Unmanned aerial vehicles (UAVs) hold significant potential for sensing services in a large scope of area, thanks to their wide coverage and adaptable deployment. Considering the complex environment dynamics and limited sensing range, navigating multiple UAVs in a distributed way becomes challenging to implement cooperative data sensing and transmission tasks. In this paper, we optimize the trajectory design of UAVs by jointly considering the collected data volume, geographical fairness and limited energy reserve during their service period. To achieve the long-term serving objective, a memory augmented multi-agent deep reinforcement learning approach is presented to ensure energy-efficient distributed trajectory design with partial observations. Specifically, the intrinsic criterion is developed to enhance UAV spatial exploration when reaching the boundary of explored regions. Then, to address the information loss caused by incomplete observations, the spatial-temporal memory augmented actor-critic architecture is designed to extract historical contextual features for multi-UAV cooperative navigation. Furthermore, the prioritized experience replay mechanism is incorporated to enhance important experience exploitation for UAV collaboration. Extensive simulations using two real-world datasets in Shenzhen and Beijing demonstrate that the proposed method outperforms the state-of-the-art methods in terms of data collection ratio, geographical fairness, and energy consumption ratio.

Multi-tier computation offloading is crucial to address capacity constraints and improve flexibility for mobile devices. However, existing research on multi-layer computing offloading faces challenges like inefficient resource utilization and poor scalability, particularly in handling diverse computational tasks. To address these challenges, this paper proposes a distributed resource allocation and mixed task offloading framework for end-edge-cloud collaborative systems that support partial and full task offloading modes. First, we propose a three-tier network computing architecture and formulate a task-offloading utility maximization problem by jointly optimizing mixed task-offloading and resource allocation. The proposed problem is a mixed integer nonlinear program (MINLP), which we solve by decomposing it into two subproblemsresource allocationandtask offloading. Edge computing resources and bandwidth allocation can be independently optimized at each edge node with a fixed task offloading strategy. Cloud computing resource allocation, while convex, involves a global constraint, which we solve in a decentralized manner using a multi-agent optimization approach. Then, we propose a joint task offloading and resource allocation optimization algorithm, CNO-TORA, to obtain the solution to the formulated problem. The algorithm is supported by strong theoretical guarantees and is almost surely convergent to a globally optimal solution. Experimental results on a real dataset demonstrate that our algorithm is scalable to large-scale networks and outperforms baselines, achieving improvements in average system utility ranging from 4.01%-28.15%.

The vehicular environment presents unique challenges, including massive data generation, stringent latency requirements for safety-critical applications, bandwidth limitations, and intermittent connectivity, which make centralized learning approaches impractical. Vehicular Federated Learning (VFL) enables distributed model training by leveraging local data from connected vehicles, while preserving data privacy and reducing network overhead. However, the dynamic nature of VFL presents several additional challenges. High vehicle mobility and unstable channels lead to inconsistent client participation, while heterogeneous vehicle capabilities result in unbalanced training workloads and competitive resource allocation. These challenges significantly degrade VFL model performance and prolong training periods. In this paper, we propose a Mobility Resilient Vehicular Federated Learning (MR-VFL) scheme, which comprises two key components: an amplification-based adaptive vehicular FL (AVFL) training scheme and a dual-timescale FL scheduler. Specifically, AVFL adapts local training epochs to vehicle capabilities to improve scheduling flexibility and alleviate the impact of insufficient local epochs on model updates, which enhances training efficiency and reduces communication competition. The dual-timescale FL scheduler includes a macro scheduling strategy that optimizes long-term VFL performance based on the correlation between convergence speed and model accuracy, and a Mamba-based real-time scheduler that enhances training efficiency and reduces decision latency in massive vehicles scenarios. Extensive simulations show that MR-VFL effectively mitigates performance degradation due to complex vehicle mobility and heterogeneity, and improves training efficiency.

Asynchronous Federated Learning (AFL) can mitigate the straggler problem due to unbalanced training time of clients in Synchronous Federated Learning (SFL), thereby reducing the aggregation time and improving the training efficiency. However, AFL introduces training bias since different updating frequencies of heterogeneous clients can cause unequal knowledge contributions to the global model. Meanwhile, if the client data are heterogeneous, the local optimum may be drifted from the global one, which exacerbates the training bias problem. In order to solve the above issues, we propose a Knowledge Alignment framework for AFL with Heterogeneous Data, termed as KAFL-HD. Firstly, considering data heterogeneity, a data quality-aware aggregation method is proposed to estimate client contributions precisely, where both model staleness and data quality are utilized in aggregation weights. Secondly, a knowledge distillation method with staleness is designed to supplement more knowledge from slow clients to the global model. Thirdly, an adaptive learning rate adjustment method is proposed to customize the local learning rate based on the aggregation frequency and weight, which aligns the knowledge contributions of clients in the local training process. Furthermore, we provide theoretical analysis under a non-convex setting to show the convergence speed of KAFL-HD. Finally, comprehensive experiments are conducted, and the results show that KAFL-HD achieves the highest accuracy and fairness performance compared to the state-of-the-art baselines.

The growth of multimedia applications such as live streaming, online gaming, and virtual conferencing has intensified the demand for robust and efficient data transmission over heterogeneous and dynamic access networks using a multipath streaming scheduler. Traditional model-based multipath schedulers lack adaptability. Although learning-based approaches promise improved adaptation, their centralized architectures introduce challenges in scalability, user privacy, and responsiveness. Furthermore, relying solely on learning at individual devices is often insufficient due to the limited volume and diversity of local data, making it challenging to derive robust and generalizable policies. To address these limitations, we propose Fed- MVP, a federated multi-homing video streaming protocol within a hierarchical cloud-edge architecture. Fed-MVP decentralizes learning and decision-making processes to edge data centers, enhancing scalability and preserving user privacy. It employs fine-tuned meta-models at the edge for real-time adaptation to local network conditions and utilizes Proximal Policy Optimization (PPO) for efficient learning. We introduce a Modified Federated Averaging (Mod-FedAvg) algorithm for effective aggregation of model updates from clients. Trace-driven emulations highlight Fed-MVP’s superior performance, demonstrating up to 61% and 43% improvement in convergence rate during test and training phases, respectively, up to 40% reduction in download time, up to 35% enhancement in video quality assessments, and up to 38% reduction in stalling time compared to state-of-the-art multipath schedulers.

Federated Learning (FL) enables decentralized model training while preserving data privacy, however, real-world deployments are often constrained by Periodic Deterministic Communication (PDC) schedules, where communication between clients and the central server occurs at fixed intervals due to bandwidth limitations, energy constraints, or regulatory restrictions. These rigid schedules introduce fundamental challenges, including delayed model updates, model drift, inefficient convergence, and heightened sensitivity to non-IID data distributions, which undermine FL performance in practical settings. To address these limitations, we propose Federated Nested Quasi-Newton Optimization (FedNQN), a novel framework that accelerates convergence and enhances FL robustness under PDC constraints. FedNQN integrates curvature-aware central acceleration with variance-controlled local adaptation, ensuring stable learning dynamics despite restricted communication. At the global level, second-order curvature information accelerates model updates, compensating for infrequent synchronization, while local updates leverage variance-controlled optimizations to mitigate drift and adapt to heterogeneous data distributions. This coordinated optimization strategy enhances convergence speed, improves model accuracy, and maintains computational efficiency, making FL more adaptable to real-world constraints. Extensive experiments on benchmark datasets validate FedNQN’s effectiveness, demonstrating superior performance over state-of-the-art FL methods in terms of stability, scalability, and resilience to communication inefficiencies.

Topological attacks, including Sybil and wormhole attacks, modify a network topology by adding fake nodes and links to bypass typical routing paths, significantly degrading the Internet of Things (IoT) network performance. The presence of fake nodes and links introduces inconsistencies between the modified topology and the actual channel states of the nodes. Therefore, a fast and precise method for detecting topological attacks is crucial to identify and count these inconsistencies, which reveal the presence of an attack and affected nodes. In this paper, we propose a straightforward numerical metric called the Number of Channel Footprint Inconsistencies (NCFI) to count inconsistencies between nodes' transmission, reception, and idle states on the channel and the network topology. In the proposed method, each node reports its channel states to a central node, which uses a Constraint Satisfaction Problem (CSP)-based approach in graph coloring to calculate the NCFI. Additionally, each pair of IoT nodes can use the NCFI in a distributed manner to detect the presence of fake links between them. Our simulation results indicate that the proposed method can accurately detect the presence of attackers in the network compared to the state-of-the-art methods.

Mobile crowdsensing (MCS) by human participants and unmanned aerial vehicles (UAVs) is an emerging air-ground collaborative data collection paradigm by navigating a group of UAVs to collaborate with human participants to provide large-scale and fine-grained sensing services. In this paper, we aim to optimize the trajectory design of UAVs by jointly considering the collected data volume, geographical sensing fairness, and limited energy reserve during the serving period. To achieve the long-term serving objective, we propose a human participants distribution prediction based multi-agent deep reinforcement learning method for efficient UAV navigation to collaborate with human participants in performing MCS tasks. Specifically, we first introduce a region division based human participant spatial distribution prediction method to help UAVs to collaborate with human participants by the predictive mobility flows. Then, we present the multi-agent proximal policy optimization (MAPPO) based method for efficient UAV navigation decision-making. Extensive simulations and trajectory visualization using the real-world mobility dataset in KAIST show that the proposed method consistently outperforms the state-of-the-art in terms of the energy efficiency when varying the number of UAVs and human participants.

Caching and sharing the content files are critical and fundamental for various future vehicular applications. However, how to satisfy the content demands in a timely manner with limited storage is an open issue owing to the high mobility of vehicles and the unpredictable distribution of dynamic requests. To better serve the requests from the vehicles, a cache-enabled multi-layer architecture, consisting of a Micro Base Station (MBS) and several Small Base Stations (SBSs), is proposed in this paper. Considering that vehicles usually travel through the coverage of multiple SBSs in a short time period, the cooperative caching and sharing strategy is introduced, which can provide comprehensive and stable cache services to vehicles. In addition, since the content popularity profile is unknown, we model the content caching problems in a Multi-Armed Bandit (MAB) perspective to minimize the total delay while gradually estimating the popularity of content files. The reinforcement learning-based algorithms with a novel Q-value updating module are employed to update the caching files in different timescales for MBS and SBSs, respectively. Simulation results show the proposed algorithm outperforms benchmark algorithms with static or varying content popularity. In the high-speed environment, the cooperation between SBSs effectively improves the cache hit rate and further improves service performance.

In this paper, we consider an energy sustainable wireless Internet of Things (IoTs) network with a reconfigurable intelligent surface (RIS). Specifically, the Hybrid Access Point (HAP) performs beamforming to transfer energy to a set of devices, and the devices then use the harvested energy for Non-orthogonal multiple access (NOMA)-based data transmissions, where a RIS is employed to enhance both the energy harvesting and data transmissions. An optimization problem is formulated to maximize the sum-rate of IoT devices by jointly optimizing the energy beamforming of the HAP, the phase shifts of RIS, the selection of devices in NOMA transmissions with power control, and the time allocation for energy harvesting. As the formulated optimization problem is a complex mixed-integer non-linear programming (MNLP) problem, we decompose it into four subproblems and apply Block Coordinate Descent (BCD) to iteratively optimize each subproblem until convergence is achieved. A novel joint optimization algorithm is proposed to select a subset of devices with transmission power control to attain the maximum sum-rate. A closed-form expression for the optimal time allocation is further derived to strike a balance between the energy harvesting and the data transmissions, considering the residual energy resulting from the previous power control. Simulations validate that the proposed solution outperforms state-of-the-art algorithms in the literature.

With the rapid development of vehicle-to-everything technology, vehicular edge caching has emerged as a crucial component for managing frequently accessed content at the network’s edge. However, due to vehicles’ high mobility, it is challenging to determine where and which content needs to be cached. To address this issue, a mobility and context-aware precache strategy is proposed to proactively prefetch and replace content in two steps. First, by integrating the traffic features from vehicles and roads, a spatial-temporal informer-based model is designed to predict long-term vehicle trajectories. Subsequently, a proactive context-aware precache strategy is proposed. By analyzing the context of different cache types, the required content can be further accurately estimated according to the cache type and workload. Extensive simulations based on real-world mobility scenarios are conducted to validate the performance of the proposed method. The results show that the proposed method can improve prediction accuracy and cache hit rate by 34.56% and 18.89%, and reduce mean response time and total energy cost by 6.1% and 2.65% compared to the existing precaching methods.

Ultradense low-Earth orbit (LEO) satellite-terrestrial network (ULSN) has evolved as a new paradigm to provide ubiquitous and high-capacity communications in next generation wireless networks. However, the direct LEO satellite broadband connectivity faces significant challenges in urban environments due to the masking effect, which limits the reliability and availability of communication links in ULSNs. To address this, reconfigurable intelligent surface (RIS) is emerging as a promising solution in ULSNs. In this article, we investigate RIS-aided downlink data transmission in urban environments of multiusers in ULSNs. We set up a mixed-integer programming (MIP) model for maximizing the sum rate of terrestrial users in ULSNs. To solve the complex MIP problem, we propose a two-phase joint optimization algorithm with a deep learning phase and an alternative optimization (AO) phase. In the deep learning phase, a deep neural network (DNN) algorithm is employed to obtain the optimal user association matrix based on the positions of terrestrial users and LEO satellites. Then in the AO phase, successive convex approximation is utilized to transform the nonconvex subproblems of beamforming and RIS phase design into convex formulations and iteratively solve them. Simulation results demonstrate that the proposed algorithm outperforms other baseline algorithms.

Federated learning (FL) in Intelligent Internet of Things (IIoT) environments faces critical challenges, including sparse client participation, non-IID local data distributions, and unreliable communication, which lead to slow convergence and high variance in global updates. To address these issues, we propose adaptive central federated momentum optimization (ACFMO), an optimization framework that enhances FL efficiency and stability under constrained participation. ACFMO integrates an adaptive central acceleration mechanism that dynamically adjusts momentum updates based on real-time client availability, preventing instability and ensuring smoother global model updates. Additionally, a variance-controlled local updating strategy refines client contributions, mitigating high variance caused by infrequent and heterogeneous updates. Extensive experiments across diverse FL scenarios demonstrate that ACFMO significantly accelerates convergence, reduces communication overhead, and improves model stability compared to state-of-the-art FL methods, making it particularly well-suited for real-world IIoT deployments where network and computational resources are constrained.

The unpredictability of network environments, limited edge resources, and the high complexity of collaborative policies are significantly hindering the development of the Industrial Internet of Things (IIoT). These challenges are particularly pronounced in healthcare, where high-priority, delay-sensitive medical tasks and large-scale personalized services face substantial obstacles. To address these challenges, this paper proposes the Self-Attention Enhanced QMIX with Multi-Pass Multi-Task Execution (SAE-MT-QMIX) algorithm, aimed at optimizing communication and computing resource allocation as well as task offloading strategies. By leveraging Digital Twin (DT) support, the algorithm achieves collaborative optimization of communication, computing, and control within the Internet of Medical Things (IoMT), significantly enhancing the quality of service for massive personalized applications. The algorithm adopts a distributed execution and centralized training framework: the distributed execution component uses the Multi-Pass Multi-Task Deep Q-Network (MPMT-DQN) algorithm to handle the complexity of parameterized action spaces in multi-task scenarios, while the centralized training component employs the Self-Attention Enhanced QMIX (SAE-QMIX) algorithm to dynamically optimize credit assignment across multiple users. Simulation results demonstrate that SAE-MT-QMIX significantly reduces delay and energy consumption compared to baseline methods. It ensures effective optimization of communication, computing, and control in dynamic IoMT, efficiently addressing diverse demands and tasks while enhancing service quality and system adaptability.

Indoor Sound Source Localization (ISSL) is under growing focus with the rapid development of smart IOT intelligence. The predominant approaches typically involve constructing large microphone (Mic) array systems or extracting multiple angles of arrival (AOAs). However, the performance of these solutions is often constrained by the physical size of the array. Besides, there has been limited focus on 3D localization with a single small-sized Mic array. In this paper, we propose HearLoc, an ISSL system that can directly locate 3D sources with a ten-$cm$Mic array. We demonstrate that the localization ability and dimensional capability can be significantly enhanced by incorporating the time differences of arrival (TDOAs) between the line-of-sight (LOS) and ECHO signals from nearby reflective surfaces. Our approach involves a localization method that selectively sums the correlation powers at useful TDOAs induced by each location. We also design a data processing pipeline with interpolation, normalization and pruning techniques to improve system accuracy and efficiency. To further enhance scalability, we design an iterative algorithm for the ISSL problem with multiple sources and an array location calibration scheme. Experiments demonstrate that the HearLoc can effectively locate sound sources, exhibiting$2\times$/$3.7\times$improvements in accuracy for 2D and 3D localization, respectively, and a$4\times$increase in efficiency compared to the existing AOA-based ISSL solutions.

Video streaming performance may degrade substantially in a mobile environment due to fast-changing wireless links. On the other hand, to provide ubiquitous services, heterogeneous static and mobile access and backbone networks will be integrated in the sixth-generation (6G) systems, so mobile users can take advantage of multiple access options for better services. Multi-path transport-layer protocols like Multi-Path QUIC (MPQUIC) show promise in utilizing multiple access links to address the impact of mobility. However, the optimal link selection that aims to provide statistical QoS guarantee for video streaming in a mobile environment with both user mobility and network mobility remains an open issue. In this paper, based on a lightweight Multi-Armed Bandit (MAB) technique, we develop aQoS-drivenContextualMAB(QC-MAB) framework for MPQUIC, which makes an intelligent access network selection and adaptively enables FEC coding to trade off delay, reliability and goodput. Extensive simulation results with ns-3 show that the proposed QC-MAB framework can outperform the state-of-the-art solutions. It achieves up to ten times lower video interruption ratio and three times higher goodput in highly dynamic mobile environments.

Personalized Federated Learning (pFL) can improve the accuracy of local models and provide enhanced edge intelligence without exposing the raw data in Mobile Edge Computing (MEC). However, in the MEC environment with constrained communication resources, transmitting the entire model between the server and the clients in traditional pFL methods imposes substantial communication overhead, which can lead to inaccurate personalization and degraded performance of mobile clients. In response, we propose a Communication-Efficient pFL architecture to enhance the performance of personalized models while minimizing communication overhead in MEC. First, a Knowledge-Aware Parameter Coaching method (KAPC) is presented to produce a more accurate personalized model by utilizing the layer-wise parameters of other clients with adaptive aggregation weights. Then, convergence analysis of the proposed KAPC is developed in both the convex and non-convex settings. Second, a Bidirectional Layer Selection algorithm (BLS) based on self-relationship and generalization error is proposed to select the most informative layers for transmission, which reduces communication costs. Extensive experiments are conducted, and the results demonstrate that the proposed KAPC achieves superior accuracy compared to the state-of-the-art baselines, while the proposed BLS substantially improves resource utilization without sacrificing performance.

In the rapidly advancing ubiquitous intelligence society, the role of data as a valuable resource has become paramount. As a result, there is a growing need for the development of autonomous economic agents (AEAs) capable of intelligently and autonomously trading data. These AEAs are responsible for acquiring, processing, and selling data to entities such as software companies. To ensure optimal profitability, an intelligent AEA must carefully allocate its portfolio, relying on accurate return estimation and well-designed models. However, a significant challenge arises due to the sensitive and confidential nature of data trading. Each AEA possesses only limited local information, which may not be sufficient for training a robust and effective portfolio allocation model. To address this limitation, we propose a novel data trading market where AEAs exclusively possess local market information. To overcome the information constraint, AEAs employ federated learning (FL) that allows multiple AEAs to jointly train a model capable of generating promising portfolio allocations for multiple data products. To account for the dynamic and ever-changing revenue returns, we introduce an integration of the histogram of oriented gradients (HoGs) with the discrete wavelet transformation (DWT). This innovative combination serves to redefine the representation of local market information to effectively handle the inherent nonstationarity of revenue patterns associated with data products. Furthermore, we leverage the transform domain of local model drifts in the global model update process, effectively reducing the communication burden and significantly improving training efficiency. Through simulations, we provide compelling evidence that our proposed schemes deliver superior performance across multiple evaluation metrics, including test loss, cumulative return, portfolio risk, and Sharpe ratio.

We consider a setting engaging in collaborative learning with other machines where each individual machine has its own interests. How to effectively collaborate among machines with diverse requirements to maximize the profits of each participant poses a challenge in federated learning (FL). Our studies are motivated by the observation that in FL the global model attempts to acquire knowledge from each individual machine, while aggregating all local models into one optimal solution may not be desirable for some machines. To effectively leverage the knowledge of others while obtaining the customized solution for individual machine, we propose the accelerated federated training procedures with diversified global models. Based on the federated stochastic variance reduced gradient (FSVRG) framework, we propose the model-based grouping mechanism with adaptive central acceleration (MA-FSVRG) and gradients-based grouping mechanism with adaptive central acceleration (GA-FSVRG) to tackle the challenges of heterogeneous demands. The simulation results demonstrate the advantages of the proposed MA-FSVRG and GA-FSVRG over the state-of-the-art FL baselines. MA-FSVRG exhibits greater stability in performance and significant cost savings in local computation expenses compared to GA-FSVRG. On the other hand, GA-FSVRG attains higher test accuracy and faster convergence speed, particularly in scenarios with limited individual machine participation.

Federated Learning (FL) enables efficient and privacy-preserving Edge Intelligence (EI) in Mobile Edge Computing (MEC). However, implementing FL-enabled EI services faces critical challenges, including data and device heterogeneity, limited network resources, uneven distribution of network infrastructure, etc., which may intensify with increasing system scale. These challenges are particularly acute in multi-provider environments where edge servers are suboptimally allocated across federations, leading to degraded convergence and increased training costs. In this paper, we present a novel Multiple Hierarchical Federated Learning (MHFL) architecture for large-scale FL and design an Edge Server Reallocation scheme (ESR-MHFL) to enhance training efficiency by optimally redistributing edge servers among federations based on their contribution to model convergence. We first develop a closed-form analysis model for MHFL to quantify training time, computation, and communication costs. To improve training efficiency, we analyze the impacts of edge server allocation on convergence and formulate server reallocation as a multi-item auction problem with theoretical guarantees. We then propose ESR-MHFL, which leverages Coalition Structure Generation (CSG) and greedy matching methods to simplify the reallocation problem and enhance efficiency. Extensive numerical simulations demonstrate that ESR-MHFL not only improves model accuracy while reducing training cost but also exhibits strong compatibility with existing client selection methods, achieving improved training efficiency. The total economic expenditure combining all components

Low-earth-orbit (LEO) satellite networks have become very popular in recent years, exemplified by Starlink, OneWeb, Kuiper and others, due to the dramatically reduced launch cost and increased demand for connectivity anytime, anywhere. After an exploration of Starlink access, core and backbone networks, in this paper we focus on the satellite access network (SAN) of Starlink around the world. Particularly, we measure the access performance in terms of one-way delay and round-trip time from user terminal (UT) to ground station (GS) and point-of-presence (PoP), both inside-out and outside-in, and even on inactive dishes. It reveals the unique characteristics of Starlink SAN in terms of satellite-GS scheduling, media access control and user contention, and sheds light on the challenges and opportunities for network protocols and applications. The paper will be complemented by public dataset release and conference on-site demo for the research and industry community.

Time-sensitive applications have become increasingly prevalent in modern networks, necessitating the development of Delay-Guaranteed Routing (DGR) solutions. Finding an optimal DGR solution remains a challenging task due to the NP-hard nature of the problem and the dynamic nature of network traffic. In this paper, we propose Deadline-Driven Routing (DDR), a distributed traffic-aware adaptive routing protocol that addresses the DGR problem. Inspired by online navigation techniques, DDR leverages real-time traffic conditions to optimize routing decisions and ensure on-time packet delivery. By combining network topology-based path generation with real-time traffic knowledge, each router can adjust packet forwarding directions to meet its heterogeneous latency requirements. Comprehensive simulations on real-world network topologies demonstrate that DDR can consistently provide delay-guaranteed service in different network topologies with varying traffic conditions. In addition, DDR ensures backward compatibility with legacy devices and existing routing protocols, making it a viable solution for supporting delay-guaranteed service.

The evolution of network applications poses significant challenges to network service provisioning. Multipath routing and packet spraying techniques have become crucial in networks. TCP performance declines sharply on multipath setups where significant packet reordering occurs, as unordered transmissions are misinterpreted as packet loss and congestion signals. We propose the Multipath Routing Compatible (MPRC) congestion control, which utilizes the delay-sensitive Fast Retransmission Timeout (FastRTO) to decouple reordering from loss signals and enhance loss detection. This modification optimizes congestion window adjustments in multipath environments and handles packet reordering effectively, ensuring stable TCP throughput across multipath settings. Our algorithm was implemented on the NS-3 simulator platform and compared with other congestion control algorithms across various network topologies, in both single-path and multipath routing scenarios. The results demonstrate that MPRC can handle both sporadic and persistent packet reordering, ensuring steady throughput in multipath routing environments while maintaining compatibility and fairness in bandwidth competition, which paves the way for efficient congestion control adopting multi-path routing networks.

In underwater acoustic sensor networks (UANs), reliable packet delivery is critical in data collection and monitoring of the oceans. It primarily relies on the design of routing protocols to guarantee network durability and connectivity. However, utilizing routing design to achieve enhanced network longevity and reliable packet forwarding is challenging due to the complex underwater environment, unstable link connectivity, high transmission power, and high propagation latency. Thus, we propose a novel routing strategy called the longevity-oriented and reliable forwarding percolation (LRP) routing protocol. The goal of LRP is to ensure reliability by exploring multipath percolation-based routing and extend network longevity by energy control and residual energy optimization. Network reliability can be estimated using a built-in calculation model, and the source node controls energy and records the residual energy to extend the network lifetime. Utilizing an optimization of the network reliability requirement and residual energy, we develop a routing strategy to select the activated link set and node set for each hop in an energy-saving and reliable way. Moreover, a recursive approach is used to avoid the occurrence of void regions. Simulation results exhibit the effectiveness of the power control and routing strategy and demonstrate its superiority over the benchmarks in terms of network longevity and reliability.

In the realm of healthcare data analysis, privacy concerns have been tackled by the federated learning (FL) framework. However, in the situation that heterogeneous healthcare Internet of Things (IoT) devices collect different types of data, applying FL becomes difficult. To train a model leveraging diverse healthcare IoT devices, we propose an advanced collaborative learning framework to fill the gap. With the proposed collaborative learning framework, individual IoT devices project their sensed features into a carefully developed latent space, which are transmitted to a central server. For privacy preservation, the latent local features are encoded within this space, while the samples’ labels remain securely stored in the individual IoT devices. Collaboratively, the deep neural network model is trained by both the central server and the diverse IoT devices. The central server handles the computationally intensive training processes, while the individual IoT devices evaluate the model’s performance and initiate back-propagation based on their locally stored labels. Experimental results demonstrate that the proposed collaborative learning framework achieves performance similar to centralized training and significantly outperforms individual training while preserving data privacy.

Metaverse, a hypothetical digital environment linking the cyber world and the physical world, is expected to revolutionize the way people interact. In the metaverse, people interact with objects, the environment, and each other through digital representations of themselves or avatars across time and space. For example, in the metaverse, people can have meetings with colleagues hundreds of miles away. They can also walk through the aisles of a store, find the best fit and have it delivered to their doorstep. It is also possible to simulate the optimal process manufacturing line to adjust for product variation and minimize bottlenecks, or test an innovative aircraft wing design without building expensive prototypes.

Metaverse, a hypothetical digital environment linking the cyber world and the physical world, is expected to revolutionize the way people interact. In the metaverse, people interact with objects, the environment, and each other through digital representations of themselves or avatars across time and space. For example, in the metaverse, people can have meetings with colleagues hundreds of miles away. They can also walk through the aisles of a store, find the best fit, and have it delivered to their doorstep. It is also possible to simulate the optimal process manufacturing line to adjust for product variation and minimize bottlenecks, or test an innovative aircraft wing design without building expensive prototypes.

The information regarding the driving states of all vehicles is crucial for achieving optimal group performance in a vehicle platoon. This article focuses on the fully distributed driving state estimation problem in open vehicle platoons, which frequently experience arrivals and departures of vehicles. To address this problem, we propose a distributed driving state observer inspired by the leader–follower consensus technique. This observer can reconstruct the global driving state of the platoon, including the positions, velocities, and accelerations of all vehicles. We also derive the necessary and sufficient conditions to ensure the stability of its estimation error dynamics. The proposed observer is highly flexible in platoons with a strongly connected communication network, as it can be constructed and operated using the local knowledge of each vehicle only, without relying on global information of a platoon such as the number of vehicles. We demonstrate the observer's plug-and-play operations in the face of platoon merging and splitting and analyze its estimation stability. Extensive simulation results demonstrate the effectiveness of our theoretical results and the potential of the proposed observer for platoon control.

Average consensus is a key component of multi-agent systems coordination, while data privacy becomes a serious concern. Through the information exchange process, the initial state of an agent may be disclosed to its neighbors. The existing privacy-preserving research mainly addressed the situation of single-neighbor eavesdropping and infinite-time consensus, and they cannot deal with the cases of multi-neighbors eavesdropping and collusion inference attack or ensuring finite-time consensus. In this paper, we prove that it is impossible to preserve a node’s data privacy if all of its neighbors collusively infer the data. Otherwise, we propose a privacy-preserving framework to support conventional average consensus, push-sum consensus, and finite-time average consensus, which integrates multiplying random variables, finite-time error compensation, and updating rule jump. In this paper, each agent exchanges data with its neighbors by multiplying a random variable to its real-time state at each iteration. To eliminate errors caused by the random multiplier, a finite-time error compensation term and updating rule jump are designed, which ensure the accuracy of consensus. We prove that the proposed framework can converge and preserve privacy facing collusion inference attacks in both finite-time and infinite-time consensus, while traditional adding-noise-based methods cannot solve the finite-time case. We also derive the analytical expressions of the maximum privacy disclosure probability for the initial state of each agent, and present the impact of multiplying random variables. Extensive case studies demonstrate the effectiveness of the proposed framework.

This paper explores the joint optimization problem for trajectory planning and radio resource allocation in unmanned aerial vehicle (UAV) communications with the aim of maximizing data collection. Rather than decomposing the problem into subproblems, as most current approaches do, we express the quantity of data gathered by a UAV-assisted network as a function of both the size of the resource block allocated to all ground devices and their average upload rate. Based on this formula, it can be concluded that the problem of maximizing the average data collection can be reduced to minimizing the flight trajectory if each device communicates with the UAV within the maximum allowable coverage of the UAV. To address this issue, we propose an advanced hierarchical clustering algorithm that divides larger network-scale scenarios into many disjoint subregions to determine the initial hovering positions of the UAV. The non-convex minimization trajectory problem is decomposed into a series of convex optimizations to minimize path segments along the trajectory, based on the traveling salesman problem (TSP). Subsequently, the communication optimization process is modified to assign specific upload times for each device. The effectiveness of the optimization algorithm is demonstrated through extensive simulations, which show its superior performance in terms of average rates of data collection and upload failures.

This paper investigates secure control for homogeneous vehicle platoons in the presence of false data injection attacks with low communication and computation costs. We consider a scenario where each vehicle within the platoon transmits a local state vector to multiple neighboring vehicles. By leveraging these shared vectors from both preceding and following vehicles, we propose a novel and effective resilient controller for vehicle platoons against node/communication link attacks. More specifically, each vehicle determines the local state deviation vectors from neighboring vehicles. It then eliminates the vectors that are farthest from the origin, with the number of removed vectors equivalent to the maximum number of attacks. This approach offers a considerable advantage by mitigating the effects of abnormality and manipulation, making it robust against arbitrary information tampering within a pre-defined upper boundary for manipulated broadcast information. Importantly, we establish specific conditions for the proposed resilient design to guarantee the internal stability of the vehicle platoon under attacks. Extensive simulations and experiments involving four TurtleBot3s are conducted to demonstrate the effectiveness of the proposed resilient controller.

In unmanned aerial vehicles (UAVs) assisted cellular networks, user association plays an important role in interference control and spectrum efficiency. In this paper, we study the performance of uplink-downlink decoupled (UDDe) user association in a multi-UAV assisted network in which each user can associate with different UAVs or the macro base station (MBS) for uplink (UL) and downlink (DL) transmissions. Since some popular data may be requested by multiple users, grouping these users and applying multicasting can significantly improve spectral efficiency. Unlike traditional linear precoding that treats interference entirely as noise, we propose a rate-splitting multiple access (RSMA) policy that employs rate splitting at the transmitter and successive interference cancellation (SIC) at the receiver. To be specific, the transmitted signal is split into a common part and a private part, and the interference is partially decoded and partially treated as noise. In this context, we formulate a joint optimization problem of UL-DL association and beamforming for maximizing the sum-rate of users in UL and that of multicast groups in DL under the constraints of UAV backhaul capacity and power budget. Since the formulated problem is non-convex with intricate states and an individual UAV may not know the rewards of other UAVs, we convert it into a robust partially observable Markov decision process (POMDP). Then we resort to multi-agent deep reinforcement learning (MADRL) that enables each UAV to learn and optimize its policy in a distributed manner. To achieve an optimal policy, we further propose an improved clip and count-based proximal policy optimization (PPO) algorithm to train actor and critic networks. Simulation results demonstrate the superiority of the proposed decoupled association strategy with RSMA and the MADRL learning algorithm.

In this article, we propose a novel scheduling scheme to guarantee per-packet delay in single-hop wireless networks for delay-critical applications. We consider several classes of packets with different delay requirements, where high-class packets yield high utility after successful transmission. Considering the correla-tionship of delays among competing packets, we apply a delay-laxity concept and introduce a new output gain function for scheduling decisions. Particularly, the selection of a packet takes into account not only its output gain but also the delay-laxity of other packets. In this context, we formulate a multi-objective optimization problem aiming to minimize the average queue length while maximizing the average output gain under the constraint of guaranteeing per-packet delay. However, due to the uncertainty in the environment (e.g., time-varying channel conditions and random packet arrivals), it is difficult and often impractical to solve this problem using traditional optimization techniques. We develop a deep reinforcement learning (DRL)-based framework to solve it. Specifically, we decompose the original optimization problem into a set of scalar optimization subproblems and model each of them as a partially observable Markov Decision Process (POMDP). We then resort to a Double Deep Q Network (DDQN)-based algorithm to learn an optimal scheduling policy for each subproblem, which can overcome the large-scale state space and reduce Q-value overestimation. Simulation results show that our proposed DDQN-based algorithm outperforms the conventional Q-learning algorithm in terms of reward and learning speed. In addition, our proposed scheduling scheme can achieve significant reductions in average delay and delay outage drop rate compared to other benchmark schemes.

This paper first develops an analytical framework to investigate the performance of uplink (UL)/downlink (DL) decoupled access in cellular vehicle-to-everything (C-V2X) networks, in which a vehicle's UL/DL can be connected to different macro/small base stations (MBSs/SBSs), separately. Using the stochastic geometry analytical tool, the UL/DL decoupled access C-V2X is modeled as a Cox process, and we obtain the following theoretical results, i.e., 1) the probability of different UL/DL joint association cases i.e., both the UL and DL are associated with the different MBSs or SBSs, or they are associated with different types of BSs; 2) the distance distribution of a vehicle to its serving BSs in each case; 3) the spectral efficiency of UL/DL in each case; and 4) the UL/DL coverage probability of MBS/SBS. The analyses reveal the insights and performance gain of UL/DL decoupled access. Through extensive simulations, the accuracy of the proposed analytical framework is validated. Both the analytical and simulation results show that UL/DL decoupled access can improve spectral efficiency. The theoretical results can be directly used for estimating the statistical performance of a UL/DL decoupled access C-V2X network.

In the future 6G intelligent transportation system, the edge server will bring great convenience to the timely computing service for connected vehicles. To guarantee the quality of service, the time-critical services need to be migrated according to the future location of the vehicle. However, predicting vehicle mobility is challenging due to the time-varying of road traffic and the complex mobility patterns of vehicles. To address this issue, a spatial-temporal awareness proactive service migration strategy is proposed in this paper. First, a spatial-temporal neural network is designed to obtain accurate mobility by using gated recurrent units and graph convolutional layers extracting features from spatial road traffic and multi-time scales driving data. Then a proactive migration method is proposed to guarantee the reliability of services and reduce energy consumption. Considering the reliability of services and the real-time workload of servers, the migration problem is modeled as a multi-objective optimization problem, and the Lyapunov optimization method is utilized to obtain utility-optimal migration decisions. Extensive simulations based on real-world datasets are performed to validate the performance of the proposed method. The results show that the proposed method achieved 6% higher prediction accuracy, 10% lower dropping rate, and 10% lower energy consumption compared to state-of-the-art methods.

The Integrated Terrestrial and LEO Satellite Network (ITSN) has a high bandwidth-delay product (BDP) and high-speed movement, which makes congestion control difficult. We develop aMobility-AwareCOngestion control (MACO) algorithm for multipath QUIC (MPQUIC) in ITSN. MACO models the dynamic interactions between MPQUIC subflows and LEO networks, including handovers and outages triggered by satellite movement, and changes in network topology and link conditions. With the knowledge of network dynamics influenced by mobility, MACO can estimate changes in path BDP without solely relying on lengthy network probing. It employs a quick start (QS) and an effective congestion avoidance (CA) mechanism based on a multipath fluid model. The QS sets an appropriate initial cwnd to shorten the slow start duration. The CA applies a square root function to quickly increase the cwnd to the equilibrium and conservatively increase when approaching the BDP. We conduct a series of experiments to evaluate MACO using network simulator 3 (ns-3) based on collected data traces on Starlink. Simulation results demonstrate that MACO can achieve upto three times higher throughput and improve the convergence performance by 70.67% against benchmark algorithms.

Heterogeneous communication environments and broadcast feature of safety-critical messages bring great challenges to mode selection and resource allocation problem. In this paper, we propose a federated multi-agent deep reinforcement learning (DRL) scheme with action awareness to solve mode selection and resource allocation problem for ensuring quality of service (QoS) in heterogeneous V2X environments. The proposed scheme includes an action-observation-based DRL and a model parameter aggregation algorithm considering local model historical parameters. By observing the actions of adjacent agents and dynamically balancing the historical samples of rewards, the action-observation-based DRL can ensure fast convergence of each agent’ individual model. By randomly sampling historical model parameters and adding them to the foundation model aggregation process, the model parameter aggregation algorithm improves foundation model generalization. The generalized model is only sent to each new agent, so each old agent can retain the personality of its individual model. Simulation results show that the proposed scheme outperforms the comparison algorithms in the key performance indicators.

Multi-homing technologies are promising to support seamless handoff and non-interrupted transmissions. Scheduling packets across multiple paths, however, has the known issue of out-of-order (OFO) due to the heterogeneity of the paths, which is detrimental to users’ quality of experience (QoE). Wireless link characteristics undergo a fast change over time in mobile environments, thus aggravating the OFO issue. In this paper, we present a novel mobility-aware multipath QUIC (MMQUIC) framework in which interactions between link and transport layers are introduced so that the scheduler at a mobile sender is aware of uplink variations, and a new ACK packet structure is designed to inform the scheduler of downlink variations when the receiver is mobile. Based on MMQUIC, a Mobility-Aware Multipath Scheduler (MAMS) is developed, which forecasts the path conditions in successive time slots based on historical and current end-to-end (E2E) path conditions, along with wireless uplink/downlink conditions, and pre-allocates packets on multiple paths accordingly. We conduct a series of experiments to evaluate the performance of MAMS using network simulator 3 (ns-3). Simulation results demonstrate that MAMS effectively leverages the information related to mobility, achieving substantial performance gains w.r.t. the goodput and packet delay distribution under different mobility patterns.

High-reliability and low-latency communication is essential for timely information exchange in vehicle platooning. As a key enabler of this, the cellular vehicular-to-everything (C-V2X) network uses a sensing-based semi-persistent scheduling (SPS) protocol, where radio resources are reserved for a number of transmissions with reduced resource re-allocation and control overhead. However, consecutive access collisions may be caused by reservation conflict, which leads to long delay and threatens platoon’s stability and safety. In this paper, a coordinating resource reservation (CRR) protocol is proposed for vehicle platooning. By implementing error detection with coordination among platoon vehicles, the resource reservation is improved for reduced collisions and delay. Specifically, packet reception/loss information is sent out by platoon vehicles through their own packets. Such information is shared with transmitters and guides them to reserve new resources when access collision occurs. As a result, long delay is avoided while no extra feedback packet is introduced. Furthermore, Markov analysis is presented to evaluate the performance of SPS and the proposed CRR for vehicle platooning, providing the quantified performance gains. Finally, simulation results demonstrate the superiority of the proposed CRR in reducing packet loss and latency, compared with the legacy SPS and other state-of-the-art solutions.

The increasing number of connected vehicles exacerbates the scarcity of spectrum resources in vehicle-to-everything (V2X) communication. To optimize the utilization of wireless resources, it is crucial to allocate the limited spectrum blocks to each roadside unit (RSU) based on the real-time bandwidth demand of vehicles within their coverage. However, the complex mobility patterns of vehicles and dynamic traffic conditions make it challenging to accurately and promptly estimate the bandwidth demand. To address this issue, a spatial-temporal multi-attentional network (STMA-net) is designed to predict the future bandwidth demand of RSUs. Based on the predicted bandwidth demand, a prediction error-compensable proactive bandwidth allocation algorithm is proposed to adaptively allocate spectrum resources and narrow the discrepancy between predicted and actual demand. Experimental results with realistic traffic in Bologna demonstrate that the proposed STMA-net achieves 11.25% higher prediction accuracy compared to state-of-the-art methods. Furthermore, the proposed proactive bandwidth allocation method outperforms existing methods, providing the highest throughput and serving 5% more vehicles while reducing the service drop rate by an order of magnitude.

The next-generation wireless network is perceived to integrate with sensing capability and evolve into the perceptive mobile network (PMN), enabling massive sensing-intensive applications. However, the sensing function will affect the communication performance in cellular networks. To study the sensing and communication performance of PMNs and their interactions, this paper investigates a millimeter-wave PMN with dual-functional base stations (BSs) for simultaneous detection of unauthorized unmanned aerial vehicles (UAVs) and user communication via the unified transmit signal and beamforming. We develop a system-level theoretical framework to investigate the sensing and communication performance of PMNs based on stochastic geometry, which captures the mutual interference and resource contention between the two functions and builds a foundation for the optimization of network configurations. In addition, by leveraging the collaboration of multiple BSs in PMNs, we propose a cooperative sensing strategy combining the monostatic and bistatic sensing processes to enhance the reliability of UAV surveillance. Simulation results verify the effectiveness of the proposed theoretical framework and demonstrate the benefits of cooperative sensing in UAV detection and communication performance, as compared with the standalone sensing by individual BSs.

As one of the key applications in future vehicular networks, extended sensor sharing (ESS) requires stringent quality-of-service (QoS) for disseminating sensed data to multiple vehicles under high mobility. Ensuring QoS for ESS is challenging network dynamics. To address this issue, we propose a QoS-guaranteed clustering and routing protocol (QCRP) to make routing decisions while adapting to network changes. Specifically, QCRP uses global network information to perform topology control to ensure network connectivity and find optimal routing paths which satisfy the QoS requirements. In addition, QCRP enables re-routing at each relay vehicle based on local network observations to quickly respond to network topology changes caused by mobility. We conduct simulations to evaluate the proposed routing protocol using traffic traces of different densities in a highway scenario and show our solution can achieve the design goal and outperform the existing state-of-the-art.

The emerging artificial intelligence (AI) puts forward high requirement for big data acquisition, which is difficult to be met with the existing communication technologies in real time. In this paper, we investigate new graph learning based access management scheme for supporting the real-time big data acquisition in the sixth-generation mobile communication system (6G). We model the network scene with a mass of communication links as a fully connected graph which takes into account the accumulative interference of all links. Then, the joint admission and power control problem is formulated as a combinatorial optimization problem. We propose a graph attention network (GAT) based algorithm which can learn the system features by weighted aggregation of neighbor nodes. In addition, we construct a differentiable loss function that can accurately express the optimization objective and train the network by the change of loss. Based on the output of the GAT, we iteratively optimize the link admission and power to active more links. Simulation results demonstrate that the proposed algorithm is superior to the traditional convex optimization based algorithms and the nonmodified GAT based algorithms in the number of activated links. Moreover, the training of the constructed network is unsupervised with high computational efficiency, which makes them suitable for the big data access management.

In this paper, we study the performance of uplink-downlink decoupled (UDDe) user association in unmanned aerial vehicles (UAVs)-assisted cellular networks in which each user can associate with different UAVs or the macro base station (MBS) for uplink (UL) and downlink (DL) transmissions. Since some popular data may be requested by multiple users, grouping these users and applying multicast can significantly improve spectral efficiency. Unlike traditional linear precoding that treats interference entirely as noise, we develop a rate-splitting multiple access (RSMA) policy that employs rate splitting at the transmitter and successive interference cancellation at the receiver. In this context, we formulate a joint optimization problem of UL-DL association and beamforming for maximizing the sum-rate of users in UL and that of multicast groups in DL. Since the resultant problem is non-convex with complex states, we resort to multi-agent deep reinforcement learning (MADRL) that enables each UAV to learn and optimize its policy in a distributed manner. Simulation results show the superiority of the proposed decoupled association policy with RSMA and the MADRL learning algorithm.

Starlink and alike have attracted a lot of attention recently, however, the inner working of these low-earth-orbit (LEO) satellite networks is still largely unknown. This paper presents an ongoing measurement campaign focusing on Starlink, including its satellite access networks, gateway and point-of-presence structures, and backbone and Internet connections, revealing insights applicable to other LEO satellite providers. It also highlights the challenges and research opportunities of the integrated space-air-ground-aqua network envisioned by 6G mobile communication systems, and calls for a concerted community effort from practical and experimentation aspects.

The deployment of multipath transport protocols in the mobile environment can enhance the performance of delay-sensitive applications by enabling the simultaneous use of several network paths, resulting in faster data transmission. However, due to the heterogeneity of network paths, packets may not arrive on time or in order, affecting the performance of delay-sensitive applications. Therefore, a well-designed multipath scheduler is important to distribute data packets efficiently to guarantee the per-packet delay requirement. In this paper, we propose Meta-DAMS, a delay-aware learning-based multipath scheduler, aiming to ensure that end-to-end delay is below a predefined threshold for delay-sensitive applications. We introduce a hybrid meta reinforcement learning (meta-RL) architecture for Meta-DAMS in which offline meta-RL and online meta-RL are used to learn the optimal scheduling policy quickly and accurately in response to highly dynamic network conditions. Based on trace-driven emulation experiments, we demonstrate that Meta-DAMS surpasses state-of-the-art MP schedulers, ensuring a delay of 50 ms or less for 98% of packets after sufficient operational episodes, compared to the 83% achieved by existing MP schedulers. Even in initial operational episodes, Meta-DAMS maintains its superiority, guaranteeing 94% of packets with a delay of 50 ms or less, while the performance of the DQN-based MPQUIC scheduler drops to 72%. Meta-DAMS exhibits nearly triple the efficiency in terms of runtime compared to the DQN-based MPQUIC scheduler across varying episode numbers.

Live traffic video is vitally important for vehicles in future intelligent transport systems (ITSs). Due to the limitation of onboard sensors, vehicles may not be able to obtain a full view of the traffic situations which endangers safety for autonomous driving vehicles. In this article, we propose a traffic video multicasting scheme by using video splitting and group splitting techniques for unmanned aerial vehicles (UAVs)-assisted ITS, in which UAVs are considered as the eyes in the sky to capture real-time traffic videos. We aim to maximize the long-term video quality received by vehicles by jointly optimizing vehicle grouping and spectrum allocation. Considering the interactions among UAVs, the above optimization problem is formulated as a multiagent coordination problem in the form of a Markov game (MG). The MG is subsequently solved by leveraging a state-of-the-art multiagent deep reinforcement learning (MADRL) algorithm, namely, multiactor attention critic (MAAC), in which an attention mechanism is utilized to pay attention to other agents to make the learning process more effective and scalable. Extensive simulation results show that the MAAC-based algorithm has better performance in terms of video quality and spectrum efficiency compared with the baseline methods.

Federated learning (FL) deployed in the edge network environment is a promising approach for combining the separated training results based on the isolated local data sensed by various Internet of Things (IoT) devices. However, the limited computing resources for the training of various application models in each edge server and the communication burden among the edge server and numerous IoT devices greatly impact the realization of IoT intelligence. In this article, we propose transform-domain FL schemes based on discrete cosine transform (DCT-FA) and discrete wavelet transform (DWT-FA) to achieve better training efficiency and reduce the communication burden for IoT devices. Furthermore, when the amount of training data is limited, we propose to combine time-domain features and frequency-domain features in FL (CDCT-FA) that turns out to achieve much higher test accuracy. From the experimental results, the transform-domain FL schemes are shown to be promising, given the different constraints and requirements of various IoT intelligence applications.

Emerging intelligent transportation services are latency-sensitive with heavy demand for computing resources, which can be supported by a multi-tier computing system composed of vehicular edge computing (VEC) servers along the roads and micro servers on vehicles. In this work, we investigate the dual Uu/PC5 interface offloading and resource allocation strategy in Cellular Vehicle-to-Everything (C-V2X) enabled multi-tier VEC system. The successful transmission probability is characterized to obtain the normalized transmission rate of PC5 interface. We aim to minimize the system latency of task processing while satisfying the resource requirements of Uu and PC5 interfaces. Due to the non-convex and variables coupling, we decompose the original problem into two subproblems, i.e., resource allocation and offloading strategy subproblems. Specifically, we derive the closed-form expressions of packet transmit frequency of PC5 interface, transmission power of Uu interface, and CPU computation frequency in the resource allocation subproblem. Moreover, for the offloading strategy subproblem, the offloading ratio matrix is obtained by proposing the PC5 interface based greedy offloading (PC5-GO) algorithm, which concludes offloading decision and ratio. Simulation results are provided that the proposed PC5-GO algorithm can significantly improve the system performance compared with other baseline schemes by 13.7% at least.

In this article, we investigate the content transmission in a heavy-crowded multiple access cellular network, whose data traffic is offloaded through the combination of edge caching and unmanned aerial vehicle (UAV) communication. In this context, we formulate a novel optimization problem, which minimizes the sum content acquisition delay of users by optimizing the multiuser association and cache placement jointly with UAV trajectory and transmission power over a given flight duration. However, due to the uncertainty of the environment (e.g., random content requests and dynamic UAV positions), it is often difficult and impractical to solve the formulated problem using conventional optimization methods. To this end, we model our problem as a partially observable stochastic game where the macro base station (MBS) and UAVs act as agents to collectively interact with the environment to receive distinctive observations. Moreover, we take advantage of the Proximal Policy Optimization (PPO) learning strategy and propose a novel Dual-Clip PPO-based algorithm to solve the converted problem. To guide agent exploration, a new exploration criterion is proposed in which each UAV agent can obtain an intrinsic reward when it explores beyond the boundary of explored regions (BeBold). Note that the MBS agent has the extrinsic reward given by the environment only. Numerical results reveal that the proposed algorithm outperforms the standard PPO-based deep reinforcement learning algorithm. Moreover, the proposed joint design scheme can achieve a dramatic reduction of content acquisition delay compared with the benchmark schemes.

With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable. Faster than Nyquist (FTN) signaling, proposed in the 1970s, is a promising paradigm for improving spectrum utilization. This paper proposes the variable-packing-ratio (VPR)-based transmissions for high spectrum efficiency (SE) and security, respectively. Aided by deep learning (DL)-based estimation, the proposed scheme for high SE can achieve a higher capacity than the conventional Nyquist-criterion transmission with negligible modification to existing communication paradigms (e.g., spectrum allocation or frame structure). More importantly, for VPR-based secure transmission, a dynamic generation scheme is proposed to produce randomly distributed positions to switch the packing ratio, which can effectively avoid detections and attacks. In addition, we propose a simplified DL-based packing ratio estimation for both of these two scenarios so that the receiver can estimate the packing ratio without any in-band or out-band control messages. Simulation results show that the proposed simplified estimation achieves nearly the same accuracy and convergence speed as the original multi-branch fully-connected structure with a complexity reduction of 20 folds. Finally, we derive the SE of the proposed VPR transmission under different channels. The numerical results validate the correctness of the derivation and demonstrate the SE gains of the VPR scheme beyond conventional Nyquist transmission.

Vehicles are hesitant to upload data to edge servers in vehicle edge computing (VEC) as many vehicle data collected and perceived by various on-board sensors contain sensitive and personal information and lack economic incentive. Instead of free access to shared data, encrypted data trading will alleviate security and privacy concerns and provide an incentive for vehicle owners to share their data. The edge server needs to pay the price in data trading, and reputation management is a great method to help it trade with reliable and available vehicles. In this paper, we propose a multi-armed bandit (MAB)-based reputation management scheme, so the edge servers can select the high reputation vehicles for data trading, which can ensure the credibility and reliability of the data. The encryption scheme is applied to achieve the required transmission security level and defend the rights and interests of the edge server. On the other hand, implementing security measures will consume the computation and communication resources of the vehicles. We formulate an optimization problem that maximizes the revenue of vehicles in data trading under the constraints of time delay, energy consumption, and security level. Simulation results demonstrate that the proposed scheme is effective and efficient for vehicle reputation management, data trading selection, and resource allocation.

Many emerging intelligent transportation services are latency-sensitive with heavy demand for computing resources, which can be handled by a multi-tier computing system composed of vehicular edge computing (VEC) servers in the roadside and micro servers carried by vehicles. In multi-tier VEC system, the offloading of vehicle-to-vehicle (V2V) can be supported using the Cellular Vehicle-to-Everything (C- V2X) links, through Uu or PC5 interfaces. In this work, we investigate the offloading and resource strategy in C- V2X enabled multi-tier VEC system. The successful transmission probability of PC5 interface is modeled to characterize the normalized transmission rate of C- V2X link. We aim to minimize the total system latency of the task processing to optimize the offloading ratio matrix and packet transmit frequency of the PC5 interface, and computation resource allocation of vehicles and VEC server. Due to the non-convex and variables coupling, the latency minimization problem is decomposed into two subproblems, i.e., resource allocation and offloading strategy subproblems, and propose a PC5 interface based greedy offloading (PC5-GO) algorithm. Specifically, for the resource allocation subproblem, we derive the closed expressions of packet transmit frequency of PC5 interface and CPU computation frequency at vehicle and VEC server. For the offloading strategy subproblem, the offloading ratio matrix is obtained by the proposed PC5-GO algorithm. Simulation results are provided that the proposed PC5-GO algorithm can significantly enhance the system performance compared with other benchmark schemes by 5.88% at least.

Multi-homing technologies are promising to support seamless user mobility, as a mobile device can use multiple access links and paths for non-interrupted transmissions. Scheduling packets across multiple paths, however, has the known issue of out-of-order (OFO) due to the heterogeneity of the paths. Mobility poses new challenges due to time-varying link quality and capacity. In this paper, we present a novel Mobility-aware Multipath Quick UDP Internet Connections (MMQUIC) framework which enables collaboration between the link and transport layer. Based on MMQUIC, we develop a Mobility-Aware Multipath Scheduler (MAMS) for goodput enhancement, in which the impacts of mobility such as link outage errors and capacity variations are considered. Finally, we evaluate the performance of MAMS using network simulator 3 (ns-3). Simulation results demonstrate that our design has substantial performance gains with respect to the goodput and packet delay distribution in dynamic wireless systems.

Underwater acoustic sensor networks (UANs) have been shown as a promising technology to monitor and explore the oceans. Nevertheless, the routing design for data gathering of UANs considering the acoustic channel communication characteristics and limited energy is a pressing, open issue. To address this challenge, we propose the long-lifetime and reliable percolation routing protocol (LRP) for UANs to ensure the reliability of the network and prolong the network lifetime. The proposed protocol adaptively selects the forwarders to deliver each message. By estimating the reliability of the next-hop and considering the remaining energy of candidates, the proposed protocol takes a recursive approach to avoid trapping in a locally optimal solution. Simulations results validate the feasibility of the proposed protocol and demonstrate its superiority over the existing routing algorithms by prolonging the lifetime of LRP by up to 35%.

In this paper, we propose a novel delay-aware selective admission and scheduling algorithm for time-critical applications to guarantee the delay requirement of each packet in a single-hop downlink network. We consider a series of priorities among packets. To avoid always starving low-priority packets, we define a delay-laxity concept and introduce a new output gain model as our network utility function. In this context, we formulate a multi-objective optimization problem that minimizes the average queue backlog and maximizes the average network utility under the constraints of guaranteeing per-packet delay and achieving fairness among users. To solve this problem, we model our problem as a Markov Decision Process and propose a Double Deep Q Network-based algorithm to learn the optimal policy. Simulation results show that the proposed algorithm can achieve significant improvements in average delay, delay-outage drop rate, and goodput compared with the existing stochastic schemes. Moreover, the proposed algorithm outperforms the conventional Q-learning algorithm in terms of reward and learning speed.

Vehicle platooning utilizes automated driving and communication to let a group of vehicles travel closely, which improves road safety, traffic efficiency and fuel economy. In a platoon system, a critical task is to guarantee reliable communication among vehicles with efficient medium access control (MAC). This paper focuses on the feasibility of the distributed resource reservation MAC for communications among platoon vehicles using the cellular vehicle-to-everything (C-V2X) technology. For this purpose, a Markov chain-based model is proposed, which precisely estimates the network performance with different information flow topologies and system configurations. The state transition matrix is deduced and the stable state distribution is obtained. Given the information flow topology, we derive the probability that a platoon vehicle successfully delivers packets to all of the designated receivers. Finally, simulation results validate the analysis. To better implement the MAC protocol in practice, we also discuss the success probability for various information flow topologies in platoon communication.

TO BUILD a sustainable ecosystem, healthcare reinforced by the Internet of Things (IoT-Health) is a sector that makes a very useful contribution to society. With the aging of the world's population, the ability to monitor and protect people at home reduces costs and increases the quality of life. IoT healthcare has become a market with great potential, and IT giants such as IBM, Microsoft, and GE Healthcare develop products for specialized medical applications. Using IoT-Health for data collection and workflow automation is a great way to reduce waste and minimize human errors. However, the security of healthcare information is a major concern, and cybersecurity has become a significant threat for healthcare providers as well as governments to achieve sustainable city milestones. IT professionals must continually resolve health data security issues to help patients and the damage that healthcare security breaches can have on their lives.

To build a sustainable ecosystem, healthcare reinforced by the Internet of Things (IoT-Health) is a sector that makes a very useful contribution to society. With the aging of the world's population, the ability to monitor and protect people at home reduces costs and increases the quality of life. IoT healthcare has become a market with great potential, and IT giants such as IBM, Microsoft, and GE Healthcare develop products for specialized medical applications. Using IoT-Health for data collection and workflow automation is a great way to reduce waste and minimize human errors. However, the security of healthcare information is a major concern, and cybersecurity has become a significant threat for healthcare providers as well as governments to achieve sustainable city milestones. IT professionals must continually resolve health data security issues to help patients and the damage that healthcare security breaches can have on their lives.

To improve spectrum efficiency without interfering with licensed users, reliable prediction of spectrum occupancy plays a pivotal role in a dynamic spectrum allocation (DSA) system. A reliable machine learning method capable of exploring the long-term correlation in the data is using a neural network with long short-term memory (LSTM). However, in the situation that there are multiple sensors in the network, how to effectively exploit the spatial correlation among these sensors' data for accurate spectrum prediction remains an open issue. Directly applying LSTM to multiple series may even reduce the prediction accuracy if some series are uncorrelated. In this article, we propose a method of clustering to aid in predicting multi-dimensional received power based on the hierarchical agglomerative clustering (HAC) model, which clusters the correlated series with high spearman's rank correlation coefficient (SRCC). Similar series are grouped into clusters and trained to predict independently using HAC. By ensuring uncorrelated series do not influence each other, the LSTM prediction accuracy is improved. A low-pass filter is used to remove high-frequency noise components and further reduce the prediction error. Experimental results show that our method significantly increases the prediction accuracy in all cases.

In a mesh network, to ensure high reliability and low latency, we can explore path diversity. In other words, a packet can be transmitted using all active links in a network to reach the destination. Here, a critical, difficult issue is to calculate the end-to-end reliability of a mesh network, given the reliability of each active link. In this paper, we derive the mesh network reliability with a new approach, which is of lower computational cost and more scalable than the state-of-the-art. Based on a Markov model, the closed-form network reliability as a polynomial expression of link reliability is obtained using the Hop-State Algorithm (HSA). Furthermore, we propose two metrics to assist in selecting the links in a network for routing to ensure performance while reducing link cost. From the analysis and simulation evaluations, exploring path diversity can effectively support Ultra-Reliable Low-Latency (URLL) services.

The Integrated Terrestrial and LEO Satellite Network (ITSN) is promising for providing ubiquitous communication services, which attracts attention but also brings new challenges. In this regard, a new transport layer protocol, Multipath QUIC (MPQUIC) appears salient advantages in tackling with the challenging environment (e.g., large propagation delays, high-speed mobility, etc.). However, the standard congestion control algorithm of MPQUIC, Opportunistic Linked Increases Algorithm (OLIA), still encounters great challenges such as congestion window (cwnd) overshooting whenever handoff, which motivates our proposal, a Mobility-aware Multipath QUIC (MM-QUIC) congestion control algorithm. MM-QUIC leverages the periodical changes of path capacity and good similarity among disjoint subflows to quickly start a new round of transmission, and employs a multipath-based fluid model to determine the cwnd adjustment in the congestion avoidance phase. Finally, simulation results on NS-3 demonstrate that MM-QUIC can offer up to 50% throughput improvement compared to OLIA in ITSN.

For autonomous vehicles and smart transportation services, information exchange and fusion with low latency and high reliability is critical. The 3rd Generation Partnership Project has released the cellular vehicle-to-everything (C-V2X) Mode 4 to enable direct vehicle-to-vehicle communications regardless of the cellular coverage. Mode 4 uses the sensing-based semi-persistent resource scheduling (SPS) to support autonomous resource selection by vehicles. However, channel access collisions lead to packet losses, especially in crowded scenarios. Thus, an accurate analytical model is essential to quantify the system performance, reveal how to mitigate collision and ensure system reliability and scalability. This paper focuses on the analytical modeling of the SPS and derives the access collision ratio considering both the sensed and hidden terminals in V2X. Extended simulations are conducted to verify the correctness of the analytical framework. In addition, we investigate the impact of system parameters on performance, which provides important guidelines for improving the system configuration.

The need for wireless communication is growing at an unprecedented pace, making the wireless spectrum at a premium. To use the spectrum more efficiently, a promising solution was proposed to enable two concurrent users to transmit their signals in the same frequency at the same time, and then decode the superimposed signal. In current systems, the superimposed signal is decoded by successive interference cancellation (SIC) that requires strict power control upon each individual user. However, this requirement is infeasible for many IoT devices that are heterogeneous and often low cost. For other superimposed signal decoding technologies that require no power control, a reliable performance can be only achieved by introducing repetitive information in each transmission, which, in turn, reduces the spectrum efficiency. In this article, we introduce Chitchat, a new solution to decode the superimposed signal from two concurrent transmitters without any power control nor repetitive transmissions. Chitchat presents a rotation-code-based idea to provide both the diversity gain and the coding gain, so that it can achieve high reliability while preserving the spectrum efficiency. We implement Chitchat on a software-defined radio platform, and evaluate its performance in various scenarios.

Control performance of vehicle platooning relies on the information flow topology and quality of wireless communications. In this article, we investigate the constant-time-headway-spacing-policy-based vehicle platooning problem, where multiple predecessors' information is used by the following vehicles and communication impairments, i.e., limited communication range and random packet losses, are considered. In this article, first, when the leading vehicle moves at a constant speed, we obtain the sufficient and necessary conditions on sampling time, control gains, and internal lag, to ensure the stability of the vehicle platoon based on matrix polynomials' stability for ideal communications. Second, for time-independent homogeneous random packet losses, we provide the upper bound for the loss rate to maintain convergence in expectation by matrix eigenvalue perturbation theory when no input is set for lossy information. We also provide sufficient conditions to guarantee mean-square convergence for heterogeneous time-independent random packet losses and show the convergence time for any given accuracy and probability. Third, when historically latest information is used for input, the sufficient and necessary conditions are provided to ensure the internal stability and string stability by Markov jump linear system theory. Furthermore, we discuss the controller design when no feasible solution exists to guarantee the string stability. Extensive numerical results validate our analysis.

In vehicular ad hoc networks (VANETs), beacon broadcasting plays a critical role in improving road safety and avoiding hazardous situations. How to ensure reliability and scalability of beacon broadcasting is a difficult and open problem, due to high mobility, dynamic network topology, hidden terminal, and varying density in both the time and location domains. In this paper, wireless resources are divided into basic resource units in the time and frequency domains, and a distributed and adaptive reservation based MAC protocol (DARP) is proposed to solve the above problem. For decentralized control in VANETs, each vehicle's channel access is coordinated with its neighbors to solve the hidden terminal problem. To ensure the reliability of beacon broadcasting, different kinds of preambles are applied in DARP to support distributed reservation, detect beacon collisions, and resolve collisions. Once a vehicle reserves a resource unit successfully, it will not release it until collision occurs due to topology change. The protocol performance in terms of access collision probability and access delay are analyzed. Based on the analysis, protocol parameters, including transmission power and time slots duration, can be adjusted to reduce collision probability and enhance reliability and scalability. Using NS-3 with vehicle traces generated by simulation of urban mobility (SUMO), simulation results show that the proposed DARP protocol can achieve the design goals of reliability and scalability, and it substantially outperforms the existing standard solutions.

Internet-of-Things (IoT) is emerging, while the spectrum is at a premium. To enhance spectrum efficiency, a promising solution is Non-Orthogonal Multiple Access (NOMA) that enables users to communicate with the same resource at the same time, while decoding the superimposed signal at the receiver. Existing NOMA technologies, however, rely on strict power control to decode the superimposed signal, infeasible for heterogeneous and low-cost IoT devices. In contrast, we propose I-Talk, a new NOMA scheme that is designed for IoT and can decode the superimposed signals from two transmitters without power control. Importantly, considering the IoT systems in the wild, both the hardware imperfections and mobility are unavoidable, which can cause severe signal variations, resulting in an unreliable decoding performance. To solve this problem, we design a synthesis channel coefficient to track all signal offsets caused by the hardware imperfection. Meanwhile, we propose a diversity transmission and smart combining scheme to achieve high reliable decoding performance. To demonstrate the feasibility of this new NOMA approach in practical systems, we implement I-Talk with USRP devices and the experimental results illustrate that I-Talk achieves a one-order lower bit-error-rate and a 1.47× higher throughput gain than the state-of-the-art superimposed signal decoding scheme.

In this article, we address the security issue in a tiered small-cell network aiming at security optimization for small-cell users (SUEs) to defend against eavesdropping. Meanwhile, the transmissions from small-cell base stations (SBSs) are subject to the aggregate interference constraints of macro-cell users (MUEs). In particular, we consider two-fold information uncertainties in small cells, i.e., the uncertainties regarding the eavesdroppers and interference channels to the MUEs. As such, the SBSs compete for robust secrecy rate with robust protection for the MUEs. We adopt the generalized robust Nash equilibrium problem (GRNEP) formulation, for which we confirm the existence of equilibrium and analyze the condition for the uniqueness with variational inequality-assisted analysis. Furthermore, to solve for the equilibrium, we introduce the pricing mechanism and decompose the original GRNEP as a nonlinear complementarity problem with a priced NEP, where the former provides solution of price coefficients and the latter for resource allocation strategies based on given prices. Finally, extensive simulation results are provided to demonstrate the impacts of the interference constraint and uncertainties upon the security performance of an individual SUE and the overall network, which also corroborate the effectiveness of our proposal in security provisioning for the SUEs and interference protection for the MUEs.

Multi-path TCP (MPTCP) is increasingly popular with the widespread usage of multihomed devices. MPTCP allows data streams to be delivered across multiple simultaneous connections, providing higher bandwidth aggregation and throughput in comparison with single-path TCP. However, due to the path heterogeneity and packet losses, the occurrence of Out-of-Order (OFO) packets is inevitable for MPTCP. Although many approaches have been proposed to mitigate OFO, most of them focused on compensating path delay differences but not considered the impact of packet loss. In this paper, we take the first step towards analyzing the impact of packet loss on OFO, and propose Loss-Aware Throughput Estimation scheduler, LATE. LATE comprehensively considers each subflow's path characteristics and protocol parameters including Round Trip Time (RTT), congestion window (cwnd), and loss rate, to predict the data amount that can be sent over each subflow at a given time and determine wisely which segments should be allocated to which subflows. Experimental results show that LATE achieves a gain of 5.13% in mean goodput with long-lasting flows while reducing the completion time of short flows by about 26.68% compared to the state-of-the-art scheduler for MPTCP.

Cooperative content dissemination allows vehicles to directly retrieve content from each other by vehicle-to-vehicle (V2V) communications. It can release the downlink traffic pressure caused by repetitive of popular content downloads. A critical but open issue is how to motivate vehicles to participate in content dissemination. Most of the existing incentive mechanisms are proposed under a centralized architecture that suffers from the single point attack and trustless third-platform. In this paper, we propose a decentralized incentive mechanism for cooperative content dissemination in vehicular networks. By introducing the Directed Acyclic Graph (DAG) based blockchain technology, a decentralized architecture is proposed, which can improve incentive efficiency and reliability by removing the third-platform. Next, using the contract theory, the vehicles are divided into different types according to their route feature, and a series of contracts are designed for different types of vehicles to provide suitable incentives, as well as maximize the content generators' profit. Simulation results demonstrate the effectiveness and efficiency of our solution.

To support emerging vehicular applications such as autonomous driving, the reliability of beacon broadcasting becomes an indispensable issue which is also difficult given the highly dynamic topology and vehicle density. For the territories which are not covered with communication infrastructure, controlling and scheduling in a centralized way may not be applicable. Therefore, vehicle-to-vehicle (V2V) ad hoc communication is promising to address the ubiquitousness coverage concern. In this paper, a distributed network coding-based medium access control protocol (NC-MAC) is proposed to support V2V beacon broadcasting. We combine the preamble-based feedback mechanism, retransmissions, and network coding together to enhance broadcasting reliability. Extensive simulations are given to show the performance gain of the NC-MAC protocol compared to the existing 5G cellular vehicle-to-everything (C-V2X) MAC protocol in a wide range of scenarios.

Radio spectrum is a limited and increasingly scarce resource, which motivates alternative usage methods such as dynamic spectrum allocation (DSA). DSA of a frequency band requires an accurate prediction of spectrum usage in both the time and spatial domains with minimal sensing cost. In this paper, we address challenge in two steps. First, in order to make the best use of the limited sensors in the region, we deploy a deep learning prediction model based on convolutional neural networks (CNNs) and residual networks (ResNets), to predict spatio-temporal spectrum usage at the sensors' locations. Second, given an area enclosed by a few sensors, a Bayesian estimation model is proposed to first derive the location distribution of a transmitter, and then obtain the interference power distribution within the area. Simulation results show the efficacy and efficiency of the proposed prediction models.

Vehicle-to-everything (V2X) technology has been proposed as a leading step toward road safety improvement and intelligent transportation systems, and these vehicular-related applications rely on reliable ways of communication. For the regions which are not covered by communication infrastructure or deployment of centralized controllers are not straightforward, vehicle-to-vehicle (V2V) communication is a potential solution. Reliability is crucial in vehicular ad hoc networks (VANETs) from beacon broadcasting and security aspects. Group-casting and applying multi-hop communication can ensure reliability in V2X systems. In this paper, a distributed grouping and network coding-assisted medium access control protocol (GNC- MAC) is proposed to support reliable group-casting. We propose a new grouping protocol by combining preamble-based feedback mechanism, multi-hop communication, and network coding to improve group-casting reliability. Extensive simulations have been conducted to show the performance gain of the GNC- MAC protocol compared to the existing 5G cellular vehicle-to- everything (C-V2X) MAC protocol in a wide range of scenarios.

With tens of thousands Low Earth Orbit (LEO) satellites covering Earth, LEO satellite networks can provide coverage and services that are otherwise not possible using terrestrial communication systems. The regular and dense LEO satellite constellation also provides new opportunities and challenges for network architecture and protocol design. In this paper, we propose a new routing strategy named Directed Percolation Routing (DPR), aiming to provide Ultra-Reliable and Low-Latency Communication (URLLC) services over long distances. Given the long propagation delay and uncertainty of LEO communication links, using DPR, each satellite routes a packet over several Inter-Satellite-Links (ISLs) towards the destination, without relying on link-layer retransmissions. Considering the link redundancy overhead and delay/reliability tradeoff, DPR can control the size of percolation. Using the Starlink as an example, we demonstrate that with the proposed DPR, the inter-continent propagation delay can be reduced by about 4 to 21 ms, while the reliability can be several orders higher than single-path optimal routing.

The growth of Internet of Things (IoT) is anticipated to accelerate in the coming years. However, the wireless spectrum is insufficient to support the ever-growing IoT applications. A promising solution is to allow concurrent wireless transmissions and decode the superimposed signal. To make this solution practical for IoT systems, dynamic channel conditions and hardware imperfections are the key practical challenges, but not yet be addressed in the past work, leading to a low decoding performance. In this article, we introduce SigMix, aiming to deal with the practical challenges by proposing a solution to decode the superimposed signal, and eventually boost the spectrum efficiency. To this end, we first derive a theoretical expression that reveals the close relationship between phase shifts among concurrently transmitted signals and the error probability in decoding the superimposed signal. Then, based on the theoretical expression, we propose a rotation code and an adaptive decoding scheme to largely reduce the decoding error probability. Extensive experiments have shown that the median bit-error-rate of our scheme is one-order lower than the state-of-the-art.

In this article, we aim at maximizing the data gathering performance of the rechargeable wireless sensor network, where a mobile sink moves along the predefined path to charge sensor nodes through a wireless energy transfer technique and gather data from them. First, we show how to transform the original time-average optimization problem into a queue stability one by using the Lyapunov optimization framework, then we show how to decompose it into multiple subproblems by using the optimization decomposition. A distributed speed control and routing algorithm was proposed to reduce the computing load of the mobile sink and to obtain the near-optimal solution for data collection. Our analysis shows that there is an inherent tradeoff between the network utility and the average data queuing size, and the proposed adaptive transmission scheme can achieve the near-optimal network utility when a certain queueing delay can be tolerated.

Reliable and scalable wireless transmissions for Internet of Vehicles (IoV) are technically challenging. Each vehicle, from driver-assisted to automated one, will generate a flood of information, up to thousands of times of that by a person. Vehicle density may change drastically over time and location. Emergency messages and real-time cooperative control messages have stringent delay constraints while infotainment applications may tolerate a certain degree of latency. On a congested road, thousands of vehicles need to exchange information badly, only to find that service is limited due to the scarcity of wireless spectrum. Considering the service requirements of heterogeneous IoV applications, service guarantee relies on an in-depth understanding of network performance and innovations in wireless resource management leveraging the mobility of vehicles, which are addressed in this article. For single-hop transmissions, we study and compare the performance of vehicle-to-vehicle (V2V) beacon broadcasting using random access-based (IEEE 802.11p) and resource allocation-based (cellular vehicle-to-everything) protocols, and the enhancement strategies using distributed congestion control. For messages propagated in IoV using multihop V2V relay transmissions, the fundamental network connectivity property of 1-D and 2-D roads is given. To have a message delivered farther away in a sparse, disconnected V2V network, vehicles can carry and forward the message, with the help of infrastructure if possible. The optimal locations to deploy different types of roadside infrastructures, including storage-only devices and roadside units with Internet connections, are analyzed.

In this paper, we consider a wireless powered cooperative non-orthogonal multiple access (NOMA) relay network, in which one source is supposed to send independent messages to two users with the assistance of one energy-constrained relay that harvests energy from the source. Firstly, we study the minimum power consumption at the source node to fulfill the least required transmission rates by two users in both time switching relaying (TSR) strategy and power splitting relaying (PSR) one. Secondly, when the relay is provisioned with data buffer and energy storage, the long-term average power consumption minimization problem is formulated to take into account of the data and energy queue causality, peak transmit power constraint, and transmission mode selection. By using Lyapunov optimization framework, a novel buffer-aided transmission scheme (BATS) is proposed to asymptotically approach the optimal solution. Our analysis shows that, the PSR outperforms the TSR in terms of the realized energy efficiency, and BATS can be utilized to further improve the energy efficiency. It is disclosed that, there is an inherent trade-off between the long-term power consumption and the average queuing delay. In addition, larger user rates or less power consumption can be realized if a larger delay can be tolerated.

Many third-party services and applications have integrated the login services of popular Online Social Networks, such as Facebook and Google+, and acquired user information to enrich their services by requesting user's permission. Although users can control the information disclosed to the third parties in a certain granularity, there are still serious privacy risks due to the inference attack. Even if users conceal their sensitive information, attackers can infer their secrets by exploiting the correlations among private and public information with background knowledge. To defend against such attacks, we formulate the social network data sharing problem through an optimization-based approach, which maximizes the users' self-disclosure utility while preserving their privacy. We propose two privacy-preserving social network data sharing methods to counter the inference attack. One is the efficiency-based privacy-preserving disclosure algorithm (EPPD) targeting the high utility, and the other is to convert the original problem into a multi-dimensional knapsack problem (d-KP) using greedy heuristics with a low computational complexity. We use real-world social network datasets to evaluate the performance. From the results, the proposed methods achieve a better performance when compared with the existing ones.

In this paper, we consider a buffer-aided multiuser multiple-input single-output (MISO) network consisting of one multi-antenna access point (AP) and multiple single-antenna users, in which the AP is provisioned with data buffers for temporarily storing the data for each user either from the upper layer applications or the message by the AP. The data for one specific user must be kept confidential from all other unintended users. For such a system, we aim at maximizing the long-term average achievable secrecy rate region by carefully designing the flow control, the information signal and artificial noise beamforming, as well as the user selection. To address this issue, we first transform the time average optimization problem into a real-time one by using the Lyapunov optimization framework. Then it is proposed to decompose the optimization problem into several sub-problems by using the optimization decomposition technique. Although the information signal and artificial noise beamforming sub-problem is non-convex, we show that it can be decomposed into a two-stage optimization problem to effectively solve it by using the exact line search and DC (difference of two convex functions) algorithms. Moreover, we extend the average secrecy rate region maximization problem to the worst-case scenario, in which all unintended users are colluding in eavesdropping. Our analysis discloses that, there exists an inherent tradeoff between the average achievable secrecy rate region and the average queueing length. It is shown that a better average secrecy rate region can be realized by fully taking advantage of the buffer-aided transmission potentials in the MISO network, if a certain queueing delay is tolerable.

Both the edge and the cloud can provide computing services for mobile devices to enhance their performance. The edge can reduce the conveying delay by providing local computing services while the cloud can support enormous computing requirements. Their cooperation can improve the utilization of computing resources and ensure the QoS, and thus is critical to edge-cloud computing business models. This paper proposes an efficient framework for mobile edge-cloud computing networks, which enables the edge and the cloud to share their computing resources in the form of wholesale and buyback. To optimize the computing resource sharing process, we formulate the computing resource management problems for the edge servers to manage their wholesale and buyback scheme and the cloud to determine the wholesale price and its local computing resources. Then, we solve these problems from two perspectives: i) social welfare maximization and ii) profit maximization for the edge and the cloud. For i), we have proved the concavity of the social welfare and proposed an optimal cloud computing resource management to maximize the social welfare. For ii), since it is difficult to directly prove the convexity of the primal problem, we first proved the concavity of the wholesaled computing resources with respect to the wholesale price and designed an optimal pricing and cloud computing resource management to maximize their profits. Numerical evaluations show that the total profit can be maximized by social welfare maximization while the respective profits can be maximized by the optimal pricing and cloud computing resource management.

The well-known Queue-length-based MaxWeight scheduling algorithm (QMW) has been proved to be throughput-optimal for persistent flows only, which are long-lived with infinite traffic arrival. If the flows are dynamic ones, i.e., short-lived with finite data to transmit, QMW cannot guarantee queue stability. Given future wireless networks may support both persistent machine-to-machine flows and dynamic human-to-human flows, a Flow (File) Delay based MaxWeight scheduling algorithm (F-D-MW) has been shown to be throughput-optimal. However, new flows have to suffer a long start-up latency after arriving in the system. In this work, we present the definition of the capacity region for hybrid systems with the coexistence of persistent and dynamic flows. First, when a new arrival dynamic flow classification is known, we propose an online Hybrid Queue-length-based MaxWeight (H-QMW) scheduling algorithm, and then propose a more realistic adaptive H-QMW (A-H-QMW) scheduling algorithm for the system without the knowledge of the classification of flows. We prove that H-QMW can achieve throughput-optimality for hybrid systems. Performance evaluation not only validates the throughput-optimality of H-QMW and A-H-QMW in various types of networks but also reveals that H-QMW and A-H-QMW can achieve lower start-up and total latency for dynamic flows than F-D-MW.

Edge caching is promising to alleviate the backhaul pressure and provide low latency delivery for delay sensitive applications. However, it encounters great challenges to make adaptive content placement decisions according to the scattered explicit feedback with spatial and temporal dynamics. We propose a hybrid learning framework to obtain a more accurate prediction of users' preference by combining historical data from the central cloud and real-time data in edge networks. Two hybrid-learning algorithms, i.e., Hybrid Learning based on Alternating Least Squares (HLALS) and Hybrid Learning based on Conjugate Gradient Descent (HLCGD) are designed to achieve efficient caching decisions, where HLCGD is more efficient than HLALS at the expense of complexity. Simulation results show that, compared to the popular stochastic gradient descent strategy, the proposed algorithms can achieve superior performance thanks to more accurate prediction of users preference.

Superimposed signals are anticipated to improve wireless spectrum efficiency to support the ever-growing IoT applications. Implementing the superimposed signal demands on ideally aligned signals in both the time and frequency domains. Prior work applied an average carrier-frequency offset compensation to the superimposed signal under the assumptions of homogeneous devices and static environments. However, this will cause a significant signal distortion in practice when heterogeneous IoT devices are involved in a dynamic environment. This paper presents PhyCode, which exploits the nature of varying offsets across devices, and designs a dynamic decoding scheme which can react to the exact offsets from different signal sources simultaneously. We implement PhyCode via a software-defined radio platform and demonstrate that PhyCode achieves a lower raw BER compared with the existing state-of-the-art method.

The wide range of Internet-of-Things applications has increased the number of connected devices massively. This growth may cause more contention in accessing the channel, challenging the legacy IEEE 802.11. In the IEEE 802.11ah standard, the grouping technique is exploited to make the stations (STAs) compete in a group to mitigate the contention. However, how to group the STAs in the network is still an open issue. In this paper, we propose a new strategy to group STAs in a dense network to address the above issues. We apply the MaxMin fairness criterion to the STAs' throughput to increase the overall network's performance with better fairness. Formulation of the problem results in a non-convex integer programming optimization problem which avoids hidden terminals opportunistically. As solving the optimization problem is difficult and time consuming, we apply the Ant Colony Optimization method to the problem to find the solution. Extensive simulations have been conducted to validate the solution. The proposed approach can achieve approximately up to 40% gain in the total throughput, 37% gain in the minimum per-STA throughput in the network, and 11% reduction in the number of hidden terminals compared to the existing strategies such as K-means.

In this work, we study the buffer-aided relaying mechanism in a UAV-enabled mobile relaying system assisting the terrestrial communications. Optimal UAV trajectory design against a randomly located eavesdropper is investigated from the physical layer (PHY) security perspective considering the wireless channel dynamics as the UAV relay moves in the air. Specifically, we maximize the sum secrecy rate by optimizing the discrete trajectory anchor points based on the information causality and UAV mobility constraints. To make the non- convex problem tractable, the increments of the trajectory anchor points are optimized instead through an iterative updating procedure, and successive convex approximation technique is applied for progressive optimization. The convergence of the proposed iterative optimization technique is proved by introducing additional rate bound constraints and employing the squeeze principle. Simulation results show that the proposed optimal trajectory finding algorithm is effective and fast converging. Simulation results also reveal that the distribution of the eavesdropper location has a significant impact on the PHY security performance.

Privacy-preserving data aggregation (DA) in network systems, e.g., Internet of Things (IoT), is a challenging problem, considering the dynamic network topology, limited computing capacity, energy supply of IoT devices, etc. The difficulty is exaggerated when there exist dishonest nodes, and how to ensure privacy, accuracy, and robustness of the DA process against dishonest nodes remains an open issue. Different from the widely investigated cryptographic approaches, in this paper, we address this challenging problem by exploiting the distributed consensus technique. To mitigate the pollution from dishonest nodes, we propose an enhanced secure consensus-based DA (E-SCDA) algorithm that allows neighbors to detect dishonest nodes, and derive the error bound when there are undetectable dishonest nodes. We prove the convergence of the E-SCDA and show that the algorithm can preserve the privacy associated to nodes' initial states. Extensive simulations have shown that the proposed algorithm has a high convergence accuracy and low complexity, even when there exist dishonest nodes in the network.

The deployment of drone small cells has emerged as a promising solution to agile provisioning of Internet backbone access for Internet of Things devices, and many other types of users/devices. In this paper, we consider the problem of deploying a set of drone cells operating on multiple channels in a target area to provide access to the backbone/core network, which is formulated as a combinatorial network utility maximization problem. Since an offline and centralized solution to such a problem is not feasible, a low-complexity and distributed online algorithm is highly desired. Therefore, we propose a measurement-aided dynamic planning (MAD-P) algorithm, where the dispatched drones perform position and channel configurations autonomously on the fly based on the real-time measurement of network throughput to solve the problem in a distributed fashion during flight with minimal centralized control. We prove that the proposed MAD-P algorithm is asymptotically optimal, and investigate how long it takes for the convergence to stationarity under the MAD-P algorithm by giving a mixing time analysis. We also derive an upper bound of the performance gap in presence of measurement errors. Simulation results are provided to validate our analytic results and demonstrate the effectiveness of our algorithm.

Internet of Vehicles (IoV) is a promising Internet of Things application, where roadside unit (RSU) plays an important role for network service provisioning. How to select the number and locations of RSUs to deploy and allocate the traffic load to them is a critical and practical open problem. Most of the existing work focused on 1-D scenarios assuming unlimited RSU capacity, while a more practical 2-D case with limited RSU capacity has not been fully considered yet. In this paper, we investigate an RSU deployment problem for 2-D IoV networks considering the expected delivery delay requirements and task assignment. We formulate a novel utility-based maximization problem to solve the RSU deployment problem, where the utility function indicates the total benefit from the RSU deployment. We observe that each RSU has an irregular service area, which makes the problem much more difficult than the traditional facility location problem. Then, we design a utility-based RSU deployment algorithm (URDA), a linear programming-based clustering algorithm, to solve the problem. The gap between URDA and the optimal solution has been analyzed, which proved that the proposed URDA is near optimal if the deployment cost is low. Extensive simulations have been conducted to demonstrate the effectiveness and superiority of the proposed solution for IoV network service guarantee over other approaches.

As we enter the Internet of Things (IoT) era in which the communication network is becoming increasingly dynamic, heterogeneous, and complex, it is desirable to have cognitive communication systems and networks that possess multiple interacting capabilities for situation assessment, resource management, online/distributed learning, big-data processing, and intelligent decision making. AI techniques, such as deep learning, probabilistic graph model, and reinforcement learning, aided with big data and IoT, provide a wide variety of tools and solutions to many new problems encountered in the design, operation, and optimization of cognitive communication systems and networking, including resource management, situation assessment, channel identification, anomaly detection, root cause analysis, and online/distributed learning.

Nonorthogonal multiple access (NOMA) and device-to-device (D2D) are two promising technologies that have great potential in improving user connectivity. In this paper, we incorporate NOMA into the D2D-capable cellular networks and propose a new NOMA-aided D2D access scheme. In the proposed scheme, the D2D users (DUEs) can operate in four spectrum-sharing modes, which are the extension of the traditional underlay mode. To fully exploit the advantages of the NOMA-and-D2D integrated framework, we formulate a connectivity-maximization problem by jointly considering user pairing, mode selection, and power control under the constraints of the decoding thresholds of cellular users and DUEs. Based on the graph theory, we devise an efficient algorithm with polynomial complexity to solve the formulated problem optimally. We first analytically obtain the optimal transmission power and spectrum-sharing mode for every possible user pair through a graphical method. Based on the power control and mode selection policies, we transform the user pairing problem into a min-cost max-flow problem which can be tackled by the Ford-Fulkerson algorithm. Finally, simulation results indicate that the NOMA-aided D2D access scheme outperforms the traditional underlay mode, and the proposed algorithm yields a large performance gain in comparison with other schemes in terms of user connectivity and power consumption.

We study the computation resource management problem in mobile edge-cloud computing networks. Mobile edge servers shall first satisfy the computation requirements of mobile users and Internet of Things (IoT) devices, and then wholesale redundant computation resources to the cloud networks to maximize their profit. Due to the coarse time granularity of wholesales, computation resource buyback may happen occasionally to deal with traffic bursts. Thus, the mobile edge servers need to make a tradeoff between the wholesale profit and the buyback cost. In this paper, the computation resource management problem is modeled as profit maximization. To solve this problem, we first analyze the relationship among the reserved computation resources, the computation tasks of mobile users and IoT devices, and the buyback cost. Then, we design an efficient wholesale scheme to determine the amount of the wholesaled computation resources, by which the total expected profit of the mobile edge server can be maximized. Given the reserved computation resources, we also propose a fast-convergent realtime buyback scheme for mobile edge servers to minimize the buyback cost. Finally, the simulation results show that our proposed efficient wholesale and buyback scheme can increase the total profit while guaranteeing the computation delay of all the computation tasks, especially when the computation workloads are time-varying.

With the increasing penetration of electric vehicles (EVs) and various user preferences, charging stations often provide several different charging modes to satisfy the various requirements of EVs. How to effectively utilize the charging capacity to minimize the service dropping rate is a pressing and open issue for charging stations. Given that EV owners are price-sensitive to the charging modes, we intend to design an optimal pricing scheme to minimize the service dropping rate of the charging station. First, we formulate the operation of a dual-mode charging station as a queuing network with multiple servers and heterogeneous service rates, and analyze the relationship between the service dropping rate of the charging station and the selections of EVs. Then, we formulate a customer attrition minimization problem to minimize the number of EVs that leave the charging station without being charged and propose an optimal pricing approach to guide and coordinate the charging processes of EVs in the charging station. The simulation has been conducted to evaluate the performance of the proposed charging scheduling scheme and show the efficiency of the proposed pricing scheme.

With the rapid development of mobile Internet and urban constructions, high-volume and dynamic indoor communications bring challenges to cellular systems. High penetration loss and deep shadowing channels of indoor users may substantially degrade the transmission efficiency and system throughput. To address this issue, this paper proposes a solution using Floating Relay (FR) given the mature technologies of unmanned aerial vehicle (UAV). We target the undesirable channel conditions of indoor users, introduce the FR into the cellular system to improve transmission efficiency and maximize system throughput. Considering the capacity limit of the FR's back-haul link and the maximum transmission power of each user, an optimization problem is formulated to maximize the system throughput. The optimal power allocation strategy is then obtained for each user, and two effective online 3-D placement algorithms are proposed for the FR to approach the optimal location in the unpredictable and predictable scenarios, respectively. Extensive simulations are conducted. The achieved maximum system throughput, convergence rate, and accumulated throughput are used to evaluate the proposed algorithms. According to the comparisons between the two proposed algorithms and with off-line schemes, they show superiorities in their targeted scenarios, respectively.

In this paper, the access point (AP) in a wireless network is assumed to provide energy supply via wireless energy transfer to multiple terminals in the downlink, and all the terminals use the harvested energy to transmit their collected data to the AP in the uplink in a time division multiple access (TDMA) manner. Each terminal is provisioned with a finite energy storage and a finite data buffer to store the harvested energy and to buffer the arrived data traffic, respectively. Due to the limited data buffer and energy storage size, there might be data loss due to either data buffer overflow or energy storage depletion. Firstly, we aim at maximizing the long-term weighted sum-rate through energy beamforming vector design, power allocations, rate control, time allocations, and transmission mode selection subject to average transmit power, peak transmit power, data loss ratio requirements, practical data buffer as well as energy storage constraints. Secondly, the weighted max-min scheduling scheme is proposed to guarantee the fair access requirement by multiple terminals. Numerical analyses are presented to show that, the proposed adaptive design can substantially improve the average achievable rate region, while the proposed weighted max-min fair scheduling can effectively ensure the fair access requirements.

Connected electric vehicles (EVs) are a key component of future intelligent and green transportation systems, and the penetration of EVs depends on convenient and cost-effective charging services. In addition to being charged at home or on parking lots, a charging network is needed for EVs right off the road. This paper first focuses on the optimal charging network design for charging service providers, considering the time-varying and location-dependent demands from vehicles and constraints of power grids. To optimize the charging station locations and the number of chargers in each station, we first model the coverage area of each possible location to estimate the dynamic charging requirements of EVs. Then, we formulate the problem as profit maximization, which is a mixed-integer program. To make the problem tractable, we investigate the features of the problem and obtain a necessary condition to deploy a charging station and derive the upper and lower bounds of the number of chargers in each station. Given the analysis, we take two steps to transform and relax the problem to convex optimization. A fast-converging search algorithm is further proposed based on the profit of each possible location. Using real vehicle traces, simulation results show that the proposed algorithm can maximize the total profit when fewer charging stations and chargers are initially needed, which is more attractive for charging service providers.

In this paper, we consider the wireless powered communication network, in which K terminals are supplied by one common power station (PS) via the radio frequency (RF) energy harvesting technology to transmit their independent data to the corresponding K receivers. In addition, each terminal is assumed to be equipped with an energy storage to store the collected energy and one data buffer to cache the message to be delivered. We formulate an optimization problem to minimize the average power consumption of the PS subject to the given data arrival rates, the energy sustainability and the data buffer stability constraints at all K terminals. By using Lyapunov framework, an adaptive transmission scheme is proposed to determine the energy beamforming, the transmit power allocation and transmit mode selection based on the energy storage and data buffer status. Our analysis unveils that, there exists an inherent tradeoff between the average power consumption and the average queuing delay, and the requested information rates by multiple energy-constrained wireless powered terminals can be effectively supported with less power consumption if a certain transmission delay is tolerable.

Nonorthogonal multiple access (NOMA) exhibits superiority in spectrum efficiency and device connections in comparison with the traditional orthogonal multiple access technologies. However, the nonorthogonality of NOMA also introduces intracell interference that has become the bottleneck limiting the performance to be further improved. To coordinate the intracell interference, we investigate the dynamic user scheduling and power allocation problem in this paper. Specifically, we formulate this problem as a stochastic optimization problem with the objective to minimize the total power consumption of the whole network under the constraint of all users' long-term rate requirements. To tackle this challenging problem, we first transform it into a series of static optimization problems based on the stochastic optimization theory. Afterward, we exploit the special structure of the reformulated problem and adopt the branchand-bound technique to devise an efficient algorithm, which can obtain the optimal control policies with a low complexity. As a good feature, the proposed algorithm can make decisions only according to the instantaneous system state and can guarantee the long-term network performance. Simulation results demonstrate that the proposed algorithm has good performance in convergence and outperforms other schemes in terms of power consumption and user satisfaction.

Network systems often rely on distributed algorithms to achieve a global computation goal with iterative local information exchanges between neighbor nodes. To preserve data privacy, a node may add a random noise to its original data for information exchange at each iteration. Nevertheless, an eavesdropping node can estimate other's original data based on the information it received. The estimation accuracy and data privacy can be measured in terms of (E, δ)-data-privacy, defined as the probability of E-accurate estimate (the difference of an estimation and the original data is within E) is no larger than δ (the disclosure probability). How to optimize the estimation and analyze data privacy is a critical and open issue. In this paper, a theoretical framework is developed to investigate how to optimize the estimation of neighbor's original data using the local information received, named optimal distributed estimation. Then, we study the disclosure probability under the optimal estimation for data privacy analysis. We further apply the developed framework to analyze the data privacy of the privacy-preserving average consensus algorithm and identify the optimal noises for the algorithm.

For vehicular networks, dropboxes are very useful for assisting the data dissemination, as they can greatly increase the contact probabilities between vehicles and reduce the data delivery delay. However, due to the costly deployment of dropboxes, it is impractical to deploy dropboxes in a dense manner. In this paper, we investigate how to deploy the dropboxes optimally by considering the tradeoff between the delivery delay and the cost of dropbox deployment. This is a very challenging issue due to the difficulty of accurate delay estimation and the complexity of solving the optimization problem. To address this issue, we first provide a theoretical framework to estimate the delivery delay accurately. Then, based on the idea of dimension enlargement and dynamic programming, we design a novel optimal dropbox deployment algorithm (ODDA) to obtain the optimal deployment strategy. We prove that ODDA has a fast convergence speed, which is less than κ (κ <; n) iterations for convergence. We also prove that the computational complexity of ODDA is O(nkm logm), i.e., ODDA has a polynomial computational complexity for a given m, the number of dropboxes for deployment. Performance evaluation by simulation demonstrates the superior performance of the proposed strategies compared with the benchmark methods.

The rapid development of the Internet of Things has brought new challenges to cellular networks with super-dense devices and deep-fading channels. These challenges may substantially decrease the transmission efficiency and increase the device's power consumption, especially in the uplink. A pressing issue is to improve enhanced Node B's (eNB) scheduler considering a large number of users. In this paper, a semi-centralized cooperative control method is proposed for the cellular uplink transmissions, where the user equipment (UE) relays are randomly selected according to a certain density decided by the eNB. Two specific cooperative schemes based on device-to-device (D2D) communications are proposed, which are the random UE relay scheme and the one further applying network coding. The D2D interference is considered and modeled based on stochastic geometry. The proposed schemes are analyzed based on two distinct traffic models, i.e., the machine type communications traffic with the small-data feature and the full-buffer traffic. Extensive Monte Carlo simulations have been conducted for the small-data traffic and the closed-form theoretical results have been derived for the full-buffer traffic. Performance gains are achieved in various scenarios and the comparisons between two cooperative schemes are made as well. The results provide an important guideline for the eNB to determine how to select and configure cooperative D2D communication for uplink.

With the rapid development of the Internet of Things, it is pressing to improve wireless transmission efficiency, especially for machine type communications, due to the limited wireless spectrum. In this paper, we propose a downlink transmission scheme leveraging cooperative device-to-device (D2D) communications and network coding, which can largely reduce the cellular resource consumption and the total energy consumption. In the proposed scheme, the base station generates and broadcasts linear combinations based on the packets requested by different user equipments (UEs) until at least one mature UE can recover all the original packets. Then, a selected mature UE broadcasts new linear combinations based on the recovered original packets to neighbors via D2D until all UEs can decode their packets. A feasible and backward-compatible system design including the necessary revisions on the protocol stack based on the current cellular system architecture is also provided. Then, the closed-form probability mass functions of transmission times for both cellular and D2D transmissions are derived, where the error rates in both cellular and D2D transmissions have been considered. The feedback load is also analyzed. Simulation results with different block error rate (BLER) settings are given, which can be used as references for the cellular network to decide the target BLER and adapt the modulation and coding.

Data dissemination is a fundamental yet challenging issue in vehicular networks. Due to high mobility, the vehicular network topology is random and fast- changing in both the time and spatial domains. How to fully utilize limited wireless resources for supporting heterogeneous safety and multimedia services in vehicular networks is a pressing, open issue. In this article, we leverage the software- defined network architecture for hybrid vehicular networks, using both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) wireless communication technologies to ensure network performance and quality of services (QoS). The architecture of the software-defined vehicular network (SDVN) is introduced. A simulation study on how to take advantage of the SDVN framework for efficient and effective data dissemination has been given. Further research problems and opportunities are discussed.

This paper addresses the optimal charging scheduling problem for Electric Vehicles (EVs) in an intelligent workplace parking lot powered by both the Photovoltaic Power (PV) System and the Power Grid. Due to the uncertain charging requirements of different EVs and time-varying available renewable energy, the charging load from the parking lot may bring a new challenge to the Power Grid. By minimizing total cost of the parking lot, we design a dynamic charging scheduling scheme to manage the charging processes of EVs based on the real- time information of EVs and renewable energy from the PV system. Numerical simulations are carried out to demonstrate the efficiency of the designed charging scheduling scheme.

Due to the massive growth in the number of wireless devices and their traffic, the problem of sharing channel access has become much severe in wireless networks. Group Synchronized Distributed Coordination Function (GS-DCF) was introduced in the IEEE 802.11ah standard to group users and give each group a Restricted Access Window (RAW) to compete in. However, how to group users remains an open issue which has a great impact on the network performance. In this paper, an RSS- based grouping strategy is proposed to solve the hidden terminal problem in wireless networks. The analytical and simulation results are then provided to demonstrate the advantages of this scheme over conventional grouping strategies. According to the results, the proposed RSS-based grouping scheme will make the probability of hidden terminal negligible and thus substantially improve the network performance.

In vehicular networks, exchanging beacons among neighboring vehicles is a promising solution to guarantee vehicle safety. However, frequent beaconing under high vehicle density will cause collisions, which is harmful to safety and tracking accuracy. In this work, we propose an adaptive beaconing method for vehicle safety and tracking accuracy. Each vehicle broadcasts beacon interval requests, including the intervals needed for safety and for tracking accuracy. The road side unit allocates resources for vehicle's beaconing according to the requests from all vehicles. We formulate the resource allocation problem for maximizing the sum utility which measures the satisfaction of vehicles. We transform the optimization problem into a maximum weighted independent set problem, and propose an algorithm to solve it efficiently. Simulation results show that the proposed method outperforms the benchmark in terms of beacon reception ratio, safety guarantee, and tracking accuracy.

For disconnected Vehicular Ad hoc NETworks (VANETs), the carry-and-forward mechanism is promising to ensure the delivery success ratio at the cost of a longer delay, as the vehicle travel speed is much lower than the wireless signal propagation speed. Estimating delay is critical to select the paths with low delay, and is also challenging given the random topology and high mobility, and the difficulty to let the message propagate along the selected path. In this paper, we first propose a simple yet effective propagation strategy considering bidirectional vehicle traffic for two-dimensional VANETs, so the opposite-direction vehicles can be used to accelerate the message propagation and the message can largely follow the selected path. Focusing on the propagation delay, an analytical framework is developed to quantify the expected path delay. Using the analytical model, a source node can apply the shortest-path algorithm to select the path with the lowest expected delay. Performance evaluation by simulation show that, when the vehicle density is uneven but known, the proposed Minimum Delay Routing Algorithm can achieve a substantial reduction in delay compared with the geocast-routing approach, and its performance is close to the flooding-based Epidemic algorithm, while our solution maintains only a single copy of the message.

Device-free localization (DFL), which does not require any devices to be attached to target(s), has become an appealing technology for many applications, such as intrusion detection and elderly monitoring. To achieve high localization accuracy, most recent DFL methods rely on collecting a large number of received signal strength (RSS) changes distorted by target(s). Consequently, the incurred high energy consumption renders them infeasible for resource-constraint networks, such as wireless sensor networks. This paper introduces an energy-efficient framework for high-precision multi-target-adaptive device-free localization (E-HIPA). Compared with the existing methods, E-HIPA demands fewer transceivers, applies the compressive sensing (CS) theory to guarantee high localization accuracy with less RSS change measurements. The motivation behind the proposed E-HIPA is the sparse nature of multi-target locations in the spatial domain. Before taking advantage of this intrinsic sparseness, we theoretically prove the validity of the proposed CS-based framework problem formulation. Based on the formulation, the proposed E-HIPA primarily includes an adaptive orthogonal matching pursuit (AOMP) algorithm, by which it is capable of recovering the precise location vector with high probability, even for a more practical scenario with unknown target number. Experimental results via real testbed demonstrate that, compared with the previous state-of-the-art solutions, i.e., RTI, SCPL, and RASS approaches, E-HIPA reduces the energy consumption by up to 69 percent with meter-level localization accuracy.

With the increasing demand of media-rich entertainment and location-aware services from people on the road, how to disseminate the multimedia data in large-scale Vehicular Ad-Hoc Networks (VANETs) efficiently and reliably is a pressing issue. Due to the high mobility, large scale, and limited contact time between vehicles, it is quite challenging to support the multimedia data dissemination in VANETs. In this paper, we first utilize a hybrid framework to model the VANETs to address the mobility and scalability issues. Then, we formulate a utility-based maximization problem to find the best delivery strategy and select an optimal path for the multimedia data dissemination, where the utility function has taken the delivery delay, Quality of Services (QoS), and storage cost into consideration. With rigorous analysis, we obtain the closed-form of the expected utility of a path, and then obtain the optimal solution of the problem with the convex optimization theory. Finally, we conduct trace-driven simulations to evaluate the performance of the proposed algorithm with real traces collected by taxis in Shanghai. The simulation results demonstrate the rigorousness of our theoretical analysis, and the effectiveness of the proposed solution.

Device-to-device (D2D) communications in cellular networks are promising technologies for improving network performance. However, they may cause severe intra/inter-cell interference that can considerably degrade the performance of cellular users, and vice versa. Therefore, interference analysis has been one of the most important research topics in such a system. Focusing on an uplink resource reusing scenario, this paper presents a framework based on a probabilistic distance and path-loss model to obtain the distributions of signal, interference, and further Signal-to-Interference-plus-Noise Ratio (SINR), based on which, the performance metrics that are functions of SINR can be analyzed, such as outage probability and capacity. Different from the previous work, this proposed framework: 1) obtains interference and SINR distributions for both cellular and D2D communications, through which insights into their performance metrics and mutual influence are provided and 2) has no limitations on cell shapes, except that they are approximated by polygons or circles. The framework can also be applied to a downlink reusing scenario. Our results indicate that the developed framework is helpful for network planners to effectively tune the network parameters, and thus to achieve the optimum system performance for both cellular and D2D communications.

In this paper, the design of multi-hop physical layer network coding (PNC) is investigated. In the existing multi-hop PNC designs, the effects of error propagation and mutual-interference are not well addressed. Error propagation refers to that the estimation error at any node may propagate to the neighboring nodes, which may result in serious end-to-end bit errors. The impact of the mutual-interference from other transmitting nodes to a receiver determines the upper bound SINR of two neighboring nodes given end-to-end SNR. By carefully addressing these issues, we propose two multi-hop PNC designs, the direct multi-hop PNC (D-MPNC) and the stored multi-hop PNC (S-MPNC), where both designs achieve the throughput upper bound of one symbol per symbol duration, which is the same as that of the traditional PNC with a single relay. There is a tradeoff between the applications of D-MPNC and S-MPNC, which targets for simple-implementation and optimal end-to-end bit error rate (BER), respectively. We provide the detailed designs of D-MPNC and S-MPNC and obtain the end-to-end BER bounds theoretically. Extensive simulation results demonstrate the performance gain of the proposed multi-hop PNC compared with the traditional PNC in terms of end-to-end BER and end-to-end throughout.

This paper proposes a new relaying technique, hierarchical physical layer network coding (H-PNC), aimed at increasing the spectrum and energy efficiency in multi-hop wireless networks by supporting two bi-directional traffic flows simultaneously. H-PNC is applicable to the scenario in which two source nodes exchange data with the help of a relay, and the relay also needs to exchange data with one source node in an asymmetric two-way relay channel network, where the channel conditions of two source-relay links are asymmetric. H-PNC arranges transmissions in two stages, the multiple access (MA) stage and the broadcast (BC) stage. In the MA stage, the source node with the better channel to the relay can superimpose the symbol targeting to the relay on the symbol targeting to the other source node. In the BC stage, the relay can superimpose the symbol targeting to the source node with the better channel on the broadcast symbol. Thus, one more bidirectional information exchange is achieved beyond traditional PNC. Designs and optimizations of three H-PNC schemes are presented, and the error performance of QPSK-BPSK H-PNC is derived. Extensive simulations have been conducted to evaluate the system performance. Both theoretical analysis and simulation results demonstrated that H-PNC achieves a substantial performance gain.

Research on individuals in online social networks often requires the collection of personal information such as demographics. Due to both user privacy concerns and unformatted textual information, it is quite difficult to build a completely labeled social network directly. However, both social relations and network characteristics can help attribute inference to profile online social network users. In this paper, we propose several attribute inference models based on these two factors and implement them with Naive Bayes, Decision Tree and Logistic Regression. Also, to study network characteristics and evaluate the performance of our proposed models, we use a well-labeled Google employee social network extracted from Google+ to test the proposed models on inferring the social roles of Google employees. The experiment results demonstrate that the proposed models are effective in social role inference with Dyadic Label Model performing best.

The rapid development of the Internet of Things (IoT) has brought big challenges to the traditional cellular networks such as super dense devices and deep fading channels. These new challenges will lead to a significant transmission efficiency degradation and increase the device's power consumption, especially in the uplink. A huge pressure will be also imposed to the enhanced Node B's (eNB) scheduler due to the large number of users. In this paper, a semi- centralized controlled cooperative method is proposed for the uplink cellular transmission, where the User Equipment (UE) relay will be randomly selected according to a certain density decided by the eNB. Two specific cooperative schemes based on the Device-to- Device (D2D) are proposed, which are the random UE relay scheme and the one further combined with the Network Coding (NC). The theoretical analyses for both of them are given and corresponding closed-form results are derived. The D2D interference is considered and modelled based on the stochastic geometry. The performance gains are identified by numerical evaluations in various scenarios and the comparisons between two cooperative schemes are made as well. Also, these results can provide an important guideline for the eNB to determine the optimal density of the UE relays.

Future hybrid vehicle networks can use both Vehicle-to- Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications to provide reliable, timely, scalable, and media-rich services. In this paper, we investigate the problem that how to disseminate the data to the Road Side Unit (RSU) considering bidirectional transmissions, using vehicles to store-carry-and-forward the messages if possible, in hybrid V2I/V2V networks. We focus on the delay modeling and dissemination strategy design, aiming to minimize the delivery delay. Considering a one-dimensional vehicle network with multiple road segments, we model the process of uploading a message to an RSU either in front of or behind the source. Furthermore, based on the delay analysis, we obtain the desirable message dissemination direction, and further design the message uploading algorithm to minimize the expected deliver delay. Simulations have been conducted to verify the correctness of the analysis and illustrate the efficiency of the proposed algorithm. The analytical model can also provide important insights and guideline for the deployment of RSUs.

Network coding improves transmission efficiency by combining packets at relay nodes and thus reduces the number of packets sent to the network. It is a network layer solution to improve network throughput and transmission efficiency. However, a coded packet must be decodable by the destination, otherwise it is a waste of resource to combine them together and to deliver the coded packet. This paper addresses how to find the coding solution that guarantees decodability at the destination. We first quantify the coding gain as the number of transmissions reduced, and then provide a method for runtime check whether a coding pair can be separated at the destination. The optimal coding solution is selected as the one that provides the maximum coding gain among all the decodable pairs. The algorithms can be applied to both unicast and multicast traffic. Simulation results show the number of transmissions can be reduced significantly, especially for multicast traffic where there are rich opportunities to apply network coding.

With the rapid development of the Internet of Things (IoT), the new challenges brought by the MTC traffic imposed a great pressure to the traditional cellular networks. The new MTC features like small-data transmission, large number of devices and deep fading channels will lead to a significant spectrum efficiency degradation and thus affect the regular Human-to-Human (H2H) communications. In this paper, we proposed a semi-centralized controlled cooperative method to improve the MTC transmission efficiency in the cellular uplink, which is backward- compatible with current LTE/LTE-A protocol and easy for implementation. Two cooperative transmission schemes based on the Device-to-Device (D2D) communications were applied, which are the Random User-Equipment (UE) Relay scheme (RUR) and the Network Coding scheme (NC). The D2D interference was analyzed based on the stochastic geometry, and 2 layers of scheduling were also considered. Intensive simulations were conducted. From the simulation results, a significant gain was achieved by the proposed method especially for the small data traffic. Also, a comparison was made between the RUR and NC schemes, and the suitable scenarios for each scheme can be identified.

This paper studies the scheduling of multiple Electric Vehicles' (EVs) charging with service quality constraint at a workplace charging station, aiming to maximize the profit of the charging station under time- of-use (TOU) pricing. We develop a multi-charger framework considering both the customers' and charging station's interests. First, an admission control mechanism is proposed to guarantee that all admitted EVs' charging requirements can be satisfied before their departure time. Then, under the premise of declining the customers' requirements as little as possible, a Joint Searching (JS) scheduling algorithm is proposed to maximize the profit of the charging station. Extensive simulations based on realistic EV charging information and TOU pricing have been conducted. Simulation results exhibit that the proposed algorithm outperforms the state-of-the-art solution in terms of up to 30% profit and similar service declining probability.

Minimizing the end-to-end delay for data dissemination in a large-scale VANET with both buses of fixed schedules and taxis of random schedules is a challenging issue, due to the scalability, high-mobility, and network heterogeneity concerns. Particularly, the mix of random taxis and fixed-scheduled buses makes the delay components along a path dependent and hard to estimate. In this paper, to address the scalability and high-mobility issues, we introduce a store-and-forward framework for VANETs with extra storage using “drop boxes”, which function similar to network routers. Next, we propose an optimal link strategy which is independent of the message arrival time and can be executed in a distributed manner. Then, we derive the expected path delay, considering the dependence of the delay components along the path, and propose the optimal routing strategy to minimize the expected path delay. Trace-driven simulations have been used to validate the rigorous analysis, and demonstrate the superior performance of the proposed strategies, which result in a substantial delay reduction and a much higher delivery ratio when compared with the state-of-the-art solutions without drop boxes. The strategies can further improve the delay performance when compared with the over-simplified routing solutions which ignore the dependence of the delay components.

Throughput-optimal scheduling has been heavily investigated given its ability to fully utilize network resources and maintain network stability. Most of the existing throughput-optimal algorithms, including the classic queue-length based MaxWeight algorithm and flow-delay-based MaxWeight algorithm, however, may bring a severe unfairness problem when scheduling transmission control protocol (TCP) controlled flows. As TCP is the dominant transport layer protocol in the Internet and it controls the majority of Internet traffic, we study how to design the scheduling algorithm that can ensure both throughput optimality and be compatible to TCP flows. In this paper, we analyze the reason behind the incompatibility between the existing scheduling algorithms and TCP, and then investigate the properties of the head-of-line access delay-based scheduling algorithm (HOLD) we proposed. We prove that the proposed HOLD can fairly schedule TCP flows in wireless networks with time-varying channel conditions and achieve throughput optimality with flow-level dynamics. Simulations using OMNeT++ 4 have been conducted to validate our analytical results, and compare the performance of different scheduling algorithms comprehensively.

Passive localization is fundamental for many applications such as activity monitoring and real-time tracking. Existing received signal strength (RSS)-based passive localization approaches have been proposed in the literature, which depend on dense deployment of wireless communication nodes to achieve high accuracy. Thus, they are not cost-effective and scalable. This paper proposes the RSS distribution-based localization (RDL) technique, which can achieve high localization accuracy without dense deployment. In essence, RDL leverages the RSS and the diffraction theory to enable RSS-based passive localization in sensor networks. Specifically, we analyze the fine-grained RSS distribution properties at a variety of node distances and reveal that the structure of the triangle is efficient for low-cost passive localization. We further construct a unit localization model aiming at high accuracy localization. Experimental results show that RDL can improve the localization accuracy by up to 50%, compared to existing approaches when the error tolerance is less than 1.5 m. In addition, we apply RDL to facilitate the application of moving trajectory identification. Our moving trajectory identification includes two phases: an offline phase where the possible locations can be estimated by RDL and an online phase where we precisely identify the moving trajectory. We conducted extensive experiments to show its effectiveness for this application - the estimated trajectory is close to the ground truth.

Multipath propagation and power arrival profiles in three-dimensional (3-D) space determine the performance of the full-dimensional MIMO (FD-MIMO) systems. Field channel measurements are crucial in characterizing wireless channel properties. Nevertheless, in spatial channel measurements, estimating the direction-of-arrivals (DoAs) of multipath components (MPCs) is a challenging issue, because of the large number of propagation paths and the correlation among the multipath signals. The number of incidence angles and estimation precision in traditional methods is limited by the sensor array size and signal correlation. In this paper, we propose a scheme for measuring and estimating the 2-D DoAs of propagation paths called multipath angular estimation using the array response of PN-sequences (MAPS). By using a receiving planar antenna array (PAA), MAPS first extracts the complex path array response vector (PARV) for each propagation path and then estimates the DoAs of the paths individually and independently. The subspace-decomposition theory for MAPS is proved and extensive simulations are conducted to compare MAPS with other algorithms. Furthermore, a channel sounder using two PAAs and the probing signal of 2.6 GHz carrier modulated by PN-sequences has been developed. The simulation and field tests show that MAPS can estimate arbitrary number of resolved MPCs in a channel snapshot and effectively suppress the multipath interference.

In this paper, physical layer network coding with heterogeneous modulations (HePNC) is proposed for the asymmetric two-way relay channel (TWRC) scenario. The existing PNC solutions using the same modulation for signals transmitted from two source nodes may not be desirable for practical situations when traffic loads exchanged between the sources are unequal and channel conditions of source-relay links are heterogeneous. HePNC includes two stages: multiple access (MA) and broadcast (BC) stages. In the MA stage, the two source nodes transmit to the relay simultaneously with heterogeneous modulations selected according to the channel conditions and the ratio of traffic loads exchanged between the sources, and then the signals superimposed at the relay are mapped to a network-coded symbol by a mapping function adaptively; in the BC stage, the relay broadcasts the network-coded symbol back to both sources with a modulation selected according to the bottleneck link's channel condition. We present three HePNC designs, including QPSK-BPSK, 8PSK-BPSK and 16QAM-BPSK HePNC. How to design and optimize the mapping function is investigated and the error performance of QPSK-BPSK HePNC is analyzed. We further study the HePNC system performance, throughput upper bound and energy efficiency. Extensive simulations demonstrated that the proposed HePNC can substantially enhance the throughput and energy efficiency compared with the existing PNC.

Traditional communication system typically separates the configuration of the physical layer from the network traffic load and topology. In this paper, we study how to apply physical-layer network coding considering the locations and traffic loads of multiple nodes in multiple-input multiple-output (MIMO) networks. We propose the heterogeneous-modulation physical-layer network coding (HePNC) design for MIMO networks with asymmetric two- way relay channel (TWRC), where all nodes are equipped with multiple antennas. Comparing to the single-antenna case, we study how to ensure the goodput with a fixed per-bit-energy can be scaled up w.r.t. the number of antennas, and also achieve performance gains in terms of end-to-end bit error rate (BER). The MIMO HePNC transmission includes the multiple access (MA) and broadcast (BC) stages. As the global channel state information (CSI) may be too costly to obtain, we propose two practical MIMO HePNC protocols based on maximum likelihood (ML) multi-user detector (MUD) that do not rely on global CSI. The first protocol is a heuristic one evolved from the single-input single-output (SISO) HePNC, and the second protocol upgrades the design and performance of both of the MA and BC stages. Analytical and extensive simulations demonstrated that, with two antennas each, the proposed MIMO HePNC protocols can not only double the goodput, but also achieve a substantial reduction on error rate, which indicates that combining HePNC and MIMO is a very promising cross-layer solution. We further discuss the impacts of the bottleneck link and provide guidelines on the relay location selection.

How to coordinate multiple Electric Vehicles' (EVs) charging demands to satisfy the requirements of the customers and also maximize the profit for the charging station is an important and challenging problem. Most of the existing works mainly focus on the interest of one side. In this paper, we develop a utility based multi-charger framework to ensure a win-win situation for both the customers and the charging station. We first propose an admission control algorithm to guarantee that the non-flexible charging requirements of all admitted EVs can be satisfied before their departure time. Then, we encourage all EVs to take more charging flexibility by setting an attractive price for the additional electricity to be taken by the EVs. To maximize the profit of the charging station, a utility based charging scheduling algorithm is proposed. Extensive simulations based on practical EV charging information have been conducted, which demonstrate the effectiveness of the proposed algorithms. The results show that the proposed approach can outperform the state-of-the-art one in terms of total utility, so that the charging station can enjoy a higher profit and the customers can enjoy more cost savings.

This paper proposes a new relaying technique, Piggybacking Physical Layer Network Coding (PiPNC), which is most favorable in the scenario that two source nodes exchange data with the help of a relay, and the relay also needs to exchange data with one source node. PiPNC arranges transmissions in two stages, a multiple access (MA) stage and a broadcast (BC) stage, which compose one transmission cycle. In the MA stage, one of the source nodes (with a better channel quality to the relay) can piggyback the symbol targeting to the relay on the symbol targeting to the other source node. In the BC stage, the relay can piggyback the symbol targeting to the source node (with a better channel) on the broadcast symbol. In this way, two bidirectional information interchanges are achieved in one transmission cycle. Designs and optimizations of two PiPNC schemes are presented, and extensive simulations have been conducted to evaluate the system performance and identify the optimal location of the relay.

Intermittent connection of wireless links, caused by low duty-cycle radio operation, harsh working environment, movement of sensor nodes, etc., makes clock synchronization a challenging task. Prior synchronization approaches in wireless sensor networks (WSNs) typically require that nodes exchange time messages frequently with the reference clock, which is difficult in networks with low or intermittent connectivity. This poster presents RobSync, a robust design for clock synchronization in intermittent-connected wireless networks. Having recognized that clock skew is highly correlated to the voltage supply, we use the local voltage information as a reference for clock self-calibration, which helps reduce the frequency of time-stamp exchanges. To prevent a misuse of the voltage information, leading to error accumulation, a re-synchronization interval adjustment design is developed to make a trade-off between accuracy and energy consumption. We present the theory behind RobSync, and provide preliminary results by experiments to compare our approach and the recent approach.

In dense wireless local area networks (WLANs), the hidden stations (HSs) cause severe collisions and performance degradation. Although cooperative communications can achieve spatial diversity, how to efficiently cooperate in a wireless network is a challenging issue due to the distributed nature of the stations. The contributions of this paper are two-fold. First, we establish an analytical model for the IEEE 802.11 WLANs with HSs using the mean value analysis method, which can provide the theoretical results of collision probability, frame service time and network throughput. Second, we propose a new medium access control (MAC) protocol, named network coding cooperative MAC (NCC-MAC) to utilize cooperation to relieve the HS problem. Different from the traditional RTS/CTS scheme, NCC-MAC utilizes random linear coding (RLC) to realize opportunistic cooperative transmission without extra control messages. Furthermore, the ACK-triggering mechanism is introduced which can help to avoid repeated collision caused by HS(s) in dense WLANs and also improve the efficiency of the coded cooperation. Simulations have been conducted to compare the performance of NCC-MAC with DCF, simple cooperative MAC (SC-MAC) without network coding, and other cooperative MAC in the literature. The results show that NCC-MAC can effectively realize station cooperation and reduce the effect of HS(s), improving the network throughput and delay performance considerably.

The full-dimensional MIMO (FD-MIMO) technology is expected to increase the system capacity significantly by elevation beamforming and sectorization, especially for the users on different floors in buildings. To design and evaluate the FD-MIMO requires the indoor-to-outdoor (I2O) channel modeling in the elevation domain, with the user equipments (UEs) located at different heights. However, such channel models are still lacking. In this paper, using a channel sounder equipped with two uniform planar antenna arrays, we have performed the spatial channel measurement in an urban micro-cell (UMi) environment where the UE is located from the 1st to the 5th floors and on each floor both LOS and NLOS scenarios are considered. The power and elevation angle of arrival (EoA) of each propagation path at the base station are estimated, and the distribution of the EoA and elevation spread of arrival (ESA) at each floor are obtained. The stochastic models of EoA and ESA with respect to the UE height are also proposed. This work can help to extend the current 2-dimensional channel models by combining the elevation propagation statistics to support the FD-MIMO system design.

Device-to-device (D2D) communications in cellular networks are promising technologies for improving network throughput, spectrum efficiency, and transmission delay. In this paper, we first introduce the concept of guard distance to explore a proper system model for enabling multiple concurrent D2D pairs in the same cell. Considering the Signal to Interference Ratio (SIR) requirements for both macro-cell and D2D communications, a geometrical method is proposed to obtain the guard distances from a D2D user equipment (DUE) to the base station (BS), to the transmitting cellular user equipment (CUE), and to other communicating D2D pairs, respectively, when the uplink resource is reused. By utilizing the guard distances, we then derive the bounds of the maximum throughput improvement provided by D2D communications in a cell. Extensive simulations are conducted to demonstrate the impact of different parameters on the optimal maximum throughput. We believe that the obtained results can provide useful guidelines for the deployment of future cellular networks with underlaying D2D communications.

Considering an optimal investment problem for a retailer in electricity market, the objective is to seek the optimal investment decision that maximizes the weighted sum of the expected return and the variance of wealth. Unlike existing works, the price fluctuation of both the wholesale and retail side of electricity market is considered, and the retailer can invest its wealth in electricity market and traditional financial market simultaneously. Hence, there is a complicated wealth dynamic, which is the main challenge in our work. In this paper, by utilizing the method of Lagrange multiplier and the classical Tchebycheff inequality, we first show that the investment problem is a quadratic programming problem in terms of the decision variable, and thus has a unique optimal solution. Then, a closed-form optimal solution is derived by solving the stationary equation and comparing the feasible solution interval. Based on the optimal solution, we find the key price, which will affect the investment is the wholesale price rather than the retail price. Moreover, with a similar analysis approach, we also provide the optimal solution considering a more general model, which allows the retailer to purchase the electricity temporarily to avoid the supply shortage. Extensive simulations demonstrate the better performance of the proposed solution over the Kelly strategy widely used in the financial market.

Device-to-device (D2D) communications in cellular networks are considered a promising technology for improving network throughput, spectrum efficiency, and transmission delay. In this paper, the Power Emission Density (PED)-based interference modeling method is applied to explore proper network settings for enabling multiple concurrent D2D pairs in a sector-partitioned cell. With the constraint of the Signal-to-Interference Ratio (SIR) requirements for both the macro-cell and D2D communications, an exclusive region-based analytical model is proposed to obtain the guard distances from a D2D user to the base station, to the transmitting cellular user, and to other communicating D2D pairs, respectively, when the uplink resource is reused. With these guard distances, the bounds of the maximum throughput improvement provided by D2D communications are then derived for different sector-based resource allocation schemes. Extensive simulations are conducted to verify our analytical results. The new results obtained in this work can provide useful guidelines for the deployment of future cellular networks with underlaying D2D communications.

How to design a throughput-optimal scheduling algorithm in a heterogeneous wireless network with flow-level dynamics is a challenging problem. In this paper, we investigate the properties of a Head-of-Line (HOL) delay based scheduling algorithm, and prove that it can achieve throughput-optimality with flow-level dynamics. The algorithm is easy to implement because it requires no prior knowledge of the statistics of the arrival traffic and channel state information. Extensive simulations have been conducted to validate the theoretical conclusion and evaluate the performance. It is shown that, at the presence of flow-level dynamics, the HOL delay based scheduling algorithm can outperform the classic queue-length based MaxWeight scheduling, and can achieve a similar performance as other known throughput-optimal scheduling while it is simpler and more practical to implement.

We consider the maximum throughput scheduling problem in a millimeter-wave wireless personal area network in which users can use adaptive modulation and coding schemes to change their data rates. The scheduling problem is to map transmissions to time slots so that the total throughput is maximized. Due to the ultra-wide bandwidth of the mm Wave band, bad scheduling tends to waste significant channel resource. It is worth the effort to consider a more sophisticated scheduling scheme than the simple serial TDMA scheme. The challenge is that the achieved data rate of one flow is limited by the interference from other transmissions in the same slot, which is unknown until the scheduling decision is known. We propose two scheduling algorithms for variable data rate transmissions. The first algorithm is a greedy algorithm, which always chooses the best option at the moment; the second one uses sorting to decide the order that flows are included in a slot. Both algorithms are interference-aware. The algorithms can be applied to transmissions with omnidirectional antennas as well as directional antennas. The simulation results show that the proposed algorithms achieve higher throughput than previous work for adaptive-rate scheduling.

In today's network-centric battlefield, the ad hoc style, self-organizing networks play a more and more important role in the operation of mobile forces that are deployed quickly to meet the tactical demands. However, the unique features of the tactical scenario also pose significant challenges for networking, which makes the existing research results for the general mobile ad hoc networks (MANETs) difficult to be reused directly. In this paper, we focus on the connectivity issue of the mobile tactical networks (MTNs). To better describe the special formation-oriented mobility pattern of MTNs, a more realistic node-following mobility model is used, based on which the neighbor connectivity is obtained from both the geometrical and physical communication point of view for not only 1-D but also 2-D network scenarios. After that, we further evolve our recently proposed decomposition and recursion method to derive the end-to-end connectivity for some formations commonly used in the MTNs. All our analytical results are verified by extensive simulations. We believe that this work is able to shed new lights on the MTNs' performance characteristics, which can be used for guiding the development of the next-generation MTNs.

Physical layer network coding (PNC) has been proposed for the two-way-relay scenario. The existing PNC solutions typically use the same modulation for the source nodes' signals which may not be desirable for practical situations when the amount of data exchanged between the two source nodes are un-equal and their links to the relay node are heterogeneous. In this paper, physical layer network coding with heterogeneous modulations (HePNC) has been proposed to further improve the spectrum and energy efficiency considering the above mentioned heterogeneity. Similar to the existing PNC, HePNC also includes two stages: the multiple access (MA) stage for two source nodes to transmit data to the relay and the broadcast (BC) stage for the relay to broadcast data to both source nodes. The main difference is that at the MA stage, HePNC can select heterogeneous modulation methods according to the channel conditions and the ratio of data to be exchanged. We present two sample designs of HePNC, including QPSK-BPSK and 8PSK-BPSK, and obtain the mapping rules for the relay's de-noising and forwarding based on the neighbor clustering algorithm. In addition, we discuss the end-to-end bit-error-rate, energy efficiency and optimal relay location selection when HePNC is used. Extensive simulations demonstrated that the proposed HePNC can substantially enhance the end-to-end throughput and the energy efficiency compared to the homogeneous PNC, and thus it is a promising technology for future green communication systems.

To reduce the difficulties in calculating the aggregated interference power at an observed receiver, a Power Emission Density-based analysis method is proposed in this paper. By utilizing the new method, the traditional discrete-style calculation (i.e., obtain each concurrent interferer's impact on the observed receiver individually, and add them together) can be replaced with a concise integration over the entire network area, which could effectively reduce the complexity of interference-related studies. The accuracy of the proposed method is verified by extensive simulations in different network scenarios. The results and analytical methods given in this paper will lead to a series of geometrical-based studies on the interference in random wireless networks, which could be used to guide the design and implementation of large-scale wireless networks.

We consider a scheduling problem in a wireless network which consists of one base station, N legitimate users and one (or more) eavesdropper(s). The scheduling problem jointly considers the reliability, security and stability of the system, and is to allocate wireless resources to the legitimate users, stabilize the system and maximize the secure transmission rate. Based on the stochastic network optimization framework, the scheduling problem is decomposed to an online optimization problem. A scheduling algorithm and a low computational complexity algorithm that both do not consider power adaptation are proposed, along with a power adaptive one. Extensive simulations are conducted to show the impact of the information arrival rate and the eavesdropper's channel condition on the system performance. These observations provide important insights and guidelines for the design and resource management of future wireless networks using secure communication technologies.

Combined Heat and Power (CHP) systems are well known for their high efficiency and relatively low emissions. Existing CHP economic dispatch schemes do not use the energy buffer to minimize the average cost in the long term. Motivated by the queueing analysis and buffer management solutions in data communication systems, in this paper, we investigate how to use a battery pack and a water tank to optimize the average cost for the CHP systems by jointly considering the real-time electricity price, renewable energy generation, energy buffer states, etc. We first formulate the queueing models for the CHP systems, and then propose an algorithm based on the Lyapunov optimization technique which does not need any statistical information about the system dynamics. The optimal control actions are obtained by solving a non-convex optimization problem. We then discuss when it can be converted into a convex optimization problem. Since the battery pack queue and water tank queue are correlated by the CHP, the capacity relationship between them is further explored. Through the theoretical performance analysis, we also show the tradeoff between the cost saving and the energy buffer capacity. Finally, the effectiveness of the proposed algorithms is evaluated with practical data.

Geometrical distance distribution (GDD) between nodes in wireless communication networks plays a significant role in modeling network performance metrics. Existing work on obtaining GDD assumes the network geometry to be a regular one, such as circle and square. Due to the various complex effects of wireless signals, however, the network geometry usually is quite irregular. Therefore, this paper proposes a novel systematic and unified approach to obtain the GDD between two random nodes associated with arbitrary network geometries. To the best of our knowledge, this is the first work that will fill the gap in the literature of this field.

In cognitive vehicular ad hoc networks (CVANETs), channel conditions are highly dynamic due to both vehicle mobility and primary user activity. In this paper, to support high-quality video playback in such a challenging scenario, an adaptive video streaming algorithm built on scalable video coding (SVC) is proposed for reducing interruption ratio and improving visual quality. The proposed streaming algorithm is capable of deciding the proper number of video layers for vehicle users, by taking into account several important factors including vehicle position, velocity, the activity of primary users. Simulation results demonstrate the superiority of the proposed algorithm on playback interruption ratio and visual quality over the compared algorithm.

Video streaming is gaining popularity among mobile users. The latest mobile devices, such as smart phones and tablets, are equipped with multiple wireless network interfaces. How to efficiently and cost-effectively utilize multiple links to improve video streaming quality needs investigation. In order to maintain high video streaming quality while reducing the wireless service cost, in this paper, the optimal video streaming process with multiple links is formulated as a Markov Decision Process (MDP). The reward function is designed to consider the quality of service (QoS) requirements for video traffic, such as the startup latency, playback fluency, average playback quality, playback smoothness and wireless service cost. To solve the MDP in real time, we propose an adaptive, best-action search algorithm to obtain a sub-optimal solution. To evaluate the performance of the proposed adaptation algorithm, we implemented a testbed using the Android mobile phone and the Scalable Video Coding (SVC) codec. Experiment results demonstrate the feasibility and effectiveness of the proposed adaptation algorithm for mobile video streaming applications, which outperforms the existing state-of-the-art adaptation algorithms.

Throughput-optimal scheduling has been widely discussed due to its capability to stabilize single-hop multiuser wireless systems if possible. However, most of the previous discussions focused on the underloaded scenario, i.e., the arrival rate lies inside the achievable rate region. The behavior of throughput-optimal scheduling in overloaded multiuser wireless systems is the focus of this paper. We first show that, with the infinite buffer assumption, although all the queues are unstable, both the average throughput and a function of queue length converge as time evolves. In addition, the average throughput is the solution to a convex optimization problem whose objective is determined by the scheduling algorithm. By investigating the average throughput of two special throughput-optimal scheduling algorithms, i.e., the generalized MaxWeight and Log-Rule, we find that users can be prioritized by tuning the parameters associated with the scheduling algorithm, but the fairness is not likely to be guaranteed and some users may starve. Second, by studying the finite buffer system, we show that whether the buffer is dedicated to each queue or shared among queues has a great impact on the system performance, and the potential user starvation problem can be alleviated by a proper design.

Mobility-assisted data collection in sensor networks creates a new dimension to reduce and balance the energy consumption for sensor nodes. However, it also introduces extra latency in the data collection process due to the limited mobility of mobile elements. Therefore, how to schedule the movement of mobile elements throughout the field is of ultimate importance. In this paper, the on-demand scenario where data collection requests arrive at the mobile element progressively is investigated, and the data collection process is modelled as an$M/G/1/c$-$NJN$queuing system with an intuitive service discipline of nearest-job-next (NJN). Based on this model, the performance of data collection is evaluated through both theoretical analysis and extensive simulation. NJN is further extended by considering the possible requests combination (NJNC). The simulation results validate our models and offer more insights when compared with the first-come-first-serve (FCFS) discipline. In contrary to the conventional wisdom of the starvation problem, we reveal that NJN and NJNC have better performance than FCFS, in both the average and more importantly the worst cases, which offers the much needed assurance to adopt NJN and NJNC in the design of more sophisticated data collection schemes, as well as other similar scheduling scenarios.

The connectivity of ad hoc networks has been extensively studied in the literature. Most recently, researchers model ad hoc networks with two-dimensional lattices and apply percolation theory for connectivity study. On the lattice, given a message source and the bond probability to connect any two neighbor vertices, percolation theory tries to determine the critical bond probability above which a giant connected component appears. This paper studies a related but different problem, directed connectivity: what is the exact probability of the connection from the source to any vertex following certain directions? The existing studies in math and physics only provide approximation or numerical results. In this paper, by proposing a recursive decomposition approach, we can obtain a closed-form polynomial expression of the directed connectivity of square lattice networks as a function of the bond probability. Based on the exact expression, we have explored the impacts of the bond probability and lattice size and ratio on the lattice connectivity, and determined the complexity of our algorithm. Further, we have studied a realistic ad hoc network scenario, i.e., an urban VANET, where we show the capability of our approach on both homogeneous and heterogeneous lattices and how related applications can benefit from our results.

Synchronization is a pre-requisite for many sensor network applications. However, it remains challenging in sensor networks due to both the limited resources and the dynamic environments. In this paper, we propose a new two-phase clock synchronization scheme. The first one is the external clock synchronization phase, during which nodes update their clock by exchanging timestamp messages with the reference clock. Different from the conventional solutions, we propose to directly remove the clock skew during the external synchronization to achieve a higher synchronization accuracy and lower computational complexity. The second one is the clock self-calibration phase, as the accumulated clock skew will make the synchronized clock drift away again, we need to compensate the clock skew to maintain the clock synchronization accuracy. However, the compensation is non-trivial as the clock skew may not be constant due to the changing environment. Thus we propose the temperature-assisted clock self-calibration (TACSC) to dynamically compensate the clock skew according to the working temperature. Extensive simulation demonstrates that the proposed synchronization scheme can achieve a much lower root mean square error in the external synchronization phase. Furthermore, during the clock self-calibration phase, the TACSC scheme can improve the synchronization accuracy by more than one order of magnitude, which is verified by both simulation and testbed experimentation.

In dense IEEE 802.11 networks, improving the efficiency of contention-based media access control is an important and challenging issue. Recently, the IEEE802.11ah Task Group has discussed a group-synchronized distributed coordination function (GS-DCF) for densely deployed wireless networks with a large number of stations. By using the restricted access window (RAW) and RAW slots, the GS-DCF is anticipated to improve the throughput substantially, primarily due to relieving the channel contention. However, optimizing the MAC configurations for the RAW, i.e., the number and duration of RAW slots, is still an open issue. In this paper, we first build an analytical model to track the performance of the GS-DCF in saturated 802.11 networks. Then, we study and compare the GS-DCF throughput using both centralized and decentralized grouping schemes. The accuracy of our model has been validated with simulation results. It is observed that the GS-DCF obtains a throughput gain of seven times or more over DCF in a network of 512 or more stations. Moreover, it is demonstrated that the decentralized grouping scheme can be implemented with a small throughput loss when compared with the centralized grouping scheme.

In WSNs, accidents like traffic congestion and sensor node running out of energy may occur frequently. These accidents are usually geographically localized, resulting in some groups of nodes unusable temporarily or even forever, which are called sensitive clusters in this paper. Although the data collection methods have been intensively studied, how to dynamically optimize the routing to bypass the sensitive clusters is an interesting and open issue. A new distributed, location-based routing protocol, named sensitive artificial potential field routing (SAPFR), is proposed to deliver packets efficiently while bypassing the sensitive clusters adaptively. SAPFR can build the multihop route from a sensor node to the sink with high energy efficiency and power consumption balancing. In particular, SAPFR is highly adaptive to bypass the dynamic sensitive clusters. Simulation results show that the obtained routes proactively bypass the sensitive clusters and the transmission efficiency is improved as well. SAPFR provides a high routing success rate in the WSNs even with a large proportion of sensitive sensor nodes.

In IEEE 802.11 networks, how to improve the efficiency of contention-based media access is an important, challenging issue. Recently, the grouping strategy is introduced in the IEEE 802.11ah standard to alleviate the channel contention. In IEEE 802.11ah networks, stations can be divided into groups and each group is only allowed to access wireless channel during the designated channel access period. By limiting the number of stations participating in the channel contention, it is anticipated that such a grouping strategy could substantially improve the communication efficiency. However, how to allocate the channel among different groups and how to adjust the number and sizes of groups are still open issues. In this paper, we first study the impact of the grouping strategy on the network performance, and then propose an analytical model to track the performance under saturated traffic. The accuracy of our model has been validated by simulation results. Our analytical model and results also provide important guidelines in optimizing grouping parameters.

Without relying on devices carried by the target, device-free localization (DFL) is attractive for many applications, such as wildlife monitoring. There still exist many challenges for DFL for multiple targets without dense deployment of sensor nodes. To fit the gap, in this paper, we propose a multi-target localization method based on compressive sensing, named LCS. The key observation is that given a pair of nodes, the received signal strength (RSS) will be different when a target locates at different locations. Taking advantage of compressive sensing in sparse recovery to handle the sparse property of the localization problem, (i.e., the vector which contains the number and location information of k targets is an ideal k-sparse signal), we presented a scalable compressive sensing based multiple target counting and localization method i.e., LCS, and rigorously justify the validity of the problem formulation. The results from our realistic deployment in a 12m×12m open space are promising. For 12 people with 24 nodes, the worst localization error ratio and counting error ratio of our LCS is no more than 8.3% and 33.3% respectively.

Connectivity has been extensively studied in ad hoc networks, most recently with the application of percolation theory in two-dimensional square lattices. Given a message source and the bond probability to connect neighbor vertexes on the lattice, percolation theory tries to determine the critical bond probability above which there exists an infinite connected giant component with high probability. This paper studies a related but different problem: what is the connectivity from the source to any vertex on the square lattice following certain directions? The original directed percolation problem has been studied in statistical physics for more than half a century, with only simulation results available. In this paper, by using a recursive decomposition approach, we have obtained the analytical expressions for directed connectivity. The results can be widely used in wireless and mobile ad hoc networks, including vehicular ad hoc networks.

Optimal routing in wireless mesh networks is a challenging problem considering inter- and intra-flow interference. To solve the problem, first, we define a new routing metric, expected path bandwidth (EPBW), where the varying link rate (due to wireless channel quality) and the dynamic link load (considering the inter- and intra-flow interference) have been considered to estimate EPBW accurately. Second, based on the proposed EPBW, we propose a distributed routing protocol for WMNs, aiming to maximize network throughput. We implement the proposed protocol and the routing metric EPBW in NS-2. We then design various scenarios to evaluate the protocol performance extensively using NS-2 simulation. Simulation results show that the proposed protocol and metric can substantially out-perform the state-of-the-art routing metrics, such as expected transmission count (ETX) and expected transmission time (ETT), and previous routing protocols including AODV, DSDV, and DSR.

In this paper, we study the resource allocation problem in a K-user wireless broadcast system with N-layer superposition coding (SPC). The problem is formulated as a sumutility maximization problem based on the average throughput. Using stochastic approximation, iteratively solving an approximated problem yields the optimality. The approximated problem can be solved by selecting the user group with the maximal weighted-sum-rate, which has a high computational complexity. Two low-complexity suboptimal algorithms are proposed. The simulation results show that the SPC gain highly depends on the variability of the channel and the SNR range of users. SPC is more favourable in the scenario with small-variation fast-fading channel and a large SNR range of users. The performance of the proposed low-complexity algorithms are close to the optimal solution, and the SPC gain achieved is substantial.

In wireless machine-to-machine (M2M) networks, how to efficiently and reliably deliver short, periodic message to a large number of receivers is an important, challenging issue. Automatic Repeat reQuest (ARQ) is a promising technique to provide the reliable communications. However, ARQ affects the transmission efficiency by retransmitting the whole packet even though partial packet has been received successfully. Such effect can be more serious when delivering messages to a large number of receivers. In this paper, we propose a new multireceiver message aggregation (MRMA) scheme and a busy-tone negative acknowledgement (BT-NACK) scheme to jointly improve the communication efficiency and reliability. To further optimize the performance, an integer programming problem is formulated to explore the optimal aggregation configuration. While it is NP-hard to find a global optimal solution, low complexity heuristic algorithms are developed. Simulation results show that our schemes significantly improve the communication efficiency and communication delay.

End-to-end delay is an important QoS metric in multihop wireless networks such as sensor networks and mesh networks. End-to-end delay is defined as the total time it takes for a single packet to reach the destination. It is a result of many factors including the length of the route and the interference level along the path. In this paper we address how to minimize end-to-end delay jointly through optimizing routing and link layer scheduling. We present two cross-layer schemes, a loosely coupled cross-layer scheme and a tightly coupled cross-layer scheme. In the loosely coupled cross-layer scheme, routing is computed first and then the information of routing is used for link layer scheduling; in the tightly coupled scheme, routing and link scheduling are solved in one optimization model. The two cross-layer schemes involve interference modeling in multihop wireless networks with omnidirectional antenna. A sufficient condition on conflict-free transmission is established, which can be transformed to polynomial-sized linear constraints, and a linear program based on the sufficient condition is developed. Through simulation, we show that the proposed routing and scheduling schemes can outperform their counterparts in each layer, and the integrated cross-layer schemes are superior to the combination of the existing routing and scheduling schemes.

Theoretically, superposition coding (SPC) can achieve the capacity of a degraded Gaussian broadcast channel. A practical implementation of SPC, hierarchical modulation (HM), has recently been adopted in industry. Using HM, how to explore the multi-user diversity gain in a time-varying wireless environment to maximize throughput and maintain fairness is an open issue. Using greedy opportunistic scheduling algorithms will lead to a severe starvation problem. In this paper, we study the proportional fair scheduling (PFS) problem in an HM aided wireless network, jointly considering the user selection and utility maximization problems. Shannon capacity based and practical HM based optimal scheduling problems are formulated. An optimal algorithm and a low complexity suboptimal algorithm are proposed to solve the practical scheduling problem combining the opportunistic PFS and HM. Simulation results demonstrate that the proposed algorithms can achieve 50% to 100% throughput gain compared to the single-user opportunistic PFS solution depending on the number of users and have better fairness performance than the existing single-user and HM-based solutions.

In this paper, we consider a wireless sensor node monitoring the environment and it is equipped with a compressive-sensing based, single-pixel image camera and other sensors such as temperature and humidity sensors. The wireless node needs to send the data out in a timely and energy efficient way. This transmission control problem is challenging in that we need to jointly consider perceived video quality, quality variation, power consumption and transmission delay requirements, and the wireless channel uncertainty. We address the above issues by first building a rate-distortion model for compressive sensing video. Then we formulate the deterministic and stochastic optimization problems and design the transmission control algorithm which jointly performs rate control, scheduling and power control. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed transmission control algorithm.

How to provide reliable, cost-effective wireless services for high-speed railway (HSR) users attracts increasing attention due to the fast deployment of HSRs worldwide. A key issue is to develop reasonably accurate and mathematically tractable models for HSR wireless communication channels. Finite-state Markov chains (FSMCs) have been extensively investigated to describe wireless channels. However, different from traditional wireless communication channels, HSR communication channels have the unique features such as very high speed, deterministic mobility pattern and frequent handoff events, which are not described by the existing FSMC models. In this paper, based on the Winner II physical layer channel model parameters, we propose a novel FSMC channel model for HSR communication systems, considering the path loss, fast fading and shadowing with high mobility. Extensive simulation results are given, which validate the accuracy of the proposed FSMC channel model. The model is not only ready for performance analysis, protocol design and optimization for HSR communication systems, but also provides an effective tool for faster HSR communication network simulation.

Routing is critical for WSNs due to the nature of multi-hop message delivery and the restricted power supply and computation capacity. Routing mechanisms with efficient power utilization and low computation complexity are of fundamental importance to meet the future application demand of WSNs. In this paper, a novel location-based routing technique, the Artificial Potential Field based Routing (APFR), is proposed. APFR establishes an Artificial Potential Field (APF) for the tagged sensor node and then uses a greedy selection algorithm to determine the next hop based on the direction obtained from the APF. In addition, a backup scheme is presented to recover the route in the case of routing voids. The advantages of APFR are three-fold. First, the routing tends to point to the areas with high node density, so the transmission load is balanced among the sensor nodes. Second, when some nodes are running out of energy, the APF will be updated and the routing will change automatically according to the new APF. Third, APFR only requires local geographic information and is of low computational complexity. The simulation results show that APFR can prolong the network lifetime and improve the routing success rate compared to other routing protocols such as GEAR and GPSR, especially for densely deployed networks.

Video streaming is gaining popularity among mobile users. The latest mobile devices, such as smart phones and tablets are equipped with multiple wireless network interfaces. How to efficiently and cost-effectively utilize multiple links to improve the video streaming quality needs to be investigated. In order to maintain high video streaming quality while reduce the wireless service cost, in this paper, the optimal video streaming process with multiple links is formulated as a Markov Decision Process (MDP). The reward function is designed to consider the quality of experience (QoE) requirements for video traffic, such as the interruption rate, average playback quality, playback smoothness and wireless service cost. Using dynamic programming, the MDP can be solved to obtain the optimal streaming policy. To evaluate the performance of the proposed multi-link rate adaptation (MLRA) algorithm, we implement a testbed using the Android mobile phone and the open-source X264 video codec. Experimental results demonstrate the feasibility and effectiveness of the proposed MLRA algorithm for mobile video streaming applications, which outperforms the existing state-of-the-art one.

The Radio Signal Strength Indicator (RSSI)-based localization algorithm is an effective solution for passive object localization. However, the localization accuracy of the existing methods highly depends on the transceiver distance and deployment density. Generally speaking, to obtain higher accuracy, we need a denser sensor node deployment, which results in a higher deployment cost and more communication overheads. In this paper, we investigate this problem based on extensive measurements. According to the measurement results, we propose to localize objects using an RSSI distribution based localization (RDL) model to identify the object location by different RSSI distributions of the communicating links. Experimental results show that the RDL method can achieve higher localization accuracy with less sensor nodes.

Video is a desirable medium to provide traffic information, news, advertisements, etc. to people on-the-road. Due to the high mobility and dynamic topology of VANET, an open question is whether it is feasible to support video streaming services using license-free wireless communications between vehicles and road-side-units. In this paper, using the advanced scalable video coding (SVC), we propose an adaptive video streaming scheme for video streaming services in the highway scenario. Relying on cooperative relay among vehicles, a vehicle can download video data using a direct link or a multi-hop path to the RSUs. Considering the current download speed and the receiver buffer level, the proposed scheme can request an appropriate number of video enhancement layers to improve video quality of experience (QoE). Simulation results with real video traces have demonstrated the effectiveness of the proposed scheme which can make a good tradeoff among the perceived video quality, the startup latency and the interruption ratio.

The introduction of mobile elements in wireless sensor networks creates a new dimension to reduce and balance the energy consumption for resource-constrained sensor nodes; however, it also introduces extra latency in the data collection process due to the limited mobility of mobile elements. Therefore, how to arrange and schedule the movement of mobile elements throughout the sensing field is of ultimate importance. In this paper, the online scenario where data collection requests arrive progressively is investigated, and the data collection process is modeled as an M/G/1/c-NJN queuing system, where NJN stands for nearest-job-next, a simple and intuitive service discipline. Based on this model, the performance of data collection is evaluated through both theoretical analysis and extensive simulation. The NJN discipline is further extended by considering the possibility of requests combination (NJNC). The simulation results validate our analytical models and give more insights when comparing with the first-come-first-serve (FCFS) discipline. In contrast to the conventional wisdom of the starvation problem, we reveal that NJN and NJNC have a better performance than FCFS, in both the average and more importantly the worst cases, which gives the much needed assurance to adopt NJN and NJNC in the design of more sophisticated data collection schemes for mobile elements in wireless ad hoc sensor networks, as well as many other similar scheduling application scenarios.

Clock synchronization is a fundamental requirement for network systems. It is particularly crucial and challenging in wireless sensor networks (WSNs), because WSN environments are dynamic and unpredictable. To tackle this problem, how to accurately estimate clock skew, the inherent reason causing clock desynchronization, is investigated. According to the measurement results, clock skew is a non-stationary random process highly correlated to temperature, and its measurements contain severe noises. Based on the observation, an additional information aided multi-model Kalman filter (AMKF) algorithm is proposed, which uses temperature measurements to assist clock skew estimation. Using AMKF, an environment-aware clock synchronization (EACS) scheme is proposed to dynamically compensate clock skew. The scheme is simple, scalable, and of low computation and energy cost. Using EACS as an additional component of the conventional synchronization protocols, the clock is updated with local information before the clock re-synchronization process is triggered, so it can substantially prolong the re-synchronization period, which not only reduces the energy consumption but also is essential for the scenarios where frequent synchronization is infeasible. The theoretical lower bound of clock skew estimation error is derived as a benchmark. Extensive simulation and experimental verification results have demonstrated the feasibility and effectiveness of the proposed scheme which can prolong the time resynchronization period by an order of magnitude in dynamic environments.

In this paper, we investigate the optimal streaming strategy for dynamic adaptive streaming over HTTP (DASH). Specifically, we focus on the rate adaptation algorithm for streaming scalable video (H.264/SVC) in wireless networks. We model the rate adaptation problem as a Markov Decision Process (MDP), aiming to find an optimal streaming strategy in terms of user-perceived quality of experience (QoE) such as playback interruption, average playback quality and playback smoothness. We then obtain the optimal MDP solution using dynamic programming. We further define a reward parameter in our proposed streaming strategy, which can be adjusted to make a good trade-off between the average playback quality and playback smoothness. We also use a simple testbed to validate our solution. Experiment results show the feasibility of the proposed solution and its advantage over the existing work.

User cooperation in wireless networks is inherently a cross-layer optimization problem. We identify a new direction for cooperative communications: i.e., in addition to the point-to-point communication channel between the transmitter and the receiver, the communication configuration should take the network topology into account. In this paper, we first propose a network modulation (NM) design that can transmit bits with different SNR requirements in a single symbol transmission. We then propose an error-correction coding assisted relay (EAR) scheme that is also configured according to the network topology. We study the performance of NM and EAR in both a three-node collinear network and a two-dimensional cellular network. Extensive simulations have been conducted, which demonstrate the substantial performance gain of the proposed schemes, in terms of both a higher network throughput and a lower bit-energy consumption. Comparing between NM and EAR, NM is more beneficial for the downlink performance and EAR is more beneficial for the uplink performance. Combining NM and EAR leads to a more efficient cooperative network. It is concluded that the topology-aware physical layer design will be a promising direction with many open issues for further study.

End-to-end delay is an important QoS metric in sensor networks as well as any application that involves transferring of small-sized files. In this paper, we address how to minimize the end-to-end delay in a multihop wireless network. End-to-end delay is defined as the total time it takes for a single packet to reach the destination. It is a result of many factors including the length of the routing path and the interference level along the path. In this paper we present a transmission scheduling scheme that minimizes the end-to-end delay along a given route. The link scheduling scheme is based on integer linear programming and involves interference modeling. Using this schedule, there are no conflicting transmissions at any time. Through simulation, we show that the proposed link scheduling scheme can significantly reduce end-to- end latency regardless of the routing algorithm used.

Given the ever-increasing popularity of wireless body area networks (WBANs), in some application scenarios, many WBANs may operate densely and lead to a high mutual interference. Excessive interference may severely degrade the network performance, which is called the network co- existence problem. It is critical to fully understand the network co-existence problem to ensure the effectiveness and efficiency of WBANs. In this paper, we investigate the network interference and co-existence problem for the scenarios with densely deployed WBANs. Specifically, we model the probability distribution of interference among co- existing WBANs using the advanced Geometrical Probability approach. We then approximate the total inter-cell interference by a simple gamma distribution which is accurate according to the simulation results. We further use the interference distribution model to solve the practical network planning issues for WBANs. That is, we quantify the minimum network distance to ensure the signal to interference and noise ratio (SINR) for the boundary nodes and the average SINR of the whole system, respectively.

The design of random media access control (MAC) renews great attention for emerging challenged wireless environments where the propagation delay is long and varying, such as satellite or underwater acoustic sensor networks. In these environments, the existing MAC solutions based on slotted transmissions, carrier sensing, or channel reservation by control packets are no longer favorable or even feasible. In this paper, we propose the Flipped Diversity Aloha (FDA) MAC protocol to tackle the challenges based on a new diversity transmission scheme. Different from the existing diversity transmission schemes, each data packet and its flipped version are transmitted back-to-back, and the receiver uses the Zigzag decoding technique to decode collided packets. The performance of FDA has been evaluated by analysis and simulation. The results show that, without time synchronization or handshaking requirements, the performance of FDA is unaffected by the duration or variation of the propagation delay, and it can substantially improve system performance in terms of throughput, packet loss ratio, and network admission region.

Performance metrics in cellular systems, such as per-user link capacity and co-channel interference, are dependent on the statistical distances between communicating nodes. An analytical model based on geometric probability in cellular systems is presented here for capacity analysis and interference estimation. We first derive the closed-form distance distribution between cellular base stations and mobile users, giving the explicit probability density functions of the distance from a base station to an arbitrary user in the same hexagonal cell, or to the users in adjacent cells. Different from numerical methods or approximation, and the existing approaches in geometric probability, this unified approach provides explicit distribution functions that can lead to all statistical moments, and is not limited by coordinate distributions, either of base stations or subscribers. Analytical results on per-user link capacity and co-channel interference are derived and validated through simulation, which shows the high accuracy and promising potentials of this approach.

With the limited wireless spectrum and the ever-increasing demand for wireless services, how to enlarge wireless network throughput is a pressing issue. To exploit the wireless spatial capacity, concurrent transmissions, if controlled appropriately, can lead to overall higher spectrum utilization and network throughput. The optimal scheduling and power control for concurrent transmissions in rate-adaptive wireless networks is a very challenging NP-hard problem. In this paper, we propose an efficient power allocation and scheduling algorithm for concurrent transmissions which can improve network throughput with fairness consideration. We first formulate the optimal power allocation and scheduling problem, and convert the original non-convex problem into a series of convex problems using a two-phase approximation technique. Then, we propose the power and channel allocation with fairness (PCAF) algorithm to solve the problem efficiently. Extensive simulation results show the remarkable improvement in terms of both network throughput and fairness, comparing to the previous scheduling algorithms.

Congestion control for wireless networks is much more challenging than that for wired networks, due to the limited wireless spectrum and severe impairments of wireless medium which suffer time-varying fading, shadowing, interference, etc. Although the stability of the Internet using TCP congestion control and active queue management (AQM) schemes has been extensively investigated, effective congestion control for wireless networks is a pressing, open issue. Considering the dynamics of wireless links, in this paper, we investigate the stability of TCP/AQM wireless networks with feedback delays, which is formulated as a delay Markov jump linear system (DMJLS). First, a dynamic model based on the DMJLS for TCP/AQM wireless networks is established. Second, a novel stochastic stability analysis for autonomous time-delay systems with a cost function is presented. Delay-dependent linear matrix inequalities (LMIs) criteria for the stochastic stability conditions are obtained. It is noteworthy that this is the first time conditions for the stochastic stability have been derived subject to the linear quadratic (LQ) control strategy, and packet drop probability as the control input under the DMJLS is calculated. In addition, for practical systems where real-time tracking of states is infeasible or costly, the mode-independent congestion control is also investigated. The robustness of random early detection (RED) in wireless environment is proved. Numerical results are given to validate the analytical results which provide important insights for wireless network congestion control and resource management.

Vehicular ad hoc networks have attracted a lot of attention in recent years, in either vehicle-to-vehicle or vehicle-to-infrastructure scenarios. In this paper, we focus on the latter, particularly for vehicles to upload to roadside units, the so-called drive-thru Internet, in a secure and efficient manner. Due to the ad hoc nature and wireless communications, traditional certificate-based security schemes are either infeasible or inefficient in this scenario. Thus we propose a certificateless approach to secure upload in a drive-thru Internet. We discuss the attack model and the desired security properties, and how to achieve these properties through the proposed certificateless scheme. We implement and evaluate the proposed scheme, and also investigate how to mitigate the security overhead through the separation of security association and data transfer in a drive-thru Internet.

Channel coding such as Reed-Solomon (RS) and convolutional codes has been widely used to protect video transmission in wireless networks. However, this type of channel coding can effectively correct error bits only if the error rate is smaller than a given threshold; when the bit error rate is underestimated, the effectiveness of channel coding drops dramatically and so does the decoded video quality. In this paper, we propose a low-complex, scalable video coding architecture based on compressive sensing (SVCCS) for wireless unicast and multicast transmissions. SVCCS achieves good scalability, error resilience and coding efficiency. SVCCS encoded bitstream is divided into base and enhancement layer. The layered structure provides quality and temporal scalability. While in the enhancement layer, the CS measurements provide fine granular quality scalability. In addition, we incorporate state-of-the-art technologies of compressive sensing to improve the coding efficiency. Experimental results show that SVCCS is more effective and efficient for wireless videocast than the existing solutions.

In wireless networks using hybrid MAC, nodes can reserve time periods inside scheduling cycles, and the time which is not reserved can be used by all the nodes through contention-based access. The hybrid MAC is attractive because it can provide satisfactory QoS by resource reservation and also achieve high channel utilization by multiplexing gain in the contention periods. However, we are still lacking a clear understanding of its performance and the optimization design scheme. In this paper, we propose an analysis framework for hybrid MAC using soft-reservation, where the unused reserved time can be released and accessed by the other nodes through contention. By the mean value analysis approach, the collision probability and average service time of one frame are obtained. The hybrid MAC based on the WiMedia ECMA-368 standard has been simulated to validate the analysis and compared to the conventional contention-based MAC and the hard-reservation hybrid MAC, which shows the soft reservation has much better performance and higher capacity when the network is relatively heavily loaded.

We introduce an approach called network modulation which gives us a new dimension to improve wireless network throughput and save energy. In current wireless systems, when a source transmits data to the receiver through a single-hop or multi-hop wireless path, the physical layer modulates and demodulates the information bits hop-by-hop, and the transmission over each hop is treated the same as in a point-to-point communication link. Given the broadcast nature of wireless medium and the wide variation of wireless channel quality, we let a sender transmit messages to multiple receivers simultaneously, using a software mapping technology, called network modulation, to redefine the constellation of typical quadrature amplitude modulation (QAM) schemes. As the software-based network modulation schemes do not require specialized communication hardware, they can be implemented with low cost and high flexibility. Network modulation can be used to improve network performance in a wide range of scenarios, for anycast (broadcast, multicast and unicast) services, one-way or two-way traffic, and single-hop or multi-hop wireless paths, in infrastructure or ad hoc networks. The minimum requirement for applying network modulation is that there are no less than three nodes within each others' transmission ranges, so we can consider modulation, topology control, resource allocation, and routing jointly.

Due to the multi-path, multi-hop transmission structure in wireless cooperative systems, when packet errors occur, retransmissions may take longer time and more energy, and thus affect the system performance and user-perceived quality of services. How to reduce the error rate for cooperative systems is a critical issue. In this paper, a novel and simple error recovery scheme for wireless cooperative systems has been proposed by using the soft values of each bit, which are related to the confidence levels when the physical layer demodulates and decodes the bit. The receiver has a better chance to obtain the correct bit by combining the soft values associated with the relay and the direct transmission paths, respectively. We then derive the bit error rate (BER) performance for the amplify and forward (AF) cooperative systems with soft value combining. Next, we extend the soft value combining for the demodulation and forward (DMF) cooperative systems, where the relay demodulates the received signal and forwards it to the destination in a separate modulation type, in order to further improve spectrum efficiency. Numerical results have validated the analysis, and demonstrated that the proposed soft value combining scheme is simple to implement and can achieve near optimal performance.

One promise of Vehicular Ad-hoc Networks (VANET) is to considerably increase road safety and travel comfort by enabling inter-vehicle communications. Among a vast array of potential applications, emergency message (EM) dissemination has attracted a lot of attention in the literature. In this paper, we propose a time/location-critical (TLC) framework for EM dissemination and use our scalable modulation and coding (SMC) scheme to achieve the goal. In specific, vehicles near the accident site (or the point-of-interest location) receive guaranteed, detailed messages to take proper reaction immediately (e.g., slow down or change lanes), and vehicles further away have a high probability to be informed and make location-aware decisions accordingly (e.g., detour or reroute), with the assistance of reverse traffic when possible and necessary. The efficacy of the proposed framework is analyzed and validated by extensive numerical and simulation results. The TLC framework and the use of the SMC scheme are shown to be able to disseminate EMs effectively and efficiently by taking both the time and location criticality into account, while simplifying the design of radio transceivers and media access control protocols for VANET.

Second-order statistical parameters of wireless channels, such as level crossing rate (LCR) and average fade duration (AFD), determine how frequent and the burst length of the channel in bad conditions, so they play an important role in the performance of wireless communication systems. For user-cooperative wireless systems, due to the interactions of multiple channels, it is non-trivial to determine the LCR and AFD of the received signals, which is an open issue. In this paper, we develop an analytical framework to quantify the LCR and AFD of the amplify-and-forward (AF) cooperative system using selection combining (SC) over Rayleigh fading channels. We first analyze the statistics of the two independent fading paths, the AF relay path with a mobile-to-mobile (M2M) channel and a mobile-to-fixed (M2F) Rayleigh fading channel, and the direct path with a M2F Rayleigh fading channel. Then, we derive the expressions of the second-order statistical parameters of the AF cooperative system with SC. Numerical results verify the correctness of our model. The analytical and simulation results reveal the different effect of the motions of the source and the relay nodes, and they can be used to select better relay nodes and assist the design and optimization of error control mechanisms in different layers in wireless cooperative networks.

The Distributed Video Coding (DVC) follows an approach different from the conventional video coding. DVC has a simpler encoder but a more complicated decoder. This feature makes it possible to encode video in computation and energy constrained devices. Thus, DVC is appealing in sensor networks and other wireless networks. When transmitting real-time DVC encoded video streams, in order to adjust coding parameters according to the time-varying communication channel conditions or the dynamics of available bandwidth in the bottleneck, the source needs an efficient way to know the tradeoff of the coding parameters and the decoded video quality. However, how to quantify the DVC video quality using tractable models is an open issue. In this paper, we propose a distortion analysis model for DVC encoded Wyner-Ziv frames. The proposed closed-form distortion model for Wyner-Ziv frames is based on the reconstruction method of the "nearest neighbor binning". With the distortion analysis model, the average video frame PSNR can be estimated as a function of the codec parameters and the video statistics. Extensive simulations with different types of videos have been conducted and the results validate the accuracy of the proposed model. The model will be an enabling tool to further optimize the system parameters and network protocols for supporting DVC coded video over wireless and wired networks.

Adaptive modulation has been widely adopted in broadband wireless communication systems to improve spectrum efficiency. On the other hand, user cooperative diversity has been investigated to improve system coverage and efficiency. How to take the advantage of adaptive modulation for user cooperative transmissions to maximize network throughput under the constraint of the bit error rate (BER) requirement is an open issue. In this paper, a simple user-cooperation strategy with adaptive M-ary Quadrature Amplitude Modulation (M-QAM) is proposed to fill the gap. We use an approximate BER expression of M-QAM modulation to formulate an easy-to-solve optimization problem, so the modulation types for the source node and the relay node can be optimized in real time to maximize the throughput under the BER constraint. To maximize the throughput for the whole network, we further use a worst-link-first (WLF) matching algorithm for selecting appropriate cooperators. Numerical results show that the proposed adaptive cooperative protocol can effectively improve system spectral efficiency.

Clock synchronization is a fundamental issue in communication networks and distributed systems, and clock skew is the inherent cause for clock desynchronization. Clock skew estimation is essential to improve the efficiency and reduce the overhead of clock synchronization schemes, and it is especially beneficial for resource-constrained devices such as sensor nodes in dynamic environments. According to the measurement, clock skew is environment sensitive, and no existing clock skew estimation schemes can accurately capture such dynamic behaviors. In this paper, we investigate a general clock synchronization problem with variable clock skews and propose a new skew estimation model based on a hybrid approach to characterizing the dynamic of clock skews. To estimate the time-varying clock state vector, we employ the Interactive Multi-Model (IMM) Kalman filter, which can make soft decisions by combining results from different models. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed adaptive clock skew estimation algorithm, which achieves a better performance with moderate computational complexity.

Hybrid media access control (MAC) protocols use reservation and contention-based approaches simultaneously, so they can provide satisfactory quality-of-service to multimedia applications by resource reservation, and achieve high resource utilization with multiplexing gain during the contention periods. However, reservation can significantly affect the behavior of the contention-based access. How to split channel time between reservation periods and contention periods and how to adjust the contention scheme for hybrid MAC are important, open issues. In this paper, an analytical model for the hybrid MAC with saturated traffic is first proposed and then extended to the unsaturated traffic case. Based on the mean value analysis, the proposed models give the average frame service time and throughput for the contention-based MAC with the presence of reserved channel periods. They are also applicable to online admission control due to their low computational complexity.

In conventional wireless systems with layered architectures, the physical layer treats all data streams from upper layers equally and apply the same modulation and coding schemes. Newer systems such as Digital Video Broadcast start to introduce hierarchical modulation schemes with SuperPosition preCoding (SPC) and support data streams of different priorities. However, SPC requires specialized hardware and has high complexity beyond most existing handheld devices. We thus propose scalable modulation (s-mod) by reusing the current mainstream modulation schemes with software-based bit-remapping. In this paper, we study how to optimize the configuration of the PHY layer s-mod and coding schemes to maximize the utility of videos with Scalable Video Coding (SVC). Simulation results demonstrate significant performance gains using s-mod and the cross-layer optimization, indicating s-mod and SVC is a good combination for wireless video multicast.

Optimal scheduling for concurrent transmissions in rate-nonadaptive wireless networks is NP-hard. Optimal scheduling in rate-adaptive wireless networks is even more difficult, because, due to mutual interference, each flow's throughput in a time slot is unknown before the scheduling decision of that slot is finalized. The capacity bound derived for rate-nonadaptive networks is no longer applicable either. In this paper, we first formulate the optimal scheduling problems with and without minimum per-flow throughput constraints. Given the hardness of the problems and the fact that the scheduling decisions should be made within a few milliseconds, we propose two simple yet effective searching algorithms which can quickly move towards better scheduling decisions. Thus, the proposed scheduling algorithms can achieve high network throughput and maintain long-term fairness among competing flows with low computational complexity. For the constrained optimization problem involved, we consider its dual problem and apply Lagrangian relaxation. We then incorporate a dual update procedure in the proposed searching algorithm to ensure that the searching results satisfy the constraints. Extensive simulations are conducted to demonstrate the effectiveness and efficiency of the proposed scheduling algorithms which are found to achieve throughputs close to the exhaustive searching results with much lower computational complexity.

Minimizing energy consumption in wireless sensor networks has been a challenging issue, and grid-based clustering and routing schemes have attracted a lot of attention due to their simplicity and feasibility. Thus how to determine the optimal grid size in order to minimize energy consumption and prolong network lifetime becomes an important problem during the network planning and dimensioning phase. So far most existing work uses the average distances within a grid and between neighbor grids to calculate the average energy consumption, which we found largely underestimates the real value. In this paper, we propose, analyze and evaluate the energy consumption models in wireless sensor networks with probabilistic distance distributions. These models have been validated by numerical and simulation results, which shows that they can be used to optimize grid size and minimize energy consumption accurately. We also use these models to study variable-size grids, which can further improve the energy efficiency by balancing the relayed traffic in wireless sensor networks.

In wireless multihop ad hoc networks, the hidden terminal problem severely degrades the overall performance. On the other hand, most existing solutions cause larger blocking areas and hence a more severe exposed-terminal problem. In this paper, we present a new Enhanced Busy-tone Multiple Access (EBTMA) medium access control (MAC) protocol. The proposed protocol minimizes the negative impact of both the hidden-terminal and the exposed-terminal problems with the assistance of an out-of-band busy tone signal. The new protocol can also enhance the reliability of packet broadcast and multicast which are very important for many network control functions such as routing. Unlike the previous busy-tone schemes, such as the original Busy-tone Multiple Access (BTMA) protocol, our proposed protocol uses a non-interfering busy- tone signal in a short period of time, in order to notify all hidden terminals without blocking a large number of nodes for a long time. Our analysis, verified by simulation results, demonstrates that the proposed MAC protocol outperforms the existing ones and it can greatly improve the performance of wireless ad hoc networks. In addition, the proposed EBTMA protocol can co-exist with the existing 802.11 MAC protocol, so it can be incrementally deployed.

TCP has been the dominant congestion control protocol in the Internet. Although it is well known that TCP combined with intermediate systems with active queue management (AQM) schemes can not guarantee asymptotical stability when the feedback delay or the link capacity is large, the asymptotical stability may not be necessary for network achieving good performance in terms of resource utilization, flow throughput and queueing delay. Practical bounds are important performance index for congestion control protocols and AQM schemes. How to derive practical bounds for realistic systems with heterogeneous flows, various delays and multiple bottlenecks is an important and challenging open issue. In this paper, we study the boundedness of generalized TCP and AQM systems considering the heterogeneity of flows and the impact of multiple bottlenecks. We derive the uniform bounds and uniform ultimate bounds of flow window size, which reveal how the system and flow parameters affect the system performance. Extensive simulation results have been given to verify the correctness of the bounds.

To reserve or not for bursty video traffic over wireless access networks has been a long-debated issue. For uplink transmissions in infrastructure-based wireless networks and peer-to-peer transmissions in mesh or ad-hoc networks, reservation can ensure the Quality-of-Service (QoS) provisioning at the cost of a lower degree of resource utilization. Contention-based Medium Access Control (MAC) protocols are more flexible and efficient in sharing resources by bursty traffic to achieve a higher multiplexing gain, but the performance may degrade severely when the network is congested and collisions occur frequently. More and more wireless standards adopt a hybrid approach, which allows the coexistence of resource reservation and contention-based MAC protocols. However, how to cost-effectively support video traffic using hybrid MAC protocols is still an open issue. In this paper, we first propose how to use hybrid MAC protocols to support video streaming over wireless networks. Then, we quantify the performance of video traffic over wireless networks with contention-only, reservation-only, and hybrid MAC protocols, respectively. Admission regions for video streams with these three approaches are obtained. Using the standard WiMedia MAC protocols as an example, extensive simulations with a commonly-used network simulator (NS-2) and real video traces are conducted to verify the analysis. The analytical and simulation results reveal the tradeoff between reservation and contention-based medium access strategies, and demonstrate the effectiveness of the hybrid approach.

Millimeter-wave (mmWave) transmissions are promising technologies for high data rate (multi-Gbps) Wireless Personal Area Networks (WPANs). In this paper, we first introduce the concept of exclusive region (ER) to allow concurrent transmissions to explore the spatial multiplexing gain of wireless networks. Considering the unique characteristics of mmWave communications and the use of omni-directional or directional antennae, we derive the ER conditions which ensure that concurrent transmissions can always outperform serial TDMA transmissions in a mmWave WPAN. We then propose REX, a randomized ER based scheduling scheme, to decide a set of senders that can transmit simultaneously. In addition, the expected number of flows that can be scheduled for concurrent transmissions is obtained analytically. Extensive simulations are conducted to validate the analysis and demonstrate the effectiveness and efficiency of the proposed REX scheduling scheme. The results should provide important guidelines for future deployment of mmWave based WPANs.

Millimeter-wave (mmWave) communication technologies can achieve up to several gigabit/sec data rate over a small range, using directional antenna. To enable high data rate wireless connectivity in a large area, a multi-hop routing protocol is needed. The rate-adaptiveness of mmWave link and the use

In wireless multihop ad hoc networks, the use of the RTS/CTS mechanism does not completely eliminate the hidden-terminal problem. Considering the hidden-terminal problem adds complexity to the existing analysis for single-hop networks. In this paper, we provide precise and accurate analytical models for quantifying the throughput and delay in wireless multihop ad hoc networks. The proposed analysis is applicable to many wireless MAC protocols and applications. The accuracy of our analytical models are verified by extensive NS-2 simulations. Our analysis reveals how the throughput and delay in wireless multihop ad hoc networks are affected by the hidden-terminals and by the transmission and interference ranges of wireless devices. These results are important for network planning and protocol optimization in wireless multihop ad hoc networks.

Orthogonal frequency-division multiplexing (OFDM) is a promising technique for high data rate wireless access networks. Modeling OFDM systems for the analysis of network performance is very challenging, because of the complexity of the modulation/coding schemes and the wideband wireless channel fading in both the time and frequency domains. In this paper, a novel packet-level model based on a two-dimensional Markov chain is proposed for OFDM systems over time-varying (Nakagami-m fading), frequency-selective channels. First, the level cross rate (LCR) of the amplitude of channel frequency response is derived. Then, we develop a methodology to map the received signal-to-noise ratio (SNR) of the subcarriers into a finite number of channel states with different packet error rate (PER). The proposed model presents directly the performance of the OFDM systems and incorporates the time- and frequency-domain correlations of the fading channels. Channel coding is also considered in evaluating PER. Simulations have verified that the statistics of the BER presented by our model are consistent with those of waveform simulations. The proposed Markov model can be an effective tool to study and optimize upper-layer protocols of OFDM-based wireless networks, via both analysis and simulation.

Ultra-wideband (UWB) communication is a promising enabling technology for future broadband wireless services. A simple, scalable, distributed, efficient medium access control (MAC) protocol is of critical importance to utilize the large bandwidth UWB channels and enable numerous new applications and services cost-effectively. In this paper, by investigating the characteristics of UWB communications, we propose a Distributed, EXclusive region (DEX) based MAC protocol. The proposed DEX protocol capitalizes on the spatial multiplexing gain of UWB networks by reserving exclusive regions (ER) surrounding the sender and receiver for data and acknowledgment (ACK) transmissions, so that users can efficiently and fairly share network resources in a distributed and asynchronous manner. We further quantify the network performance bounds and derive the optimal ER size to maximize the expected network transport throughput for a dense, multi-hop UWB network. Extensive simulation results demonstrate the efficiency and effectiveness of the DEX protocol. This work explores how to effectively utilize the wireless spatial capacity of distributed, multi-hop wireless networks by optimizing protocol parameters, instead of depending on more complicated control messages.

In sensor networks, both energy and bandwidth are scarce resources. In the past, many energy efficient routing algorithms have been devised in order to maximize network lifetime, in which wireless link bandwidth has been optimistically assumed to be sufficient. This article shows that ignoring the bandwidth constraint can lead to infeasible routing solutions. As energy constraint affects how data should be routed, link bandwidth also affects not only the routing topology but also the allowed data rate on each link. In this paper, we discuss the sufficient condition on link bandwidth that makes a routing solution feasible, then provide mathematical optimization models to tackle both energy and bandwidth constraints.We first present a basic mathematical model to address using uniform transmission power for routing without data aggregation, then extend it to handle nonuniform transmission power, and then routing with data aggregation. We propose two efficient heuristics to compute the routing topology and link data rate. Simulation results show that these heuristics provide more feasible routing solutions than previous work, and provide significant improvement on throughput and lifetime.

Ultra-wideband (UWB) wireless communication technologies have been proposed to support high data rate multimedia services in office or residential environments. Due to the low transmission power of UWB, the shadowing effect by moving people can considerably reduce the received signal quality and thus significantly degrade the quality of service (QoS) of on-going transmissions. An open issue is to build a simple model which captures the temporal variation of UWB channels and the packet error rate (PER) due to the people shadowing effect (PSE), which will be a useful tool for upper layer protocol performance analysis and simulation. This paper presents an analytical study of the PSE and the temporal variation of UWB channels induced by the motion of a person. First, we derive the angular power spectral density (APSD) of the indoor UWB channel impulse response (CIR), and the PSE in terms of signal power attenuation. Second, based on a two-dimensional random walk mobility model, the PER variation due to people shadowing is modeled as a finite-state Markov chain (FSMC). The investigation of APSD provides important insights on the spatial propagation characteristics of UWB signals. The proposed packet-level channel model can be conveniently incorporated into analytical frameworks and simulation tools for evaluating upper-layer protocols of UWB networks.

In sensor networks, both energy and bandwidth are scarce resources. In the past, the energy efficient routing problem has been vastly studied in order to maximize network lifetime, but link bandwidth has been optimistically assumed to be abundant. As energy constraint affects how data should be routed, link bandwidth also affects not just the routing topology but also the allowed data rate on each link, which in turn affects lifetime. Previous works that focus on energy efficient operations in sensor networks with the sole objective of maximizing network lifetime only consider the energy constraint and ignore the bandwidth constraint. This article shows how infeasible these solutions could be if bandwidth does become a constraint, then provides a new mathematical model to tackle both energy and bandwidth constraints. Two efficient heuristics are proposed based on this model; Simulation results show these heuristics provide more feasible routing solutions than previous works, and provide significant improvement on throughput.

It has been shown that the radio channel impulse response for a pair of legitimate Ultra-wide band (UWB) transceivers can be used to generate secret keys for secure communications. Past proposed secret key generation algorithms under-exploited the available number of secret key bits from the radio channel. This paper proposes a new efficient method for generation of the shared key where the transceivers use LDPC decoders to resolve the differences in their channel impulse response measurements caused by measurement noise. To ensure secret key agreement, a method of public discussion between the two users is performed using the syndrome from Hamming (7,3) binary codes. An algorithm is proposed to check the equality of generated keys for both legitimate users, and ensure error-free secure communication. The security of this algorithm has been verified by AVISPA. Comparisons are performed with previous work on secret key generation and it has been shown that this algorithm reliably generates longer secret keys in standard UWB radio channels.

The use of directional antennae in ad hoc networks has received growing attention in recent years. However, most existing directional MAC protocols assume interchangeable directional and omnidirectional modes of operation. Such operation reduces the spatial gain and introduces the asymmetry-in-gain problem. In this paper, we propose a directional-to-directional (DtD) MAC protocol for ad-hoc networks that operates in the directional mode exclusively. The protocol is fully distributed, does not require any synchronization, eliminates the asymmetry- in-gain problem, and alleviates the deafness problem. To study the performance of the proposed DtD MAC, we develop an analytical model that estimates the saturation throughput as a function of the number of antenna sectors, packet size and number of contending nodes. The analytical results are validated by extensive simulations with the QualNet simulator. We show that the DtD MAC protocol is practical to take the advantage of directional antennae to improve network throughput and achieve better fairness in ad-hoc networks.

To facilitate network performance analysis and simulations for IPTV traffic, a two-level Markovian traffic model is proposed in the paper. The model considers both spatial and temporal correlation in MPEG encoded video sequences, so it can mimic the highly variable data rate (VBR) behavior of IPTV sources. The model contains a Group of Pictures (GoP)- level Markov chain and a frame-level Markov chain, so it can capture both the inter-GoP and intra-GoP correlations. The proposed traffic model is simple to incorporate into network simulators, and can be used to obtain closed-form solutions of queue performance. Extensive simulations have been conducted to compare the network performance using the proposed model with the performance of a variety of real video traces. The results show that the accuracy of the proposed video source model is sufficient for the study of network performance. Therefore, it is an effective tool for performance evaluation of IPTV services via analysis and/or simulation.

Delayed acknowledgment (Dly-ACK) and packet fragmentation are link-layer policies for ultra-wideband (UWB) based wireless personal area networks (WPANs) to improve the channel utilization, defined in both the IEEE 802.15.3a and ECMA-368 standards. On the other hand, the shadowing effect caused by people moving between the transmitter and receiver may severely degrade the received signal power and thus introduce channel variation. In this paper, we develop an analytical framework for studying the performance of the Dly-ACK and fragmentation over UWB fading channels. A Markov model is used to capture the time-variation of the UWB shadowing channel. The distribution of transmission delay of fragmented packets and the queuing behavior of the sender's buffer are derived. The system performance of packet delay and loss are obtained. Validated by simulations, the analytical results provide important insights and guidelines for better supporting high data rate, delay sensitive traffic in UWB-based WPANs.

In wireless networks, the physical layer adaptive modulation and coding (AMC) scheme has been proposed to improve bandwidth efficiency over the time-varying channel. In this paper, we study the performance of ultra-wideband (UWB) based wireless personal area network where AMC is coupled with the distributed reservation protocol (DRP) and the delayed- acknowledgement (Dly-ACK) schemes at the link layer. Considering the channel variation caused by the people shadowing effect, we first propose an analytical model using an embedded Markov chain to investigate the queuing behavior at sender's buffer. Second, the throughput optimization problem is formulated and the optimal transmission mode and payload length are obtained. Simulation results are given to validate the analysis. By jointly considering channel characteristics, physical layer and link layer transmission schemes, the analytical results of the paper can provide useful guidelines for cross-layer optimization, which is essential to ensure quality of services in UWB networks.

Ultra-wideband (UWB) technology is expected to provide high data rate services for future wireless personal area networks (WPANs). The WiMedia Alliance recently has launched its standard for UWB-based WPANs, where the distributed reservation protocol (DRP) is specified to allow the channel time being reserved in a distributed manner. In view of the urgent need of using DRP to support high data rate multimedia applications, we investigate the delay performance of DRP in this paper. Since the negotiation of channel time is fully distributed without centralized coordination, the reserved channel time may be non-evenly spaced. In addition, the channel dynamics due to shadowing that is notable in indoor environments can greatly affect the protocol performance. In this paper, we study the delay performance of DRP under different reservation patterns and take into account the dynamics of UWB shadowing channel. The system is modeled as a discrete-time single server queue with vacation, which can be represented by the quasi-birth and death (QBD) process and solved by the well-established matrix-geometric approach. We use numerical results to validate the accuracy of the mathematical modeling. The proposed analytical model can be useful to understand the actual performance of DRP, thereby further performance improvement can be guided.

Telecommunication service providers are racing to deliver IPTV/video on demand (VoD), voice, and data, the so called triple-play services. IPTV traffic, supported by the UDP protocol, has highly variable data rates and stringent quality of services (QoS) requirements in terms of delay and loss. Data and VoD flows are normally supported by TCP, which has its own congestion control loop to adjust the sending rate, so the traffic load is also highly dynamic. If IPTV and TCP traffic is simply multiplexed, their performance is difficult to predict and the competition between them will jeopardize their QoS. To efficiently utilize network resources and provide satisfactory QoS for both traffic types, we propose multiplexing IPTV and TCP traffic with the protection of a class based queuing (CBQ) scheme. We also develop an analytical framework to model the multiplexed IPTV traffic and TCP traffic with CBQ. The analytical results can be used as a guide to determine the admission region of IPTV and the CBQ parameters. Simulation results are presented which validate the analytical results and demonstrate the effectiveness of the proposed solution. By multiplexing IPTV and TCP traffic appropriately, network resources can be more efficiently utilized, the QoS of IPTV can be maintained, and TCP flows can obtain higher throughputs.

The Additive Increase and Multiplicative Decrease (AIMD) congestion control algorithm of TCP protocol deployed in the end systems and the Random Early Detection (RED) queue management scheme deployed in the intermediate systems contribute to Internet stability and integrity. Previous research based on the fluid-flow model analysis indicated that an AIMD/RED system may not be asymptotically stable when the feedback delays or the link capacity becomes large [3]. However, as long as the system operates near its desired equilibrium, small oscillations are acceptable and the network performance is still satisfactory. Deriving the bounds of these oscillations for the heterogeneous AIMD/RED system with time delays is non-trivial. In this paper, we study the practical stability of the AIMD/RED system with heterogeneous flows and feedback delays, and obtain theoretical bounds of the AIMD flow window size and the RED queue length, as functions of number of flows, link capacity, RED queue parameters, and AIMD parameters. Numerical results with Matlab and simulation results with NS-2 are given to validate the correctness of the theorems and demonstrate the tightness of the derived bounds. The analytical and simulation results provide important insights on which system parameters contribute to higher oscillations of the system and how to set system parameters to ensure system efficiency with bounded delay and loss.

By considering the characteristics of Ultra- wideband (UWB) communications networks, ie., short transmission range, accurate ranging, and low transmission/interference power, we propose a Distributed, Exclusive region (DEX) based MAC protocol for multi-hop UWB based wireless networks. DEX can effectively explore the spatial multiplexing gain of UWB networks and allow users to efficiently and fairly share network resources in a distributed manner by reserving exclusive regions (ER) around the sender and receiver for data and acknowledgment (ACK) transmissions. We further quantify the network performance bounds and derive the optimal ER size to maximize the expected network transport throughput for a dense multi-hop UWB network. Extensive simulation results demonstrate the efficiency and effectiveness of the DEX protocol.

Emerging broadband entertainment applications such as IPTV (Internet Protocol Television) and whole-house PVR (Personal Video Recorder) bring new challenges to existing home networks. Gigabit Ethernet is an obvious choice, but consumers are still reluctant due to the need for rewiring in most dwellings. Several “no-new-wires” technologies have been proposed in recent years, but there is little work on how to distribute IPTV, VoIP (Voice over IP) and data traffic together effectively and efficiently in a household environment. In this paper, we propose a wireless/wired-hybrid, multi-link structure for broadband home networks, and investigate its feasibility and performance through a multimedia over multi-link testbed. Our measurement study shows that the proposed multi-link structure can improve the performance, reliability and availability of home networks considerably, indicating that it is an attractive approach to multimedia in-home distribution. In addition, the paper also discusses the challenges and approaches in further improving the performance of heterogeneous, multi-link home networks.

Communications at 60GHz millimeter-wave (mmWave) band is a promising technology for future wireless personal area networks (WPAN) supporting high data rate applications. Internet Protocol TV (IPTV) is anticipated to be one of the next killer applications, which requires high data rate and st

With the capability of supporting very high data rate services in a short range, ultra-wideband (UWB) technology is appealing to multimedia applications in future wireless personal area networks (WPANs) and broadband home networks. However, the WPAN medium access control (MAC) protocol in IEEE 802.15.3 standard was originally designed for narrowband communication networks, without considering any specific features of UWB. In this paper, we explore the unique characteristics of UWB communications from which a sufficient condition for scheduling concurrent transmissions in UWB networks is derived: concurrent transmissions can improve the network throughput if all senders are outside the exclusive regions of other flows. We also study the optimal exclusive region size for a UWB network where devices are densely and uniformly located. Since the optimal scheduling problem for peer-to-peer concurrent transmissions in a WPAN is NP-hard, the induced computation load for solving the problem may not be affordable to the network coordinator, commonly a normal UWB device with limited computational power. We propose two simple heuristic scheduling algorithms with polynomial time complexity. Extensive simulations with random network topology demonstrate that, by exploiting the unique characteristics of UWB communications and allowing concurrent transmissions appropriately, the proposed exclusive-region based scheduling algorithms can significantly increase the network throughput.

By integrating wireless wide area networks (WWANs) and wireless local area networks (WLANs), mobile hotspot technologies enable seamless Internet multimedia services to users on-board a vehicle. In this paper, we investigate the performance of TCP-Friendly Rate Control (TFRC) protocol supporting multimedia services in mobile hotspots. To quantify the throughput of TFRC flows in mobile hotspots, we first develop discrete-time queuing models for the WWAN link and the WLAN link. We then derive the steady state TFRC throughput using an iterative algorithm. Analytical and extensive simulation results reveal how the end-to-end TFRC throughput is affected by the number of users in a mobile hotspot, the vehicle velocity, the WWAN/WLAN link bandwidth, the retransmission limit, and the buffer size. It is found that the WWAN channel profile and link bandwidth have significant impacts on the TFRC throughput, and therefore suitable resource allocation and admission control are indispensable for the quality and efficiency of multimedia services in mobile hotspots.

In this paper, the performance of a hybrid medium access protocol in IEEE 802.15.3 has been studied. An analytical model has been developed for the coordination between the contention access period (CAP) and the contention-free channel time allocation period (CTAP). Given the traffic characteristic and the number of contending devices, the piconet coordinator (PNC) determines the duration of the CAP. Based on the successfully received requests and the scheduling scheme, the PNC decides the duration of the CTAP. Extensive simulations are performed to validate our analysis.

An analytical model is proposed for the perfor- mance study of IEEE 802.11 distributed coordination function (DCF) with finite traffic load. Based on the model, average medium access control (MAC) sublayer service time of a frame and channel throughput are obtained. The model is further extended for the performance analysis of DCF with mixed voice and data traffic. The maximum number of voice connections supported in IEEE 802.11 WLAN under various background data traffic is derived. The results are useful for effective call admission control in IEEE 802.11 WLAN. Extensive simulations are performed to validate our analysis. I. INTRODUCTION The IEEE 802.11 standard (1) has been widely deployed around the world. Its medium access control (MAC) sublayer specifies two modes, the mandatory distributed coordination function (DCF) and the optional point coordination function (PCF). DCF is a distributed random access mechanism that is suitable for ad hoc networks, while PCF is a centralized polling based mechanism that can only work in infrastructure- based wireless LANs (WLANs). Due to its inefficient polling schemes and limited Quality-of-Service (QoS) provisioning, PCF is not widely implemented in practice. Therefore, in this paper, we study the performance of the dominant DCF in various scenarios. To date most research work in the literature (e.g., (2)-(4)) focuses on the study of DCF performance in the saturation case, in which every station in the network always has frames waiting for transmission. However, when there are more than In this paper, we first propose an analytical model to study the DCF throughput and average MAC service time under various load conditions for a single traffic type. It is based on the fundamental relationship between the mean MAC service time and the mean traffic arrival rate, and thus applicable to general traffic arrival processes. The proposed model improves the one in (10) in several aspects such as more accurate calculation of the average backoff time and the average number of transmission trials of a frame. Moreover, by comparing the obtained average MAC service time for a frame with the given average frame inter-arrival time, whether or not a station is in the saturated state can be accurately determined with the proposed model. The maximum number of stations that can be supported in such a network is also obtained. This information is critical to the design of admission control schemes that are usually adopted for QoS support in a network. It is worthy to note that this information cannot be readily obtained from the analysis of a saturation case. As VoIP over WLAN becomes more and more popular, it is instructive to study analytically the performance of DCF in a WLAN with mixed voice and data traffic. However, little work on this thread has been reported. In this paper, we carefully extend the proposed model to study the performance of DCF in such a situation. Using the extended model, the maximum number of voice stations that can be supported in the presence of a certain amount of data traffic can be obtained. On the other hand, the data throughput can also be obtained, given the number of voice stations in the WLAN. The rest of the paper is organized as follows. The IEEE 802.11 DCF is briefly reviewed in Section II. Section III presents the proposed analytical model for a single traffic type. Section IV extends the model to mixed voice and data traffic. Numerical results of both analysis and simulations for the two scenarios are given in Section V. Finally, we conclude the paper in Section VI.

Ultra-Wideband (UWB) communication is a promising technology for high data rate multimedia services in future wireless personal and home entertainment networks. To support heterogeneous multimedia applications with a wide variety of QoS requirements and maintain fairness among various traffic classes, a utility-based optimal scheduling problem for UWB networks is formulated. To fully explore the wide spectrum of UWB, its unique characteristics and features, such as the potential of allowing simultaneous transmissions and precision positioning, are considered for scheduling. The optimal schedul- ing problem of UWB network is significantly different from that in traditional networks. In particular, each link proceeds in a peer-to-peer manner, and the aggregate utilities achieved by con- current transmissions are random in nature. Thus, the optimal scheduling is formalized as a discrete stochastic optimization problem. We propose an exclusive-region based global searching algorithm (ER-GSA) to locate the global optimum efficiently. Extensive simulations demonstrate the effectiveness and efficiency of the ER-GSA.

In this paper, we investigate the unique characteristics of millimeter-wave (mmWave) communications and propose an exclusive region (ER) based resource management scheme to explore the spatial multiplexing gain of mm Wave WPANs. We develop an analytical model to study the performance of mm Wave WPANs in terms of the average number of concurrent transmissions and the spatial multiplexing capacity, considering the use of omni-directional and directional antennae. Extensive simulations are conducted to demonstrate the accuracy of the analytical model and the efficiency of the ER based resource management scheme. The analysis and simulation results should provide important guidelines for future deployment of mm Wave based WPANs. Our findings can also be extended to other wireless communications networks in general.

A heterogeneous wired and wireless network architecture is proposed for in-home IPTV distribution. To identify the bottleneck in the home network and estimate the network capacity, we develop an analytical framework to quantify the maximum number of IPTV connections that can be supported with guaranteed QoS in the wired and multi-hop wireless networks, respectively. We extend the fluid flow model analysis to capture both the burstiness of IPTV sources and the time-varying characteristics of multi-hop wireless channels. Extensive NS-2 simulations with H.264 HDTV sources over wired and multi- hop wireless paths are given, which validate the analysis. The analytical and simulation results provide important guidelines for the planning of future home networks and IPTV systems.

In this paper, we systematically study the stability of Additive Increase and Multiplicative Decrease (AIMD)/Random Early Detection (RED) system, considering heterogeneous flows and feedback delays. By applying the methods of Lyapunov functional and Lyapunov function with Lyapunov-Razumikhin condition to the fluid model of the generalized AIMD/RED system, we obtain sufficient conditions to guarantee local asymptotic stability of the system with heterogeneous feedback delays. Our study also reveals the relationship between the AIMD parameters and the average window size of competing AIMD flows. Consequently, the TCP-friendly condition is derived. Numerical results with Matlab and simulation results with NS-2 are given to validate the analytical results. The analysis and the stability conditions derived can be used as a guideline to set up the AIMD/RED system parameters in order to maintain network stability and integrity, and to enhance system performance.

In this paper, we develop a novel packet-level channel model for orthogonal frequency-division multiplexing (OFDM) systems over frequency-selective Nakagami-m fading channel. Since the subcarriers are correlated, the Level Cross Rate (LCR) in frequency response is introduced and derived. A complete model including finite state Markov chains in both the time domain and the frequency domain is proposed for the multi- carrier system, which models the received signal to noise ratio (SNR) of each subcarrier and further presents the packet error behavior of the OFDM system. This model is useful for upper layer protocol design and analysis over OFDM systems, and it speeds up significantly the simulation for OFDM networks over fading channels. The model is validated by simulations, which confirm that the statistical properties of the frequency-selective fading channel have been maintained in our model.

We study how to appropriately match users for two-user cooperative diversity systems that deploy optimal power allocation for anamplifyandforwardor aregenerateandforwardCD scheme. Optimized power allocation strategies, which minimize the total energy consumption for the cooperating pair, are proposed for both CD schemes. A novel matching algorithm with less computational complexity than, but with performance very close to the state-of-the-art optimal maximal weighted matching algorithm is developed. Numerical results show that, with optimal power allocation, the proposed matching algorithm can achieve 9 ~ 10 dB cooperative diversity gain, which is equivalent to prolonging the cell phone battery recharge time by 10 times.

In an indoor ultra-wideband (UWB) communication environment, the line-of-sight (LOS) between the transmitter and receiver may be frequently blocked by moving people. Blocking of LOS may significantly affect the quality of service (QoS) of on-going UWB communications. Based on the Angular Power Spectrum and the human blocking models, we build a packet-level UWB channel model considering the shadowing processes. The model is simple enough to be incorporated into existing network simulators and it can be used to facilitate protocol design and QoS analysis for UWB based wireless personal area networks (WPANs).

IEEE 802.15.3c has recently been formed for developing a millimeter-wave (mmWave)-based alternative physical layer (PHY) for the existing 802.15.3 wireless personal area network (WPAN) standard, using the unlicensed 57-64 GHz band. However, the existing resource management schemes are inherently inefficient and insufficient for mmWave-based WPANs, without the consideration of the unique features of mm Wave communications: high Oxygen absorption rate and atmospheric attenuation, limited communication range, stringent power control for unlicensed usage, and the use of directional antennae. In this paper, by capturing the unique physical characteristics of mm Wave communications and based on the use of omni-or directional antennae, we derive the exclusive regions (ER) to allow efficient concurrent transmissions and develop an ER based scheduling algorithm to improve the network throughput of mm Wave based WPANs by several folds. Extensive simulations are conducted to demonstrate the effectiveness and efficiency of the proposed ER scheduling algorithm. The analysis and simulation results can provide important guidelines for future deployment of mm Wave based WPANs.

In this paper, we develop a novel analytical framework for modeling and quantifying the performance of window-controlled multimedia flows in a hybrid wireless/wired network. The framework captures the traffic characteristics of window-controlled flows and is applicable to various wireless links and packet transmission schemes. We show analytically the relationship between the sender window size, the wireless link throughput distribution, and the delay distribution. We then substantiate the analysis by demonstrating how to statistically bound the end-to-end delay of flows controlled by a TCP-like datagram congestion control protocol (DCCP) over an M-state Markovian wireless link. Simulation results validate the analysis and demonstrate the effectiveness and efficiency of the proposed delay control scheme. The scheme can also be applied to other window-based transport layer protocols

We are concerned with optimally grouping active mobile users in a two-user-based cooperative diversity system to maximize the cooperative diversity energy gain in a radio cell. The optimization problem is formulated as a non-bipartite weighted-matching problem in a static network setting. The weighted-matching problem can be solved using maximum weighted (MW) matching algorithm in polynomial time O(n3). To reduce the implementation and computational complexity, we develop a Worst-Link-First (WLF) matching algorithm, which gives the user with the worse channel condition and the higher energy consumption rate a higher priority to choose its partner. The computational complexity of the proposed WLF algorithm is O(n) while the achieved average energy gain is only slightly lower than that of the optimal maximum weighted- matching algorithm and similar to that of the 1/2-approximation Greedy matching algorithm (with computational complexity of O(n2log n)) for a static-user network. We further investigate the optimal matching problem in mobile networks. By intelligently applying user mobility information in the matching algorithm, high cooperative diversity energy gain with moderate overhead is possible. In mobile networks, the proposed WLF matching algorithm, being less complex than the MW and the Greedy matching algorithms, yields performance characteristics close to those of the MW matching algorithm and better than the Greedy matching algorithm.

Mobile hotspot enabling Internet access services in moving vehicles is an important service for ubiquitous computing. In this paper, we propose an analytical framework for studying the packet loss behavior and throughput in a mobile hotspot with heterogeneous wireless links. We first develop a two-state Markov model for the integrated wireless wide area network (WWAN) and wireless local area network (WLAN). We then derive the expressions that describe the experienced packet loss probability, packet loss burst length, and throughput. Finally, we present simulation results to verify the accuracy of our analysis. It is concluded that adaptive and cross-layer approaches should be deployed to improve the performance of mobile hotspots.

In IEEE 802.11 WLANs, packet losses may be due to buffer overflow, transmission errors, or collisions. Therefore, the performance of TCP-Friendly Rate Control (TFRC) in IEEE 802.11 WLANs largely depends on its ability to differentiate packet losses resulting from network congestion (due to buffer overflow and collisions) and those from transmission errors. In this paper, an enhanced TFRC (E-TFRC) protocol is proposed to detect and identify the cause of packet loss events through a novel two-phase loss differentiation algorithm (TP-LDA). The packet losses due to buffer overflow and those due to failed transmissions in WLANs are first differentiated. For failed transmissions, the fraction of those due to collisions is obtained with the assistance of the lower layer. By employing TP-LDA, only the packet losses due to buffer overflow and collisions are notified to the sender for appropriate flow and congestion control. To quantify the performance of TFRC and E-TFRC over WLANs, a continuous-time Markov chain based on a new WLAN link model is developed by considering both collisions and transmission errors. Analytical and simulation results demonstrate that, with appropriate loss differentiation, E-TFRC can achieve higher throughput than TFRC in WLANs with different channel profiles.

This paper presents an analytical model for evaluating the capability of wireless LANs (WLANs) to provision quantitative quality of service (QoS) guarantees. We consider a distributed medium access control (MAC) with class differentiation, where mobile nodes belonging to different classes may have heterogeneous traffic arrival processes or different contention windows. With on/off inputs, our analysis shows that the WLAN admission region under the QoS constraint can be significantly improved, when the statistical multiplexing effect is taken into account. Moreover, the statistical multiplexing gain can be further improved by aggregating the downlink flows at the access point (AP). We also demonstrate that the proper selection of contention windows plays an important role in improving the WLAN QoS capability, while the optimal contention window for each class and the maximum admission region can be jointly solved in our analytical model.

With its capability of supporting high data rate services in a short range, the ultra-wide band (UWB) technology is appealing for future wireless personal area networks (WPANs). However, the WPAN medium access control (MAC) protocol in IEEE 802.15.3 standard was originally designed for narrow band communication networks, and it is inherently inefficient for UWB networks. In this paper, we explore the unique characteristics of UWB communications and propose how to schedule concurrent transmissions in UWB networks, which can significantly improve efficiency and network capacity. Since the optimal scheduling problem for peer-to-peer concurrent transmissions is NP-hard, the induced computation load for solving the problem is not affordable to the network coordinator, commonly a normal UWB device with limited computation power and energy. We propose two simple heuristic scheduling algorithms with polynomial time complexity. Extensive simulations with random network topology demonstrate that, by allowing concurrent transmissions appropriately, the proposed scheduling algorithms can significantly increase the network throughput.

Mobile hotspot enabling Internet access services in moving vehicles is an important service for ubiquitous computing. In this paper, we propose an analytical framework for studying the packet loss rate and throughput in a mobile hotspot with heterogeneous wireless links. We present simulation results to verify the accuracy of our analysis. It is concluded that adaptive and cross-layer approaches should be deployed to improve the performance of mobile hotspots.

We analyze the capacity of UWB networks supporting multimedia services by calculating the number of multimedia connections that can be supported in a UWB network based on IEEE 802.15.3 Medium Access Control (MAC) protocol, taking into consideration the overheads from different layers. We then propose how to increase the capacity by improving the MAC protocol design. To fully explore the potential of UWB technologies which favor concurrent transmissions if the interference is appropriately controlled, we study the capacity of cellular-like UWB networks. Our findings, which should provide important guidelines for UWB network planning, are a) the inter-cell interference of UWB networks is closely related to the Riemann Zeta function, and to guarantee the bounded inter-cell interference of UWB networks, the path loss exponent α must be larger than 2; b) the total throughput in an area is a concave function of the cell size; c) the best distance between adjacent cells is a function of path loss exponent, background noise level, and cross-correlation of the target signal and the interfering signal; and d) with the optimal cell size, a single flow's throughput is reduced by 2/α due to inter-cell interference. Simulation results are given to demonstrate the accuracy of the analysis.

We study how to optimally group active users in an infrastructure-based wireless network employing two-user- based cooperative diversity technology in order to maximize the cooperative diversity gain in the network. The optimization problem is formulated as a non-bipartite weighted-matching problem, which can be solved with the state-of-the-art maximum weighted-matching algorithm in polynomial time O(n 3 ) .T o reduce the computational complexity, we develop a Worst-Link- First (WLF) matching algorithm, which gives the user with the worse channel condition and the higher energy consumption rate a higher priority to choose its partner. The computational complexity of the proposed WLF algorithm is O(n 2 ) while the achieved average energy gain is only slightly lower than that with the optimal maximum weighted-matching algorithm. Numerical results demonstrate that, with the WLF matching algorithm, 5 ∼ 7 dB energy gain can be achieved.

We propose a TCP-friendly Additive Increase and Multiplicative Decrease (AIMD) based Datagram Congestion Control Protocol (DCCP) protocol for supporting multimedia traffic in hybrid wireless/wired networks. We further demonstrate how to select the protocol parameters to fairly and efficiently utilize network resources with the consideration of quality of service (QoS) requirements. Since the parameter selection procedure requires only the exchange of parameters among the application, the transport layer protocol, and the link layer protocol, our approach preserves the end-to-end semantics of the transport layer protocol and the layered structure of the Internet. Extensive simulations are performed to evaluate the proposed protocol. It is shown that the AIMD protocol can appropriately regulate multimedia traffic to efficiently utilize the wireless link and fairly share the network resources with coexisting TCP flows, and it can provide satisfactory QoS for delay-sensitive multimedia applications. In addition, AIMD protocol can outperform the non-responsive User Datagram Protocol (UDP) when transporting multimedia traffic over hybrid wireless/wired networks. With satisfactory QoS provisioning, end-systems have more incentives to voluntarily regulate multimedia traffic with an AIMD-based congestion controller, which is vital for network stability, integrity, and future proliferation.

In this paper, an analytical framework is proposed for evaluating the quality of service (QoS) of TCP-friendly rate control protocol (TFRC) in hybrid wireless/wired networks. For the wireless network, a link-level truncated automatic repeat request (ARQ) scheme is deployed to reduce the packet losses visible to the transport layer protocol. Two discrete time Markov chains (DTMC) are introduced to analyze the network performance and the QoS of the TFRC protocol, in terms of wireless link utilization, flow throughput, packet loss rate, and probability of end-to-end delay exceeding a prescribed threshold. Extensive simulations are performed to verify the accuracy of the analytical results. It is concluded that the analytical results are useful for optimizing the system parameters, such as the interface buffer size and the maximum number of retransmissions of the truncated ARQ scheme. With the optimal parameters, wireless link utilization can be maximized, and the QoS requirements of multimedia applications can be statistically guaranteed.

Distributed denial-of-service attacks pose a serious threat to today's Internet. To counter these attacks, many IP traceback schemes have been proposed; among them, distance-indexed probabilistic packet marking and its variants are attractive due to their stateless, low-overhead and incrementally-deployable design. However, some schemes may become vulnerable in practice, and the implication is yet to be quantified. In this paper, we first reveal these vulnerabilities. Sustained by efficacy analysis and numerical results, we then design several exploits that allow attackers to take full advantage of these vulnerabilities. We also examine the causes of these vulnerabilities as well as possible remedies, and discuss the distance-related buffer overflow in the context of network protocols.

A TCP-friendly additive increase and multiplicative decrease (AIMD) protocol is proposed to support time-sensitive applications in hybrid wired/wireless networks. By analyzing the performance of AIMD-controlled flows in hybrid networks, we propose a cross-layer procedure to select the AIMD protocol parameters with consideration of wireless link characteristics and application QoS requirements, in terms of delay, loss, and throughput. Since the cross-layer interaction only exchanges parameters among the application, the transport layer protocol, and the link layer protocol, our approach preserves the end-to-end semantics of the transport protocol and the layered structure of the Internet, and it is applicable to supporting various multimedia applications over heterogeneous wireless links. With appropriate parameters, AIMD-controlled flows can fairly share network resources with TCP flows, efficiently utilize wireless resources, and statistically guarantee end-to-end delay for time-sensitive applications. Extensive simulations are performed to validate the analytical results, evaluate the protocol performance, and demonstrate that the AIMD protocol can outperform the unresponsive UDP protocol when transporting multimedia traffic in hybrid networks. With satisfactory QoS, end systems have more incentives to voluntarily regulate multimedia traffic with an AIMD-based congestion controller, which is vital for network stability, integrity, and future proliferation.

We evaluate the performance of voice transmission over a single-AP WLAN analytically and via simulation. Given the parameters of the medium access control protocol and different voice codecs, the voice capacity of the WLAN, in terms of the maximum number of voice connections that can be supported by the WLAN with satisfactory user-perceived quality, is obtained. Our analysis is applicable for unsaturated-station scenarios, and considers the practical issue induced by the unbalanced traffic. Extensive simulations have been performed to validate the analytical results.

We consider generic two-tiered wireless sensor networks (WSNs) consisting of sensor clusters deployed around strategic locations, and base-stations (BSs) whose locations are relatively flexible. Within a sensor cluster, there are many small sensor nodes (SNs) that capture, encode, and transmit relevant information from a designated area, and there is at least one application node (AN) that receives raw data from these SNs, creates a comprehensive local-view, and forwards the composite bit-stream toward a BS. This paper focuses on the topology control process for ANs and BSs, which constitute the upper tier of two-tiered WSNs. Since heterogeneous ANs are battery-powered and energy-constrained, their node lifetime directly affects the network lifetime of WSNs. By proposing algorithmic approaches to locate BSs optimally, we can maximize the topological network lifetime of WSNs deterministically, even when the initial energy provisioning for ANs is no longer always proportional to their average bit-stream rate. The obtained optimal BS locations are under different lifetime definitions according to the mission criticality of WSNs. By studying intrinsic properties of WSNs, we establish the upper and lower bounds of maximal topological lifetime, which enable a quick assessment of energy provisioning feasibility and topology control necessity. Numerical results are given to demonstrate the efficacy and optimality of the proposed topology control approaches designed for maximizing network lifetime of WSNs.

In this paper, the performance of TCP-friendly generic AIMD (Additive Increase and Multiplicative Decrease) algorithms for Web-based playback and multirate multimedia applications is investigated. The necessary and sufficient TCP-friendly condition is derived, and the effectiveness and responsiveness of AIMD are studied. Due to practical implications, a Dynamic TCP-friendly AIMD (DTAIMD) algorithm is proposed. Extensive simulation results are given to verify the derived necessary and sufficient condition, and to demonstrate the performance of the proposed DTAIMD algorithm.

The equation based TCP-friendly rate control protocol (TFRC) has been proposed to support multimedia applications. In this paper, an analytical approach is proposed for quantifying the performance of equation based TFRC over wireless/Internet integrated networks. Specifically, discrete time Markov chains (DTMC) are developed to analyze the performance of TFRC-controlled multimedia flows, in terms of wireless resource utilization, flow throughput, packet loss rate, and probability of end-to-end delay exceeding a prescribed threshold. It is shown that the maximum number of link layer retransmissions and the required base station buffer size can be determined based on the application's QoS requirements.

End-to-end delay and delay jitter are critical QoS (quality of service) parameters for time sensitive applications. In wireless/IP hybrid networks, the wireless link is presumably the bottleneck. Because of the time-varying and error prone channel, the wireless channel throughput is random. On the other hand, with closed-loop AIMD (additive increase and multiplicative decrease) congestion control, the arrival process to a queue is not on-off. In this paper, given the measured channel profile and the transmission scheme used in the link layer, the wireless channel throughput distribution is derived. By analyzing the queue in the wireless domain with an AIMD controlled flow, the queuing delay distribution is derived. It is found that by appropriately setting the protocol parameters for TCP-friendly AIMD flows, the wireless spectrum can be efficiently utilized, and the QoS requirements of time sensitive applications can be statistically guaranteed.

To guarantee network stability while supporting multimedia applications over the Internet, a new dynamically adjusted TCP-friendly additive increase and multiplicative decrease (AIMD) congestion control algorithm, DTAIMD, is proposed. By studying the competition behavior of TCP and AIMD flows, the TCP-friendly condition for the AIMD (/spl alpha/, /spl beta/) congestion control is analytically derived. Quality of service (QoS) for multimedia playback applications is enhanced by choosing an appropriate parameter pair of (/spl alpha/, /spl beta/). Simulation results show that the proposed DTAIMD congestion control scheme is TCP-friendly and suitable for supporting multimedia playback applications.

We consider a two-tiered Wireless Sensor Network (WSN) consisting of sensor clusters deployed around strategic locations and base-stations (BSs) whose locations are relatively flexible. Within a sensor cluster, there are many small sensor nodes (SNs) that capture, encode and transmit relevant information from the designated area, and there is at least one application node (AN) that receives raw data from these SNs, creates a comprehensive local-view, and forwards the composite bit-stream toward a BS. In practice, both SN and AN are battery-powered and energy-constrained, and their node lifetimes directly affect the network lifetime of WSNs. In this paper, we focus on the topology control process for ANs and BSs, which constitute the upper tier of a two-tiered WSN. We propose approaches to maximize the topological network lifetime of the WSN, by arranging BS location and inter-AN relaying optimally. Based on an algorithm in Computational Geometry, we derive the optimal BS locations under three topological lifetime definitions according to mission criticality. In addition, by studying the intrinsic properties of WSNs, we establish the upper and lower bounds of their maximal topological lifetime. When inter-AN relaying becomes feasible and favorable, we continue to develop an optimal parallel relay allocation to further prolong the topological lifetime of the WSN. An equivalent serialized relay schedule is also obtained, so that each AN only needs to have one relay destination at any time throughout the mission. The experimental performance evaluation demonstrates the efficacy of topology control as a vital process to maximize the network lifetime of WSNs.