Accurately predicting network flow volume using early packet-level features is critical for real-time applications such as resource allocation, bottleneck prediction, anomaly detection and more. In this paper, we investigate the suitability of Transformer-based architectures for the flow volume regression task. Specifically, we fine-tune two foundation models, NetFound and YaTC, originally pre-trained to learn traffic representations for classification tasks, and adapt them for flow volume regression. Additionally, we train a lightweight Transformer model, and compare the predictive performance and complexity of all Transformer-based models to a simple Multi-Layer Perceptron (MLP) model. Fine-tuning and training are conducted on two public datasets: MAWI and CIC-IDS-2017. Results demonstrate that Transformer-based models significantly outperform the MLP model. The lightweight Transformer achieves the best$\mathrm{R}^{\mathrm{2}}$score of 0.963 on CIC-IDS-2017, while NetFound achieves the best performance on the real-world traffic dataset MAWI with an$\mathrm{R}^{\mathrm{2}}$of 0.812. These findings highlight the effectiveness of Transformers in capturing complex relationships in early packet-level features of network traffic for flow volume regression task.

This paper presents the LightChain-RAN-RF, a lightweight blockchain-based architecture designed to enhance the security, privacy, and efficiency of Radio Frequency Identification (RFID) systems operating in Dense Reader Environments (DRE). By combining CSMA-based anti-collision protocols with mutual authentication, encrypted communication, and InterPlanetary File System (IPFS)-backed blockchain storage, the proposed method addresses key challenges such as reader collisions, energy consumption, and vulnerability to attacks like Man-In-The-Middle (MITM) and Sybil. RFID readers act as light blockchain nodes, ensuring secure, scalable interaction across distributed networks. The architecture is fully compatible with Open Radio Access Network (O-RAN) frameworks, allowing RFID readers to function as trusted edge devices in virtualized, Artificial intelligence (AI)-driven mobile infrastructures. Simulation results confirm significant improvements in throughput (almost 60%) and decreases in energy efficiency (almost$\mathbf{1. 4 ~ J}$), demonstrating the system's suitability for modern industrial IoT and mobile network applications.

Handover management in 5G/6G networks presents significant challenges mainly due to ultradense deployments, high mobility and diversity of scenarios. This work presents a digital twin-based Deep Q-Learning (DQL) strategy to optimize handover processes and resource management in these networks. It is proposed to consider digital twins to support the implementation of real-time network conditions where DQL agents are trained to improve quality of service (QoS) parameters related to throughput. The suggested approach, deployed with the ns-3 mmWave module and ns3-ai framework, shows relevant benefits, including reduced handovers. Moreover, the consideration of digital twins allows adaptive learning and scalability, allowing networks to respond more effectively to dynamic user behavior and environmental modifications. These results underline the potential of combining DQL and digital twins as an efficient solution for smart handover management, and thus, for sustainable and efficient communication in 5G/6G networks.

Open and disaggregated O-RAN architectures foster flexibility and vendor diversity in 5G/6G networks but simultaneously expose novel attack surfaces exploitable by sophisticated adversaries. Traditional rule-based or signature-driven detection mechanisms struggle against multi-stage, polymorphic threats in such dynamic environments. This paper proposes TwinGuard, a proactive defence framework that integrates a real-time Digital Twin of a live 5G O-RAN deployment with reinforcement learning (RL) for intelligent threat anticipation and mitigation. Our system mirrors critical KPIs, including throughput, PRB utilisation, SINR, and latency, into the Digital Twin, where an RL agent trained via Proximal Policy Optimisation (PPO) learns optimal mitigation strategies. In our prototype, the RL agent identifies and blocks malicious handover attacks within 100 ms, maintaining service continuity and outperforming a DQN-based baseline. In a second prototype deployed on a containerised OpenAirInterface (OAI) 5G Core and FlexRIC-controlled RAN testbed, our xApp swiftly mitigates an E2 subscription flooding attack in under 100 ms, reducing abnormal PRB utilisation from 95% to nominal levels. TwinGuard demonstrates the feasibility and effectiveness of closed-loop, AI-driven cybersecurity in O-RAN systems, offering a blueprint for future trustworthy and resilient 6G networks.

The variety and volume of real-time video streaming services make network resources like power, frequency, and time critical to the user experience. Semantic communication, by transmitting only meaningful data, significantly reduces the load on the network, leading to faster transmission times and reduced latency-both crucial for enhancing user experience. In this paper, we develop a context-aware resource allocation framework for a semantic-aware regenerative AI-based Unmanned Aerial Vehicle (UAV) setup that accommodates both conventional and semantic communication users. Specifically, we define a semantic feature pooling mechanism, upon which a novel Quality of Experience (QoE) model is proposed. For dynamic network environments, we formulate a long-term resource allocation problem by maximizing the expected rewards. Each UAV is modeled as a learning agent, with each resource allocation solution corresponding to an action taken by the UAVs. We then develop a Multi-Agent Reinforcement Learning (MARL) framework in which each agent discovers its optimal strategy based on local observations. More specifically, we propose an agent-independent method where all agents execute a decision algorithm independently while sharing a common structure based on Q-learning. The proposed framework also facilitates intelligent traffic steering by dynamically adjusting resource distribution based on real-time context. Finally, simulation results demonstrate the effectiveness and superiority of the proposed method in terms of overall QoE.

Ensuring robust and uniform network coverage is crucial for mobile network operators, particularly at the cell edges, where interference remains a persistent challenge. Traditional solutions such as network densification are complex, costly, and prone to increased interference. This paper presents the performance of recently proposed Cell-Sweeping base stations deployment in a typical 4G LTE network with Single User-Multiple Input Multiple Output (SU-MIMO) operation, use of higher-order modulation schemes, i.e. 256-Quadrature Amplitude Modulation (256-QAM), as well as using 3D antenna radiation patterns. By dynamically sweeping the antenna radiation patterns, cell-sweeping aims to significantly enhance the cell-edge performance while at the same time also harmonises the distribution of throughput in the whole cell. System-level simulations conducted using the 3rd Generation Partnership Project (3GPP) configurations reveal significant performance gain of cell-sweeping of up to 147% improvement in cell-edge throughput observed under open-loop spatial multiplexing, i.e. Transmission Mode 3 (TM3) in 3GPP LTE compared to conventional (i.e. non cell-sweeping) cellular network deployment. The results demonstrate that integrating cell-sweeping with advanced modulation and MIMO configurations is feasible and significantly improves Signal-to-Interference-plus-Noise Ratio (SINR), throughput, and Channel Quality Indicators (CQI) distribution, particularly in dense urban environments. These findings highlight the potential of cell-sweeping as an effective and simpler deployment strategy for future radio access networks.

Mobile edge computing (MEC) enables efficient computation offloading for mission-critical applications in resource-constrained vehicles, while reconfigurable intelligent surface (RIS) help address connectivity challenges for vehicles in urban environments with severe signal blockages. Non-orthogonal multiple access (NOMA) is an appealing technique that improves spectral efficiency while mitigating multi-user interference. This work proposes the RIS-assisted NOMA-MEC in vehicular networks, considering dynamic challenges such as heterogeneous vehicle processing capability, time-varying channel from high-mobility and dynamic task workloads. We formulate a system latency minimization problem by jointly optimizing the task offloading ratio, edge server resource allocation and RIS passive beamforming, while satisfying the task deadline and Signal to Interference plus Noise Ratio (SINR) requirements. To overcome the limitations of conventional optimization methods in such dynamic environments, we propose a soft actor critic (SAC)-based deep reinforcement learning (DRL) framework, which dynamically adapts to real-time channel state information (CSI), task workload and vehicle processing capability of all vehicles. Simulation results demonstrate that our approach achieves lower latency performance compared with the Deep Deterministic Policy Gradient (DDPG) baselines. Moreover, the proposed SAC method exhibits robustness and adaptivity to various levels of uncertainty in the CSI.

The Near-Real-Time (Near-RT) Radio Access Network (RAN) Intelligent Controller (RIC) in the Open RAN (O-RAN) architecture provides flexibility and programmability, enabling dynamic network management through Machine Learning based applications known as xApps. However, scalability and strict latency requirements hinder the support of numerous xApps. Existing orchestration solutions struggle to efficiently manage large-scale xApp deployments while maintaining latency below one second. Connecting multiple next-generation NodeBs (gNBs) to a single Near-RT RIC risks performance bottlenecks and single points of failure in the O-RAN architecture. To address these challenges, we propose FlexScale, a scalable approach to enhance O-RAN system capacity for extensive xApp deployments. It dynamically scales Near-RT RIC instances connected to gNB modules (E2 Nodes) using Kubernetes-based Horizontal and Vertical Pod Autoscaling (HPA, VPA) and native load balancing. FlexScale optimizes CPU utilization while meeting latency requirements. Simulations show that FlexScale efficiently scales xApps across multiple Near-RT RIC pods, addressing system limitations. Under high E2AP traffic, HPA and VPA autoscaling reduce latency by up to 96% compared to a single Near-RT RIC deployment. Additionally, CPU usage is reduced by approximately 70%, ensuring balanced resource utilization during traffic fluctuations. FlexScale demonstrates its capability to support large-scale xApp deployments while maintaining performance and efficiency.

Random access channel (RACH) procedure is critical to support a multitude of devices transmitting small data payloads while ensuring low-latency access. In 3GPP Release 16, a two-step RACH is proposed to alleviate signaling overhead and access latency. While benefits are noticeable, collisions still persist. In this article, we propose a novel nonorthogonal multiple access (NOMA)-enhanced two-step RACH scheme (NOMA-RACH) that jointly leverages the benefits of access class barring (ACB), two-step RACH, and NOMA random access (NOMA-RA) to further enhance the performance. We conduct a holistic study that accounts for entire access latency. The scheme optimizes NOMA access probabilities, utilizes an adjustable barring mechanism for delay-sensitive devices, and identifies the optimal barring rate for low latency. We develop a Markov chain model to analyze NOMA access and derive the optimal access probabilities and throughput of NOMA blocks. To cope with the practical scenarios with constantly changing user equipment (UE) traffic, we propose a deep contextual multiarmed bandit (DCMAB) model that optimizes the NOMA throughput and dynamically adjusts the barring rate based on the observable channel feedback. Our simulation results demonstrate that the DCMAB model performs better than benchmark schemes and remains close to the optimal latency confirming the effectiveness of our proposed scheme under changing UE traffic.

Given the delay-critical nature of AI-driven industrial automation applications, industrial cyber-physical systems are evolving from centralized cloud automation to decentralized cloud-fog automation to reduce model inference delay. However, traditional centralized deep federated learning is not wellsuited for this evolution, primarily due to scalability and delay issues caused by centralized parameter synchronization. Thus, we introduce a decentralized deep federated learning (DDFL) architecture. While DDFL resolves scalability and delay concerns, decentralized parameter synchronization amplifies the delay imbalance impact caused by uneven computing and network loads. Additionally, traditional single-task load balancing approaches with fixed load balancing weights face challenges posed by diverse delay requirements across different model training tasks. To overcome these challenges, we formulate a hybrid multi-task Markov decision process with the objective of minimizing flexibly weighted computing and network load. We further propose a hybrid multi-task deep reinforcement learning (MTDRL) scheme based on the importance-weighted actor-learner architecture, which trains a hybrid-MTDRL decision model to select fog servers and paths with balanced loads suited to diverse delay requirements. Realistic trace-based simulation and testbed evaluation results demonstrate that hybrid-MTDRL outperforms benchmarks in load balancing and reducing training delay.

In a previous paper, we have shown that a recurrent neural network (RNN) can be used to detect cellular network radio signal degradations accurately. We unexpectedly found, though, that accuracy gains diminished as we added layers to the RNN. To investigate this, in this article, we build a parallel model to illuminate and understand the internal operation of neural networks (NNs), such as the RNN, which store their internal state to process sequential inputs. This model is widely applicable in that it can be used with any input domain where the inputs can be represented by a Gaussian mixture. By looking at RNN processing from a probability density function (pdf) perspective, we are able to show how each layer of the RNN transforms the input distributions to increase detection accuracy. At the same time we also discover a side effect acting to limit the improvement in accuracy. To demonstrate the fidelity of the model, we validate it against each stage of RNN processing and output predictions. As a result, we have been able to explain the reasons for RNN performance limits with useful insights for future designs for RNNs and similar types of NN.

Service Function Chaining (SFC) is widely deployed by telecom operators and cloud service providers, offering traffic QoS guarantees and other additional functions for various applications. The network state at the time of SFC deployment can differ significantly from the runtime conditions, leading to excessive resource allocation and consequent energy waste. The existing SFC reconfiguration methods face the challenge of meeting the latency requirements of delay-sensitive applications while achieving significant energy savings. This paper proposes FR-SFCO, a flow rate-aware SFC offloading framework on programmable data planes for delay-sensitive flows. Specifically, we designed a TCAM-friendly table matching method for FR-SFCO to reduce the flow entries needed for SFC offloading in programmable switches and support larger numbers of offloaded SFC. Then, we proposed a dual-threshold-based offloading trigger mechanism that, according to the real-time traffic arrival rate, can fast offload SFC flows before they default to servers. Building on this, we propose DQN-AOTA, an adaptive offloading thresholds adjustment algorithm based on Deep Q-Learning, which can wisely change the offloading thresholds by interacting with a dynamic network traffic environment to minimize the packet loss and long-term energy consumption. Finally, we build a testbed using BMv2 software switches and Docker containers for extensive evaluation. The experimental results demonstrate the effectiveness of our solution which not only meets the latency constraints for delay-sensitive SFC flows but also reduces energy expenditure by at least 14.6%.

Reconfigurable Intelligent Surfaces (RIS) and Un-manned Aerial Vehicles (UAVs) have emerged as promising tech-nologies for the 6th-Generation (6G) network. The integration of RIS with the UAV (RIS-UAV) can enhance ground communication by providing a 360°panoramic reflection. Existing RIS-UAV mainly considers passive elements which suffer from double path loss problems. This motivates the use of the hybrid RIS-UAV equipped with both active and passive RIS elements. This paper investigates the energy efficiency maximisation problem for the hybrid RIS-UAV by optimising the placement of the UAV, subject to the UAV's permitted altitude range. The non-convex optimisation problem is addressed using Particle Swarm Optimisation (PSO) tool and distributed learning algorithm. The numerical results show that the proposed distributed learning algorithm is preferred when optimising the energy efficiency of the hybrid RIS-UAV system. In addition, hybrid RIS-UAV outperforms the fully passive RIS-UAV and the active amplify-and-forward (AF) relay in terms of energy efficiency of the system.

The rapid adoption of Open Radio Access Network (Open-RAN) architectures has brought unprecedented innovation opportunities in modern telecommunications networks. However, this evolution also introduces novel security challenges, particularly in demanding scenarios where swift decision-making is critical. In this paper, we conduct an in-depth investigation into model poisoning attacks in ensemble learning, highlighting their implications for network security, and provide a detailed demonstration of our proposed Open-RAN Intrusion Detection System (IDS), which is seamlessly incorporated into the security module of the near Real-Time RAN Intelligent Controller (nearRT-RIC). The strategic placement of the IDS within the nearRT-RIC ensures its operation within the demanding 10 ms to 1 second control loop range, enabling nearRT intrusion detection capabilities. Through rigorous evaluation and experimentation, our solution showcases promising results in enhancing network security without compromising performance.

Volumetric video streaming, an innovative media application, facilitates the real-time and immersive teleportation of individuals or objects into the virtual environment of the audience. Unlike conventional video streaming applications, volumetric content is particularly vulnerable to network fluctuations, which can lead to performance degradation such as reduced FPS, delayed frame delivery. In this research, we introduce an eBPF-based network function that duplicates packets along the pathways between network nodes, ensuring timely packet delivery amid network instability. Furthermore, we propose a path elimination algorithm to discard paths incapable of delivering frames within the target latency. Our implementation and evaluation validate the rapid and robust performance achieved across various resolution levels.

Millimeter wave (mmWave) has been recognized as one of key technologies for 5G and beyond networks due to its potential to enhance channel bandwidth and network capacity. The use of mmWave for various applications including vehicular communications has been extensively discussed. However, applying mmWave to vehicular communications faces challenges of high mobility nodes and narrow coverage along the mmWave beams. Due to high mobility in dense networks, overlapping beams can cause strong interference which leads to performance degradation. As a remedy, beam switching capability in mmWave can be utilized. Then, frequent beam switching and cell change become inevitable to manage interference, which increase computational and signalling complexity. In order to deal with the complexity in interference control, we develop a new strategy called Multi-Agent Context Learning (MACOL), which utilizes Contextual Bandit to manage interference while allocating mmWave beams to serve vehicles in the network. Our approach demonstrates that by leveraging knowledge of neighbouring beam status, the machine learning agent can identify and avoid potential interfering transmissions to other ongoing transmissions. Furthermore, we show that even under heavy traffic loads, our proposed MACOL strategy is able to maintain low interference levels at around 10%.

Open Radio Access Networks (O-RANs) have revolutionized the telecom ecosystem by bringing intelligence into disaggregated RAN and implementing functionalities as Virtual Network Functions (VNF) through open interfaces. However, dynamic traffic conditions in real-life O-RAN environments may require necessary VNF reconfigurations during run-time, which introduce additional overhead costs and traffic instability. To address this challenge, we propose a multi-objective optimization problem that minimizes VNF computational costs and overhead of periodical reconfigurations simultaneously. Our solution uses constrained combinatorial optimization with deep reinforcement learning, where an agent minimizes a penalized cost function calculated by the proposed optimization problem. The evaluation of our proposed solution demonstrates significant enhancements, achieving up to 76% reduction in VNF reconfiguration overhead, with only a slight increase of up to 23% in computational costs. In addition, when compared to the most robust O-RAN system that doesn’t require VNF reconfigurations, which is Centralized RAN (C-RAN), our solution offers up to 76% savings in bandwidth while showing up to 27% overprovisioning of CPU.

An important issue for cellular network operators is how to maintain radio coverage so that any issues can be addressed before they impact user services. This is particularly important in dense small cell network deployment scenarios such as vehicular networks. Antenna electrical tilt is a key factor in this, as unintended deviations from the planned value can adversely affect coverage and service reliability. We propose a novel method to detect antenna tilt anomalies using existing data sources without the need for additional hardware to be deployed in the radio access network. Our approach goes beyond previous techniques by using federated unsupervised learning based on polar coordinates, together with a geometrical transformation to normalise data across multiple sites. By using this approach to combine scarce training data from multiple cells, we can achieve detection accuracy in excess of 95% in a way that minimises training data size as well as computing power and memory usage.

In-network caching is frequently used to reduce unnecessary network traffic and lower server workload by allowing content to be accessed from the caching nodes in both wired and wireless networks. The drastic increasing mobile data traffic demands have conceived cooperative content sharing between nodes for addressing the storage limitation of a single node. In this paper, we explore the problem of minimizing transmission cost among cooperative nodes by jointly optimizing caching and routing in a hybrid network with vital support of service differentiation. We show that the optimal routing policy is a route-to-least cost-cache (RLC) policy for fixed caching policy. We formulate the cooperative caching problem as a Markov decision process (MDP) with the goal of maximizing the long-term average caching reward, which is NP-hard even when assuming users demands are perfectly known. Our study then proposes C-MAAC, a collaborative multi-agent deep reinforcement learning (MADRL) algorithm employing actor-critic learning model. C-MAAC has a key characteristic of centralized training and decentralized execution, with which challenge from the unstable training process caused by simultaneous decision of all agents can be addressed. After completing the offline training process, each node independently makes caching decision online during the execution process. Simulation results demonstrate the effectiveness of our proposed algorithms under dynamic environment where user request traffic change rapidly.

Due to its low latency and high data rates support, mmWave communication has been an important player for vehicular communication. However, this carries some disadvantages such as lower transmission distances and inability to transmit through obstacles. This work presents a Contextual Multi-Armed Bandit Algorithm based beam selection to improve connection stability in next generation communications for vehicular networks. The algorithm, through machine learning (ML), learns about the mobility contexts of the vehicles (location and route) and helps the base station make decisions on which of its beam sectors will provide connection to a vehicle. In addition, the proposed algorithm also smartly extends, via relay vehicles, beam coverage to outage vehicles which are either in NLOS condition due to blockages or not served any available beam. Through a set of experiments on the city map, the effectiveness of the algorithm is demonstrated, and the best possible solution is presented.

In this paper, a new approach has been proposed and investigated with the help of variational auto-encoder (VAE) as a probabilistic model to reconstruct the transmitted symbol without sending the data bits out of the transmitter. The novelty of the proposed End-to-end (E2E) wireless system is in representing the symbol as a image hot vector (IHV) that contains the features of the shape such as spikes, closed squared frame, pixels index location and pixels grey-scale colours. The previously mentioned features are inferred by latent random variables (LRVs). The LRVs are used for fronthaul and backhaul data representation. The LRVs parameters have only been transmitted through the physical wireless channel instead of the original bits as in the classical modulations or the hot vectors in the Auto-encoders (AE) E2E systems. The new proposed VAE architecture achieved the reconstruction of the symbol from the received LRV. The results show that the VAE with a simple classifier can provide a better symbol error rate (SER) than both AE baseline and classical Hamming code with hard decision decoding, especially at high$E_{b}/N_{o}$.

In Internet of Vehicles (IoV), authentication technology provides a basic security means to achieve trusted communication between legitimate vehicles. Revocation checking for vehicle certificates is an indispensable procedure in the process of authentication to protect vehicular networks from attacks by non-legitimate vehicles. However, revocation checking introduces procedures that require additional time to process which challenges latency-sensitive applications in vehicular networks. This challenge grows more evidently if taking into consideration the factor of privacy preservation. In this paper, we propose to offload partial revocation tasks to network edges to lighten the revocation process in vehicles. Particularly, we design a dual-certificates model for the revocation offloading process and employ blockchain to achieve decentralized Certificate Revocation List (CRL) management. Finally, we build a prototype of our proposed solution based on Hyperledger Fabric using permission blockchain, and compare it with Proof-of-Work scheme in terms of blockchain synchronization latency performance.

In recent years, there has been a notable surge in the Internet of Things (IoT) applications. Increasingly, IoT devices are being attacked. Network intrusion detection is a tool to detect any presence of malicious activities in a network. Machine learning (ML) techniques are increasingly used for classifying network traffic. However, results from state-of-the-art studies have shown that training ML classifiers with imbalanced datasets affect their classification performance, resulting in net-work categories with fewer training instances getting classified wrongly. This study presents a Stack ensemble ML classifier for network intrusion detection in an IoT network using the Bot-IoT dataset for the classifier evaluation. According to preliminary results, the classifier showed lower metric scores for minority network categories. We applied Synthetic Minority Oversampling Technique (SMOTE) to address the class imbalance. Follow-up experiment results for the SMOTE-Stack outclassed Stack and other state-of-the-art classifiers.

Intrusion detection systems (IDS) protect networks by continuously monitoring data flow and taking immediate action when anomalies are detected. However, due to redundancy and significant network data correlation, classical IDS have shortcomings such as poor detection rates and high computational complexity. This paper proposes a novel feature selection and extraction technique (FI-PCA). Feature Importance (FI) and Principal Component Analysis (PCA) are used to preprocess the network dataset (PCA). FI identifies the most important features in the data, while PCA is used to reduce dimensionality and denoise the data. In order to detect anomalies, we employ three single classifiers: Decision Tree (DT), Naive Bayes and Logistic Regression. Preliminary results, however, show that these classifiers have achieved average classification metric scores. On this basis, we use the Stack Ensemble Learning Classifier (ELC) method of combining single classifiers to improve the classifier's performance further. Experimental results on varied feature dimensions of an IoT (Bot-IoT) dataset indicate that our proposed technique combined with the Stack ELC can maintain the same level of classification performance for reduced dataset features. A comparison of our result with state-of-the-art classifiers’ classification performance shows that our classifier is superior in terms of accuracy and detection rate. At the same time, a remarkable decrease is recorded for both training and test time.

At 5G and beyond networks, accurate localization services and nanosecond time synchronization are crucial to enabling mission-critical wireless communications technologies and techniques such as autonomous vehicles and distributed multiple-input and multiple-output (MIMO) antenna systems. This paper investigates how to improve wireless time synchronization by studying time correction based on the Real-Time Kinematics (RTK) positioning algorithm. Using the multiple Global Navigation Satellite System (GNSS) receiver references and the proposed binary GNSS satellite formation to reduce the effect of the ionosphere and troposphere delays and recede the measurement phase-range and pseudorange errors. As a result, it improves user equipment’s (UE) localization and measures the time difference between the Base Station (BS) and the UE local clocks. The results show that the positioning accuracy has been increased, and a millimetre accuracy has been achieved while attaining the sub-nanosecond time error (TE) between the UE’s and BS local clocks.

Traditional routing technologies based on a single IP address domain faces the challenge to meet the increasing demand for network services and security. Packet classification is a technique to differentiate multi-domain network traffic in a fine-grained manner using packet header fields. Packet classification requires to operate efficiently to avoid it becoming a bottleneck in the packet routing process. Tuple space search (TSS) used in SDN supports fast rule updates but low-speed packet classification. In this paper, we propose a learned Bloom filter (LBF)-based packet classification algorithm that combines LBF and TSS to promote classification speed by avoiding invalid hash table accesses. Specifically, LBF consists of multiple RNN models and one support Bloom filter (SBF), in which the learned models are trained with the positive and negative sets, and used as a pre-filter to identify the two sets. For the filter outcomes with a negative result from learned models, SBF is constructed to perform the second filtration. To ensure efficiency of RNN and SBF, we carefully select key features to be used in RNN and SBF, which can also maintain efficient search in the final stage of hash checking. Our experimental results show that the proposed algorithm saves more memory space than Tuple space pruning (TSP) given the same false positive rate. The proposed algorithm is competitive in terms of the number of memory accesses, while achieving almost one order of magnitude improvement on pre-processing time over NeuroCuts which is an advanced decision tree classifier.

The development of emerging network technologies represented by Software-Defined Networking (SDN) has made traditional routing technologies that are based on a single IP address domain unable to meet the increasing demand for network services and security. Packet classification is a technique to differentiate multi-domain network traffic in a fine-grained manner using packet header fields. Packet classification requires to operate efficiently to avoid it becoming a bottleneck in the packet routing process. In this paper, we propose a learned Bloom filter (LBF)-based packet classification algorithm that combines the RNN learned model with a support Bloom filter (SBF) to improve the classification accuracy. Specifically, the learned model is trained with the positive and negative sets, which is used as a pre-filter to identify the two sets. For the filter outcomes with a negative result from the learned model, SBF is constructed to perform the second filtration. To ensure efficiency of RNN and SBF, we carefully select key features to be used in RNN and SBF, which can also maintain efficient search in the final stage of rule matching. We perform simulation with various datasets showing the performance advantages of our proposed algorithm over existing solutions in terms of memory usage and classification accuracy.

Satellite communication system is expected to play a vital role for realizing various remote Internet-of-Things (IoT) applications in sixth-generation vision. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their energy consumptions. In this article, we focus on the reliable design and detection of RA preamble to effectively enhance the access efficiency in high-dynamic low-earth-orbit (LEO) scenarios. To avoid additional signaling overhead and detection process, a long preamble sequence is constructed by concatenating the conjugated and circularly shifted replicas of a single root Zadoff-Chu (ZC) sequence in RA procedure. Moreover, we propose a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), that is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation. Statistical analysis of the proposed metric reveals that increasing correlation length can obviously promote the output signal-to-noise power ratio, and the first-path detection threshold is independent of noise statistics. Simulation results in different LEO scenarios validate the robustness of the proposed method to severe channel distortion, and show that our method can achieve significant performance enhancement in terms of timing estimation accuracy, success probability of first access, and mean normalized access energy, compared with the existing RA methods.

Being able to accommodate multiple simultaneous transmissions on a single channel, non-orthogonal multiple access (NOMA) appears as an attractive solution to support massive machine type communication (mMTC) that faces a massive number of devices competing to access the limited number of shared radio resources. In this paper, we first analytically study the throughput performance of NOMA-based random access (RA), namely NOMA-RA. We show that while increasing the number of power levels in NOMA-RA leads to a further gain in maximum throughput, the growth of throughput gain is slower than linear. This is due to the higher-power dominance characteristic in power-domain NOMA known in the literature. We explicitly quantify the throughput gain for the very first time in this paper. With our analytical model, we verify the performance advantage of NOMA-RA scheme by comparing with the baseline multi-channel slotted ALOHA (MS-ALOHA), with and without capture effect. Despite the higher-power dominance effect, the maximum throughput of NOMA-RA with four power levels achieves over three times that of the MS-ALOHA. However, our analytical results also reveal the sensitivity of load on the throughput of NOMA-RA. To cope with the potential bursty traffic in mMTC scenarios, we propose adaptive load regulation through a practical user barring algorithm. By estimating the current load based on the observable channel feedback, the algorithm adaptively controls user access to maintain the optimal loading of channels to achieve maximum throughput. When the proposed user barring algorithm is applied, simulations demonstrate that the instantaneous throughput of NOMA-RA always remains close to the maximum throughput confirming the effectiveness of our load regulation.

Our earlier work has demonstrated that a sufficiently trained recurrent neural network (RNN) can effectively detect base station performance degradations. We encountered a performance limit however: the accuracy gain diminishes as the RNN deepens. In this paper, we investigate the performance limit of a well-trained RNN by visualising its processes and modeling its internal operation. We first illustrate that inputs following a certain probability density undergo transformation in the RNN. By linearising the RNN process, we then develop a linear model to analyse the transformation. Using the model, we not only unveil insights into RNN operational behaviour, but are also able to explain the effect of diminishing gains in deeper RNNs. Finally, we validate our model and demonstrate its ability to accurately predict the performance of a well-trained RNN.

The problem of security and privacy in vehicular networks is a vital issue and it attracts increasing attention to address the security vulnerability of vehicular networks. Authentication solutions are introduced in vehicular networks to ensure that network access only comes from authorized users. Particularly, group signature not only offers authentication services in vehicular networks, but also provides conditional privacy preservation. However, the current group signature solution for authentication in vehicular networks exhibits time-consuming signature verification, which is attributed to the centralized certificate revocation list (CRL) management. To overcome this shortcoming, we propose utilizing edge computing approach and design a flexible and efficient decentralized authentication scheme (FEDAS). In the proposed architecture, a decentralized CRL management method is developed to reduce verification delay in the authentication process. In addition, transition zone is proposed to solve reliable authentication problem in border area of the group caused by decentralized architecture. We also conduct extensive simulations to show the effectiveness of our proposed scheme.

In the 5G network, dense deployment and millimetre wave (mmWave) are some of the key approaches to boost network capacity. Dense deployment of mmWave small cells using narrow directional beams will escalate the cell and beam related handovers for high mobility of vehicles, which may in turn limits the performance gain promised by 5G-mmWave based vehicle-to-infrastructure (V2I) communication. One of the research issues in mmWave handover is to minimise the handover needs by identifying long lasting connections. In this paper, we first develop an analytical model to derive the vehicle sojourn time within a beam coverage. When multiple connections offered by nearby all mmWave small cells are available when upon a handover event, we further derive the longest sojourn time among all potential connections which represents the theoretical upper-bound limit of the sojourn time performance. We then design a Fuzzy Logic (FL) based distributed beam-centric handover decision algorithm to maximise vehicle sojourn time. Simulation experiments are conducted to validate our analytical model and show the performance advantage of our proposed FL-based solution when compared with commonly used approach of connecting to the strongest connection.

The concept of Ultra Dense Networks (UDNs) is often seen as a key enabler of the next generation mobile networks. The massive number of BSs in UDNs represents a challenge in deployment, and there is a need to understand the performance behaviour and benefit of a network when BS locations are carefully selected. This can be of particular importance to the network operators who deploy their networks in large indoor open spaces such as exhibition halls, airports or train stations where locations of BSs often follow a regular pattern. In this paper we study performance of UDNs in downlink for regular network produced by careful BS site selection and compare to the irregular network with random BS placement. We first develop an analytical model to describe the performance of regular networks showing many similar performance behaviour to that of the irregular network widely studied in the literature. We also show the potential performance gain resulting from proper site selection. Our analysis further shows an interesting finding that even for over-densified regular networks, a non-negligible system performance could be achieved.

Drone networks offer rapid network deployment to areas that can pose access difficulty. This paper investigates the deployment of multi-hop drone-based unmanned aerial vehicles networks with a focus on the self-organization aspect. When rescue drones carry out their rescue operations which may fly faraway from the gateway, relay drones are autonomously deployed to maintain connectivity. We study the multiple dedicated connections where each rescue drone is connected to the gateway via dedicated relay drones. We show that this approach lacks sharing of relay drones and thus requires more relay drones. We then propose a centralized greedy algorithm and a distributed solution to significantly reduce the number of relay drones. We show that while the distributed self-organized drones (DSOD) solution requires a slightly higher number of relay drones than the greedy algorithm, it eliminates the need for global message exchange which makes it attractive for practical use.

In this paper, we present a novel random access method for future mobile cellular networks that support machine type communications. Traditionally, such networks establish connections with the devices using a random access procedure, however massive machine type communication poses several challenges to the design of random access for current systems. State-of-the-art random access techniques rely on predicting the traffic load to adjust the number of users allowed to attempt the random access preamble phase, however this delays network access and is highly dependent on the accuracy of traffic prediction and fast signalling. We change this paradigm by using the preamble phase to estimate traffic and then adapt the network resources to the estimated load. We introduce Preamble Barring that uses a probabilistic resource separation to allow load estimation in a wide range of load conditions and combine it with multiple random access responses. This results in a load adaptive method that can deliver near-optimal performance under any load condition without the need for traffic prediction or signalling, making it a promising solution to avoid network congestion and achieve fast uplink access for massive MTC.

One of the cutting edge requirements envisioned for next-generation mobile networks is to support ultra-reliable and low latency communication (URLLC), as well as to meet massive traffic demand in the next few years. Although network densification has been considered as one of the promising solutions to boost capacity and high throughput, the impact of mobility on latency and reliability in dense networks has not been well investigated. Moreover, handovers, especially in dense networks, can cause extra delay to the communication and degrade reliability performance. In this paper, we aim to analyse the impact of different handover hysteresis parameters on the performance metrics, such as end-to-end delay and packet loss ratio (PLR). In this regard, we compare latency and PLR performance around cell borders including the handover process with the overall period of simulation. Simulation results show that the impact of mobility becomes more significant in dense networks due to frequent exposure to cell borders and handovers.

The papers in this special section focus on mobile cloud computing. The papers address variety of interesting topics covering different aspects of the Mobile Cloud, such as process offloading, work sharing, performance enhancement of Mobile Clouds, security issues in Mobile Clouds, and applications of Mobile Clouds.

Infrastructure-as-a-service (IaaS) cloud technology has attracted much attention from users who have demands on large amounts of computing resources. Current IaaS clouds provision resources in terms of virtual machines (VMs) with homogeneous resource configurations where different types of resources in VMs have similar share of the capacity in a physical machine (PM). However, most user jobs demand different amounts for different resources. For instance,high-performance-computing jobs require more CPU cores while big data processing applications require more memory. The existing homogeneous resource allocation mechanisms cause resource starvation where dominant resources are starved while non-dominant resources are wasted. To overcome this issue, we propose a heterogeneous resource allocation approach, called skewness-avoidance multi-resource allocation (SAMR), to allocate resource according to diversified requirements on different types of resources. Our solution includes a VM allocation algorithm to ensure heterogeneous workloads are allocated appropriately to avoid skewed resource utilization in PMs, and a model-based approach to estimate the appropriate number of active PMs to operate SAMR. We show relatively low complexity for our model-based approach for practical operation and accurate estimation. Extensive simulation results show the effectiveness of SAMR and the performance advantages over its counterparts.

Satellite networks form part of the modern mobile network. In multi-layer satellite-terrestrial networks, Contact Graph Routing (CGR) enables to calculate an efficient delivery path which depends on the contact information configured in the contact plan. Due to the rapid relative motion between satellites which belong to different layers, the inter-layer contacts can suffer frequent disruption. In order to keep the integrity of the network, the inter-layer contact must be carefully selected to maintain connectivity yet avoid congestion. In this paper, we propose a traffic aware inter-layer contact selection method (TACS) by considering the flow situation of the associated nodes which contains the queue size, flow size, the number of connected nodes and the contact duration. We verify the performance of the proposed design in our Identifier/Locator (ID/Loc) split based satellite- terrestrial network testbed with 95 simulation nodes. Experiments show that the proposed design is able to achieve balanced flow distribution among MEO layer, improve the delivery ratio and reduce the delivery delay.

Dynamic network conditions are inevitable in wireless sensor networks (WSNs). In multihop WSN data collection scenarios, data traffic load becomes heavier closer to the data sink. Therefore, extensive research work has been devoted to duty cycle adaptation mechanisms to meet the time-varying and/or spatially non-uniform traffic loads. In this paper, we propose a new algorithm, called Control-theoretic approach for Duty Cycle Adaptation (CDCA), based on the non-beacon-enable mode of the IEEE 802.15.4 protocol. We consider a distributed controller to be run at each node which adapts its duty cycle to achieve energy efficiency and maximise packet delivery performance under variable traffic conditions. CDCA adjusts a sensor's duty cycle using a traffic estimation mechanism based on a novel concept called a virtual queue. This is determined based on the sensor's local queue length as well as explicit packet-drop indications collected from its child nodes in a multihop tree structure rooted at the network's data sink. Based on control theory, the proposed controller is analysed to derive system stability. Performance results obtained using Network Simulator 3 demonstrate that CDCA outperforms the most recent and relevant scheme by saving more energy while also significantly improving packet delivery ratio to the data sink.

In this paper we revisit the problem of deriving the expected number of transmissions for multicasting random linear coded (RLC) packets on single-hop wireless channels. We show by deriving the closed form expression for an instance of the problem that previous analytical formulation does not accurately model the true expected number of transmissions, especially for smaller finite field size. Our understanding is that similar to the wireless multi-hop network, the problem of deriving an exact closed form expression for the expected number of transmissions for a single-hop RLC wireless network is a complex open problem. As it is unknown whether a scalable closed form expression for the problem exists, we then propose a computationally efficient Monte Carlo method to derive a good approximation of the expected number of transmissions.

User fairness and spectrum efficiency are conflicting objectives in cellular system optimization given that users share limited spectrum resources. Users at the cell edge are more likely to be unfairly treated due to their disadvantageous locations, where they experience high path losses and strong interferences if co-channel transmission exists. In this paper, a cell edge boundary is obtained and dynamically updated for each scheduling period to divide the users into cell centre and cell edge users, a tailored scheduling scheme is then performed for each group of users accordingly. Simulation results show that the boundary helps the schedulers achieve better balance between spectrum efficiency and fairness while providing the best energy efficiency, especially for smaller cell sizes (e.g. urban macro cells).

As an important component of the intelligent transportation system, internet‐of‐vehicles technology has attracted considerable attention. The dissemination of event‐driven emergency messages with high reliability and low delay is vital to ensure traffic safety and improve traffic efficiency in urban environment. In this study, the authors propose a reliable emergency message dissemination protocol taking into account the urban road characteristics and scalability requirements, which consists of a layout‐aware ready‐to‐broadcast‐emergency‐message and clear‐to‐broadcast‐emergency‐message handshake mechanism and a redundant relay node adaptation mechanism. Finally, the simulation results confirm the feasibility and effectiveness of the proposed scheme.

Fountain-code based cloud storage system provides reliable online storage solution through placing unlabeled content blocks into multiple storage nodes. Luby Transform (LT) code is one of the popular fountain codes for storage systems due to its efficient recovery. However, to ensure high success decoding of fountain codes based storage, retrieval of additional fragments is required, and this requirement could introduce additional delay. In this paper, we show that multiple stage retrieval of fragments is effective to reduce the file-retrieval delay. We first develop a delay model for various multiple stage retrieval schemes applicable to our considered system. With the developed model, we study optimal retrieval schemes given requirements on success decodability. Our numerical results suggest a fundamental tradeoff between the file-retrieval delay and the target probability of successful file decoding, and that the file-retrieval delay can be significantly reduced by optimally scheduling packet requests in a multi-stage fashion.

As the biggest big data, video data streaming in the network contributes the largest portion of global traffic nowadays and in the future. Due to heterogeneous mobile devices, networks, and user preferences, the demands of transcoding source videos into different versions have increased significantly. However, video transcoding is a time-consuming task, and how to guarantee quality-of-service (QoS) for large video data is very challenging, particularly for those real-time applications that hold strict delay requirement such as live TV. In this paper, we propose a cloud-based online video transcoding (COVT) system aiming to offer economical and QoS guaranteed solution for online large-volume video transcoding. COVT utilizes the performance profiling technique to obtain the different performances of transcoding tasks in different infrastructures. Based on the profiles, we model the cloud-based transcoding system as a queue and derive the QoS values of the system based on the queuing theory. With the analytically derived relationship between QoS values and the number of CPU cores required for transcoding workloads, COVT is able to solve the optimization problem and obtain the minimum resource reservation for specific QoS constraints. A task scheduling algorithm is further developed to dynamically adjust the resource reservation and schedule the tasks so as to guarantee the QoS in runtime. We implement a prototype system of COVT and experimentally study the performance on real-world workloads. Experimental results show that the COVT effectively provisions a minimum number of resources for predefined QoS. To validate the effectiveness of our proposed method under large-scale video data, we further perform simulation evaluation, which again shows that the COVT is capable of achieving economical and QoS-aware video transcoding in cloud environment.

In this paper, we study the source mobility problem that exists in the current named data networking (NDN) architecture and propose a proxy-based mobility support approach named PMNDN to overcome the problem. PMNDN proposes using a proxy to efficiently manage source mobility. Besides, functionalities of the NDN access routers are extended to track the mobility status of a source and signal Proxy about a handoff event. With this design, a mobile source does not need to participate in handoff signaling which reduces the consumption of limited wireless bandwidth. PMNDN also features an ID that is structurally similar to the content name so that routing scalability of NDN architecture is maintained and addressing efficiency of Interest packets is improved. We illustrate the performance advantages of our proposed solution by comparing the handoff performance of the mobility support approaches with that in NDN architecture and current Internet architecture via analytical and simulation investigation. We show that PMNDN offers lower handoff cost, shorter handoff latency, and less packet losses during the handoff process.

Software-defined networking (SDN) is a promising network paradigm for future Internet. The centralized controller and simplified switches replace the traditional complex forwarding devices, and make network management convenient. However, the switches in SDN currently have limited ternary content addressable memory to store specific routing rules from the controller. This bottleneck provokes cyber attacks to overload the switches. Despite existing some countermeasures for such attacks, they are proposed based on simplified attack patterns. In this paper, we review the table-overflow attack using a sophisticated attack pattern. In the attack pattern, attack flows are targeted at their middle hops instead of endpoints. We first define potential targets in the network topology, then we propose three specific traffic features and a monitoring mechanism to detect and locate the attackers. Further, we propose a mitigation mechanism to limit the attack rate using the token bucket model. With the control of token add rate and bucket capacity, it avoids the table overflow on the victim switch. Extensive simulations in different types of topologies and experiments in our testbed are provided to show the performance of our proposal.

Vehicular ad hoc network (VANET) is an important component for advancing the intelligent transportation system (ITS) to improve the traffic safety and enrich driving experience. In VANET safety applications, reliable and rapid dissemination of event-driven emergency messages is of great significance to obtain the traffic safety and efficiency. In this paper, we propose a RBEM/CBEM handshake mechanism to enhance the broadcast protocol, which is dedicated to the emergency message broadcast in urban road environment. The reliability of emergency message dissemination can be improved by reducing the packet delivery failures caused by the collisions. We design a process of broadcasting emergency messages through the urban roads, taking into account the road characteristics and scalability requirements. We conduct simulation to confirm the feasibility and effectiveness of the proposed mechanism.

Cloud computing is emerging as a new paradigm which enables big data computing, including high quality media processing. However, considering the media dynamics on resource consumption and the QoS criteria, dynamically providing the cloud computing resource to meet the QoS requirements of media processing is not easy. The current cloud computing infrastructure usually employs auto-scaling to dynamically adjust the computing resource allocation, which is typically performed at relatively long time scale and cannot adapt to the dynamic changes of video arrivals or content changes at relatively short time scale. In this paper, we propose to adaptively configure the video transcoding mode to deal with the short-term transcoding QoS and computing resource mismatch problem. We formulate the problem as the one to minimize the output bit-rate with the queue stability constraint, for which we use the Lyapunov optimization framework to solve it. Simulation results show that, compared with the static configuration strategy, the proposed adaptive method achieves smooth transcoding QoS degradation when system load becomes heavier and much better transcoding delay performance.

Virtualization has been an enabling technology for IaaS (Infrastructure as a Service) Clouds. Physical machine (PM) provisioning is a key problem for IaaS cloud providers on their resource utilization and quality of service to users. Proper provisioning is able to ensure the service quality while conserving unnecessary power consumption from over-provisioned PMs. However, the effectiveness of PM provisioning in current IaaS providers such as Amazon and Rackspace is severely limited by that they offer virtual machines with proportional resource provisioning on different resource types (including CPU, memory and disk etc). Such a rigid offering cannot satisfy diversified user applications in the cloud, and can cause significant over-provision on PMs in order to satisfy users' requirement on all resource types. This paper argues a more flexible approach that IaaS providers should offer virtual machines with flexible combinations on multiple resource types. We further formulate the problem of multiple resource virtual machine allocations for IaaS clouds, and develop analytical models to predict the suitable number of PMs while satisfying a predefined quality-of-service requirement. Experiments show that the proposed approach can significantly increase the resource utilization, with a reduction on the number of active PMs by 27% on average.

Virtual screen system is becoming an essential part in the mobile cloud computing platform. However, designing a low-latency interactive communication for the high-resolution screen content is still challenging due to the network dynamics and the unique characteristics of screen content. In this paper we propose a H.264 based low-latency screen sharing system. To achieve high play-out frame rate, we decouple the low-latency screen content communication problem into two parts, a scalable H.264 based encoding and an optimal scalable stream transmission scheduling. By leveraging the unique characteristics of screen content, a multi-layer scalable video encoding scheme is designed to achieve a certain error resilience while keeping good video coding efficiency. In the transmission scheduling module, an optimal frame skipping policy is proposed to schedule the frames in the buffer to maximize the play-out frame rate. In the performance evaluation, we simulate our system in both one-hop end-to-end topology and two-hop proxy-based topology. The simulation results show that the proposed scheme achieves much better performance on frame rate and average delay, especially in the low bandwidth condition.

In this paper, we consider the problem of finding the maximum routing throughput between any pair of nodes in an arbitrary multirate wireless mesh network (WMN) using multiple paths. Multipath routing is an efficient technique to maximize routing throughput in WMN, however maximizing multipath routing throughput is a NP-complete problem due to the shared medium for electromagnetic wave transmission in wireless channel, inducing collision-free scheduling as part of the optimization problem. In this work, we first provide problem formulation that incorporates collision-free schedule, and then based on this formulation we design an algorithm with search pruning that jointly optimizes paths and transmission schedule. Though suboptimal, compared to the known optimal single path flow, we demonstrate that an efficient multipath routing scheme can increase the routing throughput by up to 100% for simple WMNs.

Trust evaluation plays an important role in securing wireless sensor networks (WSNs), which is one of the most popular network technologies for the Internet of Things (IoT). The efficiency of the trust evaluation process is largely governed by the trust derivation, as it dominates the overhead in the process, and performance of WSNs is particularly sensitive to overhead due to the limited bandwidth and power. This paper proposes an energy-aware trust derivation scheme using game theoretic approach, which manages overhead while maintaining adequate security of WSNs. A risk strategy model is first presented to stimulate WSN nodes' cooperation. Then, a game theoretic approach is applied to the trust derivation process to reduce the overhead of the process. We show with the help of simulations that our trust derivation scheme can achieve both intended security and high efficiency suitable for WSN-based IoT networks.

In datacenter network (DCN) research, one key challenge is to reproduce the relative performances of different scalable DCN architectures in an unbiased and transparent way. Adequately addressing this challenge will support the validation of performance studies, and this is fundamental to building a strong research foundation and making wise datacenter investment decisions. In addressing this challenge, this paper presents the NTU-DSI-DCN initiative with a DCN simulation module based on an open-source platform called ns-3, on which the performance models of DCN topologies can be developed and made open source to support independent reproducibility of their performances. Advantages of the framework include the following: (1) it is low cost, (2) it provides transparent performance benchmarks of known DCN architectures and (3) it enables unbiased comparative performance simulations of DCN architectures, without the tedium of developing existing DCN models from scratch. In realizing this NTU-DSI-DCN initiative, the open-source performance models of the Fat tree and the BCube architectures have been implemented on the ns-3 platform. A comparative performance study between Fat tree and BCube is reported, along with a performance reproducibility study of Fat tree. The documentation and source codes for our simulation setups are publicly available at http://code.google.com/p/ntu-dsidcn/. In continually adding new DCN architectural models or their variants in future work, the NTU-DSI-DCN is a promising initiative that can evolve into a well-documented open-source simulation platform supporting quality research in DCNs.

When deploying wireless sensor networks (WSNs) in practical applications, access control systems can limit access to sensitive information only to trusted entities and provide a capability to resist against various attacks from malicious nodes. However, due to the characteristics of highly distributed and resource-constrained, applying conventional access control models to WSNs is significantly challenging. In this paper, a distributed and fine-grained access control model based on the trust and risk degree is proposed (TC-BAC). We first introduce a trust evaluation mechanism to meet the security requirements of access control systems. Then, a risk function is proposed to assess the behavior of nodes and evaluate the risk factor of the access. The simulation results show that TC-BAC can achieve both intended security and high efficiency of the network.

Luby transform (LT) codes provide an efficient way to transfer information over erasure channels. Past research has shown that LT codes can perform well for a large number of input symbols. However, mathematical analysis and simulation results have revealed that the packet overhead for LT decoders can be as large as 100% when the number of input symbols is small. Designing an efficient decoder to handle a small number of symbols becomes an imminent research issue. In this paper, we make an observation that LT decoders often fail to recover all the input symbols, while LT encoders have a high probability of producing a full-rank coefficient matrix. Motivated by this observation, we propose a novel decoding algorithm called LT-W, in which we incorporate the use of the Wiedemann solver into LT decoding to extend the decodability of LT codes. Extensive experiments show that our proposed method reduces the packet overhead significantly and yet preserves the efficiency of the original LT decoding process.

ABSTRACT In this work, we present a new generic polymorphic routing protocol tailored for vehicular ad hoc networks (VANETs). Similar to the case of mobile ad hoc networks, the routing task in VANETs comes under various constraints that can be environmental, operational, or performance based. The proposed Polymorphic Unicast Routing Protocol (PURP) uses the concept of polymorphic routing as a means to describe dynamic, multi‐behavioral, multi‐stimuli, adaptive, and hybrid routing, that is applicable in various contexts, which empowers the protocol with great flexibility in coping with the timely requirements of the routing tasks. Polymorphic routing protocols, in general, are equipped with multi‐operational modes (e.g., grades of proactive, reactive, and semi‐proactive), and they are expected to tune in to the right mode of operation depending on the current conditions (e.g., battery residue, vicinity density, traffic intensity, mobility level of the mobile node, and other user‐defined conditions). The objective is commonly maximizing and/or improving certain metrics such as maximizing battery life, reducing communication delays, improving deliverability, and so on. We give a detailed description and analysis of the PURP protocol. Through comparative simulations, we show its superiority in performance to its peers and demonstrate its suitability for routing in VANETs. Copyright © 2011 John Wiley & Sons, Ltd.

Data center consumes increasing amount of power nowadays, together with expanding number of data centers and upgrading data center scale, its power consumption becomes a knotty issue. While main efforts of this research focus on server and storage power reduction, network devices as part of the key components of data centers, also contribute to the overall power consumption as data centers expand. In this paper, we address this problem with two perspectives. First, in a macro level, we attempt to reduce redundant energy usage incurred by network redundancies for load balancing. Second, in the micro level, we design algorithm to limit port rate in order to reduce unnecessary power consumption. Given the guidelines we obtained from problem formulation, we propose a solution based on greedy approach with integration of network traffic and minimization of switch link rate. We also present results from a simulation-based performance evaluation which shows that expected power saving is achieved with tolerable delay.

Among different setups of cloud storage systems, fountain-codes based distributed cloud storage system provides reliable online storage solution through placing coded content fragments into multiple storage nodes. Luby Transform (LT) code is one of the popular fountain codes for storage systems due to its efficient recovery. However, to ensure high success decoding of fountain codes based storage, retrieval of additional fragments is required, and this requirement introduces additional delay, which is critical for content retrieval or downloading applications. In this paper, we show that multiple-stage retrieval of fragments is effective to reduce the content-retrieval delay. We first develop a delay model for various multiple-stage retrieval schemes applicable to our considered system. With the developed model, we study optimal retrieval schemes given the success decodability requirement. Our numerical results demonstrate that the content-retrieval delay can be significantly reduced by optimally scheduling packet requests in a multi-stage fashion.

With recent advocates on end-to-end congestion control, we still observe lack of consideration of network congestion status in literature. Since end-to-end congestion control mechanisms are capable of gathering path load information through data paths, taking advantage of this information, network systems have potential capacity to react promptly in presence of congestion, especially for paths with multiple congestion points. In this paper, we design a congestion control scheme with consideration of multiple congestion points along data paths. Using an improved ECN mechanism, our scheme tunes source rate with feedbacks collecting from ECNs. We further improve our scheme with saturation detection and congestion prediction mechanisms. Simulation results show that our scheme works effectively. We evaluate our scheme and compare it with DCTCP, a recently proposed congestion control scheme for data center. In scenarios of multiple congestion points, our scheme exhibits better performance in terms of reaction time and stability.

This paper investigates the problem of minimizing energy consumption for real-time video encoding on mobile devices, by dynamically configuring the clock frequency in the CPU via the dynamic voltage scaling (DVS) technology. The problem can be formulated as a constrained optimization problem, whose objective is to minimize the total energy consumption of encoding video contents while respecting a real-time delay constraint. Under a probabilistic workload model, we obtain closed-form solutions for both the optimal clock frequency configuration and the resulted minimum energy. We also compare the optimal solution with a brute force flat frequency configuration. Numerical results indicate that our derived optimal solution outperforms the brute-force approach significantly. Moreover, we apply the optimal solution for real-time H.264/AVC video encoding application. Our numerical results suggest that an energy saving of 10%-20% can be achieved, compared to the flat clock frequency scheduling.

The index coding problem is a fundamental transmission problem which occurs in a wide range of multicast networks. Network coding over a large finite field size has been shown to be a theoretically efficient solution to the index coding problem. However the high computational complexity of packet encoding and decoding over a large finite field size, and its subsequent penalty on encoding and decoding throughput and higher energy cost makes it unsuitable for practical implementation in processor and energy constraint devices like mobile phones and wireless sensors. While network coding over GF(2) can alleviate these concerns, it comes at a tradeoff cost of degrading throughput performance. To address this tradeoff, we propose a throughput optimal triangular network coding scheme over GF(2). We show that such a coding scheme can supply unlimited number of innovative packets and the decoding involves the simple back substitution. Such a coding scheme provides an efficient solution to the index coding problem and its lower computation and energy cost makes it suitable for practical implementation on devices with limited processing and energy capacity.

In this paper, we propose a scalable video adaptation mechanism to improve the overall quality of service (QoS) in wireless home networks with a mixture of IPTV and VoD users. Unlike most of the existing studies on video streaming over WLANs, which usually focus on only one type of video streams, either stored videos or live videos, here we consider a mixture of live and stored videos. We make use of the pre-buffering time of VoD users in the rate adaptation for both IPTV and VoD users so as to achieve an overall optimal QoE for all the users. In addition, we employ the standard H.264 SVC and consider a practical multi-rate scenario, where the physical data rate of a wireless user is determined according to its distance to the access point (AP). The corresponding multi-rate multi-queue MAC-layer throughput is analyzed so as to accurately estimate the bandwidth for the video streaming. The ns-2 simulations verify the effectiveness of the proposed scalable video adaptation.

Localization of mobile phones is important to location-based mobile services, but achieving good location estimation of mobile phones is difficult especially in environment whose path loss exponent is unknown. In this paper, we present a Wi-Fi localization solution specifically designed for dense WLANs with unknown path loss exponent. In order to leverage between the computational cost and localization accuracy, our solution establishes a neighbor selection scheme based on the Voronoi diagram to identify a subset of Access Points (APs) to participate in localization. It considers the identified subset of APs and a mobile phone to be located as a mass-spring system. Provided with information of known coordinates of APs, the solution estimates the path loss exponent of the physical environment, infers inter-distances between APs and the mobile phone from Wi-Fi signals received, and implements spring relaxation algorithm to approximate the geographical location of the mobile phone, where this location estimation is fed back to refine the estimated exponent iteratively. Extensive simulation results confirm that our solution is able to provide location estimation with an attractive average accuracy of below 2 m in a typical Wi-Fi setup.

Interference in wireless networks is one of the key capacity-limiting factors. Recently developed interference-embracing techniques show promising performance on turning collisions into useful transmissions. However, the interference-embracing techniques are hard to apply in practical applications due to their strict requirements. In this paper, we consider utilizing the interference-embracing techniques in a common scenario of two interfering sender-receiver pairs. By employing opportunistic listening and analog network coding (ANC), we show that compared to traditional ARQ retransmission, a higher retransmission throughput can be achieved by allowing two interfering senders to cooperatively retransmit selected lost packets at the same time. This simultaneous retransmission is facilitated by a simple handshaking procedure without introducing additional overhead. Simulation results demonstrate the superior performance of the proposed cooperative retransmission.

Due to the unique characteristics of long signal propagation, high error rate and low bandwidth in the underwater environment, the design of the medium access control (MAC) protocol for underwater acoustic networks poses significant challenges. The previous MAC protocols designed for flexible communication models have limited achievements in performance. In this paper, we consider a practical application and propose a data-centric multi-hop MAC protocol, called DC-MAC, to enhance the performance on throughput and average end-to-end packet transmission delay. Our design uses multi-channel strategy to limit transmission interference by creating multiple collision domains, and dynamic collision-free polling strategy to offer efficient protocol handshake. We analyze the saturation throughput performance and conduct extensive simulation experiments to study the throughput and delay performance. Comparing to slotted FAMA which is a potential MAC protocol candidate for the same environment, our results show that DC-MAC outperforms its peer.

Unlike most of the existing studies on video streaming over WLANs, which usually focus on only one type of video streams, either stored videos or live videos, in this paper we propose a dynamic scheduling method for transmitting a mixture of live and stored videos over WLANs. In particular, we dynamically estimate the future IPTV traffic information based on the past. Our proposed dynamic scheduling algorithm allows VoD traffic to be transmitted not only at a later time but also in advance so as to provide more flexibility to serve the overall traffic to achieve a better QoS. The ns-2 simulations verify the effectiveness of the proposed approach.

We look at the problem of network wide broadcast using the multi-rate feature of a wireless ad hoc network. Existing research has primarily focused on achieving minimum latency by construction of minimum weight connected dominating sets (WCDS) based on neighbourhood information. In this paper, we are interested in stateless multi-rate broadcasting algorithms in which nodes determine their broadcasting behaviour based on neighbourhood transmissions. The primary contribution of this paper is that we show how broadcast effectiveness at different data rates are related and how this relationship can be used to optimize algorithm design. We propose three stateless broadcasting algorithms and demonstrate the performance improvements achievable. Our simulation results show that significant benefits can be obtained in terms of minimizing both the number of forwarding nodes as well as the broadcast latency.

Existing literature has looked to exploit wireless broadcast advantage (WBA) in order to optimize the performance of a wide variety of network operations. In this paper, we obtain a measure of WBA in a multihop scenario. We consider that all nodes in the network store and propagate implicitly received information from neighbourhood transmissions, resulting in the creation of a distributed cache, which we term broadcast cache. We obtain a lower bound on the growth of the broadcast cache in terms of the fewest set of transmissions in the network, which we define as the minimum set of non-altruistic transmissions. Subsequently, we use our results to obtain feasibility conditions that determine whether WBA can be effectively utilized depending on flow requirements.

The emergence of Internet Protocol Television (IPTV) has brought potentials to revolutionize personal entertainment. Streaming TV content over the highly pervasive wireless networks allows easy access to personalized entertainment. Focusing on wireless home entertainment which is one of the main driving forces of IPTV development, we develop a Markovian framework that investigates several important issues related to network capacity and streaming quality in an IEEE 802.11e enabled wireless home network. The Markovian framework captures not only the IEEE 802.11e MAC protocol performance, but also the statistical characteristics of IPTV media streams. The inclusion of these two key descriptions allows our model to be practically used in wireless home network planning and design. To deal with the complexity in the model, we apply the efficient Matrix Geometric approach to obtain numerical results. We further perform simulations with real IPTV traffic to not only validate our analytical results, but also obtain further insight to the performance.

This paper aims at designing a congestion and priority solution for Ethernet congestion management. Following the popular approach that uses a cooperation of an Additive Increase and Multiplicative Decrease (AIMD) based rate limiter and Explicit Congestion Notification (ECN) active queue management to combat congestions in Ethernet, the proposal considers differentiated AIMD settings for rate limiters to achieve congestion control differentiation for traffic of different priorities. We illustrate that while the operations of AIMD and ECN are independent, by using different AIMD settings, we can achieve differentiated control of bandwidth utilization. We develop a control theoretic analytical model to study the effectiveness of our proposed method. Moreover, we implement our proposed method in OMNET++ simulator to conduct simulation experiments. Our analytical and simulation results both indicate the effectiveness of bandwidth ratio differentiation.

Cooperative communication techniques have earlier been applied to design of the IEEE 802.11 medium access control (MAC) and shown to perform better. High rate stations can help relay packets from low-rate stations resulting in better throughput for the entire network. However, this also involves additional energy costs on the part of the relay which can result in reducing the network lifetime. We propose a cooperative MAC protocol NetCoop with the objective of maximizing the network lifetime and achieving high throughput. Based on this design, we also propose a flexible strategy which allows cooperation to be achieved using more than one relay. We show that this can achieve at least as good throughput as that of single relay cooperation while maintaining a high network lifetime.

Existing ad-hoc network routing strategies base their operations on flooding route requests throughout the network and choosing the shortest path thereafter. However, this typically results in a large number of unnecessary transmissions, which could be expensive for resource-constrained nodes such as those in a sensor network. In this paper, we propose a new mechanism HopAlert which optimizes route establishment and packet routing by limiting the number of nodes taking part in the route discovery process while achieving a low number of hops establishment. Using analysis and simulations, we show that this results in more routes with shorter hop counts than a reactive flooding protocol such as AODV while achieving higher savings.

The introduction of physical layer network coding gives rise to the concept of turning a collision of transmissions on a wireless channel useful. In the idea of physical layer network coding, two synchronized simultaneous packet transmissions are carefully encoded such that the superimposed transmission can be decoded to produce a packet which is identical to the bitwise binary sum of the two transmitted packets. This paper explores the decoding of superimposed transmission resulted by multiple synchronized simultaneous transmissions. We devise a coding scheme that achieves the identification of individual transmission from the synchronized superimposed transmission. A mathematical proof for the existence of such a coding scheme is given.

Rateless codes such as LT codes have become more and more popular due to their abilities to handle varying channel conditions without much feedback. However, rateless codes have the drawback of unable to provide intermediate outputs when delivering layer-coded media content. Although some methods have been proposed to produce intermediate outputs based on adjusting the distribution of LT codes, they are typically content-dependent and unable to guarantee that a more important layer can always be decoded before the decoding of a less important layer. In this paper, we propose a simple joint unequal loss protection (ULP) and LT coding (ULP-LT) scheme for layered media delivery, where different numbers of FEC are allocated to different layers to guarantee the priority and LT codes are used to deal with varying channel conditions. Simulation results show that with a small amount of overhead allocated to ULP, the ULP-LT scheme can produce good intermediate performance while still enjoying the nice features provided by LT codes.

This paper proposes and studies a differentiated congestion control for Ethernet congestion management. Following the popular approach that uses a cooperation of an Additive Increase and Multiplicative Decrease (AIMD) based rate limiter and Explicit Congestion Notification (ECN) active queue management to combat the congestion in Ethernet, the proposal considers differentiated AIMD settings for rate limiters to achieve congestion control differentiation for traffic of different priorities. We illustrate that while the operation of AIMD and ECN are independent, by using different AIMD settings, we can achieve differentiated control of bandwidth utilization. We provide an analysis and its numerical results showing the effectiveness of this method. Our proposed method is also implemented in OMNET++ simulator with results showing the effectiveness of bandwidth ratio differentiation.

Currently implemented Spanning Tree Protocol (STP) cannot meet the requirement of a data center due to its poor bandwidth utilization and lack of multipathing capability. In this paper, we propose a layer-2 multipathing solution, namely dynamic load balancing multipathing (DLBMP), for data center Ethernets. With DLBMP, traffic between two communication nodes can be spread among multiple paths. The traffic load of all paths is continuously monitored so that traffic split to each path can be dynamically adjusted. In addition, per-flow forwarding is preserved to guarantee in-order frame delivery. Computer simulations show that DLBMP gives much better performance as compared to STP due to its multipathing and dynamic load balancing capability.

Many previous studies have shown that traditional TCP slow-start algorithm suffers performance degradation in high-speed and long-delay networks. This paper presents a sender-side enhancement, which makes use of TCP Vegas congestion-detecting scheme to monitor the router queue, and accordingly refines slow-start window evolution by introducing a two-phase approach to probe bandwidth more efficiently. Moreover, it achieves good fairness of bandwidth utilization in coexistence of multiple connections. Simulation results show that, compared with traditional slow-start and many other enhancements, it is able to significantly improve the startup performance without adversely affecting coexisting TCP connections.

This paper presents a simple solution suitable for the mandatory localization function for E-911 services specified by FCC. Our solution introduces zero-length spring technique to compute the estimated location based on received signal strength (RSS). The introduced zero-length spring concept permits a less detailed path loss model to use without significant impact to the location estimation. We show the stability of our algorithm by illustrating the convergence of the estimated locations computed by the algorithm. We then demonstrate with simulation the accuracy of the estimation with various settings, and compare this accuracy with two candidate localization techniques.

LT codes provide an efficient way to transfer information over erasure channels. Past research has illustrated that LT codes can perform well for a large number of input symbols. However, it is shown that LT codes have poor performance when the number of input symbols is small. We notice that the poor performance is due to the design of the LT decoding process. In this respect, we present a decoding algorithm called full rank decoding that extends the decodability of LT codes by usingWiedemann algorithm.We provide a detailed mathematical analysis on the rank of the random coefficient matrix to evaluate the probability of successful decoding for our proposed algorithm. Our studies show that our proposed method reduces the overhead significantly in the cases of small number of input symbols yet preserves the simplicity of the original LT decoding process.

Using the multi-rate feature of the IEEE 802.11 MAC protocol in a wireless mesh network (WMN) increases efficiency in network-wide message broadcasting. A challenging problem is to achieve minimal latency for network-wide broadcasting in a multi-rate wireless mesh network (MrWMN). The three features, including the multi-rate characteristics, the source scheduling to obtain the routing decision, and the multi-radio solution to avoid the interference, add great complexities to this research issue. In this paper, we propose a distributed source routing algorithm that exploits the multi-rate feature to achieve low latency network-wide broadcasting in WMNs. Multi-radio is utilized to resolve the interferences among neighboring nodes. Simulation results show that, compared to the existing best known results under various network settings, our approach produces lower latency for network-wide broadcasting in the MrWMN.

Retransmission based on packet acknowledgement (ACK/NAK) is a fundamental error control technique employed in IEEE 802.11-2007 unicast network. However the 802.11-2007 standard falls short of proposing a reliable MAC-level recovery protocol for multicast frames. In this paper we propose a latency and bandwidth efficient coding algorithm based on the principles of network coding for retransmitting lost packets in a single-hop wireless multicast network and demonstrate its effectiveness over previously proposed network coding based retransmission algorithms.

This paper makes use of the fluid-based approach to model the throughput of TCP Veno flow over wired/wireless networks. A generalized formula is derived between Veno's throughput and its window evolution parameters, packet loss rate and round-trip time. Simulation experiments and real network measurements are conducted to validate the accuracy of this model.

In this paper, we propose an adaptive system for improving videophone transmission over EDCA. We consider that a videophone contains a constant bit rate (CBR) voice source and a rate-adaptive video source. Two issues are addressed in this research. Firstly, how to solve the AP bottleneck problem, and secondly, how to adjust video source rate to improve the network performance. For the first issue, we propose the adjustment of the transmission opportunity (TXOP) to give AP a higher priority in voice transmission in order to eliminate the AC3 transmission bottleneck at the AP. For the second issue, our principle is to guarantee the throughput of voice traffic while transmitting as much video traffic as possible. Moreover, we consider more realistic multi-rate WLANs, where multiple transmission rates are used in the PHY layer depending on the underlaying channel conditions.

We consider an important performance measure of wireless sensor networks, namely, the least number of nodes, N, required to facilitate routing between any pair of nodes, allowing other nodes to remain in sleep mode in order to conserve energy. We derive the expected value and the distribution of N for single dimensional dense networks.

In this paper, we study the issue of scalable video streaming over IEEE 802.11e EDCA WLANs. Our basic idea is to control the number of "active" nodes on the channel in order to reduce collisions under heavy traffic conditions. Specifically, we propose a distributed on-off queue control (OOQC) mechanism, which is designed to maintain high network throughput while keeping packet loss due to collision as low as possible. A low priority early drop (LPED) method is also employed to drop the packets at the queue according to packet relative priority index (RPI) provided by scalable video coding. Simulation results show that our proposed OOQC scheme significantly outperforms EDCA in received video quality.

This paper presents a semi-Markov model that evaluates the performance of TCP connections with asymmetric Additive Increase and Multiplicative Decrease (AIMD) congestion control settings involved in sharing of a common drop-tail router. We study the fairness of the connections and their individual bandwidth utilizations as well as packet loss rates. We confirm that certain asymmetric AIMD settings may achieve fairness in bandwidth sharing. We also found that while connections with asymmetric AIMD settings operate at different bandwidth utilizations, they generally experience similar packet loss rate.

In mobile ad hoc networks (MANETs), packets hop from a source to a series of forwarding nodes until they reach the desired destination. Defining the hop length to be the distance between two adjacent forwarding nodes, we observe that the two adjacent forwarding nodes tend to be farther away from each other with a higher probability in a one-dimensional MANET. We derive the probability density functions for the hop lengths to confirm our observation. Applying the developed results, we further formulate the relationship between the mean number of hops and the distance between the source and the destination.

This work presents a novel cross-layer rate control scheme for optimizing 3D wavelet scalable video transmission over the IEEE 802.11e wireless local area networks. The proposed scheme consists of a macro and a micro rate control schemes residing at the application layer and the network sublayer respectively. The macro rate control uses bandwidth estimation to achieve optimal bit allocation with minimum distortion. The micro rate control employs an adaptive mapping of packets using video classifications. This prioritizes appropriately the video traffic to maximize the transmission protection to the important video packets. The performance is investigated by simulations showing advantages of our cross-layer design.

This paper aims to study the queue dynamics of TCP Veno with RED in the wired-wireless heterogeneous networks. The authors first develop a fluid-flow model of TCP Veno with RED over heterogeneous network, and then use the classical linear feedback control theory to analyze it. Analysis results reveal the relationship between the RED queue oscillation and the network parameters. The authors use simulation tool to validate our analysis, and show how to stabilize the router queue and improve the co-existence of TCP Veno with TFRC.

TFRC is TCP-friendly rate control protocol with TCP Reno's throughput equation based. It is designed to mainly provide optimal service for unicast multimedia flow operating in the best-effort Internet environment. However, due to Reno's significant performance degradation in wireless networks, TFRC has also led to unavoidable performance suffering from wireless links due to the poor performance of Reno throughput. This paper proposes an enhancement of TFRC based on the differentiating method used in TCP Veno. Specifically, it utilizes Veno's state differentiator to distinguish congestion losses and non-congestion losses during transmission. By discounting the impact of those non-congestion losses on the throughput calculation, it can effectively alleviate the throughput degradation caused by wireless links. The simulation results have shown that, our proposal can achieve throughput improvement up to 70% as compared to the original TFRC. Meanwhile, it maintains other merits of the original TFRC, such as sending rate smoothness, fairness, and TCP-friendliness.

A cross-layer design for optimizing 3-D wavelet scalable video transmission over the IEEE 802.11e networks is proposed. A thorough study on the behavior of the IEEE 802.11e protocol is conducted. Based on our findings, all timescales rate control is developed featuring a unique property of soft capacity support for multimedia delivery. The design consists of a macro timescale and a micro timescale rate control schemes residing at the application layer and the network sublayer respectively. The macro rate control uses bandwidth estimation to achieve optimal bit allocation with minimum distortion. The micro rate control employs an adaptive mapping of packets from video classifications to appropriate network priorities which preemptively drops less important video packets to maximize the transmission protection to the important video packets. The performance is investigated by simulations highlighting advantages of our cross-layer design.

We propose in this paper an optimized, polymorphic, hybrid multicast routing protocol for MANET. This new polymorphic protocol attempts to benefit from the high efficiency of proactive behavior (in terms of quicker response to transmission requests) and the limited network traffic overhead of the reactive behavior, while being power, mobility, and vicinity-density (in terms of number of neighbor nodes per specified area around a mobile node) aware. The proposed protocol is based on the principle of adaptability and multibehavioral modes of operations. It is able to change behavior in different situations in order to improve certain metrics like maximizing battery life, reducing communication delays, improving deliverability, etc. The protocol is augmented by an optimization scheme, adapted from the one proposed for the optimized link state routing protocol (OLSR) in which only selected neighbor nodes propagate control packets to reduce the amount of control overhead. Extensive simulations and comparison to peer protocols demonstrated the effectiveness of the proposed protocol in improving performance and in extending battery power longevity

A new approach for modeling and performance analysis of the IEEE 802.11 medium access control (MAC) protocol is presented. The approach is based on the so-called system approximation technique, where the protocol service time distribution of the IEEE 802.11 MAC protocol is studied and approximated by an appropriate phase-type distribution, leading to the construction of a versatile queueing model which is amenable to analysis and, at the same time, general enough to allow for bursty arrival process as well as key statistical characteristics of the protocol operations. The versatility of the model is demonstrated by considering Markov modulated and on/off arrival processes as well as various data frame size distributions. The accuracy of the analytical results is verified by simulation.

In recent years, the physical layer data rate provided by 802.11 Wireless LANs has dramatically increased thanks to significant advances in the modulation and coding techniques employed. However, previous studies show that the 802.11 MAC operation, namely the distributed coordination function (DCF), represents a limiting factor: the throughput efficiency drops as the channel bit rate increases, and a throughput upper limit does indeed exist when the channel bit rate goes to infinite high. These findings indicate that the performance of the DCF protocol will not be efficiently improved by merely increasing the channel bit rate. This paper shows that the DCF performance may significantly benefit from the adoption of two separate physical carriers: one devised to manage the channel access contention, and another devised to deliver information data. We propose a scheme, referred to as out-of-band signaling (OBS), designed to reuse (and remain backward compatible with) the existing 802.11 medium access control (MAC) specification. Performance evaluation of OBS is carried out through analytical techniques validated via extensive simulation, for both saturation and statistical traffic conditions. Numerical results show that OBS improves the throughput/delay performance, and provides better bandwidth usage compared with the in-band signaling technique employed by DCF.

Quality of service support in wireless LAN is a theme of current interest. Several solutions have been proposed in literature in order to protect time-sensitive traffic from best-effort traffic. According to the EDCA proposal, which is a completely distributed solution, the service differentiation is provided by giving probabilistically higher number of channel accesses to stations involved in real-time applications. To this purpose, the MAC parameter settings of each contending stations can be tuned dynamically. In this paper, we face the problem of tuning the EDCA MAC parameters in common scenarios in which a given number of low-rate delay-sensitive traffic flows share the channel with some stations involved in data transfer. Our contribution is threefold. First, we show that, whenever possible, the delay constraints of the high priority class can be satisfied in both the cases of contention windows differentiation and inter-frame space differentiation. However, these mechanisms have different side effects in terms of bandwidth availability for the best effort stations. Second, we propose to exploit the MAC parameter dynamic settings of EDCA in order to probabilistically guarantee the delay requirements and to jointly maximize the aggregated throughput of the network. Finally, we suggest a very simple solution to automate these parameter settings in a real scenario, where traffic flows can be activated/deactivated dynamically, by simply monitoring the channel activity. The proposed solution is very robust, since it does not require any a priori traffic model or any network load estimator.

We propose in this paper, an optimized, polymorphic, hybrid multicast routing protocol for MANET. The protocol proposed is based on the principle of adaptability and multi-behayioral modes of operations. It is able to change behavior in different situations in order to improve certain metrics like maximizing battery life, reducing communication delays, improving deliverability, etc. This new polymorphic protocol attempts to benefit of the high efficiency of proactive behavior and the low cost of network traffic of the reactive behavior, while being power, mobility and vicinity density aware. The protocol is augmented by an optimization scheme, adapted from the one proposed for the optimized link state routing protocol (OLSR) in which only selected neighbor nodes propagate control packets to reduce the amount of control overhead. Extensive simulations and comparison to peer protocols demonstrated the effectiveness of the proposed protocol in improving performance and in extending battery power longevity.

Many models have been proposed to analyze the performance of the IEEE 802.11 distributed coordination function (DCF) and the IEEE 802.11e enhanced distributed coordination function (EDCA) under saturation condition. To analyze DCF under statistical traffic, Foh and Zukerman introduce a model that uses Markovian Framework to compute the throughput and delay performance. In this paper, we analyze the protocol service time of EDCA mechanism and introduce a model to analyze EDCA under statistical traffic using Markovian Framework. Using this model, we analyze the throughput and delay performance of EDCA mechanism under statistical traffic.

In mobile ad hoc networks (MANETs), packets are forwarded by a series of nodes to the desired destination. Previously, we have studied the network connectivity of MANETs. In this paper, we study the reliability of an established route between a source to a destination. The route is considered reliable only when all links in the route remain connected for packet relaying. We analyze the link reliability under the condition that the forwarding nodes are mobile. With this analysis, we derive the time period that the established route remains reliable for packet forwarding. Simulation is conducted to validate our analysis.

With the rapid development and wide deployment of wireless local area networks (WLANs), WLAN-based positioning system employing signal-strength-based technique has become an attractive solution for location estimation in indoor environment. In recent years, a number of such systems has been presented, and most of the systems use the common nearest neighbor in signal space (NNSS) algorithm. In this paper, we propose an enhancement to the NNSS algorithm. We analyze the enhancement to show its effectiveness. The performance of the enhanced NNSS algorithm is evaluated with different values of the parameters. Based on the performance evaluation and analysis, we recommend some guidelines on optimizing the parameters of our proposed enhanced algorithm.

We propose in this paper, a polymorphic, hybrid multicast routing protocol for MANET (mobile ad hoc networks). This new polymorphic protocol attempts to take benefit of the high efficiency of proactive behavior and the low cost of network traffic of the reactive behavior. The protocol is based on the principle of adaptability and multi-behavioral modes of operations. The proposed protocol is able to change behavior in different situations in order to improve certain metrics like the battery power. Extensive simulations demonstrated the effectiveness of the proposed protocol in improving performance and in extending battery power longevity.

In this paper, we introduce a simple model for the enhanced distributed channel access (EDCA) mechanism under saturation conditions. This model captures the operation of the AIFS (arbitration inter-frame space) and contention window differentiation of the EDCA mechanism. Using this model, we analyze the throughput and delay performance of EDCA. The results of our analytical model are then verified using simulations.

In this paper, we study the performance of our earlier proposed out-of-band signaling (OBS) scheme for high speed wireless local area networks (WLANs). We employ the system approximation technique for modeling of the OBS scheme. An equivalent state dependent single server queue, that describes the OBS scheme, is constructed for the analysis of the throughput and delay performances. Moreover, we study the throughput optimization of the OBS scheme, which provides a means for optimizing the performance of the OBS scheme, given a particular network environment. Finally, we conduct several simulation experiments to validate our analytical results.

TCP Veno was recently proposed to eliminate TCP performance suffering from wireless links. Real network measurements and live Internet results have validated Veno's significant throughput improvement in wireless networks and its harmonious coexistence with TCP Reno connections in wired networks. We demonstrate the out-of-phase synchronization of Veno in one-way traffic, as opposed to the in-phase synchronization of Reno. The detailed studies of this behavior and its interaction with Reno are reported. Moreover, our careful study shows that this out-of-phase synchronization benefits network link utilization, and reduces the occurrence of congestion loss.

In this paper, a closed form network connectivity formula for a one-dimensional mobile ad hoc network (MANET) is developed. Precisely, we derive the probability that a MANET is fully connected given a certain number of nodes randomly and uniformly placed along a path between a source and destination pair. This formula is particularly useful in the process of design and deployment of a MANET. The formula also provides a critical constraint function to the problem of network optimization. It formulates the relationship between the number of mobile nodes required for a particular MANET given a desired network connectivity probability. An approximation is employed to achieve the final closed form expression. The approximation is then tested by simulation to show the accuracy of our formula under practical network conditions.

This paper proposes an alternate solution for Ethernet passive optical networks. Our solution uses a novel protocol named full utilization local loop request contention multiple-access protocol to efficiently provide communications in passive optical networks. We study the physical layer implementation, as well as medium access control (MAC) layer protocol performance to illustrate the feasibility and benefit of our solution. The performance studies show that the MAC protocol is capable of offering 95% channel utilization under heavy load conditions. The performance results also indicate that delivery of multimedia traffic with a high quality-of-service can be achieved with our solution.

We propose a new optical hybrid switching system that takes advantage of both optical burst switching (OBS) and optical circuit switching (OCS) technologies. This system classifies incoming IP traffic flows into short-lived and long-lived flows. We model the system as a single server queue in a Markovian environment. The burst generation process is assumed to follow a two-state Markov modulated Poisson process (MMPP), and the service rate fluctuates based on the number of concurrent OCS sessions. Results for the mean delay and queue size are derived.

A new technique is proposed for performance evaluation of random access protocols. This technique is based on the idea that the service probability distribution of a particular random access protocol can be described by a phase-type distribution. By modeling the service probability distribution of a protocol into a phase-type distribution, the performance of the protocol can be evaluated using an equivalent continuous time Markov chain. We demonstrate the applicability of the technique to evaluate the performance of the IEEE 802.3 and the IEEE 802.11 MAC protocols. Furthermore, we provide an example of performance evaluation of a MAC protocol subject to bursty traffic models such as the Markov modulated Poisson process. The accuracy of the results is verified by simulation.

We analyze our previously proposed protocol, CSMA/RI, and its closest related protocol, CSMA/CD. Our models include the widely used retransmission algorithm-the truncated binary exponential backoff (BEB) algorithm. The analysis is performed under two realistic traffic scenarios-the saturation and the disaster scenarios. The performance comparison of CSMA/RI and CSMA/CD is also presented. The analytic results are verified by simulations and are found to be accurate.

This paper proposes a new carrier sense multiple access (CSMA) protocol, called CSMA with reservations by interruptions (CSMA/RI). This new protocol uses a novel approach to reserve capacity for multiple users. The performance of the new protocol is studied by simulations under realistic (long range dependent) traffic conditions and compared with CSMA/CD as well as with its work conserving G/D/1 queue. It is demonstrated that CSMA/RI always offers better performance than CSMA/CD and under certain realistic assumptions regarding packet size, the performance of CSMA/RI can be very close to that of its G/D/1 benchmark.

This paper proposes an enhancement for the carrier sense multiple access with collision detection (CSMA/CD) protocol, called CSMA with reservations by interruptions (CSMA/RI). This new protocol uses a novel approach to reserve capacity by interrupting an ongoing packet transmission. The performance of the protocol is studied by simulations under realistic (long range dependent) traffic conditions and compared to the CSMA/CD, token ring protocols, as well as with the work conserving G/D/1 queue. It is demonstrated that CSMA/RI always offers better performance than CSMA/CD, and under certain realistic assumptions regarding packet size, the performance of CSMA/RI can be very close to that of token-ring and G/D/1.