Simultaneous Localization and Mapping (SLAM) represents a fundamental pillar in autonomous vehicles and robots, enabling these systems to perceive their environment, generate a real-time map of their surroundings, and simultaneously determine their precise locations. Over the past three decades, considerable efforts have been dedicated to SLAM development, and the rise of artificial intelligence has significantly increased interest and innovation in the field. Despite the advances, SLAM continues to face critical deployment challenges in real-world applications. SLAM deployment feasibility in industrial and commercial applications has been narrowly discussed in the existing surveys. To address this gap, we investigate the fundamental obstacles in the real-world deployment of SLAM and provides a comprehensive challenge-solution classification of the core open problems and the state-of-the-art solutions in the literature. To the best of the authors’ knowledge, this study is the first to put particular emphasis on hardware requirements, real-time performance, and implementation costs. By providing insights into recent developments and identifying critical directions for future research, this survey serves as a roadmap for researchers to address these persistent challenges.

Electric Vehicle (EV) charging infrastructure intersects both power and transportation networks, inheriting a wide cyberattack surface. Emerging cyber threats motivate the need for proactive measures in EV charging networks. This work introduces a specialized penetration testing framework for EV charging ecosystems to identify and mitigate vulnerabilities in charging station communications. The work presented in this paper includes developing a realistic simulation testbed of modern EV charging components, formulating targeted cyberattack scenarios, and proposing a lightweight Machine Learning-based Intrusion Detection System (IDS) to protect the charging infrastructure and ensure feasibility given system resource constraints. The developed IDS can identify each cyberattack scenario and is a critical step toward improving cybersecurity practices in the field of EV charging infrastructure.

With the growing need for real-time processing on IoT devices, optimizing machine learning (ML) models' size, latency, and computational efficiency is essential. This paper investigates a pruning method for anomaly detection in resourceconstrained environments, specifically targeting Electric Vehicle Charging Infrastructure (EVCI). Using the CICEVSE2024 dataset, we trained and optimized three models—Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and XGBoost—through hyperparameter tuning with Optuna, further refining them using SHapley Additive exPlanations (SHAP)-based feature selection (FS) and unstructured pruning techniques. The optimized models achieved significant reductions in model size and inference times, with only a marginal impact on their performance. Notably, our findings indicate that, in the context of EVCI, pruning and FS can enhance computational efficiency while retaining critical anomaly detection capabilities.

The transition from fifth-generation (5G) to sixth-generation (6G) mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017) datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity.

Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. To this end, the work presented in this paper leverages signal processing and Machine Learning (ML) methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects during pre and post-flight operation. With the help of dimensionality reduction techniques, the Random Forest algorithm exhibited the best performance and detected defective rotor blades perfectly. Additionally, a comprehensive analysis of the impact of various feature subsets is presented to gain insight into the factors affecting the model’s classification decision process.

Large language models (LLMs) are rapidly emerging in Artificial Intelligence (AI) applications, especially in the fields of natural language processing and generative AI. Not limited to text generation applications, these models inherently possess the opportunity to leverage prompt engineering, where the inputs of such models can be appropriately structured to articulate a model’s purpose explicitly. A prominent example of this is intent-based networking, an emerging approach for automating and maintaining network operations and management. This paper presents semantic routing to achieve enhanced performance in LLM-assisted intent-based management and orchestration of 5G core networks. This work establishes an end-to-end intent extraction framework and presents a diverse dataset of sample user intents accompanied by a thorough analysis of the effects of encoders and quantization on overall system performance. The results show that using a semantic router improves the accuracy and efficiency of the LLM deployment compared to stand-alone LLMs with prompting architectures.

Autonomous Vehicles (AVs), furnished with sensors capable of capturing essential vehicle dynamics such as speed, acceleration, and precise location, possess the capacity to execute intelligent maneuvers, including lane changes, in anticipation of approaching roadblocks. Nevertheless, the sheer volume of sensory data and the processing necessary to derive informed decisions can often overwhelm the vehicles, rendering them unable to handle the task independently. Consequently, a common approach in traffic scenarios involves transmitting the data to servers for processing, a practice that introduces challenges, particularly in situations demanding real-time processing. In response to this challenge, we present a novel DL-based semantic traffic control system that entrusts semantic encoding responsibilities to the vehicles themselves. This system processes driving decisions obtained from a Reinforcement Learning (RL) agent, streamlining the decision-making process. Specifically, our framework envisions scenarios where abrupt roadblocks materialize due to factors such as road maintenance, accidents, or vehicle repairs, necessitating vehicles to make determinations concerning lane-keeping or lane-changing actions to navigate past these obstacles. To formulate this scenario mathematically, we employ a Markov Decision Process (MDP) and harness the Deep Q Learning (DQN) algorithm to unearth viable solutions.

Next-generation radio access networks such as the Open Radio Access Network (O-RAN) have alleviated many of the 5G demand and management challenges. However, 0-RAN's intelligence, openness, and virtualization have signifi-cantly increased the attack surface of RAN s. This is specifically dangerous for critical 5G use cases such as Ultra-Reliable and Low-latency Communications (URLLC) due to its strict latency and reliability constraints. In this work, we focus on enhancing the security of the data streams and the security of the ML training and inference hosts in O-RAN URLLC deployments by introducing additional network security functions to 0- RAN's service function chains. Our goal is to maximize the amount of traffic examined by the security functions while adhering to 0- RAN's operational and functional constraints and upholding network defense patterns. Two security function types, encryption Virtualized Network Functions (VNFs) and intrusion detection system VNFs are chosen to achieve this objective. Encryption VNFs provide an additional layer of encryption for data traffic, while IDS VNFs protect the ML training and inference hosts of our solution. To solve this complex task, an advantage actor-critic deep reinforcement learning agent is developed, which actively allows adaptation to dynamic traffic. We demonstrate that our solution is capable of increasing the number of security functions in URLLC deployments allowing increased data protection and securing its own training and inference hosts.

Cyber-security attacks pose a significant threat to the operation of autonomous systems. Particularly impacted are the Heating, Ventilation, and Air Conditioning (HVAC) systems in smart buildings, which depend on data gathered by sensors and Machine Learning (ML) models using the captured data. As such, attacks that alter the readings of these sensors can severely affect the HVAC system operations impacting residents’ comfort and energy reduction goals. Such attacks may induce changes in the online data distribution being fed to the ML models, violating the fundamental assumption of similarity in training and testing data distribution. This leads to a degradation in model prediction accuracy due to a phenomenon known as Concept Drift (CD) — the alteration in the relationship between input features and the target variable. Addressing CD requires identifying the source of drift to apply targeted mitigation strategies, a process termed drift explanation. This paper proposes a Feature Drift Explanation (FDE) module to identify the drifting features. FDE utilizes an Auto-encoder (AE) that reconstructs the activation of the first layer of the regression Deep Learning (DL) model and finds their latent representations. When a drift is detected, each feature of the drifting data is replaced by its representative counterpart from the training data. The Minkowski distance is then used to measure the divergence between the altered drifting data and the original training data. The results show that FDE successfully identifies 85.77% of drifting features and showcases its utility in the DL adaptation method under the CD phenomenon. As a result, the FDE method is an effective strategy for identifying drifting features towards thwarting cyber-security attacks.

Deep Reinforcement Learning (DRL) models play a fundamental role in autonomous driving applications; however, they typically suffer from sample inefficiency because they often require many interactions with the environment to learn effective policies. This makes the training process time-consuming. To address this shortcoming, Prioritized Experience Replay (PER) has proven to be effective by prioritizing samples with high Temporal-Difference (TD) error for learning. In this context, this study contributes to artificial intelligence by proposing a sample-efficient DRL algorithm called Episodic-Guided Prioritized Experience Replay (EPER). The core innovation of EPER lies in the utilization of an episodic memory, dedicated to storing successful training episodes. Within this memory, expected returns for each state–action pair are extracted. These returns, combined with TD error-based prioritization, form a novel objective function for deep Q-network training. To prevent excessive determinism, EPER introduces exploration into the learning process by incorporating a regularization term into the objective function that allows exploration of state-space regions with diverse Q-values. The proposed EPER algorithm is suitable to train a DRL agent for handling episodic tasks, and it can be integrated into off-policy DRL models. EPER is employed for traffic navigation through scenarios such as highway driving, merging, roundabout, and intersection to showcase its application in engineering. The attained results denote that, compared with the PER and an additional state-of-the-art training technique, EPER is superior in expediting the training of the agent and learning a more optimal policy that leads to lower collision rates within the constructed navigation scenarios.

Experimentation in practical, end-to-end (E2E) next-generation networks deployments is becoming increasingly prevalent and significant in the realm of modern networking and wireless communications research. The prevalence of fifth-generation technology (5G) testbeds and the emergence of developing networks systems, for the purposes of research and testing, focus on the capabilities and features of analytics, intelligence, and automated management using novel testbed designs and architectures, ranging from simple simulations and setups to complex networking systems; however, with the ever-demanding application requirements for modern and future networks, 5G-and-beyond (denoted as 5G+) testbed experimentation can be useful in assessing the creation of large-scale network infrastructures that are capable of supporting E2E virtualized mobile network services. To this end, this paper presents a functional, modular E2E 5G+ system, complete with the integration of a Radio Access Network (RAN) and handling the connection of User Equipment (UE) in real-world scenarios. As well, this paper assesses and evaluates the effectiveness of emulating full network functionalities and capabilities, including a complete description of user-plane data, from UE registrations to communications sequences, and leads to the presentation of a future outlook in powering new experimentation for 6G and next-generation networks.

With the rapid development and incoming implementation of 5G networks, many use cases, such as Intelligent Transportation Systems (ITS), are being realized. Utilizing networking technologies, including Network Function Virtualization and Mobile Edge Computing, along with 5G network slicing, the Next-Generation Service Placement Problem (NGSPP) is gaining significant attention due to the criticality of its services and its resource-constrained network nodes. The placement of services on Next-Generation (NG) networks has inherent challenges, mainly ultra-low latency requirements and the complexity of NG network management and orchestration. A candidate solution to the NGSPP should provide a placement that adheres to the strict Quality of Service (QoS) requirements. This work presents the formulation of a robust optimization problem that optimizes the high-availability placement of applications in resource-constrained and multi-tenant NG networks, which complies with QoS requirements and is capable of protecting the performance of the solution under adverse conditions. Finally, a set of hierarchical clustering-based heuristic algorithms, which reduce the time-complexity of the solution are proposed. Results demonstrate that formulating the robust solution is a proactive method of injecting resilience into the system and can preserve performance across various levels of system uncertainty.

Ultra-Reliable Low Latency Communications (URLLC) is a critical use case in 5G and B5G networks enabling applications such as Augmented Reality (AR)-assisted surgery, vehicle-to-everything communications, and smart grids to consistently deliver the promised Quality of Service to the end-users. The intelligence of the 5G core has made such applications possible, and the O-Radio Access Network (O-RAN) has extended this intelligence to Radio Access Networks (RANs) through its openness, cloudification, and ability to host machine learning models at every layer. However, the cloudification of O-RAN introduces challenges, such as securing availability and ensuring latency for URLLC. In this work, we propose an Availability- and Latency-Aware O-RAN Virtual Network Function (VNF) Protection (ALAP) solution. ALAP offers a shared VNF protection scheme based on deep Q-learning, efficiently providing this protection while minimizing the number of VNF backup components compared to dedicated protection schemes. Our solution protects against resource blockages and alleviates operational costs for network service providers. In addition to these objectives, ALAP ensures that the network meets URLLC’s strict availability and end-to-end latency constraints. ALAP has shown promising results in how quickly it can learn to optimize these objectives and in its capability to achieve its goals on large-scale O-RAN deployments.

Zero-Touch Networks (ZTNs) represent a state-of-the-art paradigm shift towards fully automated and intelligent network management, enabling the automation and intelligence required to manage the complexity, scale, and dynamic nature of next-generation (6G) networks. ZTNs leverage Artificial Intelligence (AI) and Machine Learning (ML) to enhance operational efficiency, support intelligent decision-making, and ensure effective resource allocation. However, the implementation of ZTNs is subject to security challenges that need to be resolved to achieve their full potential. In particular, two critical challenges arise: the need for human expertise in developing AI/ML-based security mechanisms, and the threat of adversarial attacks targeting AI/ML models. In this survey paper, we provide a comprehensive review of current security issues in ZTNs, emphasizing the need for advanced AI/ML-based security mechanisms that require minimal human intervention and protect AI/ML models themselves. Furthermore, we explore the potential of Automated ML (AutoML) technologies in developing robust security solutions for ZTNs. Through case studies, we illustrate practical approaches to securing ZTNs against both conventional and AI/ML-specific threats, including the development of autonomous intrusion detection systems and strategies to combat Adversarial ML (AML) attacks. The paper concludes with a discussion of the future research directions for the development of ZTN security approaches.

Machine Learning (ML) and Artificial Intelligence (AI) can harness the immense amount of operational data from clouds to services, to social and communication networks. In the era of data science and connected devices of all varieties, Intelligence have found ways to improve operations and management of next generation networks, systems, and services. Further research is therefore needed to understand and improve the potential and suitability of ML/AI in the context of network, system, and service operations and management. This will provide deeper understanding and better decision making based on largely collected and available operational and management data. It will also present opportunities for improving ML/AI algorithms on aspects such as reliability, dependability, and scalability, as well as demonstrate the benefits of these methods in control and management systems. Moreover, there is an opportunity to define novel platforms that can harness the vast operational data and advance ML/AI algorithms to drive management decisions in open and highly programmable networks, clouds, and data centers.

To ensure uninterrupted access to critical VM applications, a robust VM migration plan is necessary, especially during natural disasters or various cyber incidents such as state-led actions, espionage, and attacks. Existing literature often neglects VM migration security. To address this, we formulate a model that treats secure VM migration as an optimal stopping problem with partial observability in the context of Next Generation Core Networks (NGCN). We envision a scenario wherein a malevolent actor launches attacks during migration, and a defender oversees the process to ensure security. The migration is guaranteed to occur only via a secure path fortified with security checkpoints. We postulate that the VMs host diverse 5G Core (5GC) entities, a critical component of NGCN. This scenario is modeled via a Partial Observable Markov Decision Process (POMDP), with the Generalized Proximity Policy Optimization (GePPO) algorithm employed to address the POMDP. Our results indicate that the defender's policy for secure VM migration converges quicker than the benchmark Proximity Policy Optimization (PPO) algorithm, highlighting the effectiveness of our approach in NGCN.

The goal of Next-Generation Networks is to improve upon the current networking paradigm, especially in providing higher data rates, near-real-time latencies, and near-perfect quality of service. However, existing radio access network (RAN) architectures lack sufficient flexibility and intelligence to meet those demands. Open RAN (O-RAN) is a promising paradigm for building a virtualized and intelligent RAN architecture. This paper presents a Machine Learning (ML)-based Traffic Steering (TS) scheme to predict network congestion and then proactively steer O-RAN traffic to avoid it and reduce the expected queuing delay. To achieve this, we propose an optimized setup focusing on safeguarding both latency and reliability to serve URLLC applications. The proposed solution consists of a two-tiered ML strategy based on Naive Bayes Classifier and deep$Q- \mathbf{learning}$. Our solution is evaluated against traditional reactive TS approaches that are offered as xApps in O-RAN and shows an average of 15.81 percent decrease in queuing delay across all deployed SFCs.

Fifth Generation (5G) networks have revolutionized modern networking practices by supporting an increasing number of connected devices and delivering improved performance through higher data rates and diverse application support. Enabling technologies are crucial to the development of evolving 5G networks to address user demand; however, the strategies they employ and the solutions they implement must account for the optimization of the network resources at hand. To this end, the work presented in this paper outlines a reliable Access and Mobility Function (AMF) scaling and load balancing framework that uses traffic class distributions and weights to determine the minimum number of AMF instances required to meet the projected demand and the relative capacity of the AMF instances to perform load balancing. The work outlined in this paper was conducted by creating a 5G core prototype using open-source emulation software. The presented results demonstrate how the resilience of the solution is controlled through the optimization problem formulation, and the load balancing module effectively balances the load across all instances in a set of AMFs.

Industry 5.0 aims at maximizing the collaboration between humans and machines. Machines are capable of automating repetitive jobs, while humans handle creative tasks. As a critical component of Industrial Internet of Things (IIoT) systems for service delivery, network data stream analytics often encounter concept drift issues due to dynamic IIoT environments, causing performance degradation and automation difficulties. In this article, we propose a novel multistage automated network analytics framework for concept drift adaptation in IIoT systems, consisting of dynamic data preprocessing, the proposed drift-based dynamic feature selection method, dynamic model learning and selection, and the proposed window-based weighted probability averaging ensemble model. It is a complete automated data stream analytics framework that enables automatic, effective, and efficient data analytics for IIoT systems in Industry 5.0. Experimental results on two public IoT datasets demonstrate that the proposed framework outperforms state-of-the-art methods for IIoT data stream analytics.

The Federated Learning (FL) paradigm faces several challenges that limit its application in real-world environments. These challenges include the local models’ architecture heterogeneity and the unavailability of distributed Internet of Things (IoT) nodes due to connectivity problems. These factors posit the question of “how can the available models fill the training gap of the unavailable models?”. This question is referred to as the “Oblique Federated Learning” problem. This problem is encountered in the studied environment that includes distributed IoT nodes responsible for predicting CO2concentrations. This paper proposes the Correlation-based FL (CorrFL) approach influenced by the representational learning field to address this problem. CorrFL projects the various model weights to a common latent space to address the model heterogeneity. Its loss function minimizes the reconstruction loss when models are absent and maximizes the correlation between the generated models. The latter factor is critical because of the intersection of the feature spaces of the IoT devices. CorrFL is evaluated on a realistic use case, involving the unavailability of one IoT device and heightened activity levels that reflect occupancy. The generated CorrFL models for the unavailable IoT device from the available ones trained on the new environment are compared against models trained on different use cases, referred to as the benchmark model. The evaluation criteria combine the mean absolute error (MAE) of predictions and the impact of the amount of exchanged data on the prediction performance improvement. Through a comprehensive experimental procedure, the CorrFL model outperformed the benchmark model in every criterion.

Machine learning and artificial intelligence can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, machine learning and artificial intelligence have found ways to improve operations and management of information technology and communications.

Data-driven approaches and paradigms have be-come promising solutions to efficient network performances through optimization. These approaches focus on state-of-the-art machine learning techniques that can address the needs of 5G networks and the networks of tomorrow, such as proactive load balancing. In contrast to model-based approaches, data-driven approaches do not need accurate models to tackle the target problem, and their associated architectures provide a flexibility of available system parameters that improve the feasibility of learning-based algorithms in mobile wireless networks. The work presented in this paper focuses on demonstrating a working system prototype of the 5G Core (5GC) network and the Network Data Analytics Function (NWDAF) used to bring the benefits of data-driven techniques to fruition. Analyses of the network-generated data explore core intra-network interactions through unsupervised learning, clustering, and evaluate these results as insights for future opportunities and works.

Modern vehicles, including autonomous vehicles and connected vehicles, have adopted an increasing variety of functionalities through connections and communications with other vehicles, smart devices, and infrastructures. However, the growing connectivity of the Internet of Vehicles (IoV) also increases the vulnerabilities to network attacks. To protect IoV systems against cyber threats, Intrusion Detection Systems (IDSs) that can identify malicious cyber-attacks have been developed using Machine Learning (ML) approaches. To accurately detect various types of attacks in IoV networks, we propose a novel ensemble IDS framework named Leader Class and Confidence Decision Ensemble (LCCDE). It is constructed by determining the best-performing ML model among three advanced ML algorithms (XGBoost, LightGBM, and CatBoost) for every class or type of attack. The class leader models with their prediction confidence values are then utilized to make accurate decisions regarding the detection of various types of cyber-attacks. Experiments on two public IoV security datasets (Car-Hacking and CICIDS2017 datasets) demonstrate the effectiveness of the proposed LCCDE for intrusion detection on both intra-vehicle and external networks.

Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats. Due to the lack of authentication and encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs) are essential approaches to protect modern vehicle systems from network attacks. In this paper, a transfer learning and ensemble learning-based IDS is proposed for IoV systems using convolutional neural networks (CNNs) and hyper-parameter optimization techniques. In the experiments, the proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two well-known public benchmark IoV security datasets: the Car-Hacking dataset and the CICIDS2017 dataset. This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.

Wireless networks, in the fifth-generation and beyond, must support diverse network applications which will support the numerous and demanding connections of today's and tomorrow's devices. Requirements such as high data rates, low latencies, and reliability are crucial considerations and artificial intelligence is incorporated to achieve these requirements for a large number of connected devices. Specifically, intelligent methods and frameworks for advanced analysis are employed by the 5G Core Network Data Analytics Function (NWDAF) to detect patterns and ascribe detailed action information to accommodate end users and improve network performance. To this end, the work presented in this paper incorporates a functional NWDAF into a 5G network developed using open source software. Furthermore, an analysis of the network data collected by the NWDAF and the valuable insights which can be drawn from it have been presented with detailed Network Function interactions. An example application of such insights used for intelligent network management is outlined. Finally, the expected limitations of 5G networks are discussed as motivation for the development of 6G networks.

The Open Radio Access Network (O-RAN) architecture brings openness, intelligence, and virtualization to RANs, allowing multi-vendor existence, achieving economics of scale, and enabling intelligent management and orchestration. O-RAN components such as near real-time RAN Intelligent Controllers (RICs), O-RAN Central Units (O-CUs), and O-RAN Distributed Unit (O-DUs) can be considered as virtual network functions hosted on the O-Cloud. This virtualization allows network service providers to disaggregate O-RAN functions from their hard-ware, enabling dynamic instantiation of services and reducing their capital and operating costs. However, with openness and virtualization, availability guarantees become more difficult to maintain as the network is now prone to both software and hardware failures. In this paper, we investigate a decomposition model for the design of reliable 0-RAN deployment under a dedicated virtual network function (VNF)-protection scheme. The proposed model maximizes the network's yearly availability by providing a placement decision for all 0-RAN VNFs and their backup instances. The model is solved by a column generation algorithm making it a scalable algorithm for large-scale 0-RAN deployments. Extensive computational results show that the algorithm can produce ε-optimal solutions with negligible ε (less than 0.1%) in reasonable computational times. These results significantly enlarge the exact solutions of the state-of-the-art algorithms for this problem.

In this work, a multi-stage Machine Learning (ML) pipeline is proposed for pipe leakage detection in an industrial environment. As opposed to other industrial and urban environments, the environment under study includes many interfering background noises, complicating the identification of leaks. Furthermore, the harsh environmental conditions limit the amount of data collected and impose the use of low-complexity algorithms. To address the environment's constraints, the developed ML pipeline applies multiple steps, each addressing the environment's challenges. The proposed ML pipeline first reduces the data dimensionality by feature selection techniques and then incorporates time correlations by extracting time-based features. The resultant features are fed to a Support Vector Machine (SVM) of low-complexity that generalizes well to a small amount of data. An extensive experimental procedure was carried out on two datasets, one with background industrial noise and one without, to evaluate the validity of the proposed pipeline. The SVM hyper-parameters and parameters specific to the pipeline steps were tuned as part of the experimental procedure. The best models obtained from the dataset with industrial noise and leaks were applied to datasets without noise and with and without leaks to test their generalizability. The results show that the model produces excellent results with 99 % accuracy and an F1-score of 0.93 and 0.9 for the respective datasets.

Modern vehicles, including connected vehicles and autonomous vehicles, nowadays involve many electronic control units connected through intravehicle networks (IVNs) to implement various functionalities and perform actions. Modern vehicles are also connected to external networks through vehicle-to-everything technologies, enabling their communications with other vehicles, infrastructures, and smart devices. However, the improving functionality and connectivity of modern vehicles also increase their vulnerabilities to cyber-attacks targeting both intravehicle and external networks due to the large attack surfaces. To secure vehicular networks, many researchers have focused on developing intrusion detection systems (IDSs) that capitalize on machine learning methods to detect malicious cyber-attacks. In this article, the vulnerabilities of intravehicle and external networks are discussed, and a multitiered hybrid IDS that incorporates a signature-based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks. Experimental results illustrate that the proposed system can detect various types of known attacks with 99.99% accuracy on the CAN-intrusion-dataset representing the IVN data and 99.88% accuracy on the CICIDS2017 data set illustrating the external vehicular network data. For the zero-day attack detection, the proposed system achieves high F1-scores of 0.963 and 0.800 on the above two data sets, respectively. The average processing time of each data packet on a vehicle-level machine is less than 0.6 ms, which shows the feasibility of implementing the proposed system in real-time vehicle systems. This emphasizes the effectiveness and efficiency of the proposed IDS.

This article presents a novel drift-aware approach to multivariate time-series modeling in the nonstationary industrial Internet of Things environments. The three-layered three-state (TriLS) system enables cooperation between the gateway and the cloud toward the timely adjustment of a lightweight predictive model. Concept drift is detected by the cloud with the use of the extended adaptive windowing algorithm that operates on statistics of time sequences tracked by the gateway. This system is geared toward providing accurate predictions of nonstationary industrial processes for intelligent factory automation and safety. The proposed TriLS system is evaluated on records of recurring chemical processes collected at two plants and implemented on a Raspberry Pi board. TriLS achieves a lower prediction error than the reference adaptive schemes while reducing the computational effort and memory requirements for adaptation at the gateway by over 66% and 48%, respectively. It also reduces the volume of shared data between the gateway and the cloud by 40% –72% that is a significant cut on communications overhead.

Content delivery networks (CDNs) provide efficient content distribution over the Internet. CDNs improve the connectivity and efficiency of global communications, but their caching mechanisms may be breached by cyber-attackers. Among the security mechanisms, effective anomaly detection forms an important part of CDN security enhancement. In this work, we propose a multi-perspective unsupervised learning framework for anomaly detection in CDNs. In the proposed framework, a multi-perspective feature engineering approach, an optimized unsupervised anomaly detection model that utilizes an isolation forest and a Gaussian mixture model, and a multi-perspective validation method, are developed to detect abnormal behaviors in CDNs mainly from the client Internet Protocol (IP) and node perspectives, therefore to identify the denial of service (DoS) and cache pollution attack (CPA) patterns. Experimental results are presented based on the analytics of eight days of real-world CDN log data provided by a major CDN operator. Through experiments, the abnormal contents, compromised nodes, malicious IPs, as well as their corresponding attack types, are identified effectively by the proposed framework and validated by multiple cybersecurity experts. This shows the effectiveness of the proposed method when applied to real-world CDN data.

Machine learning and artificial intelligence can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, machine learning and artificial intelligence have found ways to improve operations and management of information technology and communications.

As next-generation networks materialize, increasing levels of intelligence are required. Federated Learning has been identified as a key enabling technology of intelligent and distributed networks; however, it is prone to concept drift as with any machine learning application. Concept drift directly affects the model's performance and can result in severe consequences considering the critical and emergency services provided by modern networks. To mitigate the adverse effects of drift, this paper proposes a concept drift detection system leveraging the federated learning updates provided at each iteration of the federated training process. Using dimensionality reduction and clustering techniques, a framework that isolates the system's drifted nodes is presented through experiments using an Intelligent Transportation System as a use case. The presented work demonstrates that the proposed framework is able to detect drifted nodes in a variety of non-iid scenarios at different stages of drift and different levels of system exposure.

Due to the huge surge in the traffic of IoT devices and applications, mobile networks require a new paradigm shift to handle such demand roll out. With the 5G economics, those networks should provide virtualized multi-vendor and intelligent systems that can scale and efficiently optimize the investment of the underlying infrastructure. Therefore, the market stakeholders have proposed the Open Radio Access Network (O-RAN) as one of the solutions to improve the network performance, agility, and time-to-market of new applications. O-RAN harnesses the power of artificial intelligence, cloud computing, and new network technologies (NFV and SDN) to allow operators to manage their infrastructure in a cost-efficient manner. Therefore, it is necessary to address the O-RAN performance and availability challenges autonomously while maintaining the quality of service. In this work, we propose an optimized deployment strategy for the virtualized O-RAN units in the O-Cloud to minimize the network's outage while complying with the performance and operational requirements. The model's evaluation provides an optimal deployment strategy that maximizes the network's overall availability and adheres to the O-RAN-specific requirements.

As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT data is often dynamic data streams that change over time, causing model degradation and attack detection failure. This is because traditional data analytics models are static models that cannot adapt to data distribution changes. In this paper, we propose a Performance Weighted Probability Averaging Ensemble (PWPAE) framework for drift adaptive IoT anomaly detection through IoT data stream analytics. Experiments on two public datasets show the effectiveness of our proposed PWPAE method compared against state-of-the-art methods.

The current trend in end-user device’s advancements in computing and communication capabilities makes edge computing an attractive solution to pave the way for the coveted ultra-low latency services. The success of the edge computing networking paradigm depends on the proper orchestration of the edge servers. Several Edge applications and services are intolerant to latency, especially in 5G and beyond networks, such as intelligent video surveillance, E-health, Internet of Vehicles, and augmented reality applications. The edge devices underwent rapid growth in both capabilities and size to cope with the service demands. Orchestrating it on the cloud was a prominent trend during the past decade. However, the increasing number of edge devices poses a significant burden on the orchestration delay. In addition to the growth in edge devices, the high mobility of users renders traditional orchestration schemes impractical for contemporary edge networks. Proper segmentation of the edge space becomes necessary to adapt these schemes to address these challenges. In this paper, we introduce a segmentation technique employing lax clustering and segregated mobility-based clustering. We then apply latency mapping to these clusters. The proposed scheme’s main objective is to create subspaces (segments) that enable light and efficient edge orchestration by-reducing the processing time and the core cloud communication overhead. A bench-marking simulation is conducted with the results showing decreased mobility-related failures and reduced orchestration delay.

Vehicle-to-everything (V2X) communication and services have been garnering significant interest from different stakeholders as part of future intelligent transportation systems (ITSs). This is due to the many benefits they offer. However, many of these services have stringent performance requirements, particularly in terms of the delay/latency. Multi-access/mobile edge computing (MEC) has been proposed as a potential solution for such services by bringing them closer to vehicles. Yet, this introduces a new set of challenges such as where to place these V2X services, especially given the limit computation resources available at edge nodes. To that end, this work formulates the problem of optimal V2X service placement (OVSP) in a hybrid core/edge environment as a binary integer linear programming problem. To the best of our knowledge, no previous work considered the V2X service placement problem while taking into consideration the computational resource availability at the nodes. Moreover, a low-complexity greedy-based heuristic algorithm named “Greedy V2X Service Placement Algorithm” (G-VSPA) was developed to solve this problem. Simulation results show that the OVSP model successfully guarantees and maintains the QoS requirements of all the different V2X services. Additionally, it is observed that the proposed G-VSPA algorithm achieves close to optimal performance while having lower complexity.

Cyber-security garnered significant attention due to the increased dependency of individuals and organizations on the Internet and their concern about the security and privacy of their online activities. Several previous machine learning (ML)-based network intrusion detection systems (NIDSs) have been developed to protect against malicious online behavior. This paper proposes a novel multi-stage optimized ML-based NIDS framework that reduces computational complexity while maintaining its detection performance. This work studies the impact of oversampling techniques on the models’ training sample size and determines the minimal suitable training sample size. Furthermore, it compares between two feature selection techniques, information gain and correlation-based, and explores their effect on detection performance and time complexity. Moreover, different ML hyper-parameter optimization techniques are investigated to enhance the NIDS’s performance. The performance of the proposed framework is evaluated using two recent intrusion detection datasets, the CICIDS 2017 and the UNSW-NB 2015 datasets. Experimental results show that the proposed model significantly reduces the required training sample size (up to 74%) and feature set size (up to 50%). Moreover, the model performance is enhanced with hyper-parameter optimization with detection accuracies over 99% for both datasets, outperforming recent literature works by 1-2% higher accuracy and 1-2% lower false alarm rate.

Different challenges are facing the adoption of cloud-based applications, including high availability (HA), energy, and other performance demands. Therefore, an integrated solution that addresses these issues is critical for cloud services. Cloud providers promise the HA of their infrastructure while cloud tenants are encouraged to deploy their applications across multiple availability zones. Moreover, the environmental and cost impacts of running applications in the cloud are integral parts of incorporated responsibility where the cloud providers and tenants intend to reduce. Hence, an analytical stochastic model is needed for the tenants and providers to quantify the expected availability offered by an application deployment. If multiple deployment options can satisfy the HA requirement, the question remains, how can we choose the deployment that satisfies the other providers and tenants requirements? Therefore, this paper proposes a cloud scoring system and integrates it with a Stochastic Petri Net model. While the Petri Net model evaluates the availability of cloud applications deployments, the scoring system selects the optimal HA-aware deployment in terms of energy, operational expenditure, and other norms. We illustrate our approach with a use case that shows how we can use the various deployment options to satisfy both the cloud tenant and provider needs.

Vehicle-to-everything (V2X) services are attracting a lot of attention in the research and industry communities due to their applicability in the landscape of connected and autonomous vehicles. Such applications have stringent performance requirements in terms of complex data processing and low latency communications which are utilized to ensure road safety and improve road conditions. To address these challenges, the placement of V2X applications through leveraging of edge computing paradigm, that distributes the computing capabilities to access points in proximity to the vehicles, presents itself as a viable solution. However, the realistic implementation of the edge enabled V2X applications is hindered by the limited computational power provided at the edge and the nature of V2X applications that are composed of multiple independent V2X basic services. To address these challenges, this work targets the efficient placement of V2X basic services in a highway scenario subject to the delay constraints of V2X applications using them and the limited computational resources at the edge. To that end, this work formulates a binary integer linear programming model that minimizes the delay of V2X applications while satisfying the resource requirements of V2X basic services. To demonstrate the soundness of the approach, simulations with varying vehicle densities were conducted, and the results reported show that it can satisfy the delay requirements of V2X applications.

The shift towards a completely virtualized networking environment is triggered by the emergence of software defined networking and network function virtualization (NFV). Network service providers have unlocked immense capabilities by these technologies, which have enabled them to dynamically adapt to user needs by deploying their network services in real-time through generating Service Function Chain (SFCs). However, NFV still faces challenges that hinder its full potentials, including availability guarantees, network security, and other performance requirements. For this reason, the deployment of NFV applications remains critical as it should meet different service level agreements while insuring the security of the virtualized functions. In this paper, we tackle the challenge of securing these SFCs by introducing virtual security functions (VSFs) into the latency-aware deployment of NFV applications. This work insures the optimal placement of the SFC components including the security functions while considering the performance constraints and the VSFs' operational rules such as, functions' alliance, proximity, and anti-affinity. This paper develops a mixed integer linear programming model to optimally place all the requested SFCs while satisfying the above constraints and minimizing the latency of every SFC and the intercommunication delay between the SFC components. The simulations are evaluated against a greedy algorithm on the virtualized Evolved Packet Core use case and have shown promising results in maintaining the security rules while achieving minimum delays.

Deploying V2X services has become a challenging task. This is mainly due to the fact that such services have strict latency requirements. To meet these requirements, one potential solution is adopting mobile edge computing (MEC). However, this presents new challenges including how to find a cost efficient placement that meets other requirements such as latency. In this work, the problem of cost-optimal V2X service placement (CO-VSP) in a distributed cloud/edge environment is formulated. Additionally, a cost-focused delay-aware V2X service placement (DA-VSP) heuristic algorithm is proposed. Simulation results show that both CO-VSP model and DA-VSP algorithm guarantee the QoS requirements of all such services and illustrates the trade-off between latency and deployment cost.

In this article, a novel pseudorandom key chaining (PRKC) algorithm is introduced and evaluated. This lightweight symmetric scheme enables a transmission-triggered time variation of group keys in low-power wireless networks during broadcasting or multicasting. The proposed algorithm uses pseudorandom (PR) sequences, generated at the physical (PHY) layer of radio transceivers during a communication session, to symmetrically refresh the encryption keys on both sides of a communication link. This solution is scalable and suitable for large networks of nodes with limited resources in an Industrial Internet-of-Things (IIoT) environment. The strength of generated keys was tested with the use of the National Institute of Standards and Technology Special Publication 800-22 (NIST SP 800-22) statistical suit. No binary patterns that may indicate a vulnerability were detected. The randomness of generated key sequences was further analyzed with the use of strange attractors approach which demonstrated that these sequences are robust against attacks, such as spoofing or intelligent brute forcing. To assess the real-time delay and computational overhead of the algorithm, it was implemented on a Raspberry Pi board. The results demonstrated that the PRKC algorithm runs over 60% faster and requires over 40% less CPU effort per round than the conventional hashing-based schemes. In addition, it does not require any communication overhead and transmission energy.

As more end devices are getting connected, the Internet will become more congested. Various congestion control techniques have been developed either on transport or network layers. Active Queue Management (AQM) is a paradigm that aims to mitigate the congestion on the network layer through active buffer control to avoid overflow. However, finding the right parameters for an AQM scheme is challenging, due to the complexity and dynamics of the networks. On the other hand, the Explicit Congestion Notification (ECN) mechanism is a solution that makes visible incipient congestion on the network layer to the transport layer. In this work, we propose to exploit the ECN information to improve AQM algorithms by applying Machine Learning techniques. Our intelligent method uses an artificial neural network to predict congestion and an AQM parameter tuner based on reinforcement learning. The evaluation results show that our solution can enhance the performance of deployed AQM, using the existing TCP congestion control mechanisms.

With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased connectivity demand. Although Network Function Virtualization (NFV) has been identified as a solution, several challenges must be addressed to ensure its feasibility. In this paper, we address the Virtual Network Function (VNF) placement problem by developing a machine learning decision tree model that learns from the effective placement of the various VNF instances forming a Service Function Chain (SFC). The model takes several performance-related features from the network as an input and selects the placement of the various VNF instances on network servers with the objective of minimizing the delay between dependent VNF instances. The benefits of using machine learning are realized by moving away from a complex mathematical modelling of the system and towards a data-based understanding of the system. Using the Evolved Packet Core (EPC) as a use case, we evaluate our model on different data center networks and compare it to the BACON algorithm in terms of the delay between interconnected components and the total delay across the SFC. Furthermore, a time complexity analysis is performed to show the effectiveness of the model in NFV applications.

The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to different types of cyber-attacks such as denial of service, spoofing, and sniffing attacks. In this paper, an intelligent intrusion detection system (IDS) is proposed based on tree-structure machine learning models. The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks. Furthermore, the proposed ensemble learning and feature selection approaches enable the proposed system to achieve high detection rate and low computational cost simultaneously.

Internet of Things (IoT) allows households to have real-time access to various services through a range of smart-home technologies. These technologies allow owners to reduce expenses for security, heating, cooling, lighting, electricity, and water. In general, using IoT and smart-home technologies enables new sharing and service economy paradigms to unlock the value of surplus resources. This requires a more holistic approach, exploring synergies across and between verticals to maximize efficiencies on a wider scale. Further, there is a demand to optimize the use of existing assets and resources while maintaining the security and privacy of users through exploring new appropriate frameworks. Indeed, Software-Defined Perimeters (SDP) can play a crucial role in security and present an ideal network security framework for smart-home technologies. There are several benefits as a result of adopting SDP in smart-home technologies, such as providing light-weight authentication for devices as well as dynamically updating firewall rules. To this end, the integration between the SDP and smart-home infrastructure is examined through virtualized network testbeds. The testing results prove that SDP can provide reasoning capabilities to repel different attacks on the smart-home infrastructures, such as flooding and spoofing types of attacks, intrusion detection, and eavesdropping activities.

Security has become of paramount importance in recent times, especially due to the advent of cloud computing and Internet of Things. With so many devices in the mix, users have the choice of working from anywhere they want. But it also raises the possibility of being able to multiply the impact of any attack by using all devices at hand. Another important aspect to consider is the prevention of access to sensitive data by unauthorized users using authorized machines. Software Defined Perimeter (SDP) provides one such solution. It aims to only allow traffic from authorized users and machines to a hidden resource. This paper discusses the SDP concept and analyzes its performance in the event of a Distributed Denial of Service (DDoS) attack under two different environments - one virtual and one real-world. The results indicate that SDP provides a resilient method for protection again DDoS attacks. While it requires slightly more time for connection setup, it is offset by its exceptional performance even under duress.

Internet of Things (IoT) technology has been pervasively applied to environmental monitoring, due to the advantages of low cost and flexible deployment of IoT enabled systems. In many large-scale IoT systems, accurate and efficient data sampling and reconstruction is among the most critical requirements, since this can relieve the data rate of trunk link for data uploading while ensure data accuracy. To address the related challenges, we have proposed an unmanned aerial vehicle (UAV) enabled spatial data sampling scheme in this paper using denoising autoencoder (DAE) neural network. More specifically, a UAV-enabled edge-cloud collaborative IoT system architecture is first developed for data processing in large-scale IoT monitoring systems, where UAV is utilized as mobile edge computing device. Based on this system architecture, the UAV-enabled spatial data sampling scheme is further proposed, where the wireless sensor nodes of large-scale IoT systems are clustered by a newly developed bounded-size K-means clustering algorithm. A neural network model, i.e., DAE, is applied to each cluster for data sampling and reconstruction, by exploitation of both linear and nonlinear spatial correlation among data samples. Simulations have been conducted and the results indicate that the proposed scheme has improved data reconstruction accuracy under the sampling ratio without introducing extra complexity, as compared to the compressive sensing-based method.

Network function virtualization (NFV) has been introduced by network service providers to overcome various challenges that hinder them from satisfying the growing demand for networking services with higher return-on-investment. The association of NFV with the leading technologies of information technology virtualization and software defined networking is paving the way for flexible and dynamic orchestration of the VNFs, but still, various challenges need to be addressed. The VNFs instantiation and placement problems on data center's (DC) servers are key enablers to achieve the desired flexible and dynamic NFV applications. In this paper, we have addressed the VNF placement problem by providing a novel mixed integer linear programming (MILP) optimization model and a novel heuristic solution, Betweenness centrality Algorithm for Component Orchestration of NFV platform (BACON), for small- and large-scale DC networks. The proposed solution addresses the VNF placement while taking into consideration the carrier-grade nature of the NFV applications and at the same time, minimizing the intra- and end-to-end delays of the service function chain (SFC). Also, the proposed approach enhances the reliability and the quality of service (QoS) of the SFC by maximizing the count of the functional group members. To evaluate the performance of the proposed solution, this paper conducts a comparative analysis with an NFV-agnostic algorithm and a greedy-k-NFV approach, which is proposed in the literature work. Also, this paper defines the complexity and the order of magnitude of the MILP model and BACON. BACON outperforms the greedy algorithms especially the greedy-k-NFV solution and has a lower complexity, which is calculated as O((n3-n2)/2). The simulation results show that finding an optimized VNF placement can achieve minimal SFCs delays and enhance the QoS accordingly.

Summary Cloud computing and its service models, such as Platform as a Service (PaaS), have changed the way that computing resources are allocated to Information and Communications Technology enterprises and users. Although multiple cloud providers support dynamic service provisioning, it is necessary to facilitate the management of the cloud infrastructure and applications in order to allow the continuous refinement of cloud models. Therefore, issues are raised regarding the cloud orchestration, including the flexible portability and interoperability of cloud applications among multiple cloud providers. Having said that, there is a need for a standardized design and management of the cloud use cases (during the creation of scenarios, application's deployment, and patching) to ensure efficient applications' migration between different providers. This paper proposes an artifact, GITS, a generic input template for CloudSim and other cloud simulators. GITS can be provided by PaaS offering to manage the creation, monitoring, administration, and patching of infrastructure and applications in the cloud. GITS defines the cloud schema that can be used with conforming cloud models and independent cloud providers; thus, portability and interoperability can be enabled in PaaS cloud models. GITS focuses on the architecture‐based modeling for cloud infrastructure and application not only in terms of computational resources but also in terms of high availability properties associated with infrastructure and applications. The main objective of the GITS template is to provide the cloud user with a modular, simple, readable, and reusable model that still supports the essential components and provide them with the ability to control the applications' execution, deployment, and other management needs in addition to the allocation environment. This paper describes GITS usage, specifically as an input template for CloudSim.

Network attacks have been very prevalent as their rate is growing tremendously. Both organization and individuals are now concerned about their confidentiality, integrity and availability of their critical information which are often impacted by network attacks. To that end, several previous machine learning-based intrusion detection methods have been developed to secure network infrastructure from such attacks. In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique to tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF), Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.

Domain Name System (DNS) is a crucial component of current IP-based networks as it is the standard mechanism for name to IP resolution. However, due to its lack of data integrity and origin authentication processes, it is vulnerable to a variety of attacks. One such attack is Typosquatting. Detecting this attack is particularly important as it can be a threat to corporate secrets and can be used to steal information or commit fraud. In this paper, a machine learning-based approach is proposed to tackle the typosquatting vulnerability. To that end, exploratory data analytics is first used to better understand the trends observed in eight domain name-based extracted features. Furthermore, a majority voting-based ensemble learning classifier built using five classification algorithms is proposed that can detect suspicious domains with high accuracy. Moreover, the observed trends are validated by studying the same features in an unlabeled dataset using K-means clustering algorithm and through applying the developed ensemble learning classifier. Results show that legitimate domains have a smaller domain name length and fewer unique characters. Moreover, the developed ensemble learning classifier performs better in terms of accuracy, precision, and F-score. Furthermore, it is shown that similar trends are observed when clustering is used. However, the number of domains identified as potentially suspicious is high. Hence, the ensemble learning classifier is applied with results showing that the number of domains identified as potentially suspicious is reduced by almost a factor of five while still maintaining the same trends in terms of features' statistics.

In recent years, there has been great interest in utilizing the unlicensed spectrum for mobile data traffic, as deployment of mobile systems are facing severe challenges due to licensed spectrum scarcity. Increasing the bandwidth is a possible solution to increasing the network capacity, however, the licensed spectrum is limited and can be very costly to obtain. Additionally, the lack of available licensed band limits the network capacity which may affect the user experience. Due to this limitation, the research focuses on the coexistence of the LTE and Wi-Fi in the unlicensed band. Currently, there is minimal to no sharing mechanism between the two technologies, which will cause significant interference for the users. This project focuses on using the time-domain virtualization, where the sharing mechanism is allocated in time slots rather than allocating frequency for each technology. The coexistence mechanism of the two technologies are discussed and evaluated using the simulation of a proposed scheduling algorithm.

This work introduces a novel blind channel estimation technique for orthogonal frequency division multiplexing (OFDM) systems over time- varying mobile channels. The proposed estimator exploits the channel correlation over consecutive OFDM symbols to estimate the channel parameters blindly. In the new estimator, particular subcarriers are modulated usingM-ary phase shift keying (MPSK), and subcarriers with the same indices in the consecutive OFDM symbol are modulated usingM-ary amplitude shift keying (MASK), replacing the pilots in pilot-aided systems. Consequently, all subcarriers are data-bearing, which leads to spectral efficiency improvement. The proposed estimator uses the feature that MPSK and MASK modulated symbols have sufficient channel state information (CSI) that enable them to cooperate in order to detect the MPSK symbols coherently and blindly. Then, the CSI at the corresponding MPSK symbols can be acquired in a decision-directed (DD) fashion. The performance of the proposed estimator is evaluated in terms of symbol error rate (SER) and mean-squared error (MSE), where an exact analytical formula is obtained for the SER of binary phase shift keying (BPSK) symbols in mobile radio channels with various time-varying rates. The obtained results show that the proposed estimator produces accurate channel estimates as compared to pilot-aided and state-of-the-art systems without additional complexity.

Cloud computing has become a business reality that impacts technology users globally. It has become a cornerstone for emerging technologies and an enabler of the future Internet services. This has been coupled with emerging trend of adopting software-based network infrastructure. Paradigms such as Software-defined networks (SDNs) have gained more attention for large scale networks due to the flexibility and agility they offer to the network. Parallel to this emergence, the power consumption has rapidly increased. Hence, developing green and sustainable solutions has become a prime concern for the cloud providers. In this paper, a novel power aware algorithm titled “Green Distributed Cloud Services Provisioning” to provision the tenants' cloud services requests on an SDN-enabled cloud environment is presented. The role of the cloud and SDN controllers within the considered environment is illustrated to better highlight the interactions with the underlaying infrastructure. Furthermore, a brief review of well known cloud simulators is given. The conducted simulations show that the proposed algorithm significantly reduces the power consumption while maintaining high admission rates when compared to other work from the literature. Moreover, the average number of hops needed for a tenant to reach its requested VM was shown to remain stable with the increase in network load.

Wireless networks are facing various challenges that demand continuous and rapid improvement. Long-Term Evolution (LTE) is a preferred wireless technology because of its satisfactory performance. Owing to an exponential increase in demand and new potential applications, the core network of LTE, which is known as the Evolved Packet Core (EPC), is affected by a surge in signaling caused by a variety of control functions. The signaling overhead decreases the users' Quality of Experience (QoE). The current study attempts to improve the intelligence of location management techniques. As an extension of our previous study [1], a Self-Organizing Network (SON) that enables dynamic reconfiguration of cell-to-TAL/MME is introduced. Both centralized and distributed pooling schemes are tested in terms of signaling overhead and user power consumption. A decomposition model that reduces the original formulated problem to two sub-problems is proposed, each of which is solved optimally. In addition, a smart cell-to-TAL selection scheme is proposed to prioritize potential cells that might be visited by a user equipment (UE). Our method is shown to outperform several state-of-the-art methods presented in the literature. Finally, a heuristic algorithm is presented to obtain a less complex solution than the optimal one.

Energy-efficient communications have become one fundamental aspect for today's cutting-edge wireless technologies due to its valuable impact on the environment. In this paper, we augment our earlier study for the user equipment's (UE) energy efficiency (EE) in the long-term evolution (LTE) downlink by looking at real-time heterogeneous traffic QoS requirements. In particular, we utilize the previously proposed cloud radio access network (C-RAN) and ray tracing (RT)-based scheduling model to optimize both of the EE and the packet delay jitter for real-time applications with fixed packet delay budget subject to other traffic types requirements. Using the utility-based scheduling approach, we formulate the resource allocation problem as a weighted sum binary integer programming (BIP) problem. Due to the inherent complexity of the problem formulation which hinders finding its solution directly, four heuristic algorithms are proposed to solve the optimization problem. Numerical simulations are conducted on three different traffic types each belonging to one of the popular QoS classes; best-effort class, rate, and delay-constrained classes. The obtained results demonstrate a substantial improvement in the system's performance achieved by our proposed schemes compared to other existing schemes.

In the interconnected globe where service delivery is the success measure, cloud high availability (HA) is an indispensable area for enterprises. An HA-aware cloud system provides different approaches to handle the outages. This includes geo-redundancy, failover schemes, and HA-aware placement solutions. However, using real-cloud platforms to model HA-aware approaches is hindered by the configuration settings. To this end, simulation tools, such as CloudSim, can be used to evaluate HA solutions and a cloud resiliency against failures. CloudSim allows implementing of scheduling policies, but it does not support HA properties. This paper provides availability-aware CloudSim extension (ACE). ACE extends CloudSim with a graphical and textual modeling to ensure simplicity and reusability of cloud scenarios. ACE has added HA-aware modeling (HA metrics and failure/redundancy/interdependency models) and HA-aware scheduling (HA-aware placements, failover, repair, and load balancing policies) into CloudSim. With ACE, the creation of cloud scenarios is facilitated, and multiple HA-aware deployment solutions can be evaluated under different stochastic and deterministic events. ACE can assess the impact of different redundancy/failure models, and other performance policies to extract HA-aware lessons. In this paper, ACE is assessed on a cloud application to evaluate different redundancy/failure models and provide availability analysis of the HA-aware placement solution.

Wireless networks have faced increasing demand to cope with the exponential growth of data. Conventional architectures have hindered the evolution of network scalability. However, the introduction of cloud technology has brought tremendous flexible and scalable on demand resources. Thus, cloud radio access networks (C-RANs) have been introduced as a new trend in wireless technologies. Despite the novel advancements that C-RAN offers, remote radio head (RRH)-to-base band unit (BBU) resource allocation can cause significant downgrade in efficiency, particularly the allocation of computational resources in the BBU pool to densely deployed small cells. This causes an increase in power consumption and wasted resources. Consequently, an efficient resource allocation method is vital for achieving efficient resource consumption. In this paper, the optimal allocation of computational resources between RRHs and BBUs is modeled. This is dependent on having an optimal physical resource allocation for users to determine the required computational resources. For this purpose, an optimization problem that models the assignment of resources at these two levels is formulated. A decomposition model is adopted to solve the problem by formulating two binary integer programming subproblems; one for each level. Furthermore, two low complexity heuristic algorithms are developed to solve each subproblem. Results show that the computational resource requirements and the power consumption of BBUs and the physical machines decrease as the channel quality worsens. Moreover, the developed heuristic solution achieves a close to optimal performance while having a lower complexity. Finally, both models achieve high resource utilization, cementing the efficiency of the proposed solutions.

The recent advancement of Internet of Things (IoT) technologies has enabled many emerging applications, including smart building and connected vehicles. These advanced applications generate massive amount of data at the edge of IoT networks, which usually need to be relayed to a remote data center for further real-time processing. However, uploading all these IoT data to the cloud platform imposes a heavy burden on the underlying network. The unavoidable long delay from data exchange and processing significantly reduces the time-responsiveness of real-time IoT applications. Recently, fog computing has been introduced to IoT applications as an intermediate between end devices and cloud for primary IoT data processing. In this paper, a temporal IoT data reduction scheme through fog computing is proposed to reduce the total amount of IoT data uploaded to the cloud. More specifically, IoT data are first modeled as multivariate normal distribution by the cloud. Dual Kalman filters (KF) with identical parameters are then deployed at both the cloud and fog platforms. The same predictions are simultaneously triggered by the dual KFs at both platforms. Only the measured IoT data out of predicted range are further uploaded from fog to cloud. Otherwise, predicted values at both platforms are used instead of measurements. A simple prototype IoT system is developed for performance evaluation. Experimental results indicate that the proposed scheme significantly reduces the number of packets uploaded to the cloud platform with high data accuracy.

The term Energy Efficiency (EE) is turning out to be a radical characteristic for today's 4G networks - which provide energy-hungry wireless services - especially from the battery-limited devices' perspective. In addition to the EE, meeting firm levels for the quality-of-service (QoS) of those energy demanding applications is inevitable. In this paper we study the EE of the user equipment (UE) in the LTE downlink and the delay jitter as a fundamental QoS metric for various real-time applications. The study focuses mainly on the Voice over LTE (VoLTE) traffic as being a heavily used service. We provide a multiobjective optimization for both the EE and the delay jitter subject to fixed delay budget. To address the complexity of the proposed optimal scheduler, two different heuristic algorithms were developed. Numerical results demonstrate that our proposed schedulers achieve better trade-off for the EE versus the delay jitter compared to existing state-of-the-art schedulers.

In the literature, the spectral efficiency is considered a key performance indicator that is used to classify various communications systems, algorithms, and techniques. However, the classification is typically performed while assuming that all systems have a static structure, and without considering the quality of service (QoS) requirements or the constraints imposed by the system design. Therefore, this work presents a new reliable and accurate approach to evaluate the spectral efficiency of communications systems while considering the system dynamics, QoS requirements and design constraints. To demonstrate its effectiveness, the proposed approach is used to evaluate the spectral efficiency of various blind and pilot-aided channel estimation and synchronization algorithms. The obtained results reveal that the spectral efficiency depends on several system variables such the signal-to-noise ratio (SNR), QoS requirements, system constraints, and the channel characteristics. Contrary to what is usually believed, the obtained results show that pilot-aided systems can be more spectrally efficient than blind systems for several cases of interest.

Internet of Things (IoT) is emerging as the underlying technology of our connected society, which enables many advanced applications. In IoT-enabled applications, information of application surroundings is gathered by networked sensors, especially wireless sensors due to their advantage of infrastructure-free deployment. However, the pervasive deployment of wireless sensor nodes generate massive amount of sensor data, and data outliers are frequently incurred due to the dynamic nature of wireless channels. As operation of IoT systems relies on sensor data, data redundancy and data outliers could significantly reduce the effectiveness of IoT applications or even mislead systems into unsafe conditions. In this paper, a cluster-based data analysis framework is proposed using recursive principal component analysis (R-PCA), which can aggregate the redundant data and detect the outliers in the meantime. More specifically, at a cluster head, spatially correlated sensor data collected from cluster members are aggregated by extracting the principal components (PCs), and potential data outliers are determined by the abnormal squared prediction error score, which is defined as the square of residual value after extraction of PCs. With R-PCA, the parameters of PCA model can be recursively updated to adapt to the changes in IoT systems. Cluster-based data analysis framework also releases the computational and processing burdens on sensor nodes. Practical databases-based simulations have confirmed that the proposed framework efficiently aggregates the correlated sensor data with high recovery accuracy. The data outlier detection accuracy is also improved by the proposed method compared to other existing algorithms.

This paper presents a blind channel estimation (BCE) technique for orthogonal frequency division multiplexing communications systems. The proposed system is based on modulating particular pairs of subcarriers using amplitude shift keying and phase shift keying, which enables the realization of a decision-directed one-shot BCE (OSBCE), with complexity and accuracy that are comparable to pilot-based channel estimation techniques. The performance of the proposed estimator is evaluated in terms of the mean squared error (MSE), where an accurate analytical expression is derived and verified using Monte Carlo simulation under various channel conditions. The obtained results show that the MSE of the proposed OSBCE is comparable to pilot-based estimators, which confirms the efficiency of the proposed OSBCE.

Linux containers provide a low overhead and lightweight virtualization solution that increases significantly deployment density of containers per host. Using containers, different applications are deployed as a set of micro-services. This in turn improves resource utilization and cost efficiency, system agility and continuous development, and also upgrade and runtime modification. Therefore, containers have attracted lots of attention as an alternative to virtual machines hosting the cloud applications. In spite of several advantages of containers versus virtual machines, assuring quality of the provided service is challenging. In this paper, we survey some of the available promising container orchestration solutions with particular attention to their quality of service assurance capability.

The growing dependency of users on social media, telecommunication services, mobile applications, banking amenities, and other cloud services requires a plan that mitigates inevitable failures and ensures the always-on access to these services. This emanates high availability (HA) concerns regarding the adoption of cloud. To maintain HA, the cloud provider and/or user should design a system that is immune to both application and infrastructure failures. This paper proposes live migration approach to maintain service delivery upon a sudden failure, a virtual machine (VM)/infrastructure overload, or maintenance. It develops a mixed integer linear programming model that minimizes the migration downtime based on the VM memory pages and the optimal HA-aware placement of the VM. It also provides different design considerations to achieve HA-aware applications placement. The proposed placement is used in the migration approach to find new hosts for the VMs. It considers VMs/applications deployments in geographically distributed data centers and satisfies redundancy, applications interdependency, and other HA and performance requirements. Then the deployments are assessed using a formal Petri Net model to improve them in terms of HA. The HA-aware placement and migration approaches are evaluated on 3-tier Web applications.

To meet the increasing mobile data services demand, several solutions have been proposed such as wireless resource virtualization and device-to-device (D2D) communication. Virtualization allows for more efficient utilization of the spectrum, reduces expenditures, and can support higher peak rates. D2D communication can achieve higher data rates due to the proximity of devices while controlling the interference it causes to cellular communication. However, the increase in data rate multimedia demand has led to an increase in global energy consumption. Thus, it is crucial to employ more energy-aware schemes as this would provide both environmental and financial gains for service providers. In this paper, we extend our work in [1] by formulating the problem of power-aware wireless resource virtualization with D2D communication underlaying the LTE network. Since the problem is a mixed integer non-linear programming problem (MINLP), it is divided into four smaller linear programs, each of which is solved to optimality. Two lower complexity heuristic algorithms to solve the power allocation problems are introduced. Results show significant savings at both eNodeB and D2D devices.

Wireless sensor networks (WSNs) are widely considered as the most important information gathering platform in enabling Internet of Things (IoT). In order to evolve the traditional WSNs for low-power and low-loss IoT applications, time slotted channel hopping (TSCH) MAC protocol has been proposed to tackle the single channel and inefficient medium access drawbacks through improved network topology awareness. However, the problem of maintaining the physical topology of a WSN at the server end remains unresolved. In this paper, we propose a novel physical topology discovery scheme for WSNs by exploitation of random walk process and iterative multilateration localization algorithm. Explicitly, information specific to the sensor nodes, including IDs and neighbor tables, are collected in the random walk process. The physical topology is then reconstructed at the server end based on the collected information and the iterative multilateration localization algorithm. Simulation results indicate that the average location offset between the established topology and the ground- truth topology can be as low as 1.26m.

High availability (HA) is a main key performance indicator for cloud deployed services. Cloud providers offer different availability zones possibly located in different geographical regions. To protect cloud services against failures and natural disasters, it is recommended to deploy the applications on redundant resources across multiple zones and distribute the workload through a load-balancer. Different cloud infrastructure, located in different geographical zones with different energy source powering, hardware quality, etc., may have different reliability levels. Scheduling a cloud service on different zones while meeting the service level agreement availability requirements necessitate a solution to assess the expected availability of a given deployment. To quantify the expected availability offered by an application deployment, a formal stochastic model is required to capture the stochastic behavior of failures. This paper proposes a stochastic Petri Net model that captures the stochastic characteristics of cloud services and translates them into elements of an availability model. The model evaluates the availability of cloud services and their deployments in geographically distributed data centers (DCs). The results are useful to generate guidelines for an HA-aware scheduling.

Wireless sensor networks have already been pervasively utilized due to the rapid deployment of information and communication technology (ICT) in many industrial applications, which generate massive amount of sensor data. This development has brought several technical challenges in sensor data processing, e.g., data fault and data redundancy. Principal component analysis (PCA) has been used recently to process the massive but correlated sensor data. However, the conventional PCA method is difficult to be adapted in following the dynamic conditions of wireless sensor networks. In this paper, recursive principal component analysis (R-PCA) method is exploited to progressively update the transformation basis for extracting principal components. Furthermore, a novel R-PCA based algorithm is proposed to address data fault and data redundancy problems. Different from conventional PCA-based algorithms, the proposed algorithm is cluster-based so that the network efficiency can be further improved. Simulations based on a practical dataset have been conducted to evaluate the performance of algorithms. Simulation results show that the proposed algorithm improves the fault detection accuracy by about 20% and reduces the data restoration error by about 28%.

Abstract Wireless resource virtualization (WRV) is currently emerging as a key technology to overcome the major challenges facing the mobile network operators (MNOs) such as reducing the capital, minimizing the operating expenses, improving the quality of service, and satisfying the growing demand for mobile services. Achieving such conflicting objectives simultaneously requires a highly efficient utilization of the available resources including the network infrastructure and the reserved spectrum. In this paper, the most dominant WRV frameworks are discussed where different levels of network infrastructure and spectrum resources are shared between multiple MNOs. Moreover, we summarize the major benefits and most pressing business challenges of deploying WRV. We further highlight the technical challenges and requirements for abstraction and sharing of spectrum resources in next generation networks. In addition, we provide guidelines for implementing comprehensive solutions that are able to abstract and share the spectrum resources in next generation network. The paper also presents an efficient algorithm for base station virtualization in long‐term evolution (LTE) networks to share the wireless resources between MNOs who apply different scheduling polices. The proposed algorithm maintains a high‐level of isolation and offers throughput performance gain. Copyright © 2016 John Wiley & Sons, Ltd.

Cloud computing promises flexible integration of the compute capabilities for on-demand access through the concept of virtualization. However, uncertainties are raised regarding the high availability of the cloud-hosted applications. High availability is a crucial requirement for multi-tier applications providing business services for a broad range of enterprises. This paper proposes a novel component high availability-aware scheduling technique, CHASE, which maximizes the availability of applications without violating service level agreements with the end-users. Using CHASE, prior criticality analysis is conducted on applications to schedule them based on their impact on their execution environment and business functionality. This paper presents the advantages and shortcomings of CHASE compared to an optimal solution, Open Stack Nova scheduler, high availability-agnostic, and redundancy-agnostic schedulers. The evaluation results demonstrate that the proposed solution improves the availability of the scheduled components compared to the latter schedulers. CHASE prototype is also defined for runtime scheduling in Open Stack environment.

Cloud computing is an attractive business model offering cost-efficiency and business agility. Recently, the trend is that small and large businesses are moving their services to cloud environments. The quality of service is always negotiated between the cloud users and the cloud providers and documented in the service level agreement (SLA). Yet assuring -- or even measuring -- the quality of the provided service can be challenging. This paper proposes a formal approach for quantifying the quality of service in the cloud systems as promised in the SLA. The proposed approach uses controller synthesis to find a system configuration that meets the SLA requirement. The formal approach suggested in this paper is based on, but not limited to, %the controller synthesis of Time Petri Nets (TPN). As a case study, we focus on service availability as a key performance indicator in the SLA and for a sample set of resources providing a service, we determine the system configuration satisfying the SLA.

Migrating to the cloud is becoming a necessity for the majority of businesses. Cloud tenants require certain levels of performance in aspects like high availability and service rate and deployment options. On the other hand, Cloud providers are in constant pursuit of a system that satisfies client demands for resources, maximizes availability, minimizes power consumption and, in turn, minimizes the cloud providers' cost. A main challenge cloud providers face here is ensuring high availability (HA). High availability includes the combined reliability of components of all categories including network, computational, hardware and software components of all layers. In this work, we first address the need for a cloud simulator that enables HA algorithm testing in cloud environments and observe its impact on energy efficiency. We introduce a framework to amend cloud simulators with critical HA features. We take GreenCloud, a major simulator with a direct focus on green computing, and implement these features as an additional measurement layer. We demonstrate these added features by simulating their impact on a phased communication application (PCA).

Long term evolution (LTE) is a leading mobile technology that provides very high speeds, low latency, and better quality-of-service (QoS). However, because of the exponential growth in the number of mobile users, the variety of new handheld devices, and the incremental use of different applications, the core network experiences a significant signaling overhead. This demand requires the design of intelligent, optimized mobility management methods. The present work attempts to overcome signaling overhead expansion and to define the fundamental basis for designing a tracking area list (TAL). In this context, we differ from other studies by introducing a model that relates the tracking area list to the mobility management entity (MME) which enables more control and adds intelligence to the system. Two MME pooling schemes are investigated namely, centralized and distributed MME schemes. The proposed model is NP-hard; thus, the problem can be simplified with a few assumptions (which do not violate the constraints of the problem) to become a solvable linear problem (LP). Moreover, a low-complexity heuristic algorithm is developed by determining the percentage use of the lists/MME in each cell. The results show that the centralized scheme outperforms the distributed one. Also, the heuristic algorithm offers sub-optimal results when compared to the LP solution.

In this paper, we first present a detailed analysis for studying the effect of changing the time difference between two successive channel predictions on the outage probability and the system's reliable transmission rate. Secondly, we propose a low complexity heuristic scheduling algorithm that utilizes long-term ray tracing channel predictions to improve the system throughput in wireless networks. The proposed heuristic algorithm achieves almost identical throughput as the optimal scheduler and outperforms traditional channel-aware schedulers in terms of system's average throughput with low computational cost. The scheduler's performance is tested in the presence of Rayleigh fading and log-normal shadowing effects.

Cloud computing is continuously growing as a business model for hosting information and communication technology applications. Although on-demand resource consumption and faster deployment time make this model appealing for the enterprise, other concerns arise regarding the quality of service offered by the cloud. One major concern is the high availability of applications hosted in the cloud. This paper demonstrates the tremendous effect that the placement strategy for virtual machines hosting applications has on the high availability of the services provided by these applications. In addition, a novel scheduling technique is presented that takes into consideration the interdependencies between applications components and other constraints such as communication delay tolerance and resource utilization. The problem is formulated as a linear programming multi-constraint optimization model. The evaluation results demonstrate that the proposed solution improves the availability of the scheduled components compared to OpenStack Nova scheduler.

Mobile Network Operators (MNOs) revenues in radio platform technology standard Long Term Evolution (LTE) systems are not increasing at the same rate as traffic volume. In order to accommodate the rapid expansion of mobile data usage, more additional network capacities must be deployed. Recently, network sharing has been proposed as an integral part of the next-generation networking architecture for vehicular communications, and is considered to be a promising solution to provide low-cost framework, and accommodate increased traffic demands. The proposed framework to follow pertains to the Mobile Network resources sharing scenario, wherein MNOs achieving a different schedulers' policy are sharing evolved Node B (eNB), allowing MNOs to customize their efforts and provide service requirements according to the sharing agreement delineated within the entire LTE system. The average jitter and delays have been evaluated to verify the framework performance effectiveness in the case of non-sharing and sharing scenarios.

Elasticity is a key characteristic of cloud computing where the provisioning of resources can be directly proportional to the runtime demand. Legacy highly available applications typically rely on the underlying platform to manage their availability by monitoring heartbeats, executing recoveries, and attempting repairs to bring the system back to normal. Migrating such applications to the cloud can be particularly challenging, especially if the elasticity policies target the application only, without considering the underlying platform contributing to its high availability (HA). In this paper, we present a comprehensive framework for the elasticity of highly available applications that considers the elastic deployment of the platform and the HA placement of the application's components. We apply our approach to an IP multimedia subsystem (IMS) application and demonstrate how, within a matter of seconds, the IMS application can be scaled up while maintaining its HA status.

Energy efficiency is one of the most critical issues in wireless sensor networks, since the sensor nodes are usually battery powered. These energy-constrained sensor nodes are usually densely distributed in indoor environments, which leads to spatially correlated sensor data and low network efficiency. Thus, one way to improve energy efficiency is to reduce the redundancy caused by the correlated data. In this paper, a new sensor scheduling algorithm, based on data correlation, is proposed. The sensor nodes are clustered into groups by a new adaptive dual-metric K-means (DK-means) algorithm. Within each group, the sensor nodes take turns to work as a group representative and transmit data to the sink. Thus, the energy consumed by the redundant transmissions of the correlated sensor data is saved. Performance evaluation of the proposed mechanism is conducted through OPNET simulations. The simulation results show that the adaptive DK-means algorithm significantly improves data reliability, as compared to the adaptive K-means algorithm. Furthermore, this improvement in reliability is achieved with minimal cost in terms of complexity. Finally, it is shown that the proposed sensor scheduling algorithm achieves energy savings of up to 58%, as compared to the baseline ZigBee protocol.

In order to accommodate mobile users' consumed power with the rapid increase of multimedia-rich mobile data, additional network capacities with optimized power allocation scheduling algorithms should be deployed. Motivated by the fundamental requirement of extending the mobile devices' battery utilization time per charge, this work formulates the optimized power allocation problem in a virtualized scheme considered in the third generation partnership project-long term evolution (3GPP-LTE) uplink (UL) systems. The proposed framework efficiently shares the evolved nodeB's dedicated physical radio resources blocks of service providers having different requirements under dynamic channel conditions. The objective is to minimize the total transmission energy for all users subject to exclusive and contiguous allocation, maximum transmission power, and rate constraints. Two algorithms are developed. A binary integer programming (BIP)-based algorithm is used to solve a simplified version of the problem. A heuristic algorithm is also presented that approaches the BIP-based algorithm's performance. Simulation results show that the proposed framework offers a remarkable transmission power reduction in the virtualized scenario as compared to the non-sharing one.

Proline residues are common source of kinetic complications during folding. The X-Pro peptide bond is the only peptide bond for which the stability of the cis and trans conformations is comparable. The cis-trans isomerization (CTI) of X-Pro peptide bonds is a widely recognized rate-limiting factor, which can not only induces additional slow phases in protein folding but also modifies the millisecond and sub-millisecond dynamics of the protein. An accurate computational prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. In our earlier work, we successfully developed a biophysically motivated proline CTI predictor utilizing a novel tree-based consensus model with a powerful metalearning technique and achieved 86.58 percent Q2 accuracy and 0.74 Mcc, which is a better result than the results (70-73 percent Q2 accuracies) reported in the literature on the well-referenced benchmark dataset. In this paper, we describe experiments with novel randomized subspace learning and bootstrap seeding techniques as an extension to our earlier work, the consensus models as well as entropy-based learning methods, to obtain better accuracy through a precise and robust learning scheme for proline CTI prediction.

The continuous increase of mobile data traffic has created a substantial demand for high data rate transmission over mobile networks. However, mobile devices are provided with small batteries that can be drained quickly by high data rate transmission. Motivated by the fundamental requirement of extending the battery utilization time per charge of mobile devices, this work presents two power-efficient schedulers for mixed streaming services in LTE uplink systems. Our objective is to minimize the total transmission power for all users. The proposed schedulers are subject to rate, delay, contiguous allocation, and maximum transmission power constraints. We first consider an optimal scheduler that uses binary integer programming (BIP). Then, we propose an iterative scheduler that performs a low-complexity greedy algorithm which solves the BIP problem. We compare the performance of the proposed schedulers to the state-of-the-art schedulers such as the energy-aware resource allocation (EARA) [1] and the proportional fair (PF) [2] in terms of rate, delay, average transmission power and complexity. Simulation results show that the proposed schedulers offer a remarkable transmission power reduction as compared to the PF and the EARA schedulers, and satisfy the QoS requirements.

Due to the emergence of numerous bandwidth-hungry applications, we are motivated to investigate cheaper and faster Internet access solutions to serve in a neighborhood. We concentrate on the convergence of optical and wireless networks for the deployment of Internet access networks so that we can exploit the opportunities of both technologies. We focus on network dimensioning and placement of equipment in hybrid optical-wireless access networks. A number of integrated optical-wireless architectures have been investigated for the greenfield deployment of future access networks. A novel hybrid network infrastructure, namely PON-LTE-WiFi, has been proposed where fiber will be deployed as deeply as affordable/practical and then, wireless systems will be used to extend this connectivity to a large number of locations and ultimately connect the wireless end users. We propose a 3-phase network design optimization scheme for greenfield deployment of PON-LTE-WiFi access network infrastructure. Finally, we propose an ILP model which optimizes the greenfield deployment of LTE network based on the static distribution of mobile user equipment (MUE). The proposed model takes into account various physical layer constraints of LTE network and determines the optimal clustering of MUEs as well as the location of eNBs in a neighborhood. Computational experiments have been conducted on three different data sets consisting of 128, 256 and 512 mobile user equipment in order to evaluate the performance of the proposed scheme.

Single Carrier Frequency Division Multiple Access (SC-FDMA) is used for uplink data transmission in Long Term Evolution (LTE) systems. SC-FDMA requires contiguous resource blocks (RBs) allocation for each user, which challenges the uplink resource allocation in LTE. The contiguity constraint turns the allocation problem into a non-convex optimization problem. The optimal solution is achieved by solving a binary integer programming (BIP) problem which is computationally-expensive. In this work, we propose a genetic algorithm that is able to solve the resource allocation problem in the LTE uplink. The proposed algorithm maintains all the system constraints and provides a solution with lower complexity compared with the optimal solution. The proposed GA is evaluated and compared with the optimal approach in terms of efficiency and time complexity.

Proline cis-trans isomerization (CTI) plays a key role in the rate-determining steps of protein folding. Accurate prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. Our goal is to develop a state-of-the-art proline CTI predictor based on a biophysically motivated intelligent consensus modeling through the use of sequence information only (i.e., position specific scores generated by PSI-BLAST). The current computational proline CTI predictors reach about 70-73 percent Q2 accuracies and about 0.40 Matthew correlation coefficient (Mcc) through the use of sequence-based evolutionary information as well as predicted protein secondary structure information. However, our approach that utilizes a novel decision tree-based consensus model with a powerful randomized-metal earning technique has achieved 86.58 percent Q2 accuracy and 0.74 Mcc, on the same proline CTI data set, which is a better result than those of any existing computational proline CTI predictors reported in the literature.

This work considers the development of a realistic statistical model to represent the interference in heterogeneous wireless networks. The considered networks are comprised of one or more femtocells deployed in buildings with unknown internal structures and a preplanned cellular network. The proposed interference model is based on a novel random floor plan generator, which is used to construct a statistical rather than site-specific floor plans. The developed model is augmented with Nakagami fading to represent the femtocell interference signal in the outdoor environment. The model is then utilized to evaluate the performance of the macrocell users where closed-form formulae for the outage probability and signal-to-interference ratio at the receiver front-end are derived. The obtained results reveal that a femtocell signal propagating from an indoor transmitter to an outdoor receiver will experience a composite shadowing/fading process where the Nakagami distribution is adopted for the fading part while the shadowing is modeled by lognormal mixture distribution. Analytical and simulation results show that placing the femtocell base-station (FBS) close to the center of the house can significantly reduce the impact of the interference on the outdoor macrocell users as compared to a randomly placed FBS.

In this paper, we propose a framework for energy efficient resource allocation in multiuser localized SC-FDMA with synchronous HARQ constraints. Resource allocation is formulated as a two-stage problem where resources are allocated in both time and frequency. The impact of retransmissions on the time-frequency problem segmentation is handled through the use of a novel block scheduling interval specifically designed for synchronous HARQ to ensure uplink users do not experience ARQ blocking. Using this framework, we formulate the optimal margin adaptive allocation problem, and based on its structure, we propose two suboptimal approaches to minimize average power allocation required for resource allocation while attempting to reduce complexity. Results are presented for computational complexity and average power allocation relative to system complexity and data rate, and comparisons are made between the proposed optimal and suboptimal approaches.

Concurrent multipath transfer (CMT) using the stream control transmission protocol (SCTP) can exploit multihomed devices to enhance data communications. While SCTP is a new transport layer protocol supporting multihomed end-points, CMT provides a framework so that transport layer resources are used efficiently and effectively when sending to the same destination with multiple IP addresses. In this paper, we present two techniques for modelling the expected throughput of a CMT session; while one is based on renewal theory, the other uses a Markov chain. As far as we know, ours is the first paper to model CMT whilst considering practical transport layer resources like a shared receive buffer (RBUF). A comparison of the models showed the Markov chain to be more accurate, but suffered from scalability issues. Alternatively, the renewal model was more cost effective, but also less accurate. We also applied our models to a new problem called congestion window management, where the size of each congestion window is reconfigured for optimal performance. Again, we compared two approaches: a dynamic method that makes decisions based on instantaneous throughput, and a static method that uses an integer linear program (ILP) to generate a global solution. Results showed the static method outperforming the dynamic approach by as much as 12 percent.

This work presents a power-efficient multi-user scheduler for mixed streaming services in multi-user LTE uplink systems. We propose an optimal and iterative formulation for the sum-power minimization subject to rate, delay, contiguous allocation, and maximum transmitted power constraints. The results are presented for the rate, delay, average transmitted power and complexity.

Cloud computing is an increasingly popular computing paradigm, now proving a necessity for utility computing services. Each provider offers a unique service portfolio with a range of resources. Resource provisioning for cloud services in a comprehensive way is of crucial importance to any resource allocation model. Any model should consider both computational resources and network(data) resources to accurately represent and serve practical needs. We propose a new model to tackle the resource allocation problem for a group of cloud user requests. This includes provisioning for both data center computational resources and network resources. The model is implemented with the objective of minimizing the average tardiness of connection requests. Four combined scheduling algorithms are introduced and used to schedule virtual machines on data center servers and then schedule connection requests on the network paths available. Of the four methods, the method combining Resource Based Distribution technique and Duration Priority technique have shown the best performance getting the minimum tardiness while complying with the problem constraints.

ABSTRACT Wireless communications have revolutionized the way the world communicates. An important process used to secure that communication is authentication. As flaws in the security of a wireless network are discovered, new protocols and algorithms are required to meet those security issues. When creating new algorithms and systems, it is possible that the existing equipment may not be able to implement the new protocols, which means that integration may be required to transition from an old security protocol to the new more secure protocol. Stationary wireless networks were created without a strong need to integrate protocols and have simply developed slightly more secure protocols to protect old equipment. New protocols in stationary wireless networks are implemented without integration as a requirement. Mobile wireless networks have the requirement of allowing old equipment to use the entire network as it is advantageous to allow new mobile equipment to connect to old networking equipment to increase coverage areas and for old equipment to connect to new towers for roaming and billing. This requirement for mobile networks means that integration is required. There are flaws in this integration of Global System for Mobile Communications (GSM) into Universal Mobile Telecommunications System (UMTS) networks. Those flaws are analyzed, and two practical solutions are proposed. Copyright © 2012 John Wiley & Sons, Ltd.

This paper presents an efficient technique to jointly mitigate the severe bit error rate (BER) performance degradation caused by impulsive noise (IN) and multipath fading in broadband transmission systems. The proposed system is based on a low complexity interleaving process applied after the inverse fast Fourier transform (IFFT) in orthogonal frequency division multiplexing (OFDM) systems, hence it is denoted as time-domain interleaving (TDI). The proposed TDI introduces both time and frequency diversity, which can be used to effectively combat impairments such as IN and frequency-selective fading. In addition to its substantial BER reduction capability, the TDI does not degrade the spectral efficiency and has low computational complexity. In frequency-selective fading channels, the BER of the proposed system is mathematically equal to that of Walsh-Hadamard precoded OFDM systems [1]. In presence of IN, analytical and simulation results show that TDI can remarkably reduce the level of the error floors that are commonly observed. Specifically, TDI can achieve a BER of 10-5for less than 1 dB difference from the IN-free case.

Today's power grid is facing many challenges due to increasing load growth, aging of existing power infrastructures, high penetration of renewable, and lack of fast monitoring and control. Utilizing recent developments in Information and Communication Technologies (ICT) at the power-distribution level, various smart-grid applications can be realized to achieve reliable, efficient, and green power. Interoperable exchange of information is already standardized in the globally accepted smart-grid standard, IEC 61850, over the local area networks (LANs). Due to low installation cost, sufficient data rates, and ease of deployment, the industrial wireless LAN technologies are gaining interest among power utilities, especially for less critical smart distribution network applications. Extensive work is carried out to examine the wireless LAN (WLAN) technology within a power distribution substation. The first phase of the work is initiated with the radio noise interference measurements at 27.6- and 13.8-kV distribution substations, including circuit breaker switching operations. For a detailed investigation, the hardware prototypes of WLAN-enabled IEC 61850 devices are developed using industrial embedded systems, and the performance of smart distribution substation monitoring, control, and protection applications is analyzed for various scenarios using a round trip-time of IEC 61850 application messages. Finally, to examine the real-world field performance, the developed prototype devices are installed in the switchyard and control room of 27.6 power distribution substation, and testing results of various applications are discussed.

ABSTRACT Synchronization is a critical operation required by majority of wireless receivers. This paper presents the design, implementation, and evaluation of an orthogonal frequency‐division multiplexing baseband packet synchronizer deployed on a field‐programmable gate array (FPGA). Packet detection, carrier frequency offset estimation/correction, and time synchronization are all performed in the time domain by processing samples before the fast Fourier transform computation on the receiver. We propose techniques to reduce the area complexity of the arithmetic computations while maintaining the performance of existing approaches. FPGA implementation results are reported, and the design is evaluated by simulation under additive white Gaussian noise channel conditions. Copyright © 2011 John Wiley & Sons, Ltd.

This work considers the development of an indoor-to-outdoor signal propagation model, which can be used to analyze and reduce the interference in various wireless communication networks, particularly 4G networks with femtocells and macrocells. The developed model is based on generating a large number of floor plans with random, but realistic, designs and use signal attenuation models to analyze the statistical properties of the signal at a certain distance from the indoor transmitter after penetrating through several layers of construction materials such as wall, doors and windows. Further studies conducted using the developed model demonstrated that the walls and buildings could be exploited to act like a shield that reduces the mutual interference of indoor and outdoor transmitters as in the case of femtocells. As an application, the proposed model is used to investigate the effect of the placement of an indoor transmitter on the signal level outdoors. The obtained results demonstrated that optimizing the location of the indoor transmitter can reduce the power leakage to the outdoor environment by about 18.5 dB.

This paper proposes a new scheduling algorithm for concurrent multipath transfer (CMT) called the on-demand scheduler (ODS). Unlike previous algorithms, ODS waits for a transmission opportunity before making a scheduling decision. We showed ODS can outperform previous scheduling algorithms for CMT under various network scenarios. Moreover, this paper offers new insight into the effects of congestion and flow control on the receive buffer blocking problem associated with CMT.

This work considers the bit error rate (BER) performance of orthogonal frequency division multiplexing (OFDM) frequency hopping (FH) systems in the presence of Multitone jamming (MTJ) and multipath fading. Analytical and simulation results confirmed that optimum jamming strategies require channel and signal power side information. Moreover, the common assumption that the jamming tones and subcarriers frequencies are identical can be quite inaccurate at low signal-to-jamming power ratios (SJR), particularly for small number of jamming tones.

ABSTRACT We consider the minimization of long‐term average power consumption for packet transmission between a mobile station and the base station over Nakagami‐m fading channel. Power consumption is minimized by intelligent transmission scheduling design, with the average queuing delay and joint packet loss across MAC and physical layers being confined below certain levels. The problem is formulated as an infinite horizon constrained Markov decision problem and solved by linear programming (LP) method. The primary intention of this paper is to provide a visible paradigm on using LP method to optimize the performance of mobile wireless communication systems. We elaborate the detailed mathematical solution with consistent simulation experiments and emphasize the effectiveness of adaptive transmission scheduling for cross‐layer QoS provisioning. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper we propose framework for energy efficient (margin adaptive, MA) resource allocation in multiuser SC-FDMA under synchronous HARQ constraints. Resource allocation is formulated as a two-stage problem where resources are allocated in both time and frequency. To limit the impact of retransmissions on the time-frequency problem segmentation, we propose use of a block scheduling interval that ensures uplink users do not experience ARQ blocking. We formulate the optimal MA resource allocation problem under this framework and based on its structure, we propose a variable complexity sub-optimal approach to solving the resource allocation problem. The proposed method's performance is shown to minimize transmission energy for low complexity implementation.

Hybrid wireless-optical networks provide the inexpensive broadband bandwidth, vital for modern applications, as well as mobility and, scalability required for an access network. However, in order to provide satisfactory Quality of Service (QoS) on such a non-homogeneous network, innovative designs are required. This paper proposes a novel mechanism to significantly improve the delay guarantee, while maintaining throughput, by predicting the incoming traffic to ONUs. Based on the proposed method, two dynamic bandwidth assignment algorithms are proposed. The performance of the proposed algorithms are evaluated by extensive simulations. The results show that the delay bound of delay-sensitive traffic classes, is decreased by a factor of two, while throughput remains the same.

In this letter, we extend the study from our previous work in. First, we extend physical layer resource allocation problem to a packet based transmission scheme. The proposed packet assignment improves the implementation robustness as it allows for consideration of coded modulation schemes while still ensuring energy efficient transmission as with our previous work. Secondly, we study the impact of channel partition size selection on system performance and complexity.

Integration between Ethernet Passive Optical Network (EPON) and Worldwide Interoperability for Microwave Access (WiMAX) is a promising solution for next generation access networks. In this paper, we devise a new architecture framework for EPON-WiMAX hybrid networks that is more reliable and extend the service coverage range. In addition, we propose a new bandwidth allocation algorithm for the proposed architecture that provides per-stream QoS protection, bandwidth guarantee for real-time flows and improves the overall system performance. Through intensive simulations, we show the effectiveness of the proposed architecture and bandwidth allocation algorithm.

SCTP is a new transport layer protocol extending the functionality of TCP with support for performance enhancing services such as multihoming and multistreaming. Unfortunately, an accurate mathematical model, able to approximate performance results, still eludes the research community. Previous models either fail to provide practicality, or are too simplified in their assumptions. This paper aims to circumvent these problems by developing an analytic model containing a higher degree of information. The result is a more accurate model that approximates the steady-state throughput of an SCTP session.

In this study a wireless multiple-input multiple-output (MIMO) communication system operating over a fading channel is considered. Data packets are stored in a finite size buffer before being released into the time-varying MIMO wireless channel. The main objective of this work is to satisfy a specific quality of service (QoS) requirement, i.e. the probability of data loss because of both erroneous wireless transmission and buffer overflow, as well as to maximise the system throughput. The theoretical limit of ergodic capacity in MIMO time-variant channels can be achieved by adapting the transmission rate to the capacity evolving process. In this study, the channel capacity evolving process has been described by a suitable autoregressive model based on the capacity time correlation and a finite state Markov chain (FSMC) has been derived. The joint effect of channel outage at the physical layer and the buffer overflow at the medium access control layer has been considered to describe the probability of data loss in the system. The optimal transmission strategy must minimise that probability of data loss and has been derived analytically through the Markov decision process (MDP) theory. Analytical results show the significant improvements of the proposed optimal transmission strategy in terms of both system throughput and probability of data loss.

In this paper we focus on minimizing the long-term average power consumption of a single transmitter providing Quality of Service (QoS) enabled traffic to a single receiver. Both the transmitting and receiving stations are equipped with multiple antennas. First, we present a general {Kx M} system model where K is the number of independently buffered QoS streams and M is the number of parallel channels available through MIMO SVD eigenmode transmission. Through application of the constrained Markov decision process (MDP) framework combined with a novel MAC layer rate assignment scheme, a randomized per-buffer scheduling policy is obtained. The designed policy exploits queue state information to schedule traffic while meeting throughput, delay and loss constraints. Packets scheduled for transmission during each frame are mapped across the set eigenmode channels subject to available channel resources and the set of channel eigenvalues. Simulation results are provided for several scenarios. System drawbacks, limitations and extensions are also discussed.

In this paper we study the feasibility of utilizing wireless local area network (WLAN) technology to deliver the triple-play services (video, voice and data) over a specific system model. Existing protocols are shown to not efficiently manage the wireless channel therefore we propose a new triple-play time division multiple access (TP-TDMA) media access control (MAC) protocol to provide Quality of Service (QoS) for these Triple-Play services in a point-to-multipoint network over an existing 802.11a physical layer. Through extensive simulation analysis, the protocol is shown to offer better performance than the 802.11e enhanced distributed coordination function (EDCF). Moreover, this protocol is implemented in hardware using the MADWiFi driver to verify the real-world performance. Both results of the simulation study and hardware implementation are provided.

In enhanced distributed channel access (EDCA) protocol, small contention window (CW) sizes are used for frequent channel access by high-priority traffic (such as voice). But these small CW sizes, which may be suboptimal for a given network scenario, can introduce more packet collisions, and thereby, reduce overall throughput. This paper proposes enhanced collision avoidance (ECA) scheme for AC_VO access category queues present in EDCA protocol. The proposed ECA scheme alleviates intensive collisions between AC_VO queues to improve voice throughput under the same suboptimal yet necessary (small size) CW restrictions. The proposed ECA scheme is studied in detail using Markov chain numerical analysis and simulations carried out in NS-2 network simulator. The performance of ECA scheme is compared with original (legacy) EDCA protocol in both voice and multimedia scenarios. Also mixed scenarios containing legacy EDCA and ECA stations are presented to study their coexistence. Comparisons reveal that ECA scheme improves voice throughput performance without seriously degrading the throughput of other traffic types.

We consider the minimization of long-term average power consumption for data transmission from a mobile station to the base station, with the joint packet loss at both physical and MAC layers being constrained below the given value. The problem is formulated as an infinite horizon constrained Markov decision problem and solved by linear programming method. Simulations firmly verify the correctness of the proposed solution. Besides, we comment on the determination of power level applied to each adaptive transmission mode, and show that enlarging the range of performance levels offered by different transmission modes can further improve the optimal solution.

Wireless local area networks (WLANs) are in a period of great expansion and there is a strong need for them to support multimedia applications. The IEEE 802.11 standard defines the way WLANs work at the physical and medium access control (MAC) level. The recently-approved IEEE 802.11e amendment proposes a MAC protocol capable of offering quality of service (QoS) guarantees. This work has to do with EDCA (Enhanced Distributed Channel Access), which is one of the mechanisms introduced in IEEE 802.11e. EDCA promises to become a widely used protocol for terminals to gain access to services with QoS guarantees. It is then extremely important to assess its performance concerning the main QoS metrics, namely throughput, delay and losses under different traffic conditions, which is exactly the goal of this work. We obtain a set of equations by modeling the system using discrete-time Markov chains, and then validate these equations using simulations.

Telecommunication and grid computing applications demand high bandwidth data channels that offer guarantees with respect to service availability. Such applications include: remote surgery, remote experimentation, video on-demand, teleconferencing and bulk transfers. Furthermore, by forecasting traffic patterns internet service providers attempt to optimise network resources in order to lower operational costs during peak periods of bandwidth consumption. Advance reservation for wavelength division multiplexed networks can address some of these issues by reserving high volume communication channels (i.e. lightpaths) beforehand. The authors develop a mathematical model to solve the problem of scheduling lightpaths in advance. The optimal solution is presented as a mixed integer linear program with the assumption that all traffic is static and the network is centrally controlled. Furthermore, we have developed two novel meta-heuristics based on: 1) a greedy implementation (local search) and 2) simulated annealing. The meta-heuristics have shown to produce good approximate solutions in a reasonable amount of time.

The IEEE 802.16 WirelessMAN standard provides a comprehensive quality-of-service (QoS) control structure to enable flow isolation and service differentiation over the common wireless network interface. By specifying a particular set of service parameters, the media access control (MAC) mechanisms defined in the standard are able to offer predefined QoS provisioning on a per-connection basis. However, the design of efficient, flexible, and yet robust MAC scheduling algorithms for such QoS provisioning still remains an open topic. This paper proposes a new QoS control scheme for single-carrier point-to-multipoint mode wireless metropolitan area network (WirelessMAN) systems, which enables the predefined service parameters to control the service provided to each uplink and downlink connection. By MAC-PHY cross-layer resource allocation, the proposed scheme is robust against particular wireless link degradation. Detailed simulation experiments are presented to study the performance and to validate the effectiveness of the proposed QoS control scheme.

In this paper a single-input-single-output wireless data transmission system with adaptive modulation and coding over correlated fading channel is considered, where run-time power adjustment is not available. Higher layer data packets are enqueued into a finite size buffer space before being released into the time-varying wireless channel. Without fixing the physical layer error probability, the objective is to minimize the average joint packet loss rate due to both erroneous transmission and buffer overflow. Two optimization techniques are incorporated to achieve the best solution. The first is policy domain optimization that formulates the data rate adaptation design as classical Markov decision problem. The second is channel domain optimization that appropriately partitions the channel variation based on particular fading environment and carried traffic pattern. The derived policy domain analytical model can precisely map any policy design into various QoS performance metrics with finite buffer setup. We then propose a tractable suboptimization framework to produce different two-dimensional suboptimal solutions with scalable complexity-optimality tradeoff for practical implementations.

Abstract Distributed coordination function (DCF) is the basis protocol for IEEE 802.11 standard wireless local area networks. It is based on carrier sense multiple access with collision avoidance (CSMA/CA) mechanism. DCF uses backoff process to avoid collisions on the wireless channel. The main drawback with this process is that packets have to spend time in the backoff process which is an additional overhead in their transmission time. The channel is rendered idle when all the stations defer their transmissions due to their backoff process. Therefore, the channel utilization and the total throughput on the channel can be improved by reducing the average time spent by the packets in the backoff process. In this paper, we propose a new media access coordination function called proposed media access protocol (PMAP) that will improve the channel utilization for successful packet transmission and therefore, the total achievable throughput. In addition, we propose an analytical model for PMAP under saturated conditions. We use this model to analyze the performance of PMAP under saturated conditions. To substantiate the effectiveness of our model, we have verified the model by simulating PMAP in NS‐2. Simulation and analytical results show that under saturated conditions, PMAP shows profound improvement in the throughput performance compared to DCF. In addition, the throughput performance of PMAP under unsaturated conditions is presented. We have also presented the delay performance of PMAP and DCF through simulation in both saturated and unsaturated conditions. Simulation results show that the average delay experienced by the packets is less in PMAP compared to DCF. Further, the variance in the packet delay is same for both PMAP and DCF protocols under unsaturated conditions. From the performance results obtained for PMAP under both saturated and unsaturated conditions, it can be concluded that PMAP is superior in performance compared to DCF. Copyright © 2006 John Wiley & Sons, Ltd.

Advance reservation is a topic that is rarely discussed within the domain of wavelength division multiplexed (WDM) networks. However, for many emerging applications in the telecommunication and/or grid computing industries, a demand for a high bandwidth communication channel as well as a guarantee on resource availability certainly exists. Such applications include: remote surgery, remote experimentation with teleobservation capabilities, teleconferencing, and bulk transfers. In this paper, we present a new model for reserving advance lightpath requests in a centralized system. This model attempts to “migrate,” i.e., move previously reserved lightpaths to candidate wavelengths in order to lower the system’s blocking probability. We have tailored different lightpath migration algorithms to address two specific network objectives: 1) minimize the number of hops a new request traverses after migration, and 2) minimize the number of migrated lightpaths. In terms of blocking probability, the lightpath migration algorithms show a significant improvement over the original advance lightpath reservation model.

Self-management is an emerging computing paradigm to deal with the arising complexity in today's cooperative design environment. An essential element of self-management is a monitoring system. This paper proposed an agent-based generic monitoring system (AGeMoS) to integrate multiple probe solutions in order to support the monitoring of various systems in multiple aspects, including functionality, performance and resource utilization. An approach for specifying the monitoring tasks of architectural properties is also proposed. With the autonomy endowed by agent-oriented approach, AGeMoS is capable of automatically configuring the integrated probe solutions and processing the monitoring results according to the architectural properties' specifications at runtime. The proposed approach has been validated through a prototype implementation and experimentation to measure network performance.

Enhanced distributed channel access (EDCA) is the basis protocol in IEEE 802.11e protocol suite. It is used for providing differentiated quality of service (QoS) in IEEE 802.11e standard wireless local area networks (WLANs). One of the main drawbacks in EDCA is the small values used for minimum and maximum contention window (CW) sizes for the AC_VO access category, the queue designated for voice transmission. The AC_VO queues of the QSTAs are very aggressive in transmitting their packets on to the channel due to their small CW sizes. This leads to degradation in the actual voice throughput performance even when the network size is small. This work proposes a new modification to the collision avoidance mechanism used by AC_VO queues without seriously effecting the performance of other traffic categories such as video and data. Extensive simulations are carried out to verify the performance of the proposed enhancement to the original collision avoidance mechanism.

EPONs are designed to deliver services for multiple applications, such as VoIP, standard and high-definition video, interactive video and best effort traffic. We present the first framework that will enable for per-stream/flow QoS protection in EPON networks using a two stage admission control (AC) system. While the first stage enables the ONU to perform flow admission locally according to the bandwidth availability, the second stage allows for global admission control at the OLT. Appropriate bandwidth allocation algorithms are presented as well. An event driven simulation model is implemented to study the effectiveness of the proposed scheme in providing and protecting QoS.

In most designed adaptive modulation and coding (AMC) schemes the data link layer transmission rate is adapted only against physical layer channel variation. The highly fluctuating queuing process at the data link layer is not fully investigated. In this study, we first research on more advanced AMC mode selection algorithms and then propose a two-dimensional cross- layer optimization scheme. This scheme applies both policy domain optimization and channel domain improvement to achieve the best solution. With the same transmission power, the proposed two-dimensional optimization scheme is shown to be capable of offering lower minimum end-to-end packet loss probability than the existing one-dimensional optimization design.

This paper proposes a new distributed media access control (MAC) scheme to improve the network performance of Multiple-Input Multiple-Output (MIMO) wireless systems. In particular, this novel MAC scheme efficiently schedules two simultaneous transmissions between wireless stations (each equipped with three antennas) that are located in a single collision domain. Along with weighted nulling and intelligent packet fragmentation, this MAC scheme is capable of providing more efficient utilization of channel resources. The proposed MIMO-aware MAC scheme is compatible with the IEEE 802.11 standard. Detailed simulations are carried out to study the performance of the proposed scheme. The performance of the MIMO-aware MAC scheme is also compared with a recently proposed MIMO MAC scheme in the literature which is compatible with the IEEE 802.11 standard. Comparisons reveal that the proposed MIMO-aware MAC scheme achieves better throughput and delay performance under both saturated and unsaturated conditions.

Ethernet Passive Optical Networks (EPONs) are considered the most promising solutions for upgrading the cur- rent congested access networks to enable the delivery of broad- band integrated services. Although current EPON architectures are economically feasible, they are however bandwidth limited. In this paper, we discuss a simple upgrade architecture from EPON to WDM-PON. We present various Dynamic Wavelength and Bandwidth Allocation algorithms (DWBAs) that exploit both inter-channel and intra-channel statistical multiplexing in order to achieve good performance. We use extensive simulation experiments to validate our reasoning. I. INTRODUCTION

The IEEE 802.16 WirelessMAN standard provides a comprehensive quality-of-service (QoS) control structure to enable flow Isolation and service differentiation over the common wireless network Interface. By specifying a particular set of service parameters, the media access control (MAC) mechanisms defined in the standard are capable of offering service guarantees on the connection basis. However, the design of efficient, flexible and yet bandwidth-saving scheduling algorithms for such QoS provisioning still remains an open topic. This paper proposes a new distributed QoS control scheme that guarantees particular service parameter settings for both uplink and downlink connections. Detailed simulation experiments are presented to study the performance and to validate the effectiveness of the proposed algorithm.

Many modifications have been proposed in the literature to improve the performance of distributed coordination function (DCF) under congested environments. These improvements are achieved by modifying the contention window reset mechanism used in DCF protocol. However the main drawback with these modifications is the high backoff process overhead. Therefore we recently proposed a new DCF (N-DCF) protocol to reduce backoff process overhead. In this paper, we proposed a throughput model for N-DCF which is applicable for any arbitrary load conditions. The model was verified using NS- 2 simulations. We then compared the performance of N-DCF with the other recently proposed modifications to DCF in the literature. Comparisons have revealed that only N-DCF provides improvement in throughput performance under RTS/CTS access mechanism. Based on our observations, we further proposed two more media access control protocols to improve the performance of DCF protocol in both basic and RTS/CTS access mechanisms. Finally, we compared the performance of our proposed protocols with our original N-DCF protocol.

Ethernet passive optical networks (EPONs) are being designed to deliver multiple services and applications, such as voice communications (VoIP), standard and high-definition video (STV and HDTV), video conferencing (interactive video) and data traffic access network. However, most of the current work focuses on inter-ONU dynamic bandwidth allocation (DBA) algorithms. In this paper, we concentrate on the intra-ONU bandwidth allocation for different classes of services. We present a new intra-ONU scheduling scheme based on the Deficient Weighted Round Robin (DWRR) scheduling to achieve adaptive fairness among different classes of services. We validate our reasoning by measuring both the end-to-end delay of different traffic along with the jitter performance of high priority traffic using extensive simulation experiments.

Advances in next-generation SONET/SDH technologies have enabled many new service provisioning paradigms. A key addition is the inverse multiplexing feature which enables the splitting of connection demands across SONET/SDH domains. This paper presents a novel tiered survivability scheme that leverages this feature to support multiple levels of service survivability and achieve higher load carrying capability and service resiliency. Detailed simulation performance analysis results are also presented along with conclusions and directions for future work.

We propose a novel ring-based local access Passive Optical Network (PON) architecture that addresses some of the limitations of current tree-based PON. Specifically, we propose a simple ring-based Ethernet PON (EPON) architecture with a fully distributed control plane among the ONUs that supports a truly shared LAN capability among end users as well as upstream access to the central office. This architecture is well suited for an autonomous access environment such as a university campus or a private corporation where several buildings are closely dispersed within a 0.5-1 km diameter area. Unlike a typical ring-based PON topology in which Optical Line Terminal (OLT) and Optical Network Units (ONUs) are interconnected via a long fiber ring, under the proposed architecture, ONUs are interconnected via a short distribution fiber ring in the local loop but share the standard trunk feeder fiber for long reach connectivity to the Central Office (CO).

This paper investigates the problem of survivable traffic grooming (STG) in shared mesh optical networks and proposes different frameworks for improving the survivability of low speed demands against multiple near simultaneous failures. Capacity reprovisioning has recently been considered for improving the overall network restorability in the event of multiple failures by allocating protection resources after a failure to unprotected and vulnerable connections. In this paper we propose two different reprovisioning schemes (lightpath level reprovisioning, LLR, and connection level reprovisioning, CLR). Each of these schemes is suitable for a different survivable grooming policy. While LLR provides collective reprovisioning of connections at the lightpath level, CLR reprovisions spare bandwidth for lower speed connections instead. We study the performance of these schemes under two grooming policies (PAL and PAC), and we show that while CLR reprovisions substantially more connections than LLR, CLR yields a much better network robustness to near simultaneous failures due to its superior flexibility in using network resources.

This research aims to provide a network service for grid applications requiring high bandwidth with minimal to no delays while demanding strict scheduling constraints. More specifically, we are looking for efficient ways of scheduling advanced reservation communication requests at the network layer.

Ethernet Passive Optical Network (EPONs) are currently being designed to deliver multiple services and applications, such as voice communications (VoIP), standard and highdefinition video (STV and HDTV), video conferencing (interactive video) and data traffic access network. The emergence of new bandwidth intensive applications and the continuous demand for more bandwidth in a bandwidth limited EPON require an upgrade from current TDM to WDM-based PON which is currently of huge interest in both the academia and industry. In this paper we propose three new Dynamic Bandwidth Allocation (DBAs) schemes for QoS support in WDM-based PON networks. These schemes can comply with any ONU architecture (tunable lasers or multiple fixed transceivers). However, they vary in their performances (i.e. different jitter, delay, bandwidth utilization etc.). We study the performance of these DBAs using extensive simulation experiments.

Ethernet passive optical networks (EPONs) have emerged as a promising candidate for next-generation broadband access networks. As this technology evolves, the development of efficient dynamic bandwidth allocation (DBA) algorithms has become a key concern. This paper presents the principle and implementation issues of a new DBA scheme. Through detailed analyses some general investigations on the fairness issue and statistical bandwidth multiplexing (SBM) mechanisms existing in EPON are developed. This proposed scheme consistently maintains a robust fairness mechanism in the DBA operation. With the better maintained fairness mechanism, efficient SBM persists at the global level, whereas multiple network performance matrices are improved. Detailed simulation experiments are presented to study the performance and to validate the effectiveness of the proposed algorithm.

Ethernet passive optical networks (EPONs) have emerged as one of the most promising access network technologies. Propelled by rapid price declines in fiber optics and Ethernet components, these architectures combine the latest in optical and electronic advances and are poised to become the dominant means of delivering gigabit broadband connectivity to homes over a unified single platform. As this technology matures, related quality of service (QoS) issues are becoming a key concern. This paper proposes a novel dynamic scheduling algorithm, termed hybrid granting protocol (HGP), to support different QoS in EPON. Specifically, the proposed dynamic scheduling algorithm minimizes packet delay and jitter for delay and delay-variation sensitive traffic (e.g., voice transmissions) by allocating bandwidth in a grant-before-report (GBR) fashion. This considerably improves their performance without degrading QoS guarantees for other service types. Detailed simulation experiments are presented to validate the effectiveness of the proposed algorithm.

Ethernet passive optical networks (EPONs) have emerged as the one of the most promising candidates for next-generation access networks. These new architectures couple low-cost optics with advanced edge electronics to offer vastly improved scalability over competing digital subscriber line and cable modem offerings. This paper proposes several novel architectural enhancements for EPON, which will help increase the viability of optical access over a broader range of subscriber access scenarios. Specifically, this paper proposes a two-stage EPON architecture that allows more end-users to share an optical line terminal link, and enables longer access reach/distances (beyond the usual 25 km distance). In addition, a new dynamic bandwidth allocation (DBA) algorithm is proposed to effectively allocate bandwidths between end users. This DBA algorithm can support differentiated services in a network with heterogeneous traffic. We conduct detailed simulation experiments to study the performance and validate the effectiveness of the proposed architecture and algorithms.

One of critical issues of today's optical network planning is how to implement a dynamically reconfigurable optical transport layer coupled with suitable control and management protocols. This paper presents a novel distributed connection management protocol with reconfiguration capabilities for data-centric optical networks. A novel concept of a "token-based" distributed algorithm to reroute existing connections to optimal paths after a failure recovery is introduced. The performance of the proposed protocol is evaluated and compared via simulation in a distributed control environment.

Ethernet passive optical networks (EPONs) have emerged as a well accepted candidate for next-generation access networks. Propelled by rapid price declines in fiber optics and Ethernet components, these new EPON architectures combine the latest in optical and electronic advances, and are poised to become the dominant means of delivering bundled services over a single platform. This paper proposes a novel EPON architecture capable of delivering bandwidth-intensive voice, data video services at distances beyond 25 km in the subscriber access network. Specifically, this paper proposes a two-stage EPON architecture that allows more end-users to share an OLT link, and enables longer access reach/distances (beyond the usual 25 km distance). In addition, a new dynamic bandwidth allocation (DBA) algorithm is proposed to effectively and fairly allocate bandwidths between end users. This DBA algorithm can support differentiated services in a network with heterogeneous traffic. We conduct detailed simulation experiments to study the performance and validate the effectiveness of the proposed architecture and algorithms.

Optical dense wavelength division multiplexing (DWDM) has yielded unprecedented levels of bandwidth scalability. In order to exploit these gains, new converged multiservice transport setups have been evolved, most notably under the multiprotocol label switching (MPLS) and generalized MPLS (GMPLS) frameworks. These paradigms offer very efficient data-optical integration and enable a host of new service capabilities. As operators deploy these new technologies, the provisioning of "subwavelength" demands over wavelengths has become a crucial requirement, i.e., traffic engineering/grooming. This work addresses data-optical grooming in converged GMPLS networks. Here, novel integrated constraint-based routing algorithms are developed to provision subwavelength demands at both packet-switching and lightpath routing levels. Simulations indicate notable performance gains and resource efficiencies with the proposed schemes.

Efficient path selection combined with fast restoration algorithms is a key requirement for designing shared restorable mesh networks. In this paper we first discuss a distributed path selection algorithm for efficient routing of restorable connections in optical networks. This approach relies on the knowledge of global information, maintained at each node, to determine link sharability and compute optimal shared paths; we compare its performance to another protocol [C. Assi et al., 2002] that only requires the knowledge of local resource usage. Second, we study the network's ability to recover from single element failures in a shared mesh network and we propose a new restoration algorithm for rapid recovery upon a failure. The significant contribution of this algorithm is that the network restoration time is independent of the protection path length (i.e., the effect of propagation delay is eliminated) as well as the accumulation of the switch configuration times. We evaluate the performance of these protocols through simulation experiments.

This paper presents a novel hybrid distributed fault-management protocol for combating single-fiber failures in mesh-based DWDM optical networks. The proposed hybrid approach combines Link State Protocol to disseminate and update information only about the physical connectivity of the network and a distributed local information-based signaling algorithm for connection management. The purpose of using a hybrid approach is two advantages: (1) reducing the signaling overhead associated with the global information-based link state protocol by using a distributed approach where only local information is maintained at each node; and (2) eases the implementation of the routing protocol where physical constraints, such as link/node diversity, are imposed. The performance of the proposed hybrid approach is evaluated via comparing the dedicated-path protection and the shared-path protection schemes in terms of blocking probability, restoration time under failure assumption, and data loss incurred during the recovery phase.

This paper investigates and compares the performance of two generalized multiprotocol label switching (GMPLS)-based distributed control and management protocols for dynamic lightpath provisioning in future IP networks. The first protocol is a global information-based link state approach that consists of both an integrated RWA (routing and wavelength assignment) algorithm and a signaling algorithm. Two triggering mechanisms for LSA (link state advertisement) update procedures are considered; one is a periodically-based update and the other is a threshold-based update. The second protocol is a local-information based fixed alternate link routing approach where the signaling protocol is closely integrated with the RWA protocols.

The purpose of this work is to show the benefits gained by dynamically provisioning low-rate traffic streams at the IP/MPLS layer in future IP-centric WDM-based optical networks. First, several low-rate data flows are statistically multiplexed (groomed) onto one wavelength at the IP/MPLS router. Then, conventional dynamic lightpath provisioning schemes at the physical WDM layer, where the bandwidth of a connection request is assumed to be a full wavelength capacity, are extended to allow the provisioning of "sub-lambda" connection flow requests at the IP/MPLS layer. In this work, provisioning a connection request implies that a flow of data is successfully routed if both an active path and another alternate link and node-disjoint backup path are setup at the same time.

This paper proposes two new distributed signaling protocols for fast automatic setup and tear-down of paths across the emerging interconnection models for IP-over-optical-networks. The first scheme is probe flooding-based routing (PFBR) algorithm with backward reservation while the second scheme is based on an adaptive routing algorithm called multi-path routing (MPR) where k paths are probed simultaneously. Our objective in developing these protocols is twofold: first, to simplify the specific signaling protocols or algorithms in the components of MPLS control plane; and second, to adapt the performance optimization algorithm to the requirements of different user applications by having the flexibility to vary the relative weight assigned to each of three performance metrics [call acceptance rate (CAR), call set-up time (CST), and routing distance (RD)].