In the Metaverse, real-time digital twin (DT) updates enable immersive, interactive environments by reflecting real world states. Metaverse can engage its users to share data in order to ensure the completeness of the DT. However, the limitations and heterogeneity in IoT devices' computation and transmission resources are critical challenges to synchronizing the vast volume of real-world objects with their digital replicas. In this work, we propose a novel adaptive Vision Transformer (ViT)- based semantic communication (SemCom) system to extract semantic information from raw data collected by IoT devices. The system dynamically adjusts the model size and computational complexity according to the resource constraints of individual IoT devices, enables broader participation of heterogeneous devices, enhances feature extraction capabilities, and ensures completeness and efficient DT representation. We formulate our problem as an utility-maximization optimization to manage ViT complexity under resource constraints, allowing the Metaverse Services Provider (MSP) to select high-performing IoT devices. To guide the optimization, we profile ViT models with varying architectural complexities and conduct an empirical analysis to capture the relationship between model scale and performance. To address the scalability and privacy limitations of the optimization, we propose a decentralized solution where each IoT device independently optimizes its own utility under local constraints. The MSP, in turn, selects among these devices to ensure quality and maximize its overall utility. We show that our distributed solution achieves near-optimal performance and significantly outperforms other approaches in terms of MSP utility, semantic data quality, and overall IoT utility. © 2026 IEEE.

The integration of terrestrial and non-terrestrial networks (TN-NTN) in 6 G is essential to support real-time applications like the Metaverse and intelligent edge services, which demand ultra-reliable low-latency communications (xURLLC). Managing these networks and maintaining robust security presents significant challenges due to their complexity and high-dimensional environments. Quantum communication, particularly quantum key distribution (QKD), offers a promising solution by providing unbreakable encryption and enhancing security across TN-NTN architectures. In this paper, we propose a novel deep reinforcement learning approach for QKD-enabled resource allocation in 6 G TN-NTN Metaverse service and transform the joint resource allocation and QKD deployment cost optimization problem into a stochastic game model to ensure secure and efficient resource distribution across TN-NTN environment. We introduce a novel hierarchical multi-agent proximal policy optimization (MAPPO) framework to address the formulated optimization problem. This framework enables dynamic and secure allocation of Metaverse resources and services from multiple providers to users while minimizing QKD deployment costs. Our simulations demonstrate that the proposed framework significantly enhances network performance, reduces key generation costs, and optimizes resource utilization and service quality.

The integration of Unmanned Aerial Vehicles (UAVs) into Open Radio Access Networks (O-RAN) enhances communication in disaster management and Search and Rescue (SAR) operations by ensuring connectivity when infrastructure fails. However, SAR scenarios demand stringent security and low-latency communication, as delays or breaches can compromise mission success. While UAVs serve as mobile relays, they introduce challenges in energy consumption and resource management, necessitating intelligent allocation strategies. Existing UAV-assisted O-RAN approaches often overlook the joint optimization of security, latency, and energy efficiency in dynamic environments. This paper proposes a novel Reinforcement Learning (RL)-based framework for dynamic resource allocation in UAV relays, explicitly addressing these trade-offs. Our approach formulates an optimization problem that integrates security-aware resource allocation, latency minimization, and energy efficiency, which is solved using RL. Unlike heuristic or static methods, our framework adapts in real-time to network dynamics, ensuring robust communication. Simulations demonstrate superior performance compared to heuristic baselines, achieving enhanced security and energy efficiency while maintaining ultralow latency in SAR scenarios.

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

Open Radio Access Networks (O-RAN) are transforming telecommunications by shifting from centralized to distributed architectures, promoting flexibility, interoperability, and innovation through open interfaces and multi-vendor environments. However, O-RAN's reliance on cloud-based architecture and enhanced observability introduces significant security and resource management challenges. Efficient resource management is crucial for secure and reliable communication in O-RAN, within the resource-constrained environment and heterogeneity of requirements, where multiple User Equipment (UE) and O-RAN Radio Units (O-RUs) coexist. This paper develops a framework to manage these aspects, ensuring each O-RU is associated with UEs based on their communication channel qualities and computational resources, and selecting appropriate encryption algorithms to safeguard data confidentiality, integrity, and authentication. A Multi-objective Optimization Problem (MOP) is formulated to minimize latency and maximize security within resource constraints. Different approaches are proposed to relax the complexity of the problem and achieve near-optimal performance, facilitating tradeoffs between latency, security, and solution complexity. Simulation results demonstrate that the proposed approaches are close enough to the optimal solution, proving that our approach is both effective and efficient.

Generative AI (GAI) and Large Language Models (LLMs) have revolutionized natural language processing and content creation. However, their significant computational demands during inference often require cloud servers, which are currently the only viable option for handling complex multi-modal models like GPT-4. The inherent complexity of these models increases latency, posing challenges even within cloud environments. Furthermore, cloud reliance brings other challenges, including high bandwidth consumption to transfer diverse data types. Worse, in personalized GAI applications like virtual assistants, similar prompts frequently occur, causing redundant transmission and computation of replies, which further increases overhead. Accelerating the inference of multi-modal systems is, therefore, critical in artificial intelligence. In this paper, we aim to improve the inference efficiency through prompt caching; if a current prompt is semantically similar to a previous one, the system can reuse the earlier response without invoking the model again. We leverage collaborative edge computing to cache popular replies and store their request embeddings. New prompts are locally processed to extract embeddings, with their qualities determined by the resources available on edge servers. Our problem is formulated as an optimization to manage offloading decisions for GAI tasks, aiming to avoid cloud inferences and minimize latency while maximizing reply quality. Given its non-convex nature, we propose to solve it via Block Successive Upper Bound Minimization (BSUM). Reinforcement learning is employed to actively pre-cache prompts, tackling the complexity of unknown prompt popularity. Our approach demonstrates near-optimal performance, significantly outperforming cloud-only solutions.

In this paper, a robust reconfigurable intelligent surface (RIS)-aided carrier frequency offset (CFO)-based physical layer authentication (PLA) scheme for wireless networks is proposed. The considered network consists of a legitimate transmitter, a spoofer, and a receiver, acting as an authenticator, who aims to identify the sender's legitimacy relying on the estimated CFO from received signals. Thus, the proposed scheme exploits an RIS to increase the received signal-to-noise ratio (SNR) and enhance the authentication performance. A deep reinforcement learning framework is developed to jointly optimize the RIS phase shifts and the preamble length to maximize the authentication performance under a minimal channel capacity constraint. Then, a supervised machine learning classifier is employed for node authentication, exploiting the optimized RIS reflection and preamble length. The results show that the authentication performance is enhanced with the increase in the RIS size and the difference between the transmitters' CFOs. Also, the proposed scheme outperforms the baseline RIS-aided CSI-based one in mobility scenarios.

The industrial Internet of Things (IIoT) and network slicing (NS) paradigms are key enablers of the industrial revolution in current and future mobile networks. However, peer-to-peer (P2P) resource blocks (RBs) exchange to match supply and demand in sliced IIoT networks requires proper incentivization and renegotiations between the service providers (SPs). This paper models the business strategic interactions between seller and buyer SPs as a coalitional game in which sellers form coalitions to set RB prices and buyers join coalitions to determine their best-response RB demand. The aim is to maximize the profit of the seller coalition and minimize the expenses of the buyer coalition while jointly contributing to maximize system RB utilization. Due to the uncertainty of network traffic, we propose a coalitional game-guided multiagent reinforcement learning approach that takes the output of the coalitional game as the starting Nash equilibrium (NE) and computes the optimal price and demand strategies of the coalitions regardless of network condition changes. Simulation results and analysis prove the efficacy of the proposed approach in terms of optimizing seller and buyer coalition payoffs, as well as maximizing the overall RB utilization.

The integration of Mobile Edge Computing (MEC) with aerial platforms introduces novel potential for the Metaverse world by providing low-latency and highly reliable computing and communication services at the network edge. Nevertheless, this integration presents critical challenges, such as low Quality of Experience (QoE) due to the dynamic nature of aerial platforms, high resource demands, and the requirements for real-time data processing in the Metaverse environment. To address these challenges, we propose a Combinatorial Client-Master Multiagent Deep Reinforcement Learning (CCM-MADRL) based joint resource allocation and QoE maximization framework to enable intelligent real-time decision-making in aerial MEC enabled Metaverse services. We form a collaborative ecosystem where agents are designed to represent both Metaverse service providers and aerial platforms to promote fairness and efficiency in resource allocation, as well as optimize service delivery. By incorporating CCM, our approach considers diverse metrics, such as latency, reliability, meta-distance, and energy efficiency, to ensure a holistic optimization of Metaverse services. The MADRL approach enables adaptive decision-making, allowing the system to respond to the dynamic and unpredictable nature of Metaverse applications. Results from simulations that mimic realistic Metaverse scenarios demonstrate the effectiveness of the proposed CCM-MADRL framework in terms of improved service performance, reduced latency, cost, and virtual meta-distance, maximized average QoE utility of Metaverse users, and enhanced resource utilization compared to baseline algorithms.

In the 5G Advanced and 6G era, wireless communication systems face security challenges, notably adversarial interference from unknown jammers in multi-user scenarios. Reconfigurable Intelligent Surfaces (RIS) present a cost-effective solution due to their low power consumption and easy deployment. Existing RIS techniques typically address simple jamming scenarios with a single static jammer, focusing on a single objective. This study introduces a multi-objective optimization approach deploying UAV-mounted RIS to counter jamming threats in wireless communications within a densely populated smart city environment. The proposed solution aims to safeguard essential services from potential disruptions caused by malicious jamming attacks during public events. We employ Proximal Policy Optimization (PPO), a lightweight Deep Reinforcement Learning (DRL) technique, to concurrently optimize the trajectory of UAV and RIS passive beamforming to address computational complexity. The objectives include maximizing the average sum rate and minimizing energy consumption. Our experiments highlight the efficacy of the PPO-based strategy, demonstrating significant improvements in average sum rates and energy efficiency amid numerous mobile devices and moving jammers. Importantly, our proposed system model outperforms a baseline from related works in maximizing the sum rate and minimizing overall energy consumption.

The metaverse, the next digital frontier, demands high-performance models and quick personalization due to the dynamic nature of user tasks despite limited data availability. The frequent user customization is resource-intensive and data-heavy. Meta-learning, especially federated meta-learning (FML) known for its adaptive capabilities, is crucial for addressing the dynamics in metaverse, characterized by user heterogeneity, diverse data structures, and varied tasks. However, the diversity of tasks can compromise global training outcomes due to statistical heterogeneity. Given this, an urgent need arises for smart coalition formation that accounts for these disparities. This paper proposes a game-theoretic framework for managing FML in metaverse services, with meta-learners as workers. A blockchain-based cooperative coalition formation game is introduced, grounded on a reputation metric, the similarity of users, and their incentives. The reputation metric is derived based on our novel reputation system, which takes into account users' historical contributions and potential contributions to current tasks, by exploiting the correlations between past and new tasks. Meanwhile, the incentive mechanism is formulated as an optimization to minimize users energy cost and boost the users contribution for higher federated meta-learning efficacy. Simulations show the framework's resilience against misbehavior and its superiority over other schemes, improving service utility and worker profitability in metaverse meta-learning.

Deep Neural Networks (DNNs) have become the key technique to revolutionize the healthcare sector. However, conducting online remote inference is often impractical due to privacy constraints and latency requirements. To enable local computation, researchers have attempted network pruning with minimal accuracy loss or DNN distribution without affecting the performance. Yet, distributed inference can be inefficient due to the energy overhead and fluctuation of communication channels between participants. On the other hand, given that realistic healthcare systems use pre-trained models, local pruning and retraining relying only on the available scarce data is not possible. Even pre-pruned DNNs are limited in their ability to customize to the local load of data and device dynamics. The online pruning of DNN inferences without retraining is viable; however, it was not considered in the literature as most well-known techniques do not perform well without adjustment. In this paper, we propose a novel pruning strategy using Explainable AI (XAI) to enhance the performance of pruned DNNs without retraining, a necessity due to the scarcity and bias of local healthcare data. We combine distribution and pruning techniques to perform online distributed inference assisted by dynamic pruning when needed for highest accuracy. We use Non-Linear Integer Programming (NLP) to formulate our approach as a trade-off between resources and accuracy, and Reinforcement Learning (RL) to relax the problem and adapt to dynamic requirements. Our pruning criterion shows high performance compared to other reference techniques and ability to assist distribution by reducing resource usage while keeping high accuracy.

The metaverse, envisioned as the next digital frontier for avatar-based virtual interaction, involves high-performance models. In this dynamic environment, users’ tasks frequently shift, requiring fast model personalization despite limited data. This evolution consumes extensive resources and requires vast data volumes. To address this, meta-learning emerges as an invaluable tool for metaverse users, with federated meta-learning (FML), offering even more tailored solutions owing to its adaptive capabilities. However, the metaverse is characterized by users heterogeneity with diverse data structures, varied tasks, and uneven sample sizes, potentially undermining global training outcomes due to statistical difference. Given this, an urgent need arises for smart coalition formation that accounts for these disparities. This paper introduces a dual game-theoretic framework for metaverse services involving meta-learners as workers to manage FML. A blockchain-based cooperative coalition formation game is crafted, grounded on a reputation metric, user similarity, and incentives. We also introduce a novel reputation system based on users’ historical contributions and potential contributions to present tasks, leveraging correlations between past and new tasks. Finally, a Stackelberg game-based incentive mechanism is presented to attract reliable workers to participate in meta-learning, minimizing users’ energy costs, increasing payoffs, boosting FML efficacy, and improving metaverse utility. Results show that our dual game framework outperforms best-effort, random, and non-uniform clustering schemes -improving training performance by up to 10%, cutting completion times by as much as 30%, enhancing metaverse utility by more than 25%, and offering up to 5% boost in training efficiency over non-blockchain systems, effectively countering misbehaving users.

Although Deep Neural Networks (DNN) have become the backbone technology of several Internet of Things (IoT) applications, their execution in resource-constrained devices remains challenging. To cater for these challenges, collaborative deep inference conducted by IoT devices was introduced. However, the prevalence of DNN computation suffers from severe privacy problems, e.g. data-reverse and model leakage. Particularly, malicious participants can accurately recover the received data to access sensitive information. Furthermore, the system is composed of heterogeneous data-sources represented by different DNN models that wish to execute classifications without exposing their data and models. Though, relaying the trained models to a centralized unit managing the collaboration leads to major risks because some features can be revealed through these models, in addition to dependency and scalability problems. In this paper, we present an approach that targets the privacy of collaborative inference via controlling the amount of data assigned to different participants, to prevent them from reversing attempts. Moreover, each independent data-source requesting inference will be responsible to manage the distribution of its DNN locally. In this context, different sources are required to compete over the pervasive resources while cooperating to maintain privacy welfare. We formulate this methodology, as an integer programming problem, where we establish a trade-off between the latency of co-inference and the privacy required by heterogeneous entities. A distributed solution scheme is also developed based on the Lagrangian dual problem. Next, to relax the optimization, we shape our approach as a cooperative and competitive Multi-Agent Reinforcement Learning (MARL) that supports heterogeneous/independent agents. Our comprehensive simulations demonstrated that our method yields results on par with those of a single RL agent in terms of action performance, while maintaining the privacy of individual agents’ information. Additionally, it surpasses the Independent Q-Learning (IQL) approach, where agents operate autonomously, in safeguarding inference privacy.

Heterogeneous wireless networks (HetNets), where networks are deployed with ultra-dense small cells (SCs), is one of the main enabling technologies for future wireless networks. In such networks, signals are vulnerable to severe blockage, interference, and intermittent connectivity. This can be largely overcome using the emerging Reconfigurable Intelligent Surface (RIS) technology that can enhance HetNets performance by controlling the propagation environment. However, jointly optimizing the parameters of base stations’ (BSs’) active beamforming and RISs’ passive beamforming is a major challenge in RIS-empowered HetNets. In this paper, we investigate the issue of rate control in RIS-empowered multi-cell multiple-input single-output (MISO) HetNets via joint users’ equipment (UEs) rate fairness and SCs rate load balancing. We assume RIS-assisted SC BSs at mmWave underlying a RIS-assisted macrocell (MC) BS at sub-6GHz serving dual-connectivity UEs that can concurrently connect to the MC BS and a single SC BS. Then, we formulate an optimization problem whose objective is to jointly optimize the active transmit beamforming vectors of the MC and SCs BSs on the one hand and the passive beamforming vectors of the MC and SCs RISs on the other hand. Due to the high non-convexity and complexity of the formulated problem, we propose a novel distributed Deep Deterministic Policy Gradient (DDPG)-based multi-task deep reinforcement learning (MTDRL) scheme to solve the problem and learn network dynamics. Through deliberate definitions of MTDRL agent’s tasks and their corresponding main elements, we demonstrate via simulations that our proposed scheme guarantees a fair distribution of rates within UEs and SCs. In addition, we quantify the robustness of our proposed MTDRL scheme compared with some benchmarks in terms of convergence speed and utility values.

In this paper, we propose a Deep Reinforcement Learning (DRL) framework to optimize the secrecy performance of a Multi-User (MU)-Multiple-Input Single-Output (MISO) Underwater Optical Wireless Communication (UOWC) system. The network consists of several light-emitting diodes connected with various underwater users through optical beams. The legitimate transmission is threatened by several eavesdroppers attempting to overhear the confidential message sent to each user. Thus, digital precoding is employed to cancel the inter-user interference and maximize the per-user secrecy rate and, consequently, the secrecy sum rate (SSR). Leveraging the developed DRL algorithm, the MU-MISO precoding matrix is optimized for enhancing the system's SSR. Numerical results show the superiority of the proposed DRL framework compared to the baseline zero-forcing and random pre coding schemes, even with corrupted CSI at the transmitter due to seawater dynamics and estimation errors.

The healthcare sector has undergone a significant transformation with the widespread adoption of Deep Neural Networks (DNN). However, due to privacy constraints and stringent latency requirements, online remote inference is not a viable option in healthcare scenarios. Many efforts have been conducted to enable local computation, such as network compression using pruning or DNN distribution among multiple resource-constrained devices. Yet, it is still challenging to conduct distributed inference due to the latency and energy overheads resulting from intermediate shared data. On the other hand, given that realistic healthcare systems use pre-trained models, local pruning and fine-tuning relying only on the scarce and biased data is not possible. Even pre-pruned DNNs are not efficient as they are not customized to the local load of data and the dynamics of devices. The dynamic and online pruning of DNN without fine-tuning is a promising solution; however, it was not considered in the literature as most well-known techniques do not perform well without adjustment. In this paper, driven by the data restrictions in healthcare sector, we propose a novel pruning strategy based on Explainable AI (XAI), with a target to enhance the pruned DNN performance without fine-tuning. Moreover, to maintain the highest possible accuracy, we propose to combine distribution and pruning techniques to perform online distributed inference assisted by dynamic pruning only when needed. Our experiments show the performance of our pruning criterion compared to other reference techniques, in addition to its ability to assist the distribution by reducing the shared data, while keeping high accuracy.

Crowdsourced live streaming services (CLS) present significant challenges due to massive data size and dynamic user behavior. Service providers must accommodate personalized QoE requests, while managing computational burdens on edge servers. Existing CLS approaches use a single edge server for both transcoding and user service, potentially overwhelming the selected node with high computational demands. In response to these challenges, we propose the Reinforcement Learning-based-Collaborative Edge-Assisted Live Streaming (RL-CEALS) framework. This innovative approach fosters collaboration between edge servers, maintaining QoE demands and distributing computational burden cost-effectively. By sharing tasks across multiple edge servers, RL-CEALS makes smart decisions, efficiently scheduling serving and transcoding of CLS. The design aims to minimize the streaming delay, the bitrate mismatch, and the computational and bandwidth costs. Simulation results reveal substantial improvements in the performance of RL-CEALS compared to recent works and baselines, paving the way for a lower cost and higher quality of live streaming experience.

As deep neural networks continue to expand and become more complex, most edge devices are unable to handle their extensive processing requirements. Therefore, the concept of distributed inference is essential to distribute the neural network among a cluster of nodes. However, distribution may lead to additional energy consumption and dependency among devices that suffer from unstable transmission rates. Unstable transmission rates harm real-time performance of IoT devices causing low latency, high energy usage, and potential failures. Hence, for dynamic systems, it is necessary to have a resilient DNN with an adaptive architecture that can downsize as per the available resources. This paper presents an empirical study that identifies the connections in ResNet that can be dropped without significantly impacting the model’s performance to enable distribution in case of resource shortage. Based on the results, a multi-objective optimization problem is formulated to minimize latency and maximize accuracy as per available resources. Our experiments demonstrate that an adaptive ResNet architecture can reduce shared data, energy consumption, and latency throughout the distribution while maintaining high accuracy.

The deployment flexibility and maneuverability of unmanned aerial vehicles (UAVs) increased their adoption in various applications, such as wildfire tracking, border monitoring, etc. In many critical applications, UAVs capture images and other sensory data and then send the captured data to remote servers for inference and data processing tasks. However, this approach is not always practical in real-time applications due to the connection instability, limited bandwidth, and end-to-end latency. One promising solution is to divide the inference requests into multiple parts (layers or segments), with each part being executed in a different UAV based on the available resources. Furthermore, some applications require the UAVs to traverse certain areas and capture incidents; thus, planning their paths becomes critical particularly, to reduce the latency of making the collaborative inference process. Specifically, planning the UAVs trajectory can reduce the data transmission latency by communicating with devices in the same proximity while mitigating the transmission interference. This work aims to design a model for distributed collaborative inference requests and path planning in a UAV swarm while respecting the resource constraints due to the computational load and memory usage of the inference requests. The model is formulated as an optimization problem and aims to minimize latency. The formulated problem is NP-hard so finding the optimal solution is quite complex; thus, this article introduces a real-time and dynamic solution for online applications using deep reinforcement learning. We conduct extensive simulations and compare our results to the state-of-the-art studies demonstrating that our model outperforms the competing models.

Remote monitoring systems analyze the environment dynamics in different smart industrial applications, such as occupational health and safety, and environmental monitoring. Specifically, in Industrial Internet of Things (IIoT) systems, the huge number of devices and the expected performance put pressure on resources, such as computational, network, and device energy. Distributed training of machine and deep learning (ML/DL) models for intelligent industrial IoT applications is very challenging for resource limited devices over heterogeneous wireless networks (HetNets). Hierarchical federated learning (HFL) performs training at multiple layers offloading the tasks to nearby multiaccess edge computing (MEC) units. In this article, we propose a novel energy-efficient HFL framework enabled by wireless energy transfer (WET) and designed for heterogeneous networks with massive multiple-input–multiple-output (MIMO) wireless backhaul. Our energy-efficiency approach is formulated as a mixed-integer nonlinear programming (MINLP) problem, where we optimize the HFL device association and manage the wireless transmitted energy. However due to its high complexity, we design a heuristic resource management algorithm, namely, H2RMA, that respects energy, channel quality, and accuracy constraints, while presenting a low-computational complexity. We also improve the energy consumption of the network using an efficient device scheduling scheme. Finally, we investigate device mobility and its impact on the HFL performance. Our extensive experiments confirm the high performance of the proposed resource management approach in HFL over HetNets, in terms of training loss and grid energy costs.

Abstract Image recognition based on computers is something human beings have been working on for many years. It is one of the most difficult tasks in the field of computer science, and improvements to this system are made when we speak. In this paper, we propose a methodology to automatically propose an appropriate title and add a specific sound to the image. Two models have been extensively trained and combined to achieve this effect. Sounds are recommended based on the image scene and the headings are generated using a combination of natural language processing and state-of-the-art computer vision models. A Top 5 accuracy of 67% and a Top 1 accuracy of 53% have been achieved. It is also worth mentioning that this is also the first model of its kind to make this forecast.

Reconfigurable Intelligent Surface (RIS) has recently emerged as an enabling technology to enhance reliability and overcome blockage in future heterogeneous wireless networks (HetNets). Adjusting amplitudes and phases of the RIS elements to achieve such goals is a major challenge. In this paper, we study the problem of network rate control to achieve users (UEs) fairness and smallcells (SCs) load balancing in multi-cell RIS-assisted multiple-input single-output (MISO) HetNets. We consider dual-connectivity UEs that can simultaneously connect to mmWave-operating SCs and sub-6GHz-operating RIS-assisted macrocell (MC), where RISs are mainly deployed to enhance sub-6GHz signal reception and mitigate interference. Then, we formulate an optimization problem whose objective is to jointly control the active beamforming vectors of SCs and MC on the one hand and the passive beamforming vectors of RISs on the other hand to maximize UEs fairness and network load balancing. Due to the high complexity of the formulated problem, we propose a novel multi-task deep reinforcement learning (MTDRL) model based on the Deep Deterministic Policy Gradient (DDPG) algorithm to solve the problem and learn system dynamics. Through proper definitions of network tasks and their main elements, we show via simulations that our proposed MTDRL-based model ensures fair distribution of rates within UEs and SCs and that it outperforms key benchmarks.

In this paper, we investigate an energy-efficient Long Range (LoRa) wireless network powered by hybrid energy which consists of an energy harvesting source and the grid. The grid allows to compensate for the randomness and intermittency of the harvested energy. The aim is to propose a dynamic energy-efficient resource management scheme for LoRa wireless networks that enables green Internet of Things (IoT). Hence, we formulate a grid energy cost minimization problem subject to minimum received signal-to-noise ratio (SNR), and channel, spreading factor (SF) and energy availability constraints. The formulated problem is simplified and decoupled into two sub-problems which allows to derive the optimal resource management solution but with high computational complexity. Then, we propose a low complexity heuristic channel and SF assignment, and energy management algorithm for dynamic LoRa wireless networks. Numerical results shows the efficient use of renewable energy in green dynamic LoRa wireless networks.

Abstract The evolution of 4G telecommunication propagated various resource‐crunched clients to experience rate‐effective resources at ease. However, it extends its underlying centralised architecture, which arouses various challenges correlated with network data availability, network information protection, and operational infrastructure charges. With the recent revolution of telecommunication, 5G networks promised to provide credible schemes like the high quality of service, ultra‐low latency, and much security over the pre‐existing architecture. However, the deployment of end‐to‐end 5G network cutting‐edge systems in the present heterogeneous world limits its core idea of extensive data privacy, native interoperability, risk‐free interference, and radio spectrum sharing. Perhaps, to achieve its true capability, improved versions of blockchain technology could be aligned to strengthen various real‐time complex applications at a flourishing rate. One of the multiplexed real‐time enterprise applications is a keyword search engine where the integrity of user data files and keyword searches are bound to come under cyber hackers. On the one hand, it was found that when a 5G‐based blockchain emulated network gets deployed with intact encryption techniques, the entire system facilitates to give reliable, efficient, and risk‐free keyword search over variegated 5G network data and its complex computational calculations. Consequently, the use of blockchain‐based decentralised cloud orchestration scheme at various levels enabled the architecture to remain incorruptible and protects all the confidential files and keywords in a fully controlled file access environment. The results of the simulation kernel shows that proposed architecture which, when combined with blockchain‐based decentralised cloud orchestration network system, justify all the essential characteristics and effectuates the optimal use of 5G network sharing by each network entity.

LoRa wireless networks are considered as a key enabling technology for next-generation Internet of Things (IoT) systems. New IoT deployments (e.g., smart city scenarios) can have thousands of devices per square kilometer leading to huge amount of power consumption to provide connectivity. In this article, we investigate green LoRa wireless networks powered by a hybrid of the grid and renewable energy sources, which can benefit from harvested energy while dealing with the intermittent supply. This article proposes resource management schemes of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway energy efficiency. First, the problem of grid power consumption minimization while satisfying the system’s quality of service demands is formulated. Specifically, both scenarios the uncorrelated and time-correlated channels are investigated. The optimal resource management problem is solved by decoupling the formulated problem into two subproblems: 1) channel and SF assignment problem and 2) energy management problem. Since the optimal solution is obtained with high complexity, online resource management heuristic algorithms that minimize the grid energy consumption are proposed. Finally, taking into account the channel and energy correlation, adaptable resource management schemes based on reinforcement learning (RL) are developed. Simulation results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.

Unmanned aerial vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems relying on direct observations obtained from fixed cameras and sensors. Furthermore, thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications. However, deep neural networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed onboard. In this article, we propose a DNN distribution methodology within UAVs to enable data classification in resource-constrained devices and avoid extra delays introduced by the server-based solutions due to data communication over air-to-ground links. The proposed method is formulated as an optimization problem that aims to minimize the latency between data collection and decision-making while considering the mobility model and the resource constraints of the UAVs as part of the air-to-air communication. We also introduce the mobility prediction to adapt our system to the dynamics of UAVs and the network variation. The simulation conducted to evaluate the performance and benchmark the proposed methods, namely, optimal UAV-based layer distribution (OULD) and OULD with mobility prediction (OULD-MP), was run in an HPC cluster. The obtained results show that our optimization solution outperforms the existing and heuristic-based approaches.

Block chain provides an innovative solution to information storage, transaction execution, security, and trust building in an open environment. The block chain is technological progress for cyber security and cryptography, with efficiency-related cases varying in smart grids, smart contracts, over the IoT, etc. The movement to exchange data on a server has massively increased with the introduction of the Internet of Things. Hence, in this research, Splitting of proxy re-encryption method (Split-PRE) has been suggested based on the IoT to improve security and privacy in a private block chain. This study proposes a block chain-based proxy re-encryption program to resolve both the trust and scalability problems and to simplify the transactions. After encryption, the system saves the Internet of Things data in a distributed cloud. The framework offers dynamic, smart contracts between the sensor and the device user without the intervention of a trustworthy third party to exchange the captured IoT data. It uses an efficient proxy re-encryption system, which provides the owner and the person existing in the smart contract to see the data. The experimental outcomes show that the proposed approach enhances the efficiency, security, privacy, and feasibility of the system when compared to other existing methods.

LoRa supports the exponential growth of connected devices. In this paper, we investigate green LoRa wireless networks powered by both the grid power and a renewable energy source. The grid power compensates for the randomness and intermittency of the harvested energy. We propose an efficient and smart resource management scheme of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway (LG) energy efficiency. We formulate the problem of grid power consumption minimization while satisfying the quality of service demands. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Next, we develop an adaptable resource management schemes based on Reinforcement Learning (RL) taking into account the channel and energy correlation. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.

Training centralized machine learning (ML) models becomes infeasible in wireless networks due to the increasing number of internet of things (IoT) and mobile devices and the prevalence of the learning algorithms to adapt tasks in dynamic situations with heterogeneous networks (HetNets) and battery limited devices. Hierarchical federated learning (HFL) has been proposed as a promising learning that can preserve the data privacy of the wireless devices, tackle the communication bottlenecks in wireless networks, and improve the energy effi-ciency. We propose a novel energy-efficient HFL framework for HetNets with massive multiple-input multiple-output (MIMO) wireless backhaul enabled by wireless energy transfer (WET). We formulate a joint energy management and device association optimization problem in HFL over HetNets subject to maximal divergence constraints. Next, an optimal solution is developed, but with high complexity. To reduce the complexity, a heuristic algorithm for HFL over HetNets with energy, channel quality, and accuracy constraints, is developed in order to minimize the grid energy consumption cost and preserve the value of loss function, which captures the HFL performance. Simulation results show the efficiency of the proposed resource management approach in the HFL context in terms of grid power consumption cost and training loss.

In this paper, a Deep Q-Network (DQN) based multi-agent multi-user power allocation algorithm is proposed for hybrid networks composed of radio frequency (RF) and visible light communication (VLC) access points (APs). The users are capable of multihoming, which can bridge RF and VLC links for accommodating their bandwidth requirements. By leveraging a non-cooperative multi-agent DQN algorithm, where each AP is an agent, an online power allocation strategy is developed to optimize the transmit power for providing users’ required data rate. Our simulation results demonstrate that DQN’s median convergence time training is 90% shorter than the Q-Learning (QL) based algorithm. The DQN-based algorithm converges to the desired user rate in half duration on average while converging with the rate of 96.1% compared to the QL-based algorithm’s convergence rate of 72.3%. Additionally, thanks to its continuous state-space definition, the DQN-based power allocation algorithm provides average user data rates closer to the target rates than the QL-based algorithm when it converges.

Cloud computing is attracting a lot of attention in the past few years. Although, even with its wide acceptance, cloud security is still one of the most essential concerns of cloud computing. Many systems have been proposed to protect the cloud from attacks using attack signatures. Most of them may seem effective and efficient; however, there are many drawbacks such as the attack detection performance and the system maintenance. Recently, learning-based methods for security applications have been proposed for cloud anomaly detection especially with the advents of machine learning techniques. However, most researchers do not consider the attack classification which is an important parameter for proposing an appropriate countermeasure for each attack type. In this paper, we propose a new firewall model called Secure Packet Classifier (SPC) for cloud anomalies detection and classification. The proposed model is constructed based on collaborative filtering using two machine learning algorithms to gain the advantages of both learning schemes. This strategy increases the learning performance and the system's accuracy. To generate our results, a publicly available dataset is used for training and testing the performance of the proposed SPC. Our results show that the accuracy of the SPC model increases the detection accuracy by 20% compared to the existing machine learning algorithms while keeping a high attack detection rate.

Recognizing the patient's emotions using deep learning techniques has attracted significant attention recently due to technological advancements. Automatically identifying the emotions can help build smart healthcare centers that can detect depression and stress among the patients in order to start the medication early. Using advanced technology to identify emotions is one of the most exciting topics as it defines the relationships between humans and machines. Machines learned how to predict emotions by adopting various methods. In this survey, we present recent research in the field of using neural networks to recognize emotions. We focus on studying emotions' recognition from speech, facial expressions, and audio-visual input and show the different techniques of deploying these algorithms in the real world. These three emotion recognition techniques can be used as a surveillance system in healthcare centers to monitor patients. We conclude the survey with a presentation of the challenges and the related future work to provide an insight into the applications of using emotion recognition.

Unmanned Aerial Vehicles (UAVs) have recently attracted significant attention due to their outstanding ability to be used in different sectors and serve in difficult and dangerous areas. Moreover, the advancements in computer vision and artificial intelligence have increased the use of UAVs in various applications and solutions, such as forest fires detection and borders monitoring. However, using deep neural networks (DNNs) with UAVs introduces several challenges of processing deeper networks and complex models, which restricts their on-board computation. In this work, we present a strategy aiming at distributing inference requests to a swarm of resource-constrained UAVs that classifies captured images on-board and finds the minimum decision-making latency. We formulate the model as an optimization problem that minimizes the latency between acquiring images and making the final decisions. The formulated optimization solution is an NP-hard problem. Hence it is not adequate for online resource allocation. Therefore, we introduce an online heuristic solution, namely DistInference, to find the layers placement strategy that gives the best latency among the available UAVs. The proposed approach is general enough to be used for different low decision-latency applications as well as for all CNN types organized into pipeline of layers (e.g., VGG) or based on residual blocks (e.g., ResNet).

Internet of Things (IoT) is an innovative paradigm envisioned to provide massive applications that are now part of our daily lives. Millions of smart devices are deployed within complex networks to provide vibrant functionalities, including communications, monitoring, and controlling of critical infrastructures. However, this massive growth of IoT devices and the corresponding huge data traffic generated at the edge of the network created additional burdens on the state-of-the-art centralized cloud computing paradigm due to the bandwidth and resource scarcity. Hence, edge computing (EC) is emerging as an innovative strategy that brings data processing and storage near to the end users, leading to what is called the EC-assisted IoT. Although this paradigm provides unique features and enhanced Quality of Service (QoS), it also introduces huge risks in data security and privacy aspects. This article conducts a comprehensive survey on security and privacy issues in the context of EC-assisted IoT. In particular, we first present an overview of EC-assisted IoT, including definitions, applications, architecture, advantages, and challenges. Second, we define security and privacy in the context of EC-assisted IoT. Then, we extensively discuss the major classifications of attacks in EC-assisted IoT and provide possible solutions and countermeasures along with the related research efforts. After that, we further classify some security and privacy issues as discussed in the literature based on security services and based on security objectives and functions. Finally, several open challenges and future research directions for secure EC-assisted IoT paradigm are also extensively provided.

Mobile and IoT devices are becoming increasingly capable computing platforms that are often underutilized. In this paper, we propose RAMOS, a system that leverages the idle compute cycles in a group of heterogeneous mobile and IoT devices that can be clustered to form an edge FemtoCloud. At the heart of this system, we formulate a multi-objective, resource-aware task assignment and scheduling problem. The scheduler runs in two main modes; latency-minimization and energy-efficiency. Under the latency-minimization mode, it strives to maximize the computational throughput of the constructed FemtoCloud while maintaining the energy consumption below an operator specified threshold. Under the energy-efficient mode, it minimizes the total energy consumed in the FemtoCloud while meeting defined tasks deadlines. Due to the NP-Completeness of this scheduling problem, we design a set of heuristics to solve it. We implement a prototype of our system and use it to evaluate its performance and efficiency. Our results demonstrate the system's ability to meet different scheduling objectives while adhering to pre-specified time and energy constraints. Compared to other schedulers, RAMOS achieves 10 to 40 percent completion time improvement under latency minimization mode and up to 30 percent more energy-efficiency under the energy-efficient mode.

With the emergence of smart cities, Internet of Things (IoT) devices as well as deep learning technologies have witnessed an increasing adoption. To support the requirements of such paradigm in terms of memory and computation, joint and real-time deep co-inference framework with IoT synergy was introduced. However, the distribution of Deep Neural Networks (DNN) has drawn attention to the privacy protection of sensitive data. In this context, various threats have been presented, including black-box attacks, where a malicious participant can accurately recover an arbitrary input fed into his device. In this paper, we introduce a methodology aiming to secure the sensitive data through re-thinking the distribution strategy, without adding any computation overhead. First, we examine the characteristics of the model structure that make it susceptible to privacy threats. We found that the more we divide the model feature maps into a high number of devices, the better we hide proprieties of the original image. We formulate such a methodology, namely DistPrivacy, as an optimization problem, where we establish a trade-off between the latency of co-inference, the privacy level of the data, and the limited-resources of IoT participants. Due to the NP-hardness of the problem, we introduce an online heuristic that supports heterogeneous IoT devices as well as multiple DNNs and datasets, making the pervasive system a general-purpose platform for privacy-aware and low decision-latency applications.

Due to the aging effect and exposure to reactive gases, the response of gas sensors tends to deviate. This deviation in sensors' response is termed as drift. The drift of sensors is a challenging issue that limits the use of sensors over longer periods of time because the pattern recognition and classification systems fail to recognize the deviated response of sensors. To address this problem, this paper proposes the use of Recursive Feature Elimination (RFE) based Random Forests (RF) for compensation of small-scale drift in gas sensors. The proposed method is evaluated for the classification of six volatile compounds and is compared with multiple state-of-the-art classifiers and feature selection techniques using a benchmark dataset publicly available online. The results depict that the RF-RFE combination outperforms the other classifiers and feature selection techniques.

Leveraging video caching to collaborative Mobile Edge Computing (MEC) servers is an emerging paradigm, where cloud computing services are extended to edge networks to allocate multimedia contents close to end-users. However, despite minimizing the traffic over the content delivery networks (CDN), congestions may occur in peak hours characterized by high load demands. Involving users' devices in data offloading through Device-to-Device (D2D) connections has proved its efficiency in relieving the cellular spectrum utilization. In this paper, the Collaborative Edge network (CE) and the devices (D2D) cluster are combined to form a CE-D2D framework aiming at maximizing video caching and efficiently using cellular and backhaul bandwidths. However, since we are dealing with large sized contents, the small storage and bandwidth capacities offered by users limit the number of cached videos and restrict offloading large volume data. This makes the CE-D2D framework, so far, an incomplete solution for multimedia contents. Therefore, we propose a caching strategy to cache only the chunks of videos to be watched and instead of caching or offloading each video content by one edge node (as performed in literature), helpers (MEC and mobiles) will collaborate to store and share different chunks to optimize the storage/transmission resources usage. In this work, we model both CE and D2D frameworks as linear programs and schedule the collaboration between them constrained by resource availability. Due to the NP-hardness of the problem, we introduce an online heuristic that presents a proactive chunks caching (HLPC) and a near-optimal data offloading with polynomial complexity.

Network security is an critical subject in any distributed network. Recently, machine learning has proven their efficiency for intrusion detection. By using a comprehensive dataset with multiple attack types, a well-trained model can be created to improve the anomaly detection performance. However, high dimensional data sets are a significant challenge for machine learning. In fact, learning algorithms considering all features in the input data, may cause over-fitting to irrelevant aspects of the data and increase the computational time caused by the process of similar features that provide redundant information, which is a critical problem especially for users with constrained resources. In this paper, we propose a new and efficient feature selection technique for intrusion detection in modern networks called Iterative Per Group Feature Selection (IPGFS). IPGFS reduces the number of features in the input data and selects the best features using the performance accuracy of the classifier. The features are sorted and selected according to their accuracy score. Both the UNSW and NSLKDD datasets are used in this paper to validate the proposed model and verify its efficiency in detecting intrusions. The simulation results show that the proposed model can reduce the number of features for the two dataset while successfully detecting intrusions with better accuracy compared to state-of-the-art techniques. Index Cloud security, feature selection, accuracy, machine learning techniques.

Recently, machine learning techniques are gaining a lot of interest in security applications as they exhibit fast processing with real-time predictions. One of the significant challenges in the implementation of these techniques is the collection of a large amount of training data for each new potential attack category, which is most of the time, unfeasible. However, learning from datasets that contain a small training data of the minority class usually produces a biased classifiers that have a higher predictive accuracy for majority class(es), but poorer predictive accuracy over the minority class. In this paper, we propose a new designed attacks weighting model to alleviate the problem of imbalanced data and enhance the accuracy of minority classes detection. In the proposed system, we combine a supervised machine learning algorithm with the node1past information. The machine learning algorithm is used to generate a classifier that differentiates between the investigated attacks. Then, the system stores these decisions in a database and exploits them for the weighted attacks classification model. Thus, for each attack class, the weight that maximizes the detection of the minority classes will be computed and the final combined decision is generated. In this work, we use the UNSW dataset to train the supervised machine learning model. The simulation results show that the proposed model can effectively detect intrusion attacks and provide better accuracy, detection rates and lower false alarm rates compared to state-of-the art techniques.1In this document we will use the words “node” to represent computing, storage, physical, and virtual machines.

With the advancement of rich media generating devices, the proliferation of live Content Providers (CP), and the availability of convenient internet access, crowdsourced live streaming services have witnessed unexpected growth. To ensure a better Quality of Experience (QoE), higher availability, and lower costs, large live streaming CPs are migrating their services to geo-distributed cloud infrastructure. However, because of the dynamics of live broadcasting and the wide geo-distribution of viewers and broadcasters, it is still challenging to satisfy all requests with reasonable resources. To overcome this challenge, we introduce in this paper a prediction driven approach that estimates the potential number of viewers near different cloud sites at the instant of broadcasting. This online and instant prediction of distributed popularity distinguishes our work from previous efforts that provision constant resources or alter their allocation as the popularity of the content changes. Based on the derived predictions, we formulate an Integer-Linear Program (ILP) to proactively and dynamically choose the right data center to allocate exact resources and serve potential viewers, while minimizing the perceived delays. As the optimization is not adequate for online serving, we propose a real-time approach based on Reinforcement Learning (RL), namely RL-OPRA, which adaptively learns to optimize the allocation and serving decisions by interacting with the network environment. Extensive simulation and comparison with the ILP have shown that our RL-based approach is able to present optimal results compared to heuristic-based approaches.

To support multimedia applications, Mobile Edge Computing (MEC) servers offer storage and computing capacities to handle videos close to end-users. However, the high load in peak hours consumes the limited available bandwidth of existing cellular and backhaul links leading to low network performance. Hence, an elastic system model is required to maintain the high Quality of Experience (QoE) as the resource demands increase. Caching popular videos at mobile devices is considered a promising technique for content delivery. Yet, mobile users offer small capacities that are not adequate for large-sized video sharing. In this paper, we extend the collaborative caching and processing framework in edge networks (Collaborative Edge - CE) to include the users' mobile video sharing (Device-to-Device - D2D). We propose a caching strategy to cache only the chunks of videos to be watched and instead of offloading one video content by one edge node, helpers (MEC servers and users) will collaborate to store and share different chunks to optimize the storage/transmission resources usage. To only cache popular contents, we designed a D2D-aware proactive chunks caching on users’ devices based on our chunks popularity model. Next, we formulate this CE-D2D collaborative problem as a linear program. Due to the NP-hardness of the problem, we introduce a sub-optimal relaxation and an online heuristic using the proactive caching and presenting a near optimal data offloading and a profitable payment determination, with polynomial time complexity. The simulation results show that our policies and heuristics outperform other edge caching approaches by more than 10% in terms of hit ratio, average delay, and cost.

In this work, we present a unified framework for the performance analysis of dual-hop underwater wireless optical communication (UWOC) systems with amplify-and-forward fixed gain relays in the presence of air bubbles and temperature gradients. Operating under either heterodyne detection or intensity modulation with direct detection, the UWOC is modeled by the unified mixture Exponential-Generalized Gamma distribution that we have proposed based on an experiment conducted in an indoor laboratory setup and has been shown to provide an excellent fit with the measured data under the considered lab channel scenarios. More specifically, we derive the cumulative distribution function (CDF) and the probability density function of the end-to-end signal-to-noise ratio (SNR) in exact closed-form in terms of the bivariate Fox's H function. Based on this CDF expression, we present novel results for the fundamental performance metrics such as the outage probability, the average bit-error rate (BER) for various modulation schemes, and the ergodic capacity. Additionally, very tight asymptotic results for the outage probability and the average BER at high SNR are obtained in terms of simple functions. Furthermore, we demonstrate that the dual-hop UWOC system can effectively mitigate the short range and both temperature gradients and air bubbles induced turbulences, as compared to the single UWOC link. All the results are verified via computer-based Monte-Carlo simulations.

Flow latency monitoring is a fundamental task in network measurement. The development of software defined networking enables flexible flow latency monitoring in the control plane. Existing approaches mainly focus on direct probe-based latency measurement, which has a high measurement overhead, especially in high accuracy monitoring systems. In this paper, we revisit software defined latency monitoring framework and explore a cost-effective approach named CFlam to produce flow latency results. We observe large scale latency monitoring generates too many duplicate probe packets. Based on this observation, we attempt to measure only a very small subset of active flows to infer the rest flow latencies. We formulate the monitoring flow selection problem by an algebraic model, and develop an efficient algorithm to generate the optimal probe flow set. We implement and deploy CFlam on a SDN testbed to verify its feasibility and performance. We conduct experiments on a public-available network topology with real packet traces collected from a data center. Experiment results demonstrate that our scheme generates accurate flow latencies at minimum measurement overhead.

A unified statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical communication (UWOC) channels in the presence of air bubbles and temperature gradient for fresh and salty waters, based on experimental data. In this model, the channel irradiance fluctuations are characterized by the mixture exponential-generalized gamma (EGG) distribution. We use the expectation-maximization algorithm to obtain the maximum likelihood parameter estimation of the new model. Interestingly, the proposed model is shown to provide a perfect fit with the measured data under all channel conditions for both types of water. The major advantage of the new model is that it has a simple mathematical form making it attractive from a performance analysis point of view. Indeed, we show that the application of the EGG model leads to closed-form and analytically tractable expressions for key UWOC system performance metrics such as the outage probability, the average bit-error rate, and the ergodic capacity. To the best of our knowledge, this is the first-ever comprehensive channel model addressing the statistics of optical beam irradiance fluctuations in underwater wireless optical channels due to both air bubbles and temperature gradient.

This paper considers the problem of energy-delay tradeoff in wireless networks using aN-policy queueing system based scheduler. A novel analytical model for N-policy queueing system is proposed and tested against other established models and simulation results. Using the model, we argue that N-policy queueing system does not necessarily save more energy as N increases. Based on the analytical and simulation results, we present a scheme for the optimal selection of N for a given arrival and service rate. Simulation results based on applying this framework on sensor networks show that the proposed schemes outperforms previous work in the area. Furthermore, an adaptive N-policy system design is illustrated and shown to save energy while satisfying delay requirements.

Motivated by the poor performance of MPTCP when used for bulk transfers in multipathed networks, in this paper we propose an efficient MPTCP protocol variant named eMPTCP aiming to achieve high throughput, low latency and good bottleneck fairness. The novel MPTCP variant eMPTCP can prevent in-network buffer overflow and packet loss using a distributed and reactive approach for bandwidth allocation and adjusting the congestion window of subflows on multiple paths in a coordinated fashion. It employs ECN feedback and latency (in terms of RTT) to modulate the congestion window via a gamma correction function. This novel congestion- and latency-aware window adjustment mechanism behaves very helpful in handling the traffic bursts, easing the buffer pressure on switches, and greatly mitigates the incast issue. Besides, in this solution SDN is leveraged to compute a set of optimal available routes for subflows and actively adjust the number of subflows of each MPTCP flow according to the instantaneous traffic condition. Moreover, this novel MPTCP protocol variant eMPTCP works well with existing switch hardware and is able to coexist with legacy TCP. It ensures that a multipath flow will not take up more capacity on any shared paths than if it was a single path TCP flow using only one of those paths, which guarantees it will not unduly harm other flows. Simulation results show that eMPTCP achieves smaller MPTCP convergence time, higher aggregate throughput and lower flow completion time than existing legacy competitors, and largely improves the application performance and user experience, making the network more robust and faster.

The broadcast nature of wireless networks makes them vulnerable to eavesdropping attacks; therefore, physical layer security (PLS) becomes essential to protect the signal at the physical layer. In this paper, we investigate PLS aspects in terms of the secrecy capacity (SC) in hybrid radio frequency (RF)/visible light communication (VLC) networks. First, we design RF-and VLC-based beamforming vectors to maximize the achievable SC. Moreover, using these vectors, we solve the power minimization problem satisfying the required SC. The results provide useful insights into how the eavesdropper's location affects the power consumption profile. Finally, the results reveal that the performance of the VLC network in terms of the consumed power per bits/s/Hz is more efficient than the RF one.

Today's data center networks are usually over-provisioned for peak workloads. This leads to a great waste of energy since in practice traffic rarely ever hits peak capacity resulting in the links being under-utilized most of the time. Furthermore, the traditional non-traffic-aware routing mechanisms worsen the situation. From the perspective of resource allocation and routing, this paper aims to implement a green data center network and save as much energy as possible. With the benefit of blocking island paradigm, we present a general framework trying to maximize the network power conservation and minimize sacrifices of network performance and reliability. The bandwidth allocation mechanism together with power-aware routing algorithm achieve a bandwidth guaranteed green tighter network. Moreover, our fast efficient heuristics for allocating bandwidth enable the system to scale to large sized data centers. The evaluation result shows that achieving up to more than 50 percent power savings are feasible while guaranteeing network performance and reliability.

A novel statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical channels in the presence of air bubbles for fresh and salty waters, based on experimental data. In this model, the channel irradiance fluctuations are characterized by the mixture Exponential-Gamma distribution. We use the expectation maximization (EM) algorithm to obtain the maximum likelihood parameter estimation of the new model. Interestingly, the proposed model is shown to provide a perfect fit with the measured data under all the channel conditions for both types of water. The major advantage of the new model is that it has a simple mathematical form making it attractive from a performance analysis point of view. Indeed, the application of the Exponential-Gamma model leads to closed-form and analytically tractable expressions for key system performance metrics such as the outage probability and the average bit-error rate.

As an efficient network innovation enabler, software defined network is designed to address the networking needs that are poorly addressed by existing networks, and makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. The OpenFlow API enables secure communication between controllers and switches, and standardizes the communications. However, there exist critical issues during the procedure of distributing network policies among the switches (especially for in-band communication schemes), which impose various implicit negative impacts on network reliability and efficiency, such as additional computation overhead on both controllers and switches, communication overhead on secure channel, and waste of storage resources on switches. Based on these observations, this paper proposes four practical solutions to deal with these issues. The evaluation results reveal that the proposed solutions reduce the network latency by 50%, improve the goodput by 10-15%, and decrease the hardware cost by 25% at most, which convinces the effectiveness of proposed solutions.

This paper proposes a three-stage buffered Clos-network switch (TSBCS) architecture along with a novel batch scheduling (BS) mechanism. We found that TSBCS/BS can be mapped to a “fat” combined input-crosspoint queued (CICQ) switch. Consequently, the well-studied CICQ scheduling algorithms can be directly applied in TSBCS. Moreover, BS drastically reduces the time complexity of TSBCS scheduling when compared with ordinary CICQ switches of the same number of switch ports, which enables us to build a larger-capacity switch with reasonable scheduling complexity. We further show that TSBCS/BS can achieve 100 percent throughput under any admissible traffic if a stable CICQ scheduling algorithm is used. Direct cell forwarding schemes are proposed to overcome the performance drawback of BS under light traffic loads. With extensive simulations, we show that the performance of TSBCS/BS is comparable to that of output-queued switches and the latter are usually considered as theoretical optimal.

Rate adaptation is an essential component in today's wireless standards, which help approach the channel capacity and maximize the throughput. However, how to estimate the optimal data rate in a fluctuated channel remains of great concern. Previous wisdoms leverage PHY layer information for rate estimation, including confidence information like SoftPHY hints, and channel state information (CSI) measurements. However, when experiencing rapid time varying and frequency selective fading channel, the above metrics can be inaccurate. The reason roots from the fact that there are not enough cost-efficient pilots, which are pre-known symbols inserted in a packet for channel estimation. In this paper, we observe that by digging into both PHY layer decoder and upper layer protocol headers, more reliable data bits with high confidence level can be exploited. These data bits, termed smart pilot, can be used to calibrate the channel estimation measurements cost-efficiently. Based on the calibrated estimation, we further propose a novel greedy rate selection algorithm to track the optimal data rate, which successfully avoids the impact of deep fading subcarriers in both legacy 802.11a/g and 802.11n MIMO systems. Our experiments on GNU radio testbed show that SmartPilot quickly tracks the link variance, and improve the channel estimation accuracy by 87%. Furthermore, the trace driven simulation reveals that greedy rate selection algorithm predicts the data rate as good as the optimal rate adaptation algorithms for 802.11 standards.

To support the fast growing cloud computing services and provide a core infrastructure to meet the increasing computing and storage requirements, the number of servers in today's data centers is expanding exponentially, which leads to enormous challenges in designing an efficient and cost-effective data center network. Traditional proposals either suffer from poor reliability, endure performance bottleneck, scale too slowly, or are expensive to construct. Motivated by these challenges, this paper presents the design, analysis, and implementation of JieLin, a novel server- centric network architecture that has many desirable features for data center networking. Besides the excellent scalability and good fault tolerance, JieLin also achieves high performance in bisection bandwidth, average path length, aggregate bottleneck throughput, and cost-effectiveness. Moreover, in order to maximize the theoretical performance of JieLin a congestion- aware fault-tolerant adaptive routing algorithm has been specially designed. In addition to theoretical analysis, extensive simulations are conducted to further prove the feasibility and good performance of JieLin.

In this paper, we present the design, analysis, and implementation of a novel data center network architecture named CLOT, which delivers significant reduction in the network diameter, network latency, and infrastructure cost. CLOT is built based on a switchless torus topology by adding only a number of most beneficial low-end switches in a proper way. Forming the servers in close proximity of each other in torus topology well implements the network locality. The extra layer of switches largely shortens the average routing path length of torus network, which increases the communication efficiency. We show that CLOT can achieve lower latency, smaller routing path length, higher bisection bandwidth and throughput, and better fault tolerance compared to both conventional hierarchical data center networks as well as the recently proposed CamCube network. Coupled with the coordinate based translated IP addresses, the carefully designed POW routing algorithm helps CLOT achieve its maximum theoretical performance. The sufficient mathematical analysis and theoretical derivation prove both guaranteed and ideal performance of CLOT.

Partially overlapped channels are barely used for concurrent transmission in WiFi networks, since they lead to collisions where the collided packets cannot be decoded successfully. In this paper, we observe that the actual corrupted symbols by partial-channel interference in OFDM-based WiFi networks are not as severe as we expected. There remains extra coding redundancy that can be exploited from the corrupted symbols, and utilized for packet recovery. Accordingly, we present a novel paradigm termed Piros, in order to Push the lImits of partially concurrent transmission in WiFi networks. Piros strategically leverages the coding redundancy according to the overlap portion in a distributed manner, and extracts useful decoding information from the corrupted symbols to decode the packet with partial-channel interference.

The rapid development of software defined measurement has significantly improved network measurement and monitoring. The key challenge for software defined measurement is to design a low-cost measurement framework which has minimum impact on the network. The state-of-the-art approaches mainly focus on reducing the measurement overhead by sampling or aggregation. However, little attention has been devoted to eliminating this issue in the physical layer. We observe that the placement of the controllers significantly affects the measurement overhead for software defined measurement. Based on this observation, we rethink software defined measurement frameworks and propose a novel scheme to minimize the measurement overhead. Our approach is application-agnostic, cost-effective and robust to traffic dynamics. We formulate the measurement-aware distributed controller placement (MDCP) problem as a quadratic integer programming problem, which takes both the synchronization cost and the flow statistics collection cost into account. Due to its high computational complexity, we develop two novel algorithms to efficiently approximate near-optimal placements. In particular, we employ an algorithm with an approximation ratio of 1.61 to obtain the placement in the discrete approximation algorithm. We conduct experiments on over 240 real network topologies and the results demonstrate the effectiveness of MDCP. Trace-driven simulations verify that our proposal is robust to traffic dynamics and can reduce 40% of the measurement overhead on average.

Sketch-based measurement provides traffic data summary in a memory-efficient way with provable accuracy bound. Recent advances in software defined networking (SDN) facilitate the development and implementation of sketch-based measurement applications. However, sketch-based measurement usually requires TCAMs which are precious resource in switch to match packet fields. The key challenge for sketch-based measurement is how to accept more concurrent measurement tasks with minimum resource usage. Existing proposals attempt to achieve this goal by exploring different task assignment algorithms. We argue that by sacrificing a small amount of accuracy, the resource usage can be decreased dramatically. In this paper, we propose COSTA, a novel system to improve the performance of the task assignment for sketch-based measurement. We utilize the cross-layer information between the application and the task assignment layers to formulate the problem as a mixed integer nonlinear programming problem. Due to its high computational complexity, we divide the initial problem into two stages and develop a two-stage heuristic to produce task assignment efficiently. In particular, we present an algorithm which guarantees (1 + α) approximation ratio to solve the second stage task assignment. Extensive experiments with three different measurement tasks and real packet traces demonstrate that COSTA significantly reduces the resource usage by up to 40% and accepts 30% more tasks.

Network monitoring and measurement are crucial in network management to facilitate quality of service routing and performance evaluation. Software Defined Networking (SDN) makes network management easier by separating the control plane and data plane. Network monitoring in SDN is lightweight as operators only need to install a monitoring module into the controller. Active monitoring techniques usually introduce too many overheads into the network. The state-of-the-art approaches utilize sampling method, aggregation flow statistics and passive measurement techniques to reduce overheads. However, little work in literature has focus on reducing the communication cost of network monitoring. Moreover, most of the existing approaches select the polling switch nodes by sub-optimal local heuristics. Inspired by the visibility and central control of SDN, we propose FlowCover, a low-cost high-accuracy monitoring scheme to support various network management tasks. We leverage the global view of the network topology and active flows to minimize the communication cost by formulating the problem as a weighted set cover, which is proved to be NP-hard. Heuristics are presented to obtain the polling scheme efficiently and handle flow changes practically. We build a simulator to evaluate the performance of FlowCover. Extensive experiment results show that FlowCover reduces roughly 50% communication cost without loss of accuracy in most cases.

This paper presents the design, analysis, and implementation of a novel data center network architecture, named NovaCube. Based on regular Torus topology, NovaCube is constructed by adding a number of most beneficial jump-over links, which offers many distinct advantages and practical benefits. Moreover, in order to enable NovaCube to achieve its maximum theoretical performance, a probabilistic oblivious routing algorithm PORA is carefully designed. PORA is a both deadlock and livelock free routing algorithm, which achieves near-optimal performance in terms of average routing path length with better load balancing thus leading to higher throughput. Theoretical derivation and mathematical analysis further prove the good performance of NovaCube and PORA.

From the perspective of resource allocation and routing, this paper aims to save as much energy as possible in data center networks. We present a general framework, based on the blocking island paradigm, to try to maximize the network power conservation and minimize sacrifices of network performance and reliability. The bandwidth allocation mechanism together with power-aware routing algorithm achieve a bandwidth guaranteed tighter network. Besides, our fast efficient heuristics for allocating bandwidth enable the system to scale to large sized data centers. The evaluation result shows that up to more than 50% power savings are feasible while guaranteeing network performance and reliability.

Recently, there is a massive growth in the amount of wireless video traffics. To increase the network efficiency and cater to the needs of Quality of Experience (QoE), researchers propose hybrid MAC schemes, with multiple MAC protocols anchored in a single MAC layer. Different MAC protocols are selected for diverse users' requirements. However, the existing hybrid MAC mainly combines various MAC protocols in time domain. Only one type of traffic can be satisfied at a time, and others have to endure poor QoE. Meanwhile, the channel resources are not fully utilized due to coarse usage in time domain. Motivated by this, we propose FC-MAC, a Fine-grained Cognitive MAC for video streaming in wireless networks. Instead of dividing channel time into slots, FC-MAC splits the channel frequency into fine-grained subchannels, and hybrid different MAC protocols in frequency domain. By dynamically adjusting the bandwidth and the MAC protocols according to the users' needs and channel condition, FC-MAC ensures the QoE at low cost and achieves high multiplexing gain. We conduct extensive simulations to verify the effectiveness of FC-MAC. Numerous results show that compared with time domain hybrid MAC, FC-MAC achieves 190% performance gain.

Reducing the power consumption of packet switches is becoming increasingly significant to future networks. However, previous research all focused on reducing power in crossbar-based switches, which is either complex or not effective, especially in some extreme cases. This paper proposes to leverage the dynamic voltage and frequency scaling (DVFS) technique in the buffered crossbar-based switches, which is more flexible and simple. The basic idea is to decrease the working frequencies of the crosspoint buffers while still preserving the maximum throughput and the satisfactory delay. Traffic estimators are used at the input and output ports to estimate the traffic arrival rates, based on which the power controller can adjust the working frequencies of the crosspoint buffers at a fine-grained level. Simulation results show that the scheme is effective.

Software defined networking (SDN), which enables programmability, has the advantage of global visibility and high flexibility. However, when forwarding new flows in SDN, the interaction between the switch and the controller imposes extra latency such as round-trip time and routing path search time. Even though such latency is acceptable for elephant flows since it only takes limited ratio of total transmission time of elephant flows, it is an overkill to pay certain overheads for mice flows due to the their short transmission time. Moreover, the controller is frequently invoked by the mice flows since the number of mice flows accounts for a large portion of the total number of flows. Hence, the frequent controller invocation is mainly responsible for the controller performance degradation, and thus increasing the flow setup latency significantly. To solve this problem, we propose CheetahFlow, a novel scheme to predict frequent communication pairs via support vector machine and proactively setup wildcard rules to reduce flow setup latency. Particularly, in order to avoid congestion along a fixed path, elephant flows are detected and rerouted to the non-congestion path efficiently by applying blocking island paradigm. Extensive experiments show that CheetahFlow prominently reduces latency without any loss of flexibility of SDN.

This paper presents the design, implementation and evaluation of SprintNet, a novel network architecture for data centers. SprintNet achieves high performance in network capacity, fault tolerance, and network latency. SprintNet is also a scalable, yet low-diameter network architecture where the maximum shortest distance between any pair of servers can be limited by no more than four and is independent of the number of layers. The specially designed routing schemes for SprintNet strengthen its merits. Both theoretical analysis and simulation experiments are conducted to evaluate its overall performance with respect to average path length, aggregate bottleneck throughput, and fault tolerance.

Data center network architecture is regarded as one of the most important determinants of network performance. As the most typical representatives of architecture design, the server-centric scheme stands out due to its good performance in various aspects. However, there still exist some critical shortcomings in these server-centric architectures. In order to provide an efficient solution to these shortcomings and improve the efficiency of server-centric architectures, in this paper, we propose a hardware based approach, named “Forwarding Unit”. Furthermore, we put forward a traffic aware routing scheme for FlatNet to further evaluate the feasibility and efficiency of our approach. Both theoretical analysis and simulation experiments are conducted to measure its overall performance with respect to cost-effectiveness, fault-tolerance, system latency, packet loss ratio, aggregate bottleneck throughput, and average path length.

Rate adaptation is an essential component in today's wireless standards, for its ability to adaptively approach the channel capacity, and maximize the system throughput. The difficulty in rate adaptation stems from estimating the optimal data rate in a fluctuated channel. Previous wisdoms leverage PHY layer information for rate estimation, such as Soft PHY hints or Channel State Information. These information solely comes from one same layer, which are insufficient to track the optimal data rate. We observe that by investigating the information in both PHY layer decoder and upper layer protocol headers, more pilots can be exploited to estimate the optimal data rate across both time and frequency domain. These smart pilots help remove the residual channel effect and calibrate the CSI with minimum overhead. Based on the calibrated CSI, we propose a novel greedy rate selection algorithm to harness frequency diversity, which obtains the optimal data rate over all the subcarriers. Our experiments on GNU radio test bed show that Smart Pilot quickly tracks the link variance, and reduces the residual channel effect by 87%. Further, the trace driven simulation reveals that greedy rate selection algorithm predicts the data rate as good as the optimal rate adaptation algorithms for 802.11 standards.

New PHY and MAC layer enhancements have been introduced in different IEEE 802.11 WLAN standards. These improvements have given birth to a wide range of bandwidth channels (up to 160MHz) and very high transmission data rates (> 1Gbps) to keep up with current and up-coming multimedia applications. The major reason for the inefficiency of different WLANs is that the current MAC layer randomly allocates the entire channel to only one user as a single resource. Thus, one way to significantly improve WLAN performance is to effectively reduce the channel width and create different sized sub-channels. Based on the user's channel conditions and QoS requirements, the PHY layer resources can be dynamically allocated to several users at the same time. In this paper, we present a novel dynamic sub-channels access approach, titled DMU-Access1, with new features suitable for the forthcoming large bandwidth WLAN products. We shall evaluate its performance compared to state-of-the-art systems, using the simulator NS-3 under a variety of network conditions. Our experiments will then demonstrate that our scheme enhances IEEE 802.11 devices' performance with a much higher degree of throughput.

The articles i nthis special issue focus on switching and routing applications for scalable and energy efficient networking.

Wireless local area networks (WLANs) are becoming increasingly popular due to the recent availability of affordable devices providing multiple and high rate capabilities. New PHY and MAC layer enhancements have been introduced in different IEEE 802.11 WLAN standards. These improvements have given birth to a wide range of bandwidth channels (up to 160 MHz) and very high transmission data rates (> 1 Gbps) to keep up with current and up-coming multimedia applications. The MAC layer in current WLANs randomly allocates the entire channel to only one user as a single resource, which, unfortunately, reduces the efficiency of WLANs. One way to significantly improve WLAN performance is to reduce the channel width and create many sub-channels. Based on the user's channel conditions and QoS requirements, the PHY layer resources can be dynamically allocated to several users at the same time. In this paper, we shall present a novel sub-channels access approach, titled MU-Access, with new features suitable for the forth-coming high-speed MIMO-based WLAN products. We will evaluate its performance compared to state-of-the-art systems, using NS-3 under a variety of network conditions. Our experiments will then demonstrate that our proposed scheme enhances the performance of IEEE 802.11 devices with a much higher throughput.

It is known that current fixed spectrum assignment policy has made the spectrum resource significantly underutilized. As a promising solution, cognitive radio emerges and shows its advantages. It allows the unlicensed users to opportunistically access the spectrum not used by the licensed users. To ensure that the unlicensed users can identify the vacate spectrum fast and accurately without interfering the licensed users, cooperative sensing is explored to improve the sensing performance by leveraging spatial diversity. However, cooperation gain can be compromised dramatically with cooperation overhead. Furthermore, when sensing decisions are made, contention on spectrum access also contributes a lot to the control overhead, especially in the distributed networks. Motivated by this, we propose a novel MAC design, termed Frequency domain Cooperative sensing and Multi-channel contention (FCM) for Cognitive Radio Ad Hoc Networks (CRAHNs). FCM is proposed for OFDM (Orthogonal Frequency Division Multiplexing) modulation based communication systems, which moves cooperative sensing and multi-channel contention from time domain into frequency domain. Therefore, control overhead caused by cooperation and contention can be significantly reduced. Meanwhile, the sensing and access performance can be both guaranteed. Extensive simulation results show that FCM can effectively reduce the control overhead, and improve the average throughput by 220% over Traditional Cooperative MAC for CRAHNs.

Recently, rateless codes have displayed a promising paradigm for rate adaptation. They enable the sender to transmit packets with a fixed data rate, and the receiver to decode the packets with a data rate comparable to its channel condition. Therefore, rateless codes can achieve higher wireless throughput than the fixed rate codes, especially over time-varying channels. However, as wireless multicarrier techniques become essential technologies in current communication systems, the design of rateless code exposes a critical problem. That is, it is not able to leverage frequency diversity introduced by multi-carrier techniques, which will degrade the achievable data rate. Motivated by this, we propose a Full Duplex Rateless (FDR) code in frequency domain. It aims to take advantage of the frequency diversity and fine-grained feedback to achieve the “most appropriate” data rate on every single subcarrier in a multicarrier communication system. Extensive simulations have been conducted to verify the effective of FDR code, and the results show that FDR code achieves up to 6.5× data rate over the fixed-rate LDPC codes, and 2.3× data rate over rateless Spinal code.

Spectrum auction is widely applied in spectrum redistributions, especially under the dynamic spectrum management context. However, due to the high price asked by the spectrum holders, secondary users (SUs) with limited budget cannot benefit from such auction directly. Motivated by the recent group-buying behaviors in the Internet based service, we advocate that SUs can be grouped together to take part in the spectrum auction as a whole to increase their chances to win the channel. The cost and benefit of the won spectrum are then shared evenly among the SUs within the group. None of the existing auction models can be applied in this scenario due to three unique challenges: how can a group leader select the winning SUs and charge them fairly and efficiently; how to guarantee truthfulness of users' bids; how to match the heterogeneous channels to groups when one group would like to buy at most one channel. In this paper, we propose TASG, a Three-stage Auction framework for Spectrum Group-buying to address the above challenges and enable group-buying behaviors among SUs. In the first stage, we propose an algorithm to decide the group members and bids for the channels. In the second stage, we conduct auction between the group leaders and the spectrum holder, with a novel winner determination algorithm. In the third stage, the group leaders further distribute spectrum and bills to the SUs in the group. TASG possesses good properties such as truthfulness, individual rationality, improved system efficiency, and computational tractability.

Wireless Local Area Networks (WLANs) have become increasingly popular due to the recent availability of affordable devices providing multiple and high rate capabilities. In different IEEE 802.11 WLAN standards, new PHY and MAC layer enhancements have been introduced. These improvements have given birth to a wide range of bandwidth channels and very high transmission rates (> 1 Gbps) to keep up with current and up-coming multimedia applications. However, the major reason for the inefficiency of different WLANs is that the current MAC layer randomly allocates the entire channel to only one user as a single resource. Thus, we argue that a better way to improve WLAN performance is to effectively reduce the channel width and create different sized sub-channels, and serve several users at the same time. In this paper, we present a novel sub-channel access approach, termed MU-Access 1, with new features suitable for the forthcoming high-speed MIMO-based WLAN products. We implement our idea and evaluate its performance compared to the existing system, using NS-3. Our experiments demonstrate that our scheme allows 802.11 devices to give a better result.

Leveraging concurrent transmission is a promising way to improve throughput in wireless networks. Existing media access control (MAC) protocols like carrier sense multiple access always try to minimize the number of concurrent transmissions to avoid collision, although collisions at sender sides are harmless to the overall performance. The reason for such conservative strategy is that those protocols cannot obtain accurate channel status (who is transmitting and receiving) with low cost. They can only avoid potential collisions through rough channel status (idle or busy). To obtain additional information in a cost-efficient way, we propose a novel coding scheme, Attachment Coding, to allow control information to be “attached” on data packet. Nodes then transmit two kinds of signals simultaneously, without degrading the effective throughput of the original data traffic. Based on Attachment Coding, we propose an Attached-RTS MAC (AR-MAC) to exploit exposed terminals for concurrent transmissions. The attached control information provides accurate channel status for nodes in real time. Therefore, nodes can identify exposed terminals and utilize them for concurrent transmission. We theoretically analyze the feasibility of Attachment Coding, and implement it on the GNU Radio testbed to further verify it. We also conduct extensive simulations to evaluate the performance of Attached-RTS. The experimental results show that by leveraging Attachment Coding, AR-MAC achieves up to 180 percent in dense deployed ad hoc networks.

Femtocells' ability to improve the in-building coverage and capacity in a cost-efficient way has drawn significant attention from mobile operators. However, a mobile operator may lack a fixed-line network infrastructure, which is indispensable for enabling femtocell service. In this paper, we propose a hybrid cooperation framework where a mobile operator can collaborate with a fixed-line operator (as a virtual integrated operator) to provide femtocell service to indoor users. The framework consists of sequential game and Nash bargaining. The sequential game models the interactions of the operator and users. Specifically, the operator announces the price for wireless services first and then the users decide their spectrum demands in response to the given price. Then the two operators divide the cooperation benefit according to the Nash bargaining model, which makes the profit sharing fair and cooperation framework amenable to operators. We theoretically derive the unique closed-form equilibrium for the framework as well as the conditions that promote the cooperation. The simulation results verify that the cooperation framework can make more revenue for the operators and the spectrum efficiency is significantly improved.

Wireless Local Area Networks (WLANs) have become increasingly popular due to the recent availability of affordable devices providing multiple and high rate capabilities. Optimizing the performance of WLANs for emerging new Internet applications that demand High Throughput (HT) is an important and a highly challenging issue. In this paper, we present a new practical Rate Adaptation (RA) algorithm, termed L3S, which extends legacy schemes with new MIMO features suitable for the forthcoming 802.11n high-speed MIMO-based WLAN products. We have implemented our rate adaptation algorithm in real hardware devices and evaluated its performance and compared it to the existing rate control mechanisms. Our experiments demonstrate that our scheme allows the current 802.11n devices to have a greater adaptability to a variety of wireless channel conditions and performs better than state-of-the-art rate adaptation algorithms.

Wireless technology has become ever more popular in recent years, which results in a higher and higher density of wireless devices. In order to cope with this high density, researchers are proposing the provision of multiple concurrent transmissions by dividing a broadband channel into separate narrow band subchannels. In particular, a fine-grained channel access approach calls for efficient channel allocation mechanisms, especially in distributed networks. However, most of the current multi-channel access methods rely on costly coordination, which significantly degrades network performance. Motivated by this, we propose a cross layer design, termed Attachment Learning (AT-Learning), to achieve multi-channel allocation with low cost and high efficiency in distributed OFDMA based networks. AT-Learning utilizes a jamming and cancellation technique to attach identifier signals to data traffic, without degrading the effective throughput of the original data transmission. These identifier signals help mobile stations learn the allocation strategy by themselves. After the learning stage, mobile stations can achieve a TDMA-like performance, where stations will know exactly when to transmit and on which channel without further collisions. We conduct comprehensive simulations, comparing AT-Learning with a traditional multi-channel access method like Slotted ALOHA. The experimental results demonstrate that AT-Learning can improve the throughput by up to 300% over Slotted ALOHA.

The design of the data center network that interconnects the massive number of servers is very important to ensure the agility and robustness of the data center to meet the requirements of the applications. In response to this challenge, the research community have begun exploring novel interconnect topologies including FatTree, DCell, BCube, HyPaC, etc. However, the solutions proposed so far either scale too slowly, suffer from performance bottlenecks, are server-location dependent, inherit poor availability, or can be too complex/expensive to construct. Motivated by these very important challenges, we propose a new data center interconnect called FlatNet that combines the advantages of previous architectures while avoiding their limitations. FlatNet is a cost-effective, high-performance, reliable and scalable interconnect with almost flat architecture. For example, given an equal-sized data center, the costs of a FlatNet in terms of number of links and switches are roughly 2/3 and 2/5 that of Portland, while still delivering comparable overall performance.

The stable subscription relationships between users and Wireless Service Providers (WSPs) are established on the long-term service contracts and SIM (Subscriber Identity Module) cards. However, Apple's “Soft Sim” plan will break the subscription relationships. With SoftSim devices, users can flexibly switch WSPs at fine time scales, which provides more choices to users and benefits them. Thus more and more users will turn to SoftSim devices. But due to many challenges unsettled, it is likely that SoftSim users will coexist with regular users who are bound to certain WSPs for a long time. How to form best strategies to attract more demand, fully utilize network capacity and thus improve the revenue in the market with heterogeneous users remains a challenge for WSPs. In this paper, we address the problem of WSPs' revenue maximization in a duopoly market with the coexistence of regular and SoftSim users. We formulate the scenario as a non-cooperative two-stage game where the WSPs first announce their prices and then users decide the subscription and demand response. We derive the unique Nash Equilibrium (NE) under our framework and also give a convergence algorithm based on best response functions. The simulation results verify our theoretic conclusions.

Radio spectrum resource is shown to be significantly underutilized with fixed spectrum assignment policy. As a promising solution, cognitive radio allows unlicensed users to opportunistically access the spectrum not used by the licensed users. Cooperative sensing is further exploited to improve the sensing performance of unlicensed users by leveraging spatial diversity. However, cooperation gain can be compromised dramatically with cooperation overhead. Furthermore, when sensing decisions are made, contention on spectrum access also becomes an overhead, especially in the distributed networks. Motivated by this, we propose a novel MAC design, termed Frequency domain Cooperative sensing and Multi-channel contention (FCM). FCM moves cooperative sensing and multi-channel contention from time domain into frequency domain. Thus, the control overhead caused by cooperation and contention can be significantly reduced, without reducing the sensing and access performance. Extensive simulation results show that FCM can effectively reduce the control overhead, and improve the average throughput by 220% over Traditional Cooperative MAC for CRAHNs.

Mega data centers are being built around the world to provide various cloud computing services. As a result, data center networking has recently been a hot research topic in both academia and industry. A fundamental challenge in this research is the design of the data center network that interconnects the massive number of servers, and provides efficient and fault-tolerant routing service to upper-layer applications. In response to this challenge, the research community have begun exploring novel interconnect topologies including Fat-Tree, DCell, and BCube and etc. Understandably, this research is still in its infancy. The proposed solutions either scale too fast (i.e., double exponentially) or too slow, suffer from performance bottlenecks, or can be quite costly in both routing and construction. In this paper, we propose a cost-effective and gracefully scalable data center interconnect termed Hyper-BCube that combines the advantages of both DCell and BCube architectures while avoiding their limitations. We then propose fault-tolerant and routing mechanisms for Hyper-BCube. Finally, we propose a comprehensive benchmarking environment that can be used for accurately and practically evaluating and testing the proposed data center architecture.

Vehicular networks arc novel wireless networks particularly for inter-vehicle communications. In vehicular networks, the current rate adaptation algorithms are not applicable to the new situations {e.g., high mobility, SNR fluctuation and complicated environment). We propose a novel hybrid rate adapt it lion scheme named as GeRA (Generic Rate Adaptation). The key idea of this scheme is to make use of both context information and signal strength information to estimate current channel condition in a much more efficient and accurate way. GeRA dynamically and adaptively switches the rate selection resources between our well-designed context information empirical model and SNR prediction model according the current situation to achieve the high mobility, density and variation. In our extensive empirical experiments and performance evaluation, we compare this scheme with two types of rate adaptation algorithms and one latest vehicular networks rate adaptation. Our experiments in real vehicular environment show that GeRA performs better than other choosing algorithms under different mobility scenarios, different traffic density and different cross-layer protocols. Our scheme achieves significant higher goodput than traditional rate adaptation algorithms, up to 93%. Compared to the context information based algorithm, GeRA also has better performance in most scenarios.

This paper presents the design, implementation, and evaluation of FAST (Full-duplex Attachment System), a cross layer system to solve both Hidden terminal problem and exposed terminal problems. The main reason that CSMA-like protocol cannot well solve hidden and exposed terminal problem both at once lies in the fact that they cannot obtain accurate Channel Usage Information (CUI, who is transmitting or receiving nearby) with low cost. FAST successfully obtains cost CUI in a cost-efficient way, thus can know exactly what are the neighborhood doing currently. FAST includes Attachment Coding in PHY layer to provide cost-effective CUI, and Attachment Sense in MAC layer to utilize CUI to identify hidden and exposed nodes in real time. Extensive simulation results show FAST can well solve both hidden and exposed terminal problems, and improves an average throughput to 200% over CSMA in practical ad-hoc networks.

High-speed routers rely on well-designed packet buffers that support multiple queues, provide large capacity and short response times. Some researchers suggested combined SRAM/DRAM hierarchical buffer architectures to meet these challenges. However, these architectures suffer from either large SRAM requirement or high time-complexity in the memory management. In this paper, we present scalable, efficient, and novel distributed packet buffer architecture. Two fundamental issues need to be addressed to make this architecture feasible: 1) how to minimize the overhead of an individual packet buffer; and 2) how to design scalable packet buffers using independent buffer subsystems. We address these issues by first designing an efficient compact buffer that reduces the SRAM size requirement by (k-1)/k. Then, we introduce a feasible way of coordinating multiple subsystems with a load-balancing algorithm that maximizes the overall system performance. Both theoretical analysis and experimental results demonstrate that our load-balancing algorithm and the distributed packet buffer architecture can easily scale to meet the buffering needs of high bandwidth links and satisfy the requirements of scale and support for multiple queues.

The hidden terminal problem is known to degrade the throughput of wireless networks due to collisions, while the exposed terminal problem results in poor performance by wasting valuable transmission opportunities. As a result, extensive research has been conducted to solve these two problems, such as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). However, CSMA-like protocols cannot solve both of these two problems at once. The fundamental reason lies in the fact that they cannot obtain accurate Channel Usage Information (CUI, who is transmitting or receiving nearby) with a low cost. To obtain additional CUI in a cost-efficient way, we propose a cross layer design, FAST (Full-duplex Attachment System). FAST contains a PHY layer Attachment Coding, which transmits control information independently on the air, without degrading the effective throughput of the original data traffic, and a MAC layer Attachment Sense, which utilizes the PHY layer control information to identify the hidden and exposed nodes in real time. We theoretically analyze the feasibility of the Attachment Coding, and then implement it on a GNU Radio testbed consisting of eight USRP2 nodes. We also conduct extensive simulations to evaluate the performance of FAST, and the experimental results show that FAST can effectively solve both the hidden and the exposed terminal problems, and improve the average throughput by up to 200% over CSMA in practical ad-hoc networks.

Rate adaptation is extremely crucial to the system performance of wireless networks. Existing rate adaptation schemes mainly make use of channel information (e.g., packet error rate or signal strengths of received packets) to adapt transmission rates. In this paper, we find out that it is beneficial for rate adaptation schemes to consider priorities of packets when adapting transmission rates. We then propose a priority-based rate adaptation scheme for vehicular networks which jointly considers channel conditions and priorities of packets using game theory. In our scheme, we consider rate adaptation as a game which consists of different priorities of users and adopt a Stackelberg game model to regulate behaviors of self-interested users. Extensive ns-2 simulations demonstrate that the proposed scheme can provide much better performance for high priority users than existing schemes, while maintaining good performance for low priority users.

Virtual Output Queuing (VOQ) is widely used by input-queued (IQ) packet switches to eliminate the head-of-line (HoL) blocking problem. A lot of research has been devoted to design iterative arbitration algorithms to maximize the throughput of this architecture. Nevertheless, these approaches require either a high computation complexity or large contention resolution times for high-speed switches. We investigate in this paper the performance of various such algorithms and based on the analysis of pointer desynchronization effect, we propose a new algorithm approximating maximum size matching (MSM) called Selective Request Round Robin (SRRR) which performs extremely well under various traffic models and is easy to implement in hardware.

In this paper, we propose a 3-stage framed packet switch using an internal speedup of 2 to avoid any control loop between any two stages of the switch. The switch segments the arriving variable-length packets at each input port into fixed-size cells and assembles the cells into frames. Then the frames are switched across the shared buffers to their destined output ports, and the cells are reassembled into packets before being transmitted to the next hop. We have designed a broad class of work-conserving scheduling algorithms for the proposed switch, and they are analyzed to be stable, i.e. achieving 100% throughput, under any admissible traffic. To gain more insights into the switch practical performance, an extensive performance evaluation study is conducted using computer simulations. Our results demonstrate that the worst-case performance can be bounded. In addition, we are able to achieve a high throughput-delay performance comparable to that of the padded frame switch which uses a much more complicated scheduling algorithm.

Wireless technologies have gained tremendous popularity in recent years, resulting in a dense deployment of wireless devices. Therefore, it is desired to provide multiple concurrent transmissions by dividing a broadband channel into separate sub channels. This fine-grained channel access calls for efficient channel allocation mechanisms, especially in distributed networks. However, most of the current multichannel access methods rely on costy coordination, which significantly degrade their performance. Motivated by this, we propose a cross layer design called Attachment Learning (AT-learning) in distributed OFDMA (Orthogonal Frequency Division Multiple Access) based networks. AT-learning utilizes jamming technique to attach identifier signals on data traffic, where the identifier signals can help mobile stations to learn allocation strategy by themselves. After the learning stage, mobile stations can achieve a TDMA-like performance, where stations can know when exactly to transmit on which channel without further collisions. We conduct comprehensive simulations and the experimental results show that AT-learning can improve the throughput by up to 300% compared with traditional multichannel access method which asks mobile stations to randomly choose channels without learning.

Wireless Local Area Networks (WLANs) have become increasingly popular due to the recent availability of affordable devices providing multiple and high rate capabilities. Optimizing the performance of WLANs for emerging new internet applications that demand High Throughput (HT) is an important and a highly challenging issue. In this paper, we present a novel practical Rate Adaptation (RA) algorithm, termed L3S, which extends legacy schemes with new MIMO features suitable for the forthcoming 802.11n high-speed MIMO-based WLAN products. We implemented our rate adaptation algorithm in real hardware devices and evaluated its performance and compared it to the existing rate control mechanisms. Our experiments will demonstrate that our scheme allows the current 802.11n devices to have a greater adaptability to a variety of wireless channel conditions, and performs better than state-of-the-art rate adaptation algorithms.

High-speed routers rely on well-designed packet buffers that support multiple queues, large capacity and short response times. Some researchers suggested combined SRAM/DRAM hierarchical buffer architectures to meet these challenges. However, these architectures suffer from either large SRAM requirement or high time-complexity in the memory management. Our analysis indicates that they perform exactly the same in the worst case. In this paper, we present a novel packet buffer architecture which reduces the SRAM size requirement by (k-1)/2k, where k denotes the number of DRAMs working in parallel. We use a fast batch load scheme and per-queue Random Round Robin memory management algorithm. Our mathematical analysis and simulation results indicate that the proposed architecture provides guaranteed performance in terms of low time complexity, short access delay and upper-bounded drop rate, when a little speedup is provided.

High-speed routers rely on well-designed packet buffers that support multiple queuing, large capacity and short response times. Some researchers suggested a combined SRAM/DRAM hierarchical buffer architecture to meet these challenges. However, both the SRAM and DRAM need to maintain a large number of dynamic queues which is a real challenge in practice and limits the scalability of these approaches. In this paper, we present a scalable, efficient and novel distributed packet buffer architecture. Two fundamental issues need to be addressed to make this feasible: (a) how to design scalable packet buffers using independent buffer subsystems; and (b) how to dynamically balance the workload among multiple buffer subsystems without any blocking. We address these issues by first designing a basic framework that allows flows to dynamically switch from one subsystem to another without any blocking. Based on this framework, we further devise a load-balancing algorithm to meet the overall system requirements. Both theoretical analysis and experimental results demonstrate that our load-balancing algorithm and the distributed packet buffer architecture can easily scale to meet the buffering needs of high bandwidth links with large number of active connections.

In current high bandwidth-delay-product networks, traditional end-to-end network protocols cannot guarantee the fair allocation of network resources (i.e., a rogue source that sends at an uncontrolled rate can seize a large fraction of the buffers at an intermediate router which results in dropped packets for other connections). Fair-queuing (FQ) algorithms were proposed to overcome this drawback. However, most of these FQ algorithms either suffer from high time-complexity or greatly rely on the multiple queuing structures which are extremely difficult to implement in large scale due to the access delay of DRAM. Based on the analysis on real-life traces, we are able to determine the short-term stability of number of connections in a trunk. Taking this characteristic into consideration, a new FQ algorithm called Budget Round-Robin (BRR) is proposed in this paper. Both theoretical analysis and experimental results demonstrate that BRR and its corresponding memory hierarchy are much superior to the other FQ algorithms when we have a high bandwidth link with large number of active connections (e.g., high-speed Internet).

Dynamic spectrum sharing in cognitive radio networks can enhance flexibility, and as a result the efficiency of spectrum usage. In this paper, we address the problem of spectrum sharing in a cognitive radio network where multiple primary and secondary strategic-users are involved. In this scenario, primary users (PUs) would like to offer part of their spectrum to secondary users (SUs) to make extra revenue. PUs face a trade-off since the more spectrum that is being shared with SUs, the more Quality of Service (QoS) degradation their own service will suffer. SUs access the Internet through an Access Point (AP). They have to decide their best strategies by taking into account service satisfaction and payment lost. The profit of PUs and SUs is directly related to the bandwidth allocation and price charging through the AP. Considering this competitive relationship, we model the scenario as a noncooperative game, and analyze it by exploring the properties of Nash Equilibrium (NE) point. The simulation results support our theoretic analysis.

The time-constraint data traffic is very common in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. Time-constraint flows are those who have fixed start and stop times and try to maximize the transferred data volume during the limited connection time. From the view of users, the total data transmitted by each flow should be proportional to each one's holding time if the network resources are allocated fairly. However, we find that the traditional fairness concept solely based on flow rates is not suitable for this scenario. Therefore, we redefine the fairness concept regarding the application utility for time-constraint flows. According to this utility-based fairness definition, we propose a new practical bandwidth sharing scheme using TCP parameter tuning for transferring data with fast-moving wireless nodes such as vehicles. Simulations through ns-2 show that the proposed algorithm can achieve better utility fairness than the standard TCP and it is also friendly to TCP in the long term.

Abstract The time‐constraint data traffic is very common in vehicle‐to‐infrastructure (V2I) and vehicle‐to‐vehicle (V2V) communications. Time‐constraint flows are those who have fixed start and stop times and try to maximize the transferred data volume during the limited connection time. From the view of users, the total data transmitted by each flow should be proportional to each one's holding time if the network resources are allocated fairly. However, we find that the traditional fairness concept solely based on flow rates is not suitable for this scenario. Therefore, we redefine the fairness concept regarding the application utility for time‐constraint flows. According to this new utility‐based fairness definition, we propose two practical bandwidth sharing schemes for transferring data by fast‐moving wireless nodes such as vehicles. Simulations through ns‐2 show that the proposed algorithms can achieve better utility fairness than the standard TCP and are also friendly to TCP in the long term. Copyright © 2010 John Wiley & Sons, Ltd.

Rate adaptation techniques have been extensively studied for traditional wireless LANs as way to adjust the data transmission bandwidth as a function of the channel quality. However, existing solutions which are mostly based on statistics collection inherently experience a large delay in response to the wireless channel fluctuations, which is unsuitable for the rapid changing of the channel conditions in vehicular networks. In this paper, we propose an efficient self-adaptive model-tree-based rate adaptation scheme termed MTRA that can predict the packet error rate (PER) and adapt the data rate in real time. We also present a detailed methodology for the PER model tree building and update. We have performed comprehensive experimentations using ns-2 simulations which demonstrate that MTRA can achieve much better performance than the traditional rate adaptation approaches under various scenarios in vehicular networks.

Supporting user interactivity in peer-to-peer streaming systems is challenging. VCR-like operations, such as random seek, pause, fast forward and rewind, require timely P2P overlay topology adjustment and appropriate bandwidth resource re-allocation. If not handled properly, the dynamics caused by user interactivity may severely deteriorate users' perceived video quality, e.g., longer start-up delay, frequent playback freezing, or blackout altogether. In this paper, we propose a derivative tree-based overlay management scheme to support user interactivity in P2P streaming system. Derivative tree takes advantage of well organized buffer overlapping to support asynchronous user requests while brings high resilience to the impact of VCR-like operations. A session discovery service is introduced to quickly locate parent peer. We show that the overhead of VCR-like operations in derivative-tree based scheme is O(log(N)), where N is the number of sessions. Simulation experiments further demonstrate the efficiency of the proposed scheme.

This paper presents a novel scalable switching architecture for input queued switches with its proper arbitration algorithms. In contrast to traditional switching architectures where the scheduler is implemented by one single centralized scheduling device, the proposed architecture connects several single scheduling devices in series and a distributed scheduling algorithm is run sequentially on them, whereby the inputs of each single scheduling device build connections to a group of outputs, considering both their local transmission requests as well as global outputs availability information. We show that a pipeline pattern can be used to increase the efficiency of the scheduling scheme with scheduling algorithms running in parallel on all the separate scheduling devices. We first introduce a distributed parallel round robin scheduling algorithm (DPRR) for the proposed architecture. Through the analysis of simulation results on various admissible traffics, it is shown that the performance of DPRR is much better than, or very close to the performance of, other round robin scheduling algorithms. We also prove that under Bernoulli i.i.d. uniform traffic DPRR achieves 100% throughput. Secondly, we introduce a distributed parallel round robin scheduling algorithm with memory (DPRRM) as an improved version of DPRR to make it stable under any admissible traffic.

In this paper, we propose a new open-loop rate/link adaptation algorithm (ARFHT) for the emerging high-speed IEEE 802.11n WLANs. ARFHT extends the legacy rate adaptation algorithms for SISO WLANs to make it applicable in the context of MIMO-based 802.11n WLANs. It adapts the MIMO mode in terms of spatial multiplexing and spatial diversity, the two fundamental characteristics of the 802.11n MIMO PHY. It also modifies the link estimation and probing behavior of legacy SISO algorithms. The combined adaptation to the appropriate MIMO mode as well as the appropriate modulation coding scheme selection achieves high channel utilization. In this paper, we provide the intuition and the design details of the ARFHT algorithm. A comprehensive simulation study using ns-2 will demonstrate that ARFHT achieves excellent throughput performance in most scenarios and is highly responsive to varying link conditions, with minimum overhead.

This paper addresses the design of high-performance buffers for high-end Internet routers. The buffers are typically implemented using a combination of SRAM and DRAM technologies in order to simultaneously meet the routers' high speed and capacity requirements. The major challenge in designing router buffers is to maintain multiple flow queues in the memory, unlike computer memory buffers (i.e., memory system). The major objective is to minimize the use of expensive but fast SRAM while providing acceptable delay guarantees to packets. In this paper, we first investigate hybrid SRAM/DRAM solutions proposed in the past. We show that one of the architectural limitations of these solutions is that the required SRAM size grows linearly with the number of flows in the system. This prevents the solutions from scaling to support a large number of flows. We then break down this shortcoming by proposing a parallel hybrid SRAM/DRAM (PHSD) architecture. We design a series of memory management algorithms (MMAs) for PHSD, based on tradeoffs between the complexity of the MMAs and the guarantee of in-order delivery of packets (segmentations). We perform a detailed analysis of the proposed algorithms and conduct extensive simulations to show that PHSD can significantly outperform solutions proposed in the past in terms of the SRAM requirements and packet delay.

This paper addresses the MAC layer packet scheduling in wireless systems with frame aggregation that allows the base station to transmit two or more data streams to different users simultaneously. We also consider the scheduler with support of users' quality of service (QoS) requirements. We first formulate the scheduling problem with frame aggregation into a knapsack problem that is shown NP hard. Then we propose a simple approximation algorithm (LUUF) based on the unit urgency concept. Our analysis shows that the complexity of LUUF isO(nlogn) and it achieves an approximation ratio ofF' /Fmax. We then show that in practice the complexity can be further reduced toO(n) and the approximation ratio can be made very near to 1, which make LUUF a promising candidate for wireless systems that support frame aggregation.

In IEEE 802.11 wireless local area networks (WLANs), TCP suffers unfairness between uplink and downlink flows due to its asymmetric reactions towards data and ACK losses at the AP (Access Point), and the AP's inability to distinguish itself from other contending stations accessing the medium. In this paper, we propose a novel dual queue management (DQM) scheme with ECN (Explicit Congestion Notification) at the AP's downlink buffer to improve TCP performance in infrastructure WLANs. Our approach maintains two queues for TCP ACK and data respectively, with their total length controlled by a PI (Proportional Integral) controller to prevent congestion. ACK/data packets are marked/dequeued dependent on uplink/downlink time usage of the wireless channel. We also propose an opportunistic scheduling mechanism of the two queues exploiting "multi-rate capability" and "multi-user diversity" for more efficient link utilization. We evaluate the proposed approach in ns-2 and our simulation results demonstrate that this design with few states can significantly improve TCP congestion control, fairness performance and link utilization in WLANs.

With the new available frame aggregation technology in the 802.11n WLAN, the base station is able to transmit two or more data streams to different users in the system simultaneously. This fact enables us to design a more QoS aware scheduler from the MAC layer. In this paper, we first formulate the

This paper addresses two challenges that are encountered when applying router-assisted explicit-feedback congestion control to wireless networks. The first challenge is how to distinguish between the two kinds of packet loss (non- congestion loss and congestion loss) in wireless networks and how to react to them properly. The second challenge is how to probe the unknown bandwidth capacity of wireless links which is required in calculating the router feedback. Some practical and novel enhancements on router-assisted congestion control in such environments are introduced in this paper. We have implemented these enhancements in a router-assisted congestion control protocol termed QFCP. Extensive simulation experiments using ns-2 will demonstrate that QFCP can fairly and efficiently allocate wireless bandwidth resources among competing flows in heterogeneous networks.

Wireless local area networks (WLANs) have become Increasingly popular due to the availability of affordable devices providing multirate capabilities. Under time- varying wireless channels, a device needs to tune its transmission rate dynamically for more efficient utilization of the physical link. Hence, rate adaptation mechanism, which is intentionally unspecified by the IEEE 802.11 standards, is critical to the system performance. In this paper, we propose a practical rate adaptation algorithm, Smart Sender, which utilizes both statistics and the received signal strength indicator (RSSI) of ACK packets to determine the transmission rate that maximizes the throughput. We implement our algorithm in commercial WLAN products and carry out extensive experiments for performance evaluation. The results demonstrate that using both statistics and RSSI of ACKs greatly improves system throughput and responsiveness under various wireless environments.

In this paper we address the unfairness problem between uplink and downlink TCP (Transmission Control Protocol) flows in the IEEE 802.11 infrastructure WLANs (c). It has been shown that TCP flows exhibit resource allocation (e.g., bandwidth) inequalities mainly due to two reasons: TCP's asymmetric reactions towards data and ACK (Acknowledgement) losses when the downlink buffer overflows at the AP (Access Point); and the AP's inability to distinguish itself from other contending stations when accessing the medium with the 802.11 MAC (Medium Access Control) layer based on the DCF (Distributed Coordination Function). To overcome these problems, we propose an AQM (Active Queue Management) approach (i.e., V2PI AQM) implemented at the AP, which utilizes two virtual queues, the TCP data and the TCP ACK queue, with their lengths controlled by PI (Proportional Integral) controllers. As a result, data losses can be reduced since the AP's downlink buffer is no longer overwhelmed by ACK packets destined to the uplink stations. In addition, when a discrepancy is observed by the AP monitoring the traffic intensity on the wireless link and the lengths of both queues, it uses an AIMD (Additive Increase Multiplicative Decrease) approach to adjust its contention window size, which compensates for the unfairness induced by the DCF MAC. We demonstrate using ns- 2 simulations that fair bandwidth sharing between uplink and downlink TCP flows can be achieved by AP's buffer management in conjunction with contention window adaptation.

TCP is currently the dominate congestion control protocol for the Internet. However, as the Internet evolves into a high-speed wired-cum-wireless hybrid network, performance degradation problems of TCP have appeared, such as underutilizing high-speed links, regarding wireless loss as congestion signal, and unfairness among flows with different RTTs. In order to improve the quality of service for such high-speed hybrid networks, we propose a router-assisted congestion control protocol called quick flow control protocol (QFCP). Performance evaluation using network simulator NS-2 shows that QFCP can significantly shorten flow completion time, fairly allocate bandwidth resource, and be robust to non-congestion- related loss.

Routers need buffers to store and forward packets, especially when there is network congestion. With current memory technology, neither the SRAM nor the DRAM alone is suitable for high-speed Internet routers that require both large capacity and fast access time. Some previous work has been carried out to combine the two technologies together and made a hybrid memory system [S. Iyer et al., 2001]. In this paper, we base the router memory on the interleaved DRAM architecture and propose an efficient memory management algorithm (CM-MMA) for it. The main advantage of the CM-MMA is that it can scale to a very large capacity while only employing small enough SRAM to guarantee a fast access time. The CM-MMA is also more responsive to traffic than previously proposed solutions, especially in light traffic situations. We perform both analysis and numerical studies to the CM-MMA. We also show simulation results that conform to them very well.

The switch architecture with the combined input - crosspoint queueing (CICQ) scheme has been recognized as a practical promising solution for building cost-effective high-performance switches. In an N x N CICQ switch, the switching fabric is a nonblocking buffered crossbar, a large input buffer is provided at each input and a relatively small internal buffer is provided at each crosspoint of the buffered crossbar. Each input buffer is logically organized as N virtual output queues (VOQs). In this paper, we build the queueing model for evaluating the delay performance of a CICQ switch under i.i.d uniform 2-state Markov modulated Bernoulli process (2-MMBP) bursty traffic. The accuracy of the queuing model is examined via computer simulation, by investigating the mean cell delay in a switch as a function of the switch size, the internal buffer size, the mean offered load and the mean burst length. The numerical results show that our queueing model can well analyze the reality.

In this paper the authors address the unfairness problem between uplink and downlink TCP flows in the IEEE 802.11 infrastructure WLANs. It has been shown that TCP flows exhibit resource allocation (e.g., bandwidth) inequalities mainly due to two reasons: TCP's asymmetric reactions towards data and ACK losses when the downlink buffer overflows at the AP; and the AP's inability to distinguish itself from other contending stations when accessing the medium with the 802.11 DCF MAC. To overcome this problem, we propose an AQM (active queue management) approach (V2PI AQM) to be implemented at the AP, which utilizes two virtual queues, namely the data queue and the ACK queue, with their lengths controlled by the PI controllers. As a result, data losses can be reduced since the AP's downlink buffer is no longer overwhelmed by the ACK packets destined to the uplink stations. In addition, when a discrepancy is observed by the AP monitoring the traffic intensity on the wireless link and the lengths of both queues, it uses an AIMD (additive increase multiplicative decrease) approach to adjust its contention window size, which compensates for the unfairness induced by the DCF MAC. We demonstrate using ns-2 simulations that fair bandwidth sharing between uplink and downlink TCP flows can be achieved by the AP's buffer management in conjunction with contention window adaptation.

With the rapid development in wireless communication technologies, the IEEE 802.11 WLANs are experiencing a huge popularity and widespread deployment. Designed with traditional layered architecture, current WLANs adopt functional layer partitioning and aim at optimization at individual layers. However, in a highly dynamic and media sharing wireless environment, the capacity enhancements at individual physical layers may not necessarily benefit, and sometimes even degrade the system performance with multiple users. It has been shown that in a multiuser setting, one can increase the throughput substantially if partial knowledge of the channels at the receiver sides is known. The challenge is to make good matching of the instantaneous channel conditions of multiple users with the bandwidth and time allocation to each user. In this paper, we address the issue of cross layer design in the proposed "weighted fair scheduling based on adaptive rate control" (WFS-ARC) framework, where the PHY layer knowledge is shared with the MAC and LLC layer in order to provide efficient resource allocation. We evaluate the WFS-ARC approach in ns-2 and the simulation results demonstrate that our design can significantly improve the system throughput.

In this paper, we propose a highly scalable packet switch that is based on a multi-stage multi-layer architecture made up of many modest size switches. This new architecture resembles the famous Clos-network studied in circuit switching systems except that it has distributed shared memories in the central stage. We call it central-stage buffered Clos-network (CBC). We first analyze the memory requirements for the CBC to emulate an output-queued (OQ) switch since OQ switches are generally regarded as having the optimal delay-throughput performance. Then we design an efficient packet-scheduling algorithm for the CBC to emulate an FCFS OQ switch. We show two distinguished features of this algorithm. First, it converges to the maximum matching faster than any other scheduling algorithms using the same paradigm. Secondly, the performance of the algorithm is independent of any arriving traffic pattern, which is not seen in other scheduling algorithms, such as iSLIP, DRRM and so on...

Wireless local area networks (WLANs) have become increasingly popular due to the recent availability of affordable devices providing multirate capabilities. However, because of the time-varying characteristics of a wireless channel, no single rate can be optimal under all scenarios, and the device needs to tune its transmission rate dynamically. Therefore, rate adaptation is a critical component of its performance. In addition, the IEEE 802.11 standard does not specify an algorithm for automatic rate selection; it is intentionally left open to the vendors. In this paper, we propose a practical rate adaptation algorithm, which utilizes both statistics and RSSI of ACK to decide the transmission rate that maximizes the throughput. We implement our algorithm in commercial products and carry out extensive experiments for performance evaluation. The results demonstrate that using both statistics and RSSI of ACK greatly improves system throughput and responsiveness under various wireless environments.

IEEE 802.11 is currently the most popular standard for Wireless LANs. The Distributed Coordination Function (DCF) defines the primary medium access control of 802.11, which uses the CSMA/CA mechanism and Binary Exponential Backoff (BEB) for Contention Window (CW) adjustment when collisions occur. In this paper, we propose a new CW adjustment scheme, CW Idle-Slots-based Control (WISC), using a control-theoretic approach. Specifically, we design and implement a PD (Proportional and Derivative) controller at each contending station, that dynamically adjusts CW based on a locally available channel state, i.e., the average number of consecutive idle slots between two transmissions, such that the channel state converges to the optimal value. Simulation results demonstrate that the new scheme outperforms the standard BEB in terms of both throughput and fairness, especially at high contention levels.

The design of the medium-access control (MAC) protocol is the most crucial aspect for high-speed and high-performance local and metropolitan area networks, since the decisions made at this level determine the major functional characteristics of these networks. Most of the MAC protocols proposed in the literature are not suitable for multimedia applications, since they have been designed with one generic traffic type in mind. As a result, they perform quite well for the traffic types they have been designed for, but poorly for other traffic streams with different characteristics. In this paper, we propose an integrated MAC protocol called the Multimedia-MAC (M-MAC), which integrates different MAC protocols into a hybrid protocol in a shared-medium network to efficiently accommodate various types of multimedia traffic streams with different characteristics and quality-of-service demands, namely, a constant-bit-rate traffic, bursty traffic (say, variable-bit-rate traffic), and emergency messages (say, control messages). We have developed a mathematical framework for the analysis and performance evaluation of our M-MAC protocol, which involves a queueing system with vacation. We have applied our M-MAC design approach to a wavelength-division multiplexing network, and evaluated its performance under various traffic conditions.

In recent years, a number of sophisticated architectures have been proposed to provide video-on-demand (VoD) service using multicast transmissions. Compared to their unicast counterparts, these multicast VoD systems are highly scalable and can potentially serve millions of concurrent users. Nevertheless, these systems are designed for streaming constant-bit rate (CBR) encoded videos and thus cannot benefit from the improved visual quality obtainable from variable-bit rate (VBR) encoding techniques. To tackle this challenge, this paper presents a turbo-slice-and-patch (TSP) algorithm to support VBR video streaming in a multicast VoD system. Results obtained from trace-driven simulation of 300 VBR videos show that serving VBR videos with the TSP algorithm increases the average latency by only 9% compared to the CBR case with the same average video bit rate. Moreover, in 165 out of the 300 video titles, the TSP algorithm actually outperforms the CBR equivalent by shortening the latency by 0.04%-99%. Given that we can achieve similar visual quality by encoding VBR video at half the average rate of CBR video, this TSP algorithm can potentially serve VBR videos with more consistent visual quality and with less resource compare to CBR-based video streaming systems.

Recently, a server-less architecture has been proposed for building video streaming systems which does not need any costly dedicated video servers and yet is highly scalable and reliable. However, due to the potentially large. number of user hosts streaming video data to a receiver for playback, the aggregate network traffic can become very bursty, leading to significant packet loss at the access routers. This study tackles this problem by investigating a novel network-aware transmission scheduling algorithm called gradient-descent scheduler (GDS) to reduce the traffic burstiness. Simulation results demonstrate that GDS can reduce the congestion-induced packet loss from over 95% to 0.07% in a 500-host system. Moreover, GDS can automatically adapt to the underlying network and does not require hosts in the system to be synchronized. These are essential for practical design of server-less architectures and peer-to-peer systems.

In this paper, we present a protection and provisioning framework to provide network survivability in IP/WDM networks based on GMPLS. This framework is based on a clustering technique called blocking island paradigm. We take into the combined topology and resource availability knowledge on the IP and WDM layer to optimize the performance of the framework. We show it is a general framework and verify its effectiveness through simulations.

A Clos-network architecture is an attractive alternative for constructing scalable packet switches because of its distributed and modular design. It can be classified according to different buffer (memory) allocation schemes in its switching stages. The most studied architectures are the space-space-space (S3) and the memory-space-memory (MSM) architectures. This paper shows that these two architectures cannot achieve efficient throughput with conventional random dispatching schemes even if the fabric itself is non-blocking. Previous research hence has focused on developing intelligent scheduling algorithms for these architectures so as to improve their throughput. However, we face various challenging problems when we try to actually implement the algorithms. The problems include performance degradation under constrained arbitration time, needs of centralized or complex scheduler hardware, etc. To solve these problems and at the same time be practical, this paper proposes a novel space-memory-memory (SMM) architecture that does not need any schedulers. The SMM architecture has similar hardware complexity as the MSM architecture has, while been proven to achieve 100% throughput under any admissible traffic. Our queuing analysis demonstrates that only small size buffers are needed in the central stage. The only tradeoff for the proposed SMM architecture is to employ small extra resequencing buffers. As a result, the SMM architecture can achieve very high performance, and is readily implemented using current technology.

Our work is motivated by the desire to build a scalable packet switch with extremely large number of ports. We consider building a multi-stage packet switch from many mid-size packet switches with distributed memories in the central stage. This new architecture resembles the famous Clos-network used in circuit switching systems except that it has buffers in the central stage. We call it Central-stage buffered Clos-network (CBC). In particular, we denote the symmetric Clos-network as (n, m, k) which means k input modules with n input ports each and m central modules. Each module is a non-blocking switch. Ideally, this CBC architecture would have similar benefits as those of an output-queued switch, i.e., the delay of individual packets could be precisely controlled, allowing the provision of guaranteed qualities of service. The main result of this paper is that, if m is approximately 4 times that of n, it is theoretically possible for a CBC to emulate an FCFS output-queued packet switch with all components running at the line rate, i.e., with no speedup. Particularly, we need to double the traditional strictly non-blocking Clos-network in the number of central modules. We show that the need to double the central modules is due to resolving the input port conflicts. But it is still much more cost effective compared with scaling one stage switches which usually have a complexity proportional to the square of the number of ports. If we slightly modify the CBC structure, we can further show that CBC can emulate any QoS queuing discipline if m is approximately 4 times of n. But packets may experience some delay which is bounded within a constant time.

IEEE 802.11 Wireless Local Area Networks (WLANs) are widely deployed nowadays. With traditional layered architecture, current WLAN adopts functional layer partitioning and aims at optimization in individual layers. However, in a highly dynamic and media sharing wireless environment, the capacity enhancement at physical layers may not necessarily benefit the overall system performance. Moreover, in a multiuser setting, throughput can be increased substantially if partial knowledge of the channel is known. In this paper, we address the issue of cross layer design in the proposed "Weighted Fair Scheduling based on Adaptive Rate Control" (WFS-ARC) framework, where the PHY layer knowledge is shared with the MAC/LLC layer. It can make good matching of instantaneous channel conditions of multiple users with resource allocation to each user.

The fundamental issue of quality-of-service (QoS) routing has triggered a lot of research during the last few years. However, the proposed algorithms attempt to route communication demands only on a call by call basis, without taking into account future traffic. There are nonetheless cases where the traffic profile is known. In this paper, we address this related problem to QoS routing, more specifically, the off-line planning of bandwidth allocation to demands known in advance. Shortest-path routing is the traditional technique applied to this problem. However, this can lead to poor network utilization and even congestion. We show how an abstraction technique combined with systematic search algorithms and heuristics derived from artificial intelligence make it possible to solve this problem more efficiently and in much tighter networks, in terms of bandwidth usage. In addition, this abstraction technique also allows to explain during search why some allocation problems are indeed infeasible. Then, the network regions between which bandwidth must be added are then identified.

This paper proposes an integrated routing and grooming algorithm for IP over WDM networks. Assuming a peer model in GMPLS-Based optical networks, we take into account the combined topology and resource usage information on both IP and WDM layers. Based on a clustering technique called blocking island paradigm, we propose an enhanced blocking island graph (BIG) network model with blocking island hierarchy (BIH) to abstract network resources. The main idea of the algorithm is to keep the integrity and load balance of related blocking islands. We also combine a cost function in the routing algorithm to groom traffic flows into active lightpaths. The complexity of the algorithm is analyzed to show its efficiency. In the simulation, we compare the algorithm with three other integrated routing algorithms in terms of blocking probability. The three algorithms are: the integrated min-hop (IMH) routing algorithm, the maximum open capacity (MOCA) routing algorithm and the IP-WDM grooming (IWG) algorithm. Simulation results show our algorithm has the best performance.

A multirack switching system usually combines electronic and optical technology, using electronic buffers, optical fabric and interconnections. In this paper, we consider the design of a scalable hybrid packet switch which efficiently transmits multicast traffic. The switch is equipped with a passive optical fabric, and each of its racks operates individually and independently. Multiple multicast queues are used in input ports to partially resolve HOL problem. A simple priority fanout filling (PFF) algorithm is proposed to improve the bandwidth usage with the help of a local crossbar. PFF is shown to be fair, work-conserving and guarantees sequential delivery for multicast traffic. All these features make the switch scalable and achieve high-performance. Furthermore, from a practical point of view, the switch is easily to be implemented.

Scheduling multicast traffic has been an active research topic due to the tremendous growth of multicast traffic (audio, video, teleconferencing, etc.) on the Internet. Considerable research work has been done on input queued (IQ) switches to handle the multicast traffic. Unfortunately, all the proposed solutions were of no practical value because they either lack performance or were simply not practical. Internally buffered crossbar (IBC) switches, on the other hand, have been considered as a robust alternative to buffer-less crossbar switches to improve the switching performance. However, no work has ever been done on multicasting in IBC switches. In this paper, we fill this gap and study, for the first time, the multicasting problem in IBC switches. In particular, we propose a simple scheduling scheme named multicast cross-points round robin (MXRR) for the IBC switch architecture. Our scheme was shown to handle multicast traffic more efficiently and far better than all previous schemes. Yet, MXRR is both practical and achieves high performance.

We address the problem of the performance analysis of the stochastic fair sharing (SFS) algorithm for fair link sharing. The SFS scheme has been proposed to carry out a fair link sharing and fair sharing among virtual private networks. Depending upon the current utilization and provisioned capacities of the classes, the SFS admission control algorithm decides which sessions to accept and which to reject. In this letter, we undertake the performance evaluation of the SFS scheme analytically. We explore the tradeoff between fairness and the blocking probability by varying the trunk reservation parameter. The results show that the analytical performance measure agrees well with the simulation results.

Previous works have shown the wavelength conversion can considerably reduce the blocking probability in all-optical networks, but most of analytical models and algorithms are proposed under simplifying assumptions or restricted to specific cases. In this paper, motivated by an abstracting technique called blocking island (BI) paradigm, we propose wavelength placement algorithms both in static traffic case and dynamic traffic case. To make sure our algorithm is applicable in arbitrary topologies and any incoming traffic patterns, a simulation-based optimization approach is employed. In the simulation, we show the performance improvement obtained by full wavelength conversion can almost be achieved by using limited number of wavelength converters with careful placement.

Hybrid switch architecture with electronic buffering/processing and optical switching fabric is receiving a lot of attention as potential candidate for the design of high-performance and scalable switches/routers. However, the reconfiguration overhead of optical fabrics brings new challenges to system and scheduling algorithm design. For example, speedup is compulsory to make the switch stable; the scheduling rate has to be reduced compare to the traditional slot-by-slot scheduling in electronic switch. This paper provides instructions on how to choose speedup, scheduling algorithm and holding time. Main results include: 1) a speedup of 2 ensures switch stability and is cost-effective; 2) effect of reconfiguration delay is exaggerated at high traffic load and it is worth using complex scheduling algorithm; and 3) AVERAGE holding method performs better under most traffic scenarios.

The output queued (OQ) switching architecture shows optimal performance amongst all queuing approaches. However, OQ switches lack scalability due to high memory-bandwidth constraints. An OQ switch can be exactly emulated by a more scalable crossbar switch (i.e., input-queued - IQ - switch) and a small speedup (Chuang, S. et al., 1998). Unfortunately, this result was not of practical use due to the high complexity of the proposed scheduling scheme. A similar result was shown by B. Magill et al. (see Conf. on Commun. Control and Computing, 2002) and was based on the internally buffered crossbar (IBC) switching architecture. While the latter result seems to overcome the complexity issue, the scheduling scheme presented is costly. We extend our previous work (Mhamdi and Hamdi, IEEE ICC'03, vol.3, p.1659-63, 2003) and prove the same result as Magill et al., but with lower hardware requirements.. In particular, we propose a simple scheduling scheme, named modified current arrival first-lowest TTL (time-to-live) first (MCAF-LTF), that does not require a costly time stamping mechanism. Based on the MCAF-LTF, we prove that, with a speedup of just 2, a one-cell-internally buffered crossbar switch can exactly emulate an OQ switch. The reduced complexity of our proposed scheme makes it of high practical value and allows it to be readily implemented in ultra-high capacity networks.

Using optical technology brings with it advantages such as scalability, high bandwidth and power consumption for the design of switches/routers. However, reconfigurating the optical fabric of these switches requires significant time under current technology. Conventional slot-by-slot scheduling may severely cripple the performance of these switches due to its frequent request for fabric reconfiguration. A more appropriate way is to use a time slot assignment (TSA) scheduling approach. The switch gathers the incoming traffic periodically and schedules them in batches, while trying to minimize the transmission time. This optical switch scheduling (OSS) problem is defined in this paper and it is NP-complete. Earlier TSA algorithms normally assume the reconfiguration delay to be either zero or infinity for simplicity. To this end, we propose a /spl lambda/-ADJUST algorithm, which breaks this limitation and self-adjusts with different reconfiguration delay values. The algorithm runs at O(/spl delta/N/sup 2/logN) time complexity, guarantees a 100% throughput and a bounded worst case delay. In addition, it outperforms existing TSA algorithms across a large spectrum of reconfiguration values.

As buffer-less scheduling algorithms reach their practical limitations due to higher port numbers and data rates, buffered crossbars gained a lot of interest recently because of the great potential they have in solving the complexity and scalability issues faced by their buffer-less predecessors. In particular, the internally buffered switching architecture was shown, through distributed scheduling algorithms, to be able to sustain the current and expected increases in Internet throughput rates. In this paper, we propose a class of distributed scheduling algorithms for the internally buffered crossbar switching architecture. As will be shown, the distributed nature of these algorithms makes them of high practical value. That is, they can be implemented in real-time for high-speed input traffic. In addition, we will demonstrate, through simulation, that these scheduling algorithms outperform state-of-the-art related algorithms in this area.

Queue management and congestion control are very important to the robustness and fairness of the Internet. In this paper, a new queue management algorithm, termed self adjustable choke (SAC) is proposed to achieve both fairness and congestion control in an Internet router. It is based on the well-known RED algorithm and a recently proposed CHOKe algorithm. The SAC scheme has kept the advantages of simplicity and lower processing cost of RED and CHOKe, while solving their unfair bandwidth allocation properties. In particular, the SAC scheme treats TCP and UDP flows differently, and can adaptively adjust its parameters according to the current traffic status. As a result, using the SAC, bandwidth is distributed evenly among different flows, no matter how different these flows are. This will be demonstrated and compared to CHOKe using experimental results under various traffic scenarios.

High-performance input queued switches achieve good performance with low cost. However, with the appearance of optical techniques, the line rate is much higher than before. Scheduling algorithms require not only good performance in delay and stability but fast speed and simple implementation as well. A variety of scheduling algorithms for virtual output queuing (VOQ) packet switching architecture are proposed. Round-robin scheduling algorithms are fast and simple to implement in hardware. In particular, a group of fully desynchronized round-robin scheduling algorithms - SRR (static round robin matching), proposed recently, achieve pretty good delay performance while easy to implement. However, they are not stable under non-uniform traffic. Randomized algorithms are stable under any admissible traffic, however, their delay is high and hardware implementation is complex. Based on the concept of randomized algorithms and SRR, we propose a group of new scheduling algorithms, DRDSRR, the improved version of DRDSRR, ARDSRR and the variations of ARDSRR. They not only ensure stability but also have good performance and simple implementation. We have proved stability in the paper.

One of the challenges in the design of switches/routers is the efficient and fair use of the shared bottleneck bandwidth among different Internet flows. In particular, various active queue management (AQM) schemes have been developed to regulate transmission control protocol traffic in response to router congestion. In addition, in order to provide fair bandwidth sharing, these AQM must protect the well-behaved flows from the misbehaving flows. However, most of the existing AQM schemes cannot provide accurate fair bandwidth sharing while being scalable. The key to the scalability and fairness of the AQM schemes is the accurate estimation of certain network resources without keeping too much state information. We propose a novel technique to estimate two network resource parameters: the number of flows in the buffer and the data source rate of a flow by using a capture-recapture (CR) model. The CR model depends on simply the random capturing/recapturing of the incoming packets, and as a result, it provides a good approximation tool with low time/space complexity. These network resource parameters are then used to provide fair bandwidth sharing among the Internet flows. Our experiments and analysis will demonstrate that this new technique outperforms the existing mechanisms and closely approximates the "ideal" case, where full state information is needed.

Using optical technology for the design of packet switches/routers offers several advantages such as scalability, high bandwidth, power consumption, and cost. However, reconfiguring the optical fabric of these switches requires significant time under current technology (microelectromechanical system mirrors, tunable elements, bubble switches, etc.). As a result, conventional slot-by-slot scheduling may severely cripple the performance of these optical switches due to the frequent fabric reconfiguration that may entail. A more appropriate way is to use a time slot assignment (TSA) scheduling approach to slow down the scheduling rate. The switch gathers the incoming packets periodically and schedules them in batches, holding each fabric configuration for a period of time. The goal is to minimize the total transmission time, which includes the actual traffic-sending process and the reconfiguration overhead. This optical switch scheduling problem is defined in this paper and proved to be NP-complete. In particular, earlier TSA algorithms normally assume the reconfiguration delay to be either zero or infinity for simplicity. To this end, we propose a practical algorithm, ADJUST, that breaks this limitation and self-adjusts with different reconfiguration delay values. The algorithm runs at O(/spl lambda/N2logN) time complexity and guarantees 100% throughput and bounded worst-case delay. In addition, it outperforms existing TSA algorithms across a large spectrum of reconfiguration values.

We investigate the problem of routing and wavelength assignment as well as the problem of the placement of wavelength converters in all-optical networks. In particular, we present a general framework, based on the blocking island (BI) paradigm, to illustrate how it can be used to solve these problems in a unified way. We first give a brief introduction about the BI graph network model, and then use this model to derive simple and general algorithms that can be used in various applications in optical networks. We discuss the implementation issues of our algorithms and present simulation results to evaluate and compare our solutions with other heuristic algorithms under both static and dynamic traffic assumptions.

Abstract ‘Anytime, anywhere’ communication, information access and processing are much cherished in modern societies because of their ability to bring flexibility, freedom and increased efficiency to individuals and organizations. Wireless communications, by providing ubiquitous and tetherless network connectivity to mobile users, are therefore bound to play a major role in the advancement of our society. Although initial proposals and implementations of wireless communications are generally focused on near‐term voice and electronic messaging applications, it is recognized that future wireless communications will have to evolve towards supporting a wider range of applications, including voice, video, data, images and connections to wired networks. This implies that future wireless networks must provide quality‐of‐service (QoS) guarantees to various multimedia applications in a wireless environment. Typical traffic in multimedia applications can be classified as either Constant‐Bit‐Rate (CBR) traffic or Variable‐Bit‐Rate (VBR) traffic. In particular, scheduling the transmission of VBR multimedia traffic streams in a wireless environment is very challenging and is still an open problem. In general, there are two ways to guarantee the QoS of VBR multimedia streams, either deterministically or statistically. In particular, most connection admission control (CAC) algorithms and medium access control (MAC) protocols that have been proposed for multimedia wireless networks only provide statistical, or soft, QoS guarantees. In this paper, we consider deterministic QoS guarantees in multimedia wireless networks. We propose a method for constructing a packet‐dropping mechanism that is based on a mathematical framework that determines how many packets can be dropped while the required QoS can still be preserved. This is achieved by employing: (1) An accurate traffic characterization of the VBR multimedia traffic streams; (2) A traffic regulator that can provide bounded packet loss and (3) A traffic scheduler that can provide bounded packet delay. The combination of traffic characterization, regulation and scheduling can provide bounded loss and delay deterministically. This is a distinction from traditional deterministic QoS schemes in which a 0% packet loss are always assumed with deterministically bounding the delay. We performed a set of performance evaluation experiments. The results will demonstrate that our proposed QoS guarantee schemes can significantly support more connections than a system, which does not allow any loss, at the same required QoS. Moreover, from our evaluation experiments, we found that the proposed algorithms are able to out‐perform scheduling algorithms adopted in state‐of‐the‐art wireless MAC protocols, for example Mobile Access Scheme Based on Contention and Reservation for ATM (MASCARA) when the worst‐case traffic is being considered. Copyright © 2002 John Wiley & Sons, Ltd.

We are trying to study the traffic grooming problem by minimizing overall network cost that includes the transceivers required as well as the number of wavelengths. A mathematical formulation of the traffic grooming problem in mesh networks is proposed. We also describe a heuristic using blocking island (BI) paradigm. Simulations have been carried out to prove effectiveness of our heuristic.

The Clos-network is widely recognized as a scalable architecture for high-performance switches and routers. Since more contention points are introduced in the multistage network, cell buffers are commonly used to resolve the contention. Recently, several scheduling algorithms have been proposed for the buffered Clos-Network switches. These approaches will cause either mis-sequence or memory speedup problem. In this paper, we propose a highly scalable bufferless Clos-network switching architecture. We also propose a distributed scheduling algorithm, Distro. It is based on a novel scheduling technique termed Static Round-Robin (SRR). Our simulation results demonstrate that our algorithm achieves 100% throughput under uniform traffic.

The proportional service model is receiving a lot of attention a an attractive model for providing differentiated services on the Internet. In particular, this model is controllable, able to provide the "tuning knobs" for network operators to quantitatively differentiate the quality-of-service (QoS) of different classes, and lends itself naturally to simple pricing schemes. We focus on the issue of how to practically implement such a QoS differentiation scheme at high-speed routers using efficient buffer management and packet scheduling mechanisms. We first propose a uniform scheduler. Unlike previously proposed schedulers which can be used only for a single QoS metric, our scheduler is suitable for various QoS metrics. We then introduce a new packet dropping mechanism with an active counter resetting scheme that compare favorably with previous schemes. Finally, we develop an original and simple approach for the integration of absolute QoS constraints with the proportional differentiation paradigm.

In this paper we consider the routing and wavelength assignment problem as well as the placement of wavelength converters in a wavelength routed all-optical network. Using a clustering technique called BI (blocking island), we propose a simple and intelligent RWA (routing and wavelength assignment) algorithm: BI RWA and a converter placement algorithm. These algorithms can be used in arbitrarily connected networks and with some simple modifications, they can also be applied on various networking scenarios. We have evaluated our algorithms through extensive simulations. The simulations are carried out in two parts: static traffic and dynamic traffic. The results demonstrate that our RWA algorithm performs better than other previously proposed algorithms (in the cases we studied).

Virtual output queuing (VOQ) is a practical and high-performance packet switch architecture. There are many simple iterative arbitration algorithms proposed for the VOQ architecture. These algorithms either employ a 3-phase or a 2-phase handshaking scheme between the switch inputs and outputs. It has been shown that neither scheme can outperform the other in all traffic patterns. As a result, we propose a 2-stage matching algorithm that combines the benefit of both schemes, and also achieves the best desynchronization of arbiter pointers during their scheduling. We demonstrate that this new algorithm outperforms all the other iterative algorithms considered under various traffic models. We also propose a possible hardware implementation method of the algorithm.

We address the problem of the performance analysis of the stochastic fair sharing (SFS) algorithm for fair link sharing. The SFS scheme has been proposed (see Garg, R. and Saran, H., Infocom, 2000) to carry out a fair link sharing and fair sharing among virtual private networks (VPNs). Depending on the current utilization and provisioned capacities of the classes, the SFS admission control algorithm decides which sessions to accept and which to reject. We undertake the performance evaluation of the SFS scheme analytically. The main performance measure in our analysis is the session blocking probability. In particular, we obtain the Roberts-Kaufman like recursion (see Kaufman, J.S., IEEE Trans. Commun., vol.COM-29, no.19, p.1474-81, 1981) for the SFS scheme to compute the blocking probability. We then use linear programming techniques to compute the blocking probability from the above recursion.

Welcome to the second issue of Volume 2 of Wireless Communications and Mobile Computing/ (WCMC). We have been receiving a large number of great articles and we would like to assure you that we are grateful for your contributions. Our fast review process and publication turnaround has caught the attention of our authors and readers. We are working hard with the John Wiley staff to make the review process more efficient by introducing a new system soon. Once that is implemented, we will be able to process more manuscripts in a short span of time. In the meantime, we appreciate receiving your quality manuscripts and would like to invite you to sit back and enjoy reading the articles in this issue. As always, we welcome your contributions, whether by submitting manuscripts and/or special issues. We also appreciate any comments/feedback you have regarding WCMC. Send your comments to any of the editors and/or to the Wiley office. Multicast protocols in mobile ad-hoc networks have been an area of active research for the past couple of years. In the first paper, Papavassiliou and An discuss the issues, challenges, and protocols related to multicasting in mobile ad-hoc wireless networks. They survey several existing multicasting protocols in mobile ad-hoc networks and summarize the activities of recent advances in the field. A qualitative comparison of these protocol characteristics according to several distinct features and performance parameters is presented. Finally, they present an overview of research and development efforts in the area of group mobility modeling in mobile ad-hoc networks. Next, Cui and Bassiouni present an analysis of hierarchical cellular networks with mobile base stations, termed as totally mobile wireless networks (TMWNs). Their performance evaluation concentrated on comparing the results of a two-tier system's throughput, handoff blocking rate and new call success rate with those obtained by a one-tier model. Under all load conditions, their results show that the two-tier system outperforms the one-tier system if the number of channels is kept the same. Other conditions have been studied and results have been presented. An analytical model to compute the new call and handoff blocking probabilities in TMWN is given and evaluated. Extensive results are presented. In the third article, a software architecture for GPRS session management (SM) is presented by Haung and Lin. This software architecture is designed to accommodate 3G wireless networks. It is compatible, modular, and flexible. The aim of the architecture is to support a GPRS support node. In a situation where single-band and dual-band PCS handsets co-exist, the channel assignment becomes a concern. In the fourth article, Chang and Li propose load-balancing channel assignment schemes to improve the system performance. To further improve the system capacity, a channel reassignment scheme is also presented. Modeling and simulation results are carried out. The results indicate that both load-balancing and channel re-assignment techniques significantly increase the system capacity as the percentage of dual-band handsets increases. Furthermore, the load-balancing with channel re-assignment scheme that combines both techniques achieves the best system performance, even when the percentage of dual-band handsets is as low as 25%. To reduce the peak-to-average power ratio (PAR) of an orthogonal frequency division multiplexing (OFDM) signal, a set of fixed permutations are used by Jayalath and Tellambura. This set of permutations is applied on K − 1 interleavers are used to produce K − 1 permuted sequences from the same information sequence. To reduce the technique's complexity, an adaptive approach is adapted. Simulation results show that random interleavers and odd-even symmetric interleavers are performing equally well in reducing the PAR. Other results for the out-of-band radiation and the bit error rate performance of interleaved OFDM (IOFDM) and conventional OFDM are also presented and discussed. In the sixth paper, Liu and Li propose a new modulation scheme to improve the performance of DS-CDMA systems in fast time-dispersive fading channels called differential space–time modulation for DS-CDMA (DST-CDMA). They study three different types of receivers using this new scheme. Then, they compare the three types under different design combinations. Finally, Sheikh and Shah present an application of LMS algorithm with optimum step size (µopt-LMS) for fading channel estimation. The expression for optimum step size of the LMS algorithm is modified for use in the fading channel estimation problem. The robustness of the proposed µopt-LMS algorithm is demonstrated via simulation in different channel conditions. Thanks to all the authors and the reviewers. We appreciate your support and hope that you continue your dedication to WCMC.

Welcome to the fourth issue of the second volume of Wireless Communications and Mobile Computing (WCMC). Our Journal has been getting a lot of attention from many experts in the field and therefore many new subscriptions but we still need your continuous support as always. Even though we have enough submissions and special issues to fill all the slots for next year, we would like to encourage all readers to send us their Special Issue proposals and manuscripts in their fields of expertise to enrich the rest of the community of their new findings. I extend a special invitation to all members of the editorial board to step in and contribute at this junction, now that we are ready to take WCMC to the next level of excellence. I would like to extend special thanks to the authors who have worked hard to produce the fine articles you are about to read in this issue. Thanks also to those who have contributed whether running a special issue or publishing a regular manuscript. Of course, the continuous help from the staff of John Wiley is always very much appreciated. We welcome any comments/feedback you have regarding WCMC. Send your comments to any of the editors and/or to the Wiley office. The first article by Mähönen et al. reviews and analyzes the standards currently available and describes the European two-layer trial system developed in 1996–2000. The authors show why further development towards IP-based LMDS is useful in the future. Most of their recommendations are based on results derived from the European Union supported research project CABSINET. It had the aim of demonstrating the viability of a 40 GHz cellular digital television system with a return channel to offer interactive services. Two systems were tested: a line of sight link using QPSK, and a non-line of sight with COFDM modulation scheme. Then, they present the architectures of the transmitters, nomadic terminals, and the design of the IF/RF subsystems for both types of modulations. The discussion is focused on system engineering and selections required in order to build a full two-layer LMDS system. Jaseemuddin et al., in the second article, present a study of profiled handoff for DiffServ-based mobile nodes. They investigate the effects of handoff on service quality of mobile nodes. Several experiments are conducted using various packet metering and marking schemes with or without transferring profiles to the new router. Results indicate the relative instability period following handoff, loss of packets and delay encountered by packets in profiled or un-profiled handoffs, leading to determining suitable mix of metering and marking schemes with or without context transfer. The third and fourth articles are two parts of the same topic prepared by Nassar et al. The first part (third article) is entitled ‘Multi-carrier platform for wireless communications: High-performance, high-throughput TDMA and DS-CDMA via multi-carrier implementations.’ In part I, the authors demonstrate how a multi-carrier platform can be adopted by TDMA and DS-CDMA systems. Then, they show how, in both systems, the proposed multi-carrier platform is able to: (1) outperform existing time-based-processing receiver structures in multipath fading channels, due to better exploitation of the channel diversity and a minimization of the MAI (multi-access interference); (2) achieve increases in network capacities (measured by numbers of users or, equivalently, throughput per user); and (3) reduce receiver complexity. In part II, OFDM and MC-CDMA systems with high-performance, high-throughput via innovations in spreading, the authors demonstrate how the CI (carrier interferometry), which brought far reaching benefits to TDMA and DS-CDMA, is also able to enhance existing multi-carrier systems, namely existing OFDM and MC-CDMA architectures. By application of the CI approach to these systems, they demonstrate significant performance benefits (measured in terms of probability of error performance) and notable network capacity gains (measured by numbers of users or throughput per user). Moreover, in the case of OFDM, the CI approach eliminates the PAPR (peak to average power ratio) problem, and in the case of MC-CDMA, a simple procedure is available to resolve all PAPR concerns. Combining the results of part I and part II, the authors succeeded in presenting a common multi-carrier platform for the most popular architectures in use today. Hence, by use of the CI approach, a simple software code will switch a CI processor from one mode to another, where modes include CI/TDMA, CI/DS-CDMA, CI/MC-CDMA, and CI/OFDM. Each mode demonstrates improved performances and network capacities relative to its traditional counterpart. Next, Al-Hussaini et al. propose and analyze parallel co-channel interferers (CCI) multistage cancellation by combining RAKE and selection diversity. In order to account for channel variations, adaptive implementation of decision thresholds at the RAKE output is suggested. It is shown to provide significant improvement over either hard or soft decision techniques especially in the near-far situation. Investigation of the system robustness to imperfect channel parameter estimation is also presented. The communication channel is modeled as slowly varying Rayleigh fading discrete multipath channel. Finally, Annamalai and Srivastava outline a general numerical procedure for computing the probability of outage of a cellular mobile radio system that is equipped with a smart antenna to suppress a few strongest co-channel interferers (CCI) out of a total of NI active interferers by adaptive null-steering. Aside from the interference-limited case, refined outage criterion that either treat receiver noise as CCI or consider a minimum detectable receiver signal threshold are studied. Exact closed-form expressions for both the basic and refined outage criterion are also derived by assuming that all the CCI signals are subject to Nakagami-m fading with a positive integer fading severity index. Selected numerical examples are provided to illustrate the application of the theory which includes the investigation into the effects of fade distribution of the CCI signals and traffic loading on the outage probability, and also the study of spectrum utilization efficiency improvement using a selective co-channel interference cancellation technique.

Optical burst switching (OBS) is considered as an efficient switching technique for building the next generation optical Internet. An offset-time based scheme has recently been proposed in order to provide quality-of-service (QoS) in OBS networks. Unfortunately, the proposed service differentiation has several problems. The aim of this paper is to address these problems and introduce the concept of proportional QoS into this OBS paradigm. An intentional dropping scheme is proposed so as to give a controllable burst loss probability for different service classes. In order to achieve flexible packet delay differentiation, we extend the well-known waited-time-priority (WTP) scheduler to form a burst assembling scheme. Simulations are conducted to evaluate the performance of our proportional QoS provisioning within OBS networks in terms of burst loss probability and packet delay.

This paper describes performance degradation problems during interaction handling in several batched and centralized-buffer VoD systems. A new system, called multi-batch buffer (MBB) system is proposed. It attempts to solve these problems by employing scalable buffering techniques in the buffers of the local servers and the set-top boxes (STBs). We have performed extensive simulation for the analysis and performance evaluation of our proposed VoD system. The simulation results demonstrate that our VoD system is very scalable and outperforms related state-of-the-art VoD systems.

One of the important issues in the design of next generation high-speed networks is to provide Differentiated Services to different types of applications with various time constraints. In this paper, we study the problem of providing real-time service to either hard or soft real-time messages in conjunction with a normal transmission service to variable-length messages without time constraints in wavelength division multiplexing (WDM) optical networks. We propose an adaptive scheduling algorithm to schedule and manage message transmissions in single-hop passive-star coupler based WDM optical networks. In particular, we develop an adaptive scheme for scheduling message transmissions in order to improve the network performance when both real-time and non real-time messages are transmitted in one single topology. In addition, we have conducted extensive discrete-event simulations to evaluate the performance of the proposed algorithm. This study suggests that when scheduling message transmission in WDM networks, a differentiated service should be considered to benefit the transmission of both real-time and non real-time messages so that the overall performance of the network could be improved.

Conventional medium access control (MAC) protocols perform quite well for the traffic types they have been designed for, but poorly for other traffic streams with different characteristics. But, the emerging multimedia applications require that the MAC protocol should perform equally well for all types of traffic characteristics. We propose an integrated MAC protocol (termed the multimedia medium access control protocol (multimedia-MAC)) which integrates different MAC protocols into a hybrid protocol to efficiently accommodate various types of multimedia traffic streams with different characteristics and QoS demands. We have applied our multimedia-MAC design approach to wavelength division multiplexing (WDM) based optical network. We have developed a mathematical framework for the analysis and performance evaluation of our multimedia-MAC protocol which involves a queueing model with vacation.

The provision of scalable quality-of-service (QoS) guarantees on wavelength-division-multiplexing (WDM) networks is an important and challenging issue for the next generation Internet. One of the important performance metrics in a QoS-capable WDM network is the call blocking probability. Previously, a proportional differentiation model has been proposed as an effective method for scalable differentiated services provision. This model provides the network operators the ability of quantitatively adjusting the quality differentiation between service classes. We introduce this model into WDM networks with the aim of providing proportionally differentiated blocking probability to various traffic classes. An intentional blocking algorithm is proposed to implement this model at the wavelength level. In order to solve the link utilization degradation in this algorithm, we propose another intentional termination algorithm. Since the performance requirement from the network operator might be various, a hybrid algorithm is also given as a balance between the above two. These three algorithms are also suitable to TDM over WDM, where one connection only take part of the transmission capacity of one wavelength. Extensive simulation results demonstrate that our algorithms provide accurate and controllable differentiation on blocking probability between various traffic classes even in a bursty traffic situation. The infeasibility problem in proportional blocking probability provision is also discussed.

With a deep understanding on the properties of stable matching in the context of multiple input-queued switches, we propose the efficient schemes to guarantee the QoS of any unicast and multicast traffic with fixed- or variable-length packets in an unified way. Using these schemes, the QoS of fixed- or variable-length unicast and multicast packets can be guaranteed by independently employing suitable service disciplines at the packets' destined outputs like what is being done in an output queueing switch. One of our proposed schemes uses totally 4N input/output buffers, each with an internal speed-up of 2 independent of N, for an N/spl times/N switch to support any fixed- or variable-length multicast and unicast packets with QoS guarantees.

In this letter, we analyze the performance of multiple input-queued asynchronous transfer mode (ATM) switches that use parallel iterative matching (PIM) for scheduling the transmission of head-of-line cells in the input queues. A queueing model of the switch is developed under independently, identically distributed, two-state Markov modulated Bernoulli processes bursty traffic. The underlying Markov chain of the queueing model is a quasi-birth-death (QBD) chain. The QBD chain is solved using an iterative computing method. Interesting performance metrics of the ATM switch such as the throughput, the mean cell delay, and the cell loss probability can be derived from the model. Numerical results from both the analytical model and simulation are presented, and the accuracy of the analysis is briefly discussed.

Welcome to the fourth issue of the journal Wireless Communications and Mobile Computing (WCMC). We have been fortunate so far that we are able to attract quality papers and also meet all our publication deadlines. I am sure that you have been enjoying reading the fine articles in the previous three issues and hope that you continue to do so in the future ones. Special thanks to the authors who worked hard to produce the fine articles that we present to you in this issue. Thanks to those who have contacted us praising the efforts and quality of this publication. We welcome any comments/feedback you have regarding WCMC. Please send your comments to any of the editors and/or to the Wiley office. The first article in this issue reviews mobility management for the third-generation mobile networks. The authors, Y-B. Lin, Y-R. Haung, Y-K. Chen, and I. Chlamtac, focus on the evolution from General Packet Radio Service (GPRS) to Universal Mobile Telecommunication System (UMTS). In this evolution, the radio access network UTRAN has been introduced, and radio-related management is moved from the core network to UTRAN. They elaborate on how this architecture change affects the mobility management functionality, including the attach and detach procedures, location update, serving radio network controller relocation and intersystem change between GPRS and UMTS. Existing research in ad hoc mobile networks is mainly concerned with unicast routing. There is a growing interest in supporting multicast communication in an ad hoc mobile environment. C-K. Toh and S. Bunchua present an ad hoc mobile multicast routing using the concept of long-lived routes. They propose a new routing protocol called ABAM (associativity-based ad hoc multicast) routing protocol. They show that ABAM has many advantages in route reconstruction. Then, they demonstrate that ABAM is robust since the repair can be triggered by a node in the tree or by the migrated node itself. They also show that ABAM is capable of handling multicast group dynamics when mobile hosts decide to join and leave an existing multicast group. Their simulation results reveal that under different mobility scenarios and multicast group size, ABAM has low communication overhead and yields better throughput performance. Next, A. Andreadis, G. Benelli, G. Giambene, and B. Marzucchi discuss the analysis of the WAP protocol over SMS in GSM networks based on the PALIO Project within the fifth Research Framework of the European Commission. This deals with an information tourist service accessible through mobile phones by means of the Wireless Application Protocol (WAP). They considered a Global System for Mobile communications (GSM) network where the WAP traffic is transported by the Short Message Service (SMS) on a logical channel that also conveys signaling messages. Suitable models have been considered for both WAP browsing traffic and signaling traffic. Analytical predictions have been validated through comparisons with simulation results. Quality of Service (QoS) requirements in terms of the maximum transmission delay that is guaranteed in 95 per cent of cases was also considered. They concluded that this study proves the feasibility for the envisaged mobile service based on WAP and it also allows dimensioning WAP pages so that each user experiences reasonable browsing delays. A review of reduced-trellis equalization using the M-BCJR Algorithm is presented by C. Fragouli, N. Seshadri, and W. Turin. They review the complexity of channel equalization using the BCJR algorithm and how it grows exponentially with the channel memory. They also discuss how the M-BCJR algorithm provides a method for reduced-trellis channel equalization, but its performance varies significantly with the distribution of a channel energy to its taps. Then, they use the implementation of the BCJR algorithm in the logarithmic domain to propose a variation of the M-algorithm, which (they show) can offer robust performance. They discuss how the designer can make decision regarding delay, maximum and minimum phase transformation, and selection of states. Simulation is carried out to demonstrate the effect of different parameters on the proposed algorithm's performance. The broadcast nature of WDM star couplers makes the handoff scheme easier and reduces the number of reconnections. This is needed as the radio cells becomes gradually smaller and smaller, the procedures for mobile terminals call setup and control become complicated due to the high handoff frequency. H-C. Chao, R-C. Wang, and J-Y. Hong propose a channel allocation algorithm, called pre-empted assigned offset (PAO) scheme, for the WDM- and SCM/WDM-based personal communication network architectures. The main idea is to keep the offset conflict probability lower. Simulation results are provided to show that the proposed scheme outperforms those previously introduced schemes, especially in offset conflict probability under a moderate roaming rate for WDM and even under heavy roaming situations for SCM/WDM. Finally, A. Annamalai, Jr. discusses the effect of Gaussian error in Selection Diversity Combiners (SDC). SDC is one of the simplest and most commonly implemented diversity mechanisms for mitigating the detrimental effects of deep fades experienced on wireless channels. SDC main advantage is the reduction of the probability of deep fades. The effect of Gaussian errors in the branch gain estimates on the SDC receiver performance is investigated by deriving new closed-form expressions for the probability density function, cumulative distribution function and the moment generating function of the combiner output SNR statistic. Mathematical expressions for quantifying the degradation in the mean combined SNR, outage probability and the average symbol error rate of a broad class of binary and multilevel modulation schemes owing to imperfect branch SNR estimates in Rayleigh fading are also derived. The author then shows that combiner errors affect the mean combined SNR negligibly in comparison to their effect on the deep fades.

This is the first issue of the new journal Wireless Communications and Mobile Computing (WCMC) published by John Wiley & Sons, Ltd. WCMC promises to bring its readers outstanding published work that will enrich their horizons in their areas of expertise related to all aspects of wireless systems. It also promises authors speedy publication under the guidance of an international Advisory Board and Editorial Board drawn from academia and industry. The objective and strategy of WCMC is to carry high calibre survey papers, and a rapid publication time with a strong focus on new trends, developments, emerging technologies and industrial standards. We realize that we have a long way to go in order to attain these goals and objectives, but we have already made an excellent start. We are very proud of the highly qualified individuals who have joined our Advisory and Editorial Boards. They represent an international panel of well-known experts from around the world. In addition, we have instituted three region editors, under the leadership of a Senior Editor, in order to minimize workloads and encourage speedy manuscript processing. We have already reached a wide range of audiences around the world who have submitted and/or are in the process of submitting manuscripts to WCMC. We at WCMC are aware of the number of fine publications already in this field and the tight competition to attract the best manuscripts. Nevertheless, we feel that WCMC will target a different pool of contributions. The focus of WCMC will be towards running more special issues on hot topics that describe industry projects/standards, publishing at least one review/tutorial paper in each issue, and bridging the gap between wireless communications and mobile computing. This fresh approach to international publication will enlarge the pool of quality publications, rather than draw from the existing pool. WCMC's publication process will be fully electronic, administered by dedicated editors, which we firmly believe will contribute to a speedy review process and reduce the waiting time for authors. Finally, we would like to offer special thanks to all of those who have encouraged and contributed in the initial stages of this project, in particular the staff at John Wiley & Sons, Ltd. We also would like to thank those authors who have already sent their contributions to WCMC that you will read in this issue and in the next two issues. Furthermore, we welcome all authors and readers of WCMC and ask for their involvement and participation by submitting their manuscripts and welcome their personal subscriptions and that of their libraries. At the outset it was our priority to make sure both personal and institutional subscriptions to WCMC are affordable. For more details about the topics and how to make your electronic submission/subscription, we invite you to visit the WCMC web site at: www.interscience.wiley.com/jpages/1530-8669/ We also welcome any comments to any of the four editors listed below. Again, we welcome all of you to contribute and encourage your colleagues and libraries to subscribe to this fine publication.

This is the second issue of Wireless Communications and Mobile Computing (WCMC) published by John Wiley & Sons, Ltd. The first issue was published ahead of time and presented some excellent review papers from experts in the field. We received great feedback from a number of colleagues about its content and we promise to continue producing excellent issues in the future. If you did not receive your sample copy of the inaugural issue, please let me or any of the regional editors know to arrange to send you a copy. We have selected seven papers to appear in this second issue. These papers span a good range of topics in the area. The first is a review paper by Ralph and Aghvami. They discuss the Wireless Application Protocol (WAP) architecture and characteristics. Then, they investigate the devices that are supported by WAP as well as the drivers and services. These services include quality of service, security, billing, interoperability, and performance engineering. The variety of media types combined with the diversity of Internet connection characteristics raises momentous challenges to the achievement of ubiquitous access to Internet multimedia content. The second paper, by Margaritidis and Polyzos, addresses issues related to this topic. They identify the importance of adaptation to this category. Then, they examine several important factors that influence the design and optimization of the adaptation architecture. In conclusion, they describe current commercial solutions and future trends in application adaptation in conjunction with recent developments towards wireless access to the Internet. The third paper, by Havinga and Smit, discusses also wireless multimedia networking, but focuses more on energy efficiency. The authors identify three key problems and provide possible solutions to overcome them. The efficient management of power-controlled cellular wireless systems is necessary for their deployment success. Power control can be used as a platform for radio resource management and is important for channel fading as well as providing QoS to individual users. Xiao, Shroff and Chong review the developments of distributed power control and related resource management problems in cellular wireless systems. The discussion is concentrated on achieving high efficiency for a power-controlled system while preventing the system from failure. They then review power and rate control schemes proposed for wireless data, and present a framework for utility-based power control as a possible candidate for distributed power control of multimedia wireless systems. In the fifth paper, Arslan and Bottomley review commonly used approaches to channel estimation. This is necessary in adaptive receiver designs used in narrowband and wireless digital communication systems. They consider both time-invariant and time-varying channels in their discussion. They also provide a number of applications that will make use of this standard. On the other hand, Tepedelenliolu, Abdi, Giannakis and Kaveh talk about estimation of Doppler spread and signal strength in wireless systems. They concentrate on estimating the received signal strength, mobile velocity, and other related statistical channel parameters that apply to handoff and adaptive transmission. They provide comparison of the presented schemes based on their modeling and simulation results. Finally, Annamalai and Tellambura present a research paper discussing three new exponential-type bounds derived using the Cauchy–Schwarz bounding technique. They show that these new bounds are found to be tight and useful for a number of applications. They compare these bounds to other well-known exponential type bounds and prove that these perform at least comparable or tighter. We hope that you will enjoy reading this issue and welcome any comments to any of the four editors listed below. Again, we welcome all of you to contribute and encourage your colleagues and libraries to subscribe to this fine publication.

A major challenge in design of high-speed networks is to provide guaranteed quality of service for multimedia applications. We propose an admission control policy, a traffic regulator and a scheduling algorithm for the single-hop passive star coupled WDM optical networks. All of them are combined to ensure guaranteed deterministic performance service to multimedia applications. We set up an analytical model to evaluate the deterministic bounded message delay in the specified network based on the max-plus algebra. We conduct a trace-driven simulation with real MPEG traffic. We model MPEG traffic with the parameters of the real MPEG traffic and use them in discrete event simulations. Both simulation results show that our systematic scheme and our analysis are feasible to multimedia applications.

This paper proposes a methodology and framework for integrating different MAC protocols into a single shared medium network to efficiently accommodate various types of multimedia traffic streams with different characteristics and QoS demands. In particular, we propose an integrated MAC protocol which is termed multimedia medium access control (multimedia-MAC) protocol that can efficiently and simultaneously serve three types of multimedia traffic streams: a constant-bit-rate (CBR) traffic and two classes of variable-bit-rate (VBR) traffic. We have developed a queuing system with a vacation model and a multiple priority queuing system to establish a mathematical framework for the analysis and performance evaluation of our multimedia-MAC protocol besides using extensive computer simulations. The main purpose of our performance analysis and evaluation is to assess our multimedia-MAC protocol in its provision of QoS guarantees under a variety of realistic traffic and networking parameters. More importantly, our theoretical analysis results in an efficient approach to estimate the QoS of a shared medium network for given deadline missing rate (DMR) vs. network traffic load, that leads to more accurate admission control and higher network utilization in multimedia communication. The simulation results show that our approach can have an accurate estimation in a wide range of traffic load and deadline requirement.

A major challenge in the design of future generation high-speed networks is to provide guaranteed quality of service for the real-time multimedia applications. In this paper, we study the problem of providing guaranteed deterministic performance service to the real-time and variable length messages in wavelength division multiplexing (WDM) optical networks. In particular, we propose an admission control policy, a traffic regulator and a scheduling algorithm for the single-hop passive star coupled WDM optical networks. All of them are combined to form a systematic scheme to ensure guaranteed performance service. We set up an analytical model to evaluate the deterministic bounded message delay in the specified network base on the max-plus algebra. Our simulation result shows that the delay bound we have set up is valid.

In a real-time wireless TDMA environment, every packet generated by applications has a deadline associated with it. If the system cannot allocate enough resources to serve the packet before the deadline, the packet would be dropped. Different applications have different delay requirements that should be guaranteed by the system so as to maintain some given packet dropping probabilities. In this paper, a single-cell system traffic of multiple delay classes is mathematically analyzed, and it is proved to be independent of the scheduling algorithm used, for all work-conserving earliest-due-date (WC-EDD) scheduling algorithms. The dropping requirements of all individual applications are guaranteed using deadline-sensitive ordered-head-of-line (DSO-HoL) priority schemes. Verification of the model is shown through extensive simulations.

A major challenge in the design of future generation high-speed networks is the provision of guaranteed quality-of-service (QoS) for a wide variety of multimedia applications. In this paper we investigate the problem of providing QoS guarantees to real-time variable length messages (e.g., IP packets) in wavelength division multiplexing (WDM) optical networks. In particular, we propose a systematic mechanism comprised of admission control, traffic regulation, and message scheduling that provide guaranteed performance service for real-time application streams made up of variable-length messages. We formulate an analytical model based on the theory of max-plus algebra to evaluate the deterministic bounded message delay in a WDM network environment using our proposed QoS guarantee mechanism to determine the "schedulability conditions" of multimedia application streams, We also conduct a series of discrete-event and trace-driven simulations to verify the accuracy of the analytical model. The simulation results demonstrate that the analytic delay bound we obtained for our WDM optical network is valid and accurate.

The distance calculation in an image is a basic operation in computer vision, pattern recognition, and robotics. Several parallel algorithms have been proposed for calculating the Euclidean distance transform (EDT). Recently, Chen and Chuang proposed a parallel algorithm for computing the EDT on mesh-connected SIMD computers (1995). For an nxn image, their algorithm runs in O(n) time on a two-dimensional (2-D) nxn mesh-connected processor array. In this paper, we propose a more efficient parallel algorithm for computing the EDT on a reconfigurable mesh model. For the same problem, our algorithm runs in O(log(2)n) time on a 2-D nxn reconfigurable mesh. Since a reconfigurable mesh uses the same amount of VLSI area as a plain mesh of the same size does when implemented in VLSI, our algorithm improves the result in [3] significantly.

We report an investigation into the effect of using smart antennas on the performance of the slotted ALOHA protocol with capture in a mobile communications environment with Rayleigh and log-normal fading. Our results demonstrate that by using smart antennas, we can achieve higher performance in terms of capture probability and throughput when compared to a conventional antenna system using the slotted ALOHA protocol.

An analytical model for the performance analysis of a multiple input queued asynchronous transfer mode (ATM) switch is presented. The interconnection network of the ATM switch is internally nonblocking and each input port maintains a separate queue of cells for each output port. The switch uses parallel iterative matching (PIM) to find the maximal matching between the input and output ports of the switch. A closed-form solution for the maximum throughput of the switch under saturated conditions is derived. It is found that the maximum throughput of the switch exceeds 99% with just four iterations of the PIM algorithm. Using the tagged input queue approach, an analytical model for evaluating the switch performance under an independent identically distributed Bernoulli traffic with the cell destinations uniformly distributed over all output ports is developed. The switch throughput, mean cell delay, and cell loss probability are computed from the analytical model. The accuracy of the analytical model is verified using simulation.

The Hough transform is an important problem in image processing and computer vision. An efficient algorithm for computing the Hough transform has been proposed on a reconfigurable array by Kao et al. (1995). For a problem with an /spl radic/N/spl times//spl radic/N image and an n/spl times/n parameter space, the algorithm runs in a constant time on a three-dimensional (3-D) n/spl times/n/spl times/N reconfigurable mesh where the data bus is N/sup 1/c/-bit wide. To our best knowledge, this is the most efficient constant-time algorithm for computing the Hough transform on a reconfigurable mesh. In this paper, an improved Hough transform algorithm on a reconfigurable mesh is proposed. For the same problem, our algorithm runs in constant time on a 3-D n*n/spl times/n/spl times//spl radic/n/spl radic/n reconfigurable mesh, where the data bus is only log N-bit wide. In most practical situations, n=O(/spl radic/N). Hence, our algorithm requires much less VLSI area to accomplish the same task. In addition, our algorithm can compute the Radon transform (a generalized Hough transform) in O(1) time on the same model, whereas the algorithm in the above paper cannot be adapted to computing Radon transform easily.

We study the problem of providing real-time service to hard and soft real-time messages in wavelength division multiplexing (WDM) optical networks. We propose and evaluate a set of scheduling algorithms which schedule message transmissions in single-hop WDM passive star networks based on specific time constraints. We compare the performances of our algorithms with that of the typical WDM scheduling algorithm which does not consider the time constraint of the transmitted messages by discrete-event simulations and an analytical model. We find that the improvement on real-time performance can be attributed to our scheduling algorithms where the time constraint of messages has been taken into consideration. This study suggests that when scheduling real-time messages in WDM networks, one has to consider not only the problem of resources allocation in the network but also the problem of sequencing messages based on their time constraints.

This paper proposes a new medium access control (MAC) protocol for future wireless multimedia personal communication systems, denoted the hybrid and adaptive multiple access control (HAMAC) protocol. The HAMAC protocol integrates fixed assignment TDMA protocols, reservation-based protocols, and contention-based protocols into a single wireless network so as to simultaneously and efficiently support various classes of traffic such as constant-bit rate (CBR), variable-bit rate (VBR), and available-bit rate (ABR) traffic. In particular, the HAMAC protocol uses a novel preservation slot technique to overcome the packet contention overhead in packet reservation multiple access (PRMA) like protocols, while keeping most isochronous service features of TDMA protocols to serve voice and CBR traffic streams. We have analyzed the performance of the HAMAC protocol using extensive simulations. The results show that the HAMAC protocol can achieve very low loss rates for various multimedia traffic with stringent quality of service (QoS) requirements and outperforms state-of-the-art PRMA-like protocols. As a result, the HAMAC protocol appears to be a good candidate for future generation multimedia personal communication systems.

An analytical model for the performance analysis of a novel input access scheme for an ATM switch is developed and presented in this paper. The interconnection network of the ATM switch is internally nonblocking and each input port maintains a separate queue for each output port so as to reduce the head-of-line (HOL) blocking of conventional input queuing switches. Each input is allowed to send only one cell per time slot, and each output port is allowed to receive only one cell per time slot. Using a tagged queue approach, an analytical model with an underlying two-dimensional Markov chain with a state space of size (queue capacity/spl times/switch size) is constructed for evaluating the switch performance under i.i.d Bernoulli traffic for different offered traffic loads. The switch throughput, mean cell delay, and cell loss probability are computed from the analytical model. The accuracy of the analytical model is verified using simulation.

Parallel computing on clusters of workstations is receiving much attention from the research community. Unfortunately, many aspects of parallel computing over this parallel computing engine is not very well understood. Some of these issues include the workstation architectures, the network protocols, the communication-to-computation ratio, the load balancing strategies, and the data partitioning schemes. The aim of this paper is to assess the strengths and limitations of a cluster of workstations by capturing the effects of the above issues. This has been achieved by evaluating the performance of this computing environment in the execution of a parallel ray tracing application through analytical modeling and extensive experimentation.

The performance evaluation of ATM switches is of paramount importance in the design and analysis of ATM networks. In this paper, we focus on the evaluation of the cell loss rate (CLR) and cell delay probability (CDP) in nonblocking ATM switches using computer simulations. In particular, we investigate the potential of using importance sampling techniques as an "superfast" alternative to conventional Monte Carlo simulation in finding the CLR and CDP in nonblocking ATM switches. We propose a "split switch" method to decouple the input and output queue behaviors, along with the notion of regenerative cycles. Numerical results will demonstrate that considerable computation cost can be saved using the proposed importance sampling techniques while maintaining a high degree of accuracy.

The embedding of one interconnection network into another is a very important issue in the design and analysis of parallel algorithms. Through such embeddings, the algorithms originally developed for one architecture can be directly mapped to another architecture. This paper describes a new embedding method, based on matrix transformations, for optimally embedding hierarchical hypercube networks (HHNs) into the hypercube (binary n-cube). Thus, this embedding method has practical importance in enhancing the capabilities and extending the usefulness of the hypercube, since hierarchical hypercube networks have proven to be very cost-effective for a wide range of applications.

The Reconfigurable Array with Spanning Optical Buses (RASOB) has recently received a lot of attention from the research community. By taking advantage of the unique properties of optical transmission, the RASOB provides flexible reconfiguration and strong connectivities with low hardware and control complexities. In this paper, we use this architecture for the design of efficient sorting algorithms on the 1-D RASOB and the 2-D RASOB. Our parallel sorting algorithm on the 1-D RASOB, which sorts N data items using N processors in O(k) time where k is the size of the data items to be in bits, is based on a novel divide-and-conquer scheme. On the other hand, our parallel sorting algorithm on the 2-D RASOB is based on the sorting algorithm on the 1-D RASOB in conjunction with the well known Rotatesort algorithm. This algorithm sorts N data items on a 2-D RASOB of size N in O(k) time. These sorting algorithms outperform state-of-the-art sorting algorithms on reconfigurable arrays of processors with electronic buses.

Randomized selection algorithms on k-dimensional mesh-connected computers with multiple broad-casting are proposed in this paper. We first show that a leader can be elected in O(log N) time on any k-dimensional mesh-connected computers with multiple broadcasting of size N. We then show that we can find the p-th smallest element among a data set of size N in O((logN + k + N1/k(k + 1))) log N) expected time using a regular N1/k ×…×N1/kk-dimensional mesh and in O((log N + k2N1/k2k)) log N) expected time using an irregular N(2k-1k + 1)/(k2k)×N(2k-2k + 1)/(k2k×…×N(k + 1)/(k2kk-dimensional mesh. This leads to a selection algorithm which runs in O((log N)2) expected time on a regular ((log N/log log N)1/2)-dimensional mesh or on an irregular (log log N)-dimensional mesh each with N processors. To our best knowledge, this is the first polylogarithmic selection algorithm on meshes with multiple broadcasting.

Distributed systems such as networks of workstations are becoming an increasingly viable alternative to traditional supercomputer systems for running complex scientific applications. A large number of these applications require solving sets of partial differential equations (PDEs). In this paper, we describe the implementation and performance of SPEED (Scalable Partial differential Equation Environment on Distributed systems), a parallel platform which provides an efficient solution for time-dependent PDEs. SPEED allows the inclusion of a wide range of parameters and programming aids. PVM is employed as the underlying message-passing system. The parallel implementation has been performed using two algorithms. The first algorithm is a two-phase scheme which uses the conventional technique of alternating phases of computation and communication. The second algorithm employs a pre-computation technique that allows overlapping of computation and communication. Both methods yield significant speedups. The pre-computation technique reduces the communication time between the workstations but incurs additional overhead in buffer management. Hence, if the saving in communication time is larger than the overhead, the pre-computation technique outperforms the two-phase algorithm. SPEED also provides a performance prediction methodology that can accurately predict the performance of a given application on the system before running the application. This methodology allows the user to tune various parameters in order to identify system bottlenecks and maximize the performance.

The paper describes the implementation and performance of a parallel platform for solving partial differential equations (PDEs) on distributed systems. The platform has been implemented using PVM for a network of workstations. It allows the inclusion of a wide range of parameters and programming aids. The PDEs are specified in the form of finite difference equations. With a given set of parameters and a partitioning strategy, the platform provides facilities to record and predict the performance of an application before running it. The performance prediction model helps the user to identify the major bottlenecks of the platform such that by reducing them, the speedup can be improved. We also present analysis of various factors that can have drastic effect on the speedup, which allows the user to tune a number of parameters to maximize the performance.

Cluster-based computing, which exploits the agtractive alternative to traditional parallel computers.

The embedding of one interconnection network into another is a very important issue in the design and analysis of parallel algorithms. Through such embeddings the algorithms originally developed for one architecture can be directly mapped to another architecture. This paper describes novel methods, based on matrix transformations, for efficiently embedding different networks into the hypercube (binary n-cube). First, we use this method to embed r-ary m-cubes into a binary n-cube of the same size with dilation 1. While our method has the same dilation as traditional methods using reflected Gray code, it has the additional property of making the layout of the binary n-cube more suitable for divide-and-conquer algorithms. Second, we use our matrix transformation method to optimally embed hierarchical interconnection networks into the binary n-cube which we would not achieve using reflected Gray code embedding. Thus, this embedding method has significant practical importance in enhancing the capabilities of the hypercube.

Multimedia services, teleconferencing, high performance visualization, and distributed supercomputing are expected to accelerate the demand for high-speed local and metropolitan area networks (LAN/MANs). In this paper, we propose a new MAC protocol, called fast reservation multiple access (FRMA), which is suitable for a folded bus high-speed MANs and LANs. Its main feature is a sample and fast reservation technique that allow stations to reserve transmission slots. The goal of the reservation is to maximize the throughput of the system and to ensure fairness among the attached stations. A complete description of the FRMA protocol and the associated nodes are given. Then, we show the performance of FRMA as a function of its throughput, mean delays, and fairness using simulation results. Our protocol is shown to achieve high throughput and small transmission delays while preserving the fairness of the whole network. Thus, the FRMA protocol seem to be an appropriate choice for future generation high-speed MANs and LANs.

The performance of distributed supercomputing computing environments are mainly dependent on three factors: distributed programming tools, computing nodes, and LANs employed. In this paper, we analyze the performance of all these factors experimentally and analytically. The distributed programming tools that we employed are PVM and Express. The computing nodes that we employed are SUN and HP workstations, and the LANs that we considered are an Ethernet and FDDI networks. Extensive timing experiments, including one-to-one communications, exchange operations, and broadcast operations, have been performed and analyzed. Moreover, analytic models have been developed to analyze the behavior of the network protocols employed by the LAN-based platforms as well as to estimate the communication overhead for the computing software tools.

The use of a network of workstations as a single unit for speeding up computationally intensive applications is becoming a cost-effective alternative to traditional parallel computers. We present the implementation of an application-driven parallel platform for solving partial differential equations (PDEs) on this computing environment. The platform provides a general and efficient parallel solution for time-dependent PDEs and an easy-to-use interface that allows the inclusion of a wide range of parallel programming tools. We have used two different parallelization methods in this platform. The first method is a two-phase algorithm which uses the conventional technique of alternating computation and communication phases. The second method uses a novel pre-computation technique which allows overlapping of computation and communication. Both methods yield significant speedup. However the pre-computation technique is shown to be more efficient and scalable.>

A new interconnection network is proposed for the construction of massively parallel computers. The systematic construction of this network, denoted RCNFULL, is performed by methodically connecting together a number of basic atoms where a basic atom is a set of fully connected nodes. Key communication characteristics and efficient routing algorithms are derivedfor RCN-FULL. An 0(log(N)) sorting algorithm is shown for RCN-FULL and RCN-FULL is proven to deterministically emulate the CRCW PRAM model, with only O(log{N)) degradation in time performance. Finally, the hardware cost for the RCNFULL is estimated as a function of its pin limitations and compared favorably to that of the hypercube.

A new class of interconnection networks is presented for interconnecting the processors of general purpose massively parallel computers. This new class of interconnection networks, recursively connected networks (RCN), is constructed by methodically connecting together a number of basic networks, referred to as atoms, through the recursive application of a complete graph compound. A specific instance of this class, RCN-CUBE, where the basic atom is a binary hypercube, is shown to have desirable network properties such as small diameter, small degree, high bandwidth and optimal connectivity; and these compare favorably to those of other related networks. Convenient routing strategies are derived for the RCN class, which require only local information to route messages between nodes whenever routing within the basic atom requires only local information. RCN is shown to emulate the binary hypercube well under any permutation when the basic atom performs a binary hypercube emulation well; and RCN-CUBE can emulate the binary hypercube with only a small multiplicative factor increase in time performance. The time performance of RCN on various fundamental data movement operations frequently used in the design of parallel algorithms is analyzed and evaluated as a function of the performance on the basic atom used, and RCN-CUBE performance is shown to be very close to that required by the binary hypercube.

To efficiently use wavelet transforms in parallel mesh architectures, it is necessary to identify efficient embeddings of wavelet transform coefficients into such meshes. Two forms of 2D wavelet transform embedding into 2D meshes (with and without reconfigurability) are considered, and time performances for these embeddings over classes of image processing algorithms are compared. This demonstrates the superiority of one of these embeddings.>