While large language models (LLMs) have become the de facto framework for literature-related tasks, they still struggle to function as domain-specific literature agents due to their inability to connect pieces of knowledge and reason across domain-specific contexts, terminologies, and nomenclatures. This challenge underscores the need for a tool that facilitates such domain-specific adaptation and enables rigorous benchmarking across literature tasks. To that end, we introduce LitBench, a benchmarking tool designed to enable the development and evaluation of domain-specific LLMs tailored to literature-related tasks. At its core, LitBench uses a data curation process that generates domain-specific literature sub-graphs and constructs training and evaluation datasets based on the textual attributes of the resulting nodes and edges. The tool is designed for flexibility, supporting the curation of literature graphs across any domain chosen by the user, whether high-level fields or specialized interdisciplinary areas. In addition to dataset curation, LitBench defines a comprehensive suite of literature tasks, ranging from node and edge level analyses to advanced applications such as related work generation. These tasks enable LLMs to internalize domain-specific knowledge and relationships embedded in the curated graph during training, while also supporting rigorous evaluation of model performance. Our results show that small domain-specific LLMs trained and evaluated on LitBench datasets achieve competitive performance compared to state-of-the-art models like GPT-4o and DeepSeek-R1. To enhance accessibility and ease of use, we open-source the tool along with an AI agent tool that streamlines data curation, model training, and evaluation.

The disaggregated, cloud-native design of the 5G Core (5GC) enables flexibility and scalability but introduces significant challenges. Control-plane procedures involve complex interactions across multiple Virtual Network Functions (VNFs), while the user plane must sustain diverse and resource-intensive traffic. Existing tools often benchmark these dimensions in isolation, rely on synthetic workloads, or lack visibility into fine-grained resource usage. This paper presents 5GC-Bench, a modular framework for stress-testing the 5GC under realistic workloads. 5GC-Bench jointly emulates signaling and service traffic, supporting both VNF profiling and end-to-end service-chain analysis. By characterizing bottlenecks and resource demands, it provides actionable insights for capacity planning and performance optimization. We integrated 5GC-Bench with the OpenAirInterface (OAI) 5GC and deployed it on a real 5G testbed, demonstrating its ability to uncover resource constraints and expose cross-VNF dependencies under scenarios that mirror operational 5G deployments. To foster reproducibility and further research, we release publicly all the artifacts.

In this paper, we study a system in which a sensor forwards status updates to a receiver through an error-prone channel, while the receiver sends the transmission results back to the sensor via a reliable channel. Both channels are subject to random delays. To evaluate the timeliness of the status information at the receiver, we use the Age of Information (AoI) metric. The objective is to design a sampling policy that minimizes the expected time-average AoI, even when the channel statistics (e.g., delay distributions) are unknown. We first review the threshold structure of the optimal offline policy under known channel statistics and then reformulate the design of the online algorithm as a stochastic approximation problem. We propose a Robbins-Monro algorithm to solve this problem and demonstrate that the optimal threshold can be approximated almost surely. Moreover, we prove that the cumulative AoI regret of the online algorithm increases with rate$\mathcal {O}(\ln K)$, where$K$is the number of successful transmissions. In addition, our algorithm is shown to be minimax order optimal, in the sense that for any online learning algorithm, the cumulative AoI regret up to the$K$-th successful transmissions grows with the rate at least$\Omega (\ln K)$in the worst case delay distribution. Finally, we improve the stability of the proposed online learning algorithm through a momentum-based stochastic gradient descent algorithm. Simulation results validate the performance of our proposed algorithm.

Building on the success of text-to-image diffusion models (DPMs), image editing is an important application to enable human interaction with AI-generated content. Among various editing methods, editing within the prompt space gains more attention due to its capacity and simplicity of controlling semantics. However, since diffusion models are commonly pretrained on descriptive text captions, direct editing of words in text prompts usually leads to completely different generated images, violating the requirements for image editing. On the other hand, existing editing methods usually consider introducing spatial masks to preserve the identity of unedited regions, which are usually ignored by DPMs and therefore lead to inharmonic editing results. Targeting these two challenges, in this work, we propose to disentangle the comprehensive image-prompt interaction into several item-prompt interactions, with each item linked to a special learned prompt. The resulting framework, named D-Edit, is based on pretrained diffusion models with cross-attention layers disentangled and adopts a two-step optimization to build item-prompt associations. Versatile image editing can then be applied to specific items by manipulating the corresponding prompts. We demonstrate state-of-the-art results in four types of editing operations including image-based, text-based, mask-based editing, and item removal, covering most types of editing applications, all within a single unified framework. Notably, D-Edit is the first framework that can (1) achieve item editing through mask editing and (2) combine image and text-based editing. We demonstrate the quality and versatility of the editing results for a diverse collection of images through both qualitative and quantitative evaluations.

Wireless networks are vulnerable to jamming attacks due to the shared communication medium, which can severely degrade performance and disrupt services. Despite extensive research, current jamming detection methods often rely on simulated data or proprietary over-the-air datasets with limited cross-layer features, failing to accurately represent the real state of a network and thus limiting their effectiveness in real-world scenarios. To address these challenges, we introduce JamShield, a dynamic jamming detection system trained on our own collected over-the-air and publicly available dataset. It utilizes hybrid feature selection to prioritize relevant features for accurate and efficient detection. Additionally, it includes an autoclassification module that dynamically adjusts the classification algorithm in real-time based on current network conditions. Our experimental results demonstrate significant improvements in detection rate, precision, and recall, along with reduced false alarms and misdetections compared to state-of-the-art detection algorithms, making JamShield a robust and reliable solution for detecting jamming attacks in real-world wireless networks.

With the advent of large language models (LLMs), managing scientific literature via LLMs has become a promising direction of research. However, existing approaches often overlook the rich structural and semantic relevance among scientific literature, limiting their ability to discern the relationships between pieces of scientific knowledge, and suffer from various types of hallucinations. These methods also focus narrowly on individual downstream tasks, limiting their applicability across use cases. We propose LitFM, the first literature foundation model designed for a wide variety of practical downstream tasks on domain-specific literature, with a focus on citation information. At its core, LitFM contains a novel graph retriever that can provide accurate and diverse recommendations for LLM to integrate graph structure information and relevant literature. LitFM also leverages a knowledge-infused LLM, fine-tuned through a well-developed instruction paradigm. It enables LitFM to extract domain-specific knowledge from literature and reason relationships among them. By integrating citation graphs during both training and inference, LitFM can generalize to unseen papers and accurately assess their relevance within existing literature. Additionally, we introduce new large-scale literature citation benchmark datasets on three academic fields, featuring sentence-level citation information and local context. Extensive experiments validate the superiority of LitFM, achieving 28.1% improvement on retrieval task in precision, and an average improvement of 7.52% over state-of-the-art across six downstream literature-related tasks.

The ubiquity of dynamic data in domains such as weather, healthcare, and energy underscores a growing need for effective interpretation and retrieval of time-series data. These data are inherently tied to domain-specific contexts, such as clinical notes or weather narratives, making cross-modal retrieval essential not only for downstream tasks but also for developing robust time-series foundation models by retrieval-augmented generation (RAG). Despite the increasing demand, time-series retrieval remains largely underexplored. Existing methods often lack semantic grounding, struggle to align heterogeneous modalities, and have limited capacity for handling multi-channel signals. To address this gap, we propose TRACE, a generic multimodal retriever that grounds time-series embeddings in aligned textual context. TRACE enables fine-grained channel-level alignment and employs hard negative mining to facilitate semantically meaningful retrieval. It supports flexible cross-modal retrieval modes, including Text-to-Timeseries and Timeseries-to-Text, effectively linking linguistic descriptions with complex temporal patterns. By retrieving semantically relevant pairs, TRACE enriches downstream models with informative context, leading to improved predictive accuracy and interpretability. Beyond a static retrieval engine, TRACE also serves as a powerful standalone encoder, with lightweight task-specific tuning that refines context-aware representations while maintaining strong cross-modal alignment. These representations achieve state-of-the-art performance on downstream forecasting and classification tasks. Extensive experiments across multiple domains highlight its dual utility, as both an effective encoder for downstream applications and a general-purpose retriever to enhance time-series models.

Frontier large language models (LLMs) have shown great success in text modeling and generation tasks across domains. However, natural language exhibits inherent semantic hierarchies and nuanced geometric structure, which current LLMs do not capture completely owing to their reliance on Euclidean operations such as dot-products and norms. Furthermore, recent studies have shown that not respecting the underlying geometry of token embeddings leads to training instabilities and degradation of generative capabilities. These findings suggest that shifting to non-Euclidean geometries can better align language models with the underlying geometry of text. We thus propose to operate fully in $\textit{Hyperbolic space}$, known for its expansive, scale-free, and low-distortion properties. To this end, we introduce $\textbf{HELM}$, a family of $\textbf{H}$yp$\textbf{E}$rbolic Large $\textbf{L}$anguage $\textbf{M}$odels, offering a geometric rethinking of the Transformer-based LLM that addresses the representational inflexibility, missing set of necessary operations, and poor scalability of existing hyperbolic LMs. We additionally introduce a $\textbf{Mi}$xture-of-$\textbf{C}$urvature $\textbf{E}$xperts model, $\textbf{HELM-MiCE}$, where each expert operates in a distinct curvature space to encode more fine-grained geometric structure from text, as well as a dense model, $\textbf{HELM-D}$. For $\textbf{HELM-MiCE}$, we further develop hyperbolic Multi-Head Latent Attention ($\textbf{HMLA}$) for efficient, reduced-KV-cache training and inference. For both models, we further develop essential hyperbolic equivalents of rotary positional encodings and root mean square normalization. We are the first to train fully hyperbolic LLMs at billion-parameter scale, and evaluate them on well-known benchmarks such as MMLU and ARC, spanning STEM problem-solving, general knowledge, and commonsense reasoning. Our results show consistent gains from our $\textbf{HELM}$ architectures – up to 4\% – over popular Euclidean architectures used in LLaMA and DeepSeek with superior semantic hierarchy modeling capabilities, highlighting the efficacy and enhanced reasoning afforded by hyperbolic geometry in large-scale language model pretraining.

Markov Decision Processes (MDPs) serve as the mathematical foundation of Reinforcement learning (RL), where a Markov process with defined states is used to model the system and the actions to be taken affect the state transitions and the corresponding rewards. The RL and deep RL (DRL) can produce the high-performing action policy to maximize the long-term reward. Although RL/DRL have been widely applied to communication and computer systems, a key limitation is that the system under consideration often does not satisfy the required mathematical properties, thus making the MDP inexact and the derived policy flawed. Therefore, we consider the periodic Markov Decision Process (pMDP), where the evolution of the underlying process and model parameters for the pMDP demonstrate some forms of periodic characteristics (e.g., periodic job arrivals and available resources) which violate the Markov property. To obtain the optimal policies for the pMDP, a policy gradient method with a multi-policy solution framework is proposed, and a deep-learning method is developed to improve the effectiveness and stability of the proposed solution. Furthermore, a layer-sharing strategy is proposed to reduce the storage complexity by reducing the number of parameters in the neural networks. The deep-learning method is applied to achieve the near-optimal allocation of resources to arriving computational tasks in a network setting corresponding to the software-defined network (SDN). Evaluation results reveal that the proposed technique is valid and capable of outperforming a baseline method that employs a single policy by 31% on average.

Future wireless networks need to support the increasing demands for high data rates and improved coverage. One promising solution is sectorization, where an infrastructure node is equipped with multiple sectors employing directional communication. Although the concept of sectorization is not new, it is critical to fully understand the potential of sectorized networks, such as the rate gain achieved when multiple sectors can be simultaneously activated. In this paper, we focus on sectorized wireless networks, where sectorized infrastructure nodes with beam-steering capabilities form a multi-hop mesh network. We present a sectorized node model and characterize the capacity region of these sectorized networks. We define the flow extension ratio and the corresponding sectorization gain, which quantitatively measure the performance gain introduced by node sectorization as a function of the network flow. Our objective is to find the sectorization of each node that achieves the maximum flow extension ratio, and thus the sectorization gain. Towards this goal, we formulate the corresponding optimization problem and develop an efficient distributed algorithm that obtains the node sectorization under a given network flow with an approximation ratio of 2/3. Additionally, we emphasize the class of Even Homogeneous Sectorizations, which simultaneously enhances the efficiency of dynamic routing schemes with unknown arrival rates and increases network capacity. We further propose that if sectorization can be adapted dynamically over time, either a backpressure-driven or maximum weighted b-matching-based routing approach can be employed, thereby expanding the achievable capacity region while preserving stability under unknown traffic conditions. Through extensive simulations, we evaluate the sectorization gain and the performance of the proposed algorithms in various network scenarios.

The efficiency of Deep Learning (DL) training jobs is critically dependent on dataloaders, which facilitate the transfer of data from storage to DL-accelerated hardware during training. Recent advancements in data loading technology have demonstrated significant improvements, not only in reducing training times but also in introducing capabilities such as seamless integration with cloud storage. This paper examines the dataloader as a distinct component within the DL workflow, offering a detailed analysis of its structure and functionalities. We present a systematic evaluation of various dataloading libraries, investigating their performance across different configurations, including worker count, batch size, GPU scaling, data access patterns and remote loading. The evaluation highlights trade-offs in functionality, usability, and performance. Additionally, we examine the impact of dataset characteristics on data loading performance, showing that throughput decreases exponentially with image resolution. To support ongoing research and practical advancements, we introduce the first open-source benchmarking suite for DL data loading, which allows the community to replicate, extend, and build upon our experiments.

This paper investigates the application of Machine Learning for credit risk assessment in Multichain Decentralized Finance (DeFi). With DeFi expanding its scope, the need for effective credit risk evaluation becomes paramount. Our study utilizes a diverse dataset gathered from multiple blockchains, including Ethereum, and employs rigorous data preprocessing techniques. DeFi-specific features are extracted, capturing transaction-related statistics. Machine learning models, such as Logistic Regression, Random Forest, XGBoost, CatBoost, LightGBM and a CNN, are deployed to predict wallet liquidations. Evaluation metrics, including accuracy, ROC curve and Area Under the Curve, demonstrate the efficacy of DeFi-related features in credit risk assessment. Furthermore, we analyze feature importance and inter-feature correlations, providing insights into critical risk factors within the DeFi ecosystem. This research contributes valuable insights to the DeFi landscape, offering data-driven approaches to credit risk management and investment strategies. Our findings hold significance for DeFi stakeholders seeking to navigate the evolving financial frontier while mitigating credit risk effectively.

Smart contracts across Blockchains provide an ecosystem of decentralized finance (DeFi), with a total locked value which had exceeded 160B USD. While DeFi comes with high rewards, it also carries plenty of risks. Many financial crimes have occurred over the years making the early detection of malicious activity an issue of high priority. The proposed framework introduces an effective method for extracting a set of features from different chains, and it is evaluated over an extensive dataset with the transactions of the 23 most widely used DeFi protocols based on a novel dataset in collaboration with Covalent. Different Machine Learning methods were employed, such as a Deep Neural Network, XGBoost, and a fine-tuned Large Language Model for identifying fraud accounts interacting with DeFi and we demonstrate that the introduction of novel DeFi-related features, significantly improves the evaluation results.

The rapid evolution of Vehicular Ad-hoc NETworks (VANETs) has ushered in a transformative era for intelligent transportation systems (ITS), significantly enhancing road safety and vehicular communication. However, the intricate and dynamic nature of VANETs presents formidable challenges, particularly in vehicle-to-infrastructure (V2I) communications. Roadside Units (RSUs), integral components of VANETs, are increasingly susceptible to cyberattacks, such as jamming and distributed denial of service (DDoS) attacks. These vulnerabilities pose grave risks to road safety, potentially leading to traffic congestion and vehicle malfunctions. Existing methods face difficulties in detecting dynamic attacks and integrating digital twin technology and artificial intelligence (AI) models to enhance VANET cybersecurity. Our study proposes a novel framework that combines digital twin technology with AI to enhance the security of RSUs in VANETs and address this gap. This framework enables real-time monitoring and efficient threat detection while also improving computational efficiency and reducing data transmission delay for increased energy efficiency and hardware durability. Our framework outperforms existing solutions in resource management and attack detection. It reduces RSU load and data transmission delay while achieving an optimal balance between resource consumption and high attack detection effectiveness. This highlights our commitment to secure and sustainable vehicular communication systems for smart cities.

Soil carbon is a critical factor in maintaining soil health and combating climate change. Understanding and managing soil carbon levels is essential for sustainable agriculture and environmental protection. However, current methods for measuring soil carbon are time-consuming and costly, hindering efforts to monitor soil health and increase carbon sequestration. In this paper, we propose Scarf, a novel soil carbon sensing approach that combines widely accessible radio frequency (RF) and optical signals to detect soil carbon contents without dedicated hardware. Our key insight is that soil carbon content closely correlates with two indicators: the effective permittivity derived from RF signals and soil lightness determined from soil surface images. We mathematically model the correlations and leverage the non-linear correlation between the two signal modalities to compute soil carbon content. We employ machine learning to model relationships that cannot be captured by traditional mathematical equations. Our experimental results indicate that Scarf can achieve high soil carbon prediction accuracy that is comparable to the state-of-the-art soil carbon sensing techniques which cost US$1000s.

Soil carbon is a key soil property for soil health management and a crucial part of the global carbon cycle. Existing methods for soil carbon determination are too expensive and time-consuming. This paper introduces Scarf, a novel soil carbon sensing technique that leverages Wi-Fi and images and eliminates the need for specialized hardware. Our analysis reveals a strong correlation between soil carbon content and both the soil permittivity obtained from Wi-Fi signals and the soil lightness captured in soil surface images. We develop mathematical models to quantify the relationships between soil carbon content and the two signal modalities, and use the models to estimate soil carbon. We apply machine learning to help handle relationships that mathematical models do not capture. Our experiments demonstrate that Scarf delivers highly accurate soil carbon estimations.

Time series forecasting has attracted significant attention in recent decades. Previous studies have demonstrated that the Channel-Independent (CI) strategy improves forecasting performance by treating different channels individually, while it leads to poor generalization on unseen instances and ignores potentially necessary interactions between channels. Conversely, the Channel-Dependent (CD) strategy mixes all channels with even irrelevant and indiscriminate information, which, however, results in oversmoothing issues and limits forecasting accuracy. There is a lack of channel strategy that effectively balances individual channel treatment for improved forecasting performance without overlooking essential interactions between channels. Motivated by our observation of a correlation between the time series model's performance boost against channel mixing and the intrinsic similarity on a pair of channels, we developed a novel and adaptable \textbf{C}hannel \textbf{C}lustering \textbf{M}odule (CCM). CCM dynamically groups channels characterized by intrinsic similarities and leverages cluster information instead of individual channel identities, combining the best of CD and CI worlds. Extensive experiments on real-world datasets demonstrate that CCM can (1) boost the performance of CI and CD models by an average margin of 2.4% and 7.2% on long-term and short-term forecasting, respectively; (2) enable zero-shot forecasting with mainstream time series forecasting models; (3) uncover intrinsic time series patterns among channels and improve interpretability of complex time series models.

Software Defined Networking has afforded numerous benefits to the network users but there are certain persisting issues with this technology, two of which are scalability and privacy. The natural solution to overcoming these limitations is a distributed SDN controller architecture where multiple controllers are deployed over the network, with each controller orchestrating a certain segment of the network. However, since the centralized control is the key attribute of SDN that allows it to be so beneficial, a centralized logical view of the network will have to be maintained by each of these controllers; this can be done through synchronization of the distributed controllers, where each controller communicates with the others to ensure that they remain informed about the entire network. There is however a network cost associated with constantly having to update each others about different aspects of the network, which will become a greater issue in dynamic wireless networks. To minimize this network cost, there is a need to consider not only when to get the update information from the neighboring controllers, but also where to dynamically place the controllers such that the network costs may be minimized. The placement should take into consideration both communication for synchronization among the distributed controllers and communication of the controllers with the network devices that they manage. In this work, we show that our multi-objective deep reinforcement learning-based method performs the best at achieving different application goals by developing policy for controller synchronization as well as placement, outperforming different other possible approaches, under a wide variety of network conditions.

We study the problem of operating a quantum switch with memory constraints. In particular, the switch has to allocate quantum memories to clients to generate link-level entanglements (LLEs), and then use these to serve end-to-end entanglements requests. The paper’s main contributions are (i) to characterize the switch’s capacity region and study how it scales with respect to the number of quantum memories and probability of successful LLEs and (ii) to propose a memory allocation policy that is throughput optimal. In addition, when the requests are bipartite and the LLE attempts are always successful, we show that the proposed policy has polynomial time complexity. We evaluate the proposed policy numerically and illustrate its performance depending on the requests arrivals characteristics and the time available to obtain a memory allocation.

Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communication bandwidth is limited. Recent works on the convergence analysis of FL have focused on unbiased client sampling, e.g., sampling uniformly at random, which suffers from slow wall-clock time for convergence due to high degrees of system heterogeneity (e.g., diverse computation and communication capacities) and statistical heterogeneity (e.g., unbalanced and non-i.i.d. data). This paper aims to design an adaptive client sampling algorithm for FL over wireless networks that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time. We obtain a new tractable convergence bound for FL algorithms with arbitrary client sampling probability. Based on the bound, we analytically establish the relationship between the total learning time and sampling probability with an adaptive bandwidth allocation scheme, which results in a non-convex optimization problem. We design an efficient algorithm for learning the unknown parameters in the convergence bound and develop a low-complexity algorithm to approximately solve the non-convex problem. Our solution reveals the impact of system and statistical heterogeneity parameters on the optimal client sampling design. Moreover, our solution shows that as the number of sampled clients increases, the total convergence time first decreases and then increases because a larger sampling number reduces the number of rounds for convergence but results in a longer expected time per-round due to limited wireless bandwidth. Experimental results from both hardware prototype and simulation demonstrate that our proposed sampling scheme significantly reduces the convergence time compared to several baseline sampling schemes. Notably, for EMNIST dataset, our scheme in hardware prototype spends 71% less time than the baseline uniform sampling for reaching the same target loss.

The growing number of AI-driven applications in mobile devices has led to solutions that integrate deep learning models with the available edge-cloud resources. Due to multiple benefits such as reduction in on-device energy consumption, improved latency, improved network usage, and certain privacy improvements, split learning, where deep learning models are split away from the mobile device and computed in a distributed manner, has become an extensively explored topic. Incorporating compression-aware methods (where learning adapts to compression level of the communicated data) has made split learning even more advantageous. This method could even offer a viable alternative to traditional methods, such as federated learning techniques. In this work, we develop an adaptive compression-aware split learning method (“deprune”) to improve and train deep learning models so that they are much more network-efficient, which would make them ideal to deploy in weaker devices with the help of edge-cloud resources. This method is also extended (“prune”) to very quickly train deep learning models through a transfer learning approach, which tradesoff little accuracy for much more network-efficient inference abilities. We show that the “deprune” method can reduce network usage by 4× when compared with a split-learning approach (that does not use our method) without loss of accuracy, while also improving accuracy over compression-aware split-learning by up to 4 percent. Lastly, we show that the “prune” method can reduce the training time for certain models by up to 6× without affecting the accuracy when compared against a compression-aware split-learning approach.

In this paper, we study an online sampling problem of the Wiener process. The goal is to minimize the mean squared error (MSE) of the remote estimator under a sampling frequency constraint when the transmission delay distribution is unknown. The sampling problem is reformulated into an optional stopping problem, and we propose an online sampling algorithm that can adaptively learn the optimal stopping threshold through stochastic approximation. We prove that the cumulative MSE regret grows with rate$\mathcal{O}(\ln k)$, where$k$is the number of samples. Through Le Cam’s two point method, we show that the worst-case cumulative MSE regret of any online sampling algorithm is lower bounded by$\Omega(\ln k)$. Hence, the proposed online sampling algorithm is minimax order-optimal. Finally, we validate the performance of the proposed algorithm via numerical simulations.

This paper introduces a new federated learning scheme that leverages over-the-air computation. A novel feature of this scheme is the proposal to employ adaptive weights during aggregation, a facet treated as predefined in other over-the-air schemes. This can mitigate the impact of wireless channel conditions on learning performance, without needing channel state information at transmitter side (CSIT). We provide a mathematical methodology to derive the convergence bound for the proposed scheme in the context of computational heterogeneity and general loss functions, supplemented with design insights. Accordingly, we propose aggregation cost metrics and efficient algorithms to find optimized weights for the aggregation. Finally, through numerical experiments, we validate the effectiveness of the proposed scheme. Even with the challenges posed by channel conditions and device heterogeneity, the proposed scheme surpasses other over-the-air strategies by an accuracy improvement of 15% over the scheme using CSIT and 30% compared to the one without CSIT.

We present a comprehensive approach to the modeling and design of clustered molecular bio-nanonetworks in which nano-machines of different clusters release an appropriate number of molecules to transmit their sensed information to their respective fusion centers. The fusion centers decode this information by counting the number of molecules received in the given time slot. Owing to the propagation properties of the biological media, this setup suffers from both inter- and intra-cluster interference that needs to be carefully modeled. We first develop a novel spatial model for this setup by modeling nano-machines as a Poisson cluster process with the fusion centers forming its parent point process. For this setup, we then derive a new set of distance distributions in the three-dimensional space, resulting in a remarkably simple result for the special case of the Thomas cluster process. Accordingly, total interference from previous symbols and different clusters is characterized and its expected value is obtained. Then, using the expected value, a simple detector suitable for biological applications is proposed. The impact of different parameters on the performance of the detector is also investigated.

A logically centralized controller architecture for SDN deployments is a well understood and implemented method, however because of issues such as scalability, privacy and more, there is a need to develop and implement a robust physically distributed SDN controller architecture. In a distributed SDN environment, a centralized logical network view needs to be maintained, which means the distributed controllers need a robust method of remaining informed about other controller's network through synchronization. This is specially true in mobile, wireless networks with changing controller and network environment, so to this end we develop a deep reinforcement and transfer learning based method that provides the controllers with an efficient policy for synchronizing with other controllers and maintaining a logically centralized view in such networks. We show that our application-centric method performs well for different kinds of applications including shortest path routing and load balancing, outperforming a reinforcement learning based method as well as a round robin method.

Incentive mechanism is crucial for federated learning (FL) when rational clients do not have the same interests in the global model as the server. However, due to system heterogeneity and limited budget, it is generally impractical for the server to incentivize all clients to participate in all training rounds (known as full participation). The existing FL incentive mechanisms are typically designed by stimulating a fixed subset of clients based on their data quantity or system resources. Hence, FL is performed only using this subset of clients throughout the entire training process, leading to a biased model because of data heterogeneity. This paper proposes a game-theoretic incentive mechanism for FL with randomized client participation, where the server adopts a customized pricing strategy that motivates different clients to join with different participation levels (probabilities) for obtaining an unbiased and high-performance model. Each client responds to the server's monetary incentive by choosing its best participation level, to maximize its profit based on not only the incurred local cost but also its intrinsic value for the global model. To effectively evaluate clients' contribution to the model performance, we derive a new convergence bound which analytically predicts how clients' arbitrary participation levels and their heterogeneous data affect the model performance. By solving a non-convex optimization problem, our analysis reveals that the intrinsic value leads to the interesting possibility of bi-directional payment between the server and clients. Experimental results using real datasets on a hardware prototype demonstrate the superiority of our mechanism in achieving higher model performance for the server as well as higher profits for the clients.

In this article, we introduce a fog system design for processing data collected from edge devices, such as mobile, sensor, and extended (mixed, augmented, virtual) reality equipment. Our system enables the network to provide hardware-accelerated processors for resource-intensive computations on data gathered from remote locations, such as 5G and beyond mobile networks. By splitting heavy computations into pipelines, and distributing them among processors in the edge, fog and the cloud, our design benefits from the processing power of the cloud, while utilizing fog devices with a lower network latency. We implement our design, and use it for distributed training and inference with industry-grade deep learning models for computer vision. We deploy our architecture in infrastructure including cloud and edge servers supporting GPU-accelerated computations. We benchmark pipelines in various deployment settings to study the overhead that they introduce. Our contributions are a new design for wide-area data processing, a framework that realizes this design and provides means of developing applications that are optimized in terms of infrastructure and hardware. These contributions are complemented with our benchmark results, which reveal the potential causes of processing overhead.

Quantum switches are envisioned to be an integral component of future entanglement distribution networks. They can provide high quality entanglement distribution service to end-users by performing quantum operations such as entanglement swapping and entanglement purification. In this work, we characterize the capacity region of such a quantum switch under noisy channel transmissions and imperfect quantum operations. We express the capacity region as a function of the channel and network parameters (link and entanglement swap success probability), entanglement purification yield and application level parameters (target fidelity threshold). In particular, we provide necessary conditions to verify if a set of request rates belong to the capacity region of the switch. We use these conditions to find the maximum achievable end-to-end user entanglement generation throughput by solving a set of linear optimization problems. We develop a max-weight scheduling policy and prove that the policy stabilizes the switch for all feasible request arrival rates. As we develop scheduling policies, we also generate new results for computing the conditional yield distribution of different classes of purification protocols. The conclusions obtained in this work can yield useful guidelines for subsequent quantum switch designs.

Towards addressing spectral scarcity and enhancing resource utilization in 5G networks, network slicing is a promising technology to establish end-to-end virtual networks without requiring additional infrastructure investments.By leveraging Software Defined Networks (SDN) and Network Function Virtualization (NFV), we can realize slices completely isolated and dedicated to satisfy the users' diverse Quality of Service (QoS) prerequisites and Service Level Agreements (SLAs).This paper focuses on the technical and economic challenges that emerge from the application of the network slicing architecture to real-world scenarios.We consider a market where multiple Network Providers (NPs) own the physical infrastructure and offer their resources to multiple Service Providers (SPs).Then, the SPs offer those resources as slices to their associated users.We propose a holistic iterative model for the network slicing market along with a clock auction that converges to a robust ǫ-competitive equilibrium.At the end of each cycle of the market, the slices are reconfigured and the SPs aim to learn the private parameters of their users.Numerical results are provided that validate and evaluate the convergence of the clock auction and the capability of the proposed market architecture to express the incentives of the different entities of the system.

Future wireless networks need to support the increasing demands for high data rates and improved coverage. One promising solution is sectorization, where an infrastructure node (e.g., a base station) is equipped with multiple sectors employing directional communication. Although the concept of sectorization is not new, it is critical to fully understand the potential of sectorized networks, such as the rate gain achieved when multiple sectors can be simultaneously activated. In this paper, we focus on sectorized wireless networks, where sectorized infrastructure nodes with beam-steering capabilities form a multi-hop mesh network for data forwarding and routing. We present a sectorized node model and characterize the capacity region of these sectorized networks. We define the flow extension ratio and the corresponding sectorization gain, which quantitatively measure the performance gain introduced by node sectorization as a function of the network flow. Our objective is to find the optimal sectorization of each node that achieves the maximum flow extension ratio, and thus the sectorization gain. Towards this goal, we formulate the corresponding optimization problem and develop an efficient distributed algorithm that obtains the node sectorization under a given network flow with an approximation ratio of 2/3. Through extensive simulations, we evaluate the sectorization gain and the performance of the proposed algorithm in various network scenarios with varying network flows. The simulation results show that the approximate sectorization gain increases sublinearly as a function of the number of sectors per node.

In this work, we study a system with a sensor forwarding status update to the receiver through an error-prone channel, and the receiver sends the transmission results to the sensor via a reliable link. We assume both transmission links suffer from random delays. We use Age of Information (AoI) to measure the freshness of the status information at the receiver. Our goal is to design a sampling policy that minimizes the expected time average AoI when the channel statistics are unknown. The problem is reformulated into a renewal-reward process optimization, and an online algorithm based on the Robbins-Monro algorithm is proposed. We prove that when the forward and backward transmission delays are bounded, the AoI difference between the online algorithm and the optimal policy decays with rate$\mathcal{O}(\ln K/K)$, where$K$is the number of successful transmissions. Simulation results validate the performance of our proposed algorithm.

This paper revisits the problem of sampling and transmitting status updates through a channel with random delay under a sampling frequency constraint. We use the Age of Information (AoI) to characterize the status information freshness at the receiver. The goal is to design a sampling policy that can minimize the average AoI when the statistics of delay is unknown. We reformulate the problem as the optimization of a renewal-reward process, and propose an online sampling strategy based on the Robbins-Monro algorithm. We prove that the proposed algorithm satisfies the sampling frequency constraint. Moreover, when the transmission delay is bounded and its distribution is absolutely continuous, the average AoI obtained by the proposed algorithm converges to the minimum AoI when the number of samples$K$goes to infinity with probability 1. We show that the optimality gap decays with rate$\mathcal {O}\left ({\ln K/K}\right)$, and the proposed algorithm is minimax rate optimal. Simulation results validate the performance of our proposed algorithm.

Federated learning (FL) allows model training from local data collected by edge/mobile devices while preserving data privacy, which has wide applicability to image and vision applications. A challenge is that client devices in FL usually have much more limited computation and communication resources compared to servers in a data center. To overcome this challenge, we propose PruneFL -a novel FL approach with adaptive and distributed parameter pruning, which adapts the model size during FL to reduce both communication and computation overhead and minimize the overall training time, while maintaining a similar accuracy as the original model. PruneFL includes initial pruning at a selected client and further pruning as part of the FL process. The model size is adapted during this process, which includes maximizing the approximate empirical risk reduction divided by the time of one FL round. Our experiments with various datasets on edge devices (e.g., Raspberry Pi) show that: 1) we significantly reduce the training time compared to conventional FL and various other pruning-based methods and 2) the pruned model with automatically determined size converges to an accuracy that is very similar to the original model, and it is also a lottery ticket of the original model.

Graph kernels are historically the most widely-used technique for graph classification tasks. However, these methods suffer from limited performance because of the hand-crafted combinatorial features of graphs. In recent years, graph neural networks (GNNs) have become the state-of-the-art method in downstream graph-related tasks due to their superior performance. Most GNNs are based on Message Passing Neural Network (MPNN) frameworks. However, recent studies show that MPNNs can not exceed the power of the Weisfeiler-Lehman (WL) algorithm in graph isomorphism test. To address the limitations of existing graph kernel and GNN methods, in this paper, we propose a novel GNN framework, termed Kernel Graph Neural Networks (KerGNNs), which integrates graph kernels into the message passing process of GNNs. Inspired by convolution filters in convolutional neural networks (CNNs), KerGNNs adopt trainable hidden graphs as graph filters which are combined with subgraphs to update node embeddings using graph kernels. In addition, we show that MPNNs can be viewed as special cases of KerGNNs. We apply KerGNNs to multiple graph-related tasks and use cross-validation to make fair comparisons with benchmarks. We show that our method achieves competitive performance compared with existing state-of-the-art methods, demonstrating the potential to increase the representation ability of GNNs. We also show that the trained graph filters in KerGNNs can reveal the local graph structures of the dataset, which significantly improves the model interpretability compared with conventional GNN models.

360-degree panoramic videos have gained considerable attention in recent years due to the rapid development of head-mounted displays (HMDs) and panoramic cameras. One major problem in streaming panoramic videos is that panoramic videos are much larger in size compared to traditional ones. Moreover, the user devices are often in a wireless environment, with limited battery, computation power, and bandwidth. To reduce resource consumption, researchers have proposed ways to predict the users’ viewports so that only part of the entire video needs to be transmitted from the server. However, the robustness of such prediction approaches has been overlooked in the literature: it is usually assumed that only a few models, pre-trained on past users’ experiences, are applied for prediction to all users. We observe that those pre-trained models can perform poorly for some users because they might have drastically different behaviors from the majority, and the pre-trained models cannot capture the features in unseen videos. In this work, we propose a novel meta learning based viewport prediction paradigm to alleviate the worst prediction performance and ensure the robustness of viewport prediction. This paradigm uses two machine learning models, where the first model predicts the viewing direction, and the second model predicts the minimum video prefetch size that can include the actual viewport. We first train two meta models so that they are sensitive to new training data, and then quickly adapt them to users while they are watching the videos. Evaluation results reveal that the meta models can adapt quickly to each user, and can significantly increase the prediction accuracy, especially for the worst-performing predictions.

Traffic forecasting can be highly challenging due to complex spatial-temporal correlations and non-linear traffic patterns. Existing works mostly model such spatial-temporal dependencies by considering spatial correlations and temporal correlations separately, or within a sliding temporal window, and fail to model the direct spatial-temporal correlations. Inspired by the recent success of transformers in the graph domain, in this paper, we propose to directly model the cross-spatial-temporal correlations on the adaptive spatial-temporal graph using local multi-head self-attentions. We then propose a novel Adaptive Graph Spatial-Temporal Transformer Network (ASTTN), which stacks multiple spatial-temporal attention layers to apply self-attention on the input graph, followed by linear layers for predictions. Experimental results on public traffic network datasets, METR-LA PEMS-BAY, PeMSD4, and PeMSD7, demonstrate the superior performance of our model.

As the containerization of network services is expanding towards the Radio Access Network (RAN), the operators seek to benefit from the paradigm of cloud-native services through a wide ecosystem of practices that are applicable to such resources. Such practices include the dynamic scaling of the services in response to the demand, with the network service being assigned more/less resources, or replicated, for accommodating the incoming demand. In such cloud-native environments, proactive decisions can be accomplished through Machine Learning models, which are efficiently trained for specific metrics that reflect the network demand. In this work, we use a real cloud-native telecommunications network and real traffic patterns, and evaluate four different Machine Learning methods for predicting the incoming demand. The decisions made based on the predictions regard the scaling of the base station (gNB/eNB) and the core network entities that deal with the User-Plane traffic (UPF/SPGW-U). Our results show that higher accuracy for such predictions can be accomplished using the tree-based methods over the Neural Network-based solutions, when each method is used for making accurate pro-active decisions for scaling of the under-study network functions.

Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server’s communication bandwidth is limited. Recent works on the convergence analysis of FL have focused on unbiased client sampling, e.g., sampling uniformly at random, which suffers from slow wall-clock time for convergence due to high degrees of system heterogeneity and statistical heterogeneity. This paper aims to design an adaptive client sampling algorithm that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time. We obtain a new tractable convergence bound for FL algorithms with arbitrary client sampling probabilities. Based on the bound, we analytically establish the relationship between the total learning time and sampling probabilities, which results in a non-convex optimization problem for training time minimization. We design an efficient algorithm for learning the unknown parameters in the convergence bound and develop a low-complexity algorithm to approximately solve the non-convex problem. Experimental results from both hardware prototype and simulation demonstrate that our proposed sampling scheme significantly reduces the convergence time compared to several baseline sampling schemes. Notably, our scheme in hardware prototype spends 73% less time than the uniform sampling baseline for reaching the same target loss.

Payment channel networks (PCNs) have emerged as a scalability solution for blockchains built on the concept of a payment channel: a setting that allows two parties to safely transact between themselves in high frequencies by updating pre-committed balances. Transaction requests in PCNs may be declined because of unavailability of funds due to temporary uneven distribution of the channel balances. In this paper, we investigate how to alleviate unnecessary payment blockage via proper prioritization of the transaction execution order. Specifically, we consider the scheduling problem in a payment channel: as transactions continuously arrive on both sides, nodes need to decide which ones to process and when, in order to maximize channel throughput. We introduce a stochastic model to capture the dynamics of a payment channel under discrete stochastic arrivals, with incoming transactions potentially held in buffers up until some deadline in order to enable more elaborate processing decisions. We describe a scheduling policy that maximizes the channel success rate/throughput, formally prove its optimality for fixed-amount transactions, and also show its superiority in the case of heterogeneous amounts via experiments in our discrete event simulator. Overall, our work is a step in the direction of formal research on improving PCN performance.

SNE2EE is a messaging service that protects indi-viduals in each and every stage of the data transfer process: creation, transmission, and reception. The aim of SNE2EE is to protect user communications not only when their date is being transported to another user via secure ports/protocols, but also while they are being created.

In this paper, we study an online sampling problem of the Wiener process. The goal is to minimize the mean squared error (MSE) of the remote estimator under a sampling frequency constraint when the transmission delay distribution is unknown. The sampling problem is reformulated into a renewal reward optimization problem, and we propose an online sampling algorithm that can adaptively learn the optimal sampling policy through stochastic approximation. We show that the cumulative MSE regret grows with rate O(ln k), where k is the number of samples. Through Le Cam's two point method, we show that the worst-case cumulative MSE regret of any online sampling algorithm is lower bounded by Ω (ln k). Hence, the proposed online sampling algorithm is minimax order-optimal. Finally, we validate the performance of the proposed algorithm via numerical simulations.

Cloud data centers require enormous amounts of energy to run their clusters of computers. There are huge financial and environmental incentives for cloud service providers to increase their energy efficiency without causing significant negative impacts on their customers' qualities of experience. Increasing resource utilization reduces energy consumption by consolidating workloads on fewer machines and allows cloud service providers to turn off inactive devices. While traditional architectures only allow virtual machines (VMs) to use the memory and CPU resources of a single device, VMs in a disaggregated cloud can utilize the small residual capacities of multiple separate devices. Separating VM resources across multiple devices leads to severe fragmentation that eventually negates any positive impact disaggregation has on utilization. To address the fragmentation problem, we present a method of ensuring a cloud operates using the minimal number of devices over time. Here we introduce an Energy-Aware Learning Agent (EALA) that uses reinforcement learning to guarantee the system can meet minimal quality of service requirements and provide energy savings without the need for VM migration. We evaluate the use of EALA guiding the decisions of Best-Fit compared to vanilla Best-Fit using the Google cluster trace. We show that EALA improves utilization by 2% and reduces the number of times that compute nodes switch on and off by 11% compared to vanilla Best-Fit.

The evolution of the different network functions to a cloud-native configuration creates fertile ground for the efficient management and reconfiguration of the network. Through the wide application of softwarization and virtualization, cloud-native approaches can extend even to the RAN, that has been dominated by monolithic non-configurable hardware equipment in the past generations of mobile network access. As such, a cloud-native deployment can cover the end-to-end 5G network architecture, from the Core Network to the base stations, with the respective services benefiting from several advanced features, such as automatic scaling of the deployed functions based on monitored metrics. Through the application of Machine Learning, the evolution of the metrics can be predicted and thus the respective functions can be pro-actively scaled. In this work, we use an end-to-end real-world cloud-native deployment of a 5G network, and deal with two different types of scaling, applied at three different parts of the network: vertical scaling for the base station, and horizontal scaling for control and user plane functions of the core network. We use a real-world dataset for replicating traffic over our setup and closely monitor the evolution of metrics from different parts of the network. By applying Machine Learning methods, we accurately predict the future network load and use it to decide on the pro-active allocation of resources for the RAN and the Core Network.

While recent years have witnessed a steady trend of applying Deep Learning (DL) to networking systems, most of the underlying Deep Neural Networks (DNNs) suffer two major limitations. First, they fail to generalize to topologies unseen during training. This lack of generalizability hampers the ability of the DNNs to make good decisions every time the topology of the networking system changes. Second, existing DNNs commonly operate as "blackboxes" that are difficult to interpret by network operators, and hinder their deployment in practice. In this paper, we propose to rely on a recently developed family of graph-based DNNs to address the aforementioned limitations. More specifically, we focus on a network congestion prediction application and apply Graph Attention (GAT) models to make congestion predictions per link using the graph topology and time series of link loads as inputs. Evaluations on three real backbone networks demonstrate the benefits of our proposed approach in terms of prediction accuracy, generalizability, and interpretability.

Federated learning (FL) is a distributed learning paradigm that enables a large number of devices to collaboratively learn a model without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. Theoretically, we analytically establish the relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different metric preferences. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for various datasets, different machine learning models, and heterogeneous system settings.

This paper provides a mathematical model of data center performance based on the recently introduced Quantitative Theory of Bottleneck Structures (QTBS). Using the model, we prove that if the traffic pattern is \textit{interference-free}, there exists a unique optimal design that both minimizes maximum flow completion time and yields maximal system-wide throughput. We show that interference-free patterns correspond to the important set of patterns that display data locality properties and use these theoretical insights to study three widely used interconnects---fat-trees, folded-Clos and dragonfly topologies. We derive equations that describe the optimal design for each interconnect as a function of the traffic pattern. Our model predicts, for example, that a 3-level folded-Clos interconnect with radix 24 that routes 10\% of the traffic through the spine links can reduce the number of switches and cabling at the core layer by 25\% without any performance penalty. We present experiments using production TCP/IP code to empirically validate the results and provide tables for network designers to identify optimal designs as a function of the size of the interconnect and traffic pattern.

Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process (e.g., local computations and global communications with the server) incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL in mobile edge networks that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. We establish the analytical relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different optimization metrics. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for different optimization metrics for various datasets and heterogeneous system and statistical settings.

Dynamic resource allocation to satisfy varying, concurrent and unpredictable demands from multiple applications is a key need in cloud systems. A fundamental challenge is the need to find the right balance between over-allocation, which satisfies each application’s varying needs without requiring frequent allocation changes, and system efficiency which requires that the allocation exactly matches the application needs. However, allocating resources close to current needs will result in frequent allocation changes. This can be detrimental to applications since there may be fixed costs (state replication, policy reconfiguration, etc.) that need to be incurred by applications for each allocation change. In this paper, we develop an MDP-based dynamic allocation scheme that uses reinforcement learning to satisfy unpredictable application demands. It minimizes the overall resource allocation needed to satisfy varying application demands while meeting application constraints on the rate of allocation changes. We prove convergence bounds and use real-world traces to study the performance.

Data centers are increasingly using high-speed circuit switches to cope with the growing demand and reduce operational costs. One of the fundamental tasks of circuit switches is to compute a sparse collection of switching configurations to support a traffic demand matrix. Such a problem has been addressed in the literature with variations of the approach proposed by Birkhoff in 1946 to decompose a doubly stochastic matrix exactly. However, the existing methods are heuristic and do not have theoretical guarantees on how well a collection of switching configurations (i.e., permutations) can approximate a traffic matrix (i.e., a scaled doubly stochastic matrix). In this paper, we revisit Birkhoff’s approach and make three contributions. First, we establish the first theoretical bound on the sparsity of Birkhoff’s algorithm (i.e., the number of switching configurations necessary to approximate a traffic matrix). In particular, we show that by using a subset of the admissible permutation matrices, Birkhoff’s algorithm obtains an$\epsilon $-approximate decomposition with at most$O(\log (1 / \epsilon))$permutations. Second, we propose a new algorithm,Birkhoff+, which combines the wealth of Frank-Wolfe with Birkhoff’s approach to obtain sparse decompositions in a fast manner. And third, we evaluate the performance of the proposed algorithm numerically and study how this affects the performance of a circuit switch. Our results show thatBirkhoff+is superior to previous algorithms in terms of throughput, running time, and number of switching configurations.

This paper studies Online Convex Optimization (OCO) problems where the constraints have additive perturbations that (i) vary over time and (ii) are not known at the time to make a decision. Perturbations may not be i.i.d. generated and can be used, for example, to model a time-varying budget or time-varying requests in resource allocation problems. Our goal is to design a policy that obtains sublinear regret and satisfies the constraints in the long-term. To this end, we present an online primal-dual proximal gradient algorithm that has $O(T^\epsilon \vee T^{1-\epsilon})$ regret and $O(T^\epsilon)$ constraint violation, where $\epsilon \in [0,1)$ is a parameter in the learning rate. The proposed algorithm obtains optimal rates when $\epsilon = 1/2$, and can compare against a stronger comparator (the set of fixed decisions in hindsight) than previous work.

Security threats arising in massively connected Internet of Things (IoT) devices have attracted wide attention. It is necessary to equip IoT gateways with firewalls to prevent hacked devices from infecting a larger amount of network nodes. The match-and-action mechanism of Software Defined Networking (SDN) provides the means to differentiate malicious traffic flows from normal ones, which mirrors the past firewall mechanisms but with a new flexible and dynamically reconfigurable twist. However, vulnerabilities of IoT devices and heterogeneous protocols coexisting in the same network challenge the extension of SDN into the IoT domain. To overcome these challenges, we leverage the high level of data plane programmability brought by the P4 language and design a novel two-stage deep learning method for attack detection tailored to that particular language. Our method is able to generate flow rules that match a small number of header fields from arbitrary protocols while maintaining high performance of attack detection. Evaluations using network traces of different IoT protocols show significant benefits in accuracy, efficiency and universality over state-of-the-art methods.

5G technologies promise to revolutionize mobile networks and push them to the limits of resource utilization. Besides better capacity, we also need better resource management via virtualization. End-to-end network slicing not only involves the core but also the Radio Access Network (RAN) which makes this a challenging problem. This is because multiple alternative radio access technologies exist (e. g. ,LTE, WLAN, and WiMAX), and there is no unifying abstraction to compare and compose from diverse technologies. In addition, existing work assumes that all RAN infrastructure exists under a single administrative domain. Software-Defined Radio Access Network (SD-RAN) offers programmability that facilitates a unified abstraction for resource sharing and composition across multiple providers harnessing different technology stacks. In this paper we propose a new architecture for heterogeneous RAN slicing across multiple providers. A central component in our architecture is a service orchestrator that interacts with multiple network providers and service providers to negotiate resource allocations that are jointly optimal. We propose a double auction mechanism that captures the interaction among selfish parties and guarantees convergence to optimal social welfare in finite time. We then demonstrate the feasibility of our proposed system by using open source SD-RAN systems such as EmPOWER (WiFi) and FlexRAN (LTE).

We study the problem of in-network execution of data analytic services using multi-grade VNF chains. The nodes host VNFs offering different and possibly time-varying gains for each stage of the chain, and our goal is to maximize the analytics performance while minimizing the data transfer and processing costs. The VNFs' performance is revealed only after their execution, since it is data-dependent or controlled by third-parties, while the service requests and network costs might also vary with time. We devise an operation algorithm that learns, on the fly, the optimal routing policy and the composition and length of each chain. Our algorithm combines a lightweight sampling technique and a Lagrange-based primal-dual iteration, allowing it to be scalable and attain provable optimality guarantees. We demonstrate the performance of the proposed algorithm using a video analytics service, and explore how it is affected by different system parameters. Our model and optimization framework is readily extensible to different types of networks and services.

Recent advances in network virtualization and programmability enable innovative service models such as Service Chaining (SC), where flows can be steered through a pre-defined sequence of service functions deployed at different cloud locations. A key aspect dictating the performance and efficiency of a SC is its instantiation onto the physical infrastructure. While existing SC Embedding (SCE) algorithms can effectively address the instantiation of SCs consuming computation and communication resources, they lack efficient mechanisms to handle the increasing data-intensive nature of next-generation services. Differently from computation and communication resources, which are allocated in a dedicated per request manner, storage resources can be shared to satisfy multiple requests for the same data. To fill this gap, in this paper, we formulate the data-intensive SCE problem with the goal of minimizing storage, computation, and communication resource costs subject to resource capacity, service chaining, and data sharing constraints. Using a randomized rounding technique that exploits a novel data-aware linear programming decomposition procedure, we develop a multi-criteria approximation algorithm with provable performance guarantees. Evaluation results show that the proposed algorithm achieves near-optimal resource costs with up to 27.8% of the cost savings owed to the sharing of the data.

In this paper, we demonstrate the implementation of a matching theory based algorithm that extends the well known Top-Trading-Cycles (TTC) in the resource allocation mechanism of the NITOS networking testbed. We first provide an overview of prominent networking testbeds and the common approach that is used for the allocation of testbed resources to experimenters. We briefly describe our extended TTC algorithm and how it is applied in the enhanced architecture of NITOS resource allocation framework, by illustrating the interactions that take place between the testbed users and the testbed management services.

In this paper, we examine the problem of the efficient allocation of resources in networking testbeds, which cannot be shared among the experimenters. We highlight the similarities with the housing market where indivisible network resources play the role of houses, while experimenters the role of owners. We adopt the Top-Trading-Cycles (TTC) algorithm for providing Pareto efficient allocations and we compare this approach with the current mechanism of the simple First-Come-First-Served (FCFS) approach used in most networking testbeds. A formulation of the problem is provided where we describe the average utility of the system as a function of the desired testbed resources of the experimenters and the final allocation of the resources to them. In the performance evaluation we observe that TTC outperforms FCFS in all the examined scenarios and achieves almost 95% better average utility in certain cases.

The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network edge, in proximity to the end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be pre-stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in dense MEC networks with multidimensional constraints. We show that this problem generalizes several well-known placement and routing problems and propose an algorithm that achieves close-to-optimal performance using a randomized rounding technique. Evaluation results demonstrate that our approach can effectively utilize available storage, computation, and communication resources to maximize the number of requests served by low-latency edge cloud servers.

The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network-periphery, in proximity to end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in MEC-enabled multi-cell networks with multidimensional (storage-computation-communication) constraints. We show that this problem generalizes several problems in literature and propose an algorithm that achieves close-to-optimal performance using randomized rounding. Evaluation results demonstrate that our approach can effectively utilize the available resources to maximize the number of requests served by low-latency edge cloud servers.

Since the early development of Software-Defined Network (SDN) technology, researchers have been concerned with the idea of physical distribution of the control plane to address scalability and reliability challenges of centralized designs. However, having multiple controllers managing the network while maintaining a “logically-centralized” network view brings additional challenges. One such challenge is how to coordinate the management decisions made by the controllers which is usually achieved by disseminating synchronization messages in a peer-to-peer manner. While there exist many architectures and protocols to ensure synchronized network views and drive coordination among controllers, there is no systematic methodology for deciding the optimal frequency (or rate) of message dissemination. In this paper, we fill this gap by introducing the SDN synchronization problem: how often to synchronize the network views for each controller pair. We consider two different objectives; first, the maximization of the number of controller pairs that are synchronized, and second, the maximization of the performance of applications of interest which may be affected by the synchronization rate. Using techniques from knapsack optimization and learning theory, we derive algorithms with provable performance guarantees for each objective. Evaluation results demonstrate significant benefits over baseline schemes that synchronize all controller pairs at equal rate.

Data gathered from dense sensor networks is often highly correlated across collocated sensors. For example, in video surveillance networks, multiple cameras can observe the same object from multiple angles. Despite the spatial and temporal dependencies between video frames from different cameras, the deep learning algorithms used in today's video analytics problems treat all frames as independent inputs to image classifiers and object detectors. The outputs of these classifiers and detectors on multiple frames are then fused to extract information about the underlying sensor region. We present a cooperative learning framework that allows sensors to train deep learning systems on their own local data and compressed insights from neighboring sensors' input data. This system fuses sensor data before classification to allow learning agents to more naturally handle correlated inputs and cooperate with neighboring sensors with minimal communication costs.

Software defined networking (SDN) can be beneficial in mobile ad hoc networks (MANETs) to increase flexibility, provide programmability and simplify management. The high dynamics in mobile networks, however, raise new reliability challenges to the conventional centralized control plane of SDN. To increase reliability, methods such as placing multiple controllers in the network have been considered that add redundancy in the control plane in a brute force manner. However, these methods cannot by themselves fundamentally solve the reliability problem. To address this issue, this paper complements the controller placement methods with a new architecture that has a hybrid structure splitting the routing decision logic between the controllers and the data plane nodes. Specifically, the controllers can break the routing path into segments, similar to the segment routing technique, and broadcast the list of segment labels to the data plane nodes. The latter are able to make the actual forwarding decisions for each segment in a distributed manner, e.g., by running an existing MANET protocol like OLSR. Experiments on a testbed built from commercial mobile devices with integrated SDN functionality highlight the feasibility and benefits of the proposed architecture.

In this paper, we study the problem of how to allocate bandwidth and computation resources to deliver data analytics services at the edge. The types of services we envision consist of a chain of tasks that must be carried out sequentially, and where the number of tasks executed in the chain determines the grade in which a service is delivered. An example of such type of service is video analytics where different deep-learning algorithms are combined to provide a more accurate description of a scene. The contributions of the paper are to formulate the static resource allocation problem as a linear program, to discuss the challenges of static formulations in dynamic settings, and to propose a control-type formulation that uses approximate system dynamics and time-varying cost functions. The work also highlights the need for policies that can operate the network and learn its characteristics simultaneously.

In-network caching constitutes a promising approach to reduce traffic loads and alleviate congestion in both wired and wireless networks. In this article, we study the joint caching and routing problem in congestible networks of arbitrary topology (JoCRAT) as a generalization of previous efforts in this particular field. We show that JoCRAT extends many previous problems in the caching literature that are intractable even with specific topologies and/or assumed unlimited bandwidth of communications. To handle this significant but challenging problem, we develop a novel approximation algorithm with guaranteed performance bound based on a randomized rounding technique. Evaluation results demonstrate that our proposed algorithm achieves near-optimal performance over a broad array of synthetic and real networks, while significantly outperforming the state-of-the-art methods.

WiFi is increasingly used by carriers for opportunistically offloading the cellular network infrastructure or even for increasing their revenue through WiFi-only plans and WiFi on-demand passes. Despite the importance and momentum of this technology, the current deployment of WiFi access points (APs) by the carriers follows mostly a heuristic approach. In addition, the prevalent free-of-charge WiFi access policy may result in significant opportunity costs for the carriers, as this traffic could yield non-negligible revenue. In this paper, we study the problem of optimizing the deployment of WiFi APs and pricing the WiFi data usage with the goal of maximizing carrier profit. Addressing this problem is a prerequisite for the efficient integration of WiFi to next-generation carrier networks. Our framework considers various demand models that predict how traffic will change in response to alteration in price and AP locations. We present both optimal and approximate solutions and reveal how key parameters shape the carrier profit. Evaluations on a dataset of WiFi access patterns indicate that WiFi can indeed help carriers reduce their costs while charging users about 50% lower than the cellular service.

Distributed caching architectures have been proposed for bringing content close to requesters, and the key problem is to design caching algorithms for reducing content delivery delay, which determines to an extent the user Quality of Experience (QoE). This problem obtains an interesting new twist with the advent of advanced layered-video encoding techniques such as Scalable Video Coding. In this paper, we show that the problem of finding the caching configuration of video encoding layers that minimizes delivery delay for a network operator is NP-Hard, and we establish a pseudopolynomial-time optimal solution by using a connection with the multiple-choice knapsack problem. Next, we design caching algorithms for multiple network operators that cooperate by pooling together their co-located caches, in an effort to aid each other, so as to avoid large delays due to fetching content from distant servers. We derive an approximate solution to this cooperative caching problem by using a technique that partitions the cache capacity into amounts dedicated to own and other operators' caching needs. Trace-driven evaluations demonstrate up to 25 percent reduction in delay over existing caching schemes. As a side benefit, our algorithms achieve smoother playback for video streaming applications, with fewer playback stalls and higher decoded quality.

We consider the problem of throughput-optimal broadcasting in time-varying wireless network with an underlying Directed Acyclic Graph (DAG) topology. Known broadcast algorithms route packets along pre-computed spanning trees. In large wireless networks with time-varying connectivities, the optimal trees are difficult to compute and maintain. In this paper we propose a new online throughput-optimal broadcast algorithm, which takes packet-by-packet scheduling and routing decisions, obviating the need for maintaining any global topological structures, such as spanning-trees. Our algorithm utilizes certain queue-like system-state information for making transmission decisions and hence, may be thought of as a generalization of the well-known back pressure algorithm, which makes point-to-point unicast transmission decisions based on local queue-length information. Technically, the back-pressure algorithm is derived by stabilizing the packet-queues. However, because of packet-duplications, the work-conservation principle is violated and appropriate queuing processes are difficult to define in the broadcast setting. To address this fundamental issue, we identify certain state-variables whose dynamics behave like virtual queues. By stochastically stabilizing these virtual queues, we devise a throughput-optimal broadcast policy. We also derive new characterizations of the broadcast-capacity of time-varying wireless DAGs and derive an efficient algorithm to compute the capacity exactly under certain assumptions, and a poly-time approximation algorithm for computing the capacity approximately under less restrictive assumptions.

Nowadays, there is a fast-paced shift from legacy telecommunication systems to novel software-defined network (SDN) architectures that can support on-the-fly network reconfiguration, therefore, empowering advanced traffic engineering mechanisms. Despite this momentum, migration to SDN cannot be realized at once especially in high-end networks of Internet service providers (ISPs). It is expected that ISPs will gradually upgrade their networks to SDN over a period that spans several years. In this paper, we study the SDN upgrading problem in an ISP network: which nodes to upgrade and when we consider a general model that captures different migration costs and network topologies, and two plausible ISP objectives: 1) the maximization of the traffic that traverses at least one SDN node, and 2) the maximization of the number of dynamically selectable routing paths enabled by SDN nodes. We leverage the theory of submodular and supermodular functions to devise algorithms with provable approximation ratios for each objective. Using real-world network topologies and traffic matrices, we evaluate the performance of our algorithms and show up to 54% gains over state-of-the-art methods. Moreover, we describe the interplay between the two objectives; maximizing one may cause a factor of 2 loss to the other. We also study the dual upgrading problem, i.e., minimizing the upgrading cost for the ISP while ensuring specific performance goals. Our analysis shows that our proposed algorithm can achieve up to 2.5 times lower cost to ensure performance goals over state-of-the-art methods.

Distributed software-defined networks (SDN), consisting of multiple inter-connected network domains, each managed by one SDN controller, is an emerging networking architecture that offers balanced centralized control and distributed operations. Under such a networking paradigm, most existing works focus on designing sophisticated controller-synchronization strategies to improve joint controller-decision-making for inter-domain routing. However, there is still a lack of fundamental understanding of how the performance of distributed SDN is related to network attributes, thus it is impossible to justify the necessity of complicated strategies. In this regard, we analyze and quantify the performance enhancement of distributed SDN architectures, which is influenced by intra-/inter-domain synchronization levels and network structural properties. Based on a generic network model, we establish analytical methods for performance estimation under four canonical inter-domain synchronization scenarios. Specifically, we first derive an asymptotic expression to quantify how dominating structural and synchronization-related parameters affect the performance metric. We then provide performance analytics for an important family of networks, where all links are of equal preference for path constructions. Finally, we establish fine-grained performance metric expressions for networks with dynamically adjusted link preferences. Our theoretical results reveal how network performance is related to synchronization levels and intra-/inter-domain connections, the accuracy of which is confirmed by simulations based on both real and synthetic networks. To the best of our knowledge, this is the first work quantifying the performance of distributed SDN in terms of network structural properties and synchronization levels.

Cloud-RAN based architectures are widely considered a fundamental part of 5G networks. As a consequence, in the upcoming standards for 5G RAN, disaggregating the RAN functionality between a Central Unit (CU) and multiple Distributed Units (DUs) is considered, addressing the splitting of the 5G protocol stack at the PDCP/RLC point. This split is expected to bring numerous advantages to mobile network operators, as through the isolation of the stack from the PDCP layer and upwards, the CU will be able to act as the Cloud-based convergence point among multiple heterogeneous technologies in the provisioned networks and hence able to serve multiple heterogeneous DUs. Moreover, data rate requirements for this type of split are not very demanding, thus allowing the IP-based transferring of data from the DU to CU and vice-versa. In this work, we propose, implement and evaluate a protocol for a Cloud-RAN based architecture allowing the selection and dynamic switching of different heterogeneous networks in the RAN. We rely on the open source OpenAirInterface platform and extend it to support data plane splitting of the LTE functionality, and the subsequent data injection to WiFi networks. We evaluate the platform using a real network setup, under several scenarios of network selection and different delay settings.

Ultra-Dense Heterogeneous Network deployments are expected to boost the offered network capacity and enhance the user-perceived Quality of Experience, through the simultaneous offering of multiple technologies using distinct or shared wireless spectrum. In such environments with a plethora of available Radio Access Technologies (RATs), the network UEs shall decide either independently or assisted through operator based services on which network they shall use to better serve their needs. In this work, we model the network selection problem in a Multi- RAT system, based on the Paris Metro Pricing (PMP) scheme, enhanced with dynamic pricing formed by the congestion of each available technology. We assume that the network UEs are equipped with multi-homing features and thus are able to use concurrently more than one technologies, based on the requirements of the applications requesting network connectivity (e.g. UHD video streaming). We port and experimentally evaluate the proposed system model over a testbed setup, using distributed components running at the UEs and at a Core Network controller. We provide evaluation results on the average cost per UE for the selected RATs, the average data rate distribution of each UE per RAT and how these performance metrics are affected under different client ordering policies at the network controller.

In software-defined networking (SDN) paradigm, where the control and data plane are separated, the scalability of the SDN controller in the control plane is critical and can affect the overall network performance significantly. To improve controller scalability, efforts have been put into enhancing the capability of SDN switches in the data plane, to make them more autonomous in providing routine services without consulting the controller. In this regard, we investigate the service placement problem on SDN switches aiming at minimizing the average accumulated service costs for end users. To solve this problem, we propose a novel reinforcement-learning-based algorithm with guaranteed performance and convergence rate, called Q-placement. Comparing to traditional optimization techniques, Q-placement exhibits many appealing features, such as performance-tuneable optimization and off-the-shelf implementation. Extensive evaluations show that Q-placement consistently outperforms benchmarks and other state-of-the-art algorithms in both synthetic and real networks. Moreover, these evaluations reveal insights into how the network topological properties (e.g., density), servicing capacities, and controller's roles affect the accumulated service costs, which is useful in service planning tasks.

The Blockchain paradigm provides a popular mechanism for establishing trust and consensus in distributed environments. While Blockchain technology is currently primarily deployed in crypto-currency systems like Bitcoin, the concept is also expected to emerge as a key component of the Internet-of-Things (IoT), enabling novel applications in digital health, smart energy, asset tracking and smart transportation. As Blockchain networks evolve to industrial deployments with large numbers of geographically distributed nodes, the block transfer and processing delays arise as a critical issue which may create greater potential for forks and vulnerability to adversarial attacks. Motivated by these issues, we develop stochastic network models to capture the Blockchain evolution and dynamics and analyze the impact of the block dissemination delay and hashing power of the member nodes on Blockchain performance in terms of the overall block generation rate and required computational power for launching a successful attack. The results provide useful insight in crucial design issues, e.g., how to adjust the `difficulty-of-work' in the presence of delay so as to achieve a target block generation rate or appropriate level of immunity from adversarial attacks. We employ a combination of analytical calculations and simulation experiments to investigate both stationary and transient performance features, and demonstrate close agreement with measurements on a wide-area network testbed running the Ethereum protocol.

Fog architectures at the network edge are becoming a popular research trend to provide elastic resources and services to end-users, where the processing capacity resides at the network periphery as opposed to traditional data-centers. Despite their momentum, the control plane of these architectures remains complex and challenging to implement. To enhance control capability, in this work, we propose to use Software Defined Networking. SDN moves the control logic off data plane devices and onto external network entities, the controllers. We provide a proof-of-concept implementation of a multi-controller edge system and measure traffic delay and overheads. The results reveal the sensitivity of delay to the location of controllers and the magnitude of inter-controller and controller-node overheads. Guided by the above, we model the problem of determining the placement of controllers in the edge network. Using linearization and supermodular function techniques, we present approximation solutions which perform close to optimal and better than state-of-the-art methods.

The following topics are dealt with: mobile computing; learning (artificial intelligence); pattern classification; Internet of Things; smart phones; assisted living; Bluetooth; feature extraction; convolution; indoor radio.

Fog architectures at the network edge are becoming a popular research trend to provide elastic resources and services to end-users, where the processing capacity resides at the network periphery as opposed to traditional data-centers. Despite their momentum, the control plane of these architectures remains complex and challenging to implement. To enhance control capability, in this paper, we propose to use software defined networking (SDN). SDN moves the control logic off data plane devices and onto external network entities, the controllers. We provide a proof-of-concept implementation of a multi-controller edge system and measure traffic delay and overheads. The results reveal the sensitivity of delay to the location of controllers and the magnitude of inter-controller and controller-node overheads. Guided by the above, we model the problem of determining the placement of controllers in the edge network. Using linearization and supermodular function techniques, we present approximation solutions which perform close to optimal and substantially better than state-of-the-art methods. Finally, we analyze the interplay between various performance and reliability objectives.

Performance experienced by end-users supporting the popular 802.11 protocol is significantly degraded in densely populated urban areas, mainly due to the extensive spectrum sharing and the resulting 802.11 impairments such as ”hidden-terminals”, overlapping channel interference, etc. Moreover, as the unlicensed spectrum is also home for other wireless technologies and a large range of RF devices, the experienced channel conditions further deteriorate due to cross-technology interference. While the various resulting phenomena can be efficiently mitigated by isolating affected links from interference sources over spectrum, 802.11 networks currently lack unified mechanisms for characterizing the impact of different interference sources across the available channel configurations. In this work, we take advantage of spectral measurements available at the PHY-layer of commercial 802.11 equipment, in order to develop a highly accurate spectral analysis mechanism that is able to quantify the impact of interference on WLAN performance. The developed distributed mechanism concurrently operates on all network nodes and characterizes the band of interest with minimal overhead. Through the implementation of our approach on commercial 802.11n chipsets and its detailed experimental evaluation, we showcase its applicability in characterizing the impact of spectrum congestion and interference in a unified way, towards driving efficient spectrum adaptation decisions.

Centralized RAN processing has been identified as one of the major enablers for 5G mobile network access. By moving the baseband units (BBU) to the Cloud, multiple instances can be instantiated on the fly, serving several Remote Radio Head (RRH) units. The goal is to satisfy the existing demand of particular geographical areas, whereas drastically reducing the overall CAPEX and OPEX costs of the mobile operators. In this work, we present an experimental study of real Cloud-RAN deployments, with respect to different functional splits. We use as a reference architecture the 3GPP LTE stack, and argue about the functional split applicability in contemporary networks. We evaluate Layer 2 functional splits, that can be used for the convergence of multiple heterogeneous wireless technologies in an all-in-one unit. By deploying our approach in a real testbed setup, we extract the backhaul network transfer requirements for the different splits and present our experimental findings, compared with the respective simulation results.

Nowadays, there is a fast-paced shift from legacy telecommunication systems to novel Software Defined Network (SDN) architectures that can support on-the-fly network reconfiguration, therefore, empowering advanced traffic engineering mechanisms. Despite this momentum, migration to SDN cannot be realized at once especially in high-end cost networks of Internet Service Providers (ISPs). It is expected that ISPs will gradually upgrade their networks to SDN over a period that spans several years. In this paper, we study the SDN upgrading problem in an ISP network: which nodes to upgrade and when. We consider a general model that captures different migration costs and network topologies, and two plausible ISP objectives; first, the maximization of the traffic that traverses at least one SDN node, and second, the maximization of the number of dynamically selectable routing paths enabled by SDN nodes. We leverage the theory of submodular and supermodular functions to devise algorithms with provable approximation ratios for each objective. Using real-world network topologies and traffic matrices, we evaluate the performance of our algorithms and show up to 54% gains over state-of-the-art methods. Moreover, we describe the interplay between the two objectives; maximizing one may cause a factor of 2 loss to the other.

Motivated by the recent efforts in extending long term evolution (LTE) to the unlicensed spectrum, we propose a novel spectrum sharing framework for the coopetition (i.e., cooperation and competition) between LTE and Wi-Fi in the unlicensed band. Basically, the LTE network can choose to work in one of the two modes: in the competition mode, it randomly accesses an unlicensed channel, and interferes with the Wi-Fi access point using the same channel; in the cooperation mode, it onloads the Wi-Fi users' traffic in exchange for the exclusive access of the corresponding channel. We design a second-price reverse auction mechanism, which enables the LTE provider and the Wi-Fi access point owners (APOs) to effectively negotiate the operation mode. Specifically, the LTE provider is the auctioneer (buyer), and the APOs are the bidders (sellers) who compete to sell the rights of onloading the APOs' traffic to the LTE provider. In Stage I of the auction, the LTE provider announces a reserve rate, which is the maximum data rate that it is willing to allocate to the APOs in the cooperation mode. In Stage II of the auction, the APOs submit their bids, which indicate the data rates that they would like the LTE provider to offer in the cooperation mode. We show that the auction involves allocative externalities, i.e., the cooperation between the LTE provider and one APO benefits other APOs who are not directly involved in this cooperation. We characterize the APOs' unique equilibrium bidding strategies in Stage II, and analyze the LTE provider's optimal reserve rate in Stage I. Numerical results show that our framework improves the payoffs of both the LTE provider and the APOs comparing with a benchmark scheme. In particular, our framework increases the LTE provider's payoff by 70% on average, when the LTE provider has a large throughput and a small data rate discounting factor. Moreover, our framework leads to a close-to-optimal social welfare under a large LTE throughput.

Caching popular content files at small-cell base stations (SBSs) has emerged as a promising technique to meet the overwhelming growth in mobile data demand. Despite the plethora of work in this field, a specific aspect has been overlooked. It is assumed that all users remain stationary during data transfer and therefore a complete copy of the requested file can always be downloaded by the associated SBSs. In this work, we revisit the caching problem in realistic environments where moving users intermittently connect to multiple SBSs encountered at different times. Due to connection duration limits, users may download only parts of the requested files. Requests for files that failed to be delivered on time by the SBSs are redirected to the coexisting macro-cell. We introduce an optimization framework that models user movements via random walks on a Markov chain aimed at minimizing the load of the macro-cell. As the main contribution, we put forward a distributed caching paradigm that leverages user mobility predictions and innovative information-mixing methods based on the principle of network coding. Systematic experiments based on measured traces of human mobility patterns demonstrate that our approach can offload 65 percent more macro-cell traffic than existing caching schemes in realistic settings.

The present study proposes a novel collaborative traffic engineering scheme for networks of autonomous systems (ASes). Backpressure routing principles are used for deriving priority routing rules that optimally stabilize a network, while maximizing its throughput under latency considerations. The routing rules are deployed to the network following simple software-defined networking principles. The proposed scheme requires minimal, infrequent interaction with a central controller, limiting its imposed workload. Furthermore, it respects the internal structure of the ASes and their existing peering relations. In addition, it co-exists smoothly with underlying distance vector-based routing schemes. The proposed scheme combines simplicity with substantial gains in served transit traffic volume, as shown by simulations in realistic setups and proven via mathematical analysis.

The surging global mobile data traffic challenges the economic viability of cellular networks and calls for innovative solutions to reduce the network congestion and improve user experience. In this context, user-provided networks (UPNs), where mobile users share their Internet access by exploiting their diverse network resources and needs, turn out to be very promising. Heterogeneous users with advanced handheld devices can form connections in a distributed fashion and unleash dormant network resources at the network edge. However, the success of such services heavily depends on users' willingness to contribute their resources, such as network access and device battery energy. In this paper, we introduce a general framework for UPN services and design a bargaining-based distributed incentive mechanism to ensure users' participation. The proposed mechanism determines the resources that each user should contribute in order to maximize the aggregate data rate in UPN, and fairly allocate the benefit among the users. The numerical results verify that the service can always improve users' performance, and such improvement increases with the diversity of the users' resources. Quantitatively, it can reach an average 30% increase of the total served traffic for a typical scenario even with only six mobile users.

Mobile-Edge Computing (MEC) is about offering application developers and service providers cloud-computing capabilities and an IT service environment at the edge of the mobile network. However, although cloud computing can be used to meet traditional challenges, like scalability concerns and provide for fast resource provisioning times, a multifaceted analysis is required when it comes in multi-operator environments with time-critical applications and services. In this work, we claim that the service importance must be at the epicenter when it comes to the scheduling and placement decision of whether to deploy the service at the edge network or not. Virtual machine (VM) scheduling decisions should avoid SLA violations for popular or time-critical services, and be fair between the service providers. A Lyapunov optimization framework is derived to solve this stochastic optimization problem that aims to maximize the revenue of the physical infrastructure owner in a multi-network operator-sharing environment with time-critical SLAs. A series of simulation experiments validate the high effectiveness of the proposed approach over benchmarking ones.

Heterogeneous network access has been proposed as a solution for the continuously deteriorating congestion problem of cellular infrastructures. In this paper we focus on a multi- Radio Access Technology (RAT) environment and we provide a solution based on the Paris Metro Pricing (PMP) scheme, which was first applied in Paris metro. The concept of this pricing scheme was to provide differentiated service classes for customers who desired to avoid being congested, while maintaining the same wagons, characterized only by a different ticket price. The proposed solution extends the classic PMP policy by inducing dynamic prices formed by the congestion of each available technology. We investigate the performance of our dynamic PMP scheme taking into consideration a mobility model of the interested users. Through simulations and testbed experimentation we provide evaluation results on the average throughput, the acceptance capability of the incoming users and how these performance metrics are affected under different mobility conditions.

Millimeter wave (mmWave) spectrum is one of the frontiers in the evolution towards the next generation of the wireless communication systems, which can provide great performance benefits at the variform access and backbone networks. However, at the access level the typical rapidly fading behavior of the mmWave channel imposes the careful design of client association to access points (APs), as well as relaying to other clients, which can act as bridge toward the APs. This challenge is hereby addressed by a distributed approach that optimally solves the joint client association and relaying problem. The problem is posed as a novel multi-dimensional assignment problem, for which an original solution method is established by a series of transformations that lead to a tractable minimum cost flow problem. The method allows to design distributed auction algorithms where the clients and relays act asynchronously to achieve optimal client-relay-AP association. It is shown that the algorithms converge to a solution that maximizes the total network throughput within a desired bound.

This paper proposes distributed load shedding policies for regulating excessive network load. Data packets are inserted into the network to be delivered to intended destinations. The intermediate network nodes may decide to forward or shed some packets depending on temporally available resources. It is possible for some packets to traverse several nodes in the network until they are finally dropped before reaching the destination, which exacerbates energy consumption. We define a multi-objective optimization problem where we aim to minimize the used energy subject to providing maximum sum throughput. For the case of single-path unicast sessions, we show that Energy-efficient Distributed Load Shedding (E-DLS), a simple shedding mechanism combined with pushback routing, solves this load di shedding optimization. We implement E-DLS in a testbed and use the experiments to select policy parameter values that strike a good balance between energy and delay performance. We then propose a heuristic extension of E-DLS for multirate multicast routing, and showcase via testbed experiments its optimal performance.

Distributed caching architectures have been proposed for bringing content close to requesters and the key problem is to design caching algorithms for reducing content delivery delay. The problem obtains an interesting new twist with the advent of advanced layered-video encoding techniques such as Scalable Video Coding (SVC). We show that the problem of finding the caching configuration of video encoding layers that minimizes average delay for a network operator is NP-Hard, and we establish a pseudopolynomial-time optimal solution using a connection with the multiple-choice knapsack problem. We also design caching algorithms for multiple operators that cooperate by pooling together their co-located caches, in an effort to aid each other, so as to avoid large delays due to downloading content from distant servers. We derive an approximate solution to this cooperative caching problem using a technique that partitions the cache capacity into amounts dedicated to own and others' caching needs. Numerical results based on real traces of SVC-encoded videos demonstrate up to 25% reduction in delay over existing (layer-agnostic) caching schemes, with increasing gains as the video popularity distribution gets steeper, and cache capacity increases.

WiFi data offloading provides a promising auxiliary to alleviate network congestion by diverting traffic from the cellular infrastructure onto WiFi access points (APs). Despite the importance and momentum of this method, the current deployment of APs by the carriers follows mostly a heuristic approach. In addition, the prevalent free-of-charge WiFi access approach may result in significant opportunity costs for the carriers as this traffic could yield non-negligible revenues. In this paper, we propose and study the problem of optimizing the deployment of WiFi offloading infrastructure, and pricing the offloading service with the goal of maximizing carrier profits. Addressing this problem is a prerequisite for the efficient integration of WiFi technology to next generation of cellular systems and the development of carrier-grade offloading solutions. Our framework considers a fundamental, intuitive model of carrier costs and revenues, and two demand models that predict how traffic will change in response to alteration in the price and the set of deployed APs. We present both analytical and approximate solutions for this intricate problem, and reveal how key network parameters shape the offloading benefits. Using a dataset of WiFi access patterns collected from real users, we evaluate the impact of offloading for different regional markets around the world. We find that in mature markets WiFi can help carriers reduce their costs, while charging users up to 50% lower than the cellular service. The gains are higher for small "virtual carriers" who resell other's mobile data services (up to a factor of 2). However, in less mature markets where the AP deployment or access costs are higher, deploying APs can actually lead to a net loss for the carrier. Our evaluation code is publicly available for the benefit of research community.

Together with recent advancements in Radio Access Network (RAN) technologies, Wi-Fi is expected to be at the center of research on the subject of ubiquitous wireless connectivity, towards building the new 5G ecosystem. Nevertheless, the necessary testbed infrastructure to support large scale experimentally driven research seems to be missing. In this work we present the design and implementation of a novel Virtual Wi-Fi Testbed. We present how a traditional Wireless Testbed can support hundreds of virtual Wi-Fi nodes that are open to experimenters. We discuss the design and implementation of the virtualization tools. We demonstrate the accuracy and the overhead analysis of the approach in the face of actual testbed conditions. Implementation experience is also reported on the benefits of using the proposed virtualization approach for a simple association algorithm.

The wide proliferation of IEEE 802.11 compatible devices and the provisioning of costless Internet connectivity in most cases, have created fertile ground for investigating seamless client mobility and handoff management from a cellular technology to any wireless access point available. Although handoffs and client mobility are currently addressed by the IEEE 802.21 standard along with mobility management protocols such as Mobile IPv6, yet no remarkable efforts exist for the wide deployment of such solutions. Moreover, the adoption of such architectures requires considerable changes in the mobile node's networking stack. In this work, we propose a Software Defined Networking technology inspired scheme for managing client mobility among heterogeneous wireless networks, by adopting changes only on the network edges. Our solution is compatible with the existing IPv4 and IPv6 addressing solutions. By employing the OpenFlow technology on the border of our network with the Internet, we manage to keep both ends of the network aware of any topology changes, and thus preserve any already established connections, resulting in a seamless handoff process. We evaluate our technique in a real network setup, by employing WiFi and LTE technologies and benchmark it using higher layer protocols with multi-homing features, namely Stream Control Transmission Protocol and Multipath TCP.

Motivated by the recent proliferation of advanced handheld devices and the unprecedented growth of mobile data traffic, this paper proposes the concept of Mobile edge-Networks (MeNs), a solution that leverages the end-user devices to enhance the performance of emerging 5G systems. MeNs enable mobile users to collaborate with each other and address in a bottom-up fashion key problems in wireless systems, such as poor channel conditions. We design a dynamic cooperation policy that determines transmission parameters of the network in a utility-optimal fashion, ensuring that no user performs worse than she would without cooperation and that the benefits from the collaboration are shared among the users.

Motivated by the recent efforts in extending LTE to the unlicensed spectrum, we propose a novel spectrum sharing framework for the coopetition (i.e., cooperation and competition) between LTE and Wi-Fi in the unlicensed band. Basically, the LTE network chooses to work in one of the two modes: in the competition mode, it randomly accesses an unlicensed channel, and interferes with a Wi-Fi access point; in the cooperation mode, it onloads a Wi-Fi access point's traffic in exchange for the full access of the corresponding channel. Because the LTE network works in an interference-free manner in the cooperation mode, it can achieve a much larger total data rate (comparing to the competition mode) to serve both its own users and the Wi-Fi users under proper channel conditions. To achieve the maximum potential of this novel coopetition framework, we design a reverse auction mechanism, where the LTE provider is the auctioneer (buyer), and the Wi-Fi access point owners (APOs) are the bidders who compete to sell their channels to the LTE provider. An APO's bid indicates the data rate that it would like the LTE provider to offer in the cooperation mode. We show that the auction involves the allocative externalities, i.e., the cooperation between the LTE provider and an APO benefits other APOs who are not directly involved in this cooperation. As a result, a particular APO's bidding strategy is affected by its belief about other APOs' bidding strategies. This makes our analysis much more challenging than that of the standard second-price auction, where bidding truthfully is a weakly dominant strategy. We characterize the APOs' unique equilibrium bidding strategies, and analyze the LTE provider's optimal reserve rate that maximizes its payoff for a general APO type distribution. Our analysis shows that only when the LTE throughput exceeds a threshold, the LTE provider will choose a reasonably large reserve rate to cooperate with the APOs; otherwise, it will restrict the reserve rate to a small value and work in the competition mode.

The ever increasing demand for content is straining operators' networks, thereby necessitating development of alternative content delivery mechanisms. Distributed caching architectures constitute a promising solution for mitigating the effects of demand growth by placing popular content files in proximity to users, rather than in a central site. In this paper, we study the content placement problem in multiple-level hierarchical caching networks where user requests for files are routed upwards toward content servers, satisfied by intermediate nodes if the latter have cached the requested files. Our goal is to reduce the server load by serving as many requests as possible by the caches. We show that the problem is NP-Hard in its general form, but it can be solved optimally in polynomial-time when the caches are installed on a single hierarchy path. For the general case, we develop an algorithm achieving a provably better approximation ratio than the best-known counterparts. Numerical experiments show up to 56% reduction in server load over existing algorithms, with gains increasing as the content popularity distributions get steeper and more diverse across nodes, and the cache capacities at the upper hierarchy levels increase. Our evaluation code is publicly available for the benefit of research community.

As the ever growing mobile data traffic challenges the economic viability and performance of cellular networks, innovative solutions that harvest idle user-owned network resources are gaining increasing interest. In this work, we propose leasing wireless bandwidth and cache space of residential 802.11 (WiFi) access points (APs) for offloading mobile data. This solution not only reduces cellular network congestion, but, due to caching, improves also the user-perceived network performance without overloading the backhaul links of the APs. To encourage residential users to contribute their bandwidth and cache resources, we design monetary incentive (reimbursement) schemes. The offered reimbursements directly determine the amounts of available bandwidth and cache space in every AP, which in turn affect the caching policy (where to cache each content file) and the routing policy (where to route each mobile data request). In order to reduce operator's total cost for serving mobile data requests and leasing resources, we introduce a framework for the joint optimization of incentive, caching, and routing policies. Using a novel WiFi usage dataset collected from 167 residences, we show that in densely populated areas with relatively costly network capacity upgrades, our proposal can halve operator's total cost, while reimbursing up to 9€ per month each residential user.

In this paper, we describe a network coding scheme for the Broadcast Erasure Channel with multiple unicast stochastic flows, for a single source transmitting packets to N users with per-slot ACK/NACK feedback. This scheme performs only binary (XOR) operations and involves a network of queues, along with special rules for coding and moving packets among the queues, that ensure instantaneous decodability. Additionally, for the scheme to work, one has to specify which packets to select for encoding at each time, based on the received feedback. Contrary to prior work where this packet selection was explicitly specified a priori, we employ a backpressure-type policy that makes the selection based only on queue backlogs. We next provide a stability region outer bound for arbitrary N and erasure patterns and show that this bound effectively coincides with a bound on the system's information-theoretic capacity region (accounting for idle slots). Finally, for N=4 and i.i.d. erasures, we provide a policy that achieves the stability outer bound and employs the proposed XOR scheme using a restricted set of coding rules.

As the growth of mobile data traffic places significant strain on cellular networks, plans for exploiting under-utilized network resources become increasingly attractive. In this paper, we propose, design, and evaluate a data offloading architecture, where mobile users are offloaded to mesh networks, which are built and managed by residential users. Such networks are often developed in the context of community networks or, recently, as commercial services. Mobile network operators can lease capacity from these networks and offload traffic to reduce their servicing costs. We introduce an analytical framework that determines the offloading policy, i.e., which mobile users should be offloaded, based on the energy cost induced to the cellular base stations. Accordingly, we design a minimum-cost servicing policy for the mesh networks. Clearly, such architectures are realizable only if the mesh nodes agree with each other to jointly serve the offloaded traffic. To achieve this, we employ the Shapley value rule for dispensing the leasing payment among the mesh nodes. We evaluate this paper by simulating the operation of the LTE-A network, and conducting test bed experiments for the mesh network. The results reveal significant savings for eNBs power consumption and reimbursements for mesh users.

Base station sharing is currently considered one of the most promising solutions for reducing the energy consumption costs of cellular networks. This paper presents a game theoretic framework for the study of such cooperative solutions where different mobile network operators (MNOs) decide to switch off subsets of their base stations during off-peak hours and roam their traffic to the remaining stations. The solution is based on a detailed optimization framework that determines exactly which base stations should remain active and how much traffic each one of them should serve, so as to maximize the aggregate energy savings. Accordingly, using the axiomatic Shapley value rule, it is determined how the benefits from the cooperation, i.e., the cost savings, should be dispersed among the cooperating MNOs. It is proved that this coalitional game with transferrable utilities has a nonempty core, and thus there exists a cooperation solution that incentivizes the participation of all operators. Moreover, using a thorough numerical analysis, it is shown that the benefits achieved with the implementation of the cooperation strategy depend mainly on the power consumption characteristics of the MNOs, which in turn are related to the number, type, and technology of their base stations. Overall, the energy savings are found to be most sensitive to the technology of the used base stations, and more precisely to the no-load base station energy consumption which defines the energy waste in a network.

The landscape toward 5G wireless communication is currently unclear, and, despite the efforts of academia and industry in evolving traditional cellular networks, the enabling technology for 5G is still obscure. This paper puts forward a network paradigm toward next-generation cellular networks, targeting to satisfy the explosive demand for mobile data while minimizing energy expenditures. The paradigm builds on two principles; namely caching and multicast. On one hand, caching policies disperse popular content files at the wireless edge, e.g., pico-cells and femto-cells, hence shortening the distance between content and requester. On other hand, due to the broadcast nature of wireless medium, requests for identical files occurring at nearby times are aggregated and served through a common multicast stream. To better exploit the available cache space, caching policies are optimized based on multicast transmissions. We show that the multicast-aware caching problem is NP-hard and develop solutions with performance guarantees using randomized-rounding techniques. Trace-driven numerical results show that in the presence of massive demand for delay tolerant content, combining caching and multicast can indeed reduce energy costs. The gains over existing caching schemes are 19% when users tolerate delay of three minutes, increasing further with the steepness of content access pattern.

Heterogenous cellular networks (HCN) consist of macrocells and small cells that are overlaid in the same geographical area. Hence, is critical that the high power macrocell shuts off its transmissions for a fraction of the time to allow the low power small cells to transmit without interference. This is the time-domain resource partitioning (TDRP) mechanism. In this paper we investigate video communication in HCNs when TDRP is employed. More specifically we consider the problem of maximizing the average video quality of all users, by jointly optimizing the rate allocated to each specific video stream and the quality that it is streamed. The resulting mixed integer linear program (MILP) formulation is solved numerically. Simulation results indicate clearly that as the small cells and the users are increased the proposed system can improve significantly the video quality.

With the emergence of Online Social Networks (OSNs), as the most popular medium for advertisements, as source of knowledge and information, the emergence of malicious contents (viruses, false rumors, etc..) has become a critical issue that requires immediate attention. In this study we investigate on blocking the contagion of malicious things dynamically, by continuously fighting the diffusion near the source of misinformation under the Susceptible-Infectious-Recovered (SIR) model. We focus on protecting networked populations by removing key connections between nodes, and show via experimental results, that by following the infection the contagion can be controlled more efficiently and even being stopped in the earliest steps. We modify a well studied heuristic from the literature of graphs, and show that our proposed technique significantly outperforms what we believe the state-of-the-art competitors by successfully confronting the infection in real networks.

Heterogeneous cellular networks that overlay cache-endowed small-cell networks with macro-cell networks have emerged as a promising solution towards ultra-low latency, extra-high throughput, and sub-multiple energy consumption compared to the conventional cellular paradigm. A technique to further improve the energy efficiency of such multi-tier networks is to apply activation mechanisms, that dynamically power on/off a subset of the small-cells and macro-cells. In this work, we show that the activation policy should be jointly derived with the caching policy, that places popular content files at the base station caches. As a result content is fetched effectively closer to the mobile end-users and can be transported via energy-prudent links, while at the same time as many as possible of the base stations are powered off. We then formulate the energy-minimizing problem, which is NP-hard, and introduce a novel approximation framework for its efficient solution. Numerical results, that are based on system parameters driven from real trace datasets, show that our approach provides an excellent performance that is far better than the schemes that perform caching and base station activation in a disjoint manner.

Fueled by the increasing demands for content, Internet has become one of the leading players in energy consumption, with a worldwide share of more than 10%. Network devices typically consume close to the maximum energy even if lightly loaded. Hence, straight-forward energy saving techniques that power-off network devices during periods of low demand constitute the most promising mechanism for reducing energy expenses. In this work, we show how caching policies, that place popular content close to the requesters, can bring opportunities for powering-off network devices. We then formalize the energy-minimizing caching problem, prove that it is NP-Hard to approximate within any constant factor, and present a bicriteria approximation solution. Trace-driven numerical results indicate the superiority of our approach as compared to traditional caching schemes.

The recent mobile data explosion has increased the interest for mobile user-provided networks (MUPNs), where users share their Internet access by exploiting the diversity in their needs and resource availability. Although promising, MUPNs raise unique challenges. Namely, the success of such services relies on user participation which in turn can be achieved on the basis of a fair and efficient resource (i.e., Internet access and battery energy) exchange policy. The latter should be devised and imposed in a very fast time scale, based on near real-time feedback from mobile users regarding their needs, resources, and network conditions that are rapidly changing. To address these challenges we design and implement a novel cloud-controlled MUPN system, that employs software defined networking support on mobile terminals, to dynamically apply data forwarding policies with adaptive flow-control. We devise these policies by solving a coalitional game that is played among the users. We prove that the game has a non-empty core and hence the solution, which determines the servicing policy, incentivizes the users to participate. Finally, we evaluate the performance of the service in a prototype, where we investigate its performance limits, quantify the implementation overheads, and justify our architecture design choices.

In this work, we present how programmable data-plane technology (software routers) offers an easy-to-apply mechanism to create virtual wireless networks and support buffering and scheduling decisions. Furthermore, we present a feedback-based buffering mechanism that is able to provide throughput ratio guarantees per virtual network, without requiring any modifications in the 802.11 driver and without relying on statistical knowledge of the workload per virtual network or knowledge regarding the channel conditions. We implement the proposed mechanism in a software router in a 802.11 Access Point and we evaluate its performance in a wireless testbed environment. The methodology and the mechanics developed are generic and with some modifications can be applied to differentiating services for other types of guarantees like delay.

The lessons already learned from the existing protocols operation are taken into deep consideration during the standardization activities of the potential technologies opted for the future 5th Generation mobile networks. Prior research on wireless technologies in general has clearly shown the need for open programmable experimental facilities which can be used for the implementation and evaluation of novel algorithms and ideas under real world settings, even directly comparable to existing technologies and methodologies. Nevertheless, provisioning of such testbed platforms mandates the respective tools which will enable access to the testbed resources and will expose the maximum possible flexibility in configuring them. In this work, we present our efforts in building such a facility, along with the tools and services that cope with such requirements. The facility upon which we build is the long-established NITOS wireless testbed, which is offering commercial as well as open source LTE components in a 24/7 basis.

We investigate an information-resilience scheme in the context of Content-Centric Networks (CCN) for the retrieval of content in disruptive, fragmented networks cases. To resolve and fetch content when the origin is not available due to fragmentation, we exploit content cached both in in-network caches and in end-users' devices. Initially, we present the required modifications in the CCN architecture to support the proposed resilience scheme. We also present the family of policies that enable the retrieval of cached content and we derive an analytical expression/lower bound of the probability that an information item will disappear from the network (be absorbed) and the time to absorption when the origin of the item is not reachable. Extensive simulations indicate that the proposed resilience scheme is a valid tool for the retrieval of cached content in disruptive scenarios, since it allows the retrieval of content for a long period after the fragmentation of the network and the “disappearance” of the content origin.

In this work, we demonstrate and validate a novel architecture for next generation cellular networks that enables collaborative forwarding at Layer 2 among adjacent eNBs with the aid of enhanced user equipment (UE) devices, that act voluntarily as packet forwarders. We introduce an evolved-UE (eUE) which is capable of operating simultaneously over multiples eNBs in order to enable reliable multi-hop operation through relaying and to achieve low-latency communication through efficient L2/MAC forwarding. For the demonstration and the evaluation of this architecture, we used the OpenAirInterface emulation platform to implement it, and also to evaluate its performance. The obtained results show that, the proposed architecture achieves significant reduction in latency (up to 16.94%) and improvement on packet loss rate (up to 59.25%), as the number of the employed eUEs increases with increasing BLER up to 20%. Moreover, the proposed architecture enables eUEs to increase the aggregated data rate in downlink by exploiting data connection to multiple eNBs.

In this paper, we propose a novel architecture for next generation cellular networks that enables collaborative forwarding at Layer 2 among adjacent eNBs with the aid of enhanced user equipment (UE) devices, that act voluntarily as packet forwarders. Therefore, legacy UEs are leveraged as active network elements being capable of operating simultaneously over multiple base stations (eNBs). To this end, we introduce an evolved-UE (eUE) in order to enable reliable multi-hop operation through relaying and to achieve low-latency communication through efficient L2/MAC forwarding. Through extensive experimentation with OpenAirInterface emulation platform, we evaluated the performance and also validated the feasibility of the proposed architecture. Our results show that, in certain use cases corresponding to public safety and moving/small cell scenarios, the proposed architecture achieves significant reduction in latency (up to 16.94%) and improvement on packet loss rate (up to 59.25%), as the number of the employed eUEs increases with increasing BLER up to 20%. Moreover, the proposed architecture enables eUEs to increase the aggregated data rate in downlink by exploiting data connection to multiple eNBs at the expense of extra power consumption, which calls for the appropriate incentives to enable such a cooperation.

Exchange of services and resources in, or over, networks is attracting nowadays renewed interest. However, despite the broad applicability and the extensive study of such models, e.g., in the context of P2P networks, many fundamental questions regarding their properties and efficiency remain unanswered. We consider such a service exchange model and analyze the users' interactions under three different approaches. First, we study a centrally designed service allocation policy that yields the fair total service each user should receive based on the service it offers to the others. Accordingly, we consider a competitive market where each user determines selfishly its allocation policy so as to maximize the service it receives in return, and a coalitional game model where users are allowed to coordinate their policies. We prove that there is a unique equilibrium exchange allocation for both game theoretic formulations, which also coincides with the central fair service allocation. Furthermore, we characterize its properties in terms of the coalitions that emerge and the equilibrium allocations, and analyze its dependency on the underlying network graph. That servicing policy is the natural reference point to the various mechanisms that are currently proposed to incentivize user participation and improve the efficiency of such networked service (or, resource) exchange markets.

Experimental platforms (testbeds) play a significant role in the evaluation of new and existing technologies. Their popularity has been raised lately as more and more researchers prefer experimentation over simulation as a way for acquiring more accurate results. This imposes significant challenges in testbed operators since an efficient mechanism is needed to manage the testbed's resources and provision them according to the users' needs. In this paper we describe such a framework which was implemented for the management of networking testbeds. We present the design requirements and the implementation details, along with the challenges we encountered during its operation in the NITOS testbed. Significant results were extracted through the experiences of the every day operation of the testbed's management.

In this paper we present an optimization framework that formalizes the inherent trade-off between the user perceived quality of wireless video, and the energy consumption cost of the network. The former is formulated in the context of the emerging heterogeneous cellular networks (HCN) based on LTE. We also consider users that employ dynamic adaptive streaming over HTTP (DASH). Our framework quantifies this trade-off carefully, by delving into the details of DASH, the LTE network, and the HCN architecture. The result is a complex problem that is solved in two levels. The master problem is responsible for decisions regarding the the user association and the average power they are allocated. The solution of this problem also entails a decision about the encoding rate of the DASH video segments. The previous decision is used in order to perform resource allocation at a finer level by considering the technical details of LTE that allocates resource blocks and power simultaneously. Numerical results are presented with realistic parameters for the LTE network and the video traffic.

We study a one-hop broadcast channel with two receivers. The receivers have side information obtained by overhearing wireless channels. The relay takes control decisions by coding transmissions based on its knowledge of side information in the receivers. We consider two control mechanisms. In the ACK system, the relay has definite knowledge of side information announced via overhearing reports. In the NACK system, the relay has statistical knowledge of side information and receives feedback after every decoding failure. Our contribution is as follows. We provide the minimal evacuation times for the two systems and obtain analytical expressions of the throughput region for the ACK and the code-constrained region for the NACK system. When the transmission rates are the same (r1= r2) or when the receiver with the highest transmission rate has perfect side information (pf=1), we show that the two regions are equal. We then provide simple joint xor coding and scheduling policies that achieve those regions and, thus, are throughput optimal. Subsequently, we evaluate the report overhead performance for both mechanisms and reflect on the involved tradeoff with throughput. Ultimately, we demonstrate by simulations that the proposed throughput optimal policies can be appropriately enhanced to have good delay properties, particularly for protocols that utilize sequenced packet delivery.

Heterogenous cellular networks (HCN) introduce small cells within the transmission range of a macrocell. For the efficient operation of HCNs it is essential that the high-power macrocell shuts off its transmissions for an appropriate amount of time in order for the low-power small cells to transmit. This is a mechanism that allows time-domain resource partitioning (TDRP) and is critical to be optimized for maximizing the throughput of the complete HCN. In this paper, we investigate video communication in HCNs when TDRP is employed. After defining a detailed system model for video streaming in such an HCN, we consider the problem of maximizing the experienced video quality at all the users, by jointly optimizing the TDRP for the HCN, the rate allocated to each specific user, and the selected video quality transmitted to a user. The NP-hard problem is solved with a primal-dual approximation algorithm that decomposes the problem into simpler subproblems, making them amenable to fast well-known solution algorithms. Consequently , the calculated solution can be enforced in the time scale of real-life video streaming sessions. This last observation motivates the enhancement of the proposed framework to support video delivery with dynamic adaptive streaming over HTTP (DASH). Our extensive simulation results demonstrate clearly the need for our holistic approach for improving the video quality and playback performance of the video streaming users in HCNs.

This work focuses on the design, analysis and evaluation of Dynamic Weighted Round Robin (DWRR) algorithms that can guarantee CPU service shares in clusters of servers. Our motivation comes from the need to provision multiple server CPUs in cloud-based data center environments. Using stochastic control theory we show that a class of DWRR policies provide the service differentiation objectives, without requiring any knowledge about the arrival and the service process statistics. The member policies provide the data center administrator with trade-off options, so that the communication and computation overhead of the policy can be adjusted. We further evaluate the proposed policies via simulations, using both synthetic and real traces obtained from a medium scale mobile computing application.

Millimeter-wave communications in the 60-GHz band are considered one of the key technologies for enabling multigigabit wireless access. However, the special characteristics of such a band pose major obstacles to the optimal utilization of the wireless resources, where the problem of efficient client association to access points (APs) is of vital importance. In this paper, the client association in 60-GHz wireless access networks is investigated. The AP utilization and the quality of the rapidly vanishing communication links are the control parameters. Because of the tricky nonconvex and combinatorial nature of the client association optimization problem, a novel solution method is developed to guarantee balanced and fair resource allocation. A new distributed, lightweight, and easy-to-implement association algorithm, based on Lagrangian duality theory and subgradient methods, is proposed. It is shown that the algorithm is asymptotically optimal, that is, the relative duality gap diminishes to zero as the number of clients increases .

We consider a system where packets (jobs) arrive for processing using one of the policies in a given class. We study the connection between the minimal evacuation time and the stability region of the system and show that evacuation time optimal policies can be used for stabilizing the system (and for characterizing its stability region) under broad assumptions. Conversely, we show that while a stabilizing policy can be suboptimal in terms of evacuation time, one can always design a randomized version of any stabilizing policy that achieves an optimal evacuation time in the asymptotic regime when the number of evacuated packets scales to infinity.

The unprecedented growth of mobile data traffic challenges the performance and economic viability of today's cellular networks and calls for novel network architectures and communication solutions. Mobile data offloading through third-party Wi-Fi or femtocell access points (APs) can significantly alleviate the cellular congestion and enhance user quality of service (QoS), without requiring costly and time-consuming infrastructure investments. This solution has substantial benefits both for the mobile network operators (MNOs) and the mobile users, but comes with unique technical and economic challenges that must be jointly addressed. In this paper, we consider a market where MNOs lease APs that are already deployed by residential users for the offloading purpose. We assume that each MNO can employ multiple APs, and each AP can concurrently serve traffic from multiple MNOs. We design an iterative double-auction mechanism that ensures the efficient operation of the market by maximizing the differences between the MNOs' offloading benefits and APs' offloading costs. The proposed scheme takes into account the particular characteristics of the wireless network, such as the coupling of MNOs' offloading decisions and APs' capacity constraints. Additionally, it does not require full information about the MNOs and APs and creates nonnegative revenue for the market broker.

In cognitive radio networks, secondary users (SUs) may cooperate with the primary user (PU) so that the success probability of PU transmissions are improved, while SUs obtain more transmission opportunities. However, SUs have limited power resources and, therefore, they have to take intelligent decisions on whether to cooperate or not and at which power level, to maximize their throughput. Cooperation policies in this framework require the solution of a constrained Markov decision problem with infinite state space. In our work, we restrict attention to the class of stationary policies that take randomized decisions of an SU activation and its transmit power in every time slot based only on spectrum sensing. Assuming infinitely backlogged SUs queues, the proposed class of policies is shown to achieve the maximum throughput for the SUs, while significantly enlarging the stability region of PU queue. The structure of the optimal policies remains the same even if the assumption of infinitely backlogged SU queues is relaxed. Furthermore, the model is extended for the case of imperfect channel sensing. Finally, a lightweight distributed protocol for the implementation of the proposed policies is presented, which is applicable to realistic scenarios.

In recent years, ISPs in mature residential broadband markets have been moving away from flat-rate pricing schemes to ones that involve usage limits (or "data caps"). These changes have led to a lively debate about the merits of data capping. While previous work has studied the theoretical benefits of data capped pricing models, there is relatively little work that provides insights into exactly how to design data capped tariffs, and their impact on the parties involved, i.e., ISPs and their broadband subscribers. In this paper, we formulate the problem of selecting optimal data capped tariffs based on a simple, extensible model of ISP costs and revenues, and a novel model that captures how users evaluate a set of offered tariffs.

As the unprecedented growth of mobile data traffic places significant strain on cellular networks, alternative plans for exploiting already existing and under-utilized wireless infrastructure, become quite attractive. In this paper, we study cellular-to-mesh (C2M) data offloading for LTE-A cellular mobile users to WiFi mesh networks, which are built and managed collaboratively by users. Such networks are developed in the context of community networks or, recently, as commercial services among residential users. Mobile network operators can lease these mesh networks to offload their traffic and reduce their servicing cost. In this context, we introduce an analytical framework that determines which mobile users should be offloaded, based on the energy cost incurred to the cellular base stations (eNB) for serving their demands. Accordingly, we design a routing policy that the mesh network can employ so as to serve the offloaded traffic with the minimum possible cost. Moreover, the reimbursement offered by the operator should be dispensed to the different mesh users, according to their contribution and added-value significance. We address this issue by employing the Shapley value profit sharing rule, which ensures the participation of the mesh nodes in this joint task. We evaluate our work by simulating the operation of the LTE-A network, and conducting testbed experimentation for the mesh network. The results reveal significant savings for eNBs power consumption and compensation profits for mesh users.

The heterogeneous wireless network (HetNet) paradigm is based on the deployment of low power small cell base stations (SCBS) close to the user in parallel with a macrocell BS (MBS) that provides umbrella coverage. However, since these SCBSs are expected to be deployed in significant numbers, their increased density creates new problems but also new optimization opportunities. In this paper we design a video streaming system for HetNet configurations that allow the SCBSs to opportunistically cooperate, while their backhaul link can be either wireless or wired. The first component of our system is an algorithm that is executed at each SCBS and is responsible for opportunistically overhearing packet transmissions from the MBS and other SCBSs and forwarding them to the users. The second component of our system is a rate allocation algorithm that is executed at the video streaming server. Our system offers different performance benefits depending on the HetNet backhaul configuration. Our performance evaluation with high quality 4K videos, indicates that in the wireline backhaul case video streaming experiences lower delay, while in the wireless backhaul case our system improves the capacity that is available for video communication.

Cloud service provisioning on top of virtual infrastructures is of major importance in modern ICT, since it is directly correlated to the way business models are designed and revenue is generated from the cloud service providers. In this work we examine an end-to-end content replication problem over cloud-based multi-technology infrastructures. We extend the classical model where every network node is a potential replica carrier and the link weights represent hops/delay and we examine replication schemes for content that a) is requested by customers belonging in different virtual networks and b) depending on the requester there is different impact on the system operational cost. We examine both centralized and distributed content replication management policies and we evaluate their performance through extended simulations, by means of total cost, the number of object replacements and the number of iterations required.

User-provided connectivity (UPC) is a promising paradigm to achieve a low-cost ubiquitous connectivity. In this paper, we study a network-assisted UPC service model, where a mobile virtual network operator (MVNO) enables its subscribers to operate as mobile WiFi hotspots (hosts) and provide Internet connectivity for others. A unique aspect of this service model is that the MVNO offers some free data quota to hosts as reimbursements (incentives) for connectivity sharing. This reimbursing scheme, together with a usage-based pricing, constitute a revolutionary hybrid data pricing-reimbursing scheme, which has not been considered before. We analyze the different impacts of data price and reimbursement on the host's connectivity sharing decision systematically. Based on this analysis, we further derive the optimal hybrid pricing-reimbursing policy that maximizes the MVNO's revenue. Our numerical result indicates that by using the proposed hybrid pricing policy, the MVNO can increase its revenue by 20% to 135% under an elastic client demand, and by 20% to 550% under an inelastic client demand, comparing to those achieved under a pricing-only policy.

Crowd-sourced mobile Internet access services enable mobile users to connect with each other and share their Internet connections. This is a promising solution for addressing users' increasing needs for ubiquitous connectivity and alleviating network congestion. The success of such services heavily depends on users' willingness to contribute their resources. In this paper, we consider a general model for such services, and design a distributed incentive mechanism for encouraging users' participation. This bargaining based scheme ensures that the contribution of user resources, in terms of Internet access bandwidths and battery energy, and the allocation of service capacity, measured in the delivered mobile data, are Pareto efficient and proportionally fair. The numerical results verify that the service always improves users' performance and that these benefits depend on the diversity of the users' resources.

Video delivery to mobile users is one of the largest challenges that network operators face today. In this work we consider a heterogeneous cellular network with storage capable small-cell base stations, and study this problem for pre-stored video files that can be encoded with two different schemes, namely versions or layers, in various qualities. We introduce a framework for the joint derivation of video caching and routing policies for users with different quality requirements. This allows the operator to optimize a balanced objective of incurred servicing cost, and users experienced delay, according to his priorities. The numerical results indicate that versions and layers may have different impact on the delay and servicing cost, depending on the diversity of users' demand, and that the cost-delay trade off is affected by the network's load.

Recently, network coding has emerged as an effective way to increase the efficiency of the content placement in the caching networks and thus boost the content delivery to the requesters. Its superiority compared to network coding-agnostic caching schemes lies on the increased availability of the content, which can be extracted by the requesters after they receive and decode a sufficiently large amount of encoded data. Although the topics surrounding network coding and caching have been already studied in the previous literature, their potential on enhancing mobile content delivery has not been fully explored yet. Namely, most of the existing works restrict the encoded data combinations to involve only parts of the same file, since this guarantees a low number of choices and thus simplifies the analysis. In this work, we study the problem of caching linear combinations of different files in a small-cell network. Our goal is to mitigate the pressure on the macrocellular base station by serving as many as possible content requests by the cache-endowed small-cell base stations that are deployed in the cell. Because of the NP-hardness of this problem, we propose a heuristic algorithm that gradually increases the performance of the obtained solution. Numerical results for typical popularity distributions reveal the performance benefits of our approach.

Cache-endowed small cell networks constitute a timely and effective solution for mobile network operators (MNOs) who strive to serve the massive content demand of the mobile users. However, the deployment of small cell base stations (SBSs) requires significant economic investments, while site acquisition issues render it infeasible in several cases. Yet, one potentially explosive factor for network expansion remains unexploited, namely, an increasing number of residential users install in their premises privately-owned SBSs (femtocell or WiFi access points) in order to serve their own needs. In this work, we envision an MNO offering incentives to the SBS owners to cache and deliver content items requested by the nearby mobile users. We model the interaction between the MNO and the SBS owners as a Stackelberg game, and show that the incentive design problem requires to know the content caching policy, which in turn should be jointly derived with the request routing policy. We then introduce a framework for the joint derivation of incentive, caching and routing policies. Numerical results indicate that our mechanism provides a substantial potential for reducing the MNO's costs, depending on the willingness of the SBS owners to lease their resources and the spatio temporal characteristics of the mobile data demand.

In this paper we present NITOS Bikes Net, a framework for mobile sensing in a city-wide environment offering experimentation capabilities. More Specifically, we present a custom-made and modular prototype device that can be easily mounted on volunteers' bicycles dedicated to collecting environmental measurements and available WiFi networks. In addition, we present our enhancements in OMF framework through which we remotely control the operation of the developed devices, whenever they experience back-end connection. Finally, we analyze an indicative demonstration experiment which illustrates the capabilities of the developed framework.

In this paper we present the NITOS Bikes Net platform, a city-scale mobile sensing infrastructure that relies on bicycles of volunteer users. NITOS Bikes Net employs a custom-built embedded node that can be equipped with different types of sensors, and which can be easily mounted on a bicycle in order to opportunistically collect environmental and WiFi measurements in different parts of the city. Experimenters can remotely reserve and control the sensor nodes on bicycles as well as collect/visualize their measurements via the OMF/OML framework, which was extended in order to handle the intermittent connectivity and disconnected operation of the mobile nodes. We also provide a performance analysis of our node prototype in terms of sensing latency, end-to-end data transmission capability and power consumption, and report on a first experiment that was performed using NITOS Bikes Net in the city of Volos, Greece.

In this work, we present the AGILE Spectrum Adaptation system that is able to dynamically tune the channel central frequency and bandwidth of wireless links in an adaptive to the interference and traffic conditions way. The developed system is able to detect under-utilised spectrum fragments and optimally adjust the occupied spectrum. Through the online execution of 3 specifically designed experimental scenarios, we demonstrate the ability to implement distributed spectrum adaptation in commercial WLAN deployments, along with the obtained performance benefits.

We consider the problem of optimal load balancing in a server farm under overload conditions. A convex penalty minimization problem is studied to optimize queue overflow rates at the servers. We introduce a new class of α-fair penalty functions, and show that the cases of α = 0, 1, ∞ correspond to minimum sum penalty, penalty proportional fairness, and min-max fairness, respectively. These functions are useful to maximize the time to first buffer overflow and minimize the recovery time from temporary overload. In addition, we show that any policy that solves an overload minimization problem with strictly increasing penalty functions must be throughput optimal. A dynamic control policy is developed to solve the overload minimization problem in a stochastic setting. This policy generalizes the well-known join-the-shortest-queue (JSQ) policy and uses intelligent job tagging to optimize queue overflow rates without the knowledge of traffic arrival rates.

Content distribution in the Internet places content providers in a dominant position, with delivery happening directly between two end-points, that is, from content providers to consumers. Information-Centrism has been proposed as a paradigm shift from the host-to-host Internet to a host-to-content one, or in other words from an end-to-end communication system to a native distribution network. This trend has attracted the attention of the research community, which has argued that content, instead of end-points, must be at the center stage of attention. Given this emergence of information-centric solutions, the relevant management needs in terms of performance have not been adequately addressed, yet they are absolutely essential for relevant network operations and crucial for the information-centric approaches to succeed. Performance management and traffic engineering approaches are also required to control routing, to configure the logic for replacement policies in caches and to control decisions where to cache, for instance. Therefore, there is an urgent need to manage information-centric resources and in fact to constitute their missing management and control plane which is essential for their success as clean-slate technologies. In this thesis we aim to provide solutions to crucial problems that remain, such as the management of information-centric approaches which has not yet been addressed, focusing on the key aspect of route and cache management.

We investigate the problem of replication in large wireless networks that employ caching in the case of a single file whose popularity varies with time. As opposed to the case of static popularity, in this case for the network resources to be efficiently allocated the replication should vary with time. In this study, we first outline the low-level operations of wireless networks with caching, which involve decisions of combinatorial complexity, such as about the contents of all network caches. To overcome this complexity, we approximate the network optimization with a formulation based on the frequency of file replication across the network - a high-level perspective, amenable to mathematical analysis. We present a solution that is based on looking ahead into the future and has a simple graphical representation.

In this paper, we propose and characterize the performance of a novel video-aware Opportunistic Routing (OR) algorithm for multicast, using a one-hop and two-hop forwarding scheme in 802.11 mesh networks. We believe that the OR approach exploits the inherent broadcast nature of the wireless medium and adapts very well in the lossy wireless environment. Inferring from the above, we extend a state of the art routing algorithm, namely MORE, that leverages on this approach and offers multicast support, but is not efficiently applicable in video streaming. By employing our scheme, we enable support for video streaming application with hard time-constraints. In addition, we focus on the orchestration of the packet transmissions and the prioritization of the video traffic towards improving the video-perception quality of the end users. In order to evaluate the proposed scheme, we conducted experiments in a realistic medium-scale wireless testbed. Our results show that the proposed scheme increases the average video-perception quality, measured in PSNR, by up to 270% in some cases or up to 175% in average, compared to the MORE algorithm.

In this demo paper, we demonstrate and evaluate a novel Opportunistic Routing (OR) protocol for video multicast, namely Video-aware Multicast Opportunistic Routing (ViMOR), over 802.11 two-hop mesh networks. OR exploits the broadcast nature of the wireless medium and offers spatial diversity among the receivers. ViMOR extends MORE, a state of the art OR algorithm, by orchestrating packet transmissions and prioritizing video traffic, in order to conform with video streaming requirements. For the demonstration and evaluation of the proposed scheme, we proceeded with the development of the implementation on NITOS wireless testbed. Results showed a significant increase in average video-perception quality, compared to MORE protocol.

Social network analysis comprises a popular set of tools for the analysis of online social networks. Among these techniques, k-shell decomposition of a graph is a popular technique that has been used for centrality analysis, for communities discovery, for the detection of influential spreaders, and so on. The huge volume of input graphs and the environments where the algorithm needs to run i.e., large datacenters, makes none of the existing algorithms appropriate for the decomposition of graphs into shells. In this article, we develop for the first time in the literature, a distributed algorithm based on MapReduce for the k-shell decomposition of a graph. We furthermore, provide an implementation and assessment of the algorithm using real social network datasets. We analyze the tradeoffs and speedup of the proposed algorithm and conclude for its virtues and shortcomings.

In this paper we evaluate an energy-aware relay selection mechanism which exploits channel state information and the availability of buffers at relays to perform flexible relaying based on a backpressure-driven optimization model. This model ensures the maximization of the cell throughput while maintains the stability of backlog queues. Performance evaluation is conducted using a System Level Simulator (SLS) which is fully compliant with IEEE 802.16m and supports various relaying scenarios. The Below Roof Top (BRT) relaying scenario is considered in this work. A holistic and flexible energy framework is implemented to capture the energy consumption of the cellular network nodes. The model maps the RF output power radiated at the antenna elements of each node including relays on the network to the total supply power of the node equipment. Two derivatives of the proposed mechanism, half-duplex and full-duplex are proposed and evaluated. Results of the two derivatives on BRT relaying scenario revealed noticeable increases for both cell throughput and system energy efficiency of the cell-edge users compared to the conventional relaying protocol and the non cooperative scheme.

In this paper we consider packetized video transmission in cooperative relay-based wireless networks. We propose algorithms for optimized relay selection that take into account the content of each specific video packet. Our first algorithm, that is designed for a narrowband flat-fading channel and single-carrier modulation, selects jointly the optimal relay and video packet for forwarding. Our next algorithm is an extension of our main idea for OFDM modulation that is suitable for frequency-selective fading channels. In this case in addition to optimized packet and relay selection, our algorithm also jointly selects the optimal power level for each subcarrier. The key benefit of our algorithms is that they are fully distributed since they require no explicit communication among the relays but they only use passively collected information. We perform an extensive evaluation of our algorithms for different system configurations.

Experimentation in testbeds is gaining increasing ground as a necessary validation step for every theoretical study in communication networks. However, the first-come-first-served policy employed today by most testbeds does not ensure the fair and efficient utilization of their resources, which often lie idle. Ideally, every testbed should be utilized as much as possible and serve the most important requests. In this paper we introduce a novel resource scheduling mechanism for the wireless testbed NITOS. The proposed scheme is based on VCG auctions and includes an allocation and a pricing rule which induce the users to judiciously submit experiment requests. We prove theoretically and demonstrate numerically that this scheme ensures that the testbed resources (nodes and channels) are assigned to the users with the highest needs. Our mechanism can be incorporated in the next generation resource management systems for NITOS and similar testbeds.

The deployment of small cells is expected to gain huge momentum in the near future, as a solution for managing the skyrocketing mobile data demand growth. Local caching of popular files at the small cell base stations has been recently proposed, aiming at reducing the traffic incurred when transferring the requested content from the core network to the users. In this paper, we propose and analyze a novel caching approach that can achieve significantly lower traffic compared to the traditional caching schemes. Our cache design policy carefully takes into account the fact that an operator can serve the requests for the same file that happen at nearby times via a single multicast transmission. The latter incurs less traffic as the requested file is transmitted to the users only once, rather than with many unicast transmissions. Systematic experiments demonstrate the effectiveness of our approach, as compared to the existing caching schemes.

Utilization of device-to-device communication links provides an alternative way for mitigating the skyrocketing mobile data growth that challenges cellular operators nowadays. Namely, the owners of these devices can serve requests of their neighbors for the files that are stored at their caches. However, users are unwilling in general to serve requests of others, since data transmission incurs energy consumption. The issue is further perplexed, if one considers realistic parameters such as the limited capacity of the device's battery. In this work, we explicitly take into account the above aspects and formulate the content request routing problem aiming to minimize the load of the macrocellular base station. This problem is challenging to solve due to its discrete nature. We derive an optimal polynomial-time solution based on a reduction to a matching problem. Besides, we present a light-weight distributed algorithm for its solution. Simulation results reveal the performance benefits of our approach.

In cognitive radio networks, secondary users (SUs) may cooperate with the primary user (PU) in order to obtain more transmission opportunities and thus maximize their throughput. The synergy consists in the following: the SU opts to cooperate by using its own transmit power to improve the probability of successful transmission of the PU. By increasing the probability of successful packet transmission for the PU, the SU essentially increases the service rate of the PU queue and thus, for given packet arrival rate, it increases the chances that it will be empty, and the channel will be free to use. Due to power limitations however, SUs have to take intelligent decisions on whether to cooperate or not and at which power level. Cooperation policies in this framework require the solution of a constrained Markov decision problem with infinite state space. In our work, we restrict attention to the class of stationary policies that take randomized decisions of an SU activation and its transmit power in every time slot based only on spectrum sensing. The proposed class of policies is shown to achieve the same set of SU rates as the more general policies, while significantly enlarging the stability region of the PU queue. Finally, a lightweight distributed protocol based on the proposed class of policies is presented, which is amenable to implementation in realistic scenarios.

The unprecedented growth of mobile data traffic challenges the performance and economic viability of today's cellular networks and calls for novel network architectures and communication solutions. Data offloading through third-party WiFi or femtocell access points (APs) can effectively alleviate the cellular network congestion in low operational and capital expenditure. This solution requires the cooperation and agreement of mobile cellular network operators (MNOs) and AP owners (APOs). In this paper, we model and analyze the interaction among one MNO and multiple APOs (for the amount of MNO's offloading data and the respective APOs' compensations) by using thew Nash bargaining theory. Specifically, we introduce a one-to-many bargaining game among the MNO and APOs and analyze the bargaining solution (game equilibrium) systematically under two different bargaining protocols: 1) sequential bargaining, where the MNO bargains with APOs sequentially, with one APO at a time, in a given order; and 2) concurrent bargaining, where the MNO bargains with all APOs concurrently. We quantify the benefits for APOs when bargaining sequentially and earlier with the MNO, and the losses for APOs when bargaining concurrently with the MNO. We further study the group bargaining scenario where multiple APOs form a group bargaining with the MNO jointly and quantify the benefits for APOs when forming such a group. Interestingly, our analysis indicates that grouping of APOs not only benefits the APOs in the group but may also benefit some APOs not in the group. Our results shed light on the economic aspects and the possible outcomes of the MNO/APOs interactions and can be used as a roadmap for designing policies for this promising data offloading solution.

Small cells constitute a promising solution for managing the mobile data growth that has overwhelmed network operators. Local caching of popular content items at the small cell base stations (SBSs) has been proposed to decrease the costly transmissions from the macrocell base stations without requiring high capacity backhaul links for connecting the SBSs with the core network. However, the caching policy design is a challenging problem especially if one considers realistic parameters such as the bandwidth capacity constraints of the SBSs that can be reached in congested urban areas. We consider such a scenario and formulate the joint routing and caching problem aiming to maximize the fraction of content requests served locally by the deployed SBSs. This is an NP-hard problem and, hence, we cannot obtain an optimal solution. Thus, we present a novel reduction to a variant of the facility location problem, which allows us to exploit the rich literature of it, to establish algorithms with approximation guarantees for our problem. Although the reduction does not ensure tight enough bounds in general, extensive numerical results reveal a near-optimal performance that is even up to 38% better compared to conventional caching schemes using realistic system settings.

We study the case of a three-user broadcast erasure channel with multiple unicast traffic sessions, where feedback from the users is fed back to the transmitter in the form of positive acknowledgment (ACK)/negative acknowledgment (NACK) messages. The capacity region of this system has been recently derived and two capacity-achieving coding algorithms employing intersession linear network coding have been proposed. Since these algorithms suffer from large computational complexity and decoding delay, our aim, in this paper, is to design a coding algorithm with reduced computational complexity and a low decoding delay that achieves a comparable rate region to the former algorithms. We exclusively consider algorithms that require no knowledge of channel statistics, only perform XOR operations among the packets, and allow for instantaneous decoding by any receiver that successfully receives a packet. We present such an algorithm, named IXOR, which operates on a specially constructed network of virtual queues and, through intelligent packet combining, achieves the capacity under a general condition, which is satisfied in the following settings: spatially independent identically distributed erasure channels with arbitrary values of erasure probability; and spatially independent erasure channels where the maximum erasure probability does not exceed 8/9.

Small cells constitute a promising solution for managing the mobile data growth that has overwhelmed network operators. Local caching of popular content items at the small cell base stations has been proposed in order to decrease the capacity-and hence the cost- of the backhaul links that connect these base stations with the core network. However, deriving the optimal caching policy remains a challenging open problem especially if one considers realistic parameters such as the bandwidth limitation of the base stations. The latter constraint is particularly important for cases when users requests are massive. We consider such a scenario and formulate the joint caching and routing problem aiming to maximize the fraction of content requests served by the deployed small cell base stations. This is an NP-hard problem and hence we cannot obtain an exact optimal solution. Thus, we present a novel approximation framework based on a reduction to a well known variant of the facility location problem. This allows us to exploit the rich literature in facility location problems, in order to establish bounded approximation algorithms for our problem.

In this paper, we study the problem of resource allocation in the setting of multitier data centers. Our main motivation and objective is to provide applications hosted in the data center with different service levels. In such centers, there are several mechanisms the designer can use to achieve such objectives. We restrict our attention to CPU time at the service tier as the resource; the objective we consider is service differentiation, expressed as allocating prespecified percentages of this resource to applications. Then, mechanisms at the designer's disposal to provide desired service differentiation include the triplet of load balancing through the switch fabric, enqueueing at a server and scheduling at a server. We focus on the enqueueing component of control mechanisms. We provide, through analysis and simulations “rules of thumb” for situations where simple enqueueing policies can provide service differentiation.

Hierarchical topologies have been applied in many existing systems that provide public IPTV or massive content delivery services. The efficient operation of these services requires massive bandwidth resources. Data caching has emerged as an effective way in reducing bandwidth consumption and accelerating content access. In hierarchical caching systems requests for content are routed upwards until they reach a cache that stores a copy of the requested file. When the requested file is found, it is sent on the reverse path to the client. In this work, we focus on the problem of caching redundant copies of content in intermediate caches on the reverse path in order to minimize the bandwidth consumption within a given time horizon. The above problem is known to be NP-hard. However, we show that replacing the cache capacity constraints by a cost term paid each time we store a file in a cache, results to the tractable problem of minimizing the overall bandwidth and caching expenses. We use its optimal solution to establish a novel algorithm for the efficient solution of the original problem. We furthermore study the case that segments of encoded versions of the files instead of only complete files are allowed to be stored at the caches. We show that this problem is of polynomial complexity. Numerical experiments for typical popularity distributions reveal the performance distance between the proposed algorithms and heuristic algorithms that are commonly applied nowadays.

Mobile data offloading is a promising approach to alleviate network congestion and enhance quality of service (QoS) in mobile cellular networks. In this paper, we investigate the economics of mobile data offloading through third-party WiFi or femtocell access points (APs). Specifically, we consider a market-based data offloading solution, where macrocellular base stations (BSs) pay APs for offloading traffic. The key questions arising in such a marketplace are following: (i) how much traffic should each AP offload for each BS? and (ii) what is the corresponding payment of each BS to each AP? We answer these questions by using the non-cooperative game theory. In particular, we define a multi-leader multi-follower data offloading game (DOFF), where BSs (leaders) propose market prices, and accordingly APs (followers) determine the traffic volumes they are willing to offload. We characterize the subgame perfect equilibrium (SPE) of this game, and further compare the SPE with two other classic market outcomes: (i) the market balance (MB) in a perfect competition market (i.e., without price participation), and (ii) the monopoly outcome (MO) in a monopoly market (i.e., without price competition). Our results analytically show that (i) the price participation (of BSs) will drive market prices down, compared to those under the MB outcome, and (ii) the price competition (among BSs) will drive market prices up, compared to those under the MO outcome.

We study an 1-hop broadcast channel with two receivers. Due to overhearing channels, the receivers have side information which can be leveraged by interflow network coding techniques to provide throughput increase. In this setup, we consider two different control mechanisms, the deterministic system, where the contents of the receivers' buffers are announced to the coding node via overhearing reports and the stochastic system, where the coding node makes stochastic control decisions based on statistics and the performance is improved via NACK messages. We study the minimal evacuation times for the two systems and obtain analytical expressions of the throughput region for the deterministic and the code-constrained region for the stochastic. We show that maximum performance is achieved by simple XOR policies. For equal transmission rates r1= r2, the two regions are equal. If r1≠ r2, we showcase the tradeoff between throughput and overhead.

As the processing and transport capacity of the information and communication technologies (ICT) infrastructure increased vastly the last few years, the bottleneck of the information exchange process moved to the end points of the process, i.e. the consumers and the producers of information. On one hand there is the limited time that a consumer has to access the information and on the other hand there is the minimum utility level that a provider needs to provide to the society of consumers to cover it's investment cost. In this paper we present a novel decision model for a set of competing providers that wish to enter a market. It may happen that due to the competition, some competitors will not be able to cover their investment cost and therefore will disappear. We analyze the optimum way of forming the market, in order to maximize the aggregate utility of it. We show that this problem is NP-complete and present a linear programming rounding heuristic algorithm to solve it. Besides, we study a game where every player (provider) is to choose whether to join the market or not. We compute the price of anarchy of the game and present a heuristic algorithm that belongs to the family of best response dynamic algorithms. Systematic experiments on a real world data set have demonstrated the effectiveness of our proposed approach.

In this paper we describe a network coding scheme for the Broadcast Erasure Channel with multiple unicast stochastic flows, for the case of a single source transmitting packets to N users, where per-slot feedback is fed back to the transmitter in the form of ACK/NACK messages. This scheme performs only binary (XOR) operations and includes special rules for coding packets that ensure instantaneous decodability. Drawing on the results of network stability under statistical overhearing, we provide a stabilizing policy using this coding scheme. Furthermore, we show that, for N = 4 and i.i.d. erasure events, the stability region of such a system effectively coincides with its information-theoretic capacity region, and provide a stabilizing policy that employs this XOR-based scheme.

We consider the problem of storing segments of encoded versions of content files in a set of base stations located in a communication cell. These base stations work in conjunction with the main base station of the cell. Users move randomly across the space based on a discrete-time Markov chain model. At each time slot each user accesses a single base station based on it's current position and it can download only a part of the content stored in it, depending on the time slot duration. We assume that file requests must be satisfied within a given time deadline in order to be successful. If the amount of the downloaded (encoded) data by the accessed base stations when the time deadline expires does not suffice to recover the requested file, the main base station of the cell serves the request. Our aim is to find the storage allocation that minimizes the probability of using the main base station for file delivery. This problem is intractable in general. However, we show that the optimal solution of the problem can be efficiently attained in case that the time deadline is small. To tackle the general case, we propose a distributed approximation algorithm based on large deviation inequalities. Systematic experiments on a real world data set demonstrate the effectiveness of our proposed algorithms.

Cognitive radio systems have gathered a lot of research interest during the last decade. Accuracy of spectrum sensing and efficiency of free spectrum utilization are considered as the primary objectives in this emerging technology, which promises a boost in wireless network performance, through exploitation of underutilized licensed frequency bands. As the focus of researchers is usually on these two major challenges, other aspects have been in part underestimated. In this work, we consider two factors that are rather important for evaluation of cognitive platforms, namely sensing delay and energy efficiency. The first is related to the latency induced by the spectrum sensing process and its impact on sensing efficiency, which is tightly connected to both the QoS performance of secondary users and the protection of primary users. On the other hand, energy consumption is considered as a crucial issue in all types of wireless communications, due to restricted battery autonomy of mobile devices, as well as for moving towards "greener" solutions in telecommunications. Therefore, it is important to extend existing testbed experimentation tools and develop new ones, in order to equip cognitive testbeds with such advanced monitoring capabilities. In this work, we present a monitoring procedure that has been directly integrated in the experimentation tools of the CREW testbed federation and demonstrate how it aids in the online evaluation of four different cognitive platforms in terms of the aforementioned metrics.

In this paper, we present an implementation design of a TDMA protocol for the canonical diamond-topology network containing a source, two relays and a destination (single unicast session). Getting inspired by the established Lyapunov-methodology, we propose an online strategy for the relay selection/scheduling problem. In contrast to existing works, we implement this strategy inside the proposed TDMA protocol in order to operate over a CSMA enabled Wi-Fi infrastructure-less network. We elaborate a network controller within the TDMA frame to solve a global optimization problem at each time slot in a centralized manner. In our formulation, we consider the class of scheduling policies that select concurrently a non-interfering subset of links. Our architecture is tailored to achieve the objectives of stabilizing the network and either maximizing throughput or minimizing the total power consumption. Our scheme has been implemented and tested thoroughly through experimentation in the NITOS wireless testbed by exploiting Wi-Fi technology features. The results revealed significant increase in networking efficiency for throughput maximization.

We propose a resource allocation model that captures the interaction between legitimate users of a distributed service provisioning system with malicious intruders attempting to disrupt its operation. The system consists of a bank of servers providing service to incoming requests. Malicious intruders generate fake traffic to the servers attempting to degrade service provisioning. Legitimate traffic may be balanced using available mechanisms in order to mitigate the damage from the attack. We characterize the guaranteed region, i.e. the set of legitimate traffic intensities that are sustainable given specific intensities of the fake traffic, under the assumption that the fake traffic is routed using static policies. This assumption will be relaxed, allowing arbitrary routing policies, in the full version of this work.

We consider the N-user broadcast erasure channel with N unicast sessions (one for each user) where receiver feedback is regularly sent to the transmitter in the form of ACK/NACK messages. We first provide a generic outer bound to the capacity of this system; we then propose a virtual-queue-based inter-session mixing coding algorithm, determine its rate region, and show that it achieves capacity under certain conditions on channel statistics, assuming that instantaneous feedback is known to all users. Removing this assumption results in a rate region that asymptotically differs from the outer bound by 1 bit as L → ∞, where L is the number of bits per packet (packet length). For the case of arbitrary channel statistics, we present a modification of the previous algorithm whose rate region is identical to the outer bound for N = 3, when instant feedback is known to all users, and differs from the bound by 1 bit as L → ∞, when the three users know only their own ACK. The proposed algorithms do not require any prior knowledge of channel statistics.

We investigate the scalability of multihop wireless communications, a major concern in networking, for the case that users access content replicated across the nodes. In contrast to the standard paradigm of randomly selected communicating pairs, content replication is efficient for certain regimes of file popularity, cache, and network size. Our study begins with the detailed joint content replication and delivery problem on a 2-D square grid, a hard combinatorial optimization. This is reduced to a simpler problem based on replication density, whose performance is of the same order as the original. Assuming a Zipf popularity law, and letting the size of content and network both go to infinity, we identify the scaling laws and regimes of the required link capacity, ranging from$O\!\left(\!\sqrt {N}\right)$down to$O(1)$.

We consider a source node broadcasting to two receivers over a general erasure channel with receiver feedback. We characterize the capacity region of the channel and construct algorithms based on linear network coding (either randomized or depending on channel dynamics) that achieve this capacity. We then consider stochastic arrivals at the source for the two destinations and characterize the stable throughput region achieved by adapting the same algorithms that achieve capacity. Next, we modify these algorithms to improve their delay performance and characterize their stable throughput regions. Although the capacity and stability regions obtained by the algorithms are not always identical (because of the extra overhead needed for the algorithms to handle stochastic traffic), they are within a few bits of each other and have similar forms. This example exhibits an unusual relationship between capacity and stability regions and extends similar prior studies for multiple access channels.

The main promise of current research efforts in the area of Information-Centric Networking (ICN) architectures is to optimize the dissemination of information within transient communication relationships of endpoints. Efficient caching of information is key to delivering on this promise. In this paper, we look into achieving this promise from the angle of managed replication of information. Management decisions are made in order to efficiently place replicas of information in dedicated storage devices attached to nodes of the network. In contrast to traditional off-line external management systems we adopt a distributed autonomic management architecture where management intelligence is placed inside the network. Particularly, we present an autonomic cache management approach for ICNs, where distributed managers residing in cache-enabled nodes decide on which information items to cache. We propose four on-line intra-domain cache management algorithms with different level of autonomicity and compare them with respect to performance, complexity, execution time and message exchange overhead. Additionally, we derive a lower bound of the overall network traffic cost for a certain category of network topologies. Our extensive simulations, using realistic network topologies and synthetic workload generators, signify the importance of network wide knowledge and cooperation.

A key problem in wireless networking is how to choose a link activation schedule and associated powers in concert with routing decisions to optimize throughput. Back-pressure control policies are optimal in this context, but the underlying power control problem is non-convex. Back-pressure power control (BPPC) was recently shown to be NP-hard, yet amenable to successive convex approximation strategies that deliver manifold improvements in end-to-end throughput relative to the prior art in wireless networking. A drawback is that existing implementations are centralized, whereas practical power control has to be distributed across the network. This paper fills this gap by developing a distributed version of the core step of successive convex approximation of the BPPC problem, building upon the Alternating Direction Method of Multipliers (ADMoM). The resulting protocol enjoys favorable properties relative to dual decomposition - based implementations, and allows tight approximation of the BPPC objective in all interference regimes. Judicious simulations reveal that the proposed algorithm matches the performance of its centralized counterpart, as well as pertinent trade-offs in terms of the design parameters.

A key consideration in novel communication paradigms in multihop wireless networks regards the scalability of the network. We investigate the case of nodes making random requests on content stored in multiple replicas over the wireless network. We show that, in contrast to the conventional paradigm of random communicating pairs, multihop communication is a sustainable scheme for certain values of file popularity, cache and network size. In particular, we formulate the joint problem of replication and routing and compute an order optimal solution. Assuming a Zipf file popularity distribution, we vary the number of files M in the system as a function of the nodes N, let both go to infinity and identify the scaling regimes of the required link capacity, from O(√N) down to O(1).

We consider a system where jobs (packets) arrive for processing using one of the policies in a given class. We study the connection between the minimal evacuation times and the stability region of the system under the given class of policies. The result is used to establish the equality of information theoretic capacity region and system stability region for the multiuser broadcast erasure channel with feedback.

Recent research efforts in the area of future networks indicate Information-Centric Networking (ICN) as the dominant architecture for the Future Internet. The main promise of ICN is that of shifting the communication paradigm of the internetworking layer from machine endpoints to information access and delivery. Optimized content dissemination and efficient caching of information is key to delivering on this promise. Moreover, current trends in management of future networks adopt a more distributed autonomic management architecture where management intelligence is placed inside the network with respect to traditional off-line external management systems. In this paper, we present an autonomic cache management approach for ICNs, where distributed managers residing in cache-enabled nodes decide on which items to cache. We propose three online cache management algorithms with different level of autonomicity and compare them with respect to performance, complexity, execution time and message exchange overhead. Our extensive simulation-based experimentation signifies the importance of network wide knowledge and cooperation.

We study the problem of minimizing the long-term average power grid operational cost through power demand scheduling. A controller at the operator side receives consumer power demand requests with different power requirements, durations and time flexibilities for their satisfaction. Flexibility is modeled as a deadline by which a demand is to be activated. The cost is a convex function of total power consumption, which reflects the fact that each additional unit of power needed to serve demands is more expensive to provision, as demand load increases. We develop a stochastic model and introduce two online demand scheduling policies. In the first one, the Threshold Postponement (TP), the controller serves a new demand request immediately or postpones it to the end of its deadline, depending on current power consumption. In the second one, the Controlled Release (CR), a new request is activated immediately if power consumption is lower than a threshold, else it is queued. Queued demands are activated when deadlines expire or when consumption drops below the threshold. These policies admit an optimal control with switching curve threshold structure, which involves active and postponed demand. The CR policy is asymptotically optimal as deadlines increase, namely it achieves a lower bound on average cost, and the threshold depends only on active demand. Numerical results validate the benefit of our policies compared to the default one of serving demands upon arrival.

The energy efficiency of cellular networks is explored for different network deployment strategies and traffic conditions. The total network power consumption and the ratios Watts/kbps, Watts/user are used as metrics to quantify the performance and it is shown that efficiency depends on the deployment strategy and the state of operation of the network (underutilized-overutilized). As a general conclusion it is shown that a microcell based network, comprising a large number of low power stations, is the most efficient strategy but it does not present traffic proportional characteristics. For the purpose of the investigation, the paper presents a pre-processing of the database 3D ray tracing algorithm that is enhanced with an image test procedure and multiple slope diffraction mechanisms to achieve fast and accurate predictions. The algorithm is used for channel estimations over a real urban environment described in a vector format. In addition, a power control algorithm is developed that explicitly considers power levels of neighbor base stations and is used for the network power consumption estimation. Finally, network planning is achieved through an evolutionary optimization technique that combines the above mentioned algorithms.

Unstructured BitTorrent-like Peer-to-Peer networks have been extensively studied as an important enabling technology for Video-on-Demand. While most studies maintain the pull-based nature of unstructured Peer-to-Peer networks, recent ones are investigating the combination of pull and push operations. The goal of the proposed push/pull protocols is to make more effcient use of the available bandwidth, however, this paper proposes a different use, the exploitation of users' viewing patterns (random seek patterns) in Video-on-Demand applications. As viewing pattern exploitation has not been given signif cant attention so far, we propose a scheme to prioritize chunk distribution based on the video's points of interest.

Cross-layer design and operation of wireless networks has attracted significant interest in the last decade, yet some basic problems in the area remain unsolved. In this paper, we consider the joint routing and power control problem, and specifically how to choose transmission powers at the physical layer to maximize stable end-to-end throughput at the network layer for a multi-hop wireless network. This is the back-pressure power control (BPPC) problem. Earlier work had recognized that BPPC is a non-convex problem, and suggested relatively simple suboptimal strategies. Here we show that BPPC is NP-hard. This is a negative result, which however comes with a positive flip side: drawing from related developments in the digital subscriber line (DSL) literature, we devise effective ways to approximate it. We report substantial improvements in transport capacity relative to the earlier state of art, as illustrated in pertinent simulations.

Abstract-This work is concerned with scaling decentralized content-based publish/subscribe (CBPS) networks for Internet-wide content distribution. A fundamental step for CBPS networks to reach the Internet-scale is to move from the exhaustive filtering service model, where a subscription selects every relevant publication, to a service model capturing the quantitative and qualitative heterogeneity of information consumers' requirements. In previous work, we described a service model allowing information consumers to express the maximum number of publications they would like to receive per service period and how to take advantage of such knowledge to pace the dissemination process. This paper extends by introducing a generic service model that seamlessly supports content-based information retrieval and dissemination and investigates through extensive simulations the performances of six caching policies in terms of consumers satisfaction and bandwidth usage.

We consider the N-user memoryless broadcast erasure channel with N unicast sessions (one for each user) where receiver feedback is sent to the transmitter in the form of ACK/NACK messages. We first provide a generic outer bound to the capacity of this system; using concepts from network coding, we then propose a session-mixing coding algorithm applied on specially constructed and maintained virtual queues (at the transmitter side), determine its throughput region and show that it achieves capacity under certain conditions on channel statistics (assuming that instantaneous feedback is known to all users). The algorithm requires no knowledge of channel statistics or future events.

Distributed Omni Antenna Systems (OASs) have been proposed to increase the capacity of Broadband Wireless Access (BWA) systems. We investigate the extension of such systems by employing distributed Directive Antenna Arrays (DAAs) to support high speed users also employing multiple antenna configurations. The problem of beamforming in a single sector is considered where remote Base Stations (BS's) equipped with multiple antennas attempt to serve a separate user. Different positions of distributed BS's in the sector are studied. The beamforming design is based on partial Channel State Information (CSI). The case study for a WiMAX system show that DAAs using beamforming techniques significantly improve performance compared to OASs and lead to an optimum network infrastructure in BWA with high mobile users systems where high - bandwidth radio services are required.

The ultimate success of the Wireless Mesh Network paradigm (WMN) in large scale deployments depends on the ability to test it in real world scenarios. A typical application scenario which is worth to be investigated in such a context is peer-to-peer traffic management. The creation of large scale testbeds for evaluating wireless mesh technologies and protocols, and for testing their ability to support real world applications in realistic environments, is then a crucial step. OMF (cOntrol and Management Framework) is a well-established control, measurement, and management framework for wireless testbeds. In this paper we present how we integrated an OMF-based wireless testbed in the planetary-scale PlanetLab testbed, making it possible for PlanetLab users to run experiments spanning on both PlanetLab and an OMF-based wireless testbed. In order to demonstrate the usefulness of such an integrated scenario, we tested on it an innovative peer-to-peer traffic optimization technique for the BitTorrent file sharing application. The possibility of running this kind of experiments highlighted several real-world issues which could be investigated thanks to our hybrid experimental scenario.

Efficient association of a station with the appropriate access point has always been a challenging problem. The standard approach of considering only the Received Signal Strength, has recently been substituted by more efficient schemes that consider channel conditions, cell population etc. However, in spite of the large variety of approaches, several factors that determine to a large extent user throughput after association with an access point have been overlooked. In this work, we propose innovative metrics on which association should be based. First, we capture the contention from one-hop and interference from two-hop neighbors that is inherent in IEEE 802.11 WLAN environments. Second we include the PHY transmission rate and show preference to higher rates that reduce the above effects. Third, unlike most relevant approaches, we define an activity factor that reveals the anticipated activity due to backlogged traffic. We devise an association protocol suite, through which messages containing the information above are passed between the AP and the user to support association decisions for the uplink and downlink. We implement the proposed mechanism using the MAD-WiFi open source driver and moreover show through experiments in a wireless testbed that it significantly improves user performance in real conditions.

In a publish/subscribe (pub/sub) network, message delivery is guaranteed for all connected subscribers at publish time. However, in a dynamic mobile scenario where users join and leave the network, it is important that content published at the time they are disconnected is still delivered when they reconnect from a different point. In this paper, we enhance the caching mechanisms in pub/sub networks to enable client mobility. We build our mobility support with minor changes in the caching scheme while preserving the main principles of loose coupled and asynchronous communication of the pub/sub communication model. We also present a new proactive mechanism to reduce the overhead of duplicate responses. The evaluation of our proposed scheme is performed via simulations and testbed measurements.

In current publish/subscribe networks messages are not stored and only active subscribers receive published messages. However, in a dynamic scenario a user may be interested in content published before the subscription time. In this paper, we introduce a mechanism that enables storing in such networks, while maintaining the main principle of loose-coupled and asynchronous communication. Furthermore, we propose a new storage placement and replication algorithm which differentiates classes of content and minimize the clients response latency. The performance of our proposed placement and replication algorithm and the proposed storing mechanism is evaluated via simulations and insights are given for future work.

This paper proposes easily applicable distributed reputation-based policies to motivate users of a wireless community to share their Internet connection in a p2p fashion. The goal is the provision of free and good quality Internet access anytime and anywhere inside the community. We study general systems of users with different contribution and consumption profiles and show via an extensive simulation study that when our reputation-based allocation scheme is applied, mobile users enjoy a QoS Internet connection from their community in proportion to their cooperation level; the more greedy for community resources users are, the more contributive they should be to satisfy their needs.

A novel cell architecture based on Distributed Antenna Arrays for next generation mobile WiMAX systems is proposed. By exploiting Spatial Multiplexing and beamforming techniques, our target is to maximize the throughput of a high speed user by keeping low the deployment costs. Radio over Fiber is one of the best solutions to feed from a central point distributed arrays and share wireless resources with flexibility. The new architecture is compared to the case of using co-located antenna systems. Simulation results evaluate the enhancement in the system performance that is achieved by using the proposed methods at the transmitter and the receiver sides with different complexity.

The selection of high-throughput routes is a key element towards improving the performance of wireless multihop networks. While several routing metrics have been proposed in the literature, it has been shown that link-quality aware metrics can provide significantly higher end-to-end throughput. To date, the online computation of such metrics requires the periodic transmission of probe packets at all available transmission rates. However, our link level measurement study on two different 802.11 testbeds demonstrates that: (a) multi-rate probe transmissions increase the number of collisions and enforce nodes to reside in the back-off state for prolonged time periods, and (b) the extent of performance degradation depends on the network density; a network-wide throughput reduction of the order of 400% is possible. In addition, our measurements show that the impact of probing in terms of end-to-end performance can be devastating. In particular, the probing functionality can pose a significant degradation in the end-to-end throughput of a single flow, by at least 35% and as high as 90%, depending on the probing frequency and network density. Finally, we discuss different alternatives to multi-rate probing for the online computation of such metrics.

In this paper, we study p2p systems, where peers have to share their available resources between their own and other peers' needs. One such example is a system of peers who use their capacity-limited access links both for their upstream and downstream connections. In the selfish approach, each peer would like to exploit the full capacity of his access link only for his downloads. However, if all peers acted selfishly, the system would collapse. In order to motivate peers to cooperate, we propose a distributed reputation-based system according to which peers earn reputation analogous to their contributions. In this way, each peer has to trade off the capacity he will dedicate for uploading in order to increase his reputation and therefore his revenue and the capacity he will dedicate for his downloads. All peers act rationally, trying to maximize their utility. Our proposed policies lead rational peers to cooperation while promoting fairness, as peers receive resources in proportion to their contributions. Our policies outperform existing work in this area in which the slowest link becomes the bottleneck of a heterogeneous system of different link capacity peers. On the contrary, no such bottleneck appears when our policies are used, improving the performance of the system. Finally, we apply our reputation-based approach in a BitTorrent-like file sharing system and we highlight the potential performance gains.

We consider the problem of cross-layer resource allocation for wireless networks operating with rechargeable batteries under general arrival, channel state and recharge processes. The objective is to maximize total system utility, defined as a function of the long-term rate achieved per link, while satisfying energy and power constraints. A policy with decoupled admission control and power allocation decisions is proposed that achieves asymptotic optimality for sufficiently large battery capacity to maximum transmission power ratio (explicit bounds are provided). We present first a downlink resource allocation scenario; the analysis is then extended to multihop networks. The policy is evaluated via simulations and is seen to perform very well even in the non-asymptotic regime. This policy is particularly suitable for sensor networks, which typically satisfy the asymptotic conditions required by our methodology.

A multi-user MIMO/OFDMA system for the next generation broadband wireless access (BWA) networks is studied, in which the base station (BS) has only knowledge of the statistics of the channel. A combination of MIMO, OFDMA and FDD could be suitable to increase spectral efficiency in a high speed network. We investigate methods with scalable channel feedback and we analyze the trade off between the amount of channel state information (CSI) to the transmitter and the system performance. Simulation results demonstrate that substantial gain is obtained by the proposed schemes which take advantage the statistical information of the highly dynamic channel.

In a publish/subscribe network, message delivery is guaranteed for all active subscribers at publish time. However, in a dynamic scenario where users join and leave the network, a user may be interested in content published before the subscription time. In this paper, we introduce mechanisms that enable caching in such networks, while maintaining the main principle of loose-coupled and asynchronous communication. Furthermore we investigate two caching policies; caching in all candidate brokers (basic caching) which yields high survivability and low delay and caching in leaf brokers (leaf caching) which maintains low overhead and querying complexity. The comparison is performed via simulations and testbed measurements and insights are given for future work.

Efficient channel selection is essential in 802.11 mesh deployments, for minimizing contention and interference among co-channel devices and thereby supporting a plurality of QoS-sensitive applications. In this paper, we propose ARACHNE, a routing-aware channel selection protocol for wireless mesh networks. ARACHNE is distributed in nature, and motivated by our measurements on a wireless testbed. The main novelty of our protocol comes from adopting a metric that captures the end-to-end link loads across different routes in the network. ARACHNE prioritizes the assignment of low-interference channels to links that (a) need to serve high-load aggregate traffic and/or (b) already suffer significant levels of contention and interference. Our protocol takes into account the number of potential interfaces (radios) per device, and allocates these interfaces in a manner that efficiently utilizes the available channel capacity. We evaluate ARACHNE through extensive, trace-driven simulations. We observe that our protocol improves the total network throughput, as compared to three other channel allocation strategies.

We consider a content sharing network of non-cooperative peers. The strategy set of each peer comprises, (i) client strategies, namely feasible request load splits to servers, and (ii) server strategies, namely scheduling disciplines on requests. First, we consider the request load splitting game for given server strategies such as First-In-First-Out or given absolute priority policies. A peer splits its request load to servers to optimize its performance objective. We consider the class of best response load splitting policies residing between the following extremes: a truly selfish, or egotistic one, where a peer optimizes its own delay, and a pseudo-selfish or altruistic one, where a peer also considers incurred delays to others. We derive conditions for Nash equilibrium points (NEPs) and discuss convergence to NEP and properties of the NEP. For both the egotistic cases, the NEP is unique. For the altruistic case, each of the multiple NEPs is an optimum, a global one for the FIFO case and a local one otherwise. Next, we include scheduling in peer strategies. With its scheduling discipline, a peer cannot directly affect its delay, but it can affect the NEP after peers play the load splitting game. The idea is that peer i should offer high priority to (and thus attract traffic from) higher-priority peers that cause large delay to i at other servers. We devise two-stage game models, where, at a first stage, a peer selects a scheduling rule in terms of a convex combination of absolute priorities, and subsequently peers play the load splitting game. In the most sophisticated rule, a peer selects a scheduling discipline that minimizes its delay at equilibrium, after peers play the load splitting game. We also suggest various heuristics for picking the scheduling discipline. Our models and results capture the dual client-server peer role and aim at quantifying the impact of selfish peer interaction on equilibria.

Vehicular ad hoc networks have emerged recently as a platform to support intelligent inter-vehicle communication and improve traffic safety and performance. The road-constrained and high mobility of the vehicles, their unbounded power source, and the emergence of roadside wireless infrastructures make VANETs a challenging research topic. A key to the development of protocols for intervehicle communication and services lies in the knowledge of the topological characteristics of the VANET communication graph. This article provides answers to the general question: how does a VANET communication graph look like over time and space? This study is the first one that examines a very large-scale VANET graph and conducts a thorough investigation of its topological characteristics using several metrics, not examined in previous studies. Our work characterizes a VANET graph at the connectivity (link) level, quantifies the notion of ¿qualitative¿ nodes as required by routing and dissemination protocols, and examines the existence and evolution of communities (dense clusters of vehicles) in the VANET. Several latent facts about the VANET graph are revealed and incentives for their exploitation in protocol design are examined.

The impetus of the present study is to describe a downlink beamforming method that increase spectrum efficiency and significantly reduces implementation complexity and power consumption compare to beamforming technique at each sub-carrier, proposed in the ongoing IEEE 802.11n standardization. In our scheme, common transmission weight vectors are used at a set of users in all sub-carriers. Our strategy consists of simultaneously designing downlink beamformers to multiple co-channel sets of users under the constraint on providing at least a prescribed received signal-to-interference plus noise ratio (SINR) to each intended receiver. We assume that the instantaneous channel gains are known at the access point (AP) for all users. Simulation results show substantial gain for our proposed algorithms in IEEE 802.11n wireless access networks.

A dynamic resource allocation algorithm with beam steering is evaluated for providing broadband wireless access to mobile users of the emerging IEEE 802.16 m air interface standard. Thanks to our design, the coverage area and total throughput may be significantly enhanced, compared to the current IEEE 802.16 e standard. Our proposed solution supports high mobility, improves fairness among the users and is backward compatible to the mobile networks based on the 802.16 e. The simulation analysis determines the parameters which impact the system performance. Furthermore, our system presents low complexity hardware implementation and low power consumption.

During the last decade, Wireless Sensor Networks (WSNs) have emerged and matured at such point that currently support several applications like environment control, intelligent buildings, target tracking in battlefields, and many more. The vast majority of these applications require an optimization to the communication among the sensors so as to serve data in short latency and with minimal energy consumption. Cooperative data caching has been proposed as an effective and efficient technique to achieve these goals concurrently. The essence of these protocols is the selection of the sensor nodes which will take special roles in running the caching and request forwarding decisions. This article introduces a new metric to aid in the selection of such nodes. Based on this metric, we propose a new cooperative caching protocol, which is compared against the state-of-the-art competing protocols. The simulation results attest the superiority of the proposed protocol; the proposed solution achieves on the average 20% improvement w.r.t. the competing method for the examined performance measures.

The user association mechanism specified by the IEEE 802.11 standard does not consider the channel conditions and the AP load in the association process. Employing the mechanism in its plain form in wireless mesh networks we may only achieve low throughput and low user transmission rates. In this paper we design a new association framework in order to provide optimal association and network performance. In this framework we propose a new channel-quality based user association mechanism inspired by the operation of the infrastructure-based WLANs. Besides, we enforce our framework by proposing an airtime-metric based association mechanism that is aware of the uplink and downlink channel conditions as well as the communication load. We then extend the functionality of this mechanism in a cross-layer manner taking into account information from the routing layer, in order to fit it in the operation of wireless mesh networks. Lastly, we design a hybrid association scheme that can be efficiently applied in real deployments to improve the network performance. We evaluate the performance of our system through simulations and we show that wireless mesh networks that use the proposed association mechanisms are more capable in meeting the needs of QoS-sensitive applications.

The multicast beamforming problem is considered from the viewpoint of minimizing outage probability subject to a transmit power constraint. The main difference with the point-to-point transmit beamforming problem is that in multicast beamforming the channel is naturally modeled as a Gaussian mixture, as opposed to a single Gaussian distribution. The Gaussian components in the mixture model user clusters of different means (locations) and variances (spreads). It is shown that minimizing outage probability subject to a transmit power constraint is an NP-hard problem when the number of Gaussian kernels, J, is greater than or equal to the number of transmit antennas, N. Through dimensionality reduction, it is also shown that the problem is practically tractable for 2 - 3 Gaussian kernels. An approximate solution based on the Markov inequality is also proposed. This is simple to compute for any J and N, and often works well in practice.

Transmit beamforming for physical layer multicasting is emerging as an appealing transmission modality for next-generation cellular wireless systems, notably UMTS-LTE. Optimal design of the transmit beamformer(s) from a quality of service perspective is a hard computational problem; however, convex approximation tools have been shown to yield high-quality approximate solutions. Recently, Lozano proposed a particularly simple adaptive multicast beamforming algorithm that aims to serve a certain percentage of users. In parallel, convex approximation algorithms were developed for the more general problem of joint co-channel multicast beamforming and admission control. In this paper, we focus on the important (in view of recent standardization activity) special case of a single multicast group, and put the two approaches to the test. Through simple examples, we pinpoint issues regarding convergence of Lozano's algorithm. In numerical experiments with measured channel data, we show that a convex approximation approach is preferable performance- wise. At the same time, we find merits in the simplicity of Lozano's approach, and suggest a way to improve its performance.

With the advent of low-cost WLAN devices, the delivery of multimedia content is highly desirable. Such applications require high throughput and near-real time for quality viewing. The use of next-generation WLAN 802.1 In and physical layer multicast transmission for a wide range of wireless terminals introduces many significant challenges. The impetus of the present study is to describe a cross-layer approach, enabling techniques as beamforming, MIMO antennas, OFDM and low latency MAC operation. Our simulation results show a substantial improvement in network performance for our proposed strategies in IEEE802.11n wireless access networks.

As the two key technologies that have the potential to reshape the landscape of next-generation wireless network, cooperative communications and directional antenna system so far have been developed in parallel, if not in isolation from each other. In order to establish a thorough comparison between the relative system performance of cooperative diversity and directional transmission in an adhoc environment, we design and quantitatively evaluate three medium access control protocols, namely O-CoopMAC, D-NoopMAC and D-CoopMAC. The study yields the unexpected yet crucial observation that cooperative forwarding significantly limits the spatial reuse created by transmission directionality, and therefore can appreciably degrade the performance of a directional system with a sufficiently narrow antenna beam. To the best knowledge of the authors, this is the first paper to systematically compare the performance of these two technologies in an adhoc environment and reveal the key fact that cooperation and directionality can be rather foes than friends!

Due to the unreliable nature of the wireless medium, provisioning of quality of service (QoS) in wireless LANs is far more complicated than in wired networks. In order to address this challenge, IEEE 802.11e defines a framework for QoS support where packets are prioritized based on their traffic characteristics. In this paper, we propose two new QoS support schemes. One is based on a ldquouser centricrdquo approach and the other on a ldquopacket content basedrdquo approach. The new mechanisms, in addition to the traffic itself, take into consideration the identification of the station that generates the traffic or the content of the traffic. Therefore, they use a second prioritization level on top of the one that is implemented in IEEE 802.11e. In the ldquouser centricrdquo approach, the mechanism defines groups of stations based on their MAC addresses and assigns different priorities to different groups. Under this classification, stations are served based on the prioritization of the group they belong. Among stations with same priority, traffic is scheduled based on the priorities given by 802.11e. On the other hand, in the case of the ldquopacket content basedrdquo approach, the mechanism defines groups of words or phrases with their respective priority. A packet that includes words of a specific group is scheduled based on the priority that the particular content defines. The new schemes are simple yet efficient, since they are adapted to the realistic needs of todaypsilas WiFi networks. In order to evaluate the performance of these proposed schemes, we implement them using open source drivers in a Linux platform. We run experiments in a medium-size testbed. Experimentation results clearly demonstrate the performance superiority of the new schemes, as compared to the legacy IEEE 802.11e.

Wireless technology advances over the last few years lead to sophisticated physical layer designs that may interact with the access and network layer in multiple modes. Link quality related information is passed from the physical layer, to be used in access and network layer actions. At the same time several considerations belonging naturally to the physical layer, like channel coding rate, signal constellation selection, power level adjustments, frequency selection and beam steering in multiple antenna systems are to the disposal of the access layer, that may control them in various time scales. That interaction is particularly useful for full exploitation of the volatile error-prone mobile channel and the establishment of reliable broadband wireless links in the interference limited radio medium. It is clear that novel approaches are needed for architecting networks that seamlessly integrate wired and wireless components and offer the grade of service people are accustomed from the internet. In this presentation we will review a number of theoretical advances towards characterizing the capacity of wireless networks and present an optimization based framework for developing algorithms towards achieving that capacity. The necessary interaction among the different network layers will be discussed while implementation challenges both in terms of computational complexity as well as state information availability will be presented. Implications on the scaling properties of those algorithms will be given.

Dense deployments of hybrid WLANs result in high levels of interference and low end-user throughput. Many frequency allocation mechanisms for WLANs have been proposed by a large body of previous studies. However, none of these mechanisms considers the load that is carried by APs in terms of channel conditions, number of affiliated users as well as traffic-load, in conjunction. In this paper, we propose LAC, a load-aware channel allocation scheme for WLANs, which considers all the above performance determinant factors. LAC incorporates an airtime cost metric into its channel scanning process, in order to capture the effects of these factors and select the channel with the maximum long-term throughput. We evaluate LAC through extensive OPNET simulations, for many different traffic scenarios. Our simulations demonstrate that LAC outperforms other frequency allocation policies for WLANs in terms of total network throughput by up to 135%.

In 802.11-based wireless networks the stations (STAs) are associated with the available access points (APs) and communicate through them. In traditional handoff schemes the STAs get information about the active APs in their neighborhood by scanning the available channels and listen to transmitted beacons. This paper proposes a 802.11k compliant framework for cooperative handoff where the STAs are informed about the active APs by exchanging information with neighboring STAs. In this way we minimize the delay of the scanning procedure. We evaluate the performance of our mechanisms through simulations and we show that when our cooperative framework is applied the network performance is significantly improved. Consequently, our system is more capable in meeting the needs of QoS-sensitive applications.

Due to the unreliable nature of the wireless medium, provisioning of the quality of service (QoS) in wireless LANs is by far more complicated than in wired networks. In this demo we show the efficiency of a new QoS support scheme that is based on an user centric approach. The new mechanism takes under consideration the identification of the station that generates the traffic additionally to the traffic itself. Therefore, it uses a second prioritization level on the top of the one that is implemented in IEEE 802.11e. It first defines groups of stations based on their MAC addresses and it assigns different priorities to different groups. Under this classification, stations are served based on the prioritization of the group they belong. Among stations with same priority, traffic is scheduled based on the priorities given by 802.11e. The new scheme is implemented using open source drivers and 802.11g WiFi cards. In the demo, a video clip is streamed from a server to a client with high priority under heavy traffic load. The underlying wireless transport alternates between the classic IEEE 802.11e and the new prioritization scheme. The new scheme delivers a smooth and jitter-free user experience, while the video playout over IEEE 802.11e experiences noticeable jitter and frequent distortions. The demo clearly demonstrates the performance superiority of the new implemented scheme, as compared to the legacy IEEE 802.11e.

Space division multiple access (SDMA) with an antenna array at the transmitter is a promising means for increasing system capacity and supporting rate-demanding services. However, the presence of an antenna array at the physical layer raises significant issues at higher layers. In this paper, we attempt to capture the impact of SDMA on access layer channel allocation, reflected on channel reuse. This impact obtains different twists in TDMA, CDMA and OFDMA due to the different nature of co-channel and cross-channel interference and the different interaction of user spatial channel characteristics with system channels, namely time slots, codes and subcarriers. We consider these access schemes in a generalized unified framework and propose heuristic algorithms for channel allocation, downlink beamforming and transmit power control so as to increase total provisioned system rate and provide QoS to users in the form of minimum rate guarantees. We study the class of greedy algorithms that rely on criteria such as induced or received interference and signal-to-interference ratio (SIR), and a class of SIR balancing algorithms. Results show superior performance for SIR balancing resource allocation and expose the performance benefits of cross-layer design.

We study carrier assignment in a single-cell multiuser OFDM multi-carrier system so as to satisfy user rate requirements with minimal resources. Different users experience different quality in different carriers due to frequency selectivity of users' propagation channels and due to non-co-located user receivers that perceive different interference from neighboring cells across carriers. We study a static instance of the problem, specified by user carrier qualities and rate requirements. Adaptive modulation at the transmitter differentiates carriers for each user. In good quality carriers, the user satisfies per-frame rate requirements with few slots (or equivalently it satisfies its per-slot rate requirements with small occupied time slot portion). We study integral and fractional assignment, where a user is assigned to only one or several carriers. Fractional assignment is formulated as a linear programming problem. For integral assignment, we introduce two classes of iterative heuristics that use carrier reassignment to users and user substitution in carriers respectively and may be viewed as resulting from corresponding optimal fractional assignment algorithms. We use Lagrangian relaxation to obtain performance bounds and show that the two classes of heuristics arise from two relaxations. Our approach identifies efficient feasible solutions and is amenable to distributed implementation.

In this paper, we propose a new Medium Access Control (MAC) protocol for full exploitation of directional antennas in wireless networks. The protocol introduces a circular directional transmission of the Request To Send (RTS) control packet, spreading around a station information about the intended communication. The stations that receive the directional RTS, using a simple scheme of tracking the neighbors' directions, defer their transmission toward the beams that could harm the ongoing communication. In this way, the proposed protocol takes advantage of the benefits of directional transmissions as the increase of spatial reuse and of coverage range. Additionally, it reduces the hidden-terminal problem, as well as the deafness problem, two main factors for the decrease of the efficiency of directional transmissions in ad hoc networks. The performance evaluation of the protocol shows that it offers a significant improvement in static, as well as mobile, scenarios, as compared to the performance of the proposed protocols that use omnidirectional or directional transmissions.

Multiuser downlink beamforming under quality of service (QoS) constraints has attracted considerable interest in years, because it is particularly appealing from a network operator's perspective (e.g., UMTS, 802.16e). When there are many co-channel users and/or the service constraints are stringent, the problem becomes infeasible and some form of admission control is necessary. We advocate a cross-layer approach to joint multiuser transmit beamforming and admission control, aiming to maximize the number of users that can be served at their desired QoS. It is shown that the core problem is NP-hard, yet amenable to convex approximation tools. Two computationally efficient convex approximation algorithms are proposed: one is based on semidefinite relaxation of an equivalent problem reformulation; the other takes a penalized second-order cone approach. Their performance is assessed in a range of experiments, using both simulated and measured channel data. In all experiments considered, the proposed algorithms work remarkably well in terms of the attained performance-complexity trade-off, consistently exhibiting close to optimal performance at an affordable computational complexity.

Multiuser downlink beamforming under quality of service (QoS) constraints has attracted considerable interest in recent years, because it is particularly appealing from a network operator's perspective (e.g., UMTS, 802.16e). When there are many co-channel users and/or the service constraints are stringent, the problem becomes infeasible and some form of admission control is necessary. We advocate a cross-layer approach to joint multiuser transmit beamforming and admission control, aiming to maximize the number of users that can be served at their desired QoS. The core problem is NP-hard, yet amenable to convex approximation tools. We propose a computationally efficient semidefinite relaxation algorithm which works remarkably well in a range of experiments, using both simulated and measured channel data.

In IEEE 802.11-based wireless mesh networks a user is associated with an access point (AP) in order to communicate and be part of the overall network. The association mechanism specified by the IEEE 802.11 standard does not consider the channel conditions and the AP load in the association process. Employing the mechanism in its plain form in wireless mesh networks we may only achieve low throughput and low user transmission rates. In this paper, we propose an association mechanism that is aware of the uplink and downlink channel conditions. We introduce a metric that captures the channel conditions and the load of the APs in the network. The users use this metric in order to optimally associate with the available APs. We then extend the functionality of this mechanism in a cross-layer manner taking into account information from the routing layer. The novelty of the mechanism is that the routing QoS information of the back haul is available to the end users. This information can be combined with the uplink and downlink channel information for the purpose of supporting optimal end-to-end communication and providing high end-to-end throughput values. We evaluate the performance of our system through simulations and we show that 802.11-based mesh networks that use the proposed association mechanism are more capable in meeting the needs of QoS-sensitive applications.

Voice over IP (VoIP) is one of the most emerging technologies, while a respectable number of corresponding applications are being implemented or updated in daily basis. Although communications security is a constant challenge, most VoIP applications take little or no security provisions. In the present work we describe the design and implementation of a secure VoIP application that is using the security mechanisms of voice interactive personalized security protocol (VIPSec). We analyze the architectural elements and we present the implementation characteristics of the application regarding the end-to-end security. Our approach follows the client-server model for subsidiary procedures like user sign-in and the direct client connection model for key, data and voice exchange between the users. The application is developed in the Delphi/Kylix language and is compatible with all versions of MS-Windows or Linux running on an x86 compatible computer, essentially providing secure PC-to-PC voice and data communications.

Wireless systems possess attributes fairly different than those that formed the design guidelines of the Internet. Hence, novel approaches are needed for architecting networks that seamlessly integrate wired and wireless components and offer the grade of service people are accustomed to from the Internet. In this talk we will review a number of theoretical advances towards characterizing the capacity of wireless networks and present an optimization based framework towards achieving that capacity. The necessary interaction among the different network layers for realizing those resource allocation algorithms will be discussed while implementation challenges both in terms of computational complexity as well as state information available will be presented. Implications on the scaling properties of those algorithms and the associated network capacity will be given. In the last part of the talk, we will present attributes of prevailing wireless network standards that support the incorporation of optimization based resource allocation algorithms in practical network designs and discuss current approaches.

We introduce cross-layer, distributed power control algorithms that guarantee maximum possible data throughput in multihop CDMA wireless networks. Throughput maximization, for given power budget, is achieved by jointly performing dynamic routing and scheduling together with power control. The cross-layer interaction consists in differential queue length information, made available from the network layer, which is exploited by the physical layer power control function. The proposed back-pressure power control algorithms operate in real-time, i.e. in parallel with system evolution, and achieve maximum throughput without knowledge of traffic statistics.

We consider a CDMA wireless ad—hoc network in the high SINR regime. We introduce a suite of cross—layer algorithms for joint flow control, routing, scheduling and power control. The algorithms guarantee forwarding of all incoming traffic, with an energy expenditure that can get arbitrarily close to the minimum possible. When traffic arrival rates lie outside the stable throughput region supported by the wireless network, the algorithms ensure fair allocation of resources. Compared to other algorithms that have been proposed in the past in a more general setting, our scheme is of considerably lower complexity. It relies on iterative methods for solving convex throughput optimization problems for CDMA networks in the high SINR regime. The resulting cross—layer control algorithms are promising in practical implementations, for they operate in real—time, i.e., evolve in parallel with network dynamics, with limited computational complexity between successive control epochs.

In this paper, we address the rate control problem for layered multicast traffic, with the objective of solving a generalized throughput/fairness objective. Our approach is based on a combination of Lagrangian relaxation and dynamic programming. Unlike previously proposed dual-based approaches, the algorithm presented in this paper scales well as the number of multicast groups in the network increases. Moreover, unlike all existing approaches, our approach takes into account the discreteness of the receiver rates that is inherent to layered multicasting. We show analytically that our algorithm converges and yields rates that are approximately optimal. Simulations carried out in an asynchronous network environment demonstrate that our algorithm exhibits good convergence speed and minimal rate fluctuations

A single commodity network that models the information flow in an arbitrary topology sensor field that collects and forwards information to a backbone through certain designated gateway nodes is considered. Resilient operation in overload stress situations caused by unpredictable traffic or topology variations is investigated. That amounts to studying the network in instability mode, where the traffic load distribution is outside the throughput region. A fluid model is adopted where superflows model traffic forwarding and backlog formations at the network level. Quantitative performance metrics of the overload including throughput, lexicographic minimization, most balanced allocation, and amount of lost traffic due to buffer overflow are considered to capture the information loss process due to overflow in the network. Optimal superflows with respect to these metrics are characterized and a distributed asynchronous algorithm that computes such superflows is given. The characterization of the optimal superflow amounts to obtaining a structural decomposition of the network in a sequence of disjoint subregions with decreasing overload such that traffic flows only from regions of higher overload to regions of lower overload. The optimal superflow represents the smoothest trajectory to overflow, followed by the network in case of instability.

A spatially distributed set of sources is creating data that must be delivered to a spatially distributed set of sinks. A network of wireless nodes is responsible for sensing the data at the sources, transporting them over a wireless channel, and delivering them to the sinks. The problem is to find the optimal placement of nodes, so that a minimum number of them is needed. The critical assumption is made that the network is massively dense, i.e., there are so many sources, sinks, and wireless nodes, that it does not make sense to discuss in terms of microscopic parameters, such as their individual placements, but rather in terms of macroscopic parameters, such as their spatial densities. Assuming a particular interference-limited, capacity-achieving physical layer, and specifying that nodes only need to transport the data (and not to sense them at the sources, or deliver them at the sinks once their location is reached), the optimal node placement induces a traffic flow that is identical to the electrostatic field created if the sources and sinks are replaced by a corresponding distribution of positive and negative charges. Assuming a general model for the physical layer, and specifying that nodes must not only transport the data, but also sense them at the sources and deliver them at the sinks, the optimal placement of nodes is given by a scalar nonlinear partial differential equation found by calculus of variations techniques. The proposed formulation and derived equations can help in the design of large wireless sensor networks that are deployed in the most efficient manner, not only avoiding the formation of bottlenecks, but also striking the optimal balance between reducing congestion and having the data packets follow short routes.

Next generation wireless CDMA systems will provide high and variable data rates by using multicode structures, controllable code spreading gains, and transmit power adaptation. We address the emerging resource allocation problem in that context and consider maximizing the total achievable user rate while satisfying minimum rate requirements of users. We devise symbol-synchronous and asynchronous models that capture different communication scenarios. The synchronous model holds for down-link transmission in one cell. The asynchronous model captures up-link single-cell communication and down-link or up-link scenarios in multicell systems. It can also account for multipath with each path corresponding to a virtual user. We propose a class of two-stage resource allocation algorithms. First, an admissible set of codes is constructed with criteria that capture code cross-correlation, induced interference to the system, and code rates. Next, the codes are allocated to users so as to satisfy their rate requirements. In the synchronous case, the problem structure allows the distinction of the two stages. In the asynchronous case, this distinction is not feasible due to different user delay profiles perceived at the receiver. Our models and numerical results indicate interesting trends and lead to useful conclusions and design guidelines for resource allocation algorithms under the aforementioned regimes.

In recent years, it has become common consensus that independent consideration of communication layers often turns out to be inadequate in terms of providing the desired quality of service (QoS) and power efficiency in wireless networks. The need for a synergistic, cross-layer design framework has already been identified. In that respect, our work constitutes an important step towards a better understanding of the cross-layer paradigm by simultaneously targeting both the power efficiency and the end-to-end QoS in multi-hop wireless networks. More specifically, we address the joint problem of power control and scheduling with the objective of minimizing the total transmit power subject to the end-to-end bandwidth guarantees and the bit error rate constraints of each communication session. After identifying the inherent difficulty of the problem, we propose two classes of heuristic algorithms that rely on graph theory principles as well as on derived metrics such as effective interference. The first heuristic follows a top-down design strategy by solving the schedule feasibility problem as the initial step and then targeting the total power efficiency. On the other hand, the second heuristic follows a bottom-up approach that schedules one wireless link at a time by greedily filling up the available time slots. The simulation results reveal valuable insights about the performance of each strategy. The top-down design strategy turns out to address power efficiency issues better, whereas the bottom-up design strategy with a properly selected cost function for link scheduling shows better performance in finding a feasible solution, namely one that satisfies both the QoS and the transmit power constraints. Our results also illustrate the impact of routing decisions on the feasibility and the power efficiency of multi-hop wireless networks through employing different routing criteria in the experiments

We investigate the spatial distribution of wireless nodes that can transport a given volume of traffic in a sensor network, while requiring the minimum number of wireless nodes. The traffic is created at a spatially distributed set of sources, and must arrive at a spatially distributed set of sinks. Under a general assumption on the physical and medium access control (MAC) layers, the optimal distribution of nodes induces a traffic flow identical to the electrostatic field that would exist if the sources and sinks of traffic were substituted with an appropriate distribution of electric charge. This analogy between electrostatics and wireless sensor networks can be extended in a number of different ways. For example, Thomson's theorem on the distribution of electric charge on conductors gives the optimal distribution of traffic sources and sinks (that minimizes the number of nodes needed) when we have a limited degree of freedom on their initial placement. Electrostatics problems with Neumann boundary conditions and topologies with different types of dielectric materials can also be interpreted in the context of wireless sensor networks. The analogy also has important limitations. For example, if we move to a three dimensional topology, adapting our general assumption on the physical and MAC layers accordingly, or we stay in the two dimensional plane but use an alternative assumption, that is more suited to ultra wide band communication, the optimal traffic distribution is not in general irrotational, and so can not be interpreted as an electrostatic field. Finally, the analogy cannot be extended to include networks that support more than one type of traffic.

We consider the operation of a network in overload situations, that is, when the incoming traffic is outside the feasibility region determined by the network topology. In such a situation nodes will be overloaded and it is important to maintain a balanced network overload while ensuring that maximum amount of traffic reaches the sink nodes. We formulate the problem as lexicographic optimization of node overloads, study the properties of the solution and provide a distributed flow reallocation mechanism whose node overloads converge to the optimal solution

We present a novel framework for the provision of deterministic end-to-end bandwidth guarantees in wireless ad hoc networks. Guided by a set of local feasibility conditions, multi-hop sessions are dynamically offered allocations, further translated to link demands. Using a distributed TDMA protocol, nodes adapt to the demand changes on their adjacent links by local, conflict-free slot reassignments. As soon as the changes stabilize, the nodes must incrementally converge to a TDMA schedule that realizes the global link (and session) demand allocation. We first identify an inherent trade-off between the degree of topology control and fraction of feasible allocations that can be captured by the local conditions. We show that tree topologies can be maximally utilized in this respect and that a converging distributed link scheduling algorithm exists in this case. Decoupling end-to-end bandwidth allocation from link scheduling allows support of various end-to-end QoS objectives. Focusing on Available Bit Rate (ABR) service, we design an asynchronous distributed algorithm for sharing bandwidth to the sessions in a maxmin fair (MMF) manner. Finally, we present the implementation of this framework over Bluetooth, an existing wireless technology that enables the formation of ad hoc networks. This implementation is free of the usual restrictive assumptions of previous TDMA approaches: it does not require any a-priori knowledge on the number of nodes in the network nor even network-wide slot synchronization.

In this article we describe and evaluate a general class of synchronous multi-carrier CDMA methods. In the proposed methods each accessing user is encoded by a Hadamard sequence and a multi-carrier (MC) encoder. The MC-encoder may provide one or many sub-carriers for each access channel while each accessing user may distribute its transmit power over all sub-carriers of its own access channel or over all subcarriers of all access channels. Each sub-carrier then carries the transmitted symbols of all users which are distinguished by their Hadamard sequences. In the performance evaluation presented herein we have examined their tolerance to synchronization jitter.

The deployment of traditional higher layer protocols (especially the IEEE 802.11 MAC protocol at the MAC layer) with directional antennae could lead to problems from an increased number of collisions; this effect is primarily seen due to three specific effects: (i) an increase in the number of hidden terminals; (ii) the problem of deafness and, (iii) a difficulty in determining the locations of neighbors. In this work we propose a new MAC protocol that incorporates circular RTS and CTS transmissions. We show that the circular transmission of the control messages helps avoid collisions of both DATA and ACK packets from hidden terminals. Our protocol intelligently determines the directions in which the control messages ought to be transmitted so as to eliminate redundant transmissions in any given direction. We perform extensive simulations and analyze the obtained results in order to compare our scheme with previously proposed protocols that have been proposed for use in directional antenna equipped ad hoc networks. Our simulation results clearly demonstrate the benefits of incorporating both circular RTS and CTS messages in terms of the achieved aggregate throughput.

Bluetooth, a wireless technology based on a frequency-hopping physical layer, enables portable devices to form short-range wireless ad hoc networks. Bluetooth hosts are not able to communicate unless they have previously discovered each other through synchronization of their timing and frequency-hopping patterns. Thus, even if all nodes are within proximity of each other, only those nodes which are synchronized with the transmitter can hear the transmission. To support any-to-any communication, nodes must be synchronized so that the pairs of nodes, which can communicate with each other, form a connected graph. Using Bluetooth as an example, we first provide deeper insights into the issue of link establishment in frequency-hopping wireless systems. We then introduce an asynchronous distributed protocol that begins with nodes having no knowledge of their surroundings and terminates with the formation of a connected network topology satisfying all constraints posed by Bluetooth. An attractive protocol feature is its ease in implementation using the communication primitives offered by the Bluetooth Specification.

We investigate from an algorithmic perspective the maxmin fair allocation of bandwidth in wireless ad hoc networks. We formalize the maxmin fair objective under wireless scheduling constraints, and present a necessary and sufficient condition for maxmin fairness of a bandwidth allocation. We propose an algorithm that assigns weights to the sessions dynamically such that the weights depend on the congestion in the neighborhood, and schedules the sessions that constitute a maximum weighted matching. We prove that this algorithm attains the maxmin fair rates, even though it does not use any information about the statistics of the packet arrival process.

We study the fair allocation of bandwidth in multicast networks with multirate capabilities. In multirate transmission, each source encodes its signal in layers. The lowest layer contains the most important information and all receivers of a session should receive it. If a receiver's data path has additional bandwidth, it receives higher layers which leads to a better quality of reception. The bandwidth allocation objective is to distribute the layers fairly. We present a computationally simple, decentralized scheduling policy that attains the maxmin fair rates without using any knowledge of traffic statistics and layer bandwidths. This policy learns the congestion level from the queue lengths at the nodes, and adapts the packet transmissions accordingly. When the network is congested, packets are dropped from the higher layers; therefore, the more important lower layers suffer negligible packet loss. We present analytical and simulation results that guarantee the maxmin fairness of the resulting rate allocation, and upper bound the packet loss rates for different layers.

We study fairness of resource allocation in multirate, multicast networks. In multirate networks, different receivers of the same multicast session can receive service at different rates. We develop a mathematical framework to model the maxmin fair allocation of bandwidth with minimum and maximum rate constraints. We present a necessary and sufficient condition for a rate allocation to be maxmin fair in a multirate, multicast network. We propose a distributed algorithm for computing the maxmin fair rates allocated to various source-destination pairs. This algorithm has a low message exchange overhead, and is guaranteed to converge to the maxmin fair rates in finite time.

Caching is a popular mechanism for enhancing performance in various layers and applications of computer networking. We introduce both a model and algorithms for caching routing information in large, memory-limited wireless ad hoc networks. Each host can cache only a small fraction of the network and must rely on flooding to acquire information that has not been locally cached. To constrain flooding, the network uses a cooperative caching model where every node provides its route cache contents to others when they flood. Given the host memory capacity limitations, we are faced with the problem of allocating destinations to caches in an efficient manner. We propose the class of best state/best cost (BSBC) cooperative caching algorithms that aim to minimize the overall network search effort.

Efficient use of energy while providing an adequate level of connection to individual sessions is of paramount importance in multi-hop wireless networks. Energy efficiency and connection quality depend on mechanisms that span several communication layers due to the existing co-channel interference among competing flows that must reuse the limited radio spectrum. Although independent consideration of these layers simplifies the system design, it is often insufficient for wireless networks when the overall system performance is examined carefully. The multi-hop wireless extensions and the need for routing users' sessions from source to the destination only intensify this point of view. In this work, we present a framework for cross-layer design towards energy-efficient communication. Our approach is characterized by a synergy between the physical and the medium access control (MAC) layers with a view towards inclusion of higher layers as well. More specifically, we address the joint problem of power control and scheduling with the objective of minimizing the total transmit power subject to the end-to-end quality of service (QoS) guarantees for sessions in terms of their bandwidth and bit error rate guarantees. Bearing to the NP-hardness of this combinatorial optimization problem, we propose our heuristic solutions that follow greedy approaches.

We study the downlink scheduling problem in a cellular wireless network. The base stations are equipped with antenna arrays and can transmit to more than one mobile user at any time instant, provided the users are spatially separable. In previous work, an infinite traffic demand model is used to study the physical layer beamforming and power control algorithms that maximize the system throughput. We consider finite user traffic demands. A scheduling policy makes a decision based on both the queue lengths and the spatial separability of the users. The objective of the scheduling algorithm is to maintain the stability of the system. We derive an optimal scheduling policy that maintains the stability of the system if it is stable under any scheduling policy. However, this optimal scheduling policy is exponentially complex in the number of users which renders it impractical. We propose four heuristic scheduling algorithms that have polynomial complexity. The first two algorithms are for the special case of single cell systems, while the other two algorithms deal with multiple cell systems. Using a realistic multipath wireless channel model, we evaluate the performance of the proposed algorithms through computer simulations. The results demonstrate the benefits of joint consideration of queue length and dynamic base station assignment.

This paper proposes an algorithm for design and on the fly modification of the schedule of a wireless ad hoc network for provision of fair service guarantees under topological changes. The primary objective is to derive a distributed coordination method for schedule construction and modification for any wireless ad-hoc network operating under a schedule where transmissions at each slot are explicitly specified over a time period of length T. We first introduce a fluid model of the system where the conflict avoidance requirements of neighboring links are relaxed while the aspect of local channel sharing is captured. In this model we propose an algorithm where the nodes asynchronously re-adjust the rates allocated to their adjacent links using only local information. We prove that, from any initial condition, the algorithm finds the max-min fair rate allocation in the fluid model. Hence, if the iteration is performed constantly the rate allocation will track the optimal even in regimes of constant topology changes. Then we consider the slotted system and propose a modification method that applies directly on the slotted schedule, emulating the effect of the rate re-adjustment iteration of the fluid model. Through extensive experiments in networks with both fixed and time varying topologies we show that the latter algorithm achieves balanced rate allocations in the actual slotted system that are very close to the max-min fair rates. The experiments also show that the algorithm is very robust on topology variations, with very good tracking properties of the max-min fair rate allocation.

We study fairness in allocating bandwidth for loss-tolerant real-time multicast applications. We assume that the traffic is encoded in several layers so that the network can adapt to the available bandwidth and receiver processing capabilities by varying the number of layers delivered. We consider the case where receivers cannot subscribe to fractional layers. Therefore, the network can allocate only a discrete set of bandwidth to a receiver, whereas a continuous set of rates can be allocated when receivers can subscribe to fractional layers. Fairness issues differ vastly in these two different cases. Computation of lexicographic optimal rate allocation becomes NP-hard in this case, while lexicographic optimal rate allocation is polynomial complexity computable when fractional layers can be allocated. Furthermore, maxmin fair rate vector may not exist in this case. We introduce a new notion of fairness, maximal fairness. Even though maximal fairness is a weaker notion of fairness, it has many intuitively appealing fairness properties. For example, it coincides with lexicographic optimally and maxmin fairness, when maxmin fair rate allocation exists. We propose a polynomial complexity algorithm for computation of maximally fair rates allocated to various source-destination pairs, which incidentally computes the maxmin fair rate allocation, when the latter exists.

We consider the problem of scheduling packets over channels with time-varying quality. This problem has received a lot of attention lately in the context of devising methods for providing quality of service in wireless communications. Earlier work dealing with this problem considered two cases. One case is that the arrival rate vector is in the throughput region and then policies that stabilize the system are pursued. The other case is that all packet queues are saturated and then policies that optimize an objective function of the channel throughputs are investigated. In this paper, we address the case where no assumption on the arrival rates is made. We obtain a scheduling policy that maximizes the weighted sum of channel throughputs. Under the optimal policy, in the general case, the system may operate in a regime where some queues are stable, while the other become saturated. If stability for the whole system is at all possible, it is always achieved. The optimal policy is a combination of a criterion that gives priorities based on queue lengths and a strict priority rule. The scheduling mechanism switches between the two criteria based on thresholds on the queue lengths and is modulated by the availability of the channels. The analysis of the operation of the system involves the study of a vector process which in steady state has some of its components stable while others are unstable. We adopt a novel model for time-varying channel availability that dispenses with the statistical assumptions and makes a rigorous description of system dynamics possible.

A routing problem in static wireless ad hoc networks is considered as it arises in a rapidly deployed, sensor based, monitoring system known as the wireless sensor network. Information obtained by the monitoring nodes needs to be routed to a set of designated gateway nodes. In these networks, every node is capable of sensing, data processing, and communication, and operates on its limited amount of battery energy consumed mostly in transmission and reception at its radio transceiver. If we assume that the transmitter power level can be adjusted to use the minimum energy required to reach the intended next hop receiver then the energy consumption rate per unit information transmission depends on the choice of the next hop node, i.e., the routing decision. We formulate the routing problem as a linear programming problem, where the objective is to maximize the network lifetime, which is equivalent to the time until the network partition due to battery outage. Two different models are considered for the information-generation processes. One assumes constant rates and the other assumes an arbitrary process. A shortest cost path routing algorithm is proposed which uses link costs that reflect both the communication energy consumption rates and the residual energy levels at the two end nodes. The algorithm is amenable to distributed implementation. Simulation results with both information-generation process models show that the proposed algorithm can achieve network lifetime that is very close to the optimal network lifetime obtained by solving the linear programming problem.

A new algorithm for routing of messages in ad-hoc networks where the nodes are energy-constrained is presented. The routing objective is to maximize the total number of messages that can be successfully sent over the network without knowing any information regarding future message arrivals or message generation rates. From a theoretical perspective, we show that if admission control of messages is permitted, then the worst-case performance of our algorithm is within a factor of O(log(network size)) of the best achievable solution. In other words, our algorithm achieves a logarithmic competitive ratio. Our approach provides sound theoretical backing for several observations that have been made by previous researchers. From a practical perspective, we show by extensive simulations that the performance of the algorithm is very good even in the absence of admission control (the admission control being necessary only to prove the competitive ratio result), and that it also performs better than previously proposed algorithms for other suggested metrics such as network lifetime maximization. Our algorithm uses a single shortest path computation, and is amenable to efficient implementation. We also evaluate by simulations the performance impact of inexact knowledge of residual battery energy, and the impact of energy drain due to dissemination of residual energy information.

Recent advances in the area of wireless communications have revealed the emerging need for efficient wireless access in personal, local and wide area networks. Space division multiple access (SDMA) with smart antennas at the base station is recognized as a promising means of increasing system capacity and supporting rate-demanding services. However, the existence of SDMA at the physical layer raises significant issues at higher layers. In this paper, we attempt to capture the impact of SDMA on channel allocation at the media access control (MAC) layer. This impact obtains different forms in TDMA, CDMA and OFDMA access schemes, due to the different cochannel and interchannel interference instances, as well as the different effect of corresponding channels (time slots, codes or subcarrier frequencies) on user channel characteristics. We follow a unified approach for these multiple access schemes and propose heuristic algorithms to allocate channels to users and adjust down-link beamforming vectors and transmission powers, with the objective to increase achievable system rate and provide QoS to users in the form of minimum rate guarantees. We consider the class of greedy algorithms, based on criteria such as minimum induced or received interference and minimum signal-to-interference ratio (SIR), as well as the class of SIR balancing algorithms. Our results indicate that this cross-layer approach yields significant performance benefits and that SIR balancing algorithms achieves the best performance.

Service discovery is an integral part of the ad hoc networking to achieve stand-alone and self-configurable communication networks. In this paper, we discuss possible service discovery architectures along with the required network support for their implementation, and we propose a distributed service discovery architecture which relies on a virtual backbone for locating and registering available services within a dynamic network topology. Our proposal consists of two independent components: (i) formation of a virtual backbone and (ii) distribution of service registrations, requests, and replies. The first component creates a mesh structure from a subset of a given network graph that includes the nodes acting as service brokers and a subset of paths (which we refer as virtual links) connecting them. Service broker nodes (SBNs) constitute a dominating set, i.e. all the nodes in the network are either in this set or only one-hop away from at least one member of the set. The second component establishes subtrees rooted at service requesting nodes and registering servers for efficient dissemination of the service discovery probing messages. Extensive simulation results are provided for comparison of performance measures. i.e. latency, success rate, and control message overhead, when different architectures and network support mechanisms are utilized in service discovery.

In this paper, we use fluid model techniques to establish some new results for the throughput of input-buffered switches. In particular, we introduce a new class of deterministic maximal size matching algorithms that achieves 100% throughput. Dai and Prabhakar (2000) has shown that any maximal size matching algorithm with speedup of 2 achieves 100% throughput. We introduce a class of maximal size matching algorithms that we call them maximum node containing matching (MNCM) algorithms, and prove that they have 100% throughput with no speedup. We also introduce a new weighted matching algorithm, maximum first matching (MFM) with complexity O(N2.5) that belongs to MNCM. MFM, to the best of our knowledge, is the lowest complexity deterministic algorithm that delivers 100% throughput. The only assumption on the input traffic is that it satisfies the strong law of large numbers. Besides throughput, average delay is the other key performance metric for the input-buffered schedulers. We use simulation results to compare and study the delay performance of MFM. The simulation results demonstrate promising delay performance for MFM.

The problem of scheduling packets over a number of channels with time varying connectivity is considered. Policies proposed for this problem either stabilize the system when the arrival rates are within the stability region, or optimize an objective function under the assumption that all channel queues are saturated. We address the realistic situation where it is not known a priori whether the channel queues are saturated or not, and provide a scheduling policy that maximizes the weighted sum of channel throughputs. We employ a burstiness-constrained channel model that allows us to dispense of statistical assumptions and simplifies the proofs.

Web sites disseminate some of their information to surrogate caches in order to reduce the latency observed during the delivery of the information. The surrogates classify publishers under several classes with respect to their willingness-to-pay. Surrogate partitions the total cache capacity among different classes to provide loose version of quality of service. In our model, publishers try to get as large cache space as possible, while the surrogate is required to achieve fair allocation among the publishers. Specifically, each publisher should be charged the same if they receive equal share of caching space. We determine the optimal pricing strategy of the surrogate maximizing its revenue. We also analyzed the competition between surrogates under this model and determined the condition that leads to a Nash equilibrium. We showed that at equilibrium surrogates peer with each other as if there is a single combined surrogate server.

In this paper, we consider the transport capacity of ad hoc networks with a random flat topology under the present support of an infinite capacity infrastructure network. Such a network architecture allows ad hoc nodes to communicate with each other by purely using the remaining ad hoc nodes as their relays. In addition, ad hoc nodes can also utilize the existing infrastructure fully or partially by reaching any access point (or gateway) of the infrastructure network in a single or multi-hop fashion. Using the same tools as in [1], we show that the per source node capacity of T(W/log(N)) can be achieved in a random network scenario with the following assumptions: (i) The number of ad hoc nodes per access point is bounded above, (ii) each wireless node, including the access points, is able to transmit at W bits/sec using a fixed transmission range, and (iii) N ad hoc nodes, excluding the access points, constitute a connected topology graph. This is a significant improvement over the capacity of random ad hoc networks with no infrastructure support which is found as T(W/vN log(N)) in [1]. Although better capacity figures may be obtained by complex network coding or exploiting mobility in the network, infrastructure approach provides a simpler mechanism that has more practical aspects. We also show that even when less stringent requirements are imposed on topology connectivity, a per source node capacity figure that is arbitrarily close to T(1) cannot be obtained. Nevertheless, under these weak conditions, we can further improve per node throughput significantly.

Directional antennas in ad hoc networks offer many benefits compared with classical omnidirectional antennas. The most important include significant increase of spatial reuse, coverage range and subsequently network capacity as a whole. On the other hand, the use of directional antennas requires new approach in the design of a MAC protocol to fully exploit these benefits. Unfortunately, directional transmissions increase the hidden terminal problem, the problem of deafness and the problem of determination of neighbors' location. In this paper we propose a new MAC protocol that deals effectively with these problems while it exploits in an efficient way the advantages of the directional antennas. We evaluate our work through simulation study. Numerical results show that our protocol offers significant improvement compared to the performance of omni transmissions.

The use of smart antennas in extending coverage range and capacity of wireless networks dictates the employment of novel media access control protocols, with which the base station (BS) or access point (AP) provides access to users by learning their locations. We consider the class of protocols that employ beam forming and use contention-based or contention-free polling methods to locate users residing in or out of coverage range of the AP. Such protocols allow rapid media access and can be embedded in existing MAC protocols.

Utilizing optical technologies to build packet fabrics for high-capacity switches and routers has several advantages in terms of scalability, power consumption, and cost. However, several technology related problems have to be overcome to be able to use such an approach. The reconfiguration times of optical crossbars are longer than those of electronic fabrics and end-to-end clock recovery in such systems add to the reconfiguration overheads. Both these problems can limit the efficiency of optical packet fabrics. In addition, existing work on input-buffered switches mostly assumes fixed size packets (referred as envelopes in this paper). When fixed size switching is used for Internet protocol networks where packets are of variable size, the incoming packets need to be fragmented to fit the fixed size envelopes. This fragmentation can lead to, possibly large loss of bandwidth and even instability. This paper addresses all of the above issues by presenting packetization and scheduling techniques that allow optical packet fabrics to be used within switches and routers. The proposed scheme aggregates multiple packets in a single envelope and when used in combination with proper scheduling algorithms, it can provide system stability as well as bandwidth and delay guarantees. As a result of the aggregation method, the reconfiguration frequency required from the optics is reduced, facilitating the use of optical technologies in implementing packet switch fabrics.

Caches have been used extensively to store the most popular/recent requested data to improve the user latency and reduce the network load. Recently, a more systematic approach to caching has been developed within the framework of content delivery networks (CDN). A CDN is the network of caches, where the caches are geographically distributed and serve user requests on behalf of the subscriber Web sites. Users receive the requested information from the caching servers, which are closer to the users and usually much less loaded than the origin server. The objective is to minimize the user latency by intelligently distributing the content and serving the user requests from the most efficient sites. We realistically model the agents in a CDN with selfish self-maximizing behaviors and define the problem as a noncooperative game. We separate the distribution and routing subproblems and use games to solve each. We show that the subproblems have equilibrium solutions and, if the equilibrium of a subproblem is unique, we achieve the global optimum for that subproblem. We also determine that a unique equilibrium is reached if the content providers are not willing to pay high amounts and the cache sizes are sufficiently small. We noticed that the global system optimum requires the content providers to pay very high amounts, which in practice may prohibit the applicability of the distributed method. Thus, we consider an Investment strategy for the content providers, which maximizes the publishers' net benefits and leads to a near-optimum system solution. We also show that the joint distribution and routing game has an equilibrium and demonstrate its performance by numerical examples.

We investigate the behavior of the various transmission control protocol (TCP) algorithms over wireless links with correlated packet losses. For such a scenario, we show that the performance of NewReno is worse than the performance of Tahoe in many situations and even OldTahoe in a few situations because of the inefficient fast recovery method of NewReno. We also show that random loss leads to significant throughput deterioration when either the product of the square of the bandwidth-delay ratio and the loss probability when in the good state exceeds one, or the product of the bandwidth-delay ratio and the packet success probability when in the bad state is less than two. The performance of Sack is always seen to be the best and the most robust, thereby arguing for the implementation of TCP-Sack over the wireless channel. We also show that, under certain conditions, the performance depends not only on the bandwidth-delay product but also on the nature of timeout, coarse or fine. We have also investigated the effects of reducing the fast retransmit threshold.

QoS provisioning to users in the presence of volatility of the wireless channel is the most challenging issue in wireless system design. We consider the problem of scheduling constant bit rate (CBR) traffic packets over the wireless channel, subject to packet delivery deadline constraints. We cast the problem as a Markov decision process and derive the optimal scheduling policy, in the sense of minimizing long-term packet loss due to deadline expirations. Performance bounds and design guidelines for general scheduling algorithms are obtained through analysis and simulations.

The increasing popularity of wireless broadband access in local and wide area networks is the main expression of the need for flexible and ubiquitous wireless connectivity. In order to satisfy user resource requirements in the presence of volatility of the wireless medium, sophisticated multiple access and adaptation techniques are required, which alleviate channel impairments and increase system throughput. The use of multiple antennas at the base station allows intra-cell channel reuse by multiple spatially separable users through space division multiple access (SDMA) and hence enhances cell capacity. However, the employment of antennas in the physical layer raises significant issues in the medium access control (MAC) layer. We investigate the impact of antenna arrays on MAC layer channel allocation in the context of orthogonal frequency division multiplexing (OFDM), which is the predominantly proposed signaling scheme for wireless broadband access. We propose an algorithm to allocate channels to users based on their spatial separability properties, while appropriately adjusting beamforming weights and transmission rates for each user in a channel. The unified consideration of such adaptive techniques yields significant throughput benefits.

We consider scheduling policies for maxmin fair allocation of bandwidth in wireless ad hoc networks. We formalize the maxmin fair objective under wireless scheduling constraints. We propose a fair scheduling which assigns dynamic weights to the flows such that the weights depend on the congestion in the neighborhood and schedule the flows which constitute a maximum weighted matching. It is possible to prove analytically that this policy attains both short term and long term fairness. We consider more generalized fairness notions, and suggest mechanisms to attain these objectives.

In multirate multicasting, different users (receivers) within the same multicast group can receive service at different rates, depending on the user requirements and the network congestion level. Compared with unirate multicasting, this provides more flexibility to the user and allows more efficient usage of the network resources. We address the rate control problem for multirate multicast sessions, with the objective of maximizing the total receiver utility. This aggregate utility maximization problem not only takes into account the heterogeneity in user requirements, but also provides a unified framework for diverse fairness objectives. We propose an algorithm for this problem and show, through analysis and simulation, that it converges to the optimal rates. In spite of the nonseparability of the problem, the solution that we develop is completely decentralized, scalable and does not require the network to know the receiver utilities. The algorithm requires very simple computations both for the user and the network, and also has a very low overhead of network congestion feedback.

We propose a new multicast routing and scheduling algorithm called multipurpose multicast routing and scheduling algorithm (MMRS). The routing policy load balances among various possible routes between the source and the destinations, basing its decisions on the message queue lengths at the source node. The scheduling is such that the flow of a session depends on the congestion of the next hop links. MMRS is throughput optimal. In addition, it has several other attractive features. It is computationally simple and can be implemented in a distributed, asynchronous manner. It has several parameters which can be suitably modified to control the end-to-end delay and packet loss in a topology-specific manner. These parameters can be adjusted to offer limited priorities to some desired sessions. MMRS is expected to play a significant role in end-to-end congestion control in the multicast scenario.

The beamforming problem is studied in wireless networks where both the transmitters and receivers have linear adaptive antenna arrays. Algorithms are proposed that find the antenna array weight vectors at both the transmitters and receivers as well as the transmitter powers with one of the following two objectives: (1) to maximize the minimum signal-to-interference-and-noise ratio (SINR) over all receivers and (2) to minimize the sum of the total transmitted power satisfying the SINR requirements at all links. A numerical study is performed to compare the network capacity and the power consumption among systems having a different number of antenna array elements in a code division multiple access network.

In the near future, existing terrestrial radio networks are envisioned to integrate with satellite systems in order to provide global coverage. In order to establish communication for both nonhand-held and hand-held user terminals, the radio link design must allow full- and half-duplex operation, respectively, where the latter is desirable when radiation power restrictions are imposed. In addition, due to user mobility and wireless channel volatility, sophisticated resource management is required, so as to enhance system capacity. However, a major inherent problem of the satellite link is propagation delay, which may lead to inefficient resource allocation and reduced spectral efficiency. We address the resource allocation problem that arises in the context of a medium-Earth-orbit (MEO) satellite system with half-duplex communication capabilities. MEO satellite systems are characterized by large propagation delays and large intrabeam delay variations, which are shown to result in resource consumption. We propose a channel classification scheme, in which the available carriers are partitioned into classes and each class is associated with a range of propagation delays to the satellite. The suggested infrastructure results in better channel utilization and reduced call blocking rate and can be implemented with low signaling load.

Wireless broadband access is becoming increasingly popular in the telecommunications market due to the projected demand for high data-rate connections. Given the inherent volatility of the wireless channel, the accurate estimation of channel conditions and the adoption of sophisticated adaptation techniques is required, so that transmission parameters are selected based on link quality, and user throughput is maximized. We consider a simple wireless link monitoring method, which is based on counting positive and negative acknowledgments (ACKs and NACKs), and we investigate the class of adaptation policies that correspond to this method. The policy that maximizes the long-term throughput efficiency of a user is shown to be of threshold type. Due to inherent difficulties in realization of this policy, a suboptimal heuristic method to perform link adaptation is provided. Our results indicate a considerable improvement in link throughput under such adaptive techniques.

Wireless broadband access is an appealing solution to the projected trend towards reliable and easily deployable high-speed connections. In order to enhance system capacity and tolerate volatility of the wireless medium, sophisticated adaptation techniques are required. In this paper, we consider the problem of efficient resource allocation with adaptive modulation techniques in a multi-carrier wireless cellular system. We identify the inherent complexity of the problem and propose a heuristic algorithm for carrier frequency assignment to users, based on channel quality. The algorithm leads to an efficient allocation, in the sense that each user is assigned to a carrier and occupies the least number of channels (timeslots). Simulation results show that the algorithm leads to high link utilization and low blocking rate for a wide range of traffic loads and interference levels.

Multirate multicasting, where the receivers of a multicast group can receive service at different rates, is an efficient mode of data delivery for many real-time applications. We address the problem of achieving rates that maximize the total receiver utility for multirate multicast sessions. This problem not only takes into account the heterogeneity in user requirements, but also provides a unified framework for diverse fairness objectives. We propose two algorithms and prove that they converge to the optimal rates for this problem. The algorithms are distributed and scalable, and do not require the network to know the receiver utilities. We discuss how these algorithms can be implemented in a real network, and also demonstrate their convergence through simulation experiments.

We consider the rate control problem with the objective of maximizing the total user utility. It takes into account the possible differences in user requirements, and also provides a framework for achieving a wide range of fairness objectives. We propose a simple algorithm for achieving the optimal rates for this problem. The algorithm can be implemented in a distributed way and does not require the network to know the user utility functions. In our algorithm, the network communicates to the user the number of congested links on the user's path, and the user (end-host) adjusts its rate accordingly, taking into account its utility function and the network congestion feedback. We show through analysis and experimentation that our algorithm converges to the optimum rates.

Wireless broadband access is becoming increasingly popular in the telecommunications market due to the projected demand for flexible and easily deployable high, speed connections. In order to adhere to the volatility of the wireless medium, the adoption of sophisticated adaptation techniques is required. We investigate the problem of enhancing channel throughput by performing resource assignment and reuse with adaptation of physical layer parameters. We propose an algorithm to allocate channels to users with different rate requirements, while appropriately adjusting the modulation level and transmission power, based on instantaneous channel quality. Our algorithm constructs the cochannel set of users in a sequential manner, by utilizing a criterion which is based on the induced and received amounts of interference for a user and the contribution in throughput increase. Although illustrated in the context of TDMA/TDD, the proposed technique can be applied in systems which support different multiple access and signaling schemes with orthogonal channels (e.g. OFDMA, CDMA). Our results indicate a considerable increase in throughput per utilized channel under such adaptive techniques.

Wireless ad hoc networks have been a growing area of research. While there has been considerable research on the topic of routing in such networks, the topic of topology creation has not received due attention. This is because almost all ad hoc networks to date have been built on top of a single channel, broadcast based wireless media, such as 802.11 or IR LANs. For such networks the distance relationship between the nodes implicitly (and uniquely) determines the topology of the ad hoc network. Bluetooth is a promising new wireless technology, which enables portable devices to form short-range wireless ad hoc networks and is based on a frequency hopping physical layer. This fact implies that hosts are not able to communicate unless they have previously discovered each other by synchronizing their frequency hopping patterns. Thus, even if all nodes are within direct communication range of each other, only those nodes which are synchronized with the transmitter can hear the transmission. To support any-to-any communication, nodes must be synchronized so that the pairs of nodes (which can communicate with each other) together form a connected graph. Using Bluetooth as an example, this paper first provides deeper insights into the issue to link establishment in frequency hopping wireless systems. It then introduces the Bluetooth topology construction protocol (BTCP), an asynchronous distributed protocol for constructing scatternets which starts with nodes that have no knowledge of their surroundings and terminates with the formation of a connected network satisfying all connectivity constraints posed by the Bluetooth technology. To the best of our knowledge, the work presented in this paper is the first attempt at building Bluetooth scatternets using distributed logic and is quite "practical" in the sense that it can be implemented using the communication primitives offered by the Bluetooth 1.0 specifications.

We study fair allocation of resources in multicast networks with multirate capabilities. In multirate transmission, the session source hierarchically encodes its signal and the receivers subscribe to the appropriate number of layers. The objective of the network is to distribute the layers fairly. This can be attained either by computing the fair rates first, and then using a scheduling policy to attain the fair rates, or by using a scheduling policy which allocates the fair rates without computing them explicitly. The first requires knowledge of system parameters like link bandwidth, which are not generally known to the link schedulers. The second approach is more realistic. We present a scheduling policy which allocates the fair rates without computing them beforehand. We have presented analytical and experimental results demonstrating the fairness of the resulting rate allocation. In addition to guaranteeing the fair rates, this policy confines the packet losses to enhancement layers, and protects the more important base layers, when there is shortage of bandwidth. Furthermore, this policy does not require any knowledge of traffic statistics, is computationally simple, and is essentially local information based.

One of the prominent objectives of National/Global Information Infrastructure is to provide all types of users global access to information. Satellite broadcast data delivery has inherent advantages, such as scalability and location independent availability, in achieving this objective. However, users need expensive and cumbersome equipment to receive and transmit satellite signals. Furthermore, as the amount of information being broadcast increases, average user latency increases as well. Often, users in a geographical locality have similar interests, which can be better served by employing a local broadcast schedule. In this context, a two stage satellite terrestrial wireless broadcast system can provide more efficient service in terms of lower average user latency and cheaper and more convenient user equipment. In such a system, the main server broadcasts information via satellite to the geographically distributed local ground stations. Every ground station has limited buffer capacity to store the data broadcast by the satellite. According to their buffer content, and the interests of their users, local stations deliver the information to their users via terrestrial wireless channel. We develop novel methods for the joint cache management and scheduling problem encountered in these systems. Our results demonstrate that such two stage systems are feasible and they can provide more efficient data delivery compared to the single stage systems.

The concept of tracking fluid policies by packetized policies is extended to input queueing switches. It is considered that the speedup of the switch is one. One of the interesting applications of the tracking policy in TDMA satellite switches is elaborated. For the special case of 2/spl times/2 switches, it is shown that a tracking nonanticipative policy always exists. It is found that, in general, nonanticipative policies do not exist for switches with more than two input and output ports. For the general case of N/spl times/N switches, a heuristic tracking policy is provided. The heuristic algorithm is based on two notions: port tracking and critical links. These notions can be employed in the derivation of other heuristic tracking policies as well. Simulation results show the usefulness of the heuristic algorithm and the two basic concepts it relies on.

Overlapping cell coverage areas come into play in all mobile cellular systems, especially in small-cell, high-capacity microcellular networks. Calls arising in the overlap area have access to channels of more than one base station and can select the appropriate base station to establish a connection. In this case the problems of base station and channel assignment arise jointly. We address the joint problem in a linear cellular network and prove that its solution reduces to that of a plain channel allocation problem in an equivalent linear network, after executing a sequential load balancing algorithm. The algorithm achieves optimal performance in terms of number of utilized channels and can serve as a benchmark policy for more general channel allocation procedures.

An ad-hoc network of wireless static nodes is considered as it arises in a rapidly deployed, sensor-based, monitoring system. Information is generated in certain nodes and needs to reach a set of designated gateway nodes. Each node may adjust its power within a certain range that determines the set of possible one hop away neighbors. Traffic forwarding through multiple hops is employed when the intended destination is not within immediate reach. The nodes have limited initial amounts of energy that is consumed at different rates depending on the power level and the intended receiver. We propose algorithms to select the routes and the corresponding power levels such that the time until the batteries of the nodes drain-out is maximized. The algorithms are local and amenable to distributed implementation. When there is a single power level, the problem is reduced to a maximum flow problem with node capacities and the algorithms converge to the optimal solution. When there are multiple power levels then the achievable lifetime is close to the optimal (that is computed by linear programming) most of the time. It turns out that in order to maximize the lifetime, the traffic should be routed such that the energy consumption is balanced among the nodes in proportion to their energy reserves, instead of routing to minimize the absolute consumed power.

We study fairness in arbitrary networks with multicast capabilities. Multicast traffic in Internet and ATM provides a motivation for studying these networks. A study of fairness in multicast networks poses several interesting problems, e.g., the issue of inter-session fairness in addition to that of inter-session fairness in unicast networks. We develop a mathematical framework to model the fair allocation of bandwidth in multirate multicast networks with minimum and maximum rate constraints. We present distributed algorithms for computation of maxmin fair rates allocated to various source-destination pairs.

We study fairness when receivers in a multicast network can not subscribe to fractional layers. This case arises when the source hierarchically encodes its signal and the hierarchical structure is predetermined. Unlike the case of the fractional layer allocation, which has been studied extensively in (Sarkar and Tassiulas, 1999), bandwidth can be allocated in discrete chunks only. Fairness issues become vastly different. Computation of lexicographic optimal rate allocation becomes NP-hard in this case, while lexicographic optimal rate allocation is polynomial complexity computable when fractional layers can be allocated. Furthermore, the maxmin fair rate vector may not exist in this case. We introduce a new notion of fairness, maximal fairness. We propose a polynomial complexity algorithm for computation of maximally fair rates allocated to various source-destination pairs. Even though maximal fairness is a weaker notion of fairness, it coincides with lexicographic optimality and maxmin fairness, when maxmin fair rate allocation exists. So the algorithm for computing maximally fair rate allocation computes maxmin fair rate allocation, when the latter exists.

The concept of tracking policies for fluid policies is extended to input queueing switches. It is considered that the speed up of the switch is 1. For the special case of 2/spl times/2 switches it is shown that tracking policy always exists. One of the interesting applications of the tracking policy in TDMA satellite switches is elaborated upon. For the general case of N/spl times/N switches a heuristic tracking policy is provided. The heuristic algorithm is based on two notions of port tracking and critical links. These notions can be employed in derivation of other heuristic tracking policies as well. Simulation results present the usefulness of the heuristic algorithm and the two basic concepts it relies upon.

Proximity awareness in Bluetooth technology is implemented via an asymmetric point to point "sender-receiver" protocol where "senders" are trying to discover "receivers" in the vicinity. This paper tries to shed some light on the link formation delay by first identifying the delay bottlenecks in the asymmetric neighborhood discovery process and then discussing the factors that affect certain parameter decisions. A symmetric technique for establishing ad hoc connectivity is introduced which imposes each node to alternate between the "sender" and "receiver" state in a random fashion. The results show that the connection establishment delay can be reduced if appropriate decisions are made in the choice of parameters.

Low Earth orbit satellite networks can augment terrestrial wireless networks to provide global broadband services to users regardless of the users' locations. Delivering QoS guarantees to the users of LEO satellite networks is complicated since the footprints of the LEO satellites move as the satellites traverse their orbits, and thus, causing frequent user handovers between the satellites. Traffic on inter-satellite links of a particular satellite change as the user traffic served by the satellite changes with the satellite's mobility. The change in user traffic on the inter-satellite links may cause violation of QoS requirements of on-going calls. We propose a novel routing algorithm called the predictive routing protocol (PRP), that exploits the predictive nature of the LEO satellite topology to maximize the total number of users served by the system, while maintaining each user's QoS requirements. The PRP predicts the user traffic load on the inter-satellite links up to a short time in the future by using the deterministic knowledge of the LEO satellite topology, and user location information. The PRP determines multiple paths for a particular connection that effectively help avoid possible future bottlenecks as predicted by estimated future traffic on the inter-satellite links. The algorithm is compared with other non-predictive routing protocols such as IP routing by extensive simulations and it is shown that PRP can deliver deterministic QoS guarantees (such as delay jitter), without over-reserving channel bandwidth. An admission control curve has also been obtained which may be used to ensure that the desired QoS metrics may be guaranteed.

The problem of fair bandwidth sharing among adaptive (TCP) and non-adaptive (i.e. CBR-UDP) flows at an Internet gateway is considered. An algorithm that drops packet preventively, in an attempt to actively penalize the non-adaptive traffic that attempts to "steal" buffer space, and therefore bandwidth from the adaptive traffic flows, is presented. The algorithm maintains minimal flow state information and is therefore scalable. The performance of the algorithm is compared with other gateway algorithms and it is shown that, in the presence of non-adaptive traffic, it achieves a more balanced bandwidth allocation among the different flows. The behavior of a flow subjected to the given algorithm has also been analysed in detail.

We propose a new multicast routing and scheduling algorithm called multipurpose multicast routing and scheduling algorithm (MMRS). The routing policy load balances amongst various possible routes between the source and the destinations, basing its decisions on the message queue lengths at the source node. The scheduling amongst various sessions sharing links is devised such that the flow of a session depends on the congestion of the next hop links. MMRS is throughput optimal and computationally simple. It can be implemented in a distributed, asynchronous manner. It has several parameters which can be suitably modified to control the end to end delay, packet loss in a topology specific manner. These parameters can be adjusted to offer limited priorities to some desired sessions. MMRS is expected to play a significant role in end to end congestion control in the multicast scenario.

In this paper, we investigate the behavior of the various algorithms of TCP, the internet data transport protocol, over wireless links with correlated packet losses.For such a scenario, we show that the performance of NewReno is worse than the performance of Tahoe in many situations and even OldTahoe in a few situations on account of the inefficient fast recovery method of NewReno.We also show that random loss leads to sign.%csnt throughput deterioration when either the product of the square of the bandwidth-delay ratio and the loss probability when in the good state exceeds 1 or the product of the bandwidth-delay ratio and the packet success probability when in the bad state is less than two.The performance of Sack is always seen to be the best and the most robust thereby arguing for the implementation of TCP SACK over the wireless channel.We also show that under certain conditions the performance depends not only on the bandwidth-delay product but also on the nature of timeout whether coarse or fine.We have also investigated the effects of reducing the fast retransmit threshold. introduction

In this paper we have studied the behavior of the different TCP algorithms on mobiles operating at different speeds. In order to do this we first describe a method of modelling the behavior of the various TCP versions analytically. We show that TCP Sack has the best performance of all the TCP versions in terms of link utilization thereby arguing for the widespread implementation of TCP Sack. We also show that at low speeds Tahoe is a better option compared to New Reno while at high speeds the performance of Sack and New Reno is similar. We then use the analytical model to characterize the loss probability region for high link utilization. From this we also derive approximate conditions on the buffer sizes at various speeds for which the link utilization will be high. Thus we show that at low speeds the performance is sensitive to the buffer size within certain limits. On the other hand at high speeds changing buffer capacity does not lead to much change in the link utilization.

A general model of a virtual circuit network consisting of a number of servers and a number of traffic classes is considered. A traffic class is identified by the sequence of servers that should be visited and the corresponding service rates before a message (customer) of the class leaves the network. The following cases are distinguished: (1) the messages need nonpreemptive service; (2) the service of a message can be preempted at any time; (3) pipelining of the service in a sequence of servers is allowed; and (4) pipelining is not allowed. All of these cases arise in different transmission switching techniques and scheduling schemes. A fluid model that emerges when both preemption and pipelining are allowed is considered. Scheduling schemes in the fluid model are compared with corresponding ones in the network with nonpreemptive service and no pipelining. The problem of evacuating the network from an initial backlog without further arrival is identified in the fluid model. Based on that, a policy with nearly optimal evacuation time is identified for the store-and-forward case. Finally, scheduling with deadlines is considered and it is shown that in the fluid model, the evacuation problem is equivalent to a linear programming problem. The evacuation times under different work-conserving policies are considered in specific examples.

A resource allocation model that has within its scope a number of computer and communication network architectures was introduced by Tassiulas and Ephremides (1992) and scheduling methods that achieve maximum throughput were proposed. Those methods require the solution of a complex optimization problem at each packet transmission time and as a result they are not amenable to direct implementations. We propose a class of maximum throughput scheduling policies for the model introduced by Tassiulas and Ephremides that have linear complexity and can lead to practical implementations. They rely on a randomized, iterative algorithm for the solution of the optimization problem arising in the scheduling, in combination with an incremental updating rule. The proposed policy is of maximum throughput under some fairly general conditions on the randomized algorithm.

Article Joint broadcast scheduling and user's cache management for efficient information delivery Share on Authors: Chi-Jiun Su Hughes Network Systems, 11717 Exploration Lane, Germantown MD Hughes Network Systems, 11717 Exploration Lane, Germantown MDView Profile , Leandros Tassiulas Electrical Engineering Department, University of Maryland, College Park MD Electrical Engineering Department, University of Maryland, College Park MDView Profile Authors Info & Claims MobiCom '98: Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networkingOctober 1998 Pages 33–42https://doi.org/10.1145/288235.288246Online:25 October 1998Publication History 17citation338DownloadsMetricsTotal Citations17Total Downloads338Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Joint power control and beamforming schemes are proposed for cellular systems where adaptive arrays are used only at base stations. In the uplink, mobile power and receiver diversity combining vectors at the base stations are calculated jointly. The mobile transmitted power is minimized, while the signal-to-interference-and-noise ratio (SINR) at each link is maintained above a threshold. A transmit diversity scheme for the downlink is also proposed where the transmit weight vectors and downlink power allocations are jointly calculated such that the SINR at each mobile is above a target value. The proposed algorithm achieves a feasible solution for the downlink if there is one and minimizes the total transmitted power in the network. In a reciprocal network it can be implemented in a decentralized system, and it does not require global channel response measurements. In a nonreciprocal network, where the uplink and downlink channel responses are different, the proposed transmit beamforming algorithm needs to be implemented in a centralized system, and it requires a knowledge of the downlink channel responses. The performances of these algorithms are compared with previously proposed algorithms through numerical studies.

The interference reduction capability of antenna arrays and the power control algorithms have been considered separately as means to increase the capacity in wireless communication networks. The minimum variance distortionless response beamformer maximizes the signal-to-interference-and-noise ratio (SINR) when it is employed in the receiver of a wireless link. In a system with omnidirectional antennas, power control algorithms are used to maximize the SINR as well. We consider a system with beamforming capabilities in the receiver, and power control. An iterative algorithm is proposed to jointly update the transmission powers and the beamformer weights so that it converges to the jointly optimal beamforming and transmission power vector. The algorithm is distributed and uses only local interference measurements. In an uplink transmission scenario, it is shown how base assignment can be incorporated in addition to beamforming and power control, such that a globally optimum solution is obtained. The network capacity and the saving in mobile power are evaluated through numerical study.

Broadcast data delivery is encountered in many applications where there is a need to disseminate information to a large user community in a wireless asymmetric communication environment. We consider the problem of scheduling the data broadcast such that the access latency experienced by the users is low. In a push-based system, where the users cannot place requests directly to the server and the broadcast schedule should be determined based solely on the access probabilities, we formulate a deterministic dynamic optimization problem, the solution of which provides the optimal broadcast schedule. Properties of the optimal solution are obtained and then we propose a suboptimal dynamic policy which achieves mean access latency close to the lower bound. The policy has low complexity, it is adaptive to changing access statistics, and is easily generalized to multiple broadcast channels. In a pull-based system where the users may place requests about information items directly to the server, the scheduling can be based on the number of pending requests for each item. Suboptimal policies with good performance are obtained in this case as well. Finally, it is demonstrated by a numerical study that as the request generation rate increases, the achievable performance of the pull- and push-based systems becomes almost identical.

Data broadcasting has been considered as a promising way of disseminating information to a massive number of users in a wireless communication environment. In a broadcast data delivery system, there is a server which is broadcasting data to a user community. Due to the lack of communication from the users to the server, the server cannot know what a user needs. In order to access a certain item, a user has to wait until the item appears in the broadcast. The waiting time will be considerably long if the server's broadcast schedule does not match the user's access needs. If a user has a local memory, it can alleviate its access latency by selectively prefetching the items from the broadcast and storing them in the memory. A good memory management strategy can substantially reduce the user's access latency, which is a major concern in a broadcast data delivery system. An optimal memory management policy is identified that minimizes the expected aggregate latency. We present optimal memory update strategies with limited look ahead as implementable approximations of the optimal policy. Some interesting special cases are given for which the limited look-ahead policies are optimal. We also show that the same formulation can be used to find the optimal memory management policy which minimizes the number of deadline misses when users generate information requests which have to be satisfied within some given deadlines.

The worst case length of a tour for the Euclidean traveling salesman problem produced by the nearest neighbor (NN) heuristic is studied in this paper. Nearest neighbor tours for a set of arbitrarily located points in the d-dimensional unit cube are considered. A technique is developed for bounding the worst case length of a tour. It is based on identifying sequences of {\it coverings} of $[0,1]^d$. Each covering ${\cal P}_k$ consists of sets $C_i$, with diameter bounded by the {\it diameter} $D({\cal P}_k)$ of the covering. For every sequence of coverings a bound is obtained that depends on the cardinality of the coverings and their diameters. The task of bounding the worst case length of an NN tour is reduced to finding appropriate sequences of coverings. Using coverings produced by the rectangular lattice with appropriately shrinking diameter, it is shown that the worst case length of an NN tour through N points in $[0,1]^d$ is bounded by $[d\sqrt{d}/(d-1)] N^{(d-1)/d}+o(N^{(d-1)/d})$. For the unit square the tighter bound $2.482\sqrt{N}+o(\sqrt{N})$ is obtained using regular hexagonal lattice coverings.

We consider a slotted ring that allows simultaneous transmissions of messages by different nodes, known as ring with spatial reuse. To alleviate fairness problems that arise in such networks, policies have been proposed that operate in cycles and guarantee that a certain number of packets, not exceeding a given number called a quota, will be transmitted by every node in every cycle. We provide sufficient and necessary stability conditions that implicitly characterize the stability region for such rings. These conditions are derived by extending a technique developed for some networks of queues satisfying a monotonicity property. Our approach to instability is novel and its peculiar property is that it is derived from the instability of a dominant system. Interestingly, the stability region depends on the entire distribution of the message arrival process and the steady-state average cycle lengths of lower dimensional systems, leading to a region with nonlinear boundaries, the exact computation of which is in general intractable. Next, we introduce the notions of essential and absolute stability region. An arrival rate vector belongs to the former region if the system is stable under any arrival distribution with this arrival vector, while it belongs to the latter if there exists some distribution with this rate vector for which the system is stable. Using a linear programming approach, we derive bounds for these stability regions that depend only on conditional average cycle lengths. For the case of two nodes, we provide closed-form expressions for the essential stability region.

A communication network with tine-varying topology is considered. The network consists of M receivers and N transmitters that, in principle, may access every receiver. An underlying network state process with Markovian statistics is considered that reflects the physical characteristics of the network affecting the link service capacity. The transmissions are scheduled dynamically, based on information about the link capacities and the backlog in the network. The region of achievable throughputs is characterized. A transmission scheduling policy is proposed that utilizes current topology state information and achieves all throughput vectors achievable by any anticipative policy. The changing topology model applies to networks of low-Earth orbit (LEO) satellites, meteor-burst communication networks, and networks with mobile users.

The provision of personal communication services is the goal of the evolution of integrated communication systems. The fundamental problem underlying any phase (hand-off, new connection, etc.) of a dynamic resource allocation algorithm in a wireless network is to assign transmission powers, forward (downstream) and reverse (upstream) channels, and base stations such that every mobile of the system can establish a connection. Each one of these problems separately has been studied extensively. We consider the joint problem in a system with two base stations. An algorithm that achieves the optimal assignment is provided. It involves the computation of a maximum matching in a graph that captures the topological characteristics of the mobile locations. The traffic capacities, in terms of expected number of connections per channel, of the forward and reverse channel are obtained and compared, for both cases of power control and nonpower control. It turns out that when the transmission power is fixed, the capacities of the forward and reverse channel are different, while when power control is allowed they are the same. For systems with two mobiles the capacities of the forward and reverse channels are studied analytically. Finally, several versions of the two-way channel assignment problem are studied.

A virtual circuit network with arbitrary topology is considered. The traffic streams follow prespecified routes, different in general for each stream, to reach their destination. A fluid traffic model is adopted and a processor sharing service discipline is considered. A policy is proposed for setting adaptively the fractions of the transmission capacity, which is allocated to the different traffic streams in the processor sharing discipline at each link. The amount of traffic arrived at the originating node of each link is measured for each stream. The fraction of the link capacity allocated to each stream is set to be proportional to the measured traffic. The traffic is measured continuously and the fractions are updated regularly based on the most recent traffic measurements. It is shown that eventually, the transmission capacity allocated to each stream converges to a quantity proportional to the average rate of the stream. Hence, if the capacity condition is satisfied, sufficient fractions of the capacity are allocated at each link for each stream. End-to-end performance guarantees are provided, if the traffic is regulated. The policy is distributed since each link adjusts the service fractions based on observations of the traffic arriving at its originating node only. Furthermore, it is adaptive since no information on the traffic characteristics is needed for the application of the policy.

We consider the ring network with spatial reuse. Traffic streams may enter and exit the network at any node. We adopt an arrival traffic model with deterministic constraints on its sample paths, which conforms to the output traffic of a leaky bucket rate control mechanism. A transmission policy specifies each time at which the traffic stream will be transmitted at the outgoing link by each node. We provide an upper bound on the asymptotic backlog of the ring that holds for all work-conserving policies and is independent of the initial conditions. This bound remains finite as long as the maximum load of every link is less than one. The latter condition is also necessary for the existence of an asymptotic bound that is independent of the initial conditions.

A unidirectional ring network is considered. A node may transmit at most one packet per slot to its downstream neighbor. Potentially all nodes may transmit at the same slot. The achievable performance is studied and policies are proposed for both the evacuation mode and continual operation. In the evacuation mode each node has initially an amount of packets destined for every other node of the ring, and no more packets are generated later. It is shown that the furthest destination first (FDF) policy, that gives priority to the packet with the longest way to go at each node, minimizes the time until every packet reaches its destination. Furthermore it is shown that the closest destination first (CDF) policy, that gives priority to the packet with the shortest way to go at each node, minimizes the average packet delivery time. A formula for the optimal evacuation time is obtained. The continual operation of the ring is considered then where packets are generated according to some arrival process. For any arrival sample path, the PDF maximizes the fraction of the time at which the ring is empty. The performance analysis of individual origin-destination traffic streams under FDF is facilitated based on the following. For each traffic stream, a single server priority queue is identified such that the average sojourn time of the traffic stream in the ring is equal to the aggregate transmission time plus the queueing delay of the low priority stream in the queue. Formulas for the sojourn time are obtained for iid arrivals. The performance of CDF and FIFO in continual operation is studied by simulation. It turns out that the CDF, minimum delay policy for the evacuation, has the worst performance in continual operation.>

Reuse partitioning is a technique for providing more efficient spectrum reuse in cellular radio systems. A cell in such a system is divided into concentric zones, each associated with an overlaid cell plan. Calls that arise in the periphery of the cell have fewer channels in their availability than those arising close to the base station and therefore they experience higher blocking rates. The authors consider the problem of balancing uniformly the blocking probability throughout the cell offering a fair treatment to the whole area within the cell, by controlling the allocation do the different channel layers. A policy that minimizes the maximum blocking probability experienced at any location of the cell is identified and is shows to be of threshold type, An adaptive scheme that adjusts the threshold based on estimates of the blocking probabilities in the different zones of the cell as proposed. This scheme tracks the optimal threshold effectively without any knowledge of the traffic parameters. Simulation study shows that substantial capacity improvements care achieved by the application of the optimal channel assignment policy, over the uncontrolled system.>

A service discipline for the link transmission scheduling in a virtual circuit network with arbitrary topology is proposed. The scheduling of each link is based on the traffic at its origin node only. It is performed in two levels operating in different time scales. In the slower time scale the fraction of the link capacity to be allocated to the different streams is updated such that it remains proportional to the traffic of each stream that has arrived at the origin node of the link. In the faster time scale the link capacity is shared, such that the fractions of the capacity allocated by the bandwidth allocation policy are honored. A fluid traffic model is considered and processor sharing service is assumed. The policy is analyzed and it is shown that the existence of rates of the arrival streams guarantees that the allocated bandwidth at each stream will converge to a value larger than the link load. When the arrival streams are regulated to satisfy certain burstiness constraints, then the backlog at each network node is bounded as well.>

Considers a ring network with spatial reuse. Traffic streams may enter and exit the network at any node. The burstiness of each traffic stream is bounded by a deterministic bound. A transmission policy specifies at each time which traffic stream will be transmitted at the outgoing link by each node. The authors provide an upper bound on the asymptotic backlog of the ring that holds for all work-conserving policies and is independent of the initial conditions. This bound remains finite as long as the maximum load of every link is less than one. The latter condition is also necessary for the existence of an asymptotic bound that is independent of the initial conditions.>

Reuse partitioning is a technique for providing more efficient spectrum reuse in cellular radio systems. A cell in such a system is divided into concentric zones, each associated with an overlaid cell plan. Calls that arise in the periphery of the cell have fewer channels in their availability than those arising close to the base station and therefore they experience higher blocking rates. In this paper we consider the problem of balancing uniformly the blocking probability throughout the cell offering a fair treatment to the whole area within the cell, by controlling the allocation to the different channel layers. A policy that minimizes the maximum blocking probability experienced at any location of the cell is identified and is shown to be of threshold type. The policy satisfies any achievable constraint on the blocking rate uniformly throughout the cell. An adaptive scheme that adjusts the threshold based on estimates of the blocking probabilities in the different zones of the cell is proposed. This scheme tracks the optimal threshold effectively without any knowledge of the traffic parameters. Simulation study shows that substantial capacity improvements are achieved by the application of the optimal channel assignment policy, over the uncontrolled system.>

Study the problem of optimal buffer space priority control in an ATM network node. The buffer of a transmission link is shared among the cells of several traffic classes waiting for transmission through the link. When the number of cells to be stored in the buffer exceeds the available buffer space, certain cells have to be dropped. Different traffic classes have different sensitivities to cell losses. By appropriately selecting the classes of cells which are dropped or blocked in case of overflow, one can have the more sensitive classes suffering smaller cell losses. Depending on the control that on the system, three classes of policies are distinguished. In each one, policies that schedule the buffer allocation in some optimal manner are identified.>

The problem of optimal buffer space priority control in an asynchronous transfer mode (ATM) network node is studied. The buffer of a transmission link is shared among the cells of several traffic classes waiting for transmission through the link. When the number of cells to be stored in the buffer exceed the available buffer space, certain cells have to be dropped. Different traffic classes have different sensitivities to cell losses. By appropriate selection of the classes of cells that are dropped in case of overflow, the more sensitive classes can be made to suffer smaller cell losses. Arriving cells might be blocked from entering the system or they may be dropped after they are already in the buffer. Depending on the control that is on the system, three classes of policies are distinguished. In each one, policies that schedule the buffer allocation in some optimal manner are identified.>

Consider N parallel queues competing for the attention of a single server. At each time slot each queue may be connected to the server or not depending on the value of a binary random variable, the connectivity variable. Allocation at each slot; is based on the connectivity information and on the lengths of the connected queues only. At the end of each slot, service may be completed with a given fixed probability. Such a queueing model is appropriate for some communication networks with changing topology. In the case of infinite buffers, necessary and sufficient conditions are obtained for stabilizability of the system in terms of the different system parameters. The allocation policy that serves the longest connected queue stabilizes the system when the stabilizability conditions hold. The same policy minimizes the delay for the special case of symmetric queues. In a system with a single buffer per queue, an allocation policy is obtained that maximizes the throughput and minimizes the delay when the arrival and service statistics of different queues are identical.>

A multihop packet radio network is considered with a single traffic class and given end-to-end transmission requirements. A transmission schedule specifies at each time instant the set of links which are allowed to transmit. The purpose of a schedule is to prevent interference among transmissions from neighboring links. Given amounts of information are residing initially at a subset of the network nodes and must be delivered to a prespecified set of destination nodes. The transmission schedule that evacuates the network in minimum time is specified. The decomposition of the problem into a pure routing and a pure scheduling problem is crucial for the characterization of the optimal transmission schedule.>