In resource-constrained Internet of Things (IoT) environments, lightweight deep learning is crucial for Radio Frequency Fingerprint Identification (RFFI). However, existing lightweight designs primarily rely on group convolution with a “hard split" topology, which strictly isolates channels and blocks inter-group information sharing, impairing feature extraction. To address this, we propose overlap convolution, a novel operator employing a “soft coverage" mechanism to facilitate inter-group interaction. By enabling tunable channel overlapping, this method unifies and generalizes standard and group convolutions, restoring inter-group interaction without requiring additional mixing. We then analyze the approach by introducing structural constraint entropy as an interpretive framework to investigate the information flow capacity. Theoretical analysis demonstrates that overlap convolution offers expanded structural flexibility to achieve enhanced learning ability. Furthermore, we establish the OverlapConv framework for systematic integration of the operator into networks. Within this framework, we develop an automatic parameter acquisition strategy based on a differentiable-to-discrete transition mechanism to efficiently narrow down the search space for optimal settings. Extensive evaluations across diverse datasets (LoRa and UAV RFFI) and multiple backbones (MobileNet, ShuffleNet, and EfficientNet) confirm the method’s effectiveness as a universal plug-and-play module. Notably, our approach achieves an accuracy gain of 29.22% on the Fire block, and improves the accuracy (averaging 2.1% and 2.47% on the two datasets) of FasterNet, EfficientNet, and LMSCNet with reduced FLOPs.

Considering the mobility of high-speed railway (HSR) trains, we propose an interruption-avoiding transmission paradigm for content delivery in HSR communication systems, enabled by a space-terrestrial integrated network (STIN) that supports both out-carriage and in-carriage communications. Each content delivery task must be fully transmitted from a satellite or terrestrial base station to the corresponding carriage femto-cell access point (FAP) before handoff occurs. To minimize the maximum transmission latency, we formulate the Content delivery task Scheduling and Resource Allocation problem for both Out-carriage and In-carriage communications (CSRA-OI) over a long-term scheduling period. To solve the CSRA-OI problem, we decompose it into two interrelated sub-problems, i.e., a transmission rate control and in-carriage bandwidth allocation (RCIBA) sub-problem, and a content delivery task scheduling and out-carriage resource management (CSORM) sub-problem. We show that the optimal solution to the RCIBA sub-problem can be obtained by solving a series of linear programming problems. To solve the CSORM sub-problem, we propose a multi-agent deep reinforcement learning (DRL)-based framework assisted by a graph attention network (GAT). We present a novel training scheme for the CSORM framework, where the policy networks are updated rapidly in a distributed manner while the critic network and GAT are updated jointly at a slower rate. Extensive experiments validate the advantages of our approach.

This paper studies network resource allocation for multiple IoT devices as physical systems (PSs) tasked with maintaining their digital twins (DTs) at a shared edge server (ES) through a shared communication channel. The problem is formulated as a constrained Markov decision process with an objective of reducing the average transmission power of the PSs while keeping the age of information (Aol) at the DTs below predetermined targets. A hybrid decision making frame is proposed, where multiple agent reinforcement learning is used to make decisions for the transmission power of the PSs in a distributed way, and a centralized and deterministic algorithm is proposed to allocate the computation resources of the ES among the DTs. Simulation results show that, compared with the multiagent duelling double deep Q-Network, the proposed multi-agent deep deterministic policy gradient for power allocation together with the urgency-baed computation resource allocation solution achieves much lower the average power consumption of the PSs while maintaining low AoI violation rate at the DTs.

In federated learning (FL), clients update their local machine learning models using private data that is not to be shared with others. In each update period, the local models are then shared with a central server that maintains a global model that is used by all the clients. In this paper we consider the problem of scheduling and bandwidth assignment for vehicles that share a wireless communication channel during the FL. The objective is to minimize the update period duration so that global model updates can occur as quickly as possible. This is done by creating a transmission schedule and a fractional bandwidth assignment for each FL update period. The problem is modeled as a mixed-integer nonlinear program (MINLP) and since the problem is NP-complete, approximation algorithms are introduced that yield near-optimal solutions. This is done by doing a binary search on the update duration using a fractional relaxation and then by applying different dependent rounding procedures to obtain valid solutions. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solutions when compared to the results obtained by an optimum direct solver on the same inputs.

A digital twin (DT) is a software version of a physical system (PS) that interacts with other objects on its behalf. In order to do so, changes in the PS must be communicated to the DT in a timely fashion, and this updating is referred to as DT synchronization. This paper addresses the Minimum Synchronization Period (MSP) problem in vehicular networks, which seeks to place DTs on execution servers (ESs) so as to minimize the maximum synchronization period for all physical systems and their DTs (PS-DT pairs), while satisfying communication and computation requirements. A novel solution is proposed by modelling the MSP problem as a multi-commodity quickest flow evacuation problem, which treats the synchronization data and processing as flow network inputs to be evacuated in the shortest possible time. Transmission and computation components are represented as network flows with linear edge delays, which enables the use of well-known techniques to find the quickest flow solution. To ensure that each DT is placed at a single execution server, an unsplittable flow rounding procedure is used that assigns DTs to servers without significantly increasing the synchronization objective. Simulation results demonstrate the quality of the MSP solutions produced by our algorithm using the optimal fractional solution as a lower bound for the optimal integral solution.

Vehicle Edge Computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications. However, during peak hours of urban road traffic, the scarce computational resources available at edge servers could pose challenges in fulfilling the processing needs of vehicles. Introducing Unmanned Aerial Vehicles (UAVs) as supplementary edge computing nodes could significantly mitigate the aforementioned issue. In this paper, we propose a flexible edge computing framework in which a fleet of UAVs function as mobile computational service providers, offering computation offloading services to multiple vehicles. We design and optimize a computation offloading model for the UAV-enabled vehicle edge computing environment. The proposed model tackles the task offloading challenge, aiming to optimize UAV revenue and task processing efficiency while considering the constraints of UAVs’ restricted computational power and energy resources. Towards this end, our model jointly considers two key factors: task partitioning and computational resource allocation. To tackle the challenges posed by the aforementioned non-convex optimization problem, we construct a Markov Decision Process (MDP) model for the multi-UAV-enabled mobile edge computing system and introduce an innovative Multi-Agent Deep Reinforcement Learning (MADRL) framework addressing the decision-making challenge represented by MDP model. Comprehensive simulation outcomes illustrate that our devised task offloading technique outperforms other optimization methods.

The inherent nonlinear and memory-dependent input-output characteristics of piezoelectric actuators pose challenges to the precision of piezoelectric positioning systems. In order to solve this problem, this paper firstly transforms the Jiles-Atherton (JA) model into a neural network structure, designs the Jiles-Atherton neural network (JANN), and combines JANN with nonlinear autoregressive exogenous input (NARX) neural network. A hybrid JA-NARX neural network model is proposed for the first time. This model has the advantages of simple structure, high modeling accuracy, and good interpretability. The effectiveness of the proposed JA-NARX neural network model is validated through a series of experiments, specifically assessing its capacity to accurately capture rate-dependent and asymmetric hysteresis characteristics. The results show that although the proposed neural network model has fewer layers and relatively simple structure, it can realize the high-precision modeling of piezoelectric hysteresis dynamics at a lower computational cost. The experimental data shows that, under the excitation of 60 Hz input signal, the model's PV error only accounts for 0.82% of the full scale range, and the modeling performance is far superior to other models.

Abstract In order to solve the related problems of detection and tracking of shallow marine organisms, this paper designs a YOLO v5 multi‐target detection and tracking algorithm with attention mechanism. When working underwater, the authors usually encounter many difficulties. Different luminosity and complex coral background usually affect the detection of marine organisms. At the same time, the unrestricted movement of marine organisms, the ability to hide behind rocks and algae, and their mutual occlusion while swimming pose additional challenges to this task. Considering the characteristics of shallow marine organisms activity environment, the attention mechanism is added to the feature extraction network of YOLO v5 to reduce redundant information and improve the detection accuracy of shallow marine organism targets in complex environment. The improved algorithm improves the average detection accuracy of marine organisms target detection by 3.2%. Aiming at the problem of shallow marine organisms target tracking, a shallow marine organisms multi‐target tracking algorithm based on improved Deep Simple Online And Realtime Tracking (SORT) is designed. The improved YOLO v5 algorithm is used to replace Faster R‐CNN (Faster Region‐Convolutional Neural Networks) as the detector of DeepSORT tracking algorithm, and the cascade matching strategy is adopted to solve the problem that the target cannot be tracked continuously when it is occluded for a long time. The experimental results show that the shallow marine organisms multi‐target tracking algorithm based on improved DeepSORT reduces the number of id transformation of marine biological target tracking in shallow sea environment, and improves the accuracy of shallow marine organisms multi‐target tracking.

This paper introduces algorithms for static task class partitioning in mobile computation offloading (MCO). The objective is to partition a given set of task classes into two sets that are either executed locally by the mobile device (MD) or those classes that are permitted to contend for remote edge server (ES) execution. The goal is to find the task class partition that gives the minimum mean MD power consumption subject to task completion deadlines. The paper generates these partitions for both soft and hard task completion deadlines. Two variations of the problem are considered. The first assumes that the wireless and computational capacities are given and the second generates both capacity assignments subject to an additional resource cost budget constraint. The proposed partitioning algorithms are based on heuristic class ordering methods. The paper introduces two class ordering methods, a simpler one based on a task latency criterion, and an hierarchical version that first sorts and groups classes based on a mean power consumption criterion and then orders the task classes within each group based on a task completion time criterion. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solutions for both given and optimized network resource assignments.

Digital twins (DTs) can be used to represent the behavior of real physical systems (PSs) in their interaction with other objects. Each DT periodically communicates with its PS and uses these updates to implement features that reflect the real behavior of the PS. A given feature can be implemented using different models that create the feature with differing levels of system accuracy. In this article, we study the DT model selection problem, where the DTs of multiple PSs are hosted at an execution server (ES). The objective is to maximize the minimum feature accuracy for the requested features by making appropriate model selections subject to the synchronization and ES execution constraints. The model selection problem is first formulated as an NP-complete integer program. It is then decomposed into multiple subproblems, each consisting of a modified Knapsack problem. A polynomial-time approximation algorithm is proposed using dynamic programming to solve it efficiently, by violating its constraints by at most a given factor. A generalization of the model selection problem is then given and an approximation algorithm using relaxation and dependent rounding is proposed to solve the problem efficiently with guaranteed constraint violations. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solutions.

To support safety driving and various on-board services, efficient resource allocation is crucial for the promising implement of vehicle platooning in intelligent transportation systems (ITSs). The resource allocation of vehicle-to-everything (V2X) communications for vehicular platoons is studied in this article. First, a multiobjective function is formulated to jointly optimize sub-band and power allocation to satisfy Quality-of- Service (QoS) in vehicular networks. With the advantage of dealing with complex decision-making problems in multiagent systems, distributed multiagent deep reinforcement learning (MADRL) stands out for resource allocation of vehicular networks. However, it faces the challenge of cooperation aging when every agent is only learning from information of others to form a cooperation model in the training process. Considering the random and dynamic combination of vehicles in vehicle platooning, a tactic-interactive MADRL method named as TacNet is then proposed to improve the cooperation efficiency of multiple agents. In TacNet, the tactics of other agents will be encoded and transmitted through interactive communications among agents. In addition, with the development of vehicular edge computing (VEC), digital twin (DT) networks are constructed to assist offloading computation-intensive resource allocation tasks in vehicles to the edge. The superiority of the proposed method is verified through extensive simulation results, which refers to convergence and performance of satisfying diversified QoS requirements compared with state-of-the-art MADRL methods.

With the ever-increasing number of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliability and high-speed demand of cellular vehicle-to-everything (C-V2X) communication in scenarios where vehicles are moving at high speeds poses a significant challenge. Recently, multiconnectivity technology has become a promising network access paradigm for improving network performance and reliability for C-V2X in the 5G and B5G era. To this end, this article proposes an analytical framework for the performance of downlink in multiconnectivity C-V2X networks. Specifically, by modeling the vehicles and base stations (BSs) as 1-D Poisson point processes, we first derive and analyze the joint distance distribution of multiconnectivity. Then through leveraging the tools of stochastic geometry, the coverage probability and spectral efficiency are obtained based on the previous results for general multiconnectivity cases in C-V2X. Additionally, we evaluate the effect of the path-loss exponent and the density of downlink BS on system performance indicators. We demonstrate through extensive Monte Carlo simulations that multiconnectivity technology can effectively enhance network performance in C-V2X. Our findings have important implications for the research and application of multiconnectivity C-V2X in the 5G and B5G era.

Federated Learning (FL) allows clients to keep local datasets and train collaboratively by uploading model gradients, which achieves the goal of learning from fragmented sensitive data. Although FL prevents clients’ datasets from being shared directly, local private information may be leaked through gradients. To mitigate this problem, we combine game theory to design an FL scheme (IMFL) based on the incentive mechanism and differential privacy (DP). Firstly, we explore three DP variants, all of which are resistant to deep leakage from gradients (DLG) but differ in their level of privacy protection. In addition, we perform the convergence analysis of the FL model based on DP. Then, with the assistance of game theory, we analyze the natural state of the server and clients in the FL process and formulate the utility function of both sides under the case of considering the attack. Finally, we establish the optimization problem as a Stackelberg game and solve for the optimal strategy of the server and clients by deriving the Nash equilibrium to achieve personalized protection. Theoretical proof demonstrates that both types of entities can achieve optimal actions by maximizing their utility functions upon reaching the Nash equilibrium. Besides, extensive experiments are conducted on real-world datasets to demonstrate that the IMFL is efficient and feasible.

Digital twin (DT)-empowered platoon can improve platoon management efficiency and driving safety. However, the resource allocation scheme of low-latency platoon DT (PDT) and the interactions with platoon control strategy are important issues in the study of PDTs. In this article, we study the resource allocation in the PDT network and the interaction mechanism between PDT and platoon control for an unmanned aerial vehicle (UAV)-hosted PDT. We introduce the Age of Information (AoI) metrics to characterize the freshness of the DTs. To explore the impact of the PDT resource allocation scheme on the platoon control strategy, we propose a joint optimization model for power resource allocation and platoon control. Specifically, the allocation of power resources affects the PDT’s AoI, and the high-latency PDT in turn affects the platoon control strategy. Our objective is minimize the weighted sum of the system’s average energy consumption and the PDT’s average peak AoI. To solve the problem, we first reformulate the power resource allocation problem over a period of time as a Markov decision process (MDP) model, and then propose the Dirichlet deep deterministic policy gradient (DDPG)-based power allocation (D3PGPA) method based on Dirichlet distribution and DDPG algorithm. The method can not only effectively explores the state space while satisfying the constraints of limited resources but also improve the stability of the algorithm. Numerical results show that the D3PGPA method can host a PDT with low AoI and improve the stability of the platoon. Besides, our proposed method performs stably and outperforms other benchmark methods.

With the development of Internet of Vehicles (IoV) technology and the application of artificial intelligence-based algorithms, platoon driving based on connected autonomous vehicles (CAVs) has become one of the effective solutions to reduce environmental pollution and improve traffic safety. However, the connectivity, autonomy, and passenger comfort in platooning vehicles cannot be realized without the support of advanced communication technologies and auxiliary computing. In this work, we research the problem of computation offloading and resource allocation within a platoon network. Considering the comprehensive effects of vehicle mobility, co-channel interference, and multivehicle cooperation, we propose a system optimization model for joint computation offloading and power allocation (COPA). Our objective is to minimize the weighted sum of the system average energy consumption and task data processing delay. In the dynamic platoon network, we design a multiagent deep deterministic policy gradient (DDPG)-based joint COPA scheme, which can learn the temporal correlation of environment states and make more accurate power allocation actions. Moreover, we conduct extensive computer simulations to demonstrate the robustness and effectiveness of the DDPG-based COPA scheme. Numerical results demonstrate that the proposed scheme has a better performance compared with other benchmark schemes.

In mobile computation offloading (MCO), mobile devices (MDs) can choose to either execute tasks locally or have them executed on a remote edge server (ES). This paper addresses the problem of assigning the wireless communication bandwidth and the ES capacity used for the task execution, so that task completion time constraints are satisfied. The objective is to minimize the average power consumption of the mobile devices, subject to a cost budget constraint for obtaining the communication and computation resources. The paper includes contributions for both soft and hard task completion deadline constraints. The problems are first formulated as mixed integer nonlinear programs (MINLPs). Approximate solutions are then obtained by decomposing the problems into a collection of convex subproblems that can be efficiently solved. Results are presented that demonstrate the quality of the proposed solutions, which can achieve near optimum performance over a wide range of system parameters.

Convolutional neural networks have been widely used in intrusion detection and proactive network defense strategies such as network security situation prediction (NSSP). The interaction between cross-channel features and the dependencies between elements in the input data are essential factors that affect the prediction model’s performance. However, existing works have ignored these, resulting in performance that needs to be improved. To this end, we propose a GResNeSt model that combines the advantages of the global context block and ResNeSt to improve the NSSP performance. The GResNeSt model strengthens traditional convolutional neural networks in two ways: it effectively captures cross-feature interactions and obtains long-range dependencies of the input data. This enhances its performance in capturing associations among different elements, making it more effective in extracting critical information from data to identify network attacks. We used the Salp swarm algorithm to select optimal hyperparameters for improving the model’s performance. Furthermore, based on the attack impact, we calculated network security situation values of two public network datasets. Finally, comprehensive experiments on the datasets verified our model design and demonstrated that our scheme is superior to other models in terms of NSSP ability.

Digital twins (DTs) are virtual implementations of real physical systems (PSs) that interact with other objects on their behalf. Each PS periodically communicates with its digital twin so that the state of the DT is always sufficiently current. Using these updates, a DT can provide features that represent the real behavior of its PS using models that yield differing levels of system accuracy. In this paper, we study the DT model selection problem in wireless networks where the DTs of multiple PSs are hosted at an edge server (ES). The accuracy obtained from a given model is a function of its required amount of PS input data, the updating frequency, and the amount of computational capacity needed at the ES. The objective is to maximize the minimum achieved accuracy among the requested features by making appropriate model selections subject to wireless channel and ES resource availability. The problem is first formulated as an NP-complete integer program. The paper then uses relaxation and dependent rounding, and introduces a polynomial time approximation algorithm to obtain good solutions. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solution.

The traditional convolutional neural network (CNN) has a limited receptive field and cannot accurately identify the importance of each channel, making it difficult to solve increasingly complex network security problems.To solve these problems, this paper combines ResNeXt with the Efficient Channel Attention (ECA) module and the Contextual Transformer (COT) block to construct a model to assess network conditions.The optimal hyperparameters of the model are selected by the Harris Hawks Optimization (HHO) algorithm.The model can accurately obtain the importance of each channel to assign weights to each channel while making full use of the rich contexts among neighbour keys, effectively enhancing the convolutional neural network.Furthermore, this paper calculates the network security situation value (NSSV) of the adopted datasets based on attack impact.Lastly, experiments on two cybersecurity datasets show that the comprehensive performance of the model on the three indicators of accuracy, precision and F-scores, as well as network security situation assessment, are superior to other models.

The accuracy and reliability of continuous space curve estimation is the key to global exploration. An improved artificial intelligence algorithm is proposed for continuous space analysis. First, a wavelet basis ANN algorithm is proposed to determine the discretization strategy in continuous space. The hidden layer node transfer function in a BP neural network is replaced by a wavelet basis function, and the modified BP neural network is composed of a wavelet neural network. Second, an improved wolf algorithm is established. The core wolf system ensures the precision of the whole exploration task. Finally, main and auxiliary double cores and a five-class decision factor are used to establish a population classification model to solve the convergence of the algorithm.

Digital twins (DTs) are virtual implementations of physical systems (PSs) and can represent the states of the PSs in realtime. In order to update the DTs with changes in their corresponding PSs, the PSs should regularly send their state information data to the DTs. Each DT must be assigned to an execution server (ES) that processes the forwarded data from its corresponding PS. The output is then made available to applications that are operating at an Internet cloud server. In this article, we consider the problem of DT placement such that the maximum data request–response delay experienced by the application over all PSs is minimized, subject to maximum data age target constraints at the DTs and the application server. The problem is first formulated as an integer quadratic program (IQP) and then transformed into a semidefinite program (SDP). The problem is NP-complete. Since exact polynomial solutions are unavailable, several practical polynomial-time approximation algorithms are introduced. The algorithms are designed to give solutions with different tradeoffs between the accommodation of the application input timing latency and the achievement of data age targets.

This paper proposes a method of joint wireless network and job service allocation for use with mobile computation offloading where task completion times have deadline constraints. In this design, mobile devices (MDs) may execute a computational task locally or offload the task through a wireless network for execution on an edge server (ES). The network owner offers to lease wireless communication channels at a given set of base stations along with edge server capacity that is used for job execution. The objective is to obtain a wireless and service capacity allocation that minimizes the total energy consumption of the mobile devices, subject to a cost budget constraint and constraints on the delay incurred by offloaded task execution. The design is first formulated as a mixed integer nonlinear programming problem. An approximate solution is then obtained by decomposing it into a collection of convex subproblems that can be efficiently solved. Results are presented that demonstrate that the proposed solution achieves near optimum performance over a wide range of system parameters.

Digital twin (DT) has attracted a lot of attention from both industry and academia since it was proposed over a decade ago. A DT can be viewed as a virtual implementation of a real physical system (PS) and used as a representation of the PS for various applications. Despite the great potential of DTs in various fields, implementing DTs to obtain the desired functionality is not always straightforward. Specifically, accurate real-time synchronization between the features at a PS and its DT is essential for the DT to represent the PS. In this case, appropriate networking support is a key component to enable future DT development and applications. Currently, the research on DTs from a networking standpoint is still at an early stage, and only limited work has been done on DT implementation in practical systems. To fill this gap, this article investigates networking-related issues for DTs. Based on the existing literature, a feature-based method is provided for describing the desired properties and quality of DTs from the networking perspective. A stage-based implementation framework is presented for creating large-scale DTs for complex PSs by considering various networking constraints. Networking-related challenging issues and open research topics are discussed at the end.

Prolonging the battery lifetime of sensors has been one of the most important issues in wireless sensor networks (WSNs). With the development of Wireless Power Transfer (WPT) technology, sensors can be recharged and possibly have infinite lifetime. One common approach to achieving this is having a wireless charging vehicle (WCV) move in the system coverage area and charge sensors nearby when it stops. The duration that the WCV stays at each charging location, the amount of traffic that each sensor carries, and the transmission power of individual sensors are closely related, and their joint optimization affects not only the data transmissions in the WSN but also energy consumption of the system. This problem is formulated as a mixed integer and non-convex optimization problem, which is transformed and solved optimally. Numerical results demonstrate that the average power consumption of the system can be significantly reduced by optimizing the charging time of individual sensors instead of that of a cluster of sensors.

This work considers opportunistic mobile computation offloading between a requestor and a helper. The requestor device may offload some of its computation-intensive tasks to the helper device. The availability of the helper, however, is random. The objective of this work is to find the optimum offloading decisions for the requestor to minimize its energy consumption, subject to a mean delay constraint of the tasks. The problem is formulated as a constrained Markov decision process by taking into consideration the random task arrivals, availability of the helper, and time-varying channel conditions. Optimal offline solution is first obtained through linear programming. An online algorithm is then designed to learn the optimum offloading policy by introducing post-decision states into the problem. Simulation results demonstrate that the proposed online algorithm achieves close-to-optimum performance with much lower complexity.

This paper considers the problem of algorithmic efficiency in mobile computation offloading with Concurrent Local Execution (CLE). Online energy optimal algorithms can be developed when CLE is used to guarantee hard task deadlines while offloading over Markovian wireless channels. Unfortunately, these algorithms often have a high computational complexity, which prohibits their use in online mobile implementations. Three algorithms are introduced to reduce this complexity: Markovian Compression (MC), Time Compression (TC) and Preemption Using Continuous Offloading (Preemption-CO). MC and TC reduce the state space of the offloading Markovian process, by using a novel notion of geometric similarity, or by running an optimal online offloading algorithm in periodic time steps. In Preemption-CO, while a task is offloaded preemptively, the offloading decision at every time-slot is based on non-preemptive calculations. Our simulations show that, by applying these methods, the running times of the algorithms can be significantly reduced without suffering unreasonable performance degradation compared with the optimal energy performance.

This paper considers mobile computation offloading where task completion times are subject to hard deadline constraints. Hard deadlines are difficult to meet in conventional computation offloading due to the stochastic nature of the wireless channels involved. Rather than using binary offload decisions, we permit concurrent remote and local job execution when it is needed to ensure task completion deadlines. The paper addresses this problem for homogeneous Markovian wireless channel models. An online energy-optimal computation offloading algorithm, OnOpt, is proposed. Its energy optimality is shown by constructing a time-dilated absorbing Markov process and applying dynamic programming. Closed form results are derived for general Markovian processes, and the Gilbert-Elliott channel model is used to show how the particular structure of the Markov chain can be exploited in computing optimal offload initiation times more efficiently. It is shown that job completion time probabilities can be computed recursively, which leads to a significant reduction in the computational complexity of OnOpt. The performance of the proposed algorithm is compared to three others, namely, Immediate Offloading, Channel Threshold, and Local Execution. Performance results show that the proposed algorithm can significantly improve mobile device energy consumption compared to the other approaches while guaranteeing hard task execution deadlines.

Mobile edge computing enables mobile devices to offload their computation intensive applications to servers deployed at the network edge for improving energy efficiency. Multiple mobile users may compete for the wireless network resources when uploading their applications, and coordinating the subcarrier resource allocations among the users is important to optimize the system level energy performance. Partitioning an application further provides the flexibility that allows the mobile device to offload the optimum portion of the application in order to take the best advantage of the available radio resources in computation offloading. In this paper, we study joint partitioning decisions and subcarrier assignments in a multi-cell networks. The objective is to minimize the total energy consumption of the mobile users while satisfying the hard completion time requirements of the applications. The problem is formulated as a mixed-integer nonlinear programming, which is then decomposed into two coupled sub-problems, one linear programming for the offloading decisions under latency constraint of each application, and another matching game with externalities for subcarrier assignments. A joint subcarrier allocation and offloading decision algorithm is proposed, in which matching theory is used to design the user-subcarrier assignments, based on which the optimal offloading ratio is derived for each user. Simulation results demonstrate that the proposed algorithm can greatly reduce the average energy consumption of the mobile users, compared to several other subcarrier assignment and offloading schemes.

Multi-decision mobile computation offloading occurs when a task to be remotely executed is uploaded in separate parts. Since the upload is partitioned, separate decisions are needed to determine the best time to initiate each upload. The multi-decision problem is considered for the case where execution completion times are subject to hard deadline constraints and where task offloads occur over a Markovian wireless channel. An online energy-optimal computation offloading algorithm, Multiopt (Multi-decision online Optimum), is introduced, whose optimality is proven using Markovian stopping theory. The paper presents results using the Gilbert-Elliott channel model, where task completion time probabilities can be efficiently computed using Dynamic Programming. Although the proposed algorithm is proven to be energy optimal, its performance is also compared to four others, namely, Immediate Offloading, Channel Threshold, Local Execution, as well as optimal single-part offloading. Results show that the proposed algorithm can significantly improve mobile device energy consumption compared to the other approaches while guaranteeing hard task execution deadlines.

Advances in Internet of Things (IoT) bring massive intelligent applications, many of which are computation intensive and time sensitive. With limited resources of IoT devices, mobile computation offloading can be exploited to offload part of the applications to nearby devices that have more powerful computing resources, thereby speeding up the applications and reducing the energy consumption. In this paper, we consider application partitioning and collaborative computation offloading in IoT networks, in order to meet the completion deadline of the applications while minimizing the overall energy consumption. The problem is formulated as a binary integer linear programming problem, which is transformed into a weighted bipartite matching problem and then solved by the centralized Kuhn-Munkres algorithm. To fit the large-scale IoT scenarios, three distributed algorithms are then introduced from different perspectives. The first one is referred to as the noncooperative matching (NCM) algorithm, where each node makes offloading decision based on its own interest in minimizing energy consumption. Afterward, an asynchronous greedy matching (AGM) algorithm is developed by considering the mutual interest of the requestor and collaborator pairs in terms of their energy consumptions. Finally, a maximum differential energy matching (MDEM) algorithm is devised by relaxing the network stability requirement, which can further benefit the energy efficiency for all network nodes. Theoretical analysis and simulation results demonstrate that both the NCM and AGM algorithms guarantee the network stability and improve the energy saving compared with entirely local execution, while the MDEM algorithm can further achieve near-optimal energy consumption at the expense of higher implementation overheads.

Consider a network consisting of one multi-antenna base station (BS) and multiple pairs of multi-antenna user equipment's (UEs). For each UE pair, the communication between transmitter and receiver is established either through BS or via device-to-device (D2D) link. We assume that the D2D transmission and cellular transmission are equally prioritized and share the same resources. To improve the network throughput, we maximize the sum rate by jointly optimizing the transmission mode of each UE pair and the associated transceivers. Due to the NP-hardness of this problem, we first perform some efficient approximation to it and then design an iterative algorithm, which is guaranteed to converge to a stationary solution by solving a series of weighted minimum mean square error (WMMSE) problems. The proposed algorithm has two distinguishing features. First, it only solves the WMMSE problem inexactly in each iteration, which thereby has a simplified algorithm structure and accelerated convergence behavior than the classical one. Second, we further fit the WMMSE problem into the alternating direction method of multipliers (ADMM) framework, making it amenable to parallel and distributed computation. Finally, the approximated problem is solved efficiently and distributively, with simple closed-form solutions in each step.

In this paper, we consider how to maintain the communication quality for high-speed users in array transmission. Due to high user speed, the array transmission angle changes quickly. As a consequence, the phase shifters (beamformers) of traditional phase arrays need to be updated frequently to aim at the user, thus yielding high implementation cost. To alleviate this, we propose a novel frequency diverse array (FDA) approach, which intentionally introduces some frequency offsets across the array antennas to activate an angle-range-time dependent beampattern; i.e., the FDA beampattern peak automatically moves in space. This motivates us to carefully design FDA parameters such that the beampattern peak accompanies the quickly-moving users. To this end, we maximize the average beampattern gain along some given user trace by optimizing the frequency offsets. The block successive upper-bound minimization (BSUM) method is applied to obtain a stationary solution to this non-convex problem. Compared with phase array beamforming, the FDA approach maintains service quality for high-speed users by updating frequency offsets less frequently, thus reducing the implementation cost remarkably.

With the rapid development of the Internet of Things, location information becomes increasingly important for various applications. However, the localization information of network devices is vulnerable to various attacks and not always trustworthy. In this paper, we propose a location verification scheme that allows the access point (AP) to verify credibility of a reported location from a network node with assistance of an obstacle that moves actively in the network at a random speed. When the obstacle blocks the transmissions between the AP and a network node, the received signal strength (RSS) at the node is reduced. For location verification, a network node is asked to report its location and the RSS for a period of time. Based on the changes in the reported RSS and the mobility information of the obstacle, the AP can determine whether the network node has reported the correct location. An analytical model is developed to find the performance of the proposed scheme. Simulation results show that the proposed scheme achieves high probability of detecting malicious nodes and low probability of treating legitimate nodes as malicious. Simulation results have also verified the accuracy of the analysis.

Acoustic ranging based indoor positioning solutions have the advantage of higher ranging accuracy and better compatibility with commercial-off-the-self consumer devices. However, similar to other time-domain based approaches using Time-of-Arrival and Time-Difference-of-Arrival, they suffer from performance degradation in presence of multi-path propagation and low received signal-to-noise ratio (SNR) in indoor environments. In this paper, we improve upon our previous work on asynchronous acoustic indoor positioning and develop ARABIS, a robust and low-cost acoustic positioning system (IPS) for mobile devices. We develop a low-cost acoustic board custom-designed to support large operational ranges and extensibility. To mitigate the effects of low SNR and multi-path propagation, we devise a robust algorithm that iteratively removes possible outliers by taking advantage of redundant TDoA estimates. Experiments have been carried in two testbeds of sizes 10.67m × 7.76m and 15m × 15m, one in an academic building and one in a convention center. The proposed system achieves average and 95% quantile localization errors of 7.4cm and 16.0cm in the first testbed with 8 anchor nodes and average and 95% quantile localization errors of 20.4cm and 40.0cm in the second testbed with 4 anchor nodes only.

Delivering media traffic that requires very high bandwidth and good reconstruction quality in wireless mesh networks (WMNs) has been a challenging problem due to the limited bandwidth and multihop transmissions. In this paper, we study the problem of multicasting multiple description coded (MDC) media traffic in WMNs. A cross layer routing scheme is designed that builds multiple multicast trees, each of which delivers one description to the mobile stations (MSs) through multihop transmissions. The distortion of recovered media at an MS is determined by which description or descriptions have been correctly received. Our objective is to minimize the worst distortion at all MSs. A linear optimization problem is formulated for the routing and multicast-tree construction problem, and a heuristic scheme with much lower complexity is then proposed. Simulation results show that the proposed heuristic scheme achieves much lower min-max distortion than a two-step method for building multicast trees, and in some parameter settings can achieve close-to-optimum min-max distortion.

Computation offloading to nearby devices offers resources-constrained mobile devices the opportunity to reduce local computation load and energy consumption when supporting applications with intensive computation load. In this paper, we consider joint job partitioning and collaborative computation offloading in a multi-user wireless network, aiming at minimizing the energy consumption of the overall network under the completion time constraint of each application. The problem is formulated as a binary integer linear programming problem and then transformed into a weighted bipartite graph matching problem. The Kuhn-Munkres algorithm is firstly adopted to obtain the optimal partition and scheduling. Three distributed scheduling algorithms are then introduced. The first one is referred to as stable matching, where each mobile node chooses matching collaborator or requester based on its own interests. Next an asynchronous greedy algorithm is proposed, where every requester-collaborator pair cooperatively reduces their total energy consumption. Finally, based on the greedy algorithm, a modified version, referred to as Maximum Differential Energy Matching (MDEM) algorithm is devised, which minimizes the overall network energy cost by relaxing the stability criterion in order to benefit energy consumption of all nodes. Simulation results show that the MDEM algorithm achieves near-optimal performance in terms of total energy consumption for the network.

Mobile computation offloading is an emerging technology to migrate resource-intensive computations from resource-limited mobile devices (MDs) to resource-rich devices (such as a cloud server) via wireless access. Accessing to remote cloud server usually introduces a long delay to first deliver parameters to the server and then retrieve the results back. For applications that are time sensitive, offloading to nearby cloudlets is preferred. However, the link duration between an MD and a single cloudlet can be very limited in a vehicular network. As a result, offloading actions taken by an MD may fail due to link breakage caused by mobility. Meanwhile, some vehicles, such as buses, always follow relatively fixed routes, and their locations can be predicted much easier than other vehicles. By taking advantage of this fact, we propose a bus-based cloudlet cooperation strategy, where the bus-based cloudlets act as computation service providers for the MDs in vehicles, and an application generated by an MD includes a series of tasks that have dependency among each other. The proposed bus-based cloudlet cooperation strategy (BCCS) finds the optimal set of tasks to be offloaded to each cloudlet. Experimental results show that the proposed BCCS strategy can reduce both the energy consumption of the MDs and completion time of the applications.

This paper considers a set of mobile users that employ cloud-based computation offloading. In order to execute jobs in the cloud, the user uploads must occur over a base station channel that is shared by all of the uploading users. Since the job completion times are subject to hard deadline constraints, this restricts the feasible set of jobs that can be processed. The system is modelled as a competitive game in which each user is interested in minimizing its own energy consumption. The game is subject to the real-time constraints imposed by the job execution deadlines, user specific channel bit rates, and the competition over the shared communication channel. The paper shows that for a wide range of parameters, a game where each user independently sets its offloading decisions always has a pure Nash equilibrium, and a Gauss-Seidel-like method for determining this equilibrium is introduced. Results are presented that illustrate that the system always converges to a Nash equilibrium using the Gauss-Seidel method. Data is also presented that show the number of iterations required, and the quality of the solutions. We find that the solutions perform well compared to a lower bound on total energy performance.

Applications typically exhibit extremely different performance characteristics depending on the accelerator. Back propagation neural network (BPNN) has been parallelized into different platforms. However, it has not yet been explored on speculative multicore architecture thoroughly. This paper presents a study of parallelizing BPNN on a speculative multicore architecture, including its speculative execution model, hardware design and programming model. The implementation was analyzed with seven well-known benchmark data sets. Furthermore, it trades off several important design factors in coming speculative multicore architecture. The experimental results show that: (1) the BPNN performs well on speculative multicore platform. It can achieve similar speedup (17.7x to 57.4x) compared with graphics processors (GPU) while provides a more friendly programmability. (2) 64 cores' computing resources can be used efficiently and 4k is the proper speculative buffer capacity in the model.

In this study, the authors study transmission scheduling for a two‐way relay network in time‐varying fading channels, where the relay node can opportunistically use traditional one‐way relay technique or network coding to forward traffic to the end nodes. They formulate a stochastic dynamic programme with the objective of minimising the long‐run cost, defined as a function of both the transmission power and data transmission delay. An unconstrained Markov decision process model is developed and solved for the average and discounted cost problems. The optimal solution requires high computational and modelling complexity when the state space is large. For this reason, they develop heuristic solutions with lower complexity. For the discounted cost problem, a simulation‐based dynamic programming algorithm is proposed that not only simplifies the modelling process and reduces the computational complexity, but also achieves close‐to‐optimum cost. For the average cost problem, a heuristic scheduling scheme is proposed, which makes transmission decisions based on estimated costs in the current and next time slots. The heuristic scheme achieves close‐to‐optimum cost performance while greatly reducing the computational complexity.

Abstract In this paper, we study multicasting multiple description traffic to a group of mobile stations (MSs). The traffic is pre‐cached at a number of access points (APs), and the MSs have different quality requirements in terms of number of required descriptions. Each AP transmits one description and forms a single‐hop multicast group to reach a certain number of MSs. Different APs can transmit the same or different descriptions. MSs requiring multiple descriptions should be covered by at least the same number of the APs that transmit different descriptions. We study two problems, description assignments, and power allocations. The former is to assign a description for each AP, and the latter is to allocate the transmission power for each AP. Two objectives are considered subject to satisfying the requirements of the MSs, one is to minimize the total transmission power of all the APs, and another is to minimize the maximum transmission power of the APs. For each objective, a centralized and a distributed scheme are proposed, and their performance is compared with the optimum. Numerical results show very good performance of the heuristic schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract In this paper, we study packet transmission scheduling for a network with bidirectional relaying links, where the relay station can use network coding to combine packets to multiple receivers and opportunistically decide the number of packets to be combined in each transmission. Two cases are considered, depending on whether nodes are allowed to overhear transmissions of each other. A constrained Markov decision process is first formulated with an objective to minimize the average delay of packet transmissions, subject to the maximum and average transmission power limits of the relay node. The complexity for solving the constrained Markov decision process (MDP) is prohibitively high, although the computational complexity for the no‐overhearing case can be greatly reduced. Heuristic schemes are then proposed, one applies to the general case, and another applies to only the no‐overhearing case. Numerical results demonstrate that the heuristic schemes can achieve close‐to‐optimum average packet transmission delay, and furthermore, the second scheme achieves lower maximum delay while keeping the same average packet transmission delay and relay node power consumption as the first one. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract Using network coding in a wireless network can potentially improve the network throughput. On the other hand, it increases the complexity of resource allocations as the quality of one transmission is affected by the link conditions of the transmitter to multiple receivers. In this work, we study time slot scheduling and channel allocations jointly for a network with bidirectional relaying links, where the two end nodes of each link can exchange data through a relay node. Two scenarios are considered when the relay node forwards packets to the end nodes. In the first scenario, the relay node always forwards network‐coded packets to both end nodes simultaneously; in the second scenario, the relay node opportunistically uses network coding for two‐way relaying and traditional one‐way relaying. For each scenario, an optimization problem is first formulated for maximizing the total network throughput. The optimum scheduling is not causal because it requires future information of channel conditions. We then propose heuristic scheduling schemes. The slot‐based scheduling maximizes the total transmission rate of all the nodes at each time slot, and the node‐based scheduling schedules transmissions based on achievable transmission rates of individual nodes at different channels. The node‐based one has lower complexity than the slot‐based one. Our results indicate that although the node‐based scheduling achieves slightly lower throughput than the slot‐based one, both the proposed scheduling schemes are very effective in the sense that the difference between their throughput and the optimum scheduling is relatively small in different network settings. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we study multicasting media traffic that uses multiple description coding (MDC) in a wireless mesh network (WMN), where an access point (AP) transmits multiple descriptions to the mobile stations (MSs) through relay stations (RSs). The MSs have different quality of service (QoS) requirements in terms of number of required descriptions, and each RS can forward at most one description. All RSs forwarding the same descriptions form a multicast tree. Our objective is to minimize total transmission power of the RSs, subject to satisfying the QoS requirements of the MSs. We study two problems, building node-disjoint multicast trees and allocating transmission power. The former is to decide which RSs should forward the same description, and the latter is to determine an adequate transmission power level for each RS. An optimization problem is first formulated, and two heuristic schemes are then proposed. The first scheme is a greedy method that iteratively adds new paths to individual multicast trees and assigns transmission power to RSs, and the second one is a simplified version of the first. Numerical results demonstrate that both schemes achieve much lower power consumption compared to a spanning-tree-based scheme that builds the multicast trees one after another, and the power consumption of the first scheme is much lower than the second one at a price of higher complexity.

In this paper, we present a framework of resource allocations for multicast device-to-device (D2D) communications underlaying a cellular network. The objective is to maximize the sum throughput of active cellular users (CUs) and feasible D2D groups in a cell, while guaranteeing a certain level of the signal-to-interference-plus-noise ratio (SINR) for both the CUs and D2D groups. We formulate the problem of power and channel allocations as a mixed integer nonlinear programming (MINLP) problem where each D2D group can reuse the channel of at most one CU and each CU can share their resources with at most one D2D group. A maximum weight bipartite matching based scheme is developed to assign the optimal channel for each feasible D2D group to reuse. A heuristic algorithm is then proposed which has less complexity compared to the matching algorithm. The performance of both schemes is evaluated through simulations. Numerical results demonstrate that the proposed heuristic scheme outperforms other heuristic schemes in the literature and can achieve close-to-optimal performance.

In this paper we consider mobile users that employ computation offloading. In computational offloading, users can reduce energy consumption by executing jobs on a remote cloud server, rather than locally. In order to execute a job in the cloud, a mobile user must upload the job over a base station channel which is shared by all of the uploading users. The jobs are subject to hard deadline constraints, and since the channel quality may be different for each user, this may restrict the users ability to reduce energy usage. The system is modelled as a competitive game where each user is interested in minimizing its own energy use. The game is subject to the real-time constraints imposed by job execution deadlines, user specific channel bit rates, and the competition over the shared communication channel. The paper shows that for known classes of parameters, a game where each user independently adjusts its offload decisions always has a pure Nash equilibrium, and a Gauss-Seidel-like method for determining this equilibrium is presented. Results are then presented which illustrate that the system always converges to a Nash equilibrium using Gauss-Seidel. Data is presented which show the number of Nash equilibria that are found, the number of iterations required, and the quality of the solutions obtained. In particular, we find that the solutions perform well compared to a lower bound on total energy performance.

Network coding NC is a technique that allows intermediate nodes to combine the received packets from multiple links and forwarded to subsequent nodes. Compared with pure relaying, using NC in a wireless network, one can potentially improve the network throughput, but it increases the complexity of resource allocations as the quality of one transmission is often affected by the transmission conditions of multiple links. In this paper, we consider an ad hoc network, where all the links have bidirectional communications, and a relay node forwards traffic between the source and the destination nodes using NC. All transmissions share the same frequency channel, and simultaneous transmissions cause interference to each other. We consider both digital NC and analog NC strategies, referred to as DNC and ANC, respectively, and schedule transmission time and power of the nodes in order to maximize the overall network throughput. For DNC, an optimum scheduling is formulated and solved by assuming that a central controller is available to collect all the link gain information and make the scheduling decisions. Distributed scheduling schemes are proposed for networks using DNC and ANC. Our results indicate that the proposed scheduling scheme for DNC achieves higher throughput than pure relaying, and the scheduling scheme for ANC can achieve higher throughput than both DNC and pure relaying under certain conditions. Copyright © 2013 John Wiley & Sons, Ltd.

In a relay-assisted cellular network, the transmission mode either direct transmission or relaying and the transmit power of the source and relay nodes affect not only transmission rates of individual links but also the rates of other links sharing the same channel. In this paper, we propose a cross-layer design that jointly considers the transmission mode/relay node selection MRS with power allocation PA to optimize the system rate. We first formulate an optimization problem for a cellular system, where the same frequency channel can be reused in different cells. A low complexity heuristic MRS scheme is proposed on the basis of the link and interference conditions of the source and potential relay nodes. Given the transmission mode and relay node if the relaying mode is chosen of each link, the transmit power of the source and relay nodes can be solved by geometric programming. This method for MRS and PA can achieve a close-to-optimum performance, but implementing the PA requires heavy signalling exchanged among cells. To reduce the signalling overheads, we finally proposed a heuristic and distributed method for MRS and PA inspired by some asymptotic analysis. Numerical results are conducted to demonstrate the rate performance of the proposed methods.Copyright ©2013 John Wiley & Sons, Ltd.

In this paper we consider a system that uses computation offloading, where an infrastructure-based cloud server executes jobs on behalf of a set of mobile devices. In this type of system, mobile job completion times include the latency needed for uploading to the cloud server. Since the processed jobs are subject to hard deadline constraints, this can introduce energy unfairness where mobile devices with poor channel conditions do not fully benefit from computation offloading. This unfairness however, can be compensated for, by dynamic scheduling at the cloud server. We first derive an offline scheduler using an integer linear program which uses a min-max energy objective and non-preemptive cloud server scheduling. We then introduce three online scheduling algorithms. The first is referred to as First-Generated-First-Served (FGFS) where jobs that are generated earlier are given priority at the cloud server. A modified version, referred to as γ-Ratio Accepted FGFS (γ-FGFS) is proposed where acceptance of a job execution partition is subject to an energy threshold test. We also introduce a version of this algorithm, γ-Ratio Accepted Earliest Deadline First (γ-EDF) which uses earliest deadline first scheduling to test for job partition feasibility. Various performance results are presented which show the improvements in energy fairness possible with the proposed schedulers.

ABSTRACT In this paper, we study the delay performance in a wireless sensor network (WSN) with a cluster‐tree topology. The end‐to‐end delay in such a network can be strongly dependent on the relative location between the sensors and the sink and the resource allocations of the cluster heads (CHs). For real‐time traffic, packets transmitted with excessive delay are dropped. Given the timeline allocations of each CH for local and inter‐cluster traffic transmissions, an analytical model is developed to find the distribution of the end‐to‐end transmission delay for packets originated from different clusters. Based on this result, the packet drop rate is derived. A heuristic scheme is then proposed to jointly find the timeline allocations of all the CHs in a WSN in order to achieve the minimum and balanced packet drop rate for traffic originated from different levels of the cluster tree. Simulation results are shown to verify the analysis and to demonstrate the effectiveness of the proposed CH timeline allocation scheme. Copyright © 2012 John Wiley & Sons, Ltd.

Video transport in wireless mesh networks is a challenging problem because of frequent link failures, limited link capacity, and multihop communications. However, the mesh topology provides some degree of freedom in designing error resilient video broadcasting scheme, as multiple links can exist between a source and a destination. In this paper, we combine the mesh nature with Multiple Description Coding (MDC) technique to design a video broadcasting scheme. An Access Point (AP) is broadcasting video traffic to the Mobile Stations (MSs) via a number of Relay Stations (RSs). The AP utilizes MDC technique to encode video traffic into equal descriptions. Each description is multicasted to several RSs, which further broadcast the descriptions to MSs. Whether or not an MS can successfully receive a description from an RS depends on the transmission power of the RS and the channel conditions between the RS and the MS. For each MS, the quality of the received video depends on the total number of correctly received descriptions. We study how to allocate the transmission power of the RSs so that to satisfy the quality of the received video at each MS while minimizing the maximum transmission power at the RSs. An optimization problem is first formulated, and then a heuristic power adjustment scheme is proposed to find the transmission power of each RS. Our numerical results show a good match between optimal solution and proposed heuristic.

Using network coding in a wireless network can potentially improve the network throughput. On the other hand, it increases the complexity of resource allocations as the quality of one transmission is often affected by the link conditions of the transmitter node to multiple destination nodes. In this paper we consider a multi-channel wireless network with a star topology. Network coding is used for data transmissions of each bidirectional link through a relay node, which has multiple radios, each for one bidirectional link. We study the channel and time allocations in this network, so that the overall network throughput is maximized. An optimum problem is first formulated and solved, and two heuristic schemes, Concurrent Node Selection and Channel Assignment (CNSCA) and Sequential Node Selection and Channel Assignment (SNSCA), with different complexity are proposed. Our results indicate that the CNSCA scheme has high complexity and achieves close-to-optimum performance, and the SNSCA scheme requires lower complexity and achieves relatively lower throughput.

The non-saturated performance of the IEEE 802.11 broadcast scheme in the two-dimensional (2-D) mobile ad hoc networks is studied in this paper. A simplified yet reasonable system model is built to derive the closed-form expressions of average packet reception probability, non-saturated throughput, and average queuing delay. Compared with the existing work in the literature, the proposed system model takes into account the effect of arbitrary transmission queue length. Moreover, our analysis considers both the impact of concurrent transmissions caused by identical backoff interval selection and that of hidden nodes caused by limited sensing range on an observed transmitter. All the analysis results are validated by extensive simulations.

In this paper we study the problem of providing fair throughput for mobile stations (MSs) in a wireless mesh network (WMN) by jointly considering the handoff management of the MSs and the resource allocations at the access points (APs). An optimization problem is formulated based on long-term proportional fairness, so that all the MSs in the entire WMN can receive fair average throughput, while the total throughput of the MSs is maximized. The optimum solution is based on the assumption of having global information about the channel conditions and mobility information of all the MSs, and cannot be easily implemented in a practical system. A heuristic scheme is then proposed, which allows each AP to allocate its resources based on local information only, and the handoff decisions of the MSs are based on information exchanged between neighboring APs. Numerical results show that performance of the proposed heuristic scheme is very close to the optimum in terms of both fairness and throughput.

In this paper we study transmission scheduling in a bidirectional communication link, where two end nodes are communicating with each other through a relay node. Each of the two end nodes injects data into a separate buffer at the relay node, which can either forward the data from one end node to the other at a given time (referred to as one-way relay), or simultaneously forwarding the data to both the end nodes by using network coding (referred to as two-way relay). An optimization problem is formulated to find the optimum scheduling in order to maximize the total transmission throughput in both communication directions, and a heuristic scheduling scheme is proposed to opportunistically take advantages of both the channel conditions between the two end nodes and the one-way and two-way relays. Numerical results show that the proposed scheme achieves high and close-to-optimum throughput.

A novel multichannel multiple access (MMA) protocol is proposed for vehicular ad hoc networks in this paper. For increasing channel efficiency, two control channels are utilized in MMA to finish request-to-send/clear-to-send (RTS/CTS) interactions and acknowledgments (ACKs) separately. When a collision happens in the control channel, the waiting period before the next re-transmission is adjusted by a relative mobility based back-off scheme. Benefiting from the fully distributed channel selection scheme, which is also included in MMA, the data transmissions carried in the chosen service channels can be successfully accomplished without collision, and the influence of exposed terminals can also be effectively alleviated. Simulation results show that MMA outperforms the classic single channel and multichannel MAC protocols in average channel utilization, packet delay, and packet drop rate.

In a cellular network with cooperative relaying, each node may choose to communicate with the source node directly or use the cooperative communication mode with the assistance of a relay node. The transmission mode (either direct or cooperative) and the transmit power of the source and relay nodes affect not only performance of individual links, but also the overall system capacity. In this paper, we propose a cross-layer design that jointly considers the transmission mode/relay node selection (MRS) with power allocations (PA) in order to optimize the network capacity. This is first formulated as an optimization problem for a cellular system, where the same frequency channel can be reused in different cells. We then design a low complexity heuristic MRS scheme based on the link and interference conditions of the source and potential relay nodes. Given the transmission mode and relay node (if the cooperative mode is chosen) of each link, the transmit power of the source and relay nodes are solved by the geometric programming method. Numerical results are demonstrated to verify the effectiveness of the joint MRS and PA method.

The saturation performance of the IEEE 802.11 broadcast scheme in two dimensional (2-D) mobile ad hoc networks is studied in this paper. A simplified yet reasonable system model is built to derive the closed-form expressions of two broadcast reliability metrics, Distance-based Packet Reception Probability and Average Packet Reception Probability. Compared with the existing work in the literature, the proposed system model accounts for more general 2-D network scenarios, and considers both the impact of concurrent transmissions caused by identical backoff intervals and that of hidden nodes caused by a limited sensing range on a tagged reception. All the analysis results are validated by extensive simulations.

A lot of access points (APs) in wireless mesh networks (WMNs) are battery powered. In order to minimize their energy consumption, the APs can adaptively adjust their transmission power and time based on various network conditions. In this paper, the problem of minimizing the AP energy consumption is formulated as an optimization problem with the constraint to satisfy the throughput requirements of associated mobile stations (MSs). Balancing the energy consumption of the APs can be important to prolong the network lifetime, particularly in a network with high and random user mobility, which can easily result in unbalanced traffic load and energy consumption among the APs. Two distributed handoff schemes are proposed for a WMN with adaptive transmission power and rate. One scheme attempts to achieve balanced energy consumption among the APs during each scheduling interval, and the other scheme balances the AP energy consumption over a longer term. Numerical results show that both schemes achieve much longer network lifetime than the traditional distance-based handoff scheme, while the long-term based handoff scheme can achieve the objective with approximately the same number of handoffs as the distance-based handoff.

The energy balancing is one of the main concerns in wireless sensor networks (WSNs). Different from current methods, this paper attempts to solve the unbalanced energy consumption problem in the orthogonal frequency division multiplexing (OFDM) system for a WSN. The objective is to increase the sum throughput with almost equal energy consumption among the sensor nodes. Inspired by this goal, we formulated the optimization problem. Then a heuristic method is designed to solve this complex problem. The impact of number of subcarriers and sensor nodes on the fairness are studied in the numerical simulation. Comparisons with different common methods are made to show that the proposed method could achieve the excellent energy consumption fairness and better achievable throughput.

Vehicular ad-hoc networks will soon support a wide variety of inter-vehicle and vehicle-to-roadside applications. In many cases these networks will also co-exist with battery operated networks such as those deployed for sensor and mesh networked applications. In these latter types of networks, mesh node energy efficiency is often of paramount importance. This paper proposes improvements in mesh network energy efficiency by opportunistically ferrying delay tolerant traffic through co-existent vehicular networks. This is accomplished by dynamically routing traffic through vehicular paths when this forwarding is compatible with traffic flow quality-of-service constraints. The proposed mechanism is referred to as opportunistic vehicle assisted forwarding (OVAF). A model is formulated which provides an optimum routing bound under different objectives using hybrid multi-hop mesh routing and vehicular packet forwarding. A heuristic is then presented which can be used in practical networks which use OVAF. Simulation results show large improvements in network performance when this is used compared to conventional single-network multi-hop forwarding.

It is demonstrated that the suitable relay selection or power control method is very important to network performance. However, there is little work concerning the combination of them in the multi-cell scenario. In this paper, we propose a cross-layer design to associate relay selection with power control scheme to increase the average network capacity, as well as to mitigate interference, where the criteria of relay selection is based on the maximum capacity. Then a path table is established to determine whether to perform the power control scheme. Finally, the comparisons of capacities among several methods are made to verify the effectiveness of the cross-layer method proposed in this paper.

Traditional wireless sensor networks (WSNs) working in the license-free spectrum suffer from uncontrolled interference as the license-free spectrum becomes increasingly crowded. Designing a WSN based on cognitive radio can be promising in the near future in order to provide data transmissions with quality of service requirements. In this paper we introduce a cognitive radio sensor network (CRSN) and analyze its performance for supporting real-time traffic. The network opportunistically accesses vacant channels in the licensed spectrum. When the current channel becomes unavailable, the devices can switch to another available channel. Two types of channel switchings are considered, in periodic switching (PS) the devices can switch to a new channel only at the beginning of each channel switching (CS) interval, while in triggered switching (TS) the devices can switch to a new channel as soon as the current channel is lost. We consider two types of real-time traffic, i) a burst of packets are generated periodically and the number of packets in each burst is random, and ii) packet arrivals follow a Poisson process. We derive the average packet transmission delay for each type of the traffic and channel switching mechanisms. Our results indicate that real-time traffic can be effectively supported in the CRSN with small average packet transmission delay. For the network using PS, packets with the Poisson arrivals experience longer average delay than the bursty arrivals; while for the network using TS, packets with the bursty arrivals experience longer average delay.

With the increasing popularity of cognitive radio networks (CRNs), providing data transmissions with quality of service (QoS) requirements in such networks becomes one of the most important topics. In this paper we study QoS provisioning in an ad hoc CRN with spectrum underlay. Both streaming traffic and non-real-time (nrt) data traffic are considered. The former requires a low session outage probability and the latter requires a minimum average throughput. A joint connection admission and packet scheduling scheme is proposed in this paper. The connection admission control scheme limits the amount of traffic admitted into the system so that all admitted connections can receive their required QoS through the proposed packet scheduling schemes. Two packet transmission scheduling schemes are designed, one is optimum and assumes that a central controller can collect the global channel and interference conditions within the CRN and between the primary network and the CRN, while the other one is a heuristic scheme based on the link and interference conditions measured by the nodes in the CRN. In addition to achieving the required QoS, the scheduling schemes utilize the available radio resources fairly and efficiently. Numerical results demonstrate that when the traffic load at the primary network is relatively stationary, the proposed scheduling scheme can achieve very low and fair outage probability for all admitted streaming connections, and all admitted non-real-time data connections can receive the required throughput and fair delay performance.

We propose a distributed quality of service (QoS)-aware MAC protocol for multi-channel cognitive radio networks supporting multimedia applications. Specifically, based on the channel usage patterns of primary users (PUs), secondary users (SUs) determine a set of channels for channel sensing and data transmissions to satisfy their QoS requirements. We further enhance the QoS provisioning of the proposed cognitive MAC by applying differentiated arbitrary sensing periods for various types of traffic. An analytical model is developed to study the performance of the proposed MAC, taking the activities of both PUs and SUs into consideration. Extensive simulations validate our analysis and demonstrate that our proposed MAC can achieve multiple levels of QoS provisioning for various types of multimedia applications in cognitive radio networks.

Roadside infrastructure can be used provide a wide variety of commercial services in vehicular ad hoc networks. One particular challenge is that of providing roadside radio coverage in highway locations where wired electricity is not available. In this case, roadside access points (APs) powered by renewable energy such as solar power, is a viable alternative. The cost of provisioning this type of roadside infrastructure is dependent on the average power consumption of the AP, and can be reduced by energy efficient scheduling. In this paper, we consider the problem of satisfying vehicle communication requirements while minimizing the energy needed by the roadside access point. The problem is formulated as a Mixed Integer Linear Program (MILP) which provides an upper bound for the performance of any realizable scheduling algorithm. We then propose a Nearest Fastest Set (NFS) scheduler that uses vehicle location and velocity inputs to address the problem. Results from a variety of experiments show that the proposed scheduling algorithm performs well when compared to the performance bound.

In this paper we study quality-of-service (QoS) provisioning in an ad hoc cognitive radio network with spectrum underlay. Both streaming traffic and non-real-time (nrt) data traffic are considered. An admission control scheme is first proposed based on relatively static information such as path loss and average interference levels in order to guarantee the average throughput of each connection. Outage performance of the streaming traffic and throughput of the nrt traffic are managed through slot-by-slot packet transmission scheduling, which opportunistically takes advantage of the randomly varying channel and interference conditions. Numerical results demonstrate that very small outage probability can be achieved for the streaming traffic, and the non-real-time connections receive throughput higher than the minimum required.

Traditional wireless sensor networks (WSNs) working in the license-free spectrum suffer from uncontrolled interference as the license-free spectrum becomes increasingly crowded. Designing a WSN based on cognitive radios can be promising in the near future as the quality of service requirement for data transmissions increases. In this paper we design and analyze performance of a cognitive radio sensor network (CRSN), which opportunistically accesses spectrum of licensed spectrum unused by other networks and supports both real-time constant-bit-rate (CBR) traffic and best effort (BE) traffic. We consider two different policies for prioritizing the resource allocations, develop analytical models to find delay and capacity performance for the CBR traffic and amount of resources for best effort (BE) data transmissions. The analysis is verified by computer simulations. Our results indicate that satisfactory real-time performance can be achieved in the CRSN. Depending on the service policy used, the amount of resources for serving the BE traffic is different.

Traditional wireless sensor networks (WSNs) working in the license-free spectrum suffer from uncontrolled interference as the license-free spectrum becomes increasingly crowded. A WSN based on cognitive radios can potentially provide data transmissions with quality of service, while avoiding the high cost for accessing the licensed spectrum and uncontrolled interference in the license-free spectrum. Our previous works have studied delay performance of constant bit rate traffic in a cognitive radio sensor network (CRSN). In this paper we study the delay performance of random traffic with burst arrivals in the network. We derive the average delay for transmitting the random traffic using different channel sensing mechanisms, and verify the results using computer simulation. Our results show that satisfactory latency performance can be provided in the CRSN.

In a wireless sensor network (WSN) with specially deployed cluster heads (CHs), association relation between sensor nodes and the CHs is important as it affects the radio resource allocations, which further determine the overall network throughput, energy consumption, and other performance. In a lot of cases, the CHs are placed in random locations, and strong overlapping may exist between their coverage areas so that sensor nodes can choose to associate to different CHs. In this paper we first formulate two optimization problems that jointly consider sensor node association and radio resource allocations, one for maximizing the network level throughput, and another for balancing the energy consumption among the CHs. For each of the optimization problems, a heuristic scheme is designed that jointly considers both the timeline allocations of the CHs and the sensor node association. Numerical results based on computer simulation demonstrate that the proposed schemes achieve close-to-optimum performance. In addition, the schemes achieve much better throughput and energy performance than the straightforward association schemes.

This paper proposes an Energy Efficient Clustering Scheme for mobile ad hoc networks. In the initial clustering stage, a node's residual energy, nearby topology, relative location and relative mobility are used for determining whether the node is suitable for being a cluster head. In the cluster maintaining stage, a strategy called Distance Estimation Broadcasting is designed to help a cluster member to estimate the distance between itself and its cluster head. Thus, the cluster members can use less energy to accomplish the data transmission. Moreover, a dynamic calculated Off-Duty Threshold is proposed to trigger the re-clustering operation when needed. Simulation results show that the proposed clustering scheme performs better than the previous Weighted Clustering Algorithm and Distributed Weighted Clustering Algorithm.

In this paper we consider supporting real-time traffic in a cognitive radio sensor network (CRSN). A lot of traffic in wireless sensor networks (WSNs) requires strict quality of service (QoS) requirements, while most existing WSNs working in the license-free spectrum cannot provide guaranteed QoS. A cognitive radio network (CRN) can possibly support traffic with strict QoS requirements while avoiding high cost for accessing the licensed spectrum. The CRSN studied in this paper is cluster-based and supports both real-time traffic and best effort traffic. We consider two resource allocation policies in order to provide a higher priority to the real-time traffic. Mathematical models are developed to analyze the performance of the real-time traffic, and the analytical results are verified by computer simulations. Our results indicate that satisfactory real-time performance can be achieved in the CRSN.

This paper addresses the clustering problem for highly mobile ad hoc networks. In the proposed scheme, Doppler shifts associated with received signal are used to estimate the relative speed between cluster head and cluster members. With the estimated speed, a node can predict its stay time in every nearby cluster. In the initial clustering stage, a node joins a cluster that can provide it with the longest stay time in order to reduce the number of re-affiliations. In the cluster maintaining stage, strategies are designed to help node cope with connection loss caused by channel fading and node mobility. Simulation results show that the proposed clustering scheme can reduce the number of re- affiliations and the average disconnection time compared with previous schemes.

Vehicular ad-hoc networks (VANETs) will enable a wide variety of future inter-vehicle and vehicle-to-roadside applications. These services will span a large range of functionality, such as those supporting vehicular safety, to those used for best-effort roadside advertising. To support this wide range, the IEEE 802.11p standard defines seven communication channels, consisting of a single control channel for safety applications, and six service channels which can be used for other purposes. To allow a single radio interface to support both types of applications, the standard defines a channel coordination mechanism that allows the vehicular radio to alternately access the control and service channels. When this happens it is very important that safety messages are transmitted with high reliability and low latency. Using analytical models, this paper provides a performance evaluation of vehicular safety message delivery. Our results show that the mechanism defined in the standard can satisfy the needed latency requirements, but cannot satisfy the required reliability for safety message delivery.

In this paper we study fair rate scheduling in an ad hoc cognitive wireless network with spectrum underlay. Transmissions in the network are allowed provided their interference to the primary network is below a predefined threshold. An optimal scheduling problem is formulated with an objective to achieve proportional fairness (PF) of the long-term average transmission rates among different links. Implementing the optimum scheduling requires high complexity. Two practical scheduling schemes are then proposed. In the first scheme, transmission priorities of the links are determined by their potential contributions to an objective utility function, assuming there is no co-channel interference within the network. In the second scheme, transmission priorities are derived from both the objective function and interference to the primary network. We also consider using exclusive regions to limit interference among simultaneous transmissions in order to improve the system throughput. The scheduling schemes can be implemented distributively in the ad hoc cognitive wireless network with limited assistance from the primary network. Our results show that the proposed PF scheduling schemes can achieve high overall throughput and close-to-optimum fairness, and using exclusive regions can improve the system utility without compromising the fairness performance.

In this paper we study fair rate scheduling in an ad hoc cognitive wireless network with spectrum underlay. Transmissions in the network are allowed provided their interference to the primary network is below a predefined threshold. An optimal scheduling problem is formulated with an objective to achieve proportional fairness (PF) of the long-term average transmission rates among different links in the network. Two scheduling schemes are proposed for achieving PF of the average rates, each using different criteria to make scheduling decisions. Both scheduling schemes can be implemented distributively in the ad hoc cognitive network with limited assistance from the primary network. Our results show that the proposed PF scheduling schemes can achieve high overall throughput and close-to-optimum fairness.

As specified by the third-generation (3G) wireless networks such as the universal mobile telecommunication system (UMTS), interactive data services, such as Web browsing, voice messaging, and file transfer, represent a major service class in operation nowadays. In this paper, we develop an analytical approach to evaluate the performance of interactive data services under sharing and preemptive scheduling. Specifically, we take into account user interactions in data sessions and the heavy-tailed data file size. Both the mean and the standard deviation of data transfer delay are investigated for the two representative scheduling disciplines. Numerical results are given to show the validity of the evaluation approach and the impact of the on-off user behavior under the scheduling disciplines.

This paper studies power distribution in the downlink of a wireless CDMA network, where mobile stations (MSs) cooperatively relay traffic for their peer stations, and the destination station (DS) combines signals received from both the base station (BS) and the relay station (RS). Both the DS and the RSs transmit at the same frequency band. We consider both decode-and-forward (DF) and amplify-and- forward (AF). An optimization problem is first formulated for distributing the transmission power of the BS and the RSs. The objective is to minimize the transmission power of the BS and the total transmission power of the RSs, subject to the average transmission rate and signal-to-interference-plus-noise ratio (SINR) requirement of the user traffic. The optimum power distribution requires link gains among different MSs, which are usually not available at the BS. We then propose a practical power distribution scheme based on link gains of the RS and DS to the BS. Our results show that i) the proposed link-gain based power distribution scheme achieves close-to-optimum performance, ii) by appropriately selecting the RS and forwarding techniques, cooperative relaying in the downlink of a CDMA network can reduce the communication outage probability and significantly save the BS transmission power in the downlink transmissions, and iii) cooperatively relaying requires very low transmission power from the RSs.

Cooperative diversity has the ability of achieving multiplexing and diversity gains by taking advantage of the features and potentials of spatial distribution. Based on the asymptotic outage probability, we define the Cooperative Gain (CG) to evaluate the benefits of cooperation over direct transmission under high Signal-to-Noise Ratio (SNR). With the presence of Rayleigh fading and path loss, our analytical results illustrate the appropriate Cooperative Region (CR), in which the source prefers to coordinately work with the relay rather than transmit directly. Finally, we provide visible CRs and numerical results on the exact outage performance by Monte Carlo simulations.

This paper studies throughput improvement for TCP traffic in IEEE 802.11-based multihop ad hoc wireless networks. Due to the incompatibility between TCP and the IEEE 802.11 distributed coordination function (DCF) protocols, the reaction of TCP in case of packet losses can significantly reduce TCP end-to-end throughput. In this paper, we propose an opportunistic link scheduling (OLS), which is a simple enhancement to the IEEE 802.11 DCF protocol and intends to improve the compatibility between TCP and MAC layer protocols in multihop ad hoc networks. With OLS, a link with a good channel condition is allowed to transmit multiple packets consecutively as a burst, while the burst size depends on both physical channel fading and MAC layer collisions. The protocol also includes a mechanism to prevent starvation of nodes with poor channel conditions. An analytical model is developed for a four-hop chain to study the effect of the burst size and TCP congestion window size on the end-to-end transmission throughput in opportunistic link scheduling. Our results show that OLS can significantly improve the end-to-end transmission throughput, while keeping reasonably low transmission delay. The protocol is easy to implement and requires only slight modifications to the IEEE 802.11 protocol.

Packet transmission scheduling for supporting real-time traffic in a WMN is difficult, and one of the main challenges is to coordinate temporal operations of the mesh access points (APs) in order to provide strict latency guarantee while efficiently utilizing the radio resources. In this paper a connection-based scheduling (CBS) scheme is proposed. Connections with more hops are given a higher priority, and connections with a lower priority can only use resources remaining from serving all higher priority ones. For each multihop connection, the scheduling minimizes latency between successive hops. A connection-based optimization problem is formulated with an objective to minimize the amount of required AP resources, subject to the latency requirement of the connections. Numerical results show that the proposed scheduling scheme achieves close-to-optimum performance at both the connection and packet levels.

In a cognitive wireless network with spectrum underlay, secondary links can transmit at the same spectrum as primary links as long as the interference to the primary links is below a pre-negotiated interference threshold. This paper studies the effect of setting different interference thresholds on the transmission rate of the secondary links and how secondary transmissions affect the primary links. An optimization problem is formulated to find the maximum achievable rate for the secondary links given the interference threshold, and an analytical model is developed to find the interference level at the primary link receiver. Our results show some interesting relationship among the interference threshold, transmission rate of secondary links, and transmission power increase of the primary links in such networks.

This paper studies real-time traffic scheduling in wireless mesh networks (WMNs). Optimum packet transmission scheduling providing strict latency guarantee for realtime traffic while efficiently utilizing the network resources in a WMN is difficult, and one of the main challenges is to coordinate temporal operations of the mesh access points (APs). In this paper a connection-based scheduling scheme is proposed. Scheduling decisions for connections with a larger number of hops are made first, and that for connections with a fewer number of hops are done by using the remaining resources. When scheduling packet transmissions for each connection, the transmitting time of the AP with the highest traffic load along the route of the connection is determined first. At each hop, the transmitting time of a packet is determined to minimize the latency to the upstream hop or from the downstream hop while keeping the total amount of required AP resources small. A connection-based optimization problem is formulated and solved with an objective to minimize the total amount of required AP resources, subject to the latency requirement of the connection. Numerical results show that the proposed scheduling scheme achieves close-to-optimum performance at both the connection and packet levels.

In this paper, a credit-based user authentication scheme is proposed for delay tolerant mobile wireless networks. The proposed authentication scheme isolates the uncertain network condition in the high-delay wireless backhaul with high error rate, and accelerates the overall authentication process when the mobile terminal roams in the visited network. The performance evaluation demonstrates that the proposed credit-based authentication scheme is secure and effectively reduces the overall delay and overhead in user authentication for delay tolerant mobile wireless networks.

This paper studies power distribution in the uplink channel of a wireless CDMA network, where peer stations cooperatively relay traffic for each other, and the destination, i.e., the base station (BS), combines signals received from both the source station (SS) and the relay station (RS). Two different cooperative schemes are presented. In the first scheme, the SS transmits packets in all time slots, and two RSs forward received signals from the SS to the BS in odd and even time slots alternatively. In the second scheme, the SS transmits in the odd time slots, and a single RS receives from the SS in the odd time slots and forwards to the BS in the even time slots. Two forwarding techniques, i.e., decode-and- forward (DF) and amplify-and-forward (AF), are considered. For each of the cooperation scheme and forwarding technique combinations, an optimization problem is formulated with an objective to minimize the total transmission power subject to the average transmission rate and signal-to-interference-plus- noise ratio (SINR) requirement of the user traffic. Our results show that by appropriately selecting the RSs and forwarding techniques, cooperative communications in a CDMA network can significantly reduce the communication outage probability and save total transmission power.

One of the reasons that cause low transmission throughput in IEEE 802.11-based multihop networks is packet losses. Compared to transmitting a new packet, retransmitting a packet in 802.11-based networks has a lower priority in accessing the channel and requires a longer channel idle time for backoffs. When the number of retransmissions exceeds a certain threshold, a packet is dropped at the link layer. For TCP traffic, this will eventually result in TCP timeout, and the lost packet will be retransmitted at the transport layer, causing end- to-end throughput degradation. Furthermore, the mechanism that TCP adjusts its congestion window size negatively affects the transmission throughput in 802.11-based multihop networks. In this paper we propose an opportunistic link scheduling (OLS) protocol, which schedules transmissions of the links based on their channel conditions, including both channel fading and co-channel interference. Links with good channel conditions are given a higher priority to access the channel and allowed to transmit a limited number of packets consecutively without repeatedly competing the channel. OLS also includes mechanisms to avoid buffer overflow and prevent starving links with poor channel conditions. Our results show that OLS can significantly improve the end-to-end transmission throughput, while keeping reasonably low transmission delay. The protocol is easy to implement, and requires minor changes to the 802.11 protocol.

This paper studies real-time traffic support in IEEE 802.16-based backhaul networks, where each subscriber station (SS) may be responsible for forwarding packets for a number of real-time voice connections. In an 802.16-based network, the SS requests bandwidth to the base station (BS) in the uplink, and bandwidth grants are sent back from the BS through the downlink. The 802.16 standard specifies that the bandwidth requests are for individual connections and pass only the number of bytes requested from each connection. The standard does not specify a resource allocation strategy. In this paper we propose a simple enhancement to the bandwidth request mechanism in 802.16 for supporting packet voice traffic. First, the SS combines the bandwidth requests of multiple connections associated to it and sends aggregate bandwidth requests to the BS. This makes the bandwidth request process more efficient by saving transmission time of both the BS and the SSs. Second, in order to facilitate the BS to make resource allocation decisions, the aggregate bandwidth requests include information about the latency requirements of buffered real-time packets at the SSs. We propose three different bandwidth request and packet scheduling schemes, each of which requires a different amount of information to be included in the bandwidth requests. Our results show that there is an optimum point about how much delay information the SS should report to the BS in order to best utilize the uplink resources while providing satisfactory real-time performance for the voice traffic.

In this paper, a versatile WLAN/cellular networks integrated roaming service framework with billing support is proposed, which can lift off the requirement of peer-to-peer roaming agreements to provide seamless user roaming between WLAN hotspots and cellular networks operated by independent wireless network service providers. Within the framework, an adaptive authentication and an event-tracking scheme have been developed considering the anonymity of the mobile users and allowing the integrated billing service to be applied to cellular network if it still uses a traditional authentication scheme. Security analysis and overhead evaluation demonstrate that the proposed roaming service framework is secure and efficient.

A bottleneck first scheduling (BFS) scheme was proposed in our previous work for scheduling constant bit rate (CBR) real-time traffic in a wireless mesh network with a tree topology. In this paper we extend the BFS scheme for scheduling CBR real-time traffic in a mesh network with a more general topology. We consider an IEEE 802.11-based infrastructure wireless mesh network, where access points (APs) form a graph topology for forwarding traffic inside the mesh network or between the mesh network and the wireline backbone network. The new scheduling scheme is referred to as G-BFS, where "G" stands for "graph". We also model the scheduling problem and find the suboptimal performance. Our results show that the proposed G-BFS scheduling scheme achieves a capacity very close to the suboptimal capacity, while keeping low packet transmission delay.

This paper studies throughput fairness among different basic service sets (BSSs) in infrastructure-based IEEE 802.11 mesh networks, where inter-BSS interference is unavoidable because of the difficulty in frequency and coverage planning and the limited number of non-overlapping frequency channels available for IEEE 802.11 networks. An analytical model is first developed to find the aggregated BSS transmission throughput in a mesh environment. Two approaches are studied for achieving fair throughput among the co-channel BSSs: (i) NAV-blocking which uses network allocation vectors (NAVs) to temporarily stop transmissions in some BSSs in order to control the inter-BSS interference, and (ii) forced handoff (FHO) which forces mobile stations (MSs) to hand- off between neighboring BSSs in order to balance the traffic load among the BSSs. An optimization problem is formulated for each of the approaches for achieving the required throughput fairness while keeping high transmission throughput in the BSSs. Our results show that (i) using NAV-blocking can always achieve the required throughput fairness and its performance does not depend on coverage overlapping between the BSSs; and (ii) performance of throughput fairness using FHO depends on coverage overlapping between the BSSs, and when there is strong coverage overlapping among the BSSs, this approach can achieve throughput fairness among the BSSs.

In this paper we consider vertical handoff for dualmode (DM) cellular/WLAN handsets. When the handset roams out of WLAN coverage, the DM's cellular interface is used to maintain the call by anchoring it through a PSTN gateway/PBX. Soft handoff can be achieved in this case if the gateway supports basic conference bridging, since a new leg of the call can be established to the conference bridge while the existing media stream path is active. Unfortunately this requires that all intraenterprise calls be routed through the gateway when the call is established. In this paper we consider conferenced dual-mode handoff and propose a much more scalable mechanism whereby active calls are handed off into the conference bridge just prior to the initiation of the vertical handoff. Results are presented which are taken from dual-mode handset simulations which characterize the scalability of the proposed mechanism.

Vertical handoff is a switching process between heterogeneous wireless networks in a hybrid 3G/WLAN network. Vertical handoffs from WLAN to 3G network often fail due to the abrupt degrade of the WLAN signal strength in the transition areas. In this paper, a Handoff Trigger Table is introduced to improve the performance of vertical handoff. Based on this table, a proactive handoff scheme is proposed. Simulation results show that with the proposed scheme, the vertical handoff decisions will be more efficient so that dropping probability can be decreased dramatically.

This paper studies throughput fairness among different basic service sets (BSSs) in infrastructure-based IEEE 802.11 mesh networks, where inter-BSS interference is unavoidable because of the difficulty in frequency and coverage planning and the limited number of non-overlapping frequency channels available for IEEE 802.11 networks. A distributed inter-access points (APs) coordination scheme is proposed for achieving fair throughput among the BSSs sharing the same frequency channel. In this scheme, throughput information of the BSSs is exchanged among the APs periodically. Based on this, each AP dynamically controls transmission activities of the mobile stations (MSs) in its own BSS coverage area as well as the interference level to other BSSs. An analytical model is developed for throughput of the BSSs in a mesh environment, and an optimized solution is obtained for achieving throughput fairness among BSSs. Numerical results show that the proposed scheme can achieve fair throughput among BSSs under different network conditions and the achieved throughput values are close to the optimum ones.

This paper studies the real-time traffic scheduling in IEEE 802.11 infrastructure-based wireless mesh networks. Providing strict latency guarantee for real-time traffic in a wireless mesh network is difficult, and one of the main challenges is the difficulty in coordinating temporal operations of the mesh access points (APs). In this paper we propose a bottleneck-first scheduling scheme (BFS) for voice traffic. In the proposed scheme, a central station is responsible for making scheduling decisions for all the real-time packet transmissions at the APs. Scheduling decisions at the APs with a higher traffic load are done before those with a lower traffic load. At each AP, voice packets with more end-to-end hops are scheduled first. Numerical results show that the proposed scheduling scheme achieves low transmission delay and high capacity in the mesh networks, compared to the simple first-come-first-serve scheduling scheme.

This paper studies TCP throughput performance in IEEE 802.11-based multi-hop wireless networks where nodes can have different physical transmission modes, e.g., modulation schemes and packet transmission rates. The physical transmission modes affect the link layer transmission performance, which further affects the TCP throughput performance. Selecting the TCP congestion window size is important for improving TCP throughput. We use simulation to demonstrate the effect of different physical layer modes on the TCP throughput performance and find the optimal TCP window size upper bound.

This paper studies throughput fairness among different basic service sets (BSSs) in infrastructure-based IEEE 802.11 mesh networks, where inter-BSS interference is unavoidable because of the difficulty in frequency and coverage planning and the limited number of non-overlapping frequency channels available for IEEE 802.11 networks. Two approaches are proposed for achieving fair throughput among the co-channel BSSs: (i) dynamic network allocation vectors (DNAV), which dynamically adjusts the co-channel interference by temporarily stopping transmissions in some BSSs using network allocation vectors (NAVs), and (ii) forced handoffs (FHO), which forces mobile stations (MSs) to handoff between neighboring BSSs. An optimization problem is formulated as well as a distributed scheme is proposed for each of the approaches for achieving the required throughput fairness while keeping high transmission throughput in the BSSs. Our results show that (i) the DNAV scheme can always achieve the required throughput fairness; (ii) performance of throughput fairness using the FHO scheme depends on coverage overlapping between the BSSs, and when there is strong coverage overlapping among the BSSs, the scheme achieves a higher throughput than the DNAV while achieving the required throughput fairness; and (iii) the converged throughput performance of the proposed distributed schemes is very close to the corresponding optimized one.

This paper studies real-time voice traffic scheduling in IEEE 802.11 infrastructure-based wireless mesh networks. Providing strict latency guarantee for real-time traffic in such network is difficult, and one of the main challenges is the difficulty in coordinating temporal operations of the mesh access points (APs). In this paper scheduling problem for constant-rate voice traffic is formulated as a binary linear programming problem and its optimal solution is given. The computational complexity may prevent the optimum scheduling from implementing in practice. Then a bottleneck-first scheduling scheme is proposed where scheduling decisions at the APs with a higher traffic load are done before those with a lower traffic load. At each AP, voice packets with more hops to their destinations are scheduled first. Numerical results show that the proposed scheduling scheme can achieve the same network capacity as the optimal one while keeping reasonably low transmission delay.

In the past decade, there has been a huge proliferation of wireless local area networks (WLANs) based on the IEEE 802.11 WLAN standard. As 802.11 connectivity becomes more ubiquitous, multihop communications will be increasingly used for access point range extension and coverage enhancement. In this paper, we present a design for an IEEE 802. 11 -based power saving access point (PSAP), intended for use in multihop battery and solar/battery powered applications. These types of APs have many practical applications and can be deployed very quickly and inexpensively to provide coverage enhancement in situations such as campuses, building complexes, and fast deployment scenarios. Unlike conventional wired access points, in this type of system, power saving on the AP itself is an important objective. A key design constraint is that the proposed PSAP be backward compatible to a wide range of IEEE 802.11 functionality and existing wired access points. In this paper, we introduce the protocols required to achieve this compatibility, show the constraints imposed by this restriction, and present performance results for the proposed system.

In this paper, we consider the capacity of cellular code division multiple access (CDMA) when there is out-of-band ad hoc traffic relaying. The mobile stations (MSs) are dual-mode, having both ad hoc and cellular CDMA radios. An active MS is free to choose any available relay station (RS) within its ad hoc radio coverage area for dual-hop communication with the CDMA base station (BS). Communications between the RSs and the MSs use bandwidth which is available to the ad hoc radio and does not consume the CDMA capacity. Using this mechanism, CDIVIA interference can be reduced by dynamically selecting RSs which have more favorable CDMA link characteristics. Several relay station selection criteria are considered, namely, ad hoc relaying with low relative interference (ARRI), with best link gain (ARLG), and with shortest distance (ARSD). The relay station selection protocols are compatible with existing wireless local area network (WLAN) standards such as IEEE 802.11. An analytic model is used to compute the effects on uplink and downlink CDMA capacities when out-of-band relaying is added. The results show that very significant capacity improvements are possible by using these criteria compared with conventional CDMA with hard or soft handoff. Ad hoc relaying which dynamically tracks CDMA link quality can achieve greater capacity improvements than that using a distance-based relay station selection. Relaying, which considers both signal and interference conditions, achieves better capacity than that based on signal link quality alone.

A two-phase soft handoff scheme, which includes an initial power allocation phase followed by a power redistribution phase, is proposed. The initial power allocation phase makes a handoff decision for each connection by assigning a connection to the BS with the best link quality and allocating a minimum amount of power from the BS for the connection. The initial handoff decisions are made for individual connections independent of other connections or the BS power availability. Therefore, there might be heavily loaded and lightly loaded BSs because (i) traffic load may not be equally distributed in all cells, and (ii) the channel condition of the connections is random. The power re-distribution phase is to smooth out the loading on the system by coordinating the power allocations among neighboring BSs so that more connections can receive reliable transmissions. We then develop an analytical model for studying the connection reliability with the proposed soft handoff scheme. Our results show that the proposed two-phase soft handoff scheme can significantly improve connection reliability and increase system capacity in downlink transmissions.

A two-phase power distribution scheme for supporting quality-of-service (QoS) and best effort traffic is proposed. We first formulate the power distribution for QoS traffic as an optimization problem so that the number of simultaneously transmitting connections is maximized. Optimum power distribution is difficult to implement in practice due to both the computational complexity and the requirement for global information about the mobile station (MS) locations, connection channel conditions, and traffic load in the system. We then propose a heuristic scheme of power distribution for soft handoff (SHO) connections. The full scheme includes an initial power distribution (IPD) and a power distribution adjustment (PDA). IPD allocates BS power resource based on the channel condition of each individual connection, while PDA further coordinates the power distribution between neighboring base stations (BSs) in order to accommodate more connections. The proposed power distribution scheme can achieve a capacity close to that of the optimum power distribution, while providing much higher transmission throughput for best effort data traffic. The proposed power distribution scheme can be applied to existing SHO schemes for efficient BS power resource usage. The scheme does not require global information, and its implementation can be further simplified by performing IPD only with slight performance degradation.

Abstract We propose an access control scheme for ad hoc networks where nodes compete for transmissions selfishly in order to maximize their own payoffs. A game theory‐based model is first formulated and an equilibrium transmission probability is derived based on the total number of competing nodes, packet transmission error probabilities and other parameters. Packet transmission performance, including both transmission throughput and delay, is then analyzed. Our results show that the proposed channel access scheme allows good spatial reuse of the same channel and its saturation throughput can be much higher than the physical transmission rate of the channel under relatively good channel conditions. The mean packet transmission delay can be kept very low (e.g., less than 10 ms) so long as the offered traffic load is below the capacity. The main advantage of this access control scheme is that it allows fair channel access even nodes are selfish and do not corporate with each other in competing for transmissions. Copyright © 2006 John Wiley & Sons, Ltd.

IEEE 802.11 access point (AP) power saving is an important feature for solar/battery powered ESS mesh networks. In this paper we propose a media access control (MAC) protocol for power-aware multihop infrastructure, based on extensions to IEEE 802.11(e). A power saving mesh AP includes a network allocation map (NAM) in its beacon broadcasts which specifies its temporal operation, and thus coordinates traffic delivery and power saving at both end stations and at the AP. A simple algorithm is proposed for dynamically updating channel activities so that best-effort traffic load changes can be quickly accommodated. Simulation and analytic results are presented for the proposed system which show that the proposed protocol and algorithm can achieve good AP power consumption without any significant end station performance degradation

In this paper we study admission control in IEEE 802.11-based ESS mesh networks. An analytical model is developed for studying the mesh network capacity. Based on this, a distributed connection admission control (CAC) scheme is proposed, which incorporates load balancing in selecting a mesh path for a new connection. Our results show that connection level performance, including both average number of connections in the network and connection blocking probability, can be greatly improved using the proposed admission control compared to other admission control schemes.

In the past decade there has been a huge proliferation of wireless local area networks (WLANs) based on the IEEE 802.11 WLAN standard. Unfortunately, there is currently a large variety of commercial installations already deployed using APs with poor capabilities for supporting real-time QoS and power saving. This presents a problem for voice handset manufacturers and network operators. In this paper we consider the use of a power saving real-time gateway (i.e., POWSAR gateway). The gateway is installed on the wired infrastructure and filters all traffic towards a set of existing APs. A single gateway can support a large number of APs since infrastructure data rates are typically much higher than those of the air interface. We first consider the worse-case situation where the APs involved support neither station power saving nor packet QoS. Following this, performance comparisons are presented for a WiFi-compatible power saving solution and the POWSAR gateway approach. Results are presented which suggest that a POWSAR gateway has the potential for greatly improving power saving and real-time performance in certain legacy WLAN situations.

In this paper we consider the capacity of a cellular CDMA system when there is out-of-band ad hoc traffic relaying. In the system studied, the mobile stations (MS) are dual-mode, having both ad hoc and cellular CDMA radios. An active MS is free to choose any available relay station (RS) within its ad hoc radio coverage area for communication with the CDMA base station (BS). A relay station selection protocol, referred to as ad hoc relaying with best link gain (ARLG), is proposed which dynamically tracks the CDMA link quality when making this determination. An analytic model is used to compute the effects on uplink and downlink CDMA capacities when using the proposed protocol. The results show that very significant capacity improvements are possible by using this algorithm. Multihop relaying of this kind can be used to improve the performance of a cellular system., or to improve the performance in CDMA hotspot cells.

Future mobile handsets will often be multi-mode, containing both wireless LAN (WLAN) and cellular air interfaces. Vertical handoffs will commonly be used to pass voice calls to a cellular network when the user roams outside of WLAN radio coverage. Unfortunately, the transition from WLAN hotspot to cellular coverage is often very abrupt and leads to unacceptable call dropping rates. In this paper we propose and investigate the use of explicit WLAN/cellular handoff triggering. A simple Wi-Fi handoff trigger node (HTN) can be installed in the WLAN/cellular transition region, and generates link layer triggers which cause the initiation of the vertical handoff process. A key function provided by the HTN is to significantly reduce the call dropping rate even when there is very little collaboration between the cellular and WLAN hotspot providers. Results are presented which show that the call dropping probability can be dramatically reduced by the use of a handoff trigger node.

IEEE 802.11 WLANs are increasingly used to support real-time services such as voice and video. Reliable portable operation, however, is often difficult due to factors such as imperfect customer access point (AP) installation, unpredictable WLAN coverage, and unexpected co-channel interferers. In this paper we propose and investigate the use of IEEE 802.11 ad hoc-assisted handoff (AAHO). In AAHO, a single additional ad hoc hop may he used by a mobile station (MS) to obtain the range extension or channel quality needed to maintain its real-time voice connection. There are three versions of IEEE 802.11 AAHO. In backward ad hoc assisted handoff (BAAHO), the additional hop uses a relay station which already has an IEEE 802.11 association with the AP that the MS is using. In forward ad hoc assisted handoff (FAAHO) the additional hop uses a relay station whose AP is different from the one that the MS is currently using. Hybrid ad hoc assisted handoff (HAAHO) is a combination of the two and allows an MS to perform either BAAHO or FAAHO. The proposed AAHO designs are backward compatible, and thus can be implemented as a transparent overlay across existing IEEE 802.11 infrastructure deployments. Performance results show that AAHO can greatly improve performance in many practical situations.

In the past decade there has been a huge proliferation of wireless local area networks (WLANs) based on the IEEE 802.11 WLAN standard. As 802.11 connectivity becomes more ubiquitous, multi-hop communications is increasingly used for range extension and coverage enhancement purposes. In this paper we present a design for an IEEE 802.11-based power saving access point (PSAP), intended for use in multi-hop battery and solar/battery powered applications. These types of APs have many practical applications and can be deployed very quickly and inexpensively to provide coverage enhancement in situations such as campuses, building complexes and fast deployment scenarios. Unlike conventional wired access points, in this type of system power saving on the AP itself is an important objective. A key design constraint is that the proposed PSAP be backward compatible to a wide range of legacy IEEE 802.11 end stations and existing wired access points. In this paper we describe the protocols required to achieve this compatibility, show the constraints imposed by this restriction, and present performance results for the proposed system.

In this paper we consider the capacity improvements in a cellular CDMA hotspot with out-of-band ad hoc traffic relaying. Two different multihop relaying options are compared, and an analytic model is used to compute the effects on CDMA uplink and downlink capacities. The results show that significant capacity improvements are possible using ad hoc relaying, but these are dependent upon the ad hoc to cellular coverage ratio and other parameters. It is also shown that in some situations ad hoc relaying should be used to shrink the cells in question, but in other cases it should be used to shed traffic from the hotspot cell. The results give a useful indication of the potential role of ad hoc relaying in CDMA hotspot traffic situations.

Power distribution and soft handoff in cellular CDMA downlinks are studied in this paper. An optimum power distribution law is developed in order to guarantee the required signal-to-interference ratio for each connection. Based on this, soft handoff decisions are made at the mobile switching center for each connection based on the current channel conditions and resource availability in each cell. Simulation results show that soft handoff can improve the connection reliability and the system capacity in the downlink transmissions.

A novel radio resource management (RRM) scheme for the support of packet-switched transmission in cellular CDMA systems is proposed by jointly considering the physical, link, and network layer characteristics. The proposed resource management scheme is comprised of a combination of power distribution, rate allocation, service scheduling, and connection admission control. Power distribution allows individual connections to achieve their required signal-to-interference-plus-noise ratio, while rate allocation guarantees the required delay/jitter for real-time traffic and the minimum transmission rate requirement for non-real-time traffic. Efficient rate allocation is achieved by making use of the randomness and burstiness; of the packet generation process. At the link layer, a packet scheduling scheme is developed based on information derived from power distribution and rate allocation to achieve quality of service (QoS) guarantee. Packet scheduling efficiently utilizes the system resources in every time slot and improves the packet throughput for non-real-time traffic. At the network layer, a connection admission control (CAC) scheme based on the lower layer resource allocation information is proposed. The CAC scheme makes use of user mobility information to reduce handoff connection dropping probability (HCDP). Theoretical analysis of the grade of service performance, in terms of new connection blocking probability, HCDP, and resource utilization, is given. Numerical results show that the proposed RRM scheme can achieve both effective QoS guarantee and efficient resource utilization.

An analytical model is proposed to study the grade-of-service (GOS) performance for cellular communication systems supporting heterogeneous services, where GOS includes handoff connection dropping probability (HCDP), new connection blocking probability (NCBP) and system resource utilization. In the analytical model, a higher priority is given to a handoff connection than a new connection by resource reservation. The amount of the reserved resource depends on the current traffic load, and can be adjusted to achieve different HCDP. Numerical results are given to demonstrate the GOS performance.

Quality-of-service (QoS) support by power distribution and rate allocation for heterogeneous services in multicode code division multiple access (MC-CDMA) systems is studied. Power distribution among all participating connections achieves high resource utilization subject to satisfaction of SINR specifications for the allocated transmission rates. Rate allocation ensures that the required delay/jitter and loss requirements for different connections, while efficiently utilizing the system resources by statistically multiplexing the random and bursty traffic streams. The resultant call level grade of service (GOS) performance, including resource utilization and connection blocking probability (CBP), is derived. Numerical results are given to demonstrate the GOS performance.

Power distribution for multicode code division multiple access (MC-CDMA) systems supporting heterogeneous services is investigated. Power distribution laws under perfect and imperfect power control conditions are derived for the MC-CDMA systems to satisfy the required quality-of-service (QoS) while supporting more simultaneously transmitting users. Numerical results calculated based on the analyses are shown to demonstrate the impact of imperfect power control on the required receive powers.

A distributed call admission control (CAC) scheme for heterogeneous services is proposed. The scheme is based on a non-preemptive priority polling scheme and takes advantages of statistically multiplexing user mobility information. It is shown that the scheme can achieve both high resource utilization and low handoff dropping probability.