Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges related to device constraints and device-server interactions, necessitating heterogeneous, user-adaptive model training with limited and uncertain communication. While knowledge cache-driven federated learning offers a promising FEL solution for demanding edge environments, its logits-based interaction design provides poor richness of exchanged information for on-device model optimization. To tackle this issue, we introduce DistilCacheFL, a novel personalized FEL architecture that enhances the exchange of optimization insights while delivering state-of-the-art performance with efficient communication. DistilCacheFL incorporates the benefits of both dataset distillation and knowledge cache-driven federated learning by storing and organizing distilled data as knowledge in the server-side knowledge cache, allowing devices to periodically download and utilize personalized knowledge for local model optimization. Moreover, a device-centric cache sampling strategy is introduced to tailor transferred knowledge for individual devices within controlled communication bandwidth. Extensive experiments on five datasets covering image recognition, audio understanding, and mobile sensor data mining tasks demonstrate that (1) DistilCacheFL significantly outperforms state-of-the-art methods regardless of model structures, data distributions, and modalities. (2) DistilCacheFL can train splendid personalized on-device models with at least 28.6 improvement in communication efficiency.

Remote sensing (RS) applications increasingly operate over distributed infrastructures that integrate space-airground- sea resources with edge intelligence, yet remains challenging to centralize due to geographic dispersion, cross-institution barriers and privacy regulations. Federated learning (FL), a promising privacy-preserving distributed learning paradigm, has garnered wide attention. However, the practical application of FL for RS encounters the issue of label noise stemming from inevitable annotation errors. In this work, we pioneer an early investigation of label noise in distributed RS tasks. We introduce the Federated Representation Optimal Matching (FedROM) framework, which guides robust representation alignment in the presence of noisy labels without requiring auxiliary data or transmitting extra sensitive information. Specifically, FedROM focuses on the robust local updating process, where clients first identify underlying noisy samples from the perspectives of both per-sample loss value and latent representation space. Subsequently, inspired by the optimal transport technique, we adaptively align the latent representations of identified noisy samples with their corresponding closest class centroids with the least representation matching distance, where class centroids are averaged by the latent representations of other relatively clean samples. This reduces the misleading effects caused by noisy samples and guides the model to capture more robust semantic features in the latent representation space. Theoretical analysis proves the robustness and convergence of FedROM. Extensive experiments on two real-world distributed RS datasets covering multi-source domains and varying label noise rates demonstrate the robustness of FedROM against eighteen baseline methods. Meanwhile, FedROM also surpasses its counterparts in conditions of no label noise, narrowing the gap with the centralized training. To facilitate related communities, our code is open-sourced athttps://github.com/Sprinter1999/ROM.

Heterogeneous device-edge-cloud computing infrastructures have become the backbone of modern telecommunication operators and Wide Area Networks (WANs), providing multi-tier computational support for emerging intelligent applications. With the rapid proliferation of Large Language Model (LLM) services, efficiently coordinating inference tasks and reducing communication burden within these multi-tier network architectures becomes a critical deployment challenge. Current LLM serving paradigms exhibit significant limitations: on-device deployment restricts service to lightweight LLMs due to hardware constraints, while cloud-centric deployment encounters resource congestion and considerable prompt communication overhead during peak periods. Model-cascading inference, though better suited for multi-tier networks, depends on static, manually-tuned thresholds that cannot adapt to dynamic network conditions or varying task complexities. To address these challenges, we propose RecServe, a recursive offloading framework tailored for LLM serving in multi-tier networks. RecServe introduces a task-specific hierarchical confidence evaluation mechanism that guides offloading decisions based on inferred task complexity in progressively scaled LLMs across device, edge, and cloud tiers. To further enable intelligent task routing across tiers, RecServe employs a sliding-window-based dynamic offloading strategy with quantile interpolation, enabling real-time tracking of historical confidence distributions and adaptive offloading threshold adjustments. This design allows inference tasks to be recursively offloaded to higher tiers only when necessary, optimizing heterogeneous resource utilization while reducing cross-tier communication with little compromise on service quality. Theoretical analysis provides distinct conditions under which RecServe is expected to achieve reduced communication burden and computational costs. Experiments on eight datasets demonstrate that RecServe outperforms CasServe in both service quality and communication efficiency, and reduces the communication burden by over 50% compared to centralized cloud-based serving.

The existing federated learning (FL) methods for spatio-temporal forecasting fail to capture the inherent spatio-temporal heterogeneity, which calls for personalized FL (PFL) methods to model the spatio-temporally variant representations. While contrastive learning is promising in tackling spatio-temporal heterogeneity, the existing methods are noneffective in distinguishing positive and negative pairs and can hardly apply to PFL paradigm. To tackle this limitation, we propose a novel PFL method, named Federated dUal sEmantic aLignment-based contraStive learning (FUELS), which can adaptively align positive and negative pairs based on semantic similarity, thereby injecting precise spatio-temporal heterogeneity into the latent representation space by auxiliary contrastive tasks. From temporal perspective, a hard negative filtering module is introduced to dynamically align heterogeneous temporal representations for the supplemented intra-client contrastive task. From spatial perspective, we design lightweight-but-efficient prototypes as client-level semantic representations, based on which the server evaluates spatial similarity and yields client-customized global prototypes for the supplemented inter-client contrastive task. Extensive experiments demonstrate that FUELS outperforms state-of-the-art methods, with impressive communication cost reduction.

To effectively manage and utilize massive distributed data at the network edge, Federated Learning (FL) has emerged as a promising edge computing paradigm across data silos. However, FL still faces two challenges: system heterogeneity (i.e., the diversity of hardware resources across edge devices) and statistical heterogeneity (i.e., non-IID data). Although sparsification can extract diverse submodels for diverse clients, most sparse FL works either simply assign submodels with artificially-given rigid rules or prune partial parameters using heuristic strategies, resulting in inflexible sparsification and poor performance. In this work, we propose Learnable Personalized Sparsification for heterogeneous Federated learning (FedLPS), which achieves the learnable customization of heterogeneous sparse models with importance-associated patterns and adaptive ratios to simultaneously tackle system and statistical heterogeneity. Specifically, FedLPS learns the importance of model units on local data representation and further derives an importance-based sparse pattern with minimal heuristics to accurately extract personalized data features in non-IID settings. Furthermore, Prompt Upper Confidence Bound Variance (P-UCBV) is designed to adaptively determine sparse ratios by learning the superimposed effect of diverse device capabilities and non-IID data, aiming at resource self-adaptation with promising accuracy. Extensive experiments show that FedLPS outperforms status quo approaches in accuracy and training costs, which improves accuracy by 1.28% – 59.34% while reducing running time by more than 68.80%.

Federated Prompt Learning (FPL) enhances federated learning by exchanging optimized prompt vectors instead of full model parameters, reducing communication costs and privacy risks. However, existing methods like PromptFL struggle under Non-IID conditions, where heterogeneous client data distributions cause local drift, slow convergence, and catastrophic forgetting. While federated personalization improves local adaptation by optimizing client-specific prompts, it lacks emphasis on global prompt optimization, limiting generalization to new clients. To address these challenges, we propose FedPTR (Federated Prompt Tuning with Retraining), which leverages server-side prompt retraining to enhance global prompt generalization. By integrating feature distillation for drift control and template prompt alignment for forgetting reduction, FedPTR enhances global prompt stability and generalization. Experimental results show that FedPTR significantly improves stability in Non-IID settings. Especially on CIFAR-10, FedPTR consistently achieves stable accuracy (89.62%–92.95%), outperforming existing methods and providing an effective solution for federated prompt learning under Non-IID conditions.

Federated Learning (FL) has emerged as a privacy-preserving training paradigm, which enables distributed devices to jointly learn a shared model without raw data sharing. However, the inaccessible client-side data and unverifiable local training leave FL vulnerable to Byzantine attacks. Most defense strategies focus on penalizing malicious clients in server-side aggregations and ignore clients-side weight units poisoning assessment, failing to maintain robustness and convergence in non-IID settings. In this paper, we propose Federated learning with Benignity-assessable Bayesian Dropout and variational Attention (FedBDA) to achieve local robust training based on fine-grained benignity indicators and guarantee global robustness over non-IID data. Specifically, FedBDA integrates variational inference explanation of dropout into local training, where each client individually quantifies the benign degree of weight units to determine a resilient dropping pattern for the local Bayesian model, enabling client-side robust training with Bayesian interpretability. To accommodate variational distributions of local Bayesian models and globally assess their benign potentials, we design a joint attention mechanism based on Jensen-Shannon divergence among local, global, and median distributions for robust weighted aggregation. Theoretical analysis proves the robustness and convergence of FedBDA. We conduct extensive experiments on four benchmark datasets with five typical attacks, and the results demonstrate that FedBDA outperforms status quo approaches in model performance and running efficiency.

Multiple federated learning (FL) methods are proposed for traffic flow forecasting (TFF) to avoid heavy-transmission and privacy-leaking concerns resulting from the disclosure of raw data in centralized methods. However, these FL methods adopt offline learning which may yield subpar performance, when concept drift occurs, i.e., distributions of historical and future data vary. Online learning can detect concept drift during model training, thus more applicable to TFF. Nevertheless, the existing federated online learning method for TFF fails to efficiently solve the concept drift problem and causes tremendous computing and communication overhead. Therefore, we propose a novel method named Resource-Efficient Federated Online Learning (REFOL) for TFF, which guarantees prediction performance in a communication-lightweight and computation-efficient way. Specifically, we design a data-driven client participation mechanism to detect the occurrence of concept drift and determine clients’ participation necessity. Subsequently, we propose an adaptive online optimization strategy, which guarantees prediction performance and meanwhile avoids meaningless model updates. Then, a graph convolution-based model aggregation mechanism is designed, aiming to assess participants’ contribution based on spatial correlation without importing extra communication and computing consumption on clients. Finally, we conduct extensive experiments on real-world datasets to demonstrate the superiority of REFOL in terms of prediction improvement and resource economization.

In remote industrial Internet of Things (IIoT) monitoring systems, the uncrewed aerial vehicle (UAV) serves as supplementary infrastructure to aggregate data from a large number of distributed sensors, and achieve industrial operation intelligence. In the wireless data aggregation process, using conventional orthogonal multiple access techniques face challenges such as scarce bandwidth, high communication latency and energy consumption. To tackle these issues, the over-the-air computation (AirComp) technique has emerged. It allows concurrent data transmissions from sensors, as well as integrates communication and computation processes, ultimately enabling fast data aggregation. However, the energy consumption issue remains unresolved. In this paper, we exploit spatial correlations among sensor measurements, and design an energy-efficient AirComp in UAV-assisted IIoT networks, where only a subset of sensors transmit data instead of all sensors. Then, we derive a closed-form expression for the mean square error (MSE) of each combination under a specific number of sensor transmissions. By jointly optimizing the UAV deployment and pre-coding coefficients of sensors, we formulate the problem of minimizing MSE for each combination of transmitted sensors. Furthermore, the MSE optimization algorithm is developed to output the average MSE of all combinations. Finally, we evaluate the average MSE and network lifetime performance of proposed scheme.

Federated learning has been a popular distributed training paradigm that enables to train a shared model with data privacy protection. However, non-Independent Identically Distribution and long-tailed data distribution characteristics across mobile devices results in evident performance degradation, especially for classification tasks. Although plenty of research studies devote to alleviating classification performance degradation caused by highly-skewed data distribution, they still cannot improve the distinguishability of model representation on hard-to-learn tail classes, and face obvious divergence of local classifiers in FL setting. To this end, we propose Federated Classifier Representation Adjustment and Calibration to improve the representation distinguishability of tail classes and achieve inter-client representation alignment with acceptable resource consumption on attaching operations. We first design a Class Similarity-Aware Margin matrix to enlarge class representation discrepancy and improve local classifier discriminability on tail classes during client-side local training process. To mitigate the divergence of local classifiers across clients, we further propose the Self Distillation Classifier Calibration to achieve the aggregated global classifier calibration with the assistance of generated pseudo representation samples via self-distillation manner. We conduct various experiments under wide-range long-tailed and heterogeneous data settings. Experimental results show that FedCRAC outperforms state-of-the-art methods in terms of accuracy and resource consumption.

For distributed network traffic prediction with data localization and privacy protection, Federated Learning (FL) enables collaborative training without raw data exchange across Base Stations (BSs). Nevertheless, traffic across BSs exhibit inherently heterogeneous trend burst and smooth fluctuation properties, but existing FL methods model single-scale series from only one view, which cannot simultaneously capture diverse trend and fluctuation properties, especially distinct burst distributions. In this paper, we proposePersonalized Federated Forecasting with Multi-property Self-fusion (P2FMS), which can represent multi-scale traffic properties from different views. With precise multi-property representations, a fusion-level prediction decision is learned for each client in a personalized manner to promptly sense traffic bursts and improve forecasting performance in non-IID settings. Specifically, P2FMS decomposes the traffic series into distinct time scales, based on which, we effectively extract closeness, period, and trend properties from different views. The closeness and period are embedded through global-view representations with spatial correlations, while non-stationary trends are individually fitted from the client-side view. Furthermore, a personalized combiner is designed to accurately quantify the proportion of general fluctuation raws (i.e., closeness and period) and specific trend property in predictions, which enables multi-property self-fusion for each client to accommodate heterogeneous traffic patterns and enhance prediction accuracy. Besides, an alternant training mechanism is introduced to optimize property representation and fusion modules with the convergence guarantee. Extensive experiments on real-world datasets show that P2FMS outperforms status quo methods in both prediction performance and convergence time.

Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive applications without sacrificing the sensitive private information of clients. However, the data quality of client datasets can not be guaranteed since corresponding annotations of different clients often contain complex label noise of varying degrees, which inevitably causes the performance degradation. Intuitively, the performance degradation is dominated by clients with higher noise rates since their trained models contain more misinformation from data, thus it is necessary to devise an effective optimization scheme to mitigate the negative impacts of these noisy clients. In this work, we propose a two-stage framework FedELC to tackle this complicated label noise issue. The first stage aims to guide the detection of noisy clients with higher label noise, while the second stage aims to correct the labels of noisy clients' data via an end-to-end label correction framework which is achieved by learning possible ground-truth labels of noisy clients' datasets via back propagation. We implement sixteen related methods and evaluate five datasets with three types of complicated label noise scenarios for a comprehensive comparison. Extensive experimental results demonstrate our proposed framework achieves superior performance than its counterparts for different scenarios. Additionally, we effectively improve the data quality of detected noisy clients' local datasets with our label correction framework. The code is available at https://github.com/Sprinter1999/FedELC.

Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy. As computing tasks are increasingly performed by a combination of cloud, edge, and end devices, FL can benefit from this End-Edge-Cloud Collaboration (EECC) paradigm to achieve collaborative device-scale expansion with real-time access. Although Hierarchical Federated Learning (HFL) supports multitier model aggregation suitable for EECC, prior works assume the same model structure on all computing nodes, constraining the model scale by the weakest end devices. To address this issue, we propose Agglomerative Federated Learning (FedAgg), which is a novel EECC-empowered FL framework that allows the trained models from end, edge, to cloud to grow larger in size and stronger in generalization ability. FedAgg recursively organizes computing nodes among all tiers based on Bridge Sample Based Online Distillation Protocol (BSBODP), which enables every pair of parent-child computing nodes to mutually transfer and distill knowledge extracted from generated bridge samples. This design enhances the performance by exploiting the potential of larger models, with privacy constraints of FL and flexibility requirements of EECC both satisfied. Experiments under various settings demonstrate that FedAgg outperforms state-of-the-art methods by an average of 4.53% accuracy gains and remarkable improvements in convergence rate. Our code is available at https://github.com/wuzhiyuan2000/FedAgg.

Federated learning (FL) is a privacy-preserving machine learning paradigm in which the server periodically aggregates local model parameters from cli ents without assembling their private data. Constrained communication and personalization requirements pose severe challenges to FL. Federated distillation (FD) is proposed to simultaneously address the above two problems, which exchanges knowledge between the server and clients, supporting heterogeneous local models while significantly reducing communication overhead. However, most existing FD methods require a proxy dataset, which is often unavailable in reality. A few recent proxy-data-free FD approaches can eliminate the need for additional public data, but suffer from remarkable discrepancy among local knowledge due to client-side model heterogeneity, leading to ambiguous representation on the server and inevitable accuracy degradation. To tackle this issue, we propose a proxy-data-free FD algorithm based on distributed knowledge congruence (FedDKC). FedDKC leverages well-designed refinement strategies to narrow local knowledge differences into an acceptable upper bound, so as to mitigate the negative effects of knowledge incongruence. Specifically, from perspectives of peak probability and Shannon entropy of local knowledge, we design kernel-based knowledge refinement (KKR) and searching-based knowledge refinement (SKR) respectively, and theoretically guarantee that the refined-local knowledge can satisfy an approximately-similar distribution and be regarded as congruent. Extensive experiments conducted on three common datasets demonstrate that our proposed FedDKC significantly outperforms the state-of-the-art on various heterogeneous settings while evidently improving the convergence speed.

Traffic flow forecasting (TFF) is of great importance to the construction of Intelligent Transportation Systems. To mitigate communication burden and tackle with the problem of privacy leakage aroused by centralized forecasting methods, Federated Learning (FL) has been applied to TFF. However, existing FL-based approaches employ batch learning manner, which makes the pre-trained models inapplicable to subsequent traffic data, thus exhibiting subpar prediction performance. In this paper, we perform the first study of forecasting traffic flow adopting online learning manner in FL framework and then propose a novel prediction method named Online Spatio-Temporal Correlation-based Federated Learning (FedOSTC), aiming to guarantee performance gains regardless of traffic fluctuation. Specifically, clients employ Gated Recurrent Unit (GRU)-based encoders to obtain the internal temporal patterns inside traffic data sequences. Then, the central server evaluates spatial correlation among clients via Graph Attention Network (GAT), catering to the dynamic changes of spatial closeness caused by traffic fluctuation. Furthermore, to improve the generalization of the global model for upcoming traffic data, a period-aware aggregation mechanism is proposed to aggregate the local models which are optimized using Online Gradient Descent (OGD) algorithm at clients. We perform comprehensive experiments on two real-world datasets to validate the efficiency and effectiveness of our proposed method and the numerical results demonstrate the superiority of FedOSTC.

To alleviate the pressure on the ground base station (BS) from intensive video requests, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has become a promising and flexible solution. The UAV carries a MEC server to provide caching and transcoding services for adaptive bitrate video streaming, which can reduce duplicate transmissions of the BS and the content acquisition latency of users, while improving the flexibility of video delivery. However, considering the uncertainty of user requests and content popularity distribution, improving the robustness of video caching is a challenge to promote practical applications. Thus, by integrating caching and transcoding on the UAV, as well as backhaul retrieving, we study the bitrate-aware video caching and processing with uncertain popularity distribution. Then, the problem of joint cache placement and video delivery scheduling under the worst-case distribution is formulated to minimize the total expected system latency with energy consumption constrained. Specifically, we use$\zeta$-structure probability metrics to characterize the uncertainty and construct confidence sets of arrival distribution. Furthermore, a distributionally robust latency optimization algorithm based on convex optimization theory is designed to obtain a robust solution. Finally, we conduct extensive simulations using real-world datasets to evaluate the effectiveness and robustness of the proposed scheme.

Federated Learning (FL) is an emerging distributed learning paradigm with the privacy-preserving advantage of collaboratively training a shared model across multiple participants. Considering the prevailing device heterogeneity circumstance in practice, asynchronous interaction is introduced into FL to break the straggler barrier of synchronization, at the cost of significant accuracy degradation derived from model staleness. Although quite a few works attempt to partially mitigate the detrimental impact after occurring staleness issue, they neglect to control the overall staleness degree of clients-side local models from the whole training perspective, resulting in highly-stale models for aggregation and slow convergence speed. To this end, we propose a Staleness-Controlled Asynchronous Federated Learning (SC-AFL) method, which enables to restrict staleness degree of local models within a certain bound via dynamically tuning the aggregated strategy of each round, aiming to strike a good balance between accuracy guarantee and convergence acceleration. Specifically, we leverage the Lyapunov optimization framework to decouple the troublesome round-coupling problem into the single-round sequential solving problem, and further develop a deterministic algorithm that selects the aggregated number of clients to minimize training time under the constraint of maintaining staleness queue stability. Besides, we derive the theoretical convergence analysis of SC-AFL and also present the upper bound of the performance gap with the optimum. Extensive experiments on three datasets demonstrate the superiority of SC-AFL in terms of time-to-accuracy speedup on both IID and Non-IID data distributions, achieving a good balance between model accuracy and convergence efficiency in AFL system.

Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos distributed among end devices in EI, Federated Learning (FL) is proposed for collaborative training of shared AI models across multiple devices without compromising data privacy. However, the prevailing FL approaches cannot guarantee model generalization and adaptation on heterogeneous clients. Recently, Personalized Federated Learning (PFL) has drawn growing awareness in EI, as it enables a productive balance between local-specific training requirements inherent in devices and global-generalized optimization objectives for satisfactory performance. However, most existing PFL methods are based on the Parameters Interaction-based Architecture (PIA) represented by FedAvg, which suffers from unaffordable communication burdens due to large-scale parameters transmission between devices and the edge server. In contrast, Logits Interaction-based Architecture (LIA) allows to update model parameters with logits transfer and gains the advantages of communication lightweight and heterogeneous on-device model allowance compared to PIA. Nevertheless, previous LIA methods attempt to achieve satisfactory performance either relying on unrealistic public datasets or increasing communication overhead for additional information transmission other than logits. To tackle this dilemma, we propose a knowledge cache-driven PFL architecture, named FedCache, which reserves a knowledge cache on the server for fetching personalized knowledge from the samples with similar hashes to each given on-device sample. During the training phase, ensemble distillation is applied to on-device models for constructive optimization with personalized knowledge transferred from the server-side knowledge cache. Empirical experiments on four datasets demonstrate that FedCache achieves comparable performance with state-of-art PFL approaches, with more than two orders of magnitude improvements in communication efficiency. Our code and DEMO are available athttps://github.com/wuzhiyuan2000/FedCache.

Neighbor discovery (ND) is a critical network initialization stage, particularly challenging for highly-dynamic unmanned aerial vehicle (UAVs) with directional antennas. Considering that directional antennas focus signal energy in one direction, successful ND requires a pair of UAVs to point antennas towards each other simultaneously. However, due to the inherent constraints of autonomous UAVs (e.g., high mobility and decentralized coordination), spatial alignment of directional beams is difficult. Existing works resort to ideal assumptions (e.g., clock synchronization, assistance of omni-directional antennas and prior information) for simplification. Moreover, previous ND algorithms assume unlimited switching capability for directional antennas, often unrealistic for traditional mechanically steered antennas. In this paper, we proposeSkyOrbs, a fast directional ND algorithm for UAV networks without these ideal assumptions. To reduce ND latency,SkyOrbspresents a skip scanning strategy, dynamically adjusting antenna rotation speed to enhance discovery probability. Furthermore, to mitigate the uncertain rotation overhead induced by time-variant angular speed,SkyOrbsdesigns a novel antenna scanning path that accommodates limited mechanical rotation capacity. We analyze the theoretical delay performance ofSkyOrbs, and expand its applicability to broader scenarios. Evaluation results show thatSkyOrbscan reduce discovery latency by 40.8% and rotation overhead by 55.0% compared to the baseline method.

For mobile robotic networks in industrial scenarios, reliable and energy-efficient communications are crucial yet challenging. Fortunately, collaborative beamforming (CB) emerges as a promising solution, which can increase the transmission gain and reduce the transmit power of robots by constructing a mobile robot-enabled virtual antenna array (MRVAA). The performance of CB is tightly related to robot positions, necessitating proper robot selection. However, robot selection may expose the network to the risk of unbalanced energy distribution, reducing network lifetime. Additionally, the mobility and variable numbers of robots require flexible and scalable robot selection algorithms. To tackle these challenges, we first formulate a multi-objective optimization problem to reduce the maximum sidelobe level (MSLL) of MRVAA while minimizing the standard deviation of the network energy distribution (SDNED) by selecting robots for CB. Then, based on distributed multi-agent learning (MARL), we propose an effective and scalable robot selection algorithm with energy considered (RoSE) to solve the problem, where difference-rewards function (DRF) and policy sharing are designed for enhancing convergence rate and policy stability. Simulation results show that the RoSE has the scalability to positions and numbers of robots. Furthermore, RoSE surpasses existing selection algorithms in network lifetime and time efficiency, while still maintaining comparable MSLL.

The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in Multi-access Edge Computing (MEC). Diverse user behaviors call for personalized services with heterogeneous Machine Learning (ML) models on different devices. Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC. Introducing knowledge distillation into FMTL can simultaneously enable efficient communication and model heterogeneity among clients, whereas existing methods rely on a public dataset, which is impractical in reality. To tackle this dilemma,Federated MultI-task Distillation for Multi-access EdgeCompuTing (FedICT) is proposed. FedICT direct local-global knowledge aloof during bi-directional distillation processes between clients and the server, aiming to enable multi-task clients while alleviating client drift derived from divergent optimization directions of client-side local models. Specifically, FedICT includes Federated Prior Knowledge Distillation (FPKD) and Local Knowledge Adjustment (LKA). FPKD is proposed to reinforce the clients' fitting of local data by introducing prior knowledge of local data distributions. Moreover, LKA is proposed to correct the distillation loss of the server, making the transferred local knowledge better match the generalized representation. Extensive experiments on three datasets demonstrate that FedICT significantly outperforms all compared benchmarks in various data heterogeneous and model architecture settings, achieving improved accuracy with less than 1.2% training communication overhead compared with FedAvg and no more than 75% training communication round compared with FedGKT in all considered scenarios.

Unmanned Aerial Vehicles (UAVs) exhibit great advantages in data collection from ground sensors in vast tracts of fields. Due to their limited power supply, most works assume that the UAV simply traverses each sensor’s fixed transmission range to collect data, thereby shortening the flight path. However, they neglect the quality of collected data, which may deteriorate dramatically as the transmission distance increases. In this paper, by leveraging the physical-layer protocol – LoRa, we propose a Packet Reception Ratio (PRR)-based probabilistic coverage model to evaluate the quality of data transmission, which directly determines the data acquisition efficiency. On this basis, to minimize the energy consumption of UAV and sensors while ensuring high-quality data acquisition, we formulate the UAV trajectory planning as a joint Energy Consumption and data Acquisition Efficiency (ECAE) optimization problem. To tackle the ECAE problem, we propose a Deep Reinforcement Learning (DRL)-based two-stage scheme. First, an attention-based encoder-decoder model is trained to generate an initial trajectory. Then an intuitive optimization algorithm is devised to further explore the optimal trajectory. Evaluation results show that our scheme can reduce the total energy cost of UAV and sensors by 27.1% as compared to the best baseline’s policy while maintaining a promising PRR.

Digital Twin technology is a valuable approach for modeling and simulating complex network systems. It is used to comprehensive analysis of network performance, protocols and configurations. The ns-3 simulator has the capability to simulate real-world network environments, supporting the implementation of Network Digital Twins. However, the current ns-3 simulator pays more attention to simulating network protocol algorithm, while ignoring the network packet processing in device node. It can not simulate packet processing based on OS. In this paper we propose a new architecture based on ns-3 for simulating OS packet processing mechanism, called NS3 - Modular Packet Processing Simulation Architecture (NS3-MPPSA). In NS3-MPPSA, various hardware resources involved in packet processing are modeled. Packet processing mechanisms can be implemented by selecting and combining processing units flexibly. The obtained results indicate that NS3-MPPSA enhances the capabilities of ns-3, enabling accurate and realistic simulation of packet processing. Thus, it provides better support for the implementation of Network Digital Twins.

In the federated learning scenario, geographically distributed clients collaboratively train a global model. Data heterogeneity among clients significantly results in inconsistent model updates, which evidently slow down model convergence. To alleviate this issue, many methods employ regularization terms to narrow the discrepancy between client-side local models and the server-side global model. However, these methods impose limitations on the ability to explore superior local models and ignore the valuable information in historical models. Besides, although the up-to-date representation method simultaneously concerns the global and historical local models, it suffers from unbearable computation cost. To accelerate convergence with low resource consumption, we innovatively propose a model regularization method named FedTrip, which is designed to restrict global-local divergence and decrease current-historical correlation for alleviating the negative effects derived from data heterogeneity. FedTrip helps the current local model to be close to the global model while keeping away from historical local models, which contributes to guaranteeing the consistency of local updates among clients and efficiently exploring superior local models with negligible additional computation cost on attaching operations. Empirically, we demonstrate the superiority of FedTrip via extensive evaluations. To achieve the target accuracy, FedTrip outperforms the state-of-the-art baselines in terms of significantly reducing the total overhead of client-server communication and local computation.

Federated Learning (FL) emerges as a distributed machine learning paradigm without end-user data transmission, effectively avoiding privacy leakage. Participating devices in FL are usually bandwidth-constrained, and the uplink is much slower than the downlink in wireless networks, which causes a severe uplink communication bottleneck. A prominent direction to alleviate this problem is federated dropout, which drops fractional weights of local models. However, existing federated dropout studies focus on random or ordered dropout and lack theoretical support, resulting in unguaranteed performance. In this paper, we propose Federated learning with Bayesian Inference-based Adaptive Dropout (FedBIAD), which regards weight rows of local models as probability distributions and adaptively drops partial weight rows based on importance indicators correlated with the trend of local training loss. By applying FedBIAD, each client adaptively selects a high-quality dropping pattern with accurate approximations and only transmits parameters of non-dropped weight rows to mitigate uplink costs while improving accuracy. Theoretical analysis demonstrates that the convergence rate of the average generalization error of FedBIAD is minimax optimal up to a squared logarithmic factor. Extensive experiments on image classification and next-word prediction show that compared with status quo approaches, FedBIAD provides 2× uplink reduction with an accuracy increase of up to 2.41% even on non-Independent and Identically Distributed (non-IID) data, which brings up to 72% decrease in training time.

Industrial cognitive radio sensor networks (ICRSNs) extend channel resources by occupying the vacant licensed channels in the absence of licensed users. In ICRSNs, industrial devices should switch to a common available channel to set up a communication link. However, channel switching leads to severe energy consumption. As the energy resources of battery-powered industrial devices are limited, it is crucial to carefully allocate channels to prolong the network lifetime of multi-hop ICRSNs. This paper is the first work that studies the channel allocation problem to optimize the network lifetime by considering the channel-switching (CS) energy consumption and the time-critical requirements of industrial applications. The problem is formulated to maximize the minimum residual energy at each round of data transmission, which is linearized as integer linear programming. As the channel allocation results will affect the residual energy at subsequent rounds, we propose a switching distance-optimized channel allocation (SDOCA) scheme that shortens the CS distances to improve the residual energy of each device. Moreover, we analyze the characteristics of SDOCA, i.e., convergent CS distance and guaranteed end-to-end delay. Extensive simulation results show that SDOCA can adaptively allocate channels according to the end-to-end delay requirement and significantly prolong the network lifetime.

Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner. However, data with noisy labels are ubiquitous in reality since high-quality labeled data require expensive human efforts, which cause severe performance degradation. Although a lot of methods are proposed to directly deal with noisy labels, these methods either require excessive computation overhead or violate the privacy protection principle of FL. To this end, we focus on this issue in FL with the purpose of alleviating performance degradation yielded by noisy labels meanwhile guaranteeing data privacy. Specifically, we propose a Local Self-Regularization method, which effectively regularizes the local training process via implicitly hindering the model from memorizing noisy labels and explicitly narrowing the model output discrepancy between original and augmented instances using self distillation. Experimental results demonstrate that our proposed method can achieve notable resistance against noisy labels in various noise levels on three benchmark datasets. In addition, we integrate our method with existing state-of-the-arts and achieve superior performance on the real-world dataset Clothing1M.The code is available at https://github.com/Sprinter1999.

As a popular privacy-preserving model training technique, Federated Learning (FL) enables multiple end-devices to collaboratively train Deep Neural Network (DNN) models without exposing local privately-owned data. According to the FL paradigm, resource-constrained end-devices in IoT should perform model training which is computation-intensive, whereas the edge server occupied with powerful computation capability only performs model aggregation. Due to the above unbalanced computation pattern, IoT-oriented FL is time-consuming and inefficient. In order to alleviate the computation burden of end-devices, recent countermeasures introduce the edge server to assist end-devices in model training. However, existing works neither efficiently address the computation heterogeneity across end-devices nor reduce the leakage risk of data privacy. To this end, we propose a Federated Synergy Learning (FedSyL) paradigm which innovatively strikes a balance between training efficiency and data leakage risk. We explore the complicated relationship between the local training latency and multi-dimensional training configurations, and design a uniform training latency prediction method by applying the polynomial quadratic regression analysis. Additionally, we design the optimal model offloading strategy with the consideration of resource limitation and computation heterogeneity of end-devices, so as to accurately assign capability=matched device-side sub-models for heterogeneous end-devices. We implement FedSyL on a real test-bed comprising multiple heterogeneous end-devices. Experimental results demonstrate the superiority of FedSyL on training efficiency and privacy protection.

In cognitive radio networks (CRNs), due to the external interference from primary users, secondary users (SUs) cannot reserve a common control channel (CCC). Hence, it is essential to consider the impact of channel rendezvous on the end-to-end delay in multi-hop CRNs. For this reason, we propose a High Probabilistic Transmission Efficiency Multi-hop Routing (HPTEMR) protocol without utilizing a CCC. In HPTEMR, we design an efficient waiting channel hopping sequence to achieve fast channel rendezvous between neighborhood SUs. We then propose a novel link metric, i.e., transmission efficiency, which characterizes the transmission distance and channel-rendezvous delay. Based on the link metric, a sender SU transmits data packets to the receiver SU with the highest probability that data packets can be forwarded to the destination SU with the shortest end-to-end delay. Evaluation results verify the effectiveness of HPTEMR and show its superiority in end-to-end delay and ratio of effective packets.

Blockchain is multi-centralized, immutable and traceable, thus is very suitable for distributed storage, privacy and security management in IoTs. However, most existing researches focus on the integration of public blockchain and IoTs. In fact, problems such as slow consensus, low transmission throughput, and completely open storage on the public blockchain are intolerable in IoT scenarios. Although consortium blockchain represented by Hyperledger Fabric has improved the transmission rate, its data security completely relies on the PKI-based certificate mechanism, resulting in transmission inefficiency and privacy leakage. In this paper, a key-derived Controllable Lightweight Secure Certificateless Signature (CLS2) algorithm is proposed to significantly improve the transmission efficiency and keep similar computation overhead of consortium blockchain. Compared with the existing certificateless signatures, CLS2achieves more secure transactions, whose controllable anonymity and key-derived mechanism not only prevents public key replacement attacks and forged signature attacks, but also supports hierarchical privacy protection. Armed with CLS2, we design a consortium blockchain security architecture based on Hyper-ledger Fabric and edge computing. To the best of our knowledge, this is the first implementation of certificateless signature in consortium blockchain. We formally prove the security of our schemes in the random oracle model. Specifically, the security of the proposed scheme is reduced to the Elliptic curve discrete logarithm problem (ECDLP). Security analysis and experiments in IoT scenarios verify the feasibility and effectiveness of CLS2.

With the ubiquitous demand for wireless communications, researchers have studied heterogeneous networks (HetNets) for years. Often the HetNets include a macrocell base station (MBS), several sets of macrocell users (MUs), a large number of femtocell base stations (FBSs), and femtocell users (secondary users), where the femtocells help the macrocell system relay the uplink or downlink traffic between the MUs and the MBS. In this article, we propose a novel joint information and energy cooperation method, with the aim of enhancing the spectrum and energy efficiency (EE) for cognitive HetNets. Specifically, the MUs and the femtocells harvest wireless energy from the radio frequency signal transmitted by MBS. By using the harvested energy, femtocells obtain the transmission opportunity to forward the signals of their serving users. We theoretically derive the theoretical expressions of the outage probabilities of the primary link as well as the secondary link. Then, we focus on investigating how to maximize EE by jointly considering time allocation and power control. Furthermore, we formulate the EE maximization problem, which contains the fractional form objective function and the linear inequality constraints and hence is nonconvex. To resolve this, we integrate the Dinkelbach method with convex optimization to derive the tractable and optimal solution. The numerical results demonstrate the simulations well match our theoretical analysis. Moreover, the results validate the feasibility of the proposed method for high-quality transmission without incurring extra energy consumption.

As it is difficult for power-restricted wireless sensor nodes to achieve rapid neighbor discovery under the scenarios of asynchronous clocks, misaligned time slots, and asymmetric duty-cycle (i.e., wake-up/sleep) scheduling periods, we propose a low-power neighbor discovery algorithm termed Pharos by alternately utilizing the fully and the partially awake time slots. The partially awake time slots of one node are certain to detect the counterpart's awake slots while reducing the power consumption as compared to the fully awake time slots. We analyze the theoretical neighbor discovery latency and derive the optimal parameters for both symmetric and asymmetric duty-cycle schedules. We also verify the effectiveness of the Pharos algorithm through extensive simulations. Evaluation results display that Pharos costs much less discovery latency and power than the state-of-the-art neighbor discovery algorithms.

In distributed Internet of Things (IoTs), channel hopping (CH) is an effective scheme for neighbor nodes to achieve blind rendezvous over common available channels and to establish communication links. When nodes are unaware of each other's local clocks and the global channels and have no pre-assigned CH strategies or identifiers (IDs), it is particularly challenging to guarantee blind rendezvous within a finite period of time, which has not been solved yet by using only one radio. In this paper, we propose a novel quaternary-encoding-based CH (QECH) algorithm to tackle the above issue. The QECH algorithm encodes a randomly selected channel into a quaternary string according to the 6B/8B encoding. We also append a common prefix string as well as the randomly selected channel before the quaternary string to guarantee overlaps in the asynchronous scenario. For all kinds of quaternary digits, we construct four mutually co-prime numbers to enumerate all possible combinations of the common available channels. We theoretically analyze the deterministic rendezvous principle and the upper bounded rendezvous latency of the QECH algorithm. We also verify the effectiveness of the QECH algorithm through extensive simulations. Evaluation results show the superiority of the QECH algorithm in terms of rendezvous latency.

Mobile Crowdsensing (MCS) has become an emerging paradigm to solve complex urban sensing problems by utilizing the ubiquitous sensing capacities of the crowd. One critical issue in MCS is to efficiently allocate tasks to users. We focus on addressing the task allocation problem in a distributed manner, where each user individually and freely makes his decision to undertake tasks. Existing distributed schemes simply consider users behave completely selfishly, which leads to inefficient solutions and damages the overall benefit of all users. Different from existing schemes, in this paper we integrate the social relationship into users' decision making and build a socially aware utility model for each user, which consists of both user's own utility and the weighted sum of his social neighbors' utilities. Based on this, we formulate a novel Socially Aware Task Selection (SATS) game for users in MCS. We theoretically prove the existence of pure Nash equilibrium in the SATS game with the help of a potential game framework. We further propose a distributed user selection algorithm to actually achieve the pure Nash equilibrium. Extensive simulations based on both real and synthetic social relationship graph datasets demonstrate that our approach can achieve more efficient solutions which improve users' overall benefit compared with existing schemes.

Channel qualities between UAVs vary drastically due to the mobility of UAVs. Conventional channel coding relies on channel state information (CSI) estimation and active bit rate selection and thus cannot adapt well to such varying channel conditions. In contrast, rateless codes can achieve almost optimal bit rate under varying channel conditions without CSI estimation and explicit rate selection. In rateless codes, Spinal codes are one of the most prominent solutions and perform much better than other rateless codes over the mobile fading channel between UAVs. However, Spinal codes still face the challenge of error accumulation effect, which largely hurts the transmission efficiency. In this paper, we for the first time analyze the error accumulation effect and its impact on the performance of Spinal codes under mobile fading channel conditions between UAVs. Furthermore, we propose a model for helping the decoder estimate the quality of each received symbol. Based on such model, trust function based Spinal codes (TFSC) are then proposed. Its main idea is to treat received symbols differently according to their qualities so that those symbols with better qualities can contribute more to the decoding process. Simulation results demonstrate that TFSC can significantly mitigate the error accumulation effect and improve the efficiency of Spinal codes, which achieves 1.1x to 4.4x overall performance improvement when compared with Spinal codes over the mobile fading channel between UAVs.

Effective collaborations among autonomous unmanned aerial vehicles (UAVs) rely on timely information sharing. However, the time-varying flight environment and the intermittent link connectivity pose great challenges to message delivery. In this paper, we leverage the deep reinforcement learning (DRL) technique to address the UAVs' optimal links discovery and selection problem in uncertain environments. As the multi-agent learning efficiency is constrained by the high-dimensional and continuous action spaces, we slice the whole action spaces into a number of tractable fractions to achieve efficient convergences of optimal policies in continuous domains. Moreover, for the nonstationarity issue that particularly challenges the multi-agent DRL with local perceptions, we present a multi-agent mutual sampling method that jointly interacts the intra-agent and inter-agent state-action information to stabilize and expedite the training procedure. We evaluate the proposed algorithm on the UAVs' continuous network connection task. Results show that the associated UAVs can quickly select the optimal connected links, which facilitate the UAVs' teamwork significantly.

Neighbor discovery is a significant communication primitive for adjacent unmanned aerial vehicles (UAVs) to construct a flying ad hoc network (FANET). The multi-channel nature of FANETs makes channel hopping (CH) a feasible rendezvous method for UAVs to hop to the same available channel simultaneously and initiate a connection. However, due to the intrinsic uncoordinated constraints of dispersed UAVs (e.g., lack of clock synchronization, heterogeneous local channels, symmetric roles, and oblivious identifiers), it is challenging to design a performant CH algorithm that can achieve fast neighbor discovery in dynamic FANETs. In this paper, we present a fully uncoordinated matrix-based CH algorithm termed ABIO, which consists of one fixed Anchor column and several variable Binary (i.e., I/O-bit) extended columns in each CH period. The deterministic overlaps as well as the co-primality property of channel numbers among different kinds of columns provide the rendezvous guarantee. Furthermore, for the case with frequently varying channel status, we present a probability-based dynamic discovery (PDD) algorithm. By virtue of the cumulative probability estimation and selection of the qualified channels, the PDD algorithm can achieve timely rendezvous in the unstable environment with high probability. We rigorously analyze the theoretical neighbor discovery latency. We also validate the feasibility and efficiency of the proposed algorithms through extensive simulations. Evaluation results demonstrate the superiority of our algorithms in both stable and unstable communication environments.

The legacy concept of exclusion zones (EZs) is inept at enabling efficient utilization of fallow spectrum by secondary users (SUs), since legacy EZs are static and overly-conservative. The notion of a static EZ implies that it has to protect incumbent users (IUs) from the union of likely interference scenarios, leading to a worst-case, conservative solution. In this paper, we propose the concept of dynamic, multi-tier EZs, which takes advantage of participatory spectrum sensing carried out by SUs to support efficient database-driven spectrum sharing while protecting IUs against SU-induced aggregate interference. Specifically, the database directly incentivizes SUs to participate in spectrum sensing, which augments geolocation database by defining smaller EZs with dynamic boundaries and creating additional spectrum access opportunities for SUs. We propose an incentive mechanism based on a two-level game-theoretic model, in which the database conducts dynamic pricing in a first-level Stackelberg game in the presence of SUs who strategically contribute to spectrum sensing in a second-level stochastic game. The existence of an equilibrium solution is proven. According to our findings, the proposed incentive mechanism for the concept of dynamic, multi-tier EZs is effective to improve spectrum utilization efficiency while guaranteeing incumbent protection.

Commercial advertisement dissemination is one of the most promising applications in self-organizing mobile social networks (SMSN). Lightweight incentives are essential to encourage the participation of mobile users, given that only a few users are voluntary due to limited resources in mobiles. However, existing incentives overlook dishonest behaviors of relays in overstating their costs for higher payments, which in turn can reduce the revenues and utilities of disseminators. To this end, we design a sealed-auction-based incentive named DIBS. Effective algorithms utilizing lightweight information in DIBS encourage users to provide truthful information and to spread ads in differential places, where competition is less as fewer users carry the same ads in the same areas. Moreover, we prove that DIBS possesses attractive properties theoretically for auction-based incentives, i.e., lightweight, truthfulness and individual rationality. Extensive simulation results on both real and synthetic traces verify that DIBS can improve revenues and utilities of disseminators, and achieve efficient ad dissemination.

Poor scalability is a long standing problem in multi-user MIMO (MU-MIMO) networks: in order to select concurrent uplink users with strong channel orthogonality and thus high total capacity, channel state information (CSI) feedback from users is required. However, when the user population is large, the overhead from CSI feedback can easily overwhelm the actual channel time spent on data transmission. Moreover, due to spontaneous uplink traffic, uplink user selection cannot rely on the access point's central assignment and needs a distributed realization instead, which makes the problem even more challenging.

Cooperative content offloading is a promising technology to lessen heavy burden of wireless networks and improve the quality of downloading services. Since few users are voluntary in providing free assistance, auction-based incentive mechanisms are designed to encourage participation. In existing auction-based incentive mechanisms, each provider only acts as a service seller. However, a provider could also be a partner of the requestor if having interest in the requested content. This dual identity of the provider can improve the quality of its service and cut down the payment of requestor. Based on this observation, we propose an auction-based incentive mechanism named CADRE. To the best of our knowledge, CADRE is the first auction-based incentive mechanism that considers the provider's dual identity in cooperative content offloading applications. We prove that CADRE possesses attractive characteristics, i.e., truthfulness, lightweight and privacy protection. Besides, we also demonstrate that CADRE outperforms the traditional multi-attribute second-score sealed reverse auction. Our simulation results verify the theoretical analysis.

We consider the problem of online dynamic channel accessing in multi-hop cognitive radio networks. Previous works on online dynamic channel accessing mainly focus on single-hop networks that assume complete conflicts among all secondary users. In the multi-hop multi-channel network settings studied here, there is more general competition among different communication pairs. A simple application of models for single-hop case to multi-hop case with N nodes and M channels leads to exponential time/space complexity O (MN), and poor theoretical guarantee on throughput performance. We thus novelly formulate the problem as a linearly combinatorial multi-armed bandits (MAB) problem that involves a maximum weighted independent set (MWIS) problem with unknown weights. To efficiently address the problem, we propose a distributed channel access algorithm that can achieve 1/ρ of the optimum averaged throughput where each node has communication complexity O (r2+D) and space complexity O (m) in the learning process, and time complexity O (D mρr) in strategy decision process for an arbitrary wireless network. Here ρ = 1 + ε is the approximation ratio to MWIS for a local r-hop network with m <; N nodes, and D is the number of mini-rounds inside each round of strategy decision.

In mobile ad-hoc cognitive radio networks (CRNs), end-to-end paths with available spectrum bands for secondary users may exist temporarily, or may never exist, due to the dynamism of the primary user activities. Traditional CRN routing algorithms, which typically ignore the intermittent connectivity of network topology, and traditional mobility-assisted routing algorithms, which generally overlook the spectrum availability, are obviously unsuitable. To tackle this challenge, we propose a Mobility-Assisted Routing algorithm with Spectrum Awareness (MARSA) to select relays based on not only the probability that a node meets the destination but also the chance at which there exists at least one available channel when they meet. To the best of our knowledge, this paper is the first to bring the idea of mobility-assisted routing to deal with the intermittently connected attribute of mobile ad-hoc CRNs, and the first to enhance the mobility-assisted routing by considering the temporal , spatial, and spectrum domains at the same time. Our simulation results demonstrate the superiority of MARSA over traditional algorithms in intermittently connected mobile CRNs.

We study throughput-optimum localized link scheduling in wireless networks. The majority of results on link scheduling assume binary interference models that simplify interference constraints in actual wireless communication. While the physical interference model reflects the physical reality more precisely, the problem becomes notoriously harder under the physical interference model. There have been just a few existing results on link scheduling under the physical interference model, and even fewer on more practical distributed or localized scheduling. In this paper, we tackle the challenges of localized link scheduling posed by the complex physical interference constraints. By integrating the partition and shifting strategies into the pick-and-compare scheme, we present a class of localized scheduling algorithms with provable throughput guarantee subject to physical interference constraints. The algorithm in the oblivious power setting is the first localized algorithm that achieves at least a constant fraction of the optimal capacity region subject to physical interference constraints. The algorithm in the uniform power setting is the first localized algorithm with a logarithmic approximation ratio to the optimal solution. Our extensive simulation results demonstrate performance efficiency of our algorithms.

As video applications are increasingly popular over smartphones, many cooperative video streaming mechanisms have been proposed. These mechanisms use cellular link as well device-to-device links simultaneously to provide higher quality video streaming to mobile users. However current works solely focus on throughput enhancement in static scenarios. Consequently these mechanisms result in unfairness since smartphones with higher download rate expend more cellular traffic and monetary costs. Additionally, previous works assume a static scenario that all smartpone users start to watch the same video at the same time. Obviously, the static scenario is unrealistic in actual mobile environments. Based on these insights, in this paper, we focus on a more practical dynamic cooperation scenario and propose a scheduling scheme to achieve efficient cooperative video streaming and guarantee fluent user experience. More importantly, the proposed scheduling scheme achieves a significant improvement in fairness among cooperators. Through extensive simulations across a wide range of scenarios, we show that the proposed scheme significantly outperforms other works by 52%, 24% and 27% respectively in terms of fairness, without sacrificing efficiency.

Mobile P2P networking is an enabling technology for mobile devices to self-organize in an unstructured style and communicate in a peer-to-peer fashion. Due to user mobility and/or the unrestricted switching on/off of the mobile devices, links are intermittently connected and end-to-end paths may not exist, causing routing a very challenging problem. Moreover, the limited wireless spectrum and device resources together with the rapidly growing number of portable devices and amount of transmitted data make routing even harder. To tackle these challenges, the routing algorithms must be scalable, distributed, and light-weighted. Nevertheless, existing approaches usually cannot simultaneously satisfy all these three requirements. In this paper, we propose two opportunistic routing algorithms for intermittently connected mobile P2P networks, which exploit the spatial locality, spatial regularity, and activity heterogeneity of human mobility to select relays. The first algorithm employs a depth-search approach to diffuse the data towards the destination. The second one adopts a depth-width-search approach in a sense that it diffuses the data not only towards the destination but also to other directions determined by the actively moving nodes (activists) to find better relays. We perform both theoretical analysis as well as a comparison based simulation study. Our results obtained from both the synthetic data and the real world traces reveal that the proposed algorithms outperform the state-of-the-art in terms of delivery latency and delivery ratio.

We study distributed link scheduling for throughput maximization in wireless networks. The majority of results on link scheduling assume binary interference models for simplicity. While the physical interference model reflects the physical reality more precisely, the problem becomes notoriously harder under the physical interference model. There have been just a few existing results on centralized link scheduling under the physical interference model, though distributed schedulings are more practical. In this paper, by leveraging the partition and shifting strategies and the pick-and-compare scheme, we present the first distributed link scheduling algorithm that can achieve a constant fraction of the optimal capacity region subject to physical interference constraints in the linear power setting for multihop wireless networks.

Traffic matrices (TMs) are very important for traffic engineering and if they can be predicted, the network operations can be made beforehand. However, existing prediction methods are neither accurate nor efficient in practice. In this paper, we utilize the spatio-temporal property and low rank nature to directly predict the total TMs. The problem is that conventional matrix interpolation only works well when elements are missing uniformly and randomly. But in the case of TMs prediction, an entire part of the matrix is unknown. To solve this problem, we utilize some essential properties of TMs and add the time series forecasting into the matrix interpolation. We analyze our algorithm and evaluate its performance. The experiment result shows that our method can predict TMs under an NMAE of 30% in most cases, even predicting all the elements of next 3 weeks.

Recent studies reveal that the main cause of the well-known unfairness problem in wireless networks is the ineffective coordination of CSMA-based random access due to topology asymmetry. In this paper, we take a modeling-based approach to understand and solve the unfairness problem. Compared to existing works, we advance the state of the art in two important ways. First, we propose an analytical model called the G-Model, which accurately characterizes the ineffective coordination of medium access in asymmetrical topologies. The G-Model can estimate network performance under arbitrary parameter configurations. Second, while previous works decompose a wireless network into embedded basic asymmetric topologies and study each basic topology separately, we go beyond the basic asymmetrical topology and design a model-driven optimization method called Flow Level Adjusting (FLA) to solve the unfairness problem for larger wireless networks. Through extensive simulations, we validate the proposed G-Model and show that FLA can greatly improve the overall fairness of wireless networks in which basic asymmetric topologies are embedded.

One major problem of cross-layer control algorithms in multi-hop wireless networks is that they lead to large end-to-end delays. Recently there have been many studies devoted to solving the problem to guarantee order-optimal per-flow delay. However, these approaches also bring the adverse effect of sacrificing a lot of network utility. In this paper, we solve the large-delay problem without sacrificing network utility. We take a fundamentally different approach of delay differentiation, which is based on the observation that flows in a network usually have different requirements for end-to-end delay. We propose a novel joint rate control, routing and scheduling algorithm called CLC_DD, which ensures that the flow delays are proportional to certain pre-specified delay priority parameters. By adjusting delay priority parameters, the end-to-end delays of preferential flows achieved by CLC_DD can be as small as those achieved by delay-order-optimal algorithms. In contrast to high network utility loss in previous approaches, we prove that our approach achieves maximum network utility. Furthermore, we incorporate opportunistic routing into the cross-layer design framework to improve network performance under the environment of dynamic wireless channels.

Secondary interference constraints are important, because of representing the transmission constraints of the widespread and promising IEEE 802.11 wireless technology. Under secondary interference constraints, distributed link scheduling algorithms for multihop wireless networks can only achieve a fraction of the maximum possible throughput in general, but distributed Greedy Maximal Scheduling (GMS) algorithms can achieve optimal throughput in some network graph structures. It is possibly helpful for the improvement of distributed throughput to partition a network into subnetworks such that the subnetwork assigned to each frequency channel achieves distributed throughput maximization. In this paper, we investigate the structure characteristics of the subnetwork in which GMS achieves optimal throughput under secondary-interference constraints, and define a type of network subgraph structures meeting the requirement - special chordal subgraphs. Based on this, we propose a channel assignment algorithm, including a network partitioning algorithm and a topology balancing algorithm. By simulation, we evaluate the achievable throughput and fairness in a distributed matter using our algorithm, in comparison with the existing Max K-cut based channel assignment algorithm.

Centralized WLANs widely deployed in enterprise environment or university campus often have high density of Access Point (AP). The high density leads to multi-AP diversity, which brings possibility to improve network performance. Previ ous studies have proposed different schemes to exploit multi-AP diversity, however, these schemes are all based on heuristic and cannot guarantee an optimal exploitation of multi-AP diversity. In this paper, we propose a Theory Based Centralized Scheduling (TBCS) to exploit the full potential of multi-AP diversity. TBCS is based on the well-known back-pressure scheduling. Although back-pressure scheduling is proved to be throughput-optimal, most of previous studies are purely theoretical. To make a practical use of the theoretical back-pressure scheduling, we design new mechanisms in TBCS to handle the problem caused by the wired/wireless mixed scenario of centralized WLANs and to synchronize the scheduling. We evaluate TBCS through NS 2 simulations and show that compared with previous methods, TBCS can support the largest capacity region and greatly improves the throughput of a network.

Power-aware traffic engineering puts some links to sleep by moving their traffic to other links. However, this can make the utilization of remaining links higher, especially when the traffic amount is large. There is a tradeoff between the number of sleeping links and the utilization of links. To solve this problem, we propose to use a state-of-the-art networking called Named Data Networking (NDN), which can cache and retrieve the content in the storable routers. This can facilitate power-aware traffic engineering, because some traffic does not need to travel through the core network any more, and it only ends up at the edge routers which have already cached the required content. We use NDN to balance the traffic demand between origin-destination core routers so that traffic demand through the network can be adjusted to satisfy the requirement of power-aware traffic engineering. We evaluate power-aware traffic engineering with NDN and show its advantage compared to the one with conventional networking.

Spectrum allocation algorithms are able to improve the performance of wireless mesh networks by exploiting the frequency agility of modern radios, and several such algorithms have been proposed. However, their interference constraints are at a coarse-grained level, which results in a low spectrum efficiency. To achieve higher spectrum resource utilization, we use interference pairs as a finer granularity to model the interference constraints in wireless mesh networks, and derive a sufficient and necessary condition for interference-free spectrum allocation. Based on a set of rigorous models, we formulate spectrum allocation as an optimization problem and divide it into two subproblems, for which we propose a two-phase interference pair-based distributed spectrum allocation (IPDSA) algorithm. In IPDSA, a negotiation-based frequency hierarchy mechanism heuristically determines the relation between the center frequencies of links in each interference pair; and then a dual decomposition-based spectrum allocation algorithm converges to the optimal allocation of center frequencies and spectral widths of all links. Extensive simulation results show that IPDSA is able to significantly improve spectrum utilization and thus increase network utility and aggregate throughput, thanks to a high accuracy in modeling interference constraints.

MIPv6 is one of the dominating protocols that enable a mobile node to maintain its connectivity to the Internet when moving from one access router to another. However, it suffers from long handoff latency and routing inefficiency. In this paper, we present a novel distributed mobility management scheme, ADA (Asymmetric Double-agents Architecture), which introduces two mobility agents to serve one end-to-end communication. One mobility agent is located close to the MN to limit the amount of MIPv6 signaling traffic outside the local domain. The other mobility agent is located close to the CN to minimize routing overheads. Quantitative analysis shows that ADA significantly outperforms the existing mobility management protocols.

In wireless mesh networks with frequency-agile radios, an algorithm of dynamically combining consecutive channels has recently been proposed. However, the available channel widths are limited in the algorithm. In order to further improve the fairness or the throughput under given fairness, we propose a joint variable width spectrum allocation and link scheduling optimization algorithm. Our algorithm is composed of time division multiple access for no interface conflict and frequency division multiple access for no signal interference. In the first phase, we use as few time slots as possible to assign at least one time slots to each radio link with Max-Min fairness. In the second phase, our design jointly allocates the lengths of time slots as well as the spectral widths and center frequencies of radio links in each time slot. Numerical results indicate that compared to the existing algorithm, our algorithm significantly increases the fairness or the throughput under given fairness.

As is known to us, the handover latency of FMIPv6 in its predictive mode is given little concerns. However our previous work [4] shows that FMIPv6 may suffer long handover latency in its predictive mode, and [4] identifies three key issues raising such problems. In this paper, we propose a practical cross-layer fast handover management mechanism (PCLF) to address these issues and improve success rate of mobility prediction. To solve the problem, PCLF includes a smart link layer trigger, a TBScan algorithm, a TBAPS algorithm, a buffering support Bi-Binding scheme and the smart link event notification policy. Experiment results show that our mechanism can achieve reasonable mobility prediction and seamless handover with no interruptions on upper layer applications (VoIP) in IEEE 802.11 WLANs. The average handover latency is less than 50ms, the success rate of mobility prediction is 97.7% and no packet loss is observed.

Multipath transfer utilizes multiple independent end-to-end paths to improve the total transmission throughput. Reliable transmission protocols as TCP and SCTP suffer resequencing delay due to asynchronous packet arrivals at the receiver as a result of packet reordering. The resequencing delay deteriorates the performance of some delay-sensitive applications. In this paper, we make an analysis of the mean resequencing delay for an average packet in a typical multipath transfer scenario. In the scenario, the path delay is assumed to be constant while distinct to each other. In consideration of path selecting probability, path delay and bandwidth, a model of the mean resequencing delay for an average packet in a long-term session is derived. The model is evaluated with the aid of SCTP CMT, which is a technically mature multipath transfer instance. It is shown that the results computed from the analytical model agree well with the output of the simulations. We expect the model can be used as a reference of resequencing delay estimation, aiding multipath transfer algorithms to get a better performance.

Most existing soft handoff approaches lead to plenty of out-of-order packets during downward vertical handoffs (VHOs). We have presented a soft VHO scheme, called SHORDER, to avoid packet reordering caused by downward VHOs. In this paper, we analyze the effects of our SHORDER scheme and another typical existing soft VHO method on the handoff latency and the received data size during a downward VHO for TCP applications. Then, we approximately derive the applicable conditions of the two approaches, and further propose a diagnosis-based soft vertical handoff (DSVH) mechanism which can self-adaptively deal with reordering packets. The mechanism has practical advantages of no changes to correspondent nodes and compatibility with various enhanced TCP variants. With numerical analysis and test-bed experiments, we show that the DSVH mechanism has better performance than the SHORDER scheme and the typical existing method. Furthermore, experimental and analytical results are consistent with each other.

The information of available bandwidth on an end-to-end path is important for various network applications, and several probing methods have been proposed to estimate it in recent years. However, previous methods are either based on fluid model or are only partially suitable for bursty real internet cross traffic; and the accuracy of their estimation degrades at different extents in multi-hop situations. Moreover, all previous PGM (Probing Gap Model) based methods require the knowledge of bottleneck link capacity, which may not be available in practice. In this paper, we extend the analysis of queuing behavior of probing packets from single-hop scenarios to multi-hop scenarios and propose a novel hybrid probing technique, called PATHCOS++, which integrates the advantages of both PRM (Probing Rate Model) and PGM based methods, to estimate the end-to-end available bandwidth. Unlike previous works, PATHCOS++ does not make fluid cross traffic assumption and does not require the information about bottleneck link capacity. Simulation results show that PATHCOS++ is quite efficient and provides end-to-end available bandwidth estimation that is significantly more accurate than current state-of-the-art techniques do. The accuracy of PATHCOS++ is nearly unaffected when there are multiple congestible links.

Experiments show that FMIPv6 may suffer long handover latency even in the predictive mode in the real IEEE 802.11 based WLANs. To figure out the latency reason and identify potential enhancement of FMIPv6, we analyze the handover latency of the predictive FMIPv6 from a cross layer perspective using data collected in a real IEEE 802.11 test-bed. We find that the key issues affecting the handover latency of the predictive FMIPv6 in IEEE 802.11 WLANs are the lack of assistance from the network entities, the ambiguous link layer triggering time and the inefficient interaction between the link layer and the network layer. And FMIPv6 can provide fast handover with no interruptions on the upper layer applications in IEEE 802.11 WLANs through some enhancements, which can resolve the above three issues.

Many RSS-based vertical handoff algorithms have recently been proposed. However, there are only a few models to evaluate the performance of vertical handoff algorithms and none of the existing models reflect the effect of a doorway on received signal strength (RSS). Considering that RSS from heterogeneous networks cannot be directly compared with each other, we firstly present an effective method to compare RSS of different networks, based on the corresponding bandwidth in each network. Then, we take into account signal abrupt attenuation near a doorway and construct a novel performance evaluation model for RSS-based vertical handoff algorithms. This model also reflects the correlation between RSS at two adjacent locations in a WLAN. Following that, we propose an integrative performance evaluation function based on two metrics - the decision delay and the number of handoffs. Furthermore, we analyze hysteresis and dwell-timer algorithms with our model. The results show a good match between simulation and analysis.

Existing soft handoff techniques lead to plenty of out-of- sequence packets during downward vertical handoffs (DVHOs). In this paper, we present two new network-layer soft vertical handoff schemes, called SHORDER and E-SHORDER. The former can prevent mobile nodes from receiving reordered packets during DVHOs with a low overhead. The latter further hinders their correspondent nodes from receiving out-of-order packets caused by mobile nodes' DVHOs. Then, we analyze the performance of our proposed approaches. By experiments, we show that they have a good effect in practice.

Multihoming feature enables stream control transmission protocol (SCTP) to be a handover and mobility scheme for mobile users in multihomed environment. However, there are two flaws deteriorating SCTP handover performance. First, the congestion control mechanism of SCTP makes entire association experience a slow-start phase after handover, thus leading to a sudden-shrink of throughput. Second, the diversity of round-trip time (RTT) between separate paths incurs packet reordering and likely to trigger the spurious fast-retransmit, which degrades performance of reliable transport, too. This paper presents SHOP: an integrated scheme for SCTP handover optimization. By means of available bandwidth estimation technologies, SHOP avoids the slow-start phase after handover through configuring proper congestion control variables of new primary path. Meanwhile, postpone-handover based on RTT measurement is put forward in SHOP to minimize the negative effects of packet reordering. Detailed simulation results show the effectiveness of proposed scheme.

Previous Probe Gap Model (PGM) based available bandwidth (AB) estimation methods all request the "busy assumption" that probing packet pairs should be in the same busy period when transmitted on bottleneck link, which is hard to satisfy especially for the low utilization path. In this paper, we first present a new probabilistic methodology to estimate AB under "non busy assumption". The methodology is quite accurate on the low utilization network path. Secondly, we propose a metric to weigh the busyness of a network path based on the distribution of output probe gap. Using the metric, we combine our new methodology and previous methodology, and present a new AB estimation method called Adaptive Available Bandwidth Estimation (A_ABE) which is fit for both low utilization and high utilization paths. We use NS-2 simulation and reproduce traffic from real Internet links to evaluate A_ABE. Compared with previous methods, A_ABE shows its advantages in terms of accuracy, overhead, and also the robustness when confronted with non-persistent cross traffic in multiple hop situations.

In heterogeneous wireless networks, handoff can be separated into two parts: horizontal handoff (HHO) and vertical handoff (VHO). VHO plays an important role to fulfill seamless data transfer when mobile nodes cross wireless access networks with different link layer technologies. Current VHO algorithms mainly focus on when to trigger VHO, but neglect the problem of how to synthetically consider all currently available networks (homogeneous or heterogeneous) and choose the optimal network for HHO or VHO from all the available candidates. In this paper, we present an analytical framework to evaluate VHO algorithms. Subsequently, we extend the traditional hysteresis based and dwelling-timer based algorithms to support both VHO and HHO decisions and apply them to complex heterogeneous wireless environments. We refer to these enhanced algorithms as E-HY and E-DW, respectively. Based on the proposed analytical model, we provide a formalization definition of the handoff conditions in E-HY and E-DW and analyze their performance. Subsequently, we propose a novel general handoff decision algorithm, GHO, to trigger HHO and VHO in heterogeneous wireless networks. Analysis shows that GHO can achieve better performance than E-HY and E-DW. Simulations validate the analytical results and verify that GHO outperforms traditional algorithms in terms of the matching ratio, TCP throughput and UDP throughput.

The next generation wireless networking (4G) is envisioned as a convergence of different wireless access technologies with diverse levels of performance. Vertical handoff (VHO) is the basic requirement for convergence of different access technologies and has received tremendous attention from the academia and industry all over the world. During the VHO procedure, handoff decision is the most important step that affects the normal working of communication. In this paper, we propose a novel vertical handoff decision algorithm, self- adaptive VHO algorithm (SAVA), and compare its performance with conventional algorithms. SAVA synthetically considers the long term movement region and short term movement trend of mobile hosts, and achieves a good integrative handoff performance.

In recent years, many research works have focused on vertical handoff (VHO) decision algorithms. However, evaluation scenarios in different papers are often quite different and there is no consensus on how to evaluate performance of VHO algorithms. In this paper, we address this important issue by proposing an approach for systematic and thorough performance evaluation of VHO algorithms. Firstly we define the evaluation criteria for VHO with two metrics: matching ratio and average ping-pong number. Subsequently we analyze the general movement characteristics of mobile hosts and identify a set of novel performance evaluation models for VHO algorithms. Equipped with these models and evaluation criteria, we evaluate and analyze two types of decision algorithms: hysteresis based and dwelling-timer based algorithms. The results show a good match between simulation and analytical results.

We present an original end-to-end available bandwidth measurement method, called SMART (statistics measurement for avail-bw by random train). It resolves some of the problems common for many types of existing probing methods, e.g. the long latency and large probe traffic. Contrary to traditional estimates of available bandwidth, SMART is not a methodology based on packet dispersion in packet pair or packet train, but a completely new methodology in the light of probability and statistics. The fundamental idea is to send very small packets at random moment and calculate the proportion of minimal delay ion total test samples. To reach this purpose, we redefine the available bandwidth based on probability and statistics. We have evaluated our method in controlled and reproducible environment using NS2, and the simulations show our method is accurate, efficient, quick and non-intrusive.