. Specifically, we use a dataset collected from a Wi-Fi campus network to train deep learning models to predict the number of occupants and their locations. Our preliminary experiments indicate that PSO provides an efficient approach for tuning the optimal number of hidden layers and the number of neurons in each layer of the deep learning algorithm when compared to the grid search method. Our experiments illustrate that the exploration process of the landscape of configurations to find the optimal parameters is decreased by 77 % - 85%. In fact, the PSO yields even better accuracy results.

Publish/subscribe brokers enable the efficient dissemination of events to a large number of subscribers in support of smart city applications. These events convey data gathered from devices and published to named logical channels called topics. Software-Defined Networking (SDN) can provide the advantage of balancing the load between brokers by switching topics between brokers. However, this switching results in network overhead. Besides, supporting data and decision fusion applications is a challenging task since sensory data has to be fused before being forwarded to subscribers. Therefore, we propose an algorithm utilized by the SDN controller to minimize the load difference between brokers while respecting a reconfiguration limit in support of data and decision fusion applications. We formulate minimizing brokers' load difference within a reconfiguration budget with the constraint of indivisible topics as an Integer Linear Programing (ILP) problem. We show that the problem is NP-Hard and propose a heuristic driven by long-term statistics of topics. The proposed heuristic is evaluated with realistic simulation traffic traces and compared against a threshold-based baseline heuristic driven by instantaneous statistics of topics. Results show that the proposed heuristic performs up to 2000% better load distribution than the baseline heuristic and at least 27% less topic switching.

Internet of Things (IoT) applications can benefit greatly from cloud-hosted message broker services that utilize publish-subscribe communications. The operators of IoT cloud-hosted services are often interested in delivering services that maximize their revenue given quality of service guarantees. In this paper, we formulate the problem of maximizing the profit of the service provider given the prior knowledge of the request sequence as an integer linear program and prove that it is strongly NP-complete, and thus there is no fully polynomialtime approximation scheme for the problem, unless P = NP. Due to the above-mentioned problem and the difficulty of obtaining the request sequence in advance in real-world scenarios, we propose an auction-based online algorithm that does not require the prior knowledge of the request sequence. We prove that the competitive ratio of the online algorithm is (9(log(N)), where N is the number of cloud zones that host the publish- subscribe services. Moreover, we show that no online algorithm can achieve a competitive ratio better than Ω(log(N)). Therefore, our online algorithm achieves the optimal competitive ratio in the asymptotic sense. Our simulations, based on real data traces, show that our algorithm achieves up to 83% more profit compared to a heuristic approach, while consuming 60% less resources.

The Negative Selection (NS) algorithm is the first algorithm to come from the study of natural immune systems, and is the most widely known and applied algorithm in the field. It has been used to build intrusion detection systems along with many other security-related tasks. However, it has not been possible to use the Negative Selection algorithm on many real-world scenarios. The present research shows an optimization of the negative selection algorithm to make its execution faster. The optimized algorithm remains functionally the same, providing the same results as the unoptimized algorithm. Details are given about the optimization scheme used and the optimized negative selection algorithm is tested on the UCI Breast Cancer data set. The performance of the unoptimized negative selection algorithm is compared to the performance of the algorithm with the proposed optimization. Three claims about the function of the optimized negative selection algorithm are made and tested with four experiments. The results of the experiments are used to demonstrate that the algorithm is faster and does not change the negative selection algorithm or lower its accuracy. Although there has been research into the optimization of the Negative Selection algorithm, this work will only apply to hyper-sphere detectors, which has not been done before.

Wireless communication is an increasingly ubiquitous and important resource substrate of the digital ecosystem. In the face of the rapid growth in the population of Internet of Things (IoT), however, uncoordinated access to limited resources of radio spectrum is likely to lead to mass starvation. Here we put forward a new bio-social paradigm for cognitive radio, extending previous models in which the secondary users of spectrum alternate stochastically between foraging and consuming behaviors. In this paper, we ask and resolve two questions: (1) What costs and benefits does social deference to the group yield for each of the individuals therein? and (2) Can a notion of individual “hunger” form the basis of a distributed social deference scheme that is free of group coordination costs? Through a series of simulation experiments grounded in a well-specified formal model, we show that social deference improves both the fairness and the reliability of spectrum resource allocation, and moreover, that the concept of individual “hunger” can be used to implement social deference with minimal group coordination overhead. The results have consequences both in suggesting potential improvements for distributed spectrum access, and in understanding the evolutionary pressures on the behaviors of individual devices within emerging digital IoT societies.

The energy cost of large-scale datacenters is increasing rapidly as a result of the rising demands of user and application computation and transactions. A datacenter is expected to serve clients' requests and satisfy their constraints at a minimum cost and maintain the best performance according to the needs. To satisfy different requirements efficiently, there is a need to develop and evaluate efficient, cost effective, and green computing technologies. In this paper, an optimization approach using dynamic placement of virtual machines in cloud computing is presented. The approach is evaluated in simulation environment and compared with traditional semi-static methods.

This paper evaluates the potential use of hierarchal entropy analysis to detect covert timing channels and determine the best time-scale that reveals it. A data transmission simulator is implemented to generate a collection of overt and covert channels. The hierarchical entropy analysis approach is then utilized to detect the covert timing channels and identify the type-scale that provides the highest evidence that the underlying channel is covert. Hierarchical entropy divides the stream of inter-arrival times greedily to identify the time-scale the best reveals the existence of a covert-timing channel. The lowest entropy in the sequence is the best indicator that identifies non-random patterns in the given data stream. The results show that hierarchal entropy analysis performs significantly better than the classical flat entropy approach in the detection of covert timing channels. Furthermore, the hierarchical entropy analysis provides details about the best time-scale that reveals the features of the covert timing channel.

We propose a novel roadside unit (RSU) cloud, a vehicular cloud, as the operational backbone of the vehicle grid in the Internet of Vehicles (IoV). The architecture of the proposed RSU cloud consists of traditional and specialized RSUs employing software-defined networking (SDN) to dynamically instantiate, replicate, and/or migrate services. We leverage the deep programmability of SDN to dynamically reconfigure the services hosted in the network and their data forwarding information to efficiently serve the underlying demand from the vehicle grid. We then present a detailed reconfiguration overhead analysis to reduce reconfigurations, which are costly for service providers. We use the reconfiguration cost analysis to design and formulate an integer linear programming (ILP) problem to model our novel RSU cloud resource management (CRM). We begin by solving for the Pareto optimal frontier (POF) of nondominated solutions, such that each solution is a configuration that minimizes either the number of service instances or the RSU cloud infrastructure delay, for a given average demand. Then, we design an efficient heuristic to minimize the reconfiguration costs. A fundamental contribution of our heuristic approach is the use of reinforcement learning to select configurations that minimize reconfiguration costs in the network over the long term. We perform reconfiguration cost analysis and compare the results of our CRM formulation and heuristic. We also show the reduction in reconfiguration costs when using reinforcement learning in comparison to a myopic approach. We show significant improvement in the reconfigurations costs and infrastructure delay when compared to purist service installations.

Delivering reliable service offerings to clients remain a challenging aspect in today's cloud infrastructure. A broad number of research studies have undertaken the service evaluation process from one side; that is, the infrastructure's perspective. Conversely, clients' assessment to the service has been mostly neglected. In this paper, we propose a client-side service evaluation approach which mainly relies on the clients' assessment of infrastructure's service offerings. The proposed approach utilizes the strength of the Social Network Analysis (SNA) principles in conjunction with the Generalized Extreme Value Theorem (EVT) to converge to a precise Quality of Service (QoS) model. Our goal in this research is to build precise QoS models to predict the performance of clients that exhibit similar behaviors. Thus, we develop a novel SNA-based clustering algorithm that analyzes the strength of the interconnection links between clients and cluster related clients in communities of similar behaviors. The proposed approach is effective in providing Infrastructure as a Service (IaaS) providers with a better assessment tool to evaluate and improve their service offerings. The experimental results of the proposed approach on GENI's SEATTLE platform demonstrate its ability to enhance the prediction process of the performance of IaaS service offerings.

Internet traffic classification has been researched extensively in the last 10 years, with a few different algorithms applied to it. Internet traffic classification has also become more relevant because of its potential applications in the business world. Having information about network traffic has many benefits in network design, security, management, and accounting. The classification of network traffic is most easily achieved by Machine Learning algorithms, which can automatically build a model from training data, without much input from humans. Artificial Immune System classification algorithms have been used previously to classify network connections in network security systems [1]. They have proven to be very versatile, as well as having low sensitivity to input parameters. Because of this we are encouraged to explore the value of AIS algorithms to the Internet traffic classification problem. In this research, we propose an AIS-inspired algorithm for flow-based traffic classification, where each network flow is classified into an application class. We measure the algorithm's performance with and without the use of kernel functions, using a publicly available data set. We also compare the algorithm's performance with SVM and Naive Bayes classifiers. The algorithm generalizes well and gives high accuracy even with a small training set when compared to other algorithms, although the training and classification times were higher. The algorithm is also insensitive to the use of kernels, which makes it attractive for embedded and IoT applications.

The shortcomings of today's Internet and the high demand for complex and sophisticated applications and services drive a very interesting and novel research area called Future Internet. The area of Future Internet research focuses on developing a new network with similar magnitude as today's Internet but with more demanding and complex design goals and specifications. It strives to solve the issues identified in today's Internet, by capitalizing on the advantages of emerging new technologies in the area of computer networking such as Software Defined Networking (SDN). SDN represents an extraordinary opportunity to rethink computer networks, enabling the design and deployment of a future Internet. This paper focuses on the deployment of a complete system, designed with the new requirements of the Future Internet in mind, and aims to provide, monitor and enhance the popular multimedia streaming service of today's Internet. The testing environment was built in the Global Environment for Network Innovations (GENI). The conducted experiments illustrate how such a system can function under unstable and changing network conditions, dynamically learn its environment, recognize potential service degradation problems, and react to these challenges in an autonomic manner without the need for human intervention.

The Internet today continues to be vulnerable to distributed denial of service (DDoS) attacks. We consider the design of a scalable agent-based system for collecting information about the structure and dynamics of DDoS attacks. Our system requires placement of agents on inter-autonomous system (AS) links in the Internet. The agents implement a self-organizing and totally decentralized mechanism capable of reconstructing topological information about the spatial and temporal structure of attacks. The system is effective at recovering DDoS attack structure, even at moderate levels of deployment. In this paper, we demonstrate how careful placement of agents within the system can improve the system's effectiveness and provide better tradeoffs between system parameters and the quality of structural information the system generates. We introduced two agent placement algorithms for our agent-based DDoS system. The first attempts to maximize the percentage of attack flows detected, while the second tries to maximize the extent to which we are able to trace back detected flows to their sources. We show, somewhat surprisingly, these two objectives are concomitant. Placement of agents in a manner which optimizes in the first criterion tends also to optimize with respect to the second criterion, and vice versa. Both placement schemes show a marked improvement over a system in which agents are placed randomly, and thus provide a concrete design process by which to instrument a DDoS flow reconstruction system that is effective at recovering attack structure in large networks at moderate levels of deployment.

Resources are scarce in Wireless Sensor Networks (WSN) and one of the challenges is to design a lightweight communication protocol to support efficient and uniform power consumption among nodes. In this paper, we propose an energy aware routing scheme based on a cross-layer approach for WSNs with the objective to minimize the overall consumed energy; thus, maximizing the network lifetime. The remaining battery reserve capacity, link quality and transmission power for nodes within the local communication range are taken into consideration to determine the next hop relay node to reach the network sink. Parameters from different stack layers (i.e., physical, MAC, and network) are presented to a fuzzy logic system controller which makes a next hop routing decision. The performance of the proposed cross-layer algorithm is evaluated using discrete event simulation (OMNET++ Modeler).

IEEE 802.11p Wireless Access for Vehicular Environments (WAVE) is the approved communication protocol for Vehicular Ad-hoc Networks (VANETs) and Intelligent Transportation System (ITS) applications. WAVE offers service differentiation by prioritizing packets based on an application's requested QoS. These priorities are static and do not account for network load or vehicle's context severity. In this paper, we propose a novel opportunistic service reprioritization (OSR) technique for IEEE 802.11p (WAVE). It dynamically promotes and/or demotes vehicle's load to different access categories, by taking into account the vehicle's context severity and network link layer bounds. We show the feasibility of our approach by formulating the opportunistic service reprioritization technique as a Linear Programming (LP) problem and solve it to guarantee optimal QoS with respect to severity for all access categories. We compare our opportunistic service reprioritization technique with WAVE and show significant improvement. The weighted average delay in OSR outperforms classical WAVE, on average by 90%.

Assuring Quality-of-Service (QoS) guarantees to mobile clients is still a long-standing problem in today's wireless mobile networks. In this paper, we propose new algorithms that validate the accuracy of service measurements collected from the mobile clients to construct a precise QoS model. The proposed algorithms utilize the Linear Opinion Pool (LOP) approach in conjunction with Generalize Extreme Value theorem (GEV) to converge to a precise QoS model. The main objective of this work is to construct a QoS model that excludes the out-of-profile data that is collected from the Mobile Clients (MCs). Therefore, any MC with unreliable data is considered as un-trusted. The proposed approach is effective in providing service providers with a better assessment tool to evaluate and improve their services. The results show the usefulness of our algorithms and their ability to recognize and exclude the data collected from un-trusted MCs; thus, creating a precise QoS model.

The practical limitations and challenges of radio frequency (RF) based communication networks have become increasingly apparent over the past decade, leading researchers to seek new hybrid communication approaches. One promising strategy that has been the subject of considerable interest is the augmentation of RF technology by Free Space Optics (FSO), using the strength of each communication technology to overcome the limitations of the other. In this article, we introduce a new scheme for controlling the topology in hybrid Radio-Frequency/Free Space Optics (RF/FSO) wireless mesh networks. Our scheme is based on adaptive adjustments to both transmission power (of RF and FSO transmitters) and the optical beam-width (of FSO transmitters) at individual nodes, with the objective of meeting specified Quality of Service (QoS) requirements, specifically end-to-end delay and throughput. We show how one can effectively encode the instantaneous objectives and constraints of the system as an instance of Integer Linear Programming (ILP). We demonstrate that the technique of Lagrangian Relaxation (LR), augmented with iterative repair heuristics, can be used to determine good (albeit sub-optimal) solutions for the ILP problem, making the approach feasible for mid-sized networks. We make the proposed scheme viable for large-scale networks in terms of number of nodes, number of transceivers, and number of source-destination pairs by solving the ILP problem using a Particle Swarm Optimization (PSO) implementation.

Current technologies for localization such as Global Positioning Systems (GPS) and Inertial Measurement Unit (IMU) have limitations in terms of accuracy, cost, signal attenuation and need for re-calibration over time, which leads to a compromise in performance and accuracy. In this paper, we present a novel Artificial Landmark-Based Identification System (ALIS), which can be used in both indoor and outdoor localization applications. ALIS is a multi-part landmark-based localization approach, which uses template matching techniques to find multiple landmark patterns in an image to estimate distance from the landmark. A novel combination of pre- and post-processing techniques is used to improve template matching results. The image is enhanced by using high boost filtering and histogram equalization techniques, which sharpen the feature edges and improves the image contrast, respectively. Instead of simple averaging techniques, a novel dynamic exclusion heuristic is utilized, which converges the distance estimation results closer to the actual physical distance. Significant contributions of this paper are in the use of server assistance in pattern identification, Graphic Processing Unit (GPU) for faster parallel processing of computationally intensive algorithms, and multiple pattern identification for better distance estimation.

In this paper, we present a novel algorithm that extracts frequent anomalous behaviors based on multi-scale trend analysis of individual network parameters. The proposed Frequent Anomalous Behavior Mining (FABM) algorithm utilizes multiple levels of time-scale analysis to reveal the frequent anomalous behaviors. This makes the proposed algorithm robust to unreliable, redundant, incomplete and contradictory information. FABM is simple, has low order polynomial computational complexity of O(n2), the patterns identified by FABM require space complexity of O(n) to be stored in the knowledge base of the prediction engine, provides quick and accurate response and can be easily adapted to a distributed environment. Moreover, the empirical results gathered show that using FABM an efficient prediction engine can be realized with high true positive and true negative rates.

We consider the problem of control traffic overhead in MANETs with long-lived connections, operating under a reactive routing protocol (e.g. AODV). In such settings, control traffic overhead origins can be traced principally to connection link failures, which trigger expensive global route discoveries. In this paper, we introduce a route maintenance scheme developed with the objective of reducing global route discoveries in such settings. The proposed scheme decrease the expected number of route discovery attempts by taking preemptive action to counteract impending link disconnections due to node movement. The proposed scheme was implemented as an extension of AODV in ns2, and compared with the standard AODV under different network regimes. Through the analysis of data derived from extensive simulations, we demonstrate that the proposed scheme significantly decreases overall control traffic while maintaining comparable packet delivery rates, at the cost of only very minor degradation in path optimality.

In reactive routing protocols, active routes for multihop connections retain their topological structure in spite of node movement over time. Unfortunately, node movements may make the connection route sub-optimal in terms of hop length, thereby resulting in unnecessarily high end-to-end delays, energy consumption and channel contention. In AODV, for example, a connection route is recomputed only if one of its constituent links suffers catastrophic failure, at which point global route discovery attempts repair, and after which the topological structure of the connection again returns to near-optimality.

We describe a general-purpose distributed system capable of traceback of malicious flow trajectories in the wide area despite possible source IP spoofing. Our system requires the placement of agents on a subset of the inter-autonomous system (AS) links of the Internet. Agents are instrumented with a uniform notion of attack criterion. Deployed, these agents implement a self-organizing, decentralized mechanism that is capable of reconstructing topological and temporal information about malicious flows. For example, when the attack criterion is taken to be based on excessive TCP connection establishment traffic to a destination, the system becomes a traceback service for distributed denial of service (DDoS) attacks. As another special case, when the attack criterion is taken to be based on malicious payload signature match as defined by an intrusion detection system (IDS), the agents provide a service for tracing malware propagation pathways. The main contribution of this paper, is to demonstrate that the proposed system is effective at recovering malicious flow structure even at moderate levels of deployment in large networks, including within the present Internet topology.

In this paper we describe an effective new technique by which to guarantee cooperativeness in Hybrid Radio-Frequency/Free Space Optics (RF/FSO) networks. Our approach is based on a novel Bayesian game-theoretic model, and uses a pricing scheme in which each destination node pays some amount of virtual money to the source node in order to acquire a reliable connection. We describe both single-stage and multi-stage solutions for the game in terms of its Nash and Perfect Bayesian Equilibriums. Pure strategies are found when the required conditions are met; otherwise the game is played as a mixed-strategy. Our numerical results quantify the inherent tradeoffs involved in changing the game's parameters vis-a-vis the equilibrium player strategies and game's outcomes.

The majority of vehicle-to-vehicle (V2V) protocols presented in the literature utilize a combination of controlled access techniques (e.g., TDMA, FDMA, CDMA) to provide prioritized access to the communication medium. While these protocols split the capacity of the communication link to support a maximum number of users with different priority levels, our work utilizes a combination of CDMA and TDMA techniques to increase the number of concurrent users sharing the bandwidth. The proposed protocol provides an adaptive scheme that classifies the vehicle's messages as "urgent" or "non-urgent" based on its statistical parameters (e.g., speed, acceleration, directional stability) and the state of its neighboring vehicles (e.g., normal or urgent) . Furthermore, the proposed protocol achieves intelligent scheduling and allocation of messages and the underlying bandwidth to minimize the end-to-end communication delay and the costs associated with the deployment of vehicle infrastructure integration (VII) without the need for road-side equipment.

Intelligent service management techniques play an important role in the continuously and rapidly evolving area of technologically advanced services. High tech companies are looking for better ways to deliver and preserve services to their customers in a competitive way. This paper introduces a new architecture for a scalable service monitoring and support system called Call Home Analysis and Response System (CHARS). The proposed system utilizes data de-noising and filtering techniques to meet the service management requirements of large-scale service deployments. The system utilizes intra and inter-element correlation of events to enhance the services delivered to end- users. Our results demonstrate that the proposed system is effective in covering the performance and fault management aspects for large-scale deployments of advanced services.

Abstract Current trends in computing indicate that there is a great potential for service‐oriented computing and similar technologies, such as application‐oriented networks (AONs), where services can relocate to adapt to the conditions of the underlying network. In such environments, providing and consuming services and establishing a relationship between consumers (users of services) and producers (providers of services) are still challenging and vastly researched aspects. Bearing this in mind, we define a service location and planning (SLP) problem that uniquely matches producers to consumers and accounts for realistic parameters such as, quality of service (QoS) constraints of throughput and delay, and network constraints of underlying link layer bandwidth capacities, and cost of meeting consumer requests. Our contribution lies in the mathematical formulation of the SLP problem as an integer linear programming (ILP) problem that can be solved optimally for small‐scale networks and extending this work using Lagrangean relaxation (LR) approximation techniques to solve the SLP problem for large‐scale networks. Copyright © 2009 John Wiley & Sons, Ltd.

Modern mobile ad-hoc networks (MANETs) frequently consist of nodes which exhibit a wide range of autonomy needs. This is particularly true in the settings where MANETs are most compelling, i.e. battlefield, response & rescue, and contexts requiring rapid deployment of mobile users. The time-critical nature of the underlying circumstances frequently requires deployment of both manned and unmanned nodes, and a coordination structure which provides prioritized tasking to them. Unlike consumer MANETs, these settings bring with them a common group purpose, making inter-node cooperation plausible. In this paper, we focus on how cooperation can improve MANET communications. We begin by taxonomizing all prior approaches and noting that no existing approach adequately captures networks where nodes exhibit a wide range of autonomy with respect to their mobility. To this end we present a new cooperative mobility model, developing a cost- benefit framework which enables us to explore the impact of cooperation in MANETs where nodes are, to varying extents, willing to move for the common good. In the second half of the paper, we describe the design of CoopSim, a platform for conducting simulation experiments to evaluate the impact of parameter, policy and algorithm choices on any system based on the proposed model. Finally, we present a small but illustrative case study and use the experimental evidence derived from it to give an initial evaluation of the merits of the proposed model and the efficacy of the CoopSim software.

Current trends in computing indicate that there is a great potential for service oriented computing and similar technologies, such as Cisco's Application Oriented Networks, where services provide a higher-level of abstraction to traditional applications. In such cases, providing and consuming services and establishing a relationship between consumers (users of services) and producers (providers of services) are still challenging and vastly researched aspects. We define a service location planning problem (SLP) that is novel in the approach it takes to match consumers to producers, such that the cost of providing services is minimized while taking into account quality of service constraints of throughput and delay. In previous work, we presented an integer linear programming (ILP) model to formulate the service location planning problem. In this paper, we present Lagrangean Relaxation of the model for large-scale networks. We use the approach of Trick [3] to decrease computational overheads.

Mission-oriented MANETs are characterized by implicit common group objectives which make inter-node cooperation both logical and feasible. We propose new techniques to leverage two optimizations for cognitive radio networks that are specific to such contexts: opportunistic channel selection and cooperative mobility. We present a new formal model for MANETs consisting of cognitive radio capable nodes that are willing to be moved (at a cost). We develop an effective decentralized algorithm for mobility planning, and powerful new Altering and fuzzy based techniques for both channel estimation and channel selection. Our experiments are compelling and demonstrate that the communications infrastructure-specifically, connection bit error rates-can be significantly improved by leveraging our proposed techniques. In addition, we find that these cooperative/opportunistic optimization spaces do not trade-off significantly with one another, and thus can be used simultaneously to build superior hybrid schemes. Our results have significant applications in high-performance mission-oriented MANETs, such as battlefield communications and domestic response & rescue missions.

Rapid-deployment mobile ad-hoc networks (MANETs) are frequently characterized by common overarching mission objectives which make it reasonable to expect some degree of cooperativeness on the part of their constituent nodes. In this article we demonstrate new strategies to improve MANET communications, based on inter-node cooperation with respect to node mobility. We present our model for cooperative mobility, and use this cost-benefit framework to explore the impact of cooperation in MANETs where nodes are - to varying extents - willing to be moved for the common good. We develop a mixed-integer linear programming (MILP) formulation of the model, accurately capturing its objectives and constraints. The MILP model is evaluated through simulations and found to be very effective, albeit for small networks. To make the proposed technique scale to large networks we develop a new technique for converting a large global MILP into a sequence of smaller local MILP optimizations, and demonstrate that the resulting approach is scalable and succeeds at efficiently moving cooperative nodes in a manner which optimizes connection bit error rates.

We propose a protocol to support mobility for geo-encryption by allowing mobile nodes to exchange movement parameters, so that a sender is able to geo-encrypt messages to a moving decryption zone that contains a mobile node's estimated location. We also present methods for estimating the node's movement parameters to allow for geo-encryption. Finally, we evaluate our model by measuring the induced overhead to the network and its performance in terms of decryption ratio.

We propose two lightweight techniques to detect masquerade attacks on wireless sensor networks (WSN). Our solutions take into consideration, important WSN properties like coverage, connectivity, data aggregation and specific communication patterns. The two proposed techniques complement each other when used concurrently. The mutual guarding (MG) technique does not work when nodes are not completely covered by their neighbors or when adversary has shorter transmission range than the sensor nodes. It also does not protect nodes near the boundary. The SRP technique does not have these drawbacks. In this paper, we present our proposed techniques and analyze their performance in terms of successful masquerade detection rate and traffic and computational overhead.

Rapid-deployment mobile ad-hoc networks (MANETs) are frequently characterized by common over-arching mission objectives which predicate a cooperativeness on the part of constituent nodes. In this article we present a new strategy to improve MANET communications based on node cooperation with respect to mobility. We present our model for cooperative mobility, and use this cost-benefit framework to explore the impact of cooperation in MANETs where nodes are-to varying extents--willing to be moved for the common good. We develop an effective centralized algorithm for mobility planning based on multigrid techniques. Our simulation results are compelling and demonstrate that the communication infrastructure-specifically, connection bit error rate-can be significantly improved by leveraging this proposed scheme.

In this paper, we develop a Cooperative Mobility Modelthat captures new salient features of collaborative and mission-oriented MANETs. We propose new techniques to leverage two optimizations for cognitive radio networks that are specific to such contexts: cooperative mobility and opportunistic channel selection. We present a new formal model for MANETs consisting of cognitive radio capable nodes that are willing to be moved (at a cost). We develop an effective decentralized algorithm for mobility planning, and powerful newfiltering and fuzzy based techniques for both channel estimation and channel selection. Our experiments are compelling and demonstrate that the communications infrastructure-specifically, connectionbit error rates-can be significantly improved by leveraging our proposed techniques. In addition, we find that these cooperative/opportunistic optimization spaces do not trade-off significantly with one another, and thus can be used simultaneously to build superior hybrid schemes. Our results have significant applications in high-performance mission-oriented MANETs, such as battlefield communications and domestic response rescue missions.

In this paper, we present and discuss a novel service location problem that satisfies user demand for services, by installing services on nodes in the network, which can also meet quality of service (QoS) constraints of throughput and delay defined by the user. Service location problems can arise in various different networks, we specifically consider service location in Cisco's Application Oriented Networks (AON). Our goal is to formulate and present the service location problem as an Integer Linear Programming (ILP) problem and solve it optimally for networks with small number of nodes. Our ILP and results meet "real-world" demands of traffic splitting (due to link layer capacity) and service federation (to incur minimal service installation cost). This will enable service providers to offer services in a cost-effective manner while meeting consumer's demand for quality of service for throughput and delay.

We present the results of simulation experiments that compare end-to-end error management (used in controlled access MAC protocols) against hop-by-hop error management (used in random access MAC protocols). Our experiments are novel in that we restrict both MAC schemes to identical power budgets and power distribution strategies. By making such a normalized comparison, we observe that end-to-end schemes are more effective than hop-by-hop schemes at reducing connection BER. We are also able to quantify the sensitivity of this relative advantage to various environmental parameters, including power budget size, geographic distance, the number of hops, and power distribution scheme.

Numerous approaches have been proposed for intrusion detection, especially for anomaly detection, in ad hoc networks. However, little research work has been done in actually implementing such a scheme based on statistical methods. In this paper, we present an efficient anomaly detection algorithm based on a statistical method originated from pattern recognition, which can effectively identify abnormal behavior such as mobility pattern of MANETs. In the proposed algorithm, the mobility pattern of a specific node is characterized by a multi-leaf tree structure, second-level nodes stands for the possible starting points and leaf nodes stand for the destination node of each possible path. Since our algorithm is using statistical method, a normal profile for each node is generated through extensive experiments, where the specific tree generated may have several starting points and ending with several destination points. For each path between any two nodes (parent and children), we can get the distribution of every different mobility pattern. By comparing the mobility patterns with the training data, we can distinguish abnormal nodes from normal behavior nodes in mobile Ad Hoc networks. Simulation results demonstrate that our proposed detection algorithm can achieve good performance in terms of false alarm rate and detection rate for nodes with regular mobility patterns.

We develop a new dynamic scheme which continuously redistributes a fixed power budget among the wireless nodes participating in a multi-hop wireless connection, with the objective of minimizing the end-to-end wireless connection bit error rate (BER). We compare the efficacy of our scheme with two static schemes: one that distributes power uniformly, and one that distributes it proportionally to the square of inter-hop distances. In our experiments we observed that the dynamic allocation scheme achieved superior performance, reducing BER by using its ability to distribute the power budget. We quantified the sensitivity of this performance improvement to various environmental parameters, including power budget size, geographic distance, and the number of hops.

In wavelength division multiplexing (WDM) all-optical networks, the size of a request stream may be less than the maximum capacity of a lightpath. To avoid assigning an entire lightpath to a small request, many researchers have looked at adding traffic grooming to the routing and wavelength assignment (RWA) problem. In this work, we consider the RWA problem with traffic grooming (GRWA) for mesh networks. The GRWA problem is NP-Complete since it is a generalization of the RWA problem which is known to be NP-Complete. While most of the previous work in this field focuses on optical networks without grooming or with full grooming capabilities, in this work we study networks with sparse traffic grooming and wavelength conversion resources. In this paper, we propose two novel heuristics that minimize the cost of the traffic grooming and wavelength conversion equipment used in optical network without hindering the network blocking performance. The strength of the proposed heuristics stems from their simplicity, applicability to large-scale networks, and efficiency compared to other heuristics proposed in the literature. The performance of our proposed heuristics is compared to that of other efficient heuristics proposed in the literature in terms of the total cost of traffic grooming and wavelength conversion devices used and the blocking performance of the network.

In this paper we present new energy-efficient techniques to lower the packet-level error rates of application-layer connections in wireless ad-hoc networks. In our scheme, each application-layer connection is implemented at the physical level by an overlay network. Data packets submitted at the connection source are checksummed and replicated, flowing breadth-first across the overlay network towards the destination. The destination delivers the first error-free copy of each packet, in order, to the application layer, dropping packets that are corrupt or duplicate. Specifically in this paper, we consider overlays consisting of multiple parallel node-disjoint multi-hop paths. We compare this overlay scheme with the traditional scheme in which the source transmits to the destination along a single minimum-hop path. We show that even when the two schemes are constrained by identical power consumption bounds, an overlay scheme that uses multiple multi-hop paths provides significantly lower packet-level error rates in many common situations. We describe the relationship between packet error rate, the number of paths, and the lengths of each path, and show that the qualitative nature of the relationship changes significantly, depending on available power budget.

A mobile Ad-Hoc network (MANET) is a collection of autonomous arbitrarily located wireless mobile hosts, in which an infrastructure is absent. In this paper we propose a fuzzy-based hierarchical energy efficient routing scheme (FEER) for large scale mobile ad-hoc networks that aims to maximize the network's lifetime. Each node in the network is characterized by its residual energy, traffic, and mobility. We develop a fuzzy logic controller that combines these parameters, keeping in mind the synergy between them. The value obtained, indicates the importance of a node and it is used in network formation and maintenance. We compare our approach to another energy efficient hierarchical protocol based on the dominating set (DS) idea. Our simulation shows that our design out performs the DS approach in prolonging the network lifetime.

Due to the growing interest in mobile wireless Ad-Hoc networks' (MANETs) applications, researchers have proposed many routing protocols that differ in their objective. Energy efficiency and scalability are two of the most important objectives. In our previous work, we proposed a fuzzy based hierarchical energy efficient routing protocol (FEER) for large scale MANETs that aims to maximize the network's lifetime and increase its scalability. The problem has two parts: the clustering part and the routing part. In the first part, we cluster the network into two levels of hierarchy (cluster heads and normal nodes), connect the cluster heads (backbone) with each other, and connect the normal nodes to the cluster heads while maximizing the network lifetime. In the second part, we design energy efficient routing that uses the hierarchical structure. We call the first part, the energy efficient clustering problem (EEC). In this paper, we formulate three variations of EEC as integer linear programming (ILP) problems. We first consider a network with a fully connected backbone (EEC-FCB). Then, we relax the fully connected constraint and consider a network with a connected backbone (EEC-CB), not necessarily fully connected. Finally, we consider a more reliable network (EEC-R) by electing a backup cluster head for each cluster.

Wireless networking is a growing field thanks to recent advances in mobile and wireless device technologies. Secure communication between wireless hosts is necessary in certain applications and GPS-based encryption complements traditional encryption techniques by restricting the decryption of a message to a particular geographical area and time period. Existing geo-encryption techniques have limited support for mobile nodes. Therefore, we propose a mobility model for existing geo-encryption techniques that allow mobile nodes to exchange movement parameters, so that a sender is able to geo-encrypt messages to a moving decryption zone that contains a mobile node's estimated location. We also present methods for estimating the node's movement parameters and optimizing the goals of secure wireless communication.

It has been demonstrated that advertising the availability of wavelength and converter resources in all-optical DWDM transport networks with sparse wavelength conversion capabilities can drastically improve the blocking performance of these networks thus achieving better usage of the network resources. An important aspect with consequences in network performance is when to originate the wavelength-availability and converter-availability link state advertisements (LSAs). Frequent link-state advertisements could overwhelm the network control plane with many update messages that need to be processed. On the other hand, delaying these advertisements could result in inaccurate link-state information thus degrading the network blocking performance. In this paper, we propose two different link-state update policies to advertise the availability of wavelength and converter resource in all-optical DWDM transport networks with sparse wavelength conversion capabilities. The first one is very simple and serves as our base policy for comparison. The second policy, which is very efficient in handling the dynamic nature of all-optical DWDM networks with different degrees of wavelength conversion, reduces the number of LSAs considerably while maintaining the call blocking probability.

In this paper, we propose a novel routing framework for all-optical dense wavelength-division-multiplexing transport networks with sparse wavelength conversion capabilities. The routing framework includes an integer linear programming formulation to handle the static lightpath establishment problem and a novel open shortest path first protocol extension that advertises the availability of wavelength usage and wavelength conversion resources. Our routing framework addresses the limitations of the extensions presented in the literature because it also includes: 1) an efficient flooding protocol that is suitable for the dynamic nature of these networks and 2) an efficient route and wavelength computation engine that minimizes connection costs without hindering the blocking probability.

This work focuses on the routing and wavelength assignment (RWA) problem in all-optical DWDM networks with sparse wavelength conversion (SWC) capabilities. By sparse wavelength conversion, we mean that nodes within the optical network domain might or might not support optical wavelength conversion. For these nodes that support optical wavelength conversion, the number of wavelength converters might be limited. As such, optical lightpaths might or might not be able to find the wavelength conversion resources that might be needed for it to be established. In this work, we present the RWA problem in all-optical WDWM networks with sparse wavelength conversion capabilities (RWA-SWC). We also provide integer linear programming (ILP) formulation for static lightpath establishment (SLE) in all-optical networks with sparse wavelength conversion capabilities. Finally, we propose a new opaque extension to the OSPF routing protocol to advertise wavelength usage and converter availability throughout the optical network domain.