Optimizing routing in Wireless Sensor Networks (WSNs) is pivotal for minimizing energy consumption and extending network lifetime. This paper introduces a resource-efficient compilation method for distributed quantum circuits tailored to address large-scale WSN routing problems. Leveraging a hybrid classical-quantum framework, we employ spectral clustering for network partitioning and the Quantum Approximate Optimization Algorithm (QAOA) for optimizing routing within manageable subgraphs. We formulate the routing problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, providing comprehensive mathematical formulations and complexity analyses. Comparative evaluations against traditional classical algorithms demonstrate significant energy savings and enhanced scalability. Our approach underscores the potential of integrating quantum computing techniques into wireless communication networks, offering a scalable and efficient solution for future network optimization challenges.

In this work, we introduce a Distributed Quantum Long Short-Term Memory (QLSTM) framework that leverages modular quantum computing to address scalability challenges on Noisy Intermediate-Scale Quantum (NISQ) devices. By embedding variational quantum circuits into LSTM cells, the QLSTM captures long-range temporal dependencies, while a distributed architecture partitions the underlying Variational Quantum Circuits (VQCs) into smaller, manageable subcircuits that can be executed on a network of quantum processing units. We assess the proposed framework using nontrivial benchmark problems such as damped harmonic oscillators and Nonlinear Autoregressive Moving Average sequences. Our results demonstrate that the distributed QLSTM achieves stable convergence and improved training dynamics compared to classical approaches. This work underscores the potential of modular, distributed quantum computing architectures for large-scale sequence modeling, providing a foundation for the future integration of hybrid quantum-classical solutions into advanced Quantum High-performance computing (HPC) ecosystems.

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.

The increasing complexity of deep neural networks poses significant barriers to democratizing AI to resource-limited edge devices. To address this challenge, split federated learning (SFL) has emerged as a promising solution that enables device-server co-training through model splitting. However, although system optimization substantially influences the performance of SFL, the problem remains largely uncharted. In this paper, we first provide a unified convergence analysis of SFL, which quantifies the impact of model splitting (MS) and client-side model aggregation (MA) on its learning performance, laying a theoretical foundation for this field. Based on this convergence bound, we introduce AdaptSFL, an adaptive SFL framework to accelerate SFL under resource-constrained edge computing systems. Specifically, AdaptSFL adaptively controls MS and client-side MA to balance communication-computing latency and training convergence. Extensive simulations across various datasets validate that our proposed AdaptSFL framework takes considerably less time to achieve target accuracy than existing benchmarks.

Unmanned aerial vehicle (UAV) crowdsensing (UCS) is an emerging data collection paradigm to provide reliable and high quality urban sensing services, with age-of-information (AoI) requirement to measure data freshness in real-time applications. In this paper, we explicitly consider the case to ensure that the attained AoI always stay within a specific threshold. The goal is to maximize the total amount of collected data from diverse Point-of-Interests (PoIs) while minimizing AoI and AoI threshold violation ratio under limited energy supplement. To this end, we propose a decentralized multi-agent deep reinforcement learning framework called “DRL-UCS($\text {AoI}_{th}$)” for multi-UAV trajectory planning, which consists of a novel transformer-enhanced distributed architecture and an adaptive intrinsic reward mechanism for spatial cooperation and exploration. Extensive results and trajectory visualization on two real-world datasets in Beijing and San Francisco show that, DRL-UCS($\text {AoI}_{th}$) consistently outperforms all nine baselines when varying the number of UAVs, AoI threshold and generated data amount in a timeslot.

Mobile crowdsensing (MCS) by unmanned aerial vehicles (UAVs) servicing delay-sensitive applications becomes popular by navigating a group of UAVs to take advantage of their equipped high-precision sensors and durability for data collection in harsh environments. In this paper, we aim to simultaneously maximize collected data amount, geographical fairness, and minimize the energy consumption of all UAVs, as well as to guarantee the data freshness by setting a deadline in each timeslot. Specifically, we propose a centralized control, distributed execution framework by decentralized deep reinforcement learning (DRL) for delay-sensitive and energy-efficient UAV crowdsensing, called “DRL-eFresh”. It includes a synchronous computational architecture with GRU sequential modeling to generate multi-UAV navigation decisions. Also, we derive an optimal time allocation solution for data collection while considering all UAV efforts and avoiding much data dropout due to limited data upload time and wireless data rate. Simulation results show that DRL-eFresh significantly improves the energy efficiency, as compared to the best baseline DPPO, by 14% and 22% on average when varying different sensing ranges and number of PoIs, respectively.

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.

The growing demand for edge computing resources, particularly due to increasing popularity of Internet of Things (IoT), and distributed machine/deep learning applications poses a significant challenge. On the one hand, certain edge service providers (ESPs) may not have sufficient resources to satisfy their applications according to the associated service-level agreements. On the other hand, some ESPs may have additional unused resources. In this paper, we propose a resource-sharing framework that allows different ESPs to optimally utilize their resources and improve the satisfaction level of applications subject to constraints such as communication cost for sharing resources across ESPs. Our framework considers that different ESPs have their own objectives for utilizing their resources, thus resulting in a multi-objective optimization problem. We present an${N}$-person Nash Bargaining Solution (NBS) for resource allocation and sharing among ESPs with Pareto optimality guarantee. Furthermore, we propose a distributed, primal-dual algorithm to obtain the NBS by proving that the strong-duality property holds for the resultant resource sharing optimization problem. Using synthetic and real-world data traces, we show numerically that the proposed NBS based framework not only enhances the ability to satisfy applications’ resource demands, but also improves utilities of different ESPs.

While reinforcement learning has achieved considerable successes in recent years, state-of-the-art models are often still limited by the size of state and action spaces. Model-free reinforcement learning approaches use some form of state representations and the latest work has explored embedding techniques for actions, both with the aim of achieving better generalization and applicability. However, these approaches consider only states or actions, ignoring the interaction between them when generating embedded representations. In this work, we establish the theoretical foundations for the validity of training a reinforcement learning agent using embedded states and actions. We then propose a new approach for jointly learning embeddings for states and actions that combines aspects of model-free and model-based reinforcement learning, which can be applied in both discrete and continuous domains. Specifically, we use a model of the environment to obtain embeddings for states and actions and present a generic architecture that leverages these to learn a policy. In this way, the embedded representations obtained via our approach enable better generalization over both states and actions by capturing similarities in the embedding spaces. Evaluations of our approach on several gaming, robotic control, and recommender systems show it significantly outperforms state-of-the-art models in both discrete/continuous domains with large state/action spaces, thus confirming its efficacy.

Understanding the movement patterns of humans and vehicles traveling in a city is important for many applications like emergency evacuation and rescue, as well as city planning and management. In this paper, we aim to predict citywide crowd flows within a period in the future to give aid to urban management, through modeling spatiotemporal patterns of recent crowd flows. We present a novel deep model for this task, called "AttConvLSTM", which leverages a convolutional LSTM (ConvLSTM), Convolutional Neural Networks (CNNs) along with an attention mechanism, where ConvLSTM keeps spatial information as intact as possible during sequential analysis, and the attention mechanism can focus important crowd flow variations which cannot be identified by the recurrent module. We conducted extensive experiments for performance evaluation using three large datasets, including Beijing Taxi dataset, Rome Taxi dataset, and Chengdu Didi chauffeuring trace. The experimental results show that AttConvLSTM significantly outperforms several widely-used baselines in terms of Root Mean Squared Error (RMSE), and Mean Average Percentage Error (MAPE), indicating that our approach can deal with crowd flows with different dynamics in both spatial and temporal domains, and make valid predictions several steps ahead.

Mobile crowdsensing (MCS) represents a new sensing paradigm that utilizes the smart mobile devices to collect and share data. Traditional MCS systems mainly leverages the people carried smartphones and other wearable devices which are constrained by the limited sensing capability and battery power. With the popularity of unmanned vehicles like unmanned aerial vehicles (UAVs) and driverless cars, they can provide much more reliable, accurate and cost-efficient sensing services due to to their equipped more powerful sensors. In this paper, we propose a distributed control framework for energy-efficient and DIstributed VEhicle navigation with chaRging sTations, called “e-Divert”. It is a distributed multi-agent deep reinforcement learning (DRL) solution, which uses a convolutional neural network (CNN) to extract useful spatial features as the input to the actor-critic network to produce a real-time action. Also, e-Divert incorporates a distributed prioritized experience replay for better exploration and exploitation, and a long short-term memory (LSTM) enabled N-step temporal sequence modeling module. The solution fully explores the spatiotemporal nature of the considered scenario for better vehicle cooperation and competition between themselves and charging stations, to maximize the energy efficiency, data collection ratio, geographic fairness, and minimize the energy consumption simultaneously. Through extensive simulations, we find an appropriate set of hyperparameters that achieve the best performance, i.e., 5 actors in Ape-X architecture, priority exponent 0.5, and LSTM sequence length 3. Finally, we compare with four baselines including one state-of-the-art approach MADDPG. Results show that our proposed e-Divert significantly improves the energy efficiency, as compared to MADDPG, by 3.62 and 2.36 times on average when varying different numbers of vehicles and charging stations, respectively.

Mobile edge computing seeks to provide resources to different delay-sensitive applications. This is a challenging problem as an edge cloud-service provider may not have sufficient resources to satisfy all resource requests. Furthermore, allocating available resources optimally to different applications is also challenging. Resource sharing among different edge cloud-service providers can address the aforementioned limitation as certain service providers may have resources available that can be “rented” by other service providers. However, edge cloud service providers can have different objectives orutilities. Therefore, there is a need for an efficient and effective mechanism to share resources among service providers, while considering the different objectives of various providers. We model resource sharing as a multi-objective optimization problem and present a solution framework based onCooperative Game Theory(CGT). We consider the strategy where each service provider allocates resources to its native applications first and shares the remaining resources with applications from other service providers. We prove that for a monotonic, non-decreasing utility function, the game is canonical and convex. Hence, thecoreis not empty and the grand coalition is stable. We propose two algorithms,Game-theoretic Pareto optimal allocation(GPOA) andPolyandrous-Polygamous Matching based Pareto Optimal Allocation(PPMPOA) that provide allocations from the core. Hence the obtained allocations areParetooptimal and the grand coalition of all the service providers is stable. Experimental results confirm that our proposed resource sharing framework improves utilities of edge cloud-service providers and application request satisfaction.

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. In such 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 impossible to justify the necessity of complicated strategies. In this regard, we analyse and quantify how the performance enhancement of distributed SDN architectures is influenced by inter-domain synchronization levels, in terms of the resulting number of abstracted routing clusters, and network structural properties. Based on a generic network model incorporating link preference for path constructions, we establish analytical lower bounds for quantifying the routing performance under any arbitrarily given network synchronization status. The significance of these performance bounds is that they can be used to quantify the contribution of controller synchronization levels in improving the network performance under different network parameters, which therefore serves as a fundamental guidance for future SDN performance analysis and protocol designs.

Edge machine learning involves the deployment of learning algorithms at the network edge to leverage massive distributed data and computation resources to trainartificial intelligence(AI) models. Among others, the framework offederated edge learning(FEEL) is popular for its data-privacy preservation. FEEL coordinates global model training at an edge server and local model training at devices that are connected by wireless links. This work contributes to the energy-efficient implementation of FEEL in wireless networks by designing jointcomputation-and-communication resource management($\mathrm {C}^{2}$RM). The design targets the state-of-the-art heterogeneous mobile architecture where parallel computing using both CPU and GPU, calledheterogeneous computing, can significantly improve both the performance and energy efficiency. To minimize the sum energy consumption of devices, we propose a novel$\mathrm {C}^{2}$RM framework featuring multi-dimensional control including bandwidth allocation, CPU-GPU workload partitioning and speed scaling at each device, and$\mathrm {C}^{2}$time division for each link. The key component of the framework is a set of equilibriums in energy rates with respect to different control variables that are proved to exist among devices or between processing units at each device. The results are applied to designing efficient algorithms for computing the optimal$\mathrm {C}^{2}$RM policies faster than the standard optimization tools. Based on the equilibriums, we further design energy-efficient schemes for device scheduling and greedy spectrum sharing that scavenges “spectrum holes” resulting from heterogeneous$\mathrm {C}^{2}$time divisions among devices. Using a real dataset, experiments are conducted to demonstrate the effectiveness of$\mathrm {C}^{2}$RM on improving the energy efficiency of a FEEL system.

Reinforcement Learning (RL) based methods have seen their paramount successes in solving serial decision-making and control problems in recent years. For conventional RL formulations, Markov Decision Process (MDP) and state-action-value function are the basis for the problem modeling and policy evaluation. However, several challenging issues still remain. Among most cited issues, the inefficiency in utilizing training data is an important factor that causes difficulties in accurately approximating the state-action-value function. We observe that although actions directly define the agents' behaviors, for many problems the next state after a state transition matters more than the action taken, in determining the return of such a state transition. In this regard, we propose a new learning paradigm, State Action Separable Reinforcement Learning (sasRL), wherein the action space is decoupled from the value function learning process for higher learning efficiency. Then, a light-weight transition model is learned to assist the agent to determine the action that triggers the associated state transition. Our convergence analysis reveals that under certain conditions, the convergence time of sasRL is O(T1/k), where T is the convergence time for updating the value function in the MDP-based formulation and k is a weighting factor. Experiments on several gaming scenarios show that sasRL outperforms state-of-the-art MDP-based RL algorithms by up to 75%.

Distributed computing systems often consist of hundreds of nodes (machines), executing tasks with different resource requirements. Efficient resource provisioning and task scheduling in such systems are non-trivial and require close monitoring and accurate forecasting of the state of the system, specifically resource utilisation at its constituent machines. Two challenges present themselves towards these objectives.First, collecting monitoring data entails substantial communication overhead. This overhead can be prohibitively high, especially in networks where bandwidth is limited. Second, forecasting models to predict resource utilisation should be accurate and also need to exhibit high inference speed. Mission critical scheduling and resource allocation algorithms use these predictions and rely on their immediate availability.To address the first challenge, we present a communication-efficient data collection mechanism. Resource utilisation data is collected at the individual machines in the system and transmitted to a central controller in batches. Each batch is processed by an adaptive data-reduction algorithm based on Fourier transforms and truncation in the frequency domain. We show that the proposed mechanism leads to a significant reduction in communication overhead while incurring only minimal error and adhering to accuracy guarantees. To address the second challenge, we propose a deep learning architecture using complex Gated Recurrent Units to forecast resource utilisation. This architecture is directly integrated with the above data collection mechanism to improve inference speed of the presented forecasting model. Using two real-world datasets, we demonstrate the effectiveness of our approach, both in terms of forecasting accuracy and inference speed.Our approach resolves several challenges encountered in resource provisioning frameworks and can also be generically applied to other forecasting problems.

Federated learning (FL) is an emerging technique for training machine learning models using geographically dispersed data collected by local entities. It includes local computation and synchronization steps. To reduce the communication overhead and improve the overall efficiency of FL, gradient sparsification (GS) can be applied, where instead of the full gradient, only a small subset of important elements of the gradient is communicated. Existing work on GS uses a fixed degree of gradient sparsity for i.i.d.-distributed data within a datacenter. In this paper, we consider adaptive degree of sparsity and non-i.i.d. local datasets. We first present a fairness-aware GS method which ensures that different clients provide a similar amount of updates. Then, with the goal of minimizing the overall training time, we propose a novel online learning formulation and algorithm for automatically determining the near-optimal communication and computation trade-off that is controlled by the degree of gradient sparsity. The online learning algorithm uses an estimated sign of the derivative of the objective function, which gives a regret bound that is asymptotically equal to the case where exact derivative is available. Experiments with real datasets confirm the benefits of our proposed approaches, showing up to 40% improvement in model accuracy for a finite training time.

Spatial crowdsourcing (SC) utilizes the potential of a crowd to accomplish certain location based tasks. Although worker scheduling has been well studied recently, most existing works only focus on the static deployment of workers but ignore their temporal movement continuity. In this paper, we explicitly consider the use of unmanned vehicular workers, e.g., drones and driverless cars, which are more controllable and can be deployed in remote or dangerous areas to carry on long-term and hash tasks as a vehicular crowdsourcing (VC) campaign. We propose a novel deep reinforcement learning (DRL) approach for curiosity-driven energy-efficient worker scheduling, called "DRL-CEWS", to achieve an optimal trade-off between maximizing the collected amount of data and coverage fairness, and minimizing the overall energy consumption of workers. Specifically, we first utilize a chief-employee distributed computational architecture to stabilize and facilitate the training process. Then, we propose a spatial curiosity model with a sparse reward mechanism to help derive the optimal policy in large crowdsensing space with unevenly distributed data. Extensive simulation results show that DRL-CEWS outperforms the state-of-the-art methods and baselines, and we also visualize the benefits curiosity model brings and show the impact of two hyperparameters.

Many image and vision applications require a large amount of data for model training. Collecting all such data at a central location can be challenging due to data privacy and communication bandwidth restrictions. Federated learning is an effective way of training a machine learning model in a distributed manner from local data collected by client devices, which does not require exchanging the raw data among clients. A challenge is that among the large variety of data collected at each client, it is likely that only a subset is relevant for a learning task while the rest of data has a negative impact on model training. Therefore, before starting the learning process, it is important to select the subset of data that is relevant to the given federated learning task. In this paper, we propose a method for distributedly selecting relevant data, where we use a benchmark model trained on a small benchmark dataset that is task-specific, to evaluate the relevance of individual data samples at each client and select the data with sufficiently high relevance. Then, each client only uses the selected subset of its data in the federated learning process. The effectiveness of our proposed approach is evaluated on multiple real-world image datasets in a simulated system with a large number of clients, showing up to 25% improvement in model accuracy compared to training with all data.

In cloud and edge computing systems, computation, communication, and memory resources are distributed across different physical machines and can be used to execute computational tasks requested by different users. It is challenging to characterize the capacity of such a distributed system, because there exist multiple types of resources and the amount of resources required by different tasks is random. In this paper, we define the capacity as the number of tasks that the system can support with a given overload/outage probability. We derive theoretical formulas for the capacity of distributed systems with multiple resource types, where we consider the power of d choices as the task scheduling strategy in the analysis. Our analytical results describe the capacity of distributed computing systems, which can be used for planning purposes or assisting the scheduling and admission decisions of tasks to various resources in the system. Simulation results using both synthetic and real-world data are also presented to validate the capacity bounds.

In distributed software-defined networks (SDN), multiple physical SDN controllers, each managing a network domain, are implemented to balance centralized control, scalability and reliability requirements. In such networking paradigm, controllers synchronize with each other to maintain a logically centralized network view. Despite various proposals of distributed SDN controller architectures, most existing works only assume that such logically centralized network view can be achieved with some synchronization designs, but the question of how exactly controllers should synchronize with each other to maximize the benefits of synchronization under the eventual consistency assumptions is largely overlooked. To this end, we formulate the controller synchronization problem as a Markov Decision Process (MDP) and apply reinforcement learning techniques combined with deep neural network to train a smart controller synchronization policy, which we call the Deep-Q (DQ) Scheduler. Evaluation results show that DQ Scheduler outperforms the anti-entropy algorithm implemented in the ONOS controller by up to 95.2% for inter-domain routing tasks.

Large-scale distributed computing systems often contain thousands of distributed nodes (machines). Monitoring the conditions of these nodes is important for system management purposes, which, however, can be extremely resource demanding as this requires collecting local measurements of each individual node and constantly sending those measurements to a central controller. Meanwhile, it is often useful to forecast the future system conditions for various purposes such as resource planning/allocation and anomaly detection, but it is usually too resource-consuming to have one forecasting model running for each node, which may also neglect correlations in observed metrics across different nodes. In this paper, we propose a mechanism for collecting and forecasting the resource utilization of machines in a distributed computing system in a scalable manner. We present an algorithm that allows each local node to decide when to transmit its most recent measurement to the central node, so that the transmission frequency is kept below a given constraint value. Based on the measurements received from local nodes, the central node summarizes the received data into a small number of clusters. Since the cluster partitioning can change over time, we also present a method to capture the evolution of clusters and their centroids. As an effective way to reduce the amount of computation, time-series forecasting models are trained on the time-varying centroids of each cluster, to forecast the future resource utilizations of a group of local nodes. The effectiveness of our proposed approach is confirmed by extensive experiments using multiple real-world datasets.

In this paper, we present a novel incremental and decremental learning method for the least-squares support vector machine (LS-SVM). The goal is to adapt a pre-trained model to changes in the training dataset, without retraining the model on all the data, where the changes can include addition and deletion of data samples. We propose a provably exact method where the updated model is exactly the same as a model trained from scratch using the entire (updated) training dataset. Our proposed method only requires access to the updated data samples, the previous model parameters, and a unique, fixed-size matrix that quantifies the effect of the previous training dataset. Our approach can significantly reduce the storage requirement of model updating, preserve the privacy of unchanged training samples without loss of model accuracy, and enhance the computational efficiency. Experiments on real-world image dataset validate the effectiveness of our proposed method.

In distributed software-defined networks (SDN), multiple physical SDN controllers, each managing a network domain, are implemented to balance centralised control, scalability, and reliability requirements. In such networking paradigms, controllers synchronize with each other, in attempts to maintain a logically centralised network view. Despite the presence of various design proposals for distributed SDN controller architectures, most existing works only aim at eliminating anomalies arising from the inconsistencies in different controllers' network views. However, the performance aspect of controller synchronization designs with respect to given SDN applications are generally missing. To fill this gap, we formulate the controller synchronization problem as a Markov decision process (MDP) and apply reinforcement learning techniques combined with deep neural networks (DNNs) to train a smart, scalable, and fine-grained controller synchronization policy, called the Multi-Armed Cooperative Synchronization (MACS), whose goal is to maximise the performance enhancements brought by controller synchronizations. Evaluation results confirm the DNN's exceptional ability in abstracting latent patterns in the distributed SDN environment, rendering significant superiority to MACS-based synchronization policy, which are 56% and 30% performance improvements over ONOS and greedy SDN controller synchronization heuristics.

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.

We consider assignment policies that allocate resources to users, where both resources and users are located on a one-dimensional line (0, ∞). First, we consider unidirectional assignment policies that allocate resources only to users located to their left. We propose the Move to Right (MTR) policy, which scans from left to right assigning the nearest available resource located to the right of a user, and contrast it to the Unidirectional Gale-Shapley (UGS) matching policy. While both policies among all unidirectional policies, minimize the expected distance traveled by a request, MTR is fairer. Moreover, we show that when user and resource locations are modeled by statistical point processes, and resources are allowed to satisfy more than one user, the spatial system under unidirectional policies can be mapped into bulk service queueing systems, thus allowing the application of many queueing theory results that yield closed form expressions. As we consider a case where different resources can satisfy different numbers of users, we also generate new results for bulk service queues. We also consider bidirectional policies where there are no directional restrictions on resource allocation and develop an algorithm for computing the optimal assignment which is more efficient than known algorithms in the literature when there are more resources than users. Finally, numerical evaluation of performance of unidirectional and bidirectional allocation schemes yields design guidelines beneficial for resource placement.

Many modern applications in the area of smart computing are based on machine learning techniques. To train machine learning models, a large amount of data is usually required, which is often not readily available at a central location. Federated learning enables the training of machine learning models from distributed datasets at client devices without transmitting the data to a central place, which has benefits including preserving the privacy of user data and reducing communication bandwidth. In this demonstration, we show a federated learning system deployed in an emulated wide-area communications network with dynamic, heterogeneous, and intermittent resource availability, where the network is emulated using a CORE/EMANE emulator. In our system, the environment is decentralized and each client can ask for assistance by other clients. The availability of clients is intermittent so only those clients that are available can provide assistance. A graphical interface illustrates the network connections and the user can adjust these connections through the interface. A user interface displays the training progress and each client's contribution to training.

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.

Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent-based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.

In-Network Processing (INP) is an effective way to aggregate and process data from different sources and forward the aggregated data to other nodes for further processing until it reaches the end user. There is a trade-off between energy consumption for processing data and communication energy spent on transferring the data. An essential requirement in the INP process is to ensure that the user expectation of quality of information (QoI) is delivered during the process. Using wireless sensor networks for illustration and with the aim of minimizing the total energy consumption of the system, we study and formulate the trade-off problem as a nonlinear optimization problem where the goal is to determine the optimal data reduction rate, while satisfying the QoI required by the user. The formulated problem is a Signomial Programming (SP) problem, which is a non-convex optimization problem. We propose two solution frameworks. First, we introduce an equivalent problem which is still SP and non-convex as the original one, but we prove that the strong duality property holds, and propose an efficient distributed algorithm to obtain the optimal data reduction rates, while delivering the required QoI. The second framework applies to the system with identical nodes and parameter settings. In such cases, we prove that the complexity of the problem can be reduced logarithmically. We evaluate our proposed frameworks under different parameter settings and illustrate the validity and performance of the proposed techniques through extensive simulation.

In mobile edge computing, local edge servers can host cloud-based services, which reduces network overhead and latency but requires service migrations as users move to new locations. It is challenging to make migration decisions optimally because of the uncertainty in such a dynamic cloud environment. In this paper, we formulate the service migration problem as a Markov decision process (MDP). Our formulation captures general cost models and provides a mathematical framework to design optimal service migration policies. In order to overcome the complexity associated with computing the optimal policy, we approximate the underlying state space by the distance between the user and service locations. We show that the resulting MDP is exact for the uniform 1-D user mobility, while it provides a close approximation for uniform 2-D mobility with a constant additive error. We also propose a new algorithm and a numerical technique for computing the optimal solution, which is significantly faster than traditional methods based on the standard value or policy iteration. We illustrate the application of our solution in practical scenarios where many theoretical assumptions are relaxed. Our evaluations based on real-world mobility traces of San Francisco taxis show the superior performance of the proposed solution compared to baseline solutions.

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.

Many modern applications generate a significant amount of data in dispersed geographical areas. To analyze and make use of the data, data fusion and machine learning techniques are usually applied, which has the potential to greatly enhance the amount of information extracted from the data. These algorithms traditionally run in data center environments where all the data are available at a central location. It is challenging to run them in distributed coalition environments, where it is impractical to send all the raw data to a single place due to bandwidth and security constraints. This problem has gained notable attention recently. In this paper, we provide an overview of available techniques and recent results of performing data fusion and machine learning in a distributed coalition environment, without sharing the raw data among local processing nodes. We discuss techniques for distributed model training, scoring, and outline some applications where these techniques are applicable and beneficial.

Energy efficiency is a fundamental requirement of modern data communication systems, and its importance is reflected in much recent work on performance analysis of system energy consumption. However, most works have only focused on communication and computation costs, but do not account for caching costs. Given the increasing interest in cache networks, this is a serious limitation. In this paper, we consider the energy consumption trade-off between communication, computation, and caching (C3) under a Quality of Information (QoI) guarantee in a communication network. To attain this goal, we formulate an optimization problem to capture the C3 costs, which turns out to be a non-convex Mixed Integer Non-Linear Programming (MINLP) Problem. We then propose a variant of spatial branch and bound algorithm (V-SBB), that can achieve ε -global optimal solution to the original MINLP. We show numerically that V-SBB is more stable and robust than other candidate MINLP solvers under different network scenarios. More importantly, we observe that the energy efficiency under our C3 optimization framework improves by as much as 88% compared to any C2 optimization between communication and computation or caching.

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.

Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches. We analyze the convergence rate of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.

We consider the problem of optimally compressing and caching data across a communication network. Given the data generated at edge nodes and a routing path, our goal is to determine the optimal data compression ratios and caching decisions across the network in order to minimize average latency, which can be shown to be equivalent to maximizing the compression and caching gain under an energy consumption constraint. We show that this problem is NP-hard in general and the hardness is caused by the caching decision subproblem, while the compression sub-problem is polynomial-time solvable. We then propose an approximation algorithm that achieves a $(1-1/e)$-approximation solution to the optimum in strongly polynomial time. We show that our proposed algorithm achieve the near-optimal performance in synthetic-based evaluations. In this paper, we consider a tree-structured network as an illustrative example, but our results easily extend to general network topology at the expense of more complicated notations.

Vehicular ad-hoc networks (VANETs) that enable communication among vehicles have recently attracted significant interest from researchers, due to the range of practical applications they can facilitate, particularly related to road safety. Despite the stringent performance requirements for such applications, the IEEE 802.11p standard still uses the carrier sensing medium access/collision avoidance (CSMA/CA) protocol. The latter when used in broadcast fashion employs a randomly selected backoff period from a fixed contention window (CW) range, which can cause performance degradation as a result of vehicular density changes. Concerns regarding the robustness and adaptiveness of protocols to support time-critical applications have been raised, which motivate this work. This paper investigates how the maximum CW size can be optimised to enhance performance based on vehicular density. A stochastic model is developed to obtain the optimal maximum CW that can be integrated in an amended CSMA/CA protocol to maximise the single-hop throughput among adjacent vehicles. Simulations confirm our optimised protocol can greatly improve the channel throughput and transmission delay performance, when compared to the standardised CSMA/CA, to support safety application in VANETs.

Vehicular ad-hoc networks (VANETs) that enable communication among vehicles and between vehicles and un- manned aerial vehicles (UAVs) and cellular base stations have re- cently attracted significant interest from the research community, due to the wide range of practical applications they can facilitate (e.g. road safety, traffic management, pollution monitoring and rescue missions). Despite this increased research activity, the high vehicle mobility in a VANET raises concerns regarding the robustness and adaptiveness of such networks to support system applications. Instead of allowing direct communications between every vehicle to UAVs or base stations, clustering methods will potentially be efficient to overcome bandwidth, power consump- tion and other resource issues. Using the clustering technique, neighbouring vehicles are grouped into clusters with a particular vehicle elected as the Custer Head (CH) in each cluster. Each vehicle communicates with UAVs or base stations through the CH of the associated cluster. Despite the potential advantages, a major challenge for clustering techniques is to maintain cluster stability in light of vehicle mobility and radio fluctuation. In this paper, we propose a Stable Clustering Algorithm for vehicular ad hoc networks (SCalE). Two novel features are incorporated into the algorithm: knowledge of the vehicles behaviour for efficient selection of CHs, and the employment of a backup CH to maintain the stability of cluster structures. By simulation methods, these are shown to increase stability and improve performance when compared to existing clustering algorithms.

We consider the problem of placing the minimum number of monitors in a dynamic network to identify additive link metrics from path metrics measured along cycle-free paths between monitors. Our goal is robust monitor placement, i.e., the same set of monitors can maintain network identifiability under topology changes. Our main contribution is a set of monitor placement algorithms with different performance-complexity tradeoffs that can simultaneously identify multiple topologies occurring during the network lifetime. In particular, we show that the optimal monitor placement is the solution to a generalized hitting set problem, for which we provide a polynomial-time algorithm to construct the input and a greedy algorithm to select the monitors with logarithmic approximation. Although the optimal placement is NP-hard in general, we identify non-trivial special cases that can be solved efficiently. Our secondary contribution is a dynamic triconnected decomposition algorithm to compute the input needed by the monitor placement algorithms, which is the first such algorithm that can handle edge deletions. Our evaluations on mobility-induced dynamic topologies verify the efficiency and the robustness of the proposed algorithms.

We investigate the capability of localizing node failures in communication networks from binary states (normal/failed) of end-to-end paths. Given a set of nodes of interest, uniquely localizing failures within this set requires that different observable path states associate with different node failure events. However, this condition is difficult to test on large networks due to the need to enumerate all possible node failures. Our first contribution is a set of sufficient/necessary conditions for identifying a bounded number of failures within an arbitrary node set that can be tested in polynomial time. In addition to network topology and locations of monitors, our conditions also incorporate constraints imposed by the probing mechanism used. We consider three probing mechanisms that differ according to whether measurement paths are: (i) arbitrarily controllable; (ii) controllable but cycle-free; or (iii) uncontrollable (determined by the default routing protocol). Our second contribution is to quantify the capability of failure localization through: 1) the maximum number of failures (anywhere in the network) such that failures within a given node set can be uniquely localized and 2) the largest node set within which failures can be uniquely localized under a given bound on the total number of failures. Both measures in 1) and 2) can be converted into the functions of a per-node property, which can be computed efficiently based on the above sufficient/necessary conditions. We demonstrate how measures 1) and 2) proposed for quantifying failure localization capability can be used to evaluate the impact of various parameters, including topology, number of monitors, and probing mechanisms.

Mobile micro-clouds are promising for enabling performance-critical cloud applications. However, one challenge therein is the dynamics at the network edge. In this paper, we study how to place service instances to cope with these dynamics, where multiple users and service instances coexist in the system. Our goal is to find the optimal placement (configuration) of instances to minimize the average cost overtime, leveraging the ability of predicting future cost parameters with known accuracy. We first propose an offline algorithm that solves for the optimal configuration in a specific look-ahead time-window. Then, we propose an online approximation algorithm with polynomial time-complexity to find the placement in real-time whenever an instance arrives. We analytically show that the online algorithm is 0(1)-competitive for a broad family of cost functions. Afterwards, the impact of prediction errors is considered and a method for finding the optimal look-ahead window size is proposed, which minimizes an upper bound of the average actual cost. The effectiveness of the proposed approach is evaluated by simulations with both synthetic and real-world (San Francisco taxi) usermobility traces. The theoretical methodology used in this paper can potentially be applied to a larger class of dynamic resource allocation problems.

We consider the problem of placing the minimum number of monitors in a communication network with possible topology changes to identify additive link metrics from path metrics. The core of our solution is a suite of robust monitor placement algorithms with different performance-complexity tradeoffs that guarantee network identifiability for the multiple possible topologies. In particular, we show that the optimal (i.e., minimum) monitor placement is the solution to a generalized hitting set problem, where we provide a polynomial-time algorithm to construct the input. Although the optimal placement is NP-hard in general, we identify non-trivial special cases that can be solved efficiently. We further demonstrate how the proposed algorithms can be augmented to handle unpredictable topology changes and tradeoffs between monitor cost and adaptation cost. Our evaluations on mobility-induced dynamic topologies verify the effectiveness and robustness of the proposed algorithms.

Mobile edge clouds (MECs) are small cloud-like infrastructures deployed in close proximity to users, allowing users to have seamless and low-latency access to cloud services. When users move across different locations, their service applications often need to be migrated to follow the user so that the benefit of MEC is maintained. In this paper, we propose a layered framework for migrating running applications that are encapsulated either in virtual machines (VMs) or containers. We evaluate the migration performance of various real applications under the proposed framework.

Optimization models and techniques are often used to achieve efficient allocation of limited network resources to competing demands in communication networks. In this talk, the speaker will give a brief overview of distributed optimization theory, including convex optimization problems for which iterative solution techniques exist and converge. The well-known Transport Control Protocol (TCP) is shown to be equivalent a distributed solution that achieves the optimal allocation of bandwidth in communication networks. As for wireless ad-hoc and sensor networks, each link capacity depends on the transmission power of other links due to co-channel interference. In addition, the quality of multimedia services supported by these networks cannot be represented by a concave function of the amount of allocated bandwidth. These factors unfortunately make the resource allocation problem for the wireless networks become a non-convex optimization problem. New distributed solution techniques will be presented to solve these problems and numerical examples will also be provided.

Data aggregation techniques exploit spatial and temporal correlations among data and aggregate data into a smaller volume as a means to optimize usage of limited network resources including energy. There is a trade-off among the Quality of Information (QoI) requirement and energy consumption for computation and communication. We formulate the energy-efficient data aggregation problem as a non-linear optimization problem to optimize the trade-off and control the degree of information reduction at each node subject to given QoI requirement. Using the theory of duality optimization, we prove that under a set of reasonable cost assumptions, the optimal solution can be obtained despite non-convexity of the problem. Moreover, we propose a distributed, iterative algorithm that will converge to the optimal solution. Extensive numerical results are presented to confirm the validity of the proposed solution approach.

We propose a framework for optimizing in-network processing (INP) in wireless sensor networks. INP provides a platform for processing (e.g., fusing, aggregating or compressing) the data along the transmission routes in the sensor network. This can reduce the volume of transmitted data, therefore optimizing the utilization of energy and bandwidth. However, such data processing must ensure that the end result can meet given QoI requirements. We formulate the QoI-aware INP problem as a non-linear optimization problem to identify the optimal degree of data compression at each sensor node subject to satisfying a QoI requirement for the end-user. The formulation arranges all involved sensor nodes in a tree where data is transfered and processed from nodes to their parent nodes toward the root node of the tree. Under the assumption of uniform parameter setting, we show that the processing tree can be collapsed into a linear graph where the number of nodes represents the node levels of the original processing tree. This represents a significant reduction in complexity of the problem. Numerical example are provided to illustrate the performance of the proposed approach.

This paper investigates group secret key generation problems for different types of wireless networks, by exploiting physical layer characteristics of wireless channels. A new group key generation strategy with low complexity is proposed, which combines the well-established point-to-point pairwise key generation technique, the multisegment scheme, and the one-time pad. In particular, this group key generation process is studied for three types of communication networks: 1) the three-node network; 2) the multinode ring network; and 3) the multinode mesh network. Three group key generation algorithms are developed for these communication networks, respectively. The analysis shows that the first two algorithms yield optimal group key rates, whereas the third algorithm achieves the optimal multiplexing gain. Next, for the first two types of networks, we address the time allocation problem in the channel estimation step to maximize the group key rates. This non-convex max-min time allocation problem is first reformulated into a series of geometric programming, and then, a single-condensation-method-based iterative algorithm is proposed. Numerical results are also provided to validate the performance of the proposed key generation algorithms and the time allocation algorithm.

Recognizing and retrieving multimedia content with movie/TV series actors, especially querying actor-specific videos in large scale video datasets, has attracted much attention in both the video processing and computer vision research field. However, many existing methods have low efficiency both in training and testing processes and also a less than satisfactory performance. Considering these challenges, in this paper, we propose an efficient cloud-based actor identification approach with batch-orthogonal local-sensitive hashing (BOLSH) and multi-task joint sparse representation classification. Our approach is featured by the following: 1) videos from movie/TV series are segmented into shots with the cloud-based shot boundary detection; 2) while faces in each shot are detected and tracked, the cloud-based BOLSH is then implemented on these faces for feature description; 3) the sparse representation is then adopted for actor identification in each shot; and 4) finally, a simple application, actor-specific shots retrieval is realized to verify our approach. We conduct extensive experiments and empirical evaluations on a large scale dataset, to demonstrate the satisfying performance of our approach considering both accuracy and efficiency.

Vehicular ad-hoc networks (VANETs) have received a lot of research and industrial attention, including the approval of the IEEE 802.11p standard. However, resource allocation in the standard still makes use of the traditional mechanisms (e.g., carrier sensing) without exploiting the unique characteristics of VANETs. This provides the motivation for this work. As a first step toward the goal and by considering vehicle density, this paper investigates how transmission probability can be determined to optimise throughput of VANETs. A challenging design issue of VANETs is to deal with node (vehicle) mobility, which causes various vehicular densities within the same network and consequently influences the connectivity and capacity of the network. This work shows that it is indeed possible to follow the dynamics of a network and consequently adapt the transmission probability at the MAC layer to reduce the interference and maximise the single-hop throughput between adjacent nodes. By exploiting the characteristics of VANETs, we introduce approximations in order to derive closed-form expressions of the network throughput and other performance metrics in terms of transmission probability, which would otherwise be impossible. Our extensive simulations validate the approximations and the proposed analytical model thus can serve as a promising tool to improve VANETs performance. For example, the optimal transmission probability can be used to develop efficient MAC protocols using vehicle density estimation in VANETs for our future work.

Seamless computing and data access is enabled by the emerging technology of mobile micro-clouds (MMCs). Different from traditional centralized clouds, an MMC is typically connected directly to a wireless base-station and provides services to a small group of users, which allows users to have instantaneous access to cloud services. Due to the limited coverage area of base-stations and the dynamic nature of mobile users, network background traffic, etc., the question of where to place the services to cope with these dynamics arises. In this paper, we focus on dynamic service placement for MMCs. We consider the case where there is an underlying mechanism to predict the future costs of service hosting and migration, and the prediction error is assumed to be bounded. Our goal is to find the optimal service placement sequence which minimizes the average cost over a given time. To solve this problem, we first propose a method which solves for the optimal placement sequence for a specific look-ahead time-window, based on the predicted costs in this time-window. We show that this problem is equivalent to a shortest-path problem and propose an algorithm with polynomial time-complexity to find its solution. Then, we propose a method to find the optimal look-ahead window size, which minimizes an upper bound of the average cost. Finally, we evaluate the effectiveness of the proposed approach by simulations with realworld user-mobility traces.

Dynamic event detection by using participatory sensing paradigms has received growing interests in recent years, where detection tasks are assigned to smart device users who can potentially collect needed sensory data from the equipped sensors. These data can be utilized to detect interested events like noise, air pollution, or even earthquake. Since most existing solutions focus on centralized detection approaches that, however, usually cause heavy communication overhead, it is strongly desired to design distributed solutions to reduce energy consumption while achieving a high level of detection accuracy. In this paper, we first present a novel Minimum Cut based centralized detection algorithm as the performance benchmark, and then introduce a novel distributed, energy-efficient solution, where an optimization problem is formulated and an optimal solution is derived. Simulations based on a real-trace driven data set in Beijing demonstrate the effectiveness of our proposed algorithms.

The continuously growing number of multimedia applications in current communication networks highlights the necessity for an efficient resource allocation mechanism to capture the unique characteristics of multi-tiered multimedia applications and allocate network capacity in an efficient way. This paper examines the problem of sharing the network throughput under the existence of inelastic traffic flows that follow a multi-tiered utility function. First, the concept of multi-sigmoidal utilities is introduced in order to describe user satisfaction, then, the implications of the use of such utilities are discussed for two different allocation policies; the bandwidth-proportional and the utility-proportional fairness allocation policies. In the former case, the intrinsic reasons of possible network oscillations are analyzed in detail and a heuristic to overcome such situations is proposed. In the latter one, where such oscillations are not possible, efficient ways to calculate a closed form solution for the optimal rate allocation are described. Moreover, a novel mathematical representation of such a multi-sigmoidal utility is presented and closed form solutions for a number of application types are calculated. Finally, the efficiency and robustness of the proposed algorithms is evaluated by simulations for different network topologies and compared against other work in literature.

We study the dynamic service migration problem in mobile edge-clouds that host cloud-based services at the network edge. This offers the benefits of reduction in network overhead and latency but requires service migrations as user locations change over time. It is challenging to make these decisions in an optimal manner because of the uncertainty in node mobility as well as possible non-linearity of the migration and transmission costs. In this paper, we formulate a sequential decision making problem for service migration using the framework of Markov Decision Process (MDP). Our formulation captures general cost models and provides a mathematical framework to design optimal service migration policies. In order to overcome the complexity associated with computing the optimal policy, we approximate the underlying state space by the distance between the user and service locations. We show that the resulting MDP is exact for uniform one-dimensional mobility while it provides a close approximation for uniform two-dimensional mobility with a constant additive error term. We also propose a new algorithm and a numerical technique for computing the optimal solution which is significantly faster in computation than traditional methods based on value or policy iteration. We illustrate the effectiveness of our approach by simulation using real-world mobility traces of taxis in San Francisco.

We investigate the problem of localizing node failures in a communication network from end-to-end path measurements, under the assumption that a path behaves normally if and only if it does not contain any failed nodes. To uniquely localize node failures, the measurement paths must show different symptoms under different failure events, i.e., for any two distinct sets of failed nodes, there must be a measurement path traversing one and only one of them. This condition is, however, impractical to test for large networks. Our first contribution is a characterization of this condition in terms of easily verifiable conditions on the network topology with given monitor placements under three families of probing mechanisms, which differ in whether measurement paths are (i) arbitrarily controllable, (ii) controllable but cycle-free, or (iii) uncontrollable (i.e., determined by the default routing protocol). Our second contribution is a characterization of the maximum identifiability of node failures, measured by the maximum number of simultaneous failures that can always be uniquely localized. Specifically, we bound the maximal identifiability from both the upper and the lower bounds which differ by at most one, and show that these bounds can be evaluated in polynomial time. Finally, we quantify the impact of the probing mechanism on the capability of node failure localization under different probing mechanisms on both random and real network topologies. We observe that despite a higher implementation cost, probing along controllable paths can significantly improve a network's capability to localize simultaneous node failures.

We investigate the problem of placing a given number of monitors in a communication network to identify the maximum number of link metrics from end-to-end measurements between monitors, assuming that link metrics are additive, and measurement paths cannot contain cycles. Motivated by our previous result that complete identification of all link metrics can require a large number of monitors, we focus on partial identification using a limited number of monitors. The basis to our solution is an efficient algorithm for determining all identifiable links for a given monitor placement. Based on this algorithm, we develop a polynomial-time greedy algorithm to incrementally place monitors such that each newly placed monitor maximizes the number of additional identifiable links. We prove that the proposed algorithm is optimal for 2-vertex-connected networks, and demonstrate that it is near-optimal for several real ISP topologies that are not 2-vertex-connected. Our solution provides a quantifiable tradeoff between level of identifiability and available monitor resources.

In-network processing (INP) is a promising method that allows aggregation of data while it is being transferred along the communication paths as a means to optimize the utilization of network resources without violating the quality of information (QoI) requirements. Given the large amount of data existing in dynamic environments, the optimization of INP requires a distributed framework that can adapt easily to network changes and user requirements. In this work, we develop the principle for designing a distributed mechanism in order to determine and control INP. Specifically, the proposed framework can decide, in a distributed way, which nodes along the communication paths optimally perform INP, with consideration of operational energy consumption and QoI requirements for achieving global optimal INP. The significance of the proposed distributed method is that it requires each node to make independent decisions locally for data aggregations, thus naturally enhance robustness and efficiency against network and data load dynamics. Extensive numerical results are presented to confirm the validity of the proposed approach.

Physical layer security has been recognised as a promising technique to realise secure wireless communications. A novel interference masking approach is proposed for secure broadcast scenarios. Specifically, when there is an external jamming node, precoding matrices at the source and jammer are carefully designed to ensure that each destination can detect only its own message, and not the information intended for other destinations which is masked with artificial noise. When there is no external jamming node, interference masking is implemented through user cooperation, in which destinations switch roles from jammer to relay nodes during different time slots. Both analytical and numerical results are provided to demonstrate the performance of the proposed approaches.

This paper considers a general framework of the wiretap channel with helping interference and state information (WT-HI-SI), where a transmitter-receiver pair wishes to keep the message secret from a passive eavesdropper in the presence of an interferer and a random state. The interferer is to help the legitimate transceivers to enhance their security level, and the state information is available at the transmitter, but not at the eavesdropper. For the discrete memoryless WT-HI-SI, an achievable scheme is proposed by combining the noise forward scheme and the double binning coding scheme. Some previously proposed schemes can be viewed as special cases of the proposed scheme. Then, the achievable scheme is applied to two special channels, the Gaussian WT-HI-SI and the Gaussian WT-HI, respectively. For the Gaussian WT-HI-SI, there exists an external random state noncausally available to the transmitter in advance. But for the Gaussian WT-HI, there does not exist any external random state. In this case, we propose a novel achievable scheme that requires the transmitter to artificially generate the random state whose power can be adjusted adaptively according to dynamic channel conditions. Both the analytic and numerical results are provided to demonstrate that the use of the state information can generally improve the secrecy performance. A more important contribution of this paper is that even for the scenario where no external state information is available to the transmitter, the proposed scheme with the artificial state can still achieve a strictly larger secrecy rate in comparison with existing interference assisted schemes.

Cooperative communication has been shown to have great potential in improving wireless link quality. Incorporating cooperative communications in multi-hop wireless networks has been attracting a growing interest. However, most current research focuses on either centralized solutions or schemes limited to specific network problems. In this paper, we propose a distributed framework that uses Network Utility Maximization (NUM) to optimize the following joint objectives: flow control, routing, scheduling, and relay assignment; for multi-hop wireless cooperative networks with general flow and cooperative relay patterns. We define two special graphs, Hyper Forwarding Graphs (HFG) and Hyper Conflict Graphs (HCG), to represent all possible cooperative routing policies and interference relations among the cooperative relays respectively. Based on HFG and HCG, a stochastic mixed-integer non-linear programming problem is formulated. We then propose lightweight algorithms to solve these in a fully distributed manner, and derive the theoretical performance bounds of these proposed algorithms. Simulation results verify our theoretical analysis and reveal the significant performance gains of our framework, in terms of throughput, flexibility, and scalability. To our knowledge, this is the first distributed cross-layer optimization framework for multi-hop wireless cooperative networks with general flow and cooperative relay patterns.

We investigate the problem of identifying individual link metrics in a communication network from end-to-end path measurements, under the assumption that link metrics are additive and constant. To uniquely identify the link metrics, the number of linearly independent measurement paths must equal the number of links. Our contribution is to characterize this condition in terms of the network topology and the number/placement of monitors, under the constraint that measurement paths must be cycle-free. Our main results are: 1) it is generally impossible to identify all the link metrics by using two monitors; 2) nevertheless, metrics of all the interior links not incident to any monitor are identifiable by two monitors if the topology satisfies a set of necessary and sufficient connectivity conditions; 3) these conditions naturally extend to a necessary and sufficient condition for identifying all the link metrics using three or more monitors. We show that these conditions not only facilitate efficient identifiability tests, but also enable an efficient algorithm to place the minimum number of monitors in order to identify all link metrics. Our evaluations on both random and real topologies show that the proposed algorithm achieves identifiability using a much smaller number of monitors than a baseline solution.

We study a data dissemination scenario in which data items are to be transmitted to mobile clients via one of the stationary data access points (APs) that the clients pass by en route to their destinations. The scheduler dedicates sequences of consecutive timeslots of an AP to downloading a data item to a client during the time window in which it is in range, which corresponds to assigning a job (the client's download) to a machine (the AP) among many. The transmission rate chosen for each assignment partly corresponds to setting a machine's speed, but it also has subtler effects. The APs may control transmission power to tune its transmission range making sure that no interference occurs with neighboring APs' transmissions. The problem is a generalization of an already NP-hard parallel-machine scheduling problem in which jobs' release times and deadlines depend on the machine to which they are assigned. We define this joint timeslot, power control, and rate assignment problem formally and apply both new algorithms and adaptations of existing algorithms to it. We evaluate these algorithms through simulations which show that our proposed algorithms achieve near-optimal throughput.

Admission control (AC) is highly important in packet networks with quality-of-service (QoS) requirements since the uncontrolled admission of new data connections can jeopardize the QoS of existing connections and degrade the overall network performance. It requires a priory knowledge of the network capacity, the estimation of which is intricate and complex due to the operational characteristics of various communication protocols, complex network topologies, and dynamic traffic behavior with QoS requirements. In this work, we propose a generic admission control (GAC) methodology where the network conditions between any given ingress-egress node pair are summarized into a single parameter, referred to as the "QoS index". It accounts for various traffic volumes and QoS requirements, like throughput, delay, and packet error rate. Using this QoS index we track the network performance by predicting the potential impact of a new connection admission on the index. The decision depends on whether the predicted value lies below 1. We discuss its wide applicability and feasibility to many types of packet networks, wired or wireless, independent of what communication protocols in use at various layers. We finally validate the proposed GAC methodology by extensive simulations, confirming a significant improvement in terms of network goodput and QoS outage probability compared to other existing statistics-based AC methodologies.

We investigate the problem of identifying individual link performance metrics in a communication network by measuring end-to-end metrics of selected paths between monitors, under the assumption that link metrics are additive and constant during the measurement, and measurement paths cannot contain cycles. In a previous work, we developed an algorithm that places the minimum number of monitors to identify all link metrics. However, even the minimum number can be large in some practical networks (e.g., 60% of all the nodes), suggesting high monitor deployment cost. In this paper, we study the dual problem where given a fixed number of monitors, we want to place them to maximize the number of identifiable link metrics, with concrete results for the case of two monitors. The significance of the two-monitor case is that all the tomographic computation can be performed at the destination monitor without shipping measurements to a central node, thus enabling endhost-based network monitoring. We develop an efficient algorithm to determine all identifiable links in an arbitrary network with a given placement of two monitors, based on which we propose an optimal two-monitor placement algorithm to maximize the number of identifiable links. Our evaluation on real ISP topologies shows that although a large number of monitors is needed to identify all link metrics, we can usually identify a substantial portion (up to 97%) of the links using a single pair of optimally placed monitors.

In this work we are interested in the problem of reconstructing time-varying signals for which the support is assumed to be sparse. For a single time instance it is possible to reconstruct the original signal efficiently by employing a suitable algorithm for sparse signal recovery, given the sparsity level of the signal. In the case of time-varying sparse signals the sparsity level is not necessarily known a-priori. Furthermore conventional tracking by Kalman filtering fails to promote sparsity. Instead, a hierarchical Bayesian model is used in the tracking process which succeeds in modelling sparsity. One theorem is provided that extends previous work by providing some more general results. A second theorem gives the conditions under which all sparse signals are recovered exactly. It is demonstrated that the proposed method succeeds in recovering time-varying sparse signals with greater accuracy than the classic Kalman filter approach.

We investigate the problem of identifying individual link metrics in a communication network from accumulated end-to-end metrics over selected measurement paths, under the assumption that link metrics are additive and constant during the measurement, and measurement paths cannot contain cycles. We know from linear algebra that all link metrics can be uniquely identified when the number of linearly independent measurement paths equals u, the number of links. It is, however, inefficient to collect measurements from all possible paths, whose number can grow exponentially in u, as the number of useful measurements (from linearly independent paths) is at most u. The aim of this paper is to develop efficient algorithms for constructing linearly independent measurement paths and calculating link metrics. We show that whenever there exists a set of u linearly independent measurement paths, there must exist a set of three pairwise independent spanning trees. We exploit this property to develop an algorithm that can construct u linearly independent, cycle-free paths between monitors without examining all candidate paths, whose complexity is quadratic in u. A further benefit of the proposed algorithm is that the generated paths satisfy a nested structure that allows linear-time computation of link metrics without explicitly inverting the measurement matrix. Our evaluations on both synthetic and real network topologies verify the superior efficiency of the proposed algorithms, which are orders of magnitude faster than benchmark solutions for large networks.

We investigate the problem of identifying individual link metrics in a communication network from end-to-end path measurements, under the assumption that link metrics are additive and constant. To uniquely identify the link metrics, the number of linearly independent measurement paths must equal the number of links. Our contribution is to characterize this condition in terms of the network topology and the number/placement of monitors, under the constraint that measurement paths must be cycle-free. Our main results are: (i) it is generally impossible to identify all the link metrics by using two monitors; (ii) nevertheless, metrics of all the interior links not incident to any monitor are identifiable by two monitors if the topology satisfies a set of necessary and sufficient connectivity conditions; (iii) these conditions naturally extend to a necessary and sufficient condition for identifying all the link metrics using three or more monitors. We show that these conditions not only allow efficient identifiability tests, but also enable an efficient algorithm to place the minimum number of monitors in order to identify all link metrics. Our evaluations on both random and real topologies show that the proposed algorithm achieves identifiability using a much smaller number of monitors than a baseline solution.

Topology control for ad-hoc networks is crucial due to the absence of a fixed infrastructure that can guarantee satisfactory connectivity among communication nodes. In this paper, we study the impacts of adjustment of transmission power as a means to control the network topology (connectivity) to the signal-to-interference-plus-noise (SINR) ratio and the number of hops between two nodes, where the SINR and the hop count are highly related to the network throughput and packet delay, respectively. We investigate three distinct topologies: fully, minimally and moderately connected networks. Our results show that adjusting the power level is a very effective way to control the network topology, while enabling to achieve the desirable tradeoffs among SINR and hop count.

This paper study information-theoretic security for a four-node relay-eavesdropper channel. We propose a new achievable scheme whose key idea is to combine noisy network coding for relay channels and the interference assisted strategy for wiretap channel with a helping interferer. The corresponding achievable secrecy rate is characterized for both discrete memoryless and Gaussian channels. The previous interference assisted schemes such as noise-forwarding and cooperative jamming are shown to be special cases of the proposed scheme. Moreover, in some very strong eavesdropping case where these interference assisted schemes can only achieve zero secrecy rate, the proposed secrecy scheme can still achieve a positive secrecy rate.

The continuously increasing demand for resources in modern, both wired and wireless, communication networks urges for more efficient resource allocation. Such an allocation of resources to network users can be formulated as an optimization problem. Traditional resource allocation protocols, such as TCP, operate inefficiently in cases that there is competition for resources by multimedia applications and some, or possibly all, links in the network are wireless. In this paper, the performance degradation of TCP in modern networks is quantified to highlight the necessity for a novel optimization-based resource allocation protocol. To this direction, a new optimization framework is presented that can provide the theoretical foundations of such a protocol by proving a sufficient, and in some cases also necessary, condition for distributed solution of non-convex problems. The wide applicability of this general framework is illustrated by considering a resource allocation formulation in TDMA/CDMA ad-hoc networks. The convergence properties to the optimal solution are first identified and a distributed algorithm is proposed. Moreover, a novel heuristic is developed to approximate the optimal solution when the condition does not hold and resolve network oscillations. Finally, the performance of the proposed methodology is evaluated and compared against other approaches in literature by simulation.

We study a data dissemination scenario in which data items are to be transmitted to mobile clients via one of the stationary data access points (APs) that the clients pass by en route to their destinations. The scheduler dedicates sequences of consecutive timeslots of an AP to downloading a data item to a client during the time window in which it is in range, which corresponds to assigning a job (the client's download) to a machine (the AP) among many. The transmission rate chosen for each assignment partly corresponds to setting a machine's speed, but it also has subtler effects. The APs may control transmission power to tune its transmission range making sure that no interference occurs with neighboring APs' transmissions. The problem is a generalization of an already NP-hard parallel-machine scheduling problem in which jobs' release times and deadlines depend on the machine to which they are assigned. We define this joint timeslot, power control, and rate assignment problem formally and apply both new algorithms and adaptations of existing algorithms to it. We evaluate these algorithms through simulations which show that our proposed algorithms achieve near-optimal throughput.

In this talk, the speaker will give an overview of his current research work in the area of wireless ad-hoc, mesh and sensor networks at Imperial College. By focusing on vehicular networks, the speaker will present new stochastic traffic models for vehicular ad-hoc networks (VANETs) in urban environments and their applications to quantify communication connectivity and identify locations for placing road-side communication nodes to optimize connectivity. Results on distributed optimization for ad-hoc networks with power control will be discussed. The talk will then be concluded with a discussion on future work to extend the traffic models, optimization techniques and cross-layer protocol designs for VANETs.

Wireless Sensor Networks (WSNs) have been deployed for the purpose of structural health monitoring (SHM) of bridges. SHM applications can potentially produce a very high volume of sensing data, which consumes much transmission power and thus decreases the lifetime of battery-run networks. We employ the network-coding technique to improve network efficiency and prolong its lifetime. By increasing the transmission power, we change the node connectivity and control the number of nodes that can overhear transmitted messages so as to hopefully realize the capacity gain by use of network coding. We propose here to control transmission power as a means to adjust the number of nodes that can overhear a message transmission by a neighboring node. However, too much overhearing by high power transmission consumes too much limited battery energy. We investigate the interplay between transmission power and network coding operations. We show that our solution reduces the overall volume of data transfer, thus leading to significant energy savings and prolonged network lifetime. We present the mathematical analysis of our proposed algorithm. By simulation, we study the tradeoffs between overhearing and power consumption for the network-coding scheme. Specifically, we consider a bridge with fixed length and sensor nodes are deployed at a uniform distance along one or both sides of the bridge. Our numerical results reveal that appropriate choices of transmission power can achieve the optimal extent of overhearing for network coding gain, while minimizing the overall power consumption for the WSN.

Current communication networks support a variety of applications with different quality of service (QoS) requirements which compete for its resources. This continuously increasing competition highlights the necessity for more efficient and fair resource allocation. Current Network Utility Maximization (NUM) framework fails to achieve this target and alternative approaches cannot operate in networks that consist of wireless links. This paper presents a NUM framework for wireless networks that shares resources according to the utility proportional fairness policy. This policy is shown to prevent rate oscillations in the resource allocation process, allocate resources in a more fair manner among different types of applications and lead to the calculation of closed form solutions for the optimal rate allocation function. Based on this policy, a distributed rate and power allocation algorithm is proposed that gives priority to applications with greater need of resources. Finally, numerical results on the performance of the proposed algorithm are presented and compared against other approaches in the literature.

Information theoretic security has recently emerged as an effective physical layer approach to provide secure communications. The outage performance of such a secrecy communication system is considered in this paper, since it is an important criterion to measure whether users' predefined quality of service can be met. Provided that the legitimate receiver and eavesdropper have the same noise power, many existing secure schemes cannot achieve outage probability approaching zero, regardless of how large the transmission power is. By introducing cooperative transmission into secrecy communication systems, it will be shown here that outage probability approaching zero can be achieved. In particular, scenarios with single-antenna nodes and multiple-antenna nodes will both be addressed, and the optimal design of beamforming/precoding will be investigated. Explicit expressions of the achievable outage probability and diversity-multiplexing tradeoff will be developed to demonstrate the performance of the proposed cooperative secure transmission schemes, and numerical results are presented.

We consider the problem of relay-assisted transmission for cellular networks. In the considered system, a source node together with n relay nodes are selected in a proportionally fair (PF) manner to transmit to the base station (BS), which uses the maximal ratio combining (MRC) to combine the signals received from the source node in the first half slot and the n relay nodes in the second half slot for successful reception. The proposed algorithm incorporates the PF criterion and cooperative diversity, and is called proportionally fair cooperation (PFC). Compared with the proportional fair scheduling (PFS) algorithm, PFC provides improved efficiency and fairness. The ordinary differential equation (ODE) analysis used to study PFS cannot be used for PFC; otherwise, one has to solve a large number of nonlinear and interrelated ODE equations which is time prohibited. In this paper, we present a mathematical framework for the performance of PFC. The cornerstone of our framework is a realistic yet simple model that captures node cooperation, fading, and fair resource allocation-induced dependencies. We obtain analytical expressions for the throughput gain of PFC over traditional PFS without node cooperation. Compared with the highly time-consuming ordinary differential equation analysis, our formulae are intuitive yet easy to evaluate numerically. To our knowledge, it is the first time that a closed-form expression is obtained for the throughput of relay-assisted transmission in a cellular network with the PF constraint.

In wireless sensor networks, measurements from neighboring sensor nodes are typically cross-correlated and can be aggregated and compressed locally. This process, referred to as in-network data aggregation, saves a lot of energy by reducing the amount of data that needs to be transferred to the data sink. We consider the problem of using a TDMA schedule in order to perform data aggregation in a network in which the wireless links are unreliable and heterogeneous. In order to balance the energy consumption of different sensor nodes, the nodes with the weakest links should discard more packets than nodes with the strongest links. The existing packet discarding policies are unsuitable for data aggregation because they fail to consider the dependencies between different packets. We propose three packet discarding policies and show that they are appropriate for different kinds of networks. For large networks, among the policies that we propose, the best policy is discard the oldest packet, and to transmit the oldest packet that has not been discarded.

This paper presents a non-convex optimization framework for the Network Utility Maximization problem in Wireless Networks, which incorporates the interference among links and introduces a power penalty term in the objective function to assure both convergence and energy efficiency of the method. Moreover, a distributed gradient based algorithm is proposed that converges to the optimal solution for problems with zero duality gap and a fair-allocation heuristic is presented to resolve user oscillations when they occur. Finally, numerical results regarding the performance of the heuristic and the distributed approach are presented.

Network coding can deliver multiple data streams simultaneously and make full use of broadcast nature of wireless channels. The authors propose two diversity-enabled network-coding (NC) schemes to optimise wireless uplink scheduling. The existing scheduling protocols normally have to allow the users with relatively low channel gains to transmit, and it can maintain fairness but reduce congregated throughput. The main idea of the proposed scheme is to always schedule users with the best channel condition, while the use of NC encourages the scheduled users to help others which have not been served previously. Delay and capacity performance for different network coded scheduling schemes are analysed. Round-robin and pure opportunistic scheduling are evaluated for performance comparison. In order to show the effectiveness of the proposed schemes, NC schedulers are applied to a time division duplexing code division multiple access wireless cellular networks. System-level simulation was carried out based on the third generation partnership project specifications. Per-sector average throughput and cumulative distribution function of user average throughput are adopted as the performance metrics. Analytical and simulation results show that the proposed NC schedulers can achieve a better tradeoff between fairness and throughput than those without NC.

Wireless vehicular ad hoc networks are characterized by multi-hop transmission, where a key problem is the design of routing, e.g., how to efficiently direct the information flow from the source to the destination via available intermediate nodes. In this paper, cross-layer routing is studied by applying cooperative transmission and a new strategy of path selection is proposed to achieve a better tradeoff between the transmission power consumption and end-to-end reliability. The influence of cooperative transmission to the wireless link cost is first studied, which shows that the quality of wireless links could be improved significantly. Then we focus on formulating the objective functions for the addressed cross-layer optimization problem. Specifically, according to different quality of service requirements, two different types of routing optimization are investigated to understand the effects of improved link cost to the routing decision. The closed-form expressions of the optimal solutions for two addressed optimization problems are developed and later used as quantitative criteria of the routing decision. Our developed analytical and simulation results show that the criteria using cooperative transmission typically yield more efficient routes than the comparable schemes in terms of end-to-end reliability and total transmission power.

The secure transmission of information in wireless networks without knowledge of eavesdropper channels or locations is considered. Two key mechanisms are employed: artificial noise generation from system nodes other than the transmitter and receiver, and a form of multi-user diversity that allows message reception in the presence of the artificial noise. We determine the maximum number of independently-operating and uniformly distributed eavesdroppers that can be present while the desired secrecy is achieved with high probability in the limit of a large number of system nodes. While our main motivation is considering eavesdroppers of unknown location, we first consider the case where the path-loss is identical between all pairs of nodes. In this case, a number of eavesdroppers that is exponential in the number of systems nodes can be tolerated. In the case of uniformly distributed eavesdroppers of unknown location, any number of eavesdroppers whose growth is sub-linear in the number of system nodes can be tolerated. The proposed approach significantly outperforms a power control approach based on standard multi-user diversity.

This paper proposes a generalized sequential slotted amplify and forward (GSSAF) strategy for single-antenna wireless cooperative networks. The diversity and multiplexing tradeoff (DMT) is analyzed. Applications to cooperative multiple relay channels, cooperative broadcast channels (CBC) and cooperative multiple access channels are considered and the DMT upper bound is proven to be achievable for each case. Other than proposing the best known near-optimal strategy for CBC, another important contribution is to show that GSSAF can be used as a unified strategy to achieve DMT optimality in wireless cooperative networks with unit multiplexing gains.

This paper studies the diversity benefit of different interference alignment solutions. While most research about interference alignment was aiming at deriving or realizing the maximum achievable multiplexing gain, the symbol error rate performance, which can be characterized by the diversity gain is of equal importance. Different interference alignment solutions are classified into two categories called diversity interference alignment and zero-forcing interference alignment. Although these two types of solutions are not distinguishable in terms of the multiplexing gain, this paper will show their difference lies in the fact that they have different diversity gains. In this paper, a K-user (M × N) interference channel is used, with each user sending 1 degree of freedom of information by using interference alignment precoding and receiving filters but without space-time codes. The feasibility conditions for diversity interference alignment to be achieved are analyzed and the diversity orders different solutions can provide are compared. The results imply that diversity interference alignment solutions offer both multiplexing and diversity gains simultaneously. It also tells us two important rules about the interference alignment precoding filters design: an optimal design has to take both desired and interference channel matrices into consideration and the separation of interference alignment precoding filters design and space-time codes design may not be optimal in general.

The space and time dynamics of moving vehicles regulated by traffic signals governs the node connectivity and communication capability of vehicular ad hoc networks (VANETs) in urban environments. However, none of the previous studies on node connectivity has considered such dynamics with the presence of traffic lights and vehicle interactions. In fact, most of them assume that vehicles are distributed homogeneously throughout the geographic area, which is unrealistic. We introduce in this paper a stochastic traffic model for VANETs in signalized urban road systems. The proposed model is a composite of the fluid model and stochastic model. The former characterizes the general flow and evolution of the traffic stream so that the average density of vehicles is readily computable, while the latter takes into account the random behavior of individual vehicles. As the key contribution of this paper, we attempt to approximate vehicle interactions and capture platoon formations and dissipations at traffic signals through a density-dependent velocity profile. The stochastic traffic model with approximation of vehicle interactions is evaluated with extensive simulations, and the distributional result of the model is validated against real-world empirical data in London. In general, we show that the fluid model can adequately describe the mean behavior of the traffic stream, while the stochastic model can approximate the probability distribution well even when vehicles interact with each other as their movement is controlled by traffic lights. With the knowledge of the mean vehicular density dynamics and its probability distribution from the stochastic traffic model, we determine the degree of connectivity in the communication network and illustrate that system engineering and planning for optimizing both the transport (in terms of congestion) and communication networks (in terms of connectivity) can be carried out with the proposed model.

In this letter, we study the opportunistic use of relays for secret communications, and propose two transmission schemes that do not require the knowledge of the eavesdropper's channel state information. Both analytic and numerical results are provided.

In this paper, we study the design of network coding for the multiple two-way relaying channels where multiple pairs of sources wish to exchange information with their partners. All nodes are equipped with multiple antennas, and we focus on a particular scenario where the sources have less antennas than the relay. For such a case, the application of existing protocols developed for the scenario with a single pair has to rely on the time sharing approach, which will result in some loss of reliability and throughput. In this paper, we develop a new network coding transmission protocol which can be viewed as a combination of traditional beamforming and the recently developed approaches of signal alignment. By using such a protocol, all M pairs of source nodes can accomplish information exchanging within two time slots. We have developed analytical results, such as outage probability and diversity-multiplexing tradeoff, which demonstrate that the proposed transmission protocol can achieve a larger multiplexing gain than the time sharing approach. Furthermore, the proposed network coding scheme is extended to the special case where source nodes only have a single antenna. Simulation results have been provided to demonstrate the performance of the proposed network coding schemes.

We consider a scenario where multiple pairs of users exchange information within pair, with the help of a dedicated multi-antenna relay. The protocol integrates the idea of analogue network coding in mixing two data streams originating from the same user pair, together with the spatial multiplexing of the data streams originating from different user pairs. The key feature of the protocol is that it enables both the relay and the users to participate in interference cancellation. We propose several beamforming schemes for the multi-antenna relay and evaluate the performance using information theoretical metrics such as ergodic capacity, outage probability and diversity and multiplexing tradeoff. Analytical and simulation results justify that the ergodic capacity, outage probability and diversity and multiplexing tradeoff of the proposed beamforming schemes outperform comparable schemes.

This study considers utility-based resource allocation in backbone wireless mesh networks (WMNs). Unlike single-hop cellular networks, a WMN has multi-hop transmissions with multiple contending links, and thus requires more careful design for resource allocation. To this end, we provide a clique-based method with efficient spatial reuse, which is then incorporated into proportionally fair scheduling (PFS) for fair resource management in WMNs. We call it a clique-based proportionally fair scheduling (CBPFS) algorithm. The linear and/or logarithmic rate models used to analyze PFS in single-hop cellular networks cannot be used to analyze CBPFS in backbone WMNs. Using stochastic approximation and recent results on rate modeling for Rayleigh fading channels, we conduct mathematical analysis and obtain a closed-form model to quantify CBPFS performance, without the need of the highly time-consuming ordinary differential equation (ODE) analysis. We use the derived analytical framework to estimate the link throughput of CBPFS and compare it with simulations. It is the first time a closed-form analytical model is developed to quantify the throughput of links in a multi-hop network where links are proportionally fair scheduled.

This paper is concerned with the analysis of proportionally fair scheduling (PFS), and we provide an analytical approximation for the PFS throughput over Rayleigh fading channels. Though quite accurate, the ordinary differential equation (ODE) analysis, typically used to analyze the PFS throughput, is highly time-consuming when there are lots of users. On the other hand, due to the intricate interplay among these ODE equations, the ODE analysis generally fails to provide a closed-form approximation for estimating the PFS throughput unless with simplified models such as the linear rate model to characterize channel capacity. Our aim is to provide a novel framework to evaluate PFS in Rayleigh fading without the above-mentioned limitations. To put our work on a firm base, we use results of stochastic approximation in the analysis and take the Gaussian approximation for capacity modeling for fading channels. Simulations validate this approach and show that our analytic result provides highly accurate estimate of the PFS throughput. Compared to existing studies, our work advances the state of the art in three ways. First, it goes beyond the linear rate model and applies to the commonly used Shannon rate model. Second, it provides accurate estimate of the PFS throughput without the need for the time-consuming ODE analysis. Third, it provides a unified closed-form expression for estimating the PFS throughput for both the linear rate model and the Shannon rate model. It is interesting to note that our analysis provides the same result as existing studies when assuming the linear rate model. More importantly, our formula is intuitive yet easy to evaluate numerically.

While various cooperative protocols have been developed for the simple scenario with one source-destination pair, most of them still suffer a significant loss compared with the optimal multiple-input single-output (MISO) upper bound. The diversity-multiplexing tradeoff will be used as the criterion for performance evaluation. In this paper, we propose two new half-duplex decode-forward cooperative transmission protocols, whose performance can approach the optimal MISO bound, and achieve a better diversity-multiplexing tradeoff when compared with existing cooperative protocols, particularly for large multiplexing gains. Firstly, a simple protocol of cooperative transmission is devised by combining opportunistic strategies with non-orthogonal transmission. When the number of relays is large, the proposed opportunistic decode-forward cooperative protocol can approach the optimal MISO upper bound. Due to the inter-relay interference constraint, each relay can only be used once, which limits the achievable diversity gain. Such an observation motivates our second transmission protocol which can further push the performance of cooperative transmission close to the optimal upper bound. Secondly, a relaying protocol is proposed for a four-node network where two multiple-antenna relays alternately forward messages to the destination when they can successfully cancel the inter-relay interference using the zero forcing method. Monte-Carlo simulation has also been provided to demonstrate the performance of both protocols and comparable ones.

In this paper, we study a special case of multi-way relaying channel, to which traditional beamforming cannot achieve the best performance due to insufficient antennas. A new transmission protocol is proposed by aligning the messages from the same pair with the help of relay precoding. As a result, inter-pair interference can be avoided and intra-pair interference can be coped with by using network coding. Then analytic results, such as the ergodic sum rate and the outage probability, are developed for the proposed protocol. The numerical results are also provided to demonstrate the performance of our proposed scheme. To improve the diversity gain of the proposed scheme, an optimal scheme is also presented.

Abstract We consider the problem of reporting events using wireless sensor networks. To reduce the data volume generated by each event, the correlated data from the (neighboring) nodes that detect the event must be brought together to be processed and compressed before relaying the result across several hops to the data sink. This process is supported by an aggregation tree, which specifies the flow of information towards the sink. For efficiency, aggregation trees should compress the data close to their sources. We propose two solutions to the event‐triggered reporting problem. Firstly, we propose the first protocol to use a staggered schedule in the construction of the aggregation tree. Due to the use of such a schedule, our protocol divides the tree‐construction time by roughly the number of hops in the network, and this advantage comes only at the expense of a small degradation of the quality of the obtained aggregation tree. Secondly, we consider a multi‐hop cluster‐based topology with fixed aggregation points. This topology is appropriate for large networks with unreliable radio links. We approximate the optimal cluster size distribution and evaluate the improvement over a uniform cluster size distribution. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we study the application of cooperative diversity to wireless broadcast channels, a fundamental building block of wireless communication networks. Several cooperative broadcast protocols will be proposed, and information theoretic metrics are developed to facilitate performance evaluation. Provided that there is no direct S-D link, the proposed protocols can achieve a multiplexing gain close to one, whereas the traditional two-hop scheme can only achieve the diversity gain 1/2. Provided that there are direct S-D links, the proposed protocol can still outperform the comparable scheme, particularly at high multiplexing gains.

Existing work has shown that random coding across multi-cast sessions can reduce the system delay significantly, however, such a scheme requires the strong assumption that each source has the priori information of other sources' messages. Actually the broadcasting nature of radio propagation can provide an opportunity to realize collaboration across sessions without causing much system overhead. In this paper, we propose the application of network coding to multi-source multi-destination (MSMD) scenarios and provide formal analysis for the improvement of system delay. In particular, two types of analytical results have been developed, one based on the outage probability and the other based on the use of practical convolutional codes. Monte-Carlo simulation results have also been provided to demonstrate the delay performance of the proposed network coded protocol.

In a wireless network, connectivity is arguably the most critical issue that requires significant study since a network can hardly function well if it is disconnected. On the other hand, there are extraordinary interests in exploiting cooperative techniques in wireless networks in recent years. This paper studies the connectivity problem of large cooperative ad hoc networks. Unlike traditional cooperative networks where all nodes are willing to transmit in a collaborative manner, the cooperative network we considered does not assume that all nodes would like to transmit cooperatively when relaying other nodes' traffic. In other words, each node exhibits some sense of selfishness. Specifically, we model nodes in such a way that each node cooperatively transmits with p-selfishness or location-based p-selfishness when relaying other nodes' traffic. For the considered network, we assume that nodes are generated according to a Poisson Point Process (PPP) and techniques based on stochastic geometry and percolation theory are used to analyze the connectivity in such system. To quantify the performance of the cooperative ad hoc network with selfish behavior, we provides an upper bound of node density for such network to maintain connectivity.

In this paper, we employ a stochastic geometry model to analyze the transmission capacity of the Decode-and-Forward (DAF) cooperation scheme in an overlaid wireless network where a primary (PR) network and a secondary (SR) network coexist together. The PR users employ DAF scheme and have a higher priority to access the channel, whereas the SR users use only direct transmission. Because of the fact of coexistence, the interference from SR network seriously affects the performance of PR network. Assuming that simultaneous transmitters in both networks are randomly located in space according to Poisson point processes, we develop outage probabilities for both DAF and direct transmission schemes in both deterministic and Rayleigh fading channels. By defining transmission capacity in terms of the outage probability, a desired data rate and the density of transmissions, we further quantify transmission capacities for both schemes. It shows that the use of cooperative transmission achieves much better reliability and a larger transmission capacity than the use of direct transmission in the PR network. Furthermore, such performance gain can be manipulated to increase the transmission capacity of the SR network without deteriorating the performance of the PR network. Numerical results also demonstrate the significant improvement on the transmission capacity by using cooperative transmission.

We propose a new energy efficient network structure for maintaining synchronization in access methods based on Synchronous Preamble Sampling. Our scheme limits the number of synchronization messages and increases network capacity through the use of multiple non-interfering virtual channels. It consists in constructing independent clusters based on the Weakly Connected Dominating Set (WCDS) so that they can use different virtual channels and only need to maintain internal synchronization, while still offering global connectivity. We define a distributed and self-stabilizing algorithm for constructing and maintaining the clusters. Our simulation results show that the proposed scheme has comparable energy consumption to Scheduled Channel Polling, but results in better network capacity. Moreover, it achieves better energy savings and network capacity than recently proposed Crankshaft access method.

Localization algorithms for wireless sensors can use diverse measurement hardware as a source of geographical information in order to provide better location estimates. A relatively extensive set of measurement hardware comprises of machinery capable of measuring range, angle-of-arrival and earth gravity direction. Literature covers distance vector (DV) exchange algorithms for the measurements which provide the complete distance vector. In this work we develop a method for computing the complete DV in a distributed way, using linear antenna array and ranging to calculate a complete DV. We develop a localization algorithm that uses this concept. We also determine pathological situations of flip-error occurrence, but we incorporate this knowledge into the algorithm so that we prevent the flip-errors from occurring.

Consider a (1 × 1, ½)Ksymmetric wireless interference network where K single-antenna user-pairs want to achieve ½ degrees of freedom each. It has been proved that it is almost surely infeasible to achieve interference alignment without symbol extension. While it was proved relays can not increase the degree of freedom of wireless interference networks, we show this does not preclude the usefulness of using relays to construct practical solutions to approach interference alignment with finite symbol extensions. Feasibility conditions for relay-aided interference alignment are analyzed and an example about how to design the relaying functions to approach interference alignment is given. Simulation results also justify the use of relays as a practical means to do interference alignment with finite symbol extensions.

Due to technology or policy constraints, communications across network domains usually require the intervention of gateways, and their proper deployment is crucial to the overall performance. In this paper, we study the problem of placing static gateways in mobile DTNs consisting of multiple domains. Given a limited gateway budget, the problem is to select deployment locations to optimize certain performance. The challenge is that different domains may possess heterogeneous properties. To ensure general applicability of solution, we propose a unified framework based on utility optimization, and solve utility computation and placement optimization separately. To handle heterogeneity, we decompose utility computation into individual domains and derive closed-form solutions based on key domain characteristics with focus on the routing scheme. Moreover, we develop quadratic-complexity algorithms to solve the optimization efficiently, which has guaranteed performance under certain uniformity conditions. Although certain assumptions have been made in developing the solutions, evaluations based on synthetic data and real DTN traces both show that the proposed solutions can achieve near-optimal (within 5%) performance at much lower complexities, and the results are robust with respect to the routing schemes and the mobility patterns. Compared with utility-agnostic deployments, our solutions significantly improve the end-to-end performance (by up to 50%).

This paper considers the novel area of quality-of-information (QoI)-aware network management of multitasking wireless sensor networks (WSNs). Specifically, it provides an investigation of new task admission and resource utilization mechanisms for controlling the individual QoI provided to new and existing tasks using real-time feedback-based monitoring mechanisms. The paper describes three key design elements in support of the above: (a) the QoI satisfaction index of a task, which quantifies the degree to which the required QoI is satisfied by the WSN; (b) the QoI network capacity, which expresses the ability of the WSN to host a new task with specific QoI requirements without sacrificing the attained QoI levels of other existing tasks, and (c) an adaptive, negotiation-based admission control mechanism that reconfigures and optimizes the usage of network resources in order to optimally accommodate the QoI requirements of all tasks. Finally, extensive results are presented for assessing the performance of the proposed solution for the case of an intruder detection application scenario.

Multichannel solutions are increasingly used to cope with the problem of low capacity in sensor networks resulting from high contention during wake-up periods of nodes. We can consider wake-up schedules as virtual channels, because nodes using different schedules cannot communicate with each other. In this paper, we show that multiple virtual channel solutions come at the cost of an increased energy consumption in broadcasts. We analyze the problem of broadcasting over multiple virtual channels under different classes of MAC methods and propose Clustered Virtual Channels (CVC), a new network structure that limits the number of frames needed for maintaining multiple virtual channels synchronized. Our simulation results show that CVC reduces the cost of maintaining synchronization and increases capacity while making all types of broadcasts possible.

Controlled mobile helper nodes called data ferries have recently been proposed to bridge communications between disconnected nodes in a delay-tolerant manner. While existing work has explored various trajectory designs for the data ferry by assuming either static nodes or full observations at the data ferry, the problem remains open when the nodes are mobile and the ferry only has partial observations. In this paper, we investigate the problem of dynamic ferry mobility control under limited-range sensing. Assuming the data ferries are capable of sensing node presence within certain range and adjust their movements dynamically, we aim to design control policies that maximize the number of effective contacts. We provide a comprehensive model of the control framework using Partially Observable Markov Decision Process (POMDP), based on which we study the structure of the optimal policy and propose an efficient heuristic policy which shows significant improvement over the predetermined benchmark. To the best of our knowledge, this is the first data ferry control mechanism that can handle both stochastic node mobility and incomplete ferry observations.

In this paper, we propose a spectrally efficient cooperative transmission protocol for multiple access scenarios. The key feature is to utilize multi-user diversity and fully exploit the dynamic nature of radio propagation. In particular, by carefully scheduling the multiple sources and relays' transmissions, a source with a poor connection to the destination can have higher priority to obtain help from a relay with better channel condition. As a result, the full diversity gain is achievable even though only a fraction of relays is scheduled to help each user. We developed an achievable diversity-multiplexing tradeoff for the proposed transmission protocol to assist performance evaluation. When the number of relays is large, the diversity-multiplexing tradeoff achieved by the proposed scheme can approximate the optimal multiple-input single-output upper bound. Both analytical and numerical results show that the proposed protocol outperform other comparable schemes in most conditions.

In this paper, cooperative transmission protocols are proposed for wireless broadcast channels, a fundamental building block of wireless communication networks. The concepts of cognitive radio and precoding have been introduced to broadcast channels in order to improve system performance. Information theoretic metrics, such as outage probability and diversity-multiplexing tradeoff, are developed to facilitate performance evaluation. In the absence of direct S-D links, the proposed protocols can achieve a multiplexing gain close to one, whereas the traditional two-hop scheme only achieves a diversity gain of 1/2. In the presence of direct S-D links, the proposed protocol can still outperform the comparable scheme, particularly at high multiplexing gains. Regarding to the channel state information (CSI) assumptions, in the absence of direct S-D links, the source does not need to know CSI, but it is assumed that the relays have access to their own incoming and outgoing channel information. In the presence of direct S-D links, the use of precoding requires an extra assumption that the global CSI is available at the source.

Wireless Mesh Networks (WMNs) is a promising key technology for next generation wireless backhauling that is expected to support various types of applications with different quality-of-service (QoS) demands. Advanced antenna techniques, novel scheduling algorithms and routing schemes have attracted increased research interests aiming to optimize the performance of WMNs and satisfy their vast and diverse traffic requirements. However, in such networks, uncontrolled admission of arbitrary large number of data flows, together with the distributed and dynamic nature of WMNs, can highly increase the wireless channel interference with catastrophic subsequence for the whole network. To overcome this difficulty, we provide a mathematical model to analyze and estimate the real-time "route capacity" associated with time-varying QoS requirements. This allows us to take admission control decisions for new data flows in a way that we can increase the resource utilization while maintaining all good QoS levels for users with different grade-of-service (GoS). Numerical and extensive simulations results show that the proposed scheme achieves higher network goodput while it significantly reduces the outage and session blocking probability if compared to other techniques.

We consider the problem of selective relaying in multi-hop networks. At each slot, a relay and a node along the optimal non-cooperative path are opportunistically selected to transmit to the next-hop node in a cooperative manner. Being a promising scheme for fair resource allocation, the proportional fair scheduling (PFS) algorithm provides excellent balance between throughput and fairness via multi-user diversity and game-theoretic equilibrium. To maximize the overall utility along a cooperative multi-hop path, we apply the proportional fair (PF) criterion in selecting nodes and relays for cooperative transmission. Furthermore, we analyze and provide an analytical expression for end-to-end throughput of an opportunistic relaying, cooperative multi-hop path with proportional fairness constraints over a Rayleigh flat-fading channel. To our knowledge, it is the first time that a closed-form expression is obtained for the throughput of a proportional fair relaying, cooperative multi-hop path. This research is an extension of previous theoretical work on PF for cellular networks.

We consider the problem of fair resource allocation for multi-carrier systems. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multi-user diversity. Previous work in this area has focused on the single-user scheduling in single-carrier systems over a narrowband flat-fading channel, where only one node is scheduled at a time. In wideband multi-carrier systems, multiple nodes can be scheduled concurrently over multiple narrowband channels. In this paper, we analyze proportional fair scheduling (PFS) in multi-carrier systems over a wideband frequency-selective channel. In particular, we first derive analytical expressions for the throughput of opportunistic scheduling under proportional fairness constraints in a frequency-selective channel, for both single-user and multi-user systems. Furthermore, we provide closed-form expression to quantify the throughput benefit of the multi-user PFS over the single-user PFS in frequency-selective systems. This research is an extension of our previous theoretical work on opportunistic scheduling over flat-fading channel in narrowband single-carrier systems.

We propose a near-optimal relaying strategy for cooperative broadcast channels (CBC), CBC-SSAF, based on the class of sequential slotted amplify and forward (SSAF) strategies. Our strategy allows each destination to act in turn as a relay and forward its previously received signal to other destinations. While CBC-SSAF is not a full multiplexing gain strategy, the loss is negligible when the number of destinations is large. Moreover, CBC-SSAF allows each destination to be protected by the maximum number of extra paths in order to achieve the near-optimal diversity gain in the high multiplexing gain regime. A diversity and multiplexing tradeoff (DMT) lower bound for CBC-SSAF is derived which suggests that our proposed relaying strategy approaches the multiple-input multiple-output (MIMO) DMT upper bound and is therefore asymptotically optimal.

In wireless networks, the broadcast nature of wireless transmission enables cooperation by sharing the same transmissions with nearby receivers and thus can help improve spatial reuse and boost network throughput along a multi-hop routing. The performance of wireless networks can be further improved if prior information available at the receivers can be utilized to achieve perfect interference subtraction. In this paper, we investigate performance gain on network throughput for wireless cooperative networks by using a simple MUD scheme, called overlapped transmission, in which multiple transmissions are allowed only when the information in the interfering signal is known at the receiver. It is shown that the scheme of cooperative transmission with overlapping increases network throughput by 24% compared to that of direct transmission with overlapping. We then propose a new cooperation scheme called supplementary cooperation, which improves the performance gain of direct transmission with overlapping by 42%. Analytical results are developed to show that in a general network scenario, supplementary cooperation achieves bit error rate (BER) reduction of 34.87%, compared with the conventional cooperative transmission. Furthermore, we proposed a criterion for finding the best cooperative route to achieve maximum network throughput in a general network.

Most ad hoc mobile devices in wireless networks operate on batteries and power consumption is therefore an important issue for wireless network design. In this paper, we propose and investigate a new distributed cooperative routing algorithm that realizes minimum power transmission for each composed cooperative link, given the link BER (bit error rate) constrained at a certain target level. The key contribution of the proposed scheme is to bring the performance gain of cooperative diversity from the physical layer up to the networking layer. Specifically, the proposed algorithm selects the best relays with minimum power consumption in distributed manner, and then forms cooperative links for establishing a route with appropriate error performance from a source to a destination node. Analytical results are developed to show that our cooperative transmission strategy (MPSDF) achieves average energy saving of 82.43% compared to direct transmission, and of 21.22% compared to the existing minimum power cooperation strategy. Furthermore, the proposed power efficient routing algorithm can also reduce the total power consumption by a couple dB compared to existing cooperative routing algorithms. Monte-Carlo simulation results are also provided for performance evaluation.

In this paper, we propose a novel spectrally efficient cooperative transmission protocol for multiple access scenarios. Different to some existing cooperative multiple access schemes, the proposed scheme can exploit the availability of relays as an extra dimension to increase reception robustness. By carefully scheduling the multiple sources and relays' transmission, a source with a poor connection to the destination can have higher priority to obtain better help from relays. As a result, the full diversity gain can be achievable for each user although only a fraction of the all relays is scheduled to help him. An achievable diversity-multiplexing tradeoff (DMT) is developed for the proposed transmission protocol to assist performance evaluation. With a large number of relays, the DMT achieved by the proposed scheme can approximate the optimal multiple-input single-input upper bound. Both analytical and numerical results show that the proposed protocol can outperform comparative schemes in most conditions.

One of the main challenges in the wireless broadcast channels is the co-channel interference among multiple destinations. Practical linear precoding techniques such as zero-forcing beamforming (ZFBF) is used to avoid the co-channel interference. However, the achievable diversity order of the ZFBF is bounded by the number of transmitter antennas. In this paper, we introduce a spectral efficient cooperative broadcast channels transmission protocol in the MISO channels using linear precoding. We evaluate the performance of the proposed protocol using the diversity and multiplexing tradeoff. The proposed protocol can achieve the maximum diversity order expressed as a sum of the transmitter antennas and the number of participating relays. For a large number of relay candidates, the diversity and multiplexing tradeoff of the proposed protocol completely surpasses the performance of the comparable scheme. Simulations have also shown the outage probability of the proposed protocol outperforms the existing scheme.

In the design and planning of vehicular ad-hoc networks, road-side infrastructure nodes are commonly used to improve the overall connectivity and communication capability of the networks, however, to determine the locations to install the infrastructure nodes for optimal performance based on the ever-changing density and connectivity dynamics of moving vehicles remains to be a challenging issue. In this paper, we introduce a stochastic traffic model to capture the space and time dynamics of vehicles in signalized urban road systems to identify poorly-connected regions for infrastructure node placements. To closely approximate the practical road conditions, we propose a density-dependent velocity profile to approximate vehicle interactions and capture platoons formation and dissipation at traffic signals. Numerical results are presented to evaluate the stochastic traffic model. In general, we show that the fluid model can adequately describe the mean behavior of the traffic stream, while the stochastic model can approximate the probability distribution well even when vehicles interact with each other as their movement is controlled by traffic lights. With the understandings of the vehicular density dynamics from the proposed model, we illustrate that connectivity dynamics of vehicles can be determined and consequent system engineering and planning can be carried out.

The impact of wireless network coding on multiuser diversity gain is investigated. A generic framework to calculate the CDF of the opportunistic channel gain, for different communication scenarios with a single relay, when scheduling or network coding are used, is presented. The CDF and PDF of the lognormal and Rayleigh opportunistic channels for these scenarios are calculated and the average system capacity is derived. Numerical results show that while wireless network coding most of the time can provide significant throughput gain, for low mean received SNR and high channel variations it may result to lower ergodic capacity than simple opportunistic scheduling, especially when transmission overhearing is required. For these cases, we show that although a coding gain is achieved, there may be a larger reduction on the multiuser diversity gain which results to overall lower average system capacity compare to traditional opportunistic scheduling.

In a Vehicular Ad-hoc Network (VANET), the amount of interference from neighboring nodes to a communication link is governed by the vehicle density dynamics in vicinity and transmission probability of terminals. It is obvious that vehicles are distributed non-homogeneously along a road segment due to traffic controls and speed limits at different portions of the road, the common assumption of homogeneous node distribution in the network in most of the previous work in mobile ad-hoc networks thus appears to be inappropriate in VANETs. To capture the density dynamics in generic urban routes, we utilize a fluid model to characterize the general vehicular traffic flow, and a stochastic model to capture the randomness of individual vehicles, from which we can acquire respectively the densities of the mean number of vehicles along the road and the probability distribution. With the knowledge of the vehicular density dynamics from the stochastic traffic model, we determine the throughput and progress performances of a routing strategy, and confirm the accuracy of the analytical results through simulations. The analytical model proposed in this paper serves as a fundamental building block for performance analysis of other transmission protocols and network configurations, we also demonstrate that the optimal transmission probability for optimized network performance can be found from our results, which provides insights into system engineering and protocol designs in VANETs.

Wireless sensor networks are usually equipped with single radio interfaces where a sensor node can participate in only one transmission at a time. As a result, a node's airtime is ¿shared¿ by multiple flows that pass through the node. Although it is practically important, how to effectively allocate airtime among flows has not received sufficient research attention in the existing literature. In this paper, after showing the potential gain from adaptive airtime allocation in alleviating network congestions, we formulate a new congestion control problem using network utility maximization with respect to airtime fractions, transmission power and flow rate. We prove that the new congestion control is a concave problem and develop a local algorithm for adaptively tuning airtime fractions in parallel with power and rate. Simulation results show the convergence of the optimal airtime allocation as well as its effectiveness in boosting flow rate and conserving power.

In this paper, we employ a stochastic geometry model to analyze transmission capacity in wireless cooperative networks. Assuming that simultaneous transmitters are randomly located in space according to Poisson point process with density ¿, we develop the bound performances on outage probability and outage capacity for both direct transmission and Decode-and-Forward (DAF) cooperative scheme. Due to the nature of multipath propagation of cooperative transmission, we define regional capacity as the multiplied product of average density of successful simultaneous transmissions, achieved outage capacity and transmission distance. It shows that the regional capacity for cooperative transmission scales as ¿(¿(¿)), which is the same as the transport capacity for wireless network. Furthermore, Monte Carlo simulations demonstrate the significant improvement on the transmission capacity by using cooperative transmission.

Vehicular Ad hoc Networks design has drawn a lot of research attention. High node mobility, an inherent characteristic of VANETs, results in short lived links and rapid topology changes; this makes traditional ad-hoc protocols inefficient for vehicle to vehicle communication. In this paper we propose a cross-layer approach, where physical layer information can be used by upper layers to monitor the well-being of the various links used, and to estimate their residual time. The algorithm proposed comprises of forming a time series based on physical layer measurements. Utilizing adaptive non-linear parameter estimation methods, this time series can be used to estimate the current state of the link and its residual time. The environment in which VANETs usually operate together with the effect of relative node movement introduce considerable noise. For this reason, a data-driven signal processing technique, Empirical Mode Decomposition is used for denoising. The proposed algorithms are tested against real data and simulations.

The recently permitted unlicenced use of the regulated ultra-wideband (UWB) radio spectrum (regulated first by the US FCC in 2002 and subsequently by the standardisation bodies of EU and other major countries) provides wireless ad hoc networks a cheap and promising air-interface technology for their adopted wireless data links, thus offering the potential to greatly boost their applications. The impacts of such UWB data links, mainly the more likely adopted impulse-based UWB data links for low data rate applications, on the extensively developed cooperative wireless ad hoc networks are investigated. First, the authors investigate the diversity order of data transfer of each impulse-based UWB data link working in a corresponding fading channel, and give an approximate relationship between the diversity order and the channel model parameters (here the Saleh–Valenzuela model parameters); Secondly, the authors develop efficient cooperative and decentralised diversity schemes that can utilise the widely spread and independently distributed multiple paths of the fading UWB channels. Performance analysis and simulation studies show that proposed decentralised cooperative beamforming schemes can achieve full diversity and are more efficient than their decentralised cooperative routing counterparts.

We provide an analytical framework for preamble sampling techniques for MAC protocols in wireless sensor networks, from which we derive closed-form formulas for lifetime and reliability calculations. In addition to take into account transmitter behavior that controls the form and the content of the transmitted preamble, our model also considers receiver behavior that controls the duration of preamble reception in case of successful and failed reception. Along with both transmitter and receiver behavior, our model considers a non-perfect channel and thus takes into account the impacts of transmission errors and retransmissions on lifetime and reliability of preamble sampling protocols. Numerical results show that no protocol is universally optimal; that is, each protocol has its own optimal operation point that depends on the given channel and load conditions.

Recently proposed physical-layer network coding (PNC) has demonstrated the promise to significantly improve the throughput of wireless networks whose links can be modeled as additive white Gaussian noise (AWGN) channels. However, the extension to multipath channels is problematic, since the technique would then require both amplitude and phase compensation at each transmitter. Phase compensation requires accurate distributed phase tracking, whereas the required amplitude compensation is even more troubling, as it leads to an inefficient system that yields no diversity even in the presence of perfect channel estimates. Here, a system that avoids these limitations is obtained by reaching up one level higher in the network hierarchy and performing distributed relay selection with cognizance of the PNC technique that we will employ at the physical layer. Since the resulting scheme will achieve a form of selection diversity, we term it ldquonetwork coding with diversityrdquo (NCD). To facilitate performance evaluation, two information-theoretic metrics, the outage and ergodic capacity, are studied. Our analytical and simulation results show that the proposed protocol achieves more robust performance and higher system throughput than comparable schemes. Finally, the proposed network coding is extended to the context of cooperative multiple access channels, which yields a new cooperative protocol with larger outage and ergodic capacity compared with existing transmission schemes.

In this correspondence, the performance of the network coded amplify-forward cooperative protocol is studied. The use of network coding can suppress the bandwidth resource consumed by relay transmission, and hence increase the spectral efficiency of cooperative diversity. A distributed strategy of relay selection is applied to the cooperative scheme, which can reduce system overhead and also facilitate the development of the explicit expressions of information metrics, such as outage probability and ergodic capacity. Both analytical and numerical results demonstrate that the proposed protocol can achieve large ergodic capacity and full diversity gain simultaneously.

Wireless multi-hop, mesh networks are being considered as a candidate for wireless backhaul networks which carry data traffic between access networks and the wired Internet. Although existing scheduling algorithms have been adopted for the wireless backhaul networks, they do not yield good performance. In this paper, we study the computational complexity of finding the optimal link schedule for the wireless mesh networks with time-division-duplexing (TDD) operations. We show that the problem of finding the optimal schedule for the mesh networks is #P-complete. Consequently, we propose a heuristic distributed scheduling algorithm and a link utility function for wireless mesh networks. Performance analysis shows that our proposed framework is of linear-time complexity and the proposed utility function makes the long-term throughput allocation converge to the desired level which is proportional to the requirements specified by the routing protocol in use to guarantee quality of service (QoS). Moreover, we show that our framework maintains strong temporal correlation of interference, which is required to ensure proper channel predictions for distributed scheduling and power control. Finally, we compare our scheduling algorithm with a previously proposed "tree" scheduling and a centralized "ideal" scheduling. Simulation results reveal that the proposed algorithm achieves high efficiency in terms of network objective as well as the overall network throughput.

Due to the fact that the wireless links in an ad hoc network are susceptible to attacks and the nodal mobility renders the network to have a highly dynamic topology, it becomes critical to detect major attacks against the routing protocols of such networks and also provide some extent of QoS to the network traffic. In this paper, we present a new secure routing protocol (SRP) with quality of service (QoS) support, called trustworthiness-based quality of service (TQOS) routing, which includes secure route discovery, secure route setup, and trustworthiness-based QoS routing metrics. The routing control messages are secured by using both public and shared keys, which can be generated on-demand and maintained dynamically. The message exchanging mechanism also provides a way to detect attacks against routing protocols, particularly the most difficult internal attacks. The routing metrics are obtained by combing the requirements on the trustworthiness of the nodes in the network and the QoS of the links along a route. The simulation results have demonstrated the effectiveness of the proposed secure QoS routing protocol in both security and performance.

In this paper we develop and investigate two families of efficient distributed cooperative data relaying schemes that can be adopted to forward data in the impulse-based ultra wide band wireless ad-hoc network composed of a pair of source and destination and multiple parallel two-hop relays. The new schemes combine the mechanism of the medium access control and physical layers in a cooperative and distributed way to either select the best relay from multiple available ones for data forwarding or optimally combine the synchronized data forwarding of all participating relays, such as to improve the data transfer diversity. For distributed cooperative routing, we propose efficient protocols for use at all relays to perform an enhanced ultra wide band pulse sensing multiple access with the backoff periods deterministically and optimally mapped from associated instantaneous source-relay-destination route qualities, to ensure that only the best relay can firstly and successfully forward its received data to the destination; For distributed cooperative beamforming, we propose efficient protocols for use at all relays to take advantages of the widely spread and independently distributed multiple paths of the fading ultra wide band channels in the synchronized data forwarding combination, to create an optimally combined route for source-destination data transfer. Performance analysis and simulation studies show the effectiveness and efficiency of our proposed schemes.

Proportional fair scheduling (PFS) provides good balance between throughput and fairness via multi-user diversity and game-theoretic equilibrium. Very little analytical work exists on understanding the performance of PFS. Moreover, most researches on PFS are for cellular networks and typically use linear rate model or logarithm rate model to simplify the theoretical analysis of PFS. Since the linear rate model only applies to very small SINR, most researchers prefer the logarithm rate model in their study on PFS. While previous work which is based on the logarithm rate model provides good estimate of the PFS throughput in Rayleigh fading single-antenna cellular networks, they are not valid for multi-antenna wireless mesh networks. In this paper, PFS in multi-antenna wireless mesh networks under Rayleigh fading is discussed. Specifically, we assume that orthogonal frequency-division multiplexing (OFDM) and single-input multi-output (SIMO) techniques are used in the network. In addition, a new mathematical analysis of PFS that applies to both Rayleigh and Rician fading is presented. Simulations are conducted to verify our analytic results on PFS in the proposed mesh network. To the best of our knowledge, this work is the first one investigating the PFS problem in multi-antenna mesh networks.

In this paper we present a distributed, self-organizing, localization method for wireless sensors and a scheme for reducing position estimate errors by employing a novel concept of cliques of nodes. We describe an applicable theoretical foundation of peer-to-peer distance-vector (DV) exchange algorithm, which can operate without any manual pre-configuration. This method allows for propagating the complete information of a reference system (i.e., axes orientation and origin position) using angle and range measurements. In order to take advantage of possible spatial-diversity of measurements and to produce enhanced position estimates, we propose here an enhancement method which is based on groups of interconnected nodes (cliques) to combine localization measurements using a minimum-square-error (MSE) criterion and spatial transformations. This enables propagation of the reference system among all the nodes in a clique more accurately than by regular peer-to-peer distance-vector exchanges. Computer simulation reveals that the new clique based error mitigating scheme reduces the localization error by 65% for a typical underground tunnel setting.

In wireless sensor networks, in-network aggregation is the process of compressing locally the data gathered by the sensor nodes, so that only the compressed data travel across several hops to their destination. We address the problem of aggregating data generated by sporadic events in random locations of the monitored area. The sensor nodes keep their transceivers off most of the time in order to preserve their batteries, and these sleep periods dominate the time to react to the events. We propose a distributed protocol that, after each event, constructs a routing tree to regulate the aggregation process. It is cross-layer because, in order to accelerate the tree construction process, the routing decision considers the sleep periods of the nodes. If the nodes sleep for long periods, our protocol divides the tree construction time by the number of hops when compared to centralized protocols. For a fixed maximum tolerable delay, this allows us to extend the sleep periods and thus to save energy. Our simulations reveal that this comes at the price of an aggregation tree with degraded performance, but we retain an advantage over trees not customized to each event. Our protocol requires global time synchronization and periodic link state monitoring, especially as the network size increases.

In this paper, we study the impact of cooperative transmission on the routing decision for wireless ad-hoc networks. The influence of cooperative transmission to the wireless link cost is first studied at the physical layer. Then the problem of routing optimization is investigated to understand the effects of improved link cost on the routing decision, where the closed-form solution of the optimization problem is developed and later used as a quantitative criterion of the route selection. Our developed analytical and simulation results show that the criteria using cooperative transmission typically yield more efficient routes compared with the non-cooperative schemes.

Wireless mesh networks (WMNs) is a promising key technology for next generation wireless backhauling that have recently attracted both the academic and industrial interest. Such networks are expected to have high throughput demands and support various types of applications with different quality-of-service (QoS) constraints. Opportunistic scheduling has been proven highly beneficial in such networks since it takes advantage of the dynamic nature of the channel between different wireless mesh routers. Recently a promising cross-layered framework has been proposed (IQoSR) that combines a distributed opportunistic scheduler with a multi-constrained QoS routing scheme and has been proven to outperform conventional layer 2 and 3 approaches. However, opportunistic scheduling cannot provide hard resource reservation; therefore, the admission of new flows in a route may jeopardize the QoS of the ongoing flows. In order to overcome this problem, in this work we introduce a connection admission control (CAC) scheme for different levels of QoS to efficiently manage the resources among existing and new flows. In this way, we improve the overall network performance and minimize the outage probability of the ongoing flows while we guarantee the required grade-of-service (GoS) to the underlying applications. Simulation analysis shows that the proposed scheme achieves lower blocking probability while it reduces the outage probability of the existing flows.

The proportional fair scheduling (PFS) problem is studied in the paper. PFS is considered an attractive bandwidth allocation criterion in wireless networks for supporting high resource utilization while maintaining good fairness among network flows. The most challenge of a PFS problem is the lack of an analytic expression. By rigorously mathematical derivation, we obtain a closed-form expression for the throughput of PFS in Rayleigh fading environment. The theoretical results are compared with those from simulations. The derived model is shown to provide a high accuracy in evaluating the throughput of the PFS algorithm in Rayleigh fading networks. In particular, the expression presented here will provide great help for the system design of a PFS capable network. Moreover, compared with existing analytical results on PFS, our expression is more general in that we do not require the i.i.d relationship among nodes in our derivation.

Proportional fair scheduling (PFS) provides good balance between throughput and fairness via multi-user diversity and game-theoretic equilibrium. Very little analytical work exists on understanding the performance ofPFS. Most existing prior results are for networks with Rayleigh fading. In this paper, we provide theoretical results forPFSin general fading environments. The results reveal that the average throughput of a user solely depends on its own channel statistics when its instantaneous data rate isGaussian. Based on the theoretical results, we analyze thePFSperformance under various scenarios withRayleighand/orRicianfading, and the numerical results match very well with the simulation ones. To the best of our knowledge, this work is the first one theoretically investigating thePFSproblem in general fading environments.

We extend the existing network utility maximization (NUM) framework for wired networks to wireless sensor networks by formulating it in order to take into account interference among radio links. We study the conditions under which the formulated problem is a feasible convex optimization problem. Under such conditions, a distributed algorithm is proposed to solve the problem optimally. Finally, we provide numerical results, based on computer simulations, to show the performance of the proposed algorithm and the rate of convergence of its solution.

Cross-layer routing and scheduling algorithms design for wireless backhaul mesh network has attracted much research interest recently. The network is expected to support various types of applications with different quality of service (QoS) requirements from both routing and scheduling perspectives. Existing works do not efficiently integrate these QoS constraints in route discovery and maintenance phases and overlook the interaction between medium access control (MAC) and routing algorithms. In this work, we propose a novel cross- layer framework of QoS routing and distributed opportunistic scheduling for wireless mesh network, which provides resource reservation for QoS flows. Studies with different scheduling algorithms and routing protocols have shown that our algorithm successfully guarantees various QoS requirements and achieves higher network throughput when compared with other standard techniques.

We investigate the proportional fair scheduling (PFS) algorithm, with the objective of obtaining an analytic expression for it. In this paper, we derive a closed-form expression for the throughput of PFS in wireless networks. The theoretical results from analysis are compared with those from simulations. The analytic model is shown to provide a high accuracy in evaluating the throughput of the PFS algorithm in Rayleigh fading environment.

In this paper, a quality-of-service driven routing protocol is proposed for wireless cooperative networks. The key contribution of the proposed protocol is to bring the performance gain of cooperative diversity from the physical layer up to the networking layer. Specifically, the proposed protocol uses a distributed algorithm to select the best relays based on link quality to form cooperative links for establishing a route with appropriate error performance from a source to a destination node. Furthermore, analytical results are developed to show that the proposed distributed routing protocol can perform close to the optimal in terms of error performance, especially for linear network topologies. Monte-Carlo simulation results are also provided for performance evaluation.

Transmit-power estimation is an important part in power-aware designs of mobile ad-hoc networks (MANETs). In this paper, we consider the cooperation among multiple monitor-nodes to estimate the transmit power of other nodes. Utilizing a geometric approach, we characterize the theoretical performance of such cooperative monitoring schemes and propose transmit-power estimation techniques with different number of cooperating nodes. We introduce the novel concept of confidence region that provides a fundamental confidence level for the accuracy of the power estimation and enables the development of techniques for allocating network monitors. Finally, we present a simple, distributed cooperative estimation scheme for a large-scale wireless network and give illustrative simulation results to quantify its performance.

This paper provides analytical expressions to evaluate the performance of a random access wireless network in terms of user throughput and network throughput, subject to the constraint of proportional fairness amongst users. The proportional fair scheduling (PFS) algorithm is considered an attractive bandwidth allocation criterion in wireless networks for supporting high resource utilization while maintaining good fairness among network flows. The most challenge of a PFS problem is the lack of analytic expression. Though the PFS algorithm has been a research focus for some time, the results are mainly obtained from computer simulations. It is known that a PFS problem is NP-hard and, until recently, there are very few papers which give analytic insights into the PFS algorithm. Typically, existing works use simplified form of the PF preference metric and assume simple linear model, or the given analytic expression is valid only for very limited cases. In this research, we give analytical results of the PFS algorithm by providing closed-form expressions for the throughput in Rayleigh fading networks. We use Gaussian approximation method to model the feasible data rate in Rayleigh fading environments. Results obtained from the simulation and numerical analysis verifies the high accuracy of the closed-form expressions given in the paper. In particular, the analytic expressions given here will provide great help for the system design of a PFS-enable network, not only in that it is obtained from more realistic rate model, but also it applies to various kinds of network scenarios.

High node mobility and transient connectivity in vehicular ad hoc networks have introduced numerous challenges in the design of efficient communication protocols for these networks. Toward the goal of efficient design of such networks, the focus of this work is to develop methods that will allow the estimation of a wireless link's future quality and the remaining (residual) time for which this will remain efficiently active and useful for data transmission. Such information can serve as key input to the routing algorithms and data dissemination for VANETs. Specifically, we propose a cross-layer approach to the link estimations by introducing a method that utilizes the received signal strength of data packets. Small scale fading induced by the relative movement of the nodes, limited knowledge of the signal, caused by the fact that the received energy metric will be numerically available only when packets are sent, and the small number of available samples of the metric make the problem challenging. To overcome these, a signal processing technique, empirical mode decomposition is used along with robust regression for the prediction of link quality and residual time. The validity of the proposed methods are tested and validated via extensive simulations.

Multihop wireless mesh networks (WMNs) are considered a promising technology to backhaul heterogeneous data traffic from wireless access networks to the wired Internet. WMNs are expected to support various types of applications with diverse quality of service (QoS) requirements, such as end-to-end packet delay, throughput, and packet-error-rate (PER). Recent works in this area are mainly concentrated on network layer routing algorithms with QoS provisioning that unfortunately cannot cooperate efficiently with existing medium access control (MAC) solutions to strictly guarantee multiple QoS constraints. This drawback may significantly deteriorate the and-to-end network performance and end-user experience especially for delay/throughput-sensitive applications such as voice-over IP (voIP) and interactive video. In this paper, we propose a fully distributed multi-constrained QoS scheduling algorithm to overcome this disadvantage. We show by simulation that the proposed scheduling scheme can efficiently organize the resources in physical (PHY) and MAC layers to successfully increase the network goodput, decrease the end-to-end (ETE) packet delay, and achieve less QoS outage probability if compared with other protocols.

Cooperative diversity systems rely on using relay nodes to relay copies of transmitted information to the destination such that each copy experiences different channel fading, hence increasing the diversity of the system. However, without proper processing of the message at the relays, the performance of the cooperative system may not necessarily perform better than direct transmission systems. In this paper, we proposed a distributed beamforming and power allocation algorithm which substantially improves the diversity of the system with only very limited feedback from the destination node. We also derive outage probability as well as study the outage behavior of this scheme.

In this paper, the performance of the network coded amplify-forward cooperative protocol is studied. The use of network coding can suppress the bandwidth resource consumed by relay transmission, and hence increase the spectral efficiency of cooperative diversity. A distributed strategy of relay selection is applied to the cooperative scheme, which can reduce system overhead and also facilitate the development of the explicit expressions of information metrics, such as outage probability and ergodic capacity. Both analytical and numerical results demonstrate that the proposed protocol can achieve large ergodic capacity and full diversity gain simultaneously.

Wireless multi-hop, mesh networks are being considered as a candidate to backhaul data traffic from access networks to the wired Internet. These mesh networks are referred to as wireless backhaul networks. Existing medium access control (MAC) protocols and scheduling algorithms are devised for wireless access. So although they have been adopted for the wireless backhaul networks, they do not yield good performance. In this paper, we propose a novel distributed scheduling algorithm, composed of a framework and a new utility function definition, for wireless backhaul networks. We show by analysis and simulation that in a long run the algorithm converges to the desired throughput allocation, which can be specified by the routing protocol in use to guarantee quality of service. Moreover, in terms of interference, we show that our framework maintains strong temporal correlation of interference, which is required to ensure proper channel predictions for scheduling gain and for distributed power control. Finally, simulation results reveal that the new algorithm takes advantage of the multi-user diversity in achieving high overall network throughput, when compared with the tree-structure algorithm.

This conference paper proposes and investigates the cooperative orthogonal multiple input multiple output (MIMO) relaying strategies that can be adopted to forward data within UWB ad-hoc networks. We study two related issues: We derive the formula for the cooperative orthogonal MIMO-relaying with all channels' information under the amplify-and-forward data relaying policy, and present the conditions for different data forwarding scenarios to realize orthogonal MIMO-relaying or that via own component suppression. Further, we propose three approaches to calculate the amplifying parameters at relays. Simulation studies show that for given data forwarding scenario if the relay number satisfies the least requested number, all three approaches can realize orthogonal MIMO-relaying or that via own component suppression. With the least requested number of relays, all three approaches exhibit the same diversity order (one), and that the seemingly best approach with channel-matched weights is worse than the other two approaches, i.e. the one with channel phase-matched weights and the one with all-equal weights.

This conference paper proposes and investigates a new distributed cooperative routing strategy that can be adopted to forward data in the ultra wide band (UWB) ad-hoc network via a multi-hop route with the best instantaneous quality. The strategy combines the physical (PHY) and medium-access-control (MAC) layer mechanisms to select the best route from available ones in a cooperative and distributed way. Using an example of the parallel two-hop relay network where data from a source node can be forwarded by several possible relays to the destination node, we study two related issues: First, we devise a new estimation algorithm for the UWB link received signal-to-noise ratio (SNR) that is used to determine the UWB link quality. The estimation algorithm is unbiased with estimation errors significantly lower than the reported algorithms in literature. Second, we propose a new distributed cooperative routing scheme. Each relay node uses an enhanced carrier sensing with deterministically mapped backoff period as the MAC protocol. The back-off period is chosen by each relay such that the higher the quality of the associated source-relay-destination route, the shorter the back-off time. Simulation results show that even without any feedback about the relay- destination link quality from the destination node to relays and using only its statistical information, the proposed scheme still has up to 3 dB improvement in performance as compared to the random routing. When having 1-bit such feedback, the proposed scheme can achieve full diversity and the overall performance is only 2 dB away from that with full-precision feedback.

Vehicular Ad hoc NETworks (VANETs) is a subclass of Mobile Ad hoc NETworks (MANETs). However, automotive ad hoc networks will behave in fundamentally different ways than the predominated models in MANET research. Driver behaviour, mobility constraints and high speeds create unique characteristics in the network. All of these constraints have implications on the VANET architecture at the physical, link, network, and application layers. To facilitate the cross-layer designs for VANETs, understanding of the relationship between mobility and network connectivity is of paramount importance. In this paper, we focus on studying transport systems with structured mobility (e.g., bus systems), which have unique characteristics on the road such as fixed routes that have never been explored in previous work. The main contributions of this paper are three-fold: 1) we provide an analytical framework including the design requirements of the mobility model for realistic vehicular network studies, and metrics for evaluating node connectivity in vehicular networks; 2) we demonstrate, through simulation, the impacts of marco- and micro-mobility models, and various transport elements on network connectivity; and 3) we show that multi-hop paths perform dramatically poorer than single-hop links in vehicular networks. Specifically, two- hop and three-hop (communication) paths can only respectively achieve less than 27% and 13% of the average duration of single-hop links. Such kind of knowledge of the performance of multi-hop transmission will be significant for the studies of routing algorithm and other networking functions in vehicular networks.

In this conference paper we address the problem of distributed cooperative data transfer for ultra wide band (UWB) ad-hoc networks: (1) we propose three improving techniques that can utilize some available but not-yet-utilized additional resources to improve the sub-optimal but widely adopted autocorrelation differential detection for the widely used pulse-based UWB data transfer; (2) we devise three cooperative transmission strategies, and discuss the necessary information needed for distributed implementations and how it may be obtained via proposed transmission protocols. Simulation studies confirm the effectiveness of the proposed techniques and schemes.

Wireless sensors are expected to be widely deployed in the near future for a vast variety of applications. Sensing data is not useful unless the location where the data is collected is also known to the end users. Although much work has been done on the positioning of sensor nodes, proposed algorithms often are not applicable in certain environments. Furthermore, there is always a need to reduce their complexity and cost, and to improve accuracy for these algorithms. This work is primarily motivated by a new research project called WINES to deploy wireless sensors to monitor the conditions of London underground and water systems. Global Positioning System (GPS) based positioning methods are not applicable in the underground environments. In addition, the tunnels and water pipes are not located at the same horizontal level, so existing algorithms proposed for two-dimensional deployment areas become inadequate. To overcome the challenge, we propose and study here a new localization algorithm by extending one proposed for two-dimensional service areas by Capkun et al. Our new scheme is a distributed, self-organized, infrastructure-free positioning algorithm that enables easy and flexible sensor deployment in the harsh environments. Furthermore, our computer simulation reveals that the new scheme achieves a satisfactory, relative average position error of less than 5% when the errors for distance estimations among sensors have a standard deviation of no more than 5%.

In the development of various large-scale sensor systems, a particularly challenging problem is how to dynamically organize the sensors into a wireless communication network and route sensed information from the field sensors to a remote base station. This paper presents a new energy-efficient dynamic clustering technique for large-scale sensor networks. By monitoring the received signal power from its neighboring nodes, each node estimates the number of active nodes in realtime and computes its optimal probability of becoming a cluster head, so that the amount of energy spent in both intra- and inter-cluster communications can be minimized. Based on the clustered architecture, this paper also proposes a simple multihop routing algorithm that is designed to be both energy-efficient and power-aware, so as to prolong the network lifetime. The new clustering and routing algorithms scale well and converge fast for large-scale dynamic sensor networks, as shown by our extensive simulation results.

Routing data through a wireless network is made challenging by impairments in the wireless medium, such as fading. Nevertheless, wireless routing if implemented, will result in tremendous cost-savings in next generation wireless networks. This paper examines centralized and decentralized approaches for wireless routing from a PHY/MAC perspective. We show that decentralized routing strategies are capable of realizing full spatial diversity gain if an appropriate level of channel knowledge is available at the relay terminals. Furthermore, we propose a new carrier sensing random access based routing technique designed to avoid collision and conserve network power. A numerical example is provided to demonstrate that the technique is capable of realizing full spatial diversity gain.

Abstract – In contrast to a typical single source of data updates in Internet applications, data files in a networked information system are often distributed, replicated, accessed and updated by multiple nodes. Due to concurrent updates, replicated data files must be synchronized. For certain applications, stringent concurrency control must be employed to ensure data integrity, while for other applications, periodic data synchronization may enable very efficient data sharing. For the latter applications, this paper devises the ShuffleNet and hypercube schemes for data synchronization in such networked information systems. Their performance in terms of update delay, processing complexity, failure tolerance and growth complexity is examined. Our results reveal that the ShuffleNet and hypercube scheme provide identical maximum update delay and similar processing complexity. However, as the number of nodes in the system changes (e.g., due to failure or temporary out of service for maintenance), the hypercube scheme maintains all existing synchronization sessions and greatly simplifies system administration overhead such as moving files from node to node for the purpose of data synchronization. The ShuffleNet scheme does provide a higher degree of failure tolerance for global data files, but the hypercube scheme provides more than adequate failure tolerance. Lastly, a generalization of the hypercube scheme, based on the ideas of shift registers, is also proposed for systems where the number of nodes is a perfect power of 2. I.

In this paper, we propose a novel contention-based protocol called backoff counter reservation and classifying stations for the IEEE 802.11 distributed coordination function (DCF). In the proposed scheme, each station has three states: idle, reserved, and contentious. A station is in the idle state if it has no frame ready to transmit. A station is in the reserved state if it has a frame ready to transmit and this frame's backoff counter has been successfully announced through the previous successfully transmitted frame so that other stations know this information. A station is in the contentious state if it has a frame ready to transmit, but this frame's backoff counter has not been successfully announced to other stations. All the stations in the idle state, the reserved state, and the contentious state form an idle group, a reserved group, and a contentious group, respectively. Two backoff schemes are proposed in the BCR-CS protocol based on the number of stations in the contentious group including the optimal pseudo-p-persistent scheme. The proposed schemes are compared with the DCF and the enhanced collision avoidance (ECA) scheme in the literature. Extensive simulations and some analytical analysts are carried out. Our results show that all proposed schemes outperform both the DCF and the ECA, and the BCR-CS with optimal pseudo-p-persistent scheme is the best scheme among the four schemes

As more and more wireless LANs are deployed in many popular locations, there is a need for dynamic radio resource management (RRM) schemes. This paper proposes a new dynamic RRM technique for multiple APs network. First, we develop a real-time algorithm to estimate the number of active stations from the standpoint of an AP, instead of a station. Second, we propose a dynamic RRM algorithm that not only significantly increases network performance but also reduces the co-channel interference. Third, we find the optimal switching probability that minimizes the transient time for an AP to reach its equilibrium state. For uniform traffic conditions, our scheme produces classical optimal frequency assignments. For non-uniform traffic conditions, it produces sub-optimal frequency assignments. Our simulations have shown that the proposed RRM technique adapts to dynamic network conditions and increases the channel throughput up to 65% in less than 20 iterations. The results can be applied to practical WLANs to significantly improve network performance

We study in this paper whether mobility can be supported by the 802.16d network without any change in the specification. Mobility capability involves two main issues: connection handoff and correct reception for moving terminals. We find that seamless connection handoff can be achieved within the 802.16d standard by: 1) applying some of the existing functionalities defined for the terminal initialization process; 2) devising a set of protocols for message exchanges for handoff; and 3) forwarding some of the operational parameters from the current base station to a new one via the backhaul network, instead of over the radio link. As for reception at moving terminals, our analysis of bit error rate for the OFDMA mode shows that, under typical radio conditions, the link can provide satisfactory error performance for terminal speed up to tens of kph. As a result, we show that the current 802.16d standard with our proposed technique can support user mobility.

Integration of 3G and wireless LAN (WLAN) becomes a trend in current and future wireless networks, and brings many benefits to both end users and service providers. In this paper, we provide a comprehensive survey on integration of 3G and WLAN. We discuss issues such as underline network architectures, integrated architectures, mobility management, and quality of service (QoS). We particularly study handoff QoS mapping and guarantee between 3G and WLAN, as well as how seamless voice/multimedia/data handoff becomes possible. Copyright © 2005 John Wiley & Sons, Ltd.

The Transmission Control Protocol (TCP) has been designed to provide reliable transport of packets by adjusting the transmission rate to the network congestion level. While TCP can adapt to small fluctuations in the delay between the sender and the receiver, adverse affects (most importantly, spurious timeouts) have been observed under large delay variability. We exhibit the presence of such delay spikes in wireless networks and discuss their possible origins. We then investigate a new methodology for avoiding spurious TCP timeouts by appropriately injecting additional random delay along the communication path. Different algorithms for the delay injection are presented and we assess their relative performances and merits through simulations. In particular, we show, by numerical examples, that the delay injection methodology can significantly decrease the number of timeouts and increase the achieved TCP throughput by about 8% in the network scenario considered. One of the attractive features of the new methodology is that it does not require any changes to the TCP and can be applied independently of the TCP version used.

Current and next-generation wireless networks rely on multiuser diversity and scheduling techniques, such as the commonly used proportional fair (PF) algorithm, to achieve greater system throughput and higher efficiencies for wireless data applications over a time-varying wireless channel. We show that the variability of the inter-scheduling intervals, as introduced by the PF scheduling algorithm, can have adverse effects on TCP and its congestion control mechanism and lead to spurious timeouts and unnecessarily low throughput. We propose an enhanced scheduling algorithm that is explicitly tuned towards throughput performance at the TCP layer. However, this algorithm does not use any explicit information from the TCP layer and solely relies on information readily available at the link layer at which the scheduler resides. The performance of this improved algorithm is assessed through extensive simulations to show an average TCP throughput improvement of 12% compared to PF. In addition, the TCP-level fairness across all users is increased, as is the individual user throughput.

We explore the feasibility of designing an outdoor cellular network based on the IEEE 802.11 standard, which was developed originally for wireless local-area networks. For channels typical in cellular networks, we study the radio link power budget and, via simulation, the bit-error performance of three kinds of receiver: (1) the constrained RAKE, which is limited to a 1 /spl mu/s multipath span; (2) the full RAKE, which uses the full multipath channel information; (3) the ideal equalizer, the performance of which is represented by the matched filter bound. Our link budget reveals that the maximum cell radius in an outdoor 802.11 network ranges from 0.7 to 3 km, about half that supported by WCDMA and EDGE networks. For an RMS delay spread of 1 /spl mu/s, typical for urban-area cells of this size, our simulation results show that the conventional constrained RAKE receiver may yield a satisfactory performance. The improved receivers, however, yield 1-5 dB gain over the constrained implementation. Combining these results with those in a companion paper on the MAC protocol (see Leung, K.K. et al., ibid., p.595-599), we conclude that an 802.11 based cellular network with a cell radius of a few km is feasible.

We explore the feasibility of designing an outdoor cellular network based on the IEEE 802.11 specification. Since the standard is intended for wireless local-area networks (WLAN), there are many technical challenges when applying the air interface to the outdoor environment. We study how the 802.11 medium access control (MAC) protocol can be applied and how it performs in the outdoor network. By exploiting the fact that timeout intervals are not explicitly specified, without modifying the standard, we propose a new timing structure for the distribution coordination function (DCF) and the handshake of request-to-send (RTS) and clear-to-send (CTS) to handle increased signal propagation delay in the outdoor network. We find that the DCF and RTS/CTS protocols as specified in the standard continue to work properly for a link distance up to 6 km. Our analysis reveals that the DCF performance degrades slightly in the 802.11 network with a cell size of 6 km when compared with the 600 m WLAN. Thus, as far as the MAC protocol is concerned, the 802.11 outdoor, cellular network with 6 km cell size is feasible.

In this paper, we analyze the performance of proxy functions for controlling the quality of service in wireless networks, where the proxy performs aggressive content reduction as a means to throttle traffic when the radio link is congested. The proxy may be located at different parts of the network, thus affecting the feedback delay from the base station to the proxy. A queueing model is constructed and solved to study the performance tradeoffs among system parameters and in particular, how the proxy performance is affected by the feedback control delay between base stations and the proxy. Using image compression as an example, we examine response time and fraction of compression as a function of the control delay. Our results reveal that the proxy function can effectively control response time in the case of link congestion, if the delay is reasonably small, e.g. when the proxy is located close to the radio link. We also find that to assess the effectiveness of the control mechanism, it is necessary to examine whether the proxy performs content reduction when and only when link congestion occurs. Towards this end, we study the system-state probabilities and the correlation between response time and compression.

We devise an integrated wireless network architecture using proxy servers to support mobility management. The technique takes advantage of the existing functionalities of proxy servers to provide mobility support for applications such as Web browsing and ftp, without modifying the IP protocol stack of the mobile host. The architecture uses proxy servers to force all packets originating from mobile hosts to a close-by mobility-aware router so that the latter can maintain active data connections during handoffs across different networks. By deploying multiple proxy and mobility-aware router pairs and by assigning mobile hosts to proxy servers dynamically, the proposed architecture provides efficient mobility management functionalities, and is inherently scalable.

A Kalman-filter method for power control is proposed for broadband, packet-switched time division multiple access wireless networks. By exploiting the temporal correlation of co-channel interference, a Kalman filter is used to predict future interference power. Based on the predicted interference and estimated path gain between the transmitter and receiver, the transmission power is determined to achieve a desired signal-to-interference-plus-noise ratio (SINR). A condition to ensure power stability in the packet-switched environment is established and proven for a special case of the Kalman-filter method. The condition generalizes the existing one for a fixed path-gain matrix, as for circuit-switched networks. Performance results reveal that the Kalman-filter method for power control provides a significant performance improvement. Specifically, when messages consist of ten packets on average, the 90th and 95th percentile of the SINR by the new method are 3.79 dB and 5.46 dB above those when no power control is in use, and lie just 0.96 dB and 1.14 dB below the upper-bound performance of the optimal power control, respectively, in a system with four-sector cells and an interleaved frequency assignment of a reuse factor of 2/8. In addition, the new method performs noticeably better than the delta-modulation method and a simple scheme that uses the last measurement as predicted interference power. In an example of 8-PSK modulation and average message length of 20 packets, the SINR performance gain by the new method improves the network throughput by about 150% and 70%, relative to no power control and the simple scheme, respectively.

In this paper, we prove that the problem of maximizing data throughput by adaptive modulation and power control while meeting packet error requirements is NP-complete. A heuristic algorithm for integrated link adaptation and power control is, thus, proposed to achieve specified error rates and to improve overall throughput for real-time applications in broadband wireless packet networks. The algorithm divides terminals into groups according to their signal path gains and periodically adapts transmissions based on the required error rates, actual error statistics, and average transmission power of each terminal group. Transmission power is adjusted by an enhanced Kalman-filter method to ensure successful reception. Extensive simulation results reveal that the algorithm consistently delivers the specified error performance and attempts to maximize network throughput for a wide range of parameter settings.

It has been shown that link adaptation and power control can improve the performance of our future wireless packet networks. Realizing the expected performance gain of these techniques requires accurate prediction of future interference power. We propose a new method based on Kalman filtering for interference estimation. The new method is devised by observing: (a) it is possible to identify fairly accurately the number of active co-channel interferers in the cellular networks and (b) interference power is positively correlated with the number of active interferers. The new technique uses a two-dimensional Kalman filter to exploit that correlation to enhance the prediction accuracy. Using a cellular network with 1/3 frequency reuse and partial traffic loading, the performance of the new method is compared with a simplified method using a one-dimensional Kalman filter where the number of active interferers is not considered. Further, the new method is compared with the traditional exponential filtering. Since the proposed method and the simplified method track interference and measurement errors separately, their predictions represent closely the best estimation by exponential filtering with the optimal parameter. In addition, for a typical network environment, the two-dimensional method yields the lowest prediction errors for a wide range of parameters, and provides a 0.5 dB improvement for the 90th percentile estimation error over the simplified method due to exploitation of the positive correlation between interference and the number of active interferers.

Using the MPEG-4 advanced audio coder (AAC) music as an example of streaming applications, we investigate the improvement of error performance for the streaming service by link adaptation and power control techniques in an enhanced general packet radio services (EGPRS) cellular network. A low packet error rate and variability are essential in providing a short error-burst length so that error concealment techniques can be effectively applied to music packets. We study the effects of a combined link adaptation and power control scheme (referred to as the error-based scheme) for achieving a target error rate and reducing error variability. By simulation, we compare the error performance of the error-based scheme at both the EGPRS block and AAC frame level with another adaptation algorithm (referred to as the throughput-based scheme) with a goal of maximizing overall network throughput. It is found that when offered with a similar traffic load, the former scheme can provide noticeable improvement of music quality over the throughput-based scheme. To achieve a similar AAC frame error rate, our results also show that the error-based scheme can increase the link throughput over the throughput-based scheme by 66.7% in one of our examples. These results reveal that by aiming at required error targets and thus reducing error variability, the error-based scheme for link adaptation and power control are helpful in improving quality and capacity for streaming applications.

Abstract Performance enhancing proxies have drawn considerable interests from the network community as an effective approach to improve user experience in cellular networks. Previous research on this subject has been focusing on the design of proxy servers as well as the functions that these servers provide. In this paper, we study the placement of proxy servers, especially the approach of incorporating proxies into cellular networks, which allows better, faster, and more secure proxy services. We discuss various factors that must be considered when placing proxies inside cellular networks, including the information requirements of the proxies, the network requirements for supporting the proxy functions, and mobility management. Using GPRS as an example, we lay out a procedure for adding proxies into data transmission paths in cellular networks. Copyright © 2001 John Wiley & Sons, Ltd.

In this paper, using the EDGE system as an example, we apply a Kalman filter power control method based on interference tracking and prediction to packet voice services in wireless networks. Our results reveal that power control significantly improves the spectral efficiency by enabling 1/3 frequency reuse while maintaining a stringent requirement of 2% packet loss probability for a voice service. More specifically, for allocated spectrum of 1.8, 3.6 and 5.4 MHz, the 1/3 reuse with the Kalman power control can yield 102.5%, 49.5% and 32.5% improvement in spectral efficiency, respectively, over 3/9 reuse (regardless of whether or not power control is used). We also find that the Kalman method provides 20% additional spectral efficiency when compared with a traditional SIR power control method, and the former method is more robust than the latter for increased power update period. The protocol requirements for the implementation of the Kalman method in the EDGE system are also discussed.

Performance enhancing proxies have drawn considerable interests from the network community as an effective approach to improve user experience in cellular networks. Previous research on this subject has been focusing on the design of proxy servers as well as the functions that these servers provide. We study the placement of proxy servers, especially the approach of incorporating proxies into cellular networks, which allows better, faster, and more secure proxy services. We discuss various factors that must be considered when placing proxies inside cellular networks, including the information requirements of the proxies, the network requirements for supporting the proxy functions, and mobility management. Using GPRS as an example, we lay out a procedure for adding proxies into data transmission path in cellular networks.

A power-control method based on the tracking of interference power by use of a Kalman filter, proposed earlier for packet-switched TDMA wireless networks, does not yield a performance gain in case of short message lengths and/or moderate control delays. The major reason is that the interference prediction by the filter may not be accurate enough due to little interference temporal correlation. In this paper, we enhance the power-control method by introducing an error margin in determining the transmission power. The error margin is obtained based on tracking of the interference prediction error, which automatically captures the impacts due to short message lengths and control delays. Our performance results reveal that the enhanced power-control method is capable of providing a significant performance improvement even for short messages and moderate control delays. Specifically, for the worst case where the message length L=1 (i.e., one packet per message), the 90 and 95 percentile signal-to-interference-plus-noise ratios (SINR) for the enhanced method are 2.69 and 2.96 dB above those for no power control in a system of 4-sector cells with a frequency reuse factor of 2/8. In contrast, the original Kalman-filter method with no error margin yields no SINR gain for L=1. For L=10, we also observe a similar improvement by the enhanced method for control delays up to 3 time slots.

We propose an improved sectorization scheme, called narrow-beam quad-sector cell (NBQC) for cellular networks, in which each cell is divided into four sectors and each sector is covered by a 60/spl deg/ antenna. The NBQC structure allows easy implementation of the concept of interleaved channel assignment (ICA), which can take full advantage of antenna directivity. With ICA, the NBQC system can enhance the system performance from several perspectives. First, the NBQC has better coverage performance than the current three-sector cellular architecture. Second, we demonstrate that in a typical radio environment, the NBQC system ran achieve a reuse cluster size N=2 with the signal-to-interference ratio (SIR) as high as 11 dB in 90% of the cell area, which is a 3-5-dB improvement over the existing cellular architectures. Third, as compared to the most advanced three-sector clover-leaf cell architecture with reuse cluster size N=3, the NBQC system with ICA can achieve reuse cluster size N=2 with very slight degradation in SIR performance, thereby still improving the system capacity by about 10% over a wide range of ninetieth percentile SIR requirements.

A Kalman-filter method for power control is proposed for broadband, packet-switched TDMA wireless networks. By observing the temporal correlation of cochannel interference when transmitters can send data contiguously, a Kalman filter is used to predict interference power in the future. Based on the predicted interference and estimated path gain between the transmitter and receiver, the transmission power is determined to achieve a desired signal-to-interference-plus-noise ratio (SINR). Performance results reveal that the Kalman-filter method for power control provides a significant performance improvement. Specifically, when a message consists of 10 packets on average, the 90 and 95 percentile of the SINR by the new method are 3.94 and 5.53 dB above those when no power control is in use, and lie just 0.73 and 1.04 dB below the upper-bound performance of the optimal power control, respectively, in a system with 4-sector cells and an interleaved frequency assignment of a reuse factor of 2/8. As a by-product, these results show that the cell layout and assignment scheme combined with the new method for power control can be used to support high-speed data services.

We propose and analyze a resource allocation algorithm, referred to as the enhanced staggered resource allocation (ESRA) method, for fixed broadband wireless networks with directional antennas at base stations and terminals. The new method is based on the staggered resource allocation (SRA) algorithm with consideration of reception quality at terminals. Terminals are categorized into multiple classes, depending on their ability to tolerate concurrent packet transmissions. The purpose of the ESRA method is to maximize concurrent transmissions up to an extent tolerable by the receiving terminals for throughput improvement, while avoiding major cochannel interference in the networks where frequency is reused very aggressively. For practical radio parameters, the ESRA method provides 98.69% coverage and yields a maximum downlink throughput of 36.10% per sector while meeting a 15 dB signal-to-interference ratio (SIR). As for the uplink, using full power control, the throughput reaches 16.7% per sector with a packet loss probability of less than 5/spl times/10/sup -4/. The high ESRA throughput translates into a very large network capacity, and the high quality of service reveals that the new method can support real-time traffic such as voice and video, as expected in future broadband multimedia services in fixed wireless networks.

We propose a new digital sense multiple access with delayed transmission (DSMA/DT) protocol for the reverse channel in high-speed wireless networks. The new protocol is motivated by the observation that the existing DSMA protocol does not yield satisfactory throughput for long round-trip propagation and processing delay, which occurs in outdoor, high-speed environments or when the receiver hardware requires long signal processing time. The new protocol is intended to reduce the performance impacts of the round-trip delay. Several optional features: look-ahead busy/idle flag, seizure queueing and reserved time slots are also devised for the new protocol. While requiring at most two additional status bits on the forward channel and no additional hardware capability, these features further enhance the protocol performance, and enable constant-bit-rate service with little added complexity. The channel throughput of the DSMA/DT protocol and the optional features are analyzed. Numerical results show a significant improvement of throughput for the new protocol compared to the existing DSMA protocol for non-negligible round-trip delay.

We consider use of fixed broadband wireless networks to provide packet services for telecommuting and Internet access. Each cell is divided into multiple sectors, each of them served by a sector antenna colocated with the base station (BS), and user terminals also use directional antennas mounted on the rooftops of homes or small offices and pointed to their respective BS antennas. To support a target data rate of 10 Mb/s, a bandwidth of several MHz is required. Since radio spectrum is expensive, the bandwidth needs to be reused very aggressively. Thus, efficient strategies for frequency reuse and managing cochannel interference are critically important. We propose several algorithms for dynamic radio-resource allocation in the fixed wireless networks. In particular, a method to be referred to as the staggered resource allocation (SRA) method uses a distributed scheduling algorithm to avoid major sources of interference while allowing concurrent packet transmission and meeting signal-to-interference objectives. The performance of the method is studied by analytic approximations and detailed simulation. Our results show that the combination of directional antennas plus the SRA method is highly effective in controlling cochannel interference. For reasonable system parameters, the SRA method delivers a throughput in excess of 30% per sector while permitting a given frequency band to be reused in every sector of every cell. It also provides satisfactory probability of successful packet transmission. In addition, a simple control mechanism can be applied in the method to improve performance for harsh radio environments.

This paper explores the use of replicated databases for management of customer data (e.g., mobility data, call routing logic) in global, intelligent, and wireless networks. We propose and analyze two, full and partial, data replication schemes-which are compatible with industry protocol standards-and compare them with the traditional, centralized database scheme. By identifying a set of key teletraffic and mobility parameters, we develop a modeling framework based on queueing models and apply it to assess the relative performance and merits of these schemes. The paper also addresses some implementation issues. Numerical results reveal that the full replication scheme outperforms the centralized one over a wide range of the parameters considered in this study. Furthermore, if some customer data-such as location data for highly mobile customers in wireless networks-change frequently, and if each call launches multiple queries into the databases, the partial replication scheme offers further performance improvement. In general, however, the choice of the database design would depend on the specific characteristics of the service and user behavior under consideration.

In the Personal Communication Networks (PCN), and the Universal Personal Telecommunications (UPT) services and possibly other services offered by the Advanced Intelligent Networks (AIN), customer records for call routing and other signaling functions can be distributed and replicated at multiple sites to improve access delay and system availability. We observe that these applications can tolerate data inconsistency among replicated records for a short period of time because the main consequence of accessing obsolete data is a small probability of call misroute. By exploiting this observation, we propose the Primary-Writer Protocol(PWP) as the concurrency control and commitment protocol for updating the replicated databases. The fraction of calls misrouted under the PWP is analyzed. Our results reveal that the fraction of calls misrouted under the PWP is very small for the expected customer behaviour in the PCN (wireless networks), and the UPT and other AIN services.

A new algorithm is developed for calculating normalization constants (partition functions) and moments of product-form steady-state distributions of closed queuing networks and related models. The essential idea is to numerically invert the generating function of the normalization constant and related generating functions appearing in expressions for the moments. It is known that the generating function of the normalization constant often has a remarkably simple form, but numerical inversion evidently has not been considered before. For p -dimensional transforms, as occur with queuing networks having p closed chains, the algorithm recursively performs p one-dimensional inversions. The required computation grows exponentially in the dimension, but the dimension can often be reduced by exploiting conditional decomposition based on special structure. For large populations, the inversion algorithm is made more efficient by computing large sums using Euler summation. The inversion algorithm also has a very low storage requirement. A key ingredient in the inversion algorithm is scaling. An effective static scaling is developed for multichain closed queuing networks with only single-server and (optionally) infinite-server queues. An important feature of the inversion algorithm is a self-contained accuracy check, which allows the results to be verified in the absence of alternative algorithms.

The algorithm developed in Choudhury et al. (1994) for computing (exact) steady-state blocking probabilities for each class in product-form loss networks is extended to cover general state-dependent arrival and service rates. This generalization allows to consider, for the first time, a wide variety of buffered and unbuffered resource-sharing models with non-Poisson traffic, as may arise with overflows in the context of alternative routing. As before, the authors consider noncomplete-sharing policies involving upper-limit and guaranteed-minimum bounds for the different classes, but in the present paper both bounds are discussed simultaneously. These bounds are important for providing different grades of service with protection against overloads by other classes. The algorithm is based on numerically inverting the generating function of the normalization constant, which is derived in the present paper. Major features of the algorithm are: dimension reduction by elimination of nonbinding resources and by conditional decomposition based on special structure, an effective scaling algorithm to control errors in the inversion, efficient treatment of multiple classes with identical parameters and truncation of large sums. The authors show that the computational complexity of the inversion approach is usually significantly lower than the alternative recursive approach.>

The authors introduce a deterministic fluid model and two stochastic traffic models for wireless networks. The setting is a highway with multiple entrances and exits. Vehicles are classified as calling or non-calling, depending on whether they have calls in progress. The deterministic model ignores the behavior of individual vehicles and treats them as a continuous fluid, whereas the stochastic traffic models consider the random behavior of each vehicle. However, all three model's use the same two coupled partial (or ordinary) differential equations to describe the system evolution. The call density and call handoff rate (or their expected values in the stochastic models) are readily computable by solving these equations. Numerical examples are presented to illustrate how the models can be used to investigate various aspects of time and space dynamics in wireless networks. These examples also show that the models can serve as useful tools for system engineering and planning.>

Introduces a deterministic fluid model and two stochastic traffic models for wireless networks. The setting is a highway with multiple entrances and exits. Vehicles are classified as calling or noncalling, depending upon whether or not they have calls in progress. The main interest is in the calling vehicles; but noncalling vehicles are important because they can become calling vehicles if they initiate (place or receive) a call. The deterministic model ignores the behavior of individual vehicles and treats them as a continuous fluid, whereas the stochastic traffic models consider the random behavior of each vehicle. However, all three models use the same two coupled partial differential equations (PDEs) or ordinary differential equations (ODEs) to describe the evolution of the system. The call density and call handoff rate (or their expected values in the stochastic models) are readily computable by solving these equations. Since no capacity constraints are imposed in the models, these computed quantities can be regarded as offered traffic loads. The models complement each other, because the fluid model can be extended to include additional features such as capacity constraints and the interdependence between velocity and vehicular density, while the stochastic traffic model can provide probability distributions. Numerical examples are presented to illustrate how the models can be used to investigate various aspects of time and space dynamics in wireless networks.>

We analyze an asymmetric cyclic-service system with multiple queues and nonpreemptive, time-limited service. The time limit for a server visit at each queue is exponentially distributed. Customer service times and changeover times have general distributions. Using discrete Fourier transforms, the queue-length and delay distributions are solved.>

In a priority-based computer system, besides the regular jobs, an additional job (refereed to as job A ) is invoked infrequently but requires a significant amount of CPU time. To avoid CPU hogging, job A receives (up to) a fixed amount of CPU time whenever it is served. When the time expires, job A immediately relinquishes the CPU and puts itself to sleep for a period of time. By doing so, jobs with low priority may be processed in a timely manner. When the sleep time is over, job A is awakened and waits to resume service according to its priority. Then, the whole process is repeated until job A service is completed. In this paper, such an execution/sleep (ES) scheduling policy is analyzed for serving job A in a nonpreemptive priority queuing system. The Laplace Transforms are derived for: (i) the conditional response time of job A and (ii) the response time for jobs with priorities higher and lower than job A. This work is motivated by the ES policy in a switching system in which job A is invoked in response to the failure of signaling links. The proposed model is applicable to other real-time computer systems, and the modeling techniques can be applied or generalized to analyzing other scheduling policies in which timers are involved.

The authors examine the behavior of a source subject to flow control by a credit manager. The source receives packets for transmission into a high-speed network according to a renewal process. The credit manager regulates the flow of data into the network by the following method. First, credit is generated at a fixed rate and is allowed to accumulate subject to an upper bound. Second, a packet is allowed to start transmission only if the accumulated credit is at least as large as the service time of the packet. Otherwise, the packet waits until the required amount of credit has been accumulated. Third, the credit bank is depleted at the onset of service by an amount which equals the service time. The main purpose of the credit manager is to smooth out the burstiness of the input process, thereby making it easier for the network to handle large amounts of data without undue delays, congestion, or buffer overflows. Despite the difficulty of this problem, the authors find the distributions of queue length, sojourn time, and interdeparture time by assuming a special structure for the service-time distribution and the credit bank. Numerical examples are included.>

The authors examine the behavior of a source subject to flow control by a credit manager. The source receives packets for transmission into a high speed network according to a renewal process. The credit manager regulates the flow of data into the network by the following method. First, credit is generated at a fixed rate and is allowed to accumulate, subject to an upper bound. Second, a packet is allowed to start transmission only if the accumulated credit is at least and as large as the service time of the packet. Otherwise, the packet waits until the required amount of credit has been accumulated. Third, the credit bank is depleted at the onset of service by an amount which equals the service time. The main purpose of the credit manager is to smooth out the burstiness of the input process, thereby making it easier for the network to handle large amounts of data without undue delays, congestion, or buffer overflows. Despite the difficulty of this problem, the distributions of queue length and sojourn time are found by assuming a special structure for the service time distribution and the credit bank. Numerical examples show that the algorithms can be used to solve practical problems.>

An asymmetric cyclic-service system with a probabilistically limited (PL) service policy is analyzed. In such a service policy, the maximum number of customers served at a queue during a server visit is determined by a probability which is independent of system states. Exhaustive, limited-k, and Bernoulli services are special cases of the PL policy. Customer service times and changeover times have general distribution. A numerical technique based on discrete Fourier transforms is proposed to solve for the queue-length distributions. Thus, the waiting and response-time distribution are obtained. A set of numerical examples is presented to validate the approach.>

An analytic model is proposed for estimating task response times in distributed systems with resource contentions. The model consists of two submodels. The first submodel is an extended queuing network model used for approximating module response times. This submodel is solved by a decomposition technique which reduces the computational complexity by two to three orders of magnitude when compared with a direct approach. The second submodel is a weighted control-flow graph model from which task response time can be obtained by aggregating module response time in accordance with the precedence relationships. Task response times estimated by the analytic model compare closely with simulation results. It is shown that resource contention delays depend on the availability of resources as well as on the invocation rates and response times of the modules that use the resources. The model can be used to study the tradeoffs among module assignments, scheduling policies, interprocessor communications, and resource contentions in distributed processing systems.>

An interactive numerical solution to the waiting time distributions for asymmetric token-passing systems (of more than two queues) with a limited-one service policy is proposed. Customer service times and changeover times (incurred by the server to switch from one queue to another) have general distributions. A set of four embedded Markov chains is obtained by observing the system state at the instants of (customer) service beginning, service completion (server) visit beginning, and visit completion. Using results of Eisenberg (1972) the probability generating function (PGF) for the marginal queue-length distribution for each queue at a service completion is obtained. This PGF involves an unknown PGF for the system state probabilities at visit-completion epochs, where the latter are solved by a numerical technique based on discrete Fourier transforms. Thus, the waiting time distribution for each queue is found. Several numerical examples are presented to validate the proposed approach. Areas for improvements and extensions of this numerical technique are also discussed.>

An M/G/1 queue with server vacations and nongated time-limited service is analyzed. A functional equation which characterizes the system behavior is derived. The equation is solved by a numerical technique that approximates the unknown function by a weighted sum of Laguerre functions with unknown coefficients. The functional equation is transformed into a set of linear equations from which the coefficients can be computed. By the work-decomposition and Poisson arrivals see time averages (PASTA) properties, the average customer response time can be readily obtained for the case of exponential service time. Numerical examples are included to demonstrate the validity of the technique.>

An M/G/1 queue with server vacations and gated time-limited service is analyzed. At each visit, the server serves the queue up to a fixed amount of time. When the time expires or after all candidate customers have been served, whichever occurs first, the server takes a vacation. The service policy is gated, since only those customers present at the beginning of a server visit (poling instant) are candidates for service during that visit; subsequent arrivals are deferred until the next visit. A functional equation which characterizes the amount of work, U/sub p/, at a polling instant is derived. To solve the equation, a numerical technique is utilized in which the complementary cumulative function for U/sub p/ is closely approximated by a weighted sum of Laguerre functions with unknown coefficients. The equation is then transformed into a set of linear equations from which the coefficients can be computed. By the stochastic decomposition and Poisson-arrivals-see-time-averages properties, the average customer response time can be related to the average amount of work found by an arrival. Several numerical examples are included. The model studied is applicable to communication and computer systems where timers are used to allocate service to customers.>

An analysis is conducted of an M/G/1 queue with server vacations and gated time-limited service. The authors derive a functional equation which characterizes the amount of work, U/sub p/, at the server's return from a vacation. To solve the equation, they use a numerical technique in which the complementary cumulative function for U/sub p/ is closely approximated by a weighted sum of Laguerre functions with unknown coefficients. The functional equation is transformed into a set of linear equations from which the coefficients can be computed. Using the work-decomposition and PASTA (Poisson Arrivals Sec Time Averages) properties, the average customer waiting time can be readily obtained. Several numerical examples are included to demonstrate the validity of the technique. The model studied is applicable to analyzing a specific recently proposed communication channel that alternately serves voice and data traffic, token-passing networks with token-holding timers, and other communication and computer systems where times are used to allocate service among multiple types of customers.>

Response time is an important design criterion for real-time systems. A new analytic model is developed to estimate task response time. It considers such factors as interprocessor communication, module precedence relationship, module scheduling, interconnection network delay, and assignment of modules and files to computers. Since module assignment as well as its replication have great impact on task response time, a new algorithm is developed to iteratively search for module assignments and replications that reduce task response time. An objective function is introduced that is based on the sum of task response time and delay penalty for the violations of thread response time requirements. With this objective function, good module allocations and replications, which minimize task response time and yet satisfy the thread response time requirements, can be determined by the proposed algorithm. To validate the algorithm, we compare the assignments generated by the algorithm for some sample distributed systems to the optimal module assignments obtained from exhaustive search. It shows that with a very small number of initial module assignments, our algorithm is able to generate the optimal or close-to-optimal assignments. The algorithm is also applied to a real-time distributed system for space defense applications where exhaustive search for the optimal assignment is not feasible. The generated module assignments (with replications) satisfy the specified thread response times, and compare closely with the simulation results. A series of experiments is also performed to characterize the behavior of the algorithm. In conclusion, the algorithm can serve as a valuable tool for assigning modules with replications for distributed systems.

In a wideband communication channel (> 100 MHz) local network, the propagation delay becomes comparable to the packet transmission time. As a result, CSMA-type protocols may not provide efficient channel utilization. A new channel access protocol, contention based channel reservation (CBCR) that is based on channel reservation is proposed in this paper. Our investigation reveals that the new channel access protocol yields better performance than that of CSMA-type protocols for operating in these high data rate environments. Thus, channel reservation allocation is a good alternative to the contention strategy for high speed local networks.