Rapid development of web services has led to an explosion of multimodal content, making multimodal recommender systems (MRSs) vital tools for mitigating information overload. Current MRSs have achieved remarkable progress by incorporating advanced technologies such as Graph Neural Networks (GNNs) and Large Language Models (LLMs). However, these studies still suffer from the semantic shift problem. Generally, item's multimodal content usually contain multiple objects, including target object (core content of item) and auxiliary objects (decorations of item). Existing MRSs overlooked this distinction, failing to prevent auxiliary objects from dominating the representation, leading to biased item representation. To address this issue, we propose a model-agnostic framework ''TargetMR''. Concretely, TargetMR comprises two core modules, including Object Disentangler and Object Identifier. The Object Disentangler decouples item text and image into multiple objects via text syntactic parsing and image segmentation. The Object Identifier performs knowledge distillation based on LLMs to efficiently identify the target text object. It then identifies the target image object through cross-modal semantic evaluation. Moreover, this module refines the representation of image target object by optimizing the semantic correlation. Owing to the model-agnostic design of TargetMR, it can be integrated into various backbone MRSs. Extensive experiments on three benchmark datasets show that TargetMR consistently improves the performance of five backbone MRSs, with an average improvement of 12.26%. Our codes are available at https://github.com/gutang-97/TargetMR/.

Out-of-Distribution (OOD) generalization is a promising yet challenging goal that guarantees the test performance of Graph Neural Networks (GNNs) in open-world settings. However, due to the intricate internal topology of graph-structured data, redundant information from the spurious topologies severely confuses GNNs to deviate from the labels. Extracting concise and label-relevant subgraphs from the original graphs can alleviate this problem. Unfortunately, existing methods either overlook the global structural distribution or rely heavily on manually predefined assumptions. As a result, they fall short of well capturing the structural distribution changes between input graph and extracted subgraph, thus compromising adaptability of extracted invariant subgraphs to diverse OOD scenarios. This motivates us to propose a framework called S tructural- E ntropy-guided I nformation B ottleneck (OOD-SEIB) that aims to more traceably measure the inherent information changes for better and more flexible OOD generalization. The core of OOD-SEIB lies in concise topology extraction module, where we measure the mutual information flow between input graph and extracted subgraph based on structural entropy, termed Compression Index (CI). Specifically, the CI is a quantifiable metric that calculates the codeword length required to describe entire graph structure via a biased random walk. Under this guidance, OOD-SEIB then launches a structural information bottleneck compression module that jointly optimizes both CI and label-relevance of the subgraph topology by iteratively balancing between informativeness and compression. To further improve GNN’s invariant subgraph identification capability, OOD-SEIB generates multiple augmented environments and distill the invariant subgraphs into GNN as knowledge in an inside-out manner. When iteratively optimizing in above prescribed way, OOD-SEIB progressively reinforce the invariant subgraph extraction, thereby enhancing its generalization capability. Extensive experiments on synthetic and three real-world graph-level OOD benchmarks demonstrate that our proposed OOD-SEIB improves classification accuracy by 4.85%–38.03% on average compared to state-of-the-art baselines. Additionally, we extend OOD-SEIB to two node-level benchmarks, achieving average classification accuracy improvements of 14.52% and 13.15%.

With the development of data-centric AI, the focus has shifted from model-driven approaches to improving data quality. Academic literature, as one of the crucial types, is predominantly stored in PDF formats and needs to be parsed into texts before further processing. However, parsing diverse structured texts in academic literature remains challenging due to the lack of datasets that cover various text structures. In this paper, we introduce AceParse, the first comprehensive dataset designed to support the parsing of a wide range of structured texts, including formulas, tables, lists, algorithms, and sentences with embedded mathematical expressions. Based on AceParse, we fine-tuned a multimodal model, named AceParser, which accurately parses various structured texts within academic literature. This model outperforms the previous state-of-the-art by 4.1% in terms of F1 score and by 5% in Jaccard Similarity, demonstrating the potential of multimodal models in academic literature parsing. Our dataset is available at https://github.com/JHW5981/AceParse.

Reasoning over Knowledge Graphs (KGs) plays a pivotal role in knowledge graph completion or question answering systems, providing richer and more accurate triples and attributes. As numerical attributes become increasingly essential in characterizing entities and relations in KGs, the ability to reason over these attributes has gained significant importance. Existing graph-based methods such as Graph Neural Networks (GNNs) and Knowledge Graph Embeddings (KGEs), primarily focus on aggregating homogeneous local neighbors and implicitly embedding diverse triples. However, these approaches often fail to fully leverage the potential of logical paths within the graph, limiting their effectiveness in exploiting the reasoning process. To address these limitations, we propose ChainsFormer, a novel chain-based framework designed to support numerical reasoning. Chainsformer not only explicitly constructs logical chains but also expands the reasoning depth to multiple hops. Specially, we introduces Relation-Attribute Chains (RA-Chains), a specialized logic chain, to model sequential reasoning patterns. ChainsFormer captures the step-by-step nature of multi-hop reasoning along RA-Chains by employing sequential in-context learning. To mitigate the impact of noisy chains, we propose a hyperbolic affinity scoring mechanism that selects relevant logic chains in a variable-resolution space. Furthermore, ChainsFormer incorporates an attention-based numerical reasoner to identify critical reasoning paths, enhancing both reasoning accuracy and transparency. Experimental results demonstrate that ChainsFormer significantly outperforms state-of-the-art methods, achieving up to a 20.0% improvement in performance. The implementations are available at https://github.com/zhaodazhuang2333/ChainsFormer.

Evolving graphs are prevalent in real-world systems where nodes and edges continuously expand over time, resulting in persistent topological changes across temporal snapshots. As the graph expands, the increasing structural discrepancy between earlier and later timesteps often leads to poor generalization-a phenomenon widely recognized as Temporal out-of-distribution (TOOD) generalization. This issue has spurred growing research interest in generalized graph learning. Existing approaches often rely on modeling latent environments or incorporating structural causal reasoning, but they frequently face scalability limitations when applied to rapidly expanding graphs. In this work, we carry out a detailed analysis of temporal drift in evolving graphs and identify two complementary forms: structural role drift reflected by changes in degree proportions, and semantic drift measured by embedding divergence. To capture this graph evolution, we propose PromptEG, a scalable prompt-based framework that generates node-specific prompts using degree signals and KL-divergence between representations. These prompts are optimized via consistency and contrastive objectives, and injected into GNNs to enhance temporal generalization. Experiments on largescale evolving graphs-containing up to 500 K edges and tens of thousands of nodes-demonstrate that PromptEG achieves strong long-horizon generalization while maintaining high computational efficiency.

With the explosive growth of online multimodal content, multimodal recommender systems(MRSs) have brought significant benefits to multimedia platforms. As MRSs evolve, many studies incorporate advanced technologies like graph neural networks(GNNs) and self-supervised learning(SSL), achieving remarkable results. However, these efforts still suffer from the quality disparity problem. It refers to the mixture of high and low quality across items' multiple modalities, owing to disparities in construction costs or design levels. These low-quality modalities often lack crucial details or introduce noise to the depiction of item, leading to insufficient or polluted item representation. Therefore, we propose a novel framework R2MR: Review and Rewrite Modality for Recommendation to tackle this issue. Specifically, R2MR is composed of two key components: Modality Reviewer and Modality Rewriter. The Modality Reviewer introduces a Consensus Review Mechanism. It performs perspective decomposition based on user representations and learns the consensus quality scores for modalities from diverse perspectives of multiple users. The Modality Rewriter proposes a Latent Mapping Model, which improves the quality of inferior modalities by learning various mapping patterns from high-quality modalities. Comprehensive experiments across three benchmark datasets reveal that R2MR substantially outperforms state-of-the-art methods, achieving an average improvement of 9.20%. The implementations are available at https://github.com/gutang-97/R2MR.

The best arm identification problem in multi-armed bandit model has been widely applied into many practical applications, such as spectrum sensing, online advertising, and cloud computing. Although lots of works have been devoted into this area, most of them do not consider the cost of pulling actions, i.e., a player has to pay some cost when she pulls an arm. Motivated by this, we study a ratio-based best arm identification problem, where each arm is associated with a random reward as well as a random cost. For any \(\delta\in(0,1)\) , with probability at least \(1-\delta\) , the player aims to find the arm with the largest ratio of expected reward to expected cost using as few samplings as possible. Specifically, we consider two settings: (1) the precise setting, i.e., identifying the precise optimal one; (2) the Probably Approximate Correct (PAC) setting, which identifies the \(\epsilon\) -optimal one. For the precise setting, we design the elimination-type algorithms and provide a fundamental lower bound which asymptotically matches the upper bound, while in the PAC setting, an UCB-type algorithm which amed \(\epsilon\) -RCB algorithm is proposed. We show that for all algorithms, the sample complexities, i.e., the pulling times for all arms, grow logarithmically as \(\frac{1}{\delta}\) increases. Moreover, compared to existing works, the running of our algorithms is independent of the arm-related parameters, which is more practical. Finally, we validate our theoretical results through numerical experiments.

Shuqian Sheng, Yi Xu, Tianhang Zhang, Zanwei Shen, Luoyi Fu, Jiaxin Ding, Lei Zhou, Xiaoying Gan, Xinbing Wang, Chenghu Zhou. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Graph Neural Networks (GNNs) are widely deployed in vast fields, but they often struggle to maintain accurate representations as graphs evolve. We theoretically establish a lower bound, proving that under mild conditions, representation distortion inevitably occurs over time. To estimate the temporal distortion without human annotation after deployment, one naive approach is to pre-train a recurrent model (e.g., RNN) before deployment and use this model afterwards, but the estimation is far from satisfactory. In this paper, we analyze the representation distortion from an information theory perspective, and attribute it primarily to inaccurate feature extraction during evolution. Consequently, we introduce Smart, a straightforward and effective baseline enhanced by an adaptive feature extractor through self-supervised graph reconstruction. In synthetic random graphs, we further refine the former lower bound to show the inevitable distortion over time and empirically observe that Smart achieves good estimation performance. Moreover, we observe that Smart consistently shows outstanding generalization estimation on four real-world evolving graphs. The ablation studies underscore the necessity of graph reconstruction. For example, on OGB-arXiv dataset, the estimation metric MAPE deteriorates from 2.19% to 8.00% without reconstruction.

Accurately reconstructing the global ocean deoxygenation over a century is crucial for assessing and protecting marine ecosystem. Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities. Besides, due to the high-cost data collection, the historical observations are severely sparse, leading to big challenge for precise reconstruction. In this work, we propose OxyGenerator, the first deep learning based model, to reconstruct the global ocean deoxygenation from 1920 to 2023. Specifically, to address the heterogeneity across large temporal and spatial scales, we propose zoning-varying graph message-passing to capture the complex oceanographic correlations between missing values and sparse observations. Additionally, to further calibrate the uncertainty, we incorporate inductive bias from dissolved oxygen (DO) variations and chemical effects. Compared with in-situ DO observations, OxyGenerator significantly outperforms CMIP6 numerical simulations, reducing MAPE by 38.77%, demonstrating a promising potential to understand the “breathless ocean” in data-driven manner.

With the enrichment of user-item interactions, Graph Neural Networks (GNNs) are widely used in recommender systems to alleviate information overload. Nevertheless, they still suffer from the cold-start issue. Knowledge Graphs (KGs), providing external information, have been extensively applied in GNN-based methods to mitigate this issue. However, current KG-aware recommendation methods suffer from the knowledge imbalance problem caused by incompleteness of existing KGs. This imbalance is reflected by the long-tail phenomenon of item attributes, i.e., unpopular items usually lack more attributes compared to popular items. To tackle this problem, we propose a novel framework called EditKG: Editing Knowledge Graph for Recommendation, to balance attribute distribution of items via editing KGs. EditKG consists of two key designs: Knowledge Generator and Knowledge Deleter. Knowledge Generator generates attributes for items by exploring their mutual information correlations and semantic correlations. Knowledge Deleter removes the task-irrelevant item attributes according to the parameterized task relevance score, while dropping the spurious item attributes through aligning the attribute scores. Extensive experiments on three benchmark datasets demonstrate that EditKG significantly outperforms state-of-the-art methods, and achieves 8.98% average improvement. The implementations are available at https://github.com/gutang-97/2024SIGIR-EditKG.

Network alignment, pairing corresponding nodes across the source and target networks, plays an important role in many data mining tasks. Extensive studies focus on learning node embeddings across different networks in a unified space. However, these methods have not taken the large structural discrepancy between aligned nodes into account and, thus, are largely confined by the deterministic representations of nodes. In this work, we propose a novel network alignment framework highlighted by distributional learning and globally optimal alignment. By modeling the uncertainty of each node by Gaussian distribution, our framework builds similarity matrices on the Wasserstein distance between distributions and applies Sinkhorn operation, which learns the globally optimal mapping in an end-to-end fashion. We show that each integrated part of the framework contributes to the overall performance. Under a variety of experimental settings, our alignment framework shows superior accuracy and efficiency to the state-of-the-art.

Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. However, due to the selection bias of training and testing data (e.g., training on small graphs and testing on large graphs, or training on dense graphs and testing on sparse graphs), distribution deviation is widespread. More importantly, we often observehybrid structure distribution shiftof both scale and density, despite of one-sided biased data partition. The spurious correlations over hybrid distribution deviation degrade the performance of previous GNN methods and show large instability among different datasets. To alleviate this problem, we proposeOOD-GMixupto jointly manipulate the training distribution withcontrollable data augmentationin metric space. Specifically, we first extract the graph rationales to eliminate the spurious correlations due to irrelevant information. Secondly, we generate virtual samples with perturbation on graph rationale representation domain to obtain potential OOD training samples. Finally, we propose OOD calibration to measure the distribution deviation of virtual samples by leveraging Extreme Value Theory, and further actively control the training distribution by emphasizing the impact of virtual OOD samples. Extensive studies on several real-world datasets on graph classification demonstrate the superiority of our proposed method over state-of-the-art baselines.

Graph Neural Networks (GNNs) have achieved great success in semi-supervised node classification. These methods usually assume the closed-set setting and classify unlabeled nodes to known classes. However, in reality, there exits some unknown classes due to biased-sampling, distribution shifts, anomaly, etc. Therefore, it is important to identify unknown classes while classifying known classes, which is defined as Graph Open-Set Recognition (GOSR). To alleviate this problem, we propose Entropy Message Passing (EMP) for GOSR, which takes into account the graph structure information when identifying unknown classes and automatically determines the discrimination threshold. To be specific, we calculate the likelihood of a node belonging to unknown class through entropy propagation. Then, we transform the threshold selection into entropy clustering to identify the unknown class nodes. Finally, we classify the remaining nodes. Experimental evaluations on six benchmark graph datasets demonstrate that our method outperforms state-of-the-art baseline methods in unknown class detection and graph open-set recognition tasks. Especially in unknown class detection, our method has achieved a significant reduction in FPR@95 ranging from 12.62% to 55.88%.

Nowadays, the rapid development of self-driving technology and its fusion with the current vehicle electrification process has given rise to electric self-driving taxis (es-taxis). Foreseeably, es-taxis will become a major force that serves the massive urban mobility demands not far into the future. Though promising, it is still a fundamental unsolved problem of effectively deciding when and where a city-scale fleet of es-taxis should be charged, so that enough es-taxis will be available whenever and wherever ride requests are submitted. Furthermore, charging decisions are far from isolated, but tightly coupled with the order dispatch process that matches orders with es-taxis. Therefore, in this paper, we investigate the problem of joint order dispatch and charging in es-taxi systems, with the objective of maximizing the ride-hailing platform’s long-term cumulative profit. Technically, such problem is challenging in a myriad of aspects, such as long-term profit maximization, partial statistical information on future orders, etc. We address the various arising challenges by meticulously integrating a series of methods, including distributionally robust optimization, primal-dual transformation, and second order conic programming to yield far-sighted decisions. Finally, we validate the effectiveness of our proposed methods though extensive experiments based on two large-scale real-world online ride-hailing order datasets.

Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some developing cities have few available data, which makes it infeasible to train a well-performed model. To address this challenge, cross-city knowledge transfer has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. However, the spatio-temporal graphs among different cities show irregular structures and varied features, which limits the feasibility of existing Few-Shot Learning (FSL) methods. Therefore, we propose a model-agnostic few-shot learning framework for spatio-temporal graph called ST-GFSL. Specifically, to enhance feature extraction by transferring cross-city knowledge, ST-GFSL proposes to generate non-shared parameters based on node-level meta knowledge. The nodes in target city transfer the knowledge via parameter matching, retrieving from similar spatio-temporal characteristics. Furthermore, we propose to reconstruct the graph structure during meta-learning. The graph reconstruction loss is defined to guide structure-aware learning, avoiding structure deviation among different datasets. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods.

With the tremendous expansion of graphs data, node classification shows its great importance in many real-world applications. Existing graph neural network based methods mainly focus on classifying unlabeled nodes within fixed classes with abundant labeling. However, in many practical scenarios, graph evolves with emergence of new nodes and edges. Novel classes appear incrementally along with few labeling due to its newly emergence or lack of exploration. In this paper, we focus on this challenging but practical graph few-shot class-incremental learning (GFSCIL) problem and propose a novel method called Geometer. Instead of replacing and retraining the fully connected neural network classifier, Geometer predicts the label of a node by finding the nearest class prototype. Prototype is a vector representing a class in the metric space. With the pop-up of novel classes, Geometer learns and adjusts the attention-based prototypes by observing the geometric proximity, uniformity and separability. Teacher-student knowledge distillation and biased sampling are further introduced to mitigate catastrophic forgetting and unbalanced labeling problem respectively. Experimental results on four public datasets demonstrate that Geometer achieves a substantial improvement of 9.46% to 27.60% over state-of-the-art methods.

Emerging crowdsensing paradigm enables a large number of sensing applications, where much attention is drawn to the fundamental problems of data collection and truth inference. Existing works have devised manifold techniques to discover truth from collected noisy data, but they frequently ignore various expertise of workers and dynamic information of crowdsensing system, thus leading to error-prone estimated truth and unqualified sensing data. In this paper, we design an online location-aware crowdsensing system to accurately estimate truth and efficiently assign tasks. Specifically, we unify diverse types of numerical and categorical tasks based on probabilistic graphical model, and then propose unsupervised learning methods which can dynamically infer ground truth and various worker expertise at the same time. Furthermore, we develop online task allocation schemes to gradually gather high quality data considering location awareness and inferred worker expertise. In particular, we convert the complicated task allocation into the additive form of probability improvement and entropy reduction, thereby solving the allocation problem via linearly selecting worker-task pair with low computation complexity. We finally carry out extensive evaluations using two datasets collected by our smartphones, where results demonstrate the superiority of our algorithms over the state-of-the-art approaches.

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

Recent years have witnessed the proliferation of electric vehicles (EVs) that enable environment-friendly commuting and traveling. However, the increasing number of EVs inevitably create massive charging demands that are challenging to satisfy. Oftentimes in practice, EVs have to wait in queues for a long time outside charging stations before chargers become available. To address this challenge, we fully capture the elasticity of EVs’ charging demands in response to the charging prices, and propose a dynamic charging pricing mechanism that jointly controls the lengths of the demand queues at multiple charging stations and maximizes the charging platform’s long-term profit for offering charging services. Clearly, such an approach is more feasible than the financially and temporally expensive way of constructing extra charging facilities. Technically, we augment the Lyapunov stochastic optimization technique to decompose the challenging long-term decision-making problem into a series of single-time-slot optimization programs which require zero knowledge of future system parameters. However, due to the correlation of charging demands among different stations, the aforementioned optimization program in each time slot is non-convex. We handle the non-convexity by jointly constructing independent sets of charging stations and adapting the block coordinate descent method to iteratively obtain approximately optimal charging prices. Through rigorous theoretical analysis and extensive simulations based on the real-world dataset in the Chinese city Shenzhen which consists of 4000 taxis and 171 charging stations, we demonstrate that our control policy ensures an arbitrarily close-to-optimal profit with a flexible trade-off between the profit and queue lengths, has a low computational complexity, and requires zero knowledge of future system dynamics.

Mobile edge computing enables resource-limited edge clouds (ECs) in federation to help each other with resource-hungry yet delay-sensitive service requests. Contrary to common practice, we acknowledge that mobile services are heterogeneous and the limited storage resources of ECs allow only a subset of services to be placed at the same time. This paper presents a jointly optimized design of cooperative placement and scheduling framework, named JCPS, that pursuessocial cost minimizationover time while ensuring diverse user demands. Our main contribution is a novel perspective on cost reduction by exploiting thespatial-temporal diversitiesinworkload and resource costamong federated ECs. To build a practical edge cloud federation system, we have to consider two major challenges:user deadline preferenceandECs’ strategic behaviors. We first formulate and solve the problem of spatially strategic optimization without deadline awareness, which is proved$\mathcal {NP}$-hard. By leveraging user deadline tolerance, we develop a Lyapunov-baseddeadline-drivenjoint cooperative mechanism under the scenario where the workload and resource information of ECs are known for one-shot global cost minimization. Theservice priorityimposed by deadline urgency drives time-critical placement and scheduling, which, combined with cooperative control, enables workloads migrated across different times and ECs. Given selfishness of individual ECs, we further design an auction-based cooperative mechanism to elicittruthful bidson workload and resource cost. Rigorous theoretical analysis and extensive simulations are performed, validating the efficiency of JCPS in realizing cost reduction and user satisfaction.

Many realistic mobile social networks can be characterized by evolving bipartite graphs, in which dynamically added elements are divided into two entities and connected by links between these two entities, such as users and items in recommendation networks, authors and scientific topics in scholarly networks, male and female in dating social networks, etc. However, given the fact that connections between two entities are often weighted, how to mathematically model such weighted evolving bipartite relationships, along with quantitative characterizations, remains unexplored. Motivated by this, we develop a novel evolving bipartite model (EBM), which, based on empirically validated power-law distribution on multiple realistic mobile social networks, discloses that the distribution of total weights of incoming and outgoing edges in networks is determined by the weighting scale and bounded by certain ceilings and floors. Based on these theoretical results, for evolving bipartite networks whose degree follows power-law distribution, their overall weights of vertices can be predicted by EBM. To illustrate, in recommendation networks, the evaluation of items, i.e., total rating scores, can be estimated through the given bounds; in scholarly networks, the total numbers of publications under specific topics can be anticipated within a certain range; in dating social networks, the favorability of male/female can be roughly measured. Finally, we perform extensive experiments on 10 realistic datasets and a synthetic network with varying weights, i.e., rating scales, to further evaluate the performance of EBM, and experimental results demonstrate that given weighting scales, both the upper bound and the lower bound of total weights of vertices in mobile social networks can be properly predicted by the EBM.

As a critical part of the intelligent transportation system, traffic prediction is challenging due to the time-evolving cascading behavior, i.e., the fluctuation of traffic conditions on one road will affect neighboring roads in the future. To address this issue, we propose a novel learning framework, which is able to extract the most relevant historical information for prediction by capturing the underlying cascading behavior. An encoder-decoder architecture is adopted, where the historical contextual information of each road is encoded into a sequence of historical embeddings. A spatiotemporal attention mechanism is devised to model the cascading behavior in the embedding space so that the most relevant information for prediction is concentrated. Extensive experiments on a real-world large-scale highway dataset verify the effectiveness of our proposed approach, observing 3% ~ 5% improvement over state-of-the-art methods.

Vehicular urban sensing (VUS), which uses sensors mounted on crowdsourced vehicles or on-board drivers' smartphones, has become a promising paradigm for monitoring critical urban metrics. Due to various hardware and software constraints difficult for private vehicles to satisfy, for-hire vehicles (FHVs) are usually the major forces for VUS systems. However, FHVs alone are far from enough for fine-grained spatio-temporal sensing coverage, because of their severe distribution biases. To address this issue, we propose to use a hybrid approach, where a centralized platform not only leverages FHVs to conduct sensing tasks during their daily movements of serving passenger orders, but also controls multiple dedicated sensing vehicles (DSVs) to bridge FHVs' coverage gaps. Specifically, we aim to achieve fine-grained spatio-temporal sensing coverage at the minimum long-term operational cost by systematically optimizing the repositioning policy for DSVs. Technically, we formulate the problem as a stochastic dynamic program, and solve various challenges, including long-term cost minimization, stochastic demand with partial statistical knowledge, and computational intractability, by integrating distributionally robust optimization, primal-dual transformation, and second order conic programming methods. We validate the effectiveness of our methods using a real-world dataset from Shenzhen, China, containing 726,000 trajectories of 3848 taxis spanning overall 1 month in 2017.

This paper studies the problem of source detection, which is to infer the source node out of an aftermath of a cascade, i.e., the observed infected graph GN of the network at some time. Prior arts have adopted various statistical quantities such as degree, distance or infection size to reflect the structural centrality of the source. In this paper, we propose a new metric which we call the infected tree entropy (ITE), to utilize richer underlying structural features for source detection. Our idea of ITE is inspired by the conception of structural entropy [1], which demonstrated that the minimization of average bits to encode the network structures with different partitions is the principle for detecting the natural or true structures in real-world networks. Accordingly, our proposed ITE based estimator for the source tries to minimize the coding of network partitions brought by the infected tree rooted at all the potential sources, thus minimizing the structural deviation between the cascades from the potential sources and the actual infection process included in GN. On polynomially growing geometric trees, with increasing tree heterogeneity, the ITE estimator remarkably yields more reliable detection under only moderate infection sizes. In contrast, for regular expanding trees, we still observe guaranteed detection probability of ITE estimator even with an infinite infection size, thanks to the degree regularity property. We also algorithmically realize the ITE based detection that enjoys linear time complexity via a message-passing scheme, and further extend it to general graphs. Experiments on various network topologies confirm the superiority of ITE to the baselines. For example, ITE returns an accuracy of 75% ranking the source among top 5%, far exceeding 45% of the classic algorithms on scale-free networks.

Knowledge diffusion is capable of narrowing the knowledge gap between individuals and is conducive to transforming knowledge into economically and socially valuable outcomes. Although knowledge diffusion is of particular importance, efficient modeling and analysis of knowledge diffusion may not be achieved easily. In this paper, we apply the concept of evolving knowledge graph (EKG) in modeling and analyzing knowledge diffusion and propose an EKG-based knowledge diffusion model. To characterize the relationships between researchers and papers in the proposed network model, we define three subnetworks, i.e., relationship subnetwork, citation subnetwork and author-paper subnetwork, and analyze the evolution process of the subnetworks. The knowledge diffusion process in the proposed network model is then examined. We analyze respectively the impacts of interpersonal contact and paper citation in knowledge diffusion, and propose a modified susceptible-infective-recovered-susceptible (SIRS) epidemic model-based knowledge diffusion process. Theoretical analysis is conducted to examine the expected values of the weighted degree of network entities, and the transmission threshold of knowledge diffusion process. Extensive simulations are performed to evaluate the performance of the proposed network model.

Recent years have witnessed the rising popularity of urban vehicular crowd sensing (UVCS) systems that leverage drivers' mobile devices equipped with on-board sensors for various urban sensing tasks. Because of the importance of ensuring satisfactory spatio-temporal sensing coverage in such UVCS systems, most existing work has focus on designing efficient scheduling mechanisms to maximize the task completion rate under drivers' traveling constraints. Different from prior work, we propose Hector, a joint trajectory scheduling and incentive mechanism for spatio-temporal UVCS systems, which concentrates on capturing the interactive effects between scheduling and incentive mechanisms. Technically, we first reduce the dimensions of the original scheduling problem by mapping it into an augmented set cover problem with spatio-temporal constraints. Then, based on reverse combinatorial auctions, we design Hector, whose incentive mechanism with the presence of uncertain future trajectory information makes scheduling and compensation decisions in real-time. Specifically, Hector is truthful, individual rational and computationally efficient. Furthermore, the social cost yielded by Hector is close-to-optimal, and the approximation ratio is Hm. The advantageous properties of Hector are verified by both rigorous theoretical analysis and extensive simulations based on the real world datasets in the Chinese city Shenzhen which consists of 726,000 taxi trajectories.

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. It is quite challenging due to the complicated spatiotemporal dependency and essential uncertainty brought about by the dynamic urban traffic conditions. In most of existing methods, the spatial correlation is captured by utilizing graph neural networks (GNNs) throughout a fixed graph based on local spatial proximity. However, urban road conditions are complex and changeable, which leads to the interactions between roads should also be dynamic over time. In addition, the global contextual information of roads are also crucial for accurate forecasting. In this paper, we exploit spatiotemporal correlation of urban traffic flow and construct a dynamic weighted graph by seeking both spatial neighbors and semantic neighbors of road nodes. Multi-head self-attention temporal convolution network is utilized to capture local and long-range temporal dependencies across historical observations. Besides, we propose an adaptive graph gating mechanism to extract selective spatial dependencies within multi-layer stacking and correct information deviations caused by artificially defined spatial correlation. Extensive experiments on real world urban traffic dataset from Didi Chuxing GAIA Initiative have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines. The source code of our model is publicly available at https://github.com/RobinLu1209/STAG-GCN.

The best arm identification problem in multi-armed bandit model has been widely applied into many practical applications, such as spectrum sensing, online advertising, and cloud computing. Although lots of works have been devoted into this area, most of them do not consider the cost of pulling actions, i.e., a player has to pay some cost when she pulls an arm. Motivated by this, we study a ratio-based best arm identification problem, where each arm is associated with a random reward as well as a random cost. For any δ ∈ (0, 1), with probability at least 1-δ, the player aims to find the optimal arm with the largest ratio of expected reward to expected cost using as few samplings as possible. To solve this problem, we propose three algorithms: 1) a genie-aided algorithm GA; 2) the successive elimination algorithm with unknown gaps SEUG; 3) the successive elimination algorithm with unknown gaps and variance information SEUG-V, where gaps denote the differences between the optimal arm and the suboptimal arms. We show that for all three algorithms, the sample complexities, i.e., the pulling times for all arms, grow logarithmically as 1/δ increases. Moreover, compared to existing works, the running of our elimination-type algorithms is independent of the arm-related parameters, which is more practical. In addition, we also provide a fundamental lower bound for sample complexities of any algorithms under Bernoulli distributions, and show that the sample complexities of the proposed three algorithms match that of the lower bound in the sense of log 1/δ. Finally, we validate our theoretical results through numerical experiments.

In camera sensor networks (CSNs), full view coverage, meaning that any direction of any point in the operational region is covered by at least one camera sensor, plays a significant role in object identification. While prior work is dedicated to static CSNs for the sake of critical condition to achieve full view coverage, such performance still remains unknown in mobile CSNs. In this paper, we take the initiative to address this issue, where a centralized parameter, i.e., equivalent sensing radius (ESR), is defined to unravel the critical requirement for asymptotic full view coverage in mobile heterogeneous CSNs in the sense that camera sensors of different sensing capabilities are moving around in target area. Specifically, we derive ESR under three different mobilities, i.e., 1-dimensional and 2-dimensional random walks and random rotating model, and then explore respectively the corresponding critical conditions to achieve almost surely coverage.1 The static network is introduced as a baseline in order to gain a clear understanding of how mobility affects coverage performance differently. Interestingly, we find that both 1-dimensional and 2-dimensional random walks exhibit a smaller ESR than static one whereas ESR is even larger in random rotating mobility than that in static CSNs. Moreover, the almost surely coverage is found to be around 1.225 times of the critical condition to achieve coverage with high probability,2 and therefore turns out to be a stronger result compared to the traditional coverage with high probability. We then turn to the impact of various mobility patterns on sensing energy consumption, a metric that is closely related to ESR, and show that it can be decreased by random walks under certain delay tolerance. The relationship between ESR and percentage of full view coverage is also discussed and the results unify those under homogeneous CSNs.

Cooperative offloading in mobile edge computing enables resource-constrained edge clouds to help each other with computation-intensive tasks. However, the power of such offloading could not be fully unleashed, unless trust risks in collaboration are properly managed. As tasks are outsourced and processed at the network edge, completion latency usually presents high variability that can harm the offered service levels. By jointly considering these two challenges, we propose OLCD, an Online Learning-aided Cooperative offloaDing mechanism under the scenario where computation offloading is organized based on accumulated social trust. Under co-provisioning of computation, transmission, and trust services, trust propagation is performed along the multi-hop offloading path such that tasks are allowed to be fulfilled by powerful edge clouds. We harness Lyapunov optimization to exploit the spatial-temporal optimality of long-term system cost minimization problem. By gap-preserving transformation, we decouple the series of bidirectional offloading problems so that it suffices to solve a separate decision problem for each edge cloud. The optimal offloading control can not materialize without complete latency knowledge. To adapt to latency variability, we resort to the delayed online learning technique to facilitate completion latency prediction under long-duration processing, which is fed as input to queued-based offloading control policy. Such predictive control is specially designed to minimize the loss due to prediction errors over time. We theoretically prove that OLCD guarantees close-to-optimal system performance even with inaccurate prediction, but its robustness is achieved at the expense of decreased stability. Trace-driven simulations demonstrate the efficiency of OLCD as well as its superiorities over prior related work.

Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issues of matching workers to their assigned tasks and processing the collected data. In this paper, we reexamine these issues by considering the spatio-temporal worker mobility and task arrivals, which more fit the actual situation. Specifically, we study the location-aware and location diversity based dynamic crowdsensing system, where workers move over time and tasks arrive stochastically. We first exploit offline crowdsensing by proposing a combinatorial algorithm, for efficiently distributing tasks to workers. After that, we mainly study the online crowdsensing, and further consider an indispensable aspect of worker's fair allocation. Apart from the stochastic characteristics and discontinuous coverage, the non-linear expectation is incurred as a new challenge concerning fairness issue. Based on Lyapunov optimization with perturbation parameters, we propose online control policy to overcome those challenges. Hereby, we can maintain system stability and achieve a time average sensing utility arbitrarily close to the optimum. Finally, we propose an optimization framework to aggregate the sensing data which can estimate worker expertise and task truth simultaneously. Performance evaluations on real and synthetic data set validate the proposed algorithm, where 80 percent gain of fairness is achieved at the expense of 12 percent loss of sensing value on average.

With the popularization of smart devices and social platforms (Facebook, Instagram and etc.), content transmission is becoming an increasingly prevalent form of human interaction with transmission time being a critical issue in miscellaneous applications. Extensive works have been devoted to this problem with the typical assumption that the network is non-evolving. In contrast, real-world networks which the transmission runs through are widely observed to be evolving over time and thus display distinctive properties. In this paper, we make the first study of content transmission in evolving networks. Particularly, we focus on the specific transmission time of the content, which is an important performance metric. For a comprehensive analysis, we consider both static and mobile cases. The network evolves in the following sense: each new user randomly chooses a geographic location while establishing social relations with existing users according to the evolving model, called Affiliation Networks. The transmission scheme running on the network exploits both social relationships and geographic information. While revealing the microscopic property of evolving social networks as the basis, in both static and mobile cases we manage to bound the transmission time in evolving networks which departs from results in non-evolving networks. In both cases, we find that the transmission time in evolving networks is smaller than the non-evolving counterpart under our scheme, and the gap is constantly increasing over time. Especially, the mobile case obtains such gain with less geographic knowledge. The theoretical findings are confirmed by experiments on both synthetic and real networks under different settings. We find that transmission time in evolving networks is smaller than non-evolving counterparts under the tested settings, and our proposed algorithms outperform the baselines.

Crowdsourcing is an effective paradigm in human centric computing for addressing problems by utilizing human computation power, especially in booming social Internet of Things (IoT). By leveraging mutual friendship between computing entities (i.e., workers), collaborative tasks can thus be routed and finally fulfilled by multihop friends with high expertise. However, crowdsourcing in social IoT may reveal the privacy of task requesters which results in a large dilemma. In this paper, we focus on designing a multihop routing incentive mechanism which can also preserve task requester's privacy. Specifically, a utility maximization problem under privacy and budget feasibility constraints is formulated. Defining the conditions for privacy insurance, we give guidelines on how many subtasks should an entire task be divided into, and analyze the tradeoff between privacy and task accuracy. To enable efficient crowdsourcing task routing in social IoT, we first consider 1-hop myopic routing case and propose a near-optimal task assignment algorithm with 1/2 approximation ratio for an arbitrary prior knowledge. We further design multihop payment policy to establish an equilibrium where workers are motivated to forward subtasks to their friends with the best expertise. The extensive simulations validate that our mechanism achieves a high level of average information gain with modest privacy guarantee.

Combined with many different attack forms, advanced persistent threats (APTs) are becoming a major threat to cyber security. Existing security protection works typically either focus on one-shot case, or separate detection from response decisions. Such practices lead to tractable analysis, but miss key inherent APTs persistence and risk heterogeneity. To this end, we propose a Lyapunov-based security-aware defense mechanism backed by threat intelligence, where robust defense strategy-making is based on acquired heterogeneity knowledge. By exploring temporal evolution of risk level, we introduce priority-aware virtual queues, which together with attack queues, enable security-aware response among hosts. Specifically, a long-term time average profit maximization problem is formulated. We first develop risk admission control policy to accommodate hosts' risk tolerance and response capacity. Under multiple attacker resources, defense control policy is implemented on two-stage decisions, involving proportional fair resource allocation and host-attack assignment. In particular, distributed auction-based assignment algorithm is designed to capture uncertainty in the number of resolved attacks, where high-risk host-attack pairs are prioritized over others. We theoretically prove our mechanism can guarantee bounded queue backlogs, profit optimality, no underflow condition, and robustness to detection errors. Simulations on real-world data set corroborate theoretical analysis and reveal the importance of security awareness.

The scarcity of efficient charging infrastructures has suppressed penetration rate of Electric Vehicles (EVs). This paper mainly focuses on the Fast Charging Station (FCS) placement problem, especially with the elastic demand. We first propose the distance preference and waiting time preference to capture the elastic EV charging demand. Moreover, a fixed-point equation is proposed to illustrate the relationship between serving demand rate and waiting time at station. We further formulate the problem of FCS placement with elastic demand as a bi-level optimization problem. In the upper level, we propose a heuristic algorithm to determine the optimal location of charging stations, with the goal of maximizing overall system profit. In the lower level, queueing theory is utilized to analyze the optimal capacity of each charging station. The sufficient condition for the existence of optimal station capacity is given. Simulation results demonstrate the effectiveness of our approach in improving system profit and reducing demand loss rate.

With the development of D2D (Device-to- Device) communications, the ever increasingly growing demand for content sharing caused by nearby devices can be satisfied. D2D communications based on social relationships are taken into consideration in extant literature, while how to select friends with whom users share contents efficaciously and voluntarily remains a problem. To this end, social tie, as a measurement of content similarity and mutual trust, is introduced in our work to facilitate contents sharing. In the paper, we first model friend selection problem as a finite horizon optimal stopping problem in the frame of optimal stopping theory and derive the optimal social cost threshold. We further prove the optimal social cost threshold is endowed with monotonically non- decreasing property. Moreover, we develop an iterative algorithm based on derived social cost threshold to select friends with whom users are inclined to share contents. Last but not the least, numerous simulation results validate the effectiveness of our proposed algorithm for content sharing over random D2D communications.

Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issue of matching workers to their assigned tasks. In this paper, we reexamine this issue by considering the spatiotemporal worker mobility and task arrivals, which more fits the actual situation. Specifically, we study the location-aware and location diversity based dynamic crowdsensing system, where workers move over time and tasks arrive stochastically. We first exploit offline crowdsensing by proposing a combinatorial algorithm, for efficiently distributing tasks to workers. After that, we mainly study the online crowdsensing, and further consider an indispensable aspect of worker's fair allocation. Apart from the stochastic characteristics and discontinuous coverage, the nonlinear expectation is incurred as a new challenge concerning fairness issue. Based on Lyapunov optimization with perturbation parameters, we propose online control policy to overcome those challenges. Hereby we can maintain system stability and achieve a time average sensing utility arbitrarily close to the optimum. Performance evaluation on real data set validates the proposed algorithm, where 116% gain of fairness is achieved at the expense of 12% loss of sensing value on average.

Real-time road traffic information brings great convenience for drivers. Various road information acquisitions are enabled by recent mobile crowdsensing paradigm. However, the accuracy of information can not be guaranteed, and appropriate incentive mechanism is still unavailable. In this paper, we study the problem of extracting the actual road traffic information according to the reports from an amount of unknown contributors. To obtain the accurate road traffic result with high probability, we establish a reputation system to evaluate the reliability of each contributor, which takes both location and time deviation factors into account. We also design an incentive mechanism to elicit the truthful report of each qualified contributor. Furthermore, we improve the existing answer inference methods and derive the correct result in an efficient way. Extensive simulations are carried out to evaluate the proposed algorithms.

Cache-enabled wireless networks have gained increasing popularity in recent years for facilitating the delivery of content. Most existing works assumed that the network is static without the change of users and content, which is not the case in the real world. In this paper, we study the content delivery in cache-enabled wireless networks, where the network evolves in terms of users and content. We adopt the affiliation network model to reveal the network evolution process, where new users and content are added into the network randomly and connected with existing nodes via the preferential attachment. Users with strong social relations tend to request the same content objects, thus content popularity is sharply concentrated. Therefore, caching technique is more effective. To maximize the content delivery rate, we formulate the optimization problem of caching replication jointly with routing strategy. We show that network evolution can greatly improve the content delivery rate. In particular, when per-user cache capacity is in the order of Θ(1), per-user delivery rate can achieve constant, i.e., the network can scale. Finally, theoretical results are validated based on a real-world data set from Facebook.

Autonomous vehicle industry is developing by leaps and bounds. We envisage a situation where autonomous vehicles act as taxis to transport clients. During the transportation, vehicle sharing is enabled and encouraged. We propose fair allocation in payment for passengers of a shared vehicle. The allocation is endowed with the characteristics of envy freeness and maximin utilities. Furthermore, we devise working mechanisms for autonomous vehicles, including (i) planning routes with minimum detour; (ii) choosing clients to recommend vehicle sharing; (iii) carrying passengers continuously. Our work may serve as a good preparation for the marketization of autonomous taxis.

Network tomography can help detect network link delay. End-to-end path measurement is a promising method in network tomography which could reduce measurement overhead. In end-to-end path measurement, however, deriving link states is computational complex due to the existence of massive links. Because only a few links are congested at the same time, we can locate sparsely congested links by using compressed sensing (CS). In this paper, based on expander graphs, we demonstrate that routing matrix constructed by full tree network can satisfy Restricted Isometry Property (RIP) and be used as measurement matrix. Finally, sparsely congested links are given with CS. Evaluation results show that routing matrix constructed in this paper can help achieve high link delay recovery accuracy, i.e., small recovery error by using 1i-minimization in CS. It also illustrate that the recovery error decreases with the number of congested links decrease.

Coded caching has demonstrated the superiority in mitigating traffic pressure through jointly considering content delivery and storage schemes. However, existing works mainly focus on the situation where users have uniform demands with multiple layer of caches. In this paper, we propose a multilevel hierarchical coded caching scheme when users have nonuniform demands in a multi-hop content delivery network scenario. Specifically, to maintain the symmetry constraint of coded caching we utilize K-means to separate the file set with arbitrary distribution of popularity into several file subsets. We also formulate the Jensen's inequality and derive the upper bound of the transmission rate in each layer. To evaluate the system efficiency, we leverage the open-source Netflix dataset as our file set and conduct an experiment on a content delivery network with two layers of caches. Experimental results demonstrates that our multilevel hierarchical coded caching scheme performs much better than the baseline LFU caching scheme.

In the last two decades, Multi-Cast schemes in mobile ad-hoc networks have been widely studied and lots of research results have been put into use and changed our everyday life. However, packet loss for Multi-Cast schemes in mobile ad-hoc networks has not been well studied yet. The influence of node density and relay schemes on packet loss is not clear. Thus, we study the packet loss problem for Multi-Cast schemes in MANETs in this paper. First, we present a general Multi-Cast probabilistic model to get a better understanding of the packet loss problem. Second, based on the general model and Chernoff Bounds, we prove that the up'er bound of the general packet loss for Multi-Cast is Ω(me- 2), where n is the node number and m denotes number of hops. Thus we can conclude that large node density decreases the packet loss rate by exponential factor, and bigger hops increase packet loss rate linearly. In addition, we present a reliable Multi-Cast protocol(RMP). Through an ACK Aware Tree to complete packet acknowledgement and repair host selection, RMP is able to minimize the impact of packet loss on the Multi-Cast throughput in the simulation.

In this paper, we propose a novel game-based incentive mechanism for multi-resource sharing, where users are motivated to share their idle resources in view of conditional voluntary. Through social networking service platforms, such a crowdsourcing service fully explores the significant influence and computing potential of mobile social networks. Specifically, a combination of task allocation process, profit transfer process, and reputation updating process are involved in this sharing incentive mechanism, satisfying truthfulness, individual rationality, and robustness. To maintain the social fairness-efficiency tradeoff, we further develop a resource sharing algorithm on the basis of dominant resource fairness, revealing that the sacrifice of fairness properties is necessary for the improvement of efficiency. Real-world traces from Facebook are numerically studied, validating social fairness and efficiency of our social crowdsourcing mechanism.

Crowdsourcing systems allocate tasks to a group of workers over the Internet, which have become an effective paradigm for human-powered problem solving, such as image classification, optical character recognition, and proofreading. In this paper, we focus on incentivizing crowd workers to label a set of multi-class labeling tasks under strict budget constraint. We properly profile the tasks’ difficulty levels and workers’ quality in crowdsourcing systems, where the collected labels are aggregated with sequential Bayesian approach. To stimulate workers to undertake crowd labeling tasks, the interaction between workers and the platform is modeled as a reverse auction. We reveal that the platform utility maximization could be intractable, for which an incentive mechanism that determines the winning bid and payments with polynomial-time computation complexity is developed. Moreover, we theoretically prove that our mechanism is truthful, individually rational, and budget feasible. Through extensive simulations, we demonstrate that our mechanism utilizes budget efficiently to achieve high platform utility with polynomial computation complexity.

The widespread of smart phones has brought new social-aware features to wireless networks. Users prefer to forward traffic to their social contacts in social-aware networks, which is highly different from the traditional uniform traffic pattern. To this end, we analyze a social-aware wireless network, which is modeled by the social contacts between nodes, and explore its impact on network throughput capacity. We present that it can refine social contacts, thus improving the network capacity. Moreover, directional antennas are applied to further enhance the capacity performance, which can reduce the interference brought by other simultaneous communications. We derive the throughput capacity for social-aware networks with single-beam and multi-beam directional antenna, respectively. For single-beam directional antenna, we prove that when wireless networks are dominated by traffics of short-distance social contact, the throughput capacity can be promoted from order of (θ(1/θ2n log n)1/2) to Θ(1/θ2log n). In addition, when the beamwidth θ is at order of Ω(1/(log n)1/2), the throughput capacity is a constant. Thus, the wireless network is scalable. For multi-beam antennas, we prove that when sidelobe gain Gsis at order of o(Gm), where Gmis main lobe gain, compared with the single-beam case, the network capacity can achieve the order of θ(1/θ2log n).

To accommodate exponentially increasing traffic demands, operators are seeking to offload cellular traffic to small base stations (BSs) in heterogeneous cellular networks (HCNs), which is promising in alleviating traffic congestion. In HCNs, operators are eager to balance the traffic globally, where users may be pushed to less preferred small BSs, resulting in possible conflict with user local preference. Thus, it is a big challenge to achieve dynamic load balancing for operators and provide participation incentive for users simultaneously. Due to the dynamics of network state and user traffic demand, we are inspired to utilize Lyapunov optimization to develop a congestion-aware cellular offloading scheme. Specifically, an operator profit maximization problem involving network selection and rate control is formulated. To achieve long-term network stability, we propose a congestion-aware network selection algorithm, obtaining the BS alternative set that maintains traffic congestion constraint. By exploring the heterogeneity of user quality sensitivity, we devise the optimal quality-price contract, which maximizes operator profit. With effective pricing and resource allocation, users are motivated to make proper association strategy chosen from the BS alternative set. Simulation results demonstrate the effectiveness of our scheme in improving operator profit. User incentive and network stability are also validated.

Crowd sensing systems enable a wide range of data collection, where the data are usually tagged with private locations. How to incentivize users to participate in such systems while preserving location-privacy is coming up as a critical issue. To this end, we consider location-privacy protection when motivating users to sense data instead of viewing them separately. Without loss of generality,$k$-anonymity is utilized to reduce the risk of location-privacy disclosure. Specifically, we propose a location aggregation method to cluster users into groups for$k$-anonymity preserving, and meanwhile mitigating the incurred information loss. After that, an incentive mechanism is carefully designed to select efficient users and calculate rational compensations based on clustered groups obtained in location aggregation, where the influences of both the information loss and$k$-anonymity in location-privacy preserving are captured into group values and sensing costs. Through theoretical analysis and extensive performances evaluated on real and synthetic data, we find out that the incentive payment increases sharply with more stringent privacy protection and the information loss can be further mitigated compared with conventional methods.

With the explosive development of social networks, users' behaviors are prone to be socially connected. Social Network Operator (SNO) can effectively collect communication data which reflects users' social behaviors and construct a social graph which precisely characterize relationship among users. Users who exert online influence on their friends can potentially affect their friends' demand for wireless service. Therefore, the knowledge of users' influence would help Wireless Network Operator (WNO) make optimal operating decisions to maximize profit. This paper focus on the optimal pricing strategy of WNO when users' social influence is taken into consideration. We model SNO and WNO's relationship as a cooperative game. By formulating the interaction between SNO-WNO's coalition and users as a two-stage Stackelberg game, the optimal pricing strategy is derived which brings about extra profits. Moreover, we propose a generalized bargaining scheme based on contribution factor to divide extra profits between operators. Simulation results reveal the fact that wireless network operator's possession of influence information does incur extra profits. In addition, feasibility of SNO and WNO's cooperation is validated.

When social behaviors are considered into ad hoc wireless networks, the users prefer to forward the traffic from their social contacts. In this way, the network capacity is highly related with the social characteristics. To this end, we apply a social contact based wireless network model and explore its throughput capacity. The wireless network capacity will get improved when the contacts are located nearby the source node. Directional antennas are applied to further enhance the capacity performance, which can reduce the interference brought by other simultaneous communications. Our results demonstrate that when the wireless communications are dominated by the short-distance social contact traffics, the throughput capacity can be greatly improved. Meanwhile, when the directional antenna beamwidth is at the order of Ω( 1 √log ), the throughput capacity is a constant. The wireless network with social contacts is scalable.

Data volume has been increasing explosively in recent years and learning methods are vitally important to extract key information in such mass data. Traditional offline learning requires multiple traversals to the dataset, thus frequently suffering from lack of computational resources. Online learning can benefit in shrinking total time consumed by training model and lowering computational capacity. However they often converge slowly due to memory loss. Considering partial feedback, we uniquely propose online Confidence- Weighted learning in Bandit setting (CWB) for lower cumulative error and higher convergence rate. Specifically, historical information is preserved to adjust the weights of features for speeding up the convergence rate. Moreover, we novelly integrate the random sampling into the confidence-weighted learning, which can balance the exploitation and exploration in bandit setting. Extensive experiments demonstrate efficiency and effectiveness of our proposed scheme.

Wireless sensor networks (WSNs) always collect an enormous amount of rich diverse environmental information. When WSNs grow large in scale, it is difficult for the sink to gather data due to the increasing transmission overhead. In order to improve the fidelity of data recovery and save energy, we propose a novel data aggregation and space-time global recovery scheme. The scheme exploits block Compressed Sensing (CS) to achieve both recovery fidelity and energy efficiency. We employ diffusion wavelets to partition a large WSN into sub-networks, which are regarded as blocks. For each sub-networks, diffusion wavelets are applied to get the sparse basis for the data to be compressed. In addition, we introduce temporal and spatial correlation into the optimum target function of global recovery algorithm. Simulation results show that space-time global recovery scheme holds higher fidelity of data recovery and greatly reduces the energy consumption. Typically, the normalized mean absolute error of our recovery scheme is less than 5%. Furthermore, the energy consumption is reduced more than 50% against plain CS.

Crowdclustering clusters data items in a crowdsourcing manner, which makes discovered item categories more consistent with human perception. However, due to diversity of crowdsourcing workers and fluctuation of the number of tasks assigned to each worker, inferring stable and reliable clusters is challenging. Moreover, an item may be associated with multiple attributes, and such items should be put into different clusters, which makes inferring accurate clusters more complicated. To address the challenges above, in this paper we present a robust and fast crowdclustering scheme for finding overlapping clusters of items. Distinguished from existing works, we extract reliable and stable cluster information from workers' answers by majority voting. We then formulate an optimization problem to find overlapping clusters, and develop a nonnegative matrix factorization based approach to approximate the optimal solution. Experiments show the robustness, accuracy and efficiency of our approach.

How to integrate labels from multiple labelers in order to obtain an accurate estimate of the ground truth is a major topic of crowdsourcing. One challenging issue is that, the labelers' abilities may vary significantly and the tasks distinguish each other in difficulties. Moreover, for a crowdsourcing system, task distributors have no idea in advance how many labels will be enough for each task. Consequently, an online task assignment mechanism based on the labeler expertise and question heterogeneousness becomes necessary. In this paper, we present such an online task assignment algorithm based on a probabilistic model consisting of both labeler abilities and question difficulties. We apply the online EM (Expectation Maximization) algorithm to make online estimations of system parameters, based on which we assign tasks adaptively. A series of simulation results have been demonstrated to show that our proposed scheme outperforms the conventional EM algorithm in efficiency and accuracy.

Distributed TDMA medium access protocol plays an important role in stabilizing the throughput under various network states. The major challenge of implementing such protocol lies in the difficulty of slot assignment. Previous works provide a variety of mechanisms to implement the collision-free slot assignment. However, when network topology changes frequently, existing approaches suffer from low throughput due to the waste of idle slots. In this paper, we propose a novel distributed TDMA protocol called HD-MAC to improve the throughput performance under high dynamic change of network topology. Specifically, we come up with a frame structure with fixed length and establish a network join principle regarding the slot assignment for the newly added node, which avoid the overhead of maintaining extra frame length information among nodes. In addition, we adopt a contention-based channel access mechanism for enhancing the utilization of the idle slots, thus increasing the overall throughput. Finally, simulation results are demonstrated to validate the effectiveness of HD-MAC.

Periodic broadcasting has achieved prominent performance in VoD (Video on Demand) service in wired network. However, the development of wireless VoD service still has gone on hard and slowly in comparison to the rapid growth of mobile video service. In this paper, we propose a scalable video streaming method HQOBA (Heterogeneous Quality-Oriented Bandwidth Allocation) to address the problems in wireless network. To be concrete, in order to tackle the heterogeneous client type problem, we employ variable bandwidth allocation, instead of fixed bandwidth value for each channel. Moreover, to handle the problem of scarce server bandwidth and unpredictable wireless channel conditions, the proposed method takes the advantage of FGS (Fine Granular Scalable) video coding and Periodic Broadcasting to successfully transmit video segment. Further-more, we develop a bandwidth allocation algorithm to maximize client perceptual visual quality for the proposed transmit scheme. The experimental results of HQOBA scheme also validate the effectiveness.

In this study, a hybrid user association scheme for maximising energy efficiency is proposed in a wireless uplink heterogeneous network with human‐to‐human and machine‐to‐machine communications coexistence. Different from conventional user association algorithms, the authors consider access control protocol for both the human‐type‐communications (HTCs) users equipments’ (UEs) and machine‐type‐communications (MTCs) UEs according to their characteristics during user association phase, i.e. a contention‐free access mechanism for HTC UEs and a random access based on access class barring mechanism for MTC UEs. They formulate the user association problem as a maximisation of the overall UEs’ energy efficiency in consideration of both the HTC and MTC UEs’ quality of service (QoS) guarantees and load balance among base stations simultaneously. To solve the problem, they propose a distributed iterative algorithm which adopts both the matching theory and the dual decomposition theory, and prove its convergence. Finally, simulation results show that their proposed user association scheme not only outperforms existing schemes in terms of the load balance and the overall energy efficiency but also achieves the same performance as exhaustive search in the case of less number of UEs, while satisfying both the HTC and MTC UEs’ QoS requirements.

Device-to-device (D2D) communication underlaying cellular networks is a promising technology for improving network resource utilization, and cooperative communication technology is usually used to mitigate the interference caused by D2D communication. Due to the additional signal processing cost introduced by cooperative communication, the cellular links who have the exclusive usage right of the network spectrum can charge the D2D links a fee for spectrum usage to enhance their profit. In this paper, we propose a contract-based cooperative spectrum sharing mechanism to exploit transmission opportunities for the D2D links and meanwhile achieve the maximum profit of the cellular links. We first design a cooperative relaying scheme that employs superposition coding at both the cellular transmitters and D2D transmitters. The cooperative relaying scheme can maximize the data rate of the D2D links without deteriorating the performance of the cellular links. Then, we employ a contract-theoretic framework to model the spectrum trading process based on the cooperative relaying scheme, and derive the optimal power-payment contracts for the cellular links under both the cases that the private information (i.e., channel quality) of the D2D links is continuous and discrete using tools from continuous-and discrete-time optimal control theories, respectively. Analytic and numerical results confirm the efficiency of the proposed spectrum sharing mechanism.

This paper studies the throughput capacity of wireless networks with social characteristics. We propose a simple model to reflect both the social relations between nodes and power-law node degree distribution, and then examine their impact on capacity. We show the fact that two features above lead to traffic locality and improve capacity. Moreover, multicasting may be employed to further enhance performance when information is desired to be published from the source to all its contacts, of which the number follows power-law distribution. In addition, we propose the corresponding capacity-achieving communication schemes, which optimally exploit the underlying structure. Our study is an attempt to understand how social relations may impact on network capacity from a theoretical perspective, and provides fundamental insight on the design and analysis of real wireless networks.

Delayed traffic offloading is a promising paradigm to alleviate the cellular network congestion caused by explosive traffic demands. As we all know, in mobile networks, the delay profile for traffic is remarkable due to users’ mobility. How to exploit user delay tolerance to improve the profit of operator as well as mobile users becomes a big challenge. In this paper, we model this delayed offloading process as a monopoly market based on contract theory, where operator acts as the monopolist setting up the optimal contract by statistical information on user satisfaction. We propose an incentive mechanism to motivate users to leverage their delay and price sensitivity in exchange for service cost. To capture the heterogeneity of user satisfaction, we classify users into different types. Each user chooses a proper quality–price contract item according to its type. More specifically, we investigate this delayed offloading scheme under strongly incomplete information scenario, where user type is private information. We derive an optimal contract, which maximizes operator’s profit for both the continuous-user-type model and the discrete-user-type model. Numerical results validate the effectiveness of our incentive mechanism for delayed traffic offloading in cellular networks.

Abstract With the prevalence of mobile devices, it is of much interest to study the properties of mobile ad hoc networks. In this paper, we extend the concept of diameter from static ad hoc network to mobile ad hoc network, which is the expected number of rounds for one node to transmit a message to all other nodes in the network, reflecting the worst end‐to‐end delay between any two node. Specifically, we investigate the diameter of identically and independently mobility model in cell‐partitioned network and random walk mobility model in two‐dimensional torus network, achieving the boundary , when (k=Ω(n)), andO(klog2k), respectively, wherenis the number of nodes andkis the number of cells of network and especially under random walk mobility model . A comparison is made among the diameter of mobile ad hoc networks under identically and independently mobility model, random walk mobility model and static ad hoc network, showing that mobility dramatically decreases the diameter of the network and speed is an essential and decisive factor of diameter. Copyright © 2016 John Wiley & Sons, Ltd.

Vehicular Ad Hoc Network (VANET) has been a hot topic in the past few years. Compared with vehicular networks where vehicles are densely distributed, sparse VANET have more realistic significance. The first challenge of a sparse VANET system is that the network suffers from frequent disconnections. The second challenge is to adapt the transmission route to the dynamic mobility pattern of the vehicles. Also, some infrastructural requirements are hard to meet when deploying a VANET widely. Facing these challenges, we devise an infrastructure-less unmanned aerial vehicle (UAV) assisted VANET system called Vehicle-Drone hybrid vehicular ad hoc Network (VDNet), which utilizes UAVs, particularly quadrotor drones, to boost vehicle-to-vehicle data message transmission under instructions conducted by our distributed vehicle location prediction algorithm. VDNet takes the geographic information into consideration. Vehicles in VDNet observe the location information of other vehicles to construct a transmission route and predict the location of a destination vehicle. Some vehicles in VDNet equips an on-board UAV, which can deliver data message directly to destination, relay messages in a multi-hop route, and collect location information while flying above the traffic. The performance evaluation shows that VDNet achieves high efficiency and low end-to-end delay with controlled communication overhead. Copyright © 2016 John Wiley & Sons, Ltd.

The demands of video traffic transmission have grown rapidly in recent years. The emergence of converged network alleviates heavy burden on cellular network by introducing broadcasting. The transmission cost of contents with large demands can be effectively reduced. The content distribution strategy compromising between broadcasting and unicasting plays an important role in improving the performance of the converged network. To this end, we formulate the content delivery in a converged network by a game with three players, which are broadcast network service provider, unicast network service provider and a content provider. The cooperation without reward sharing among these players is formulated as a Stackelberg game. In this game, the two service providers are leaders to determine their transmission prices in a competitive way. The content provider is the follower to decide the content distribution strategy. Its Nash Equilibrium is derived consequently. In addition, we investigate the cooperation with reward sharing among the three players by introducing Shapley Value model. According to numerical results, it shows that the proposed Shapley Value based reward sharing scheme can improve the fairness of network cooperation. The gain of content provider using converged strategy over single mode transmission is validated as well.

Traffic offloading over Delay Tolerant Networks (DTNs) is a promising paradigm to alleviate the network congestion caused by explosive traffic demands. As we all know, in mobile networks, the delay profile for traffic is remarkable due to user's mobility. How to exploit delay tolerance to improve the profit of the operator as well as mobile users becomes a big challenge. In this paper, we investigate the problem of the interrelation of delay and user QoS. Inspired by contract theory, we model the delayed offloading process as a monopoly market where the operator makes pricing by considering statistical information about user satisfaction. In addition, we propose an incentive framework to motivate users to leverage their delay and price sensitivity in exchange for service cost. To capture the heterogeneity of user satisfaction, we classify users into different types. Each user chooses an appropriate quality-price contract item according to its type. Moreover, we derive an optimal contract which is feasible and maximizes the operator's profit as well. Numerical results validate the effectiveness of our incentive framework for traffic offloading over DTNs.

Multiple Radio Access Technologies (Multi-RAT) make it possible to exploit the advantages of Heterogeneous networks (HetNets) resulting from a joint consideration of the networks as a whole. Users in HetNets can be served with a proper RAT to maximize the system-level utility. Especially, when user dynamics are considered, they can stay in a RAT or handover to another RAT with a transition probability depending on system configuration. By formulating these dynamics as a Markov chain model, the system-level utility is defined as a combinatorial object function. However, the combinatorial optimization is NP-hard, thus we can only use exhaustive search to obtain the optimum solution, which comes up with high computational complexity and is not practical. To this end, we use Markov approximation to obtain the approximate utility and transition probability. In addition, we propose a Count Down and Select (CDS) algorithm to implement the RAT selection. Numerical results validate the convergence of Markov approximation and the effectiveness of the CDS algorithm.

With the pervasion of mobile devices, crowdsourcing based received signal strength (RSS) fingerprint collection method has drawn much attention to facilitate the indoor localization since it is effective and requires no pre-deployment. However, in large open indoor environment like museums and exhibition centres, RSS measurement points cannot be collocated densely, which degrades localization accuracy. This paper focuses on measurement point collocation in different cases and their effects on localization accuracy. We first study two simple preliminary cases under assumption that users are uniformly distributed: when measurement points are collocated regularly, we propose a collocation pattern which is most beneficial to localization accuracy; when measurement points are collocated randomly, we prove that localization accuracy is limited by a tight bound. Under the general case that users are distributed asymmetrically, we show the best allocation scheme of measurement points: measurement point density ρ is proportional to (cμ)2/3in every part of the region, where μ is user density and c is a constant determined by the collocation pattern. We also give some guidelines on collocation choice and perform extensive simulations to validate our assumptions and results.

Crowdsourcing systems allocate tasks to a group of workers over the Internet, which have become an effective paradigm for human-powered problem solving such as image classification, optical character recognition and proofreading. In this paper, we focus on incentivizing crowd workers to label a set of binary tasks under strict budget constraint. We properly profile the tasks' difficulty levels and workers' quality in crowdsourcing systems, where the collected labels are aggregated with sequential Bayesian approach. To stimulate workers to undertake crowd labeling tasks, the interaction between workers and the platform is modeled as a reverse auction. We reveal that the platform utility maximization could be intractable, for which an incentive mechanism that determines the winning bid and payments with polynomial-time computation complexity is developed. Moreover, we theoretically prove that our mechanism is truthful, individually rational and budget feasible. Through extensive simulations, we demonstrate that our mechanism utilizes budget efficiently to achieve high platform utility with polynomial computation complexity.

With the development of heterogeneous networks, small cell is introduced into LTE-A networks to satisfy the demand of user equipments (UEs) with various traffic. Due to the existence of small cells and multi-traffic UEs, traditional mobility load balance (MLB) schemes have not been suitable for the current LTE-A network. By reconfiguring the handover thresholds on the basis of cell load, these algorithms are envisioned to cope with the contradiction between large network capacity demand and shortage spectrum resources. However, the existed algorithms are always focusing on the single traffic which are not efficient for the scenario with non-uniform traffic distribution in small cells. In this paper, we propose a multi-traffic load balance (MTLB) algorithm to balance the traffic load and improve the network capacity with an appropriate handover procedure. In this MTLB algorithm, a novel cell selection method will be executed to improve the quality of service (QoS) of UEs. Moreover, the adaptive adjustment method of threshold and TTT (time to triggering) will be introduced to enhance load balance and reduce the call drop rate. Meanwhile, the simulation results strongly verify that the proposed MTLB algorithm can achieve a good resource utilization and the network throughput can also be improved.

In this paper,1we investigate the throughput and delay in heterogeneous cognitive radio networks (HCRN), where the data source and the destination (S-D) is heterogeneously distributed following a rank based model and secondary users (SUs) provide relay service for primary users (PUs). We consider two scenarios: 1) PUs and SUs are both static; and 2) PUs are static and SUs are mobile. For scenario 1, we show that the primary network throughput is the same for different heterogeneous extents of S-D distribution owing to the flexible assistance of SUs, while the throughput of secondary networks is proven to be changing with the S-D heterogeneity exponent α, which depicts the variation of different heterogeneous extents of S-D distribution. In addition, the delay of both primary and secondary networks are shown to be altering with α. Further, we reveal that the number of SUs required to assist PUs can be dramatically reduced when considering the S-D heterogeneity, while achieving the same primary network throughput. For scenario 2, we utilize a modified uniform mobility (MUM) model to depict the motion of SUs and mainly focus on the analysis of throughput and delay for primary networks. It shows that the primary network throughput is also free of the heterogeneous extent of S-D distribution, while the delay changes with α. Due to the mobility of SUs, a better delay-throughput tradeoff of primary networks is achieved compared with that in scenario 1.

In this paper, we study the problem of data gathering with compressive sensing (CS) in wireless sensor networks (WSNs). Unlike the conventional approaches, which require uniform sampling in the traditional CS theory, we propose a random walk algorithm for data gathering in WSNs. However, such an approach will conform to path constraints in networks and result in the non-uniform selection of measurements. It is still unknown whether such a non-uniform method can be used for CS to recover sparse signals in WSNs. In this paper, from the perspectives of CS theory and graph theory, we provide mathematical foundations to allow random measurements to be collected in a random walk based manner. We find that the random matrix constructed from our random walk algorithm can satisfy the expansion property of expander graphs. The theoretical analysis shows that a k-sparse signal can be recovered using `1 minimization decoding algorithm when it takes m = O(k log(n=k)) independent random walks with the length of each walk t = O(n=k) in a random geometric network with n nodes. We also carry out simulations to demonstrate the effectiveness of the proposed scheme. Simulation results show that our proposed scheme can significantly reduce communication cost compared to the conventional schemes using dense random projections and sparse random projections, indicating that our scheme can be a more practical alternative for data gathering applications in WSNs.

Network throughput and packet delay are both important performance metrics in Ad-Hoc networks. While uncorrelated mobility has been proved to be able to increase the network capacity at the cost of delay, the influence of correlated mobility on network performance has not been well studied. In correlated mobility model, nodes in the same group are constrained to lie in a disc area, whose center moves uniformly according to the i.i.d. model; however, it is still an open issue how to evaluate the performance of capacity and delay tradeoff in hybrid Ad-Hoc networks. In this paper, we propose a theoretical framework to examine such issue when relay nodes are under correlated mobility and static nodes are acting as transmitters and receivers. Simulation results shows that our algorithm provides better throughput delay tradeoff than i.i.d. model.

Recent studies show that the 60GHz wireless technology could help resolving the hotspot issue in data center networks (DCNs). However, transmissions over 60GHz suffer from limitations of short transmission range and blockage, which makes it a new challenge how to appropriately establish wireless links in the DCN. In this paper, we propose to integrate wireless links into the DCN with the wireless neighborway scheme. Each top-of-rack switch (ToR) has multiple 60GHz wireless links connecting to its neighboring ToRs, which are termed as neighborways. The elephant flow from the sending ToR can be partially offloaded through neighborways, which are then delivered to the ToRs around the receiving ToR through wired links of the DCN, and finally converged to the receiving ToR. The fundamental challenge for the design is how to prevent the offloaded traffics from forming new hotspots in the network fabric. To this end, we developed a wireless network planning solution with corresponding IP address assignment and traffic engineering scheme, which can leverage the potential underutilized wired links in the DCN and meanwhile avoid forming new hotspots. The simulation results show that the proposed scheme can notably relieve the hotspots in the DCN.

Crowdsourcing is an effective paradigm in human centric computing for addressing problems by utilizing human computation power. While efforts have been made to study the crowdsourcing systems for labeling tasks such as classification, those for scoring tasks with continuous and correlative answers have not been well studied. In this paper, we propose two inference algorithms, MCE (Maximum Correlation Estimate) and WMCE (Weighted Maximum Correlation Estimate), to infer true answers based on answers submitted by workers. When estimating answers, WMCE algorithm assigns diverse weight to submitted answers of workers based on their quality while MCE algorithm assigns identical weight to submitted answers of all workers. For a fixed worker population, we reveal that the increase in task redundancy1can improve accuracy of estimated answers but such improvement is limited within a certain level. We further show that WMCE algorithm can reduce the influence of this limitation better than MCE algorithm for the same crowdsourcing system. Simulation results validate our theoretical analysis and show that WMCE algorithm outperforms MCE algorithm in the accuracy of estimated answers.

Mobile Crowdsensing is an effective paradigm to perform tasks in many scenarios by utilizing crowd intelligence and sensing resources. However, the responses from users are often unreliable due to the low-paid rewards and their biased expertise of the crowd. In this paper, we propose a multi-class labeling scheme based on BCH codes (BCH-MCL) for the mobile crowdsensing system where the quality of crowd is unknown. For multi-class labeling tasks, BCH-MCL provides each label a BCH codeword with maximum error correction capability, and maps the responses of the crowd into an estimated codeword to determine the final estimated label. The theoretical analysis characterizes the fault-tolerance capability of BCH-MCL, and derives an upper bound of the mis-label probability for the proposed BCH-MCL scheme, with a necessary condition and a sufficient condition presented. Furthermore, we prove that BCH-MCL can achieve a more accurate estimation and much lower computational complexity, compared with prior work DCFECC. Simulation results validate our theoretical analysis and indicate the effectiveness and efficiency of the proposed BCH-MCL.

Offloading data from cellular networks to WiFi or femtocell networks is an efficient way to alleviate the network congestion caused by rapidly increasing demands for mobile data. To this end, an Internet Service Provider (ISP) is willing to deploy WiFi/femtocell networks. With the advent of such networks, it is necessary to analyze how the ISP sets data plans to improve its profit while mobile data offloading is supported. In this paper, we develop a contract-based scheme to deal with data plan setting problem in two-tier networks. The contract offered by the ISP is a set of data plans which provide different combinations of data volume and price. We classify consumers into different types according to their percentages of data traffic offloaded to WiFi/femtocell networks. Each consumer can choose its own data plan based on its type, which is private information. Under asymmetric information scenario, we provide the necessary and sufficient conditions for the feasibility of a contract and then we derive the optimal contract which maximizes the ISP's profit. Numerical results validate the effectiveness of our scheme and indicate that the ISP can improve its profit by raising the throughput of its WiFi/femtocell networks and/or lowering its energy cost.

Recently, security of cognitive radio (CR) is becoming a severe issue. There is one kind of threat, which we call greedy spectrum occupancy threat (GSOT) in this paper, has long been ignored in previous work. In GSOT, a secondary user may selfishly occupy the spectrum for a long time, which makes other users suffer additional waiting time in queue to access the spectrum and leads to congestion or breakdown. In this paper, a queueing model is established to describe the system with greedy secondary user (GSU). Based on this model, the impacts of GSU on the system are evaluated. Numerical results indicate that the steady-state performance of the system is influenced not only by average occupancy time, but also by the number of users as well as number of channels. Since a sudden change in average occupancy time of GSU will produce dramatic performance degradation, the greedy second user prefers to increase its occupancy time in a gradual manner in case it is detected easily. Once it reaches its targeted occupancy time, the system will be in steady state, and the performance will be degraded. In order to detect such a cunning behavior as quickly as possible, we propose a wavelet based detection approach. Simulation results are presented to demonstrate the effectiveness and quickness of the proposed approach.

With the rapid development of vehicular and ICT technologies, Vehicular Content Networks (VCNs) play an important role in facilitating better road safety and comfortable driving. Because the content, such as traffic reports and disaster warning messages, must be delivered efficiently and timely, how to guarantee and improve the performance of VCNs becomes a hot issue. Recently, caching with the parked vehicles shows its efficiency to distribute the large-sized vehicular content. However, the related caching algorithm has not been studied thoroughly. In this paper, based on the analyses of the arrival of vehicles, mobility in parking area and access pattern of vehicular content, we propose an algorithm to decide how to use the cached replicas in the parking area to provide users with the requested content. Simulation results prove that our proposal can outperform the conventional methods.

In camera sensor networks (CSNs), full view coverage, in which any direction of any point in the operational region is covered by at least one camera sensor, is of great significance since image shot at the frontal viewpoint considerably increases the possibility to recognize the object. However, finding the critical condition to achieve full view coverage in mobile heterogeneous CSNs remains an open question. In this paper, we analyze both the static and mobile random deployed camera sensor networks. A centralized parameter - equivalent sensing radius (ESR) - is defined to evaluate the critical requirement for asymptotic full view coverage in heterogeneous CSNs. We derive the critical sensing range for full view coverage under static model, 2-dimensional random walk mobility model, 1-dimensional random walk mobility model and random rotating model. We then discuss the impact of various mobility patterns on sensing energy consumption and study the relationship between ESR and percentage of full view coverage, and show that random walk mobility model can decrease the sensing energy consumption under certain delay tolerance. To our knowledge, our work is the very first that derive the critical condition to achieve full view coverage in mobile heterogeneous CSNs.

We study the relay-based communication schemes for cooperative spectrum sharing among multiple primary users (PUs) and multiple secondary users (SUs) with incomplete information. Inspired by the matching theory, we model the network as a matching market. In this market, each PU proposes a certain proposal representing a combination of relay power and spectrum access time to attract the SUs, while each SU maximizes its utility by selecting the most suitable PU. We derive the sufficient and necessary conditions for a stable matching in which none of the PUs or SUs would like to change its decision. We further establish a distributed matching algorithm (DMA) and a DMA with utility increasing (DMA-UI) to achieve the equilibria in partially incomplete and incomplete information scenarios, respectively. Moreover, we provide detailed discussions on the implementation of the distributed algorithms in practical networks. Simulation results show that the losses of PUs' total utilities caused by incomplete information are diminished when the number of SUs increases. Specifically, the effects of the incomplete information are reduced as the competition among SUs (PUs) is more intensive than that among PUs (SUs).

In this paper, we develop a truthful and efficient combinatorial auction scheme under a novel spectrum allocation model that can achieve a worst-case approximation ratio \sqrt{m} in social welfare. We propose to tackle the dynamic spectrum access problem in cognitive radio (CR) networks with time-frequency flexibility requirements. We model the spectrum opportunity in a time-frequency division manner and the spectrum allocation as a combinatorial auction. Then we design an auction mechanism to reach the upper bound in polynomial time and propose a combined approach to improve the bound in the cost of increasing computational complexity. A truthful payment that gives incentive to the SUs for revealing the truthful valuation of the desirable bundle of slots is presented. In order to reduce the complexity, we simplify the general model to a modified model that only allows frequency flexibility, and then present a truthful, optimal and computationally efficient auction mechanism. Extensive simulation results of the social welfare and spectrum ratio show that the performance of the combined approximation algorithm is better than the sorting based greedy algorithm.

In this paper, we explore methods to generate optimal network topologies for wireless sensor networks (WSNs) with and without obstacles. Specifically, we investigate a dense network with n sensor nodes and m=nb(0v) (0 <; v ≤ 1) arbitrarily or randomly distributed obstacles, which block cells they are located in, i.e., sensor nodes cannot be placed in these cells and nodes' communication cannot cross them directly. We find that the overall throughput capacity is bounded by the transmission burden in areas around these blocked cells and introduce a novel algorithm of complexity O(M) to generate optimal sensor nodes' topologies for any given obstacles' distributions. We further analyze its performance for regularly distributed obstacles, which can be taken to estimate the lower bound of the algorithm's performance.

Recently, many dynamic spectrum allocation schemes based on economics are proposed to improve spectrum utilization in cognitive radio networks (CRNs). However, existing mechanisms do not take into account the economic efficiency and the spatial reusability simultaneously, which leaves room to further enhance the spectrum efficiency. In this paper, we introduce the coalition double auction for efficient spectrum allocation in CRNs, where secondary users (SUs) are partitioned into several coalitions and the spectrum reusability can be executed within each coalition. The partition formation process is not only related to the interference condition between SUs, but also the expected economic goals. Therefore, we propose a fully-economic spatial spectrum allocation mechanism by incorporating the coalition formation approach with auction theory. With the proposed scheme, the primary operator acts as an auctioneer, who performs multiple virtual auctions to form a stable partition of SUs and conducts a final auction to decide the winning SUs. Moreover, we propose a possible operation rules for the primary operator to iteratively change the partition, and prove that the virtual auctions could converge in finite time. Comprehensive theoretical analysis and simulation results are presented to show that our scheme can satisfy the crucial economic robustness properties of double auction, and outperform existing mechanisms.

In this paper, we study on the problem of opportunistic channel access without prior information about channels. We model it as the multi-armed bandit (MAB) problem. There are N independent arms. The player can choose one arm to play each time and get a reward. Switch cost is taken into consideration when player switches arm. Switch cost includes reward loss and switch delay. The concept of regret is used to measure the performance of an access policy. We prove that the regret of Lai-Robbins policy with switch cost grows with time at logarithmic order as that without switch cost, though with a much higher leading constant. Then we propose a policy referred as reducing switch with advanced play (RSAP), whose regret is shown to grow with time at logarithmic order with a much smaller leading constant.

The concept of femtocell access points underlying existing macrocell network has recently emerged as a key technology that provides better coverage and improves wireless system capacity. Among all three access control mechanism adopted by Femtocell Access Points (FAPs), Hybrid access is regarded as the most desirable because this mechanism makes it possible to allow both femto and macro end users to access femtocell network, which significantly enhances overall network performance. In this paper, we propose an incentive mechanism for the two-tier macro-femtocell network under hybrid access control, in which FAPs expect to lease licensed spectrum from the coexisting Macrocell Service Provider (MSP) to serve their end users (FUs), in return FAPs grant the MSP a fraction of Access Permission (ACP) so that macro end users (MUs) can access FAPs to increase the Quality of Service and macrocell network capacity. We introduce the concept of contract to specify the relationship between FAPs and the MSP. We study how the MSP can obtain the optimal contract by designing different ACP-spectrum combinations (i.e., the amount of spectrum sold from the MSP to FAPs and the corresponding quantity of ACP sold from FAPs to the MSP) for different types of FAPs. Numerical results show that both MSP and FAPs can benefit from this incentive mechanism in term of their utilities.

The problem of jointly estimating clock skew and offset for wireless sensor networks (WSNs) in a pairwise broadcast synchronization (PBS) protocol is considered. The random part of the delay is supposed to be an exponential random variable. We consider two estimators, i.e., joint maximum-likelihood estimator (JMLE) and generalized ML-like estimator (GMLLE) proposed by Leng and Wu . For both estimators, the corresponding algorithms are explicitly derived and presented. For the GMLLE, the corresponding performance bound based on the reduced set of observations is derived and the optimal value of a user-defined parameter is identified accordingly. At last, analytical results are corroborated by numerical experiments. We observe that: (i) JMLE usually outperforms GMLLE at the cost of larger computational complexity; (ii) JMLE, while achieving the same estimation accuracy as that of the LP method presented in , enjoys significantly lower computational complexity than that of the latter.

Vehicular Ad Hoc Networks (VANETs) with base stations are called hybrid VANET, where base stations are deployed to improve the throughput capacity. In this paper, we study the multicast throughput capacity for hybrid wireless VANET with a directional antenna on each vehicle and the end-to-end delay is constrained. In the hybrid VANET, there are n mobile vehicles (or nodes) distributed in a unit area with m strategically deployed base stations connected using high-bandwidth wire links. There are n_s multicast sessions and each multicast session has one source which transmits identical data to its associated p destinations. We investigate the multicast throughput capacity for two mobility models with two mobility scales, respectively, while each vehicular node is equipped with a directional antenna and with a tolerant delay D. That is, a source node transmits to its p destinations only with the help of normal nodes within D consecutive time slots. Otherwise, the transmission will be performed with in the infrastructure mode, i.e., with the help of base stations. We demonstrate that the one dimensional i.i.d. slow mobility pattern catch the main feature of VANETs. And we find that the multicast throughput capacity of the hybrid wireless VANET greatly depends on the delay constraint D, the number of base stations m, and the beamwidth of directional antenna θ. In the order of magnitude, we obtain the closed form of the multicast throughput capacity of the hybrid directional VANET, where the impact of D, m and θ on the multicast throughput capacity is analyzed. Moreover, we derive the lower bound of the muticast throughput using a similar raptor coding approach.

In this paper, we define an ad hoc network where multiple sources transmit packets to one destination as Converge-Cast network. We will study the capacity delay tradeoffs assuming that n wireless nodes are deployed in a unit square. For each session (the session is a dataflow from k different source nodes to 1 destination node), k nodes are randomly selected as active sources and each transmits one packet to a particular destination node, which is also randomly selected. We first consider the stationary case, where capacity is mainly discussed and delay is entirely dependent on the average number of hops. We find that the per-node capacity is Θ (1/√(n log n)) (given nonnegative functions f(n) and g(n): f(n) = O(g(n)) means there exist positive constants c and m such that f(n) ≤ cg(n) for all n ≥ m; f(n)= Ω (g(n)) means there exist positive constants c and m such that f(n) ≥ cg(n) for all n ≥ m; f(n) = Θ (g(n)) means that both f(n) = Ω (g(n)) and f(n) = O(g(n)) hold), which is the same as that of unicast, presented in (Gupta and Kumar, 2000). Then, node mobility is introduced to increase network capacity, for which our study is performed in two steps. The first step is to establish the delay in single-session transmission. We find that the delay is Θ (n log k) under 1-hop strategy, and Θ (n log k/m) under 2-hop redundant strategy, where m denotes the number of replicas for each packet. The second step is to find delay and capacity in multisession transmission. We reveal that the per-node capacity and delay for 2-hop nonredundancy strategy are Θ (1) and Θ (n log k), respectively. The optimal delay is Θ (√(n log k)+k) with redundancy, corresponding to a capacity of Θ (√((1/n log k) + (k/n log k)). Therefore, we obtain that the capacity delay tradeoff satisfies delay/rate ≥ Θ (n log k) for both strategies.

How to set spectrum sensing duration is an important issue in Cognitive Radio (CR) networks, which could greatly affect energy efficiency and system throughput. Over sensing would result in insufficient transmission period, while inadequate sensing would incur false alarm and miss detection. This paper studies how to choose an optimal sensing duration to strike a balance between energy consumption and system throughput. We focus on a cooperative sensing scenario, where several secondary users form a group to guarantee more accurate sensing results. By formulating the transmission cost in terms of the energy consumption of sensing process and transmission process, we propose a comprehensive utility function. The maximization of the utility function is obtained with the constraints of sufficient protect for primary users. The existence of the optimal sensing duration is proved accordingly. Numerical results show that secondary users can achieve almost the maximum throughput with significant energy saving when utilizing optimal sensing duration.

In this paper, we study the multicast capacity-delay tradeoff of a wireless network consisting of mobile wireless nodes and base stations. m = nbbase stations are regularly placed in a square region and n is the number of mobile nodes in the network. We assume that mobile nodes move according to an independent and identical distributed (i.i.d.) pattern and each desires to send packets to k = nddistinctive destinations. Under this basic network model, we study capacity, delay tradeoff in two transmission protocols: 2-hop relay algorithm with and without redundancy. We derive that the upper bound of per-node capacity of 2-hop relay algorithm without redundancy is O(n-min{1, 1 - b + d})and its corresponding delay is Θ(nmin{1, 1 - b + d}/2). The lower bound of delay of this protocol is Θ(n1-b), with a smaller capacity O(nb-2). For 2-hop relay algorithm with redundancy, the largest capacity and its corresponding delay are O(n-min{1, 1 - b + d}) and Θ(nmin{1, 1 - b + d}/2). The smallest delay and its corresponding capacity are Θ(n1 - b/2) and O(nb - 3/2). When d >; 1/2 or b >; min{1+d/3, 1 - d}, adding base stations will improve the tradeoff mentioned in [7].

Game theory has made great contributions to Cognitive Radio (CR) algorithm design and it calls up wide interest of CR researchers, while the considerable iterations and the operation complexity would hinder its implementation. And the evaluation under practical environment is an indispensable step before its application. To alleviate the concerns, we propose a distributed relay selection algorithm based on Stackelberg Game and overcome the challenges caused by the high calculation complexity to make our strategy applicable. Moreover, we utilize a real time testbed to evaluate the CR MAC algorithm, which contains the necessary components: programmable RF layer, software-defined MAC layer and adaptive network layer to fulfill the requirements of CR research. It is based on system-on-chip processors with strong configure ability which can develop large scale network. We make experiments on our platform to reveal the game equilibrium's properties and verify our scheme's performance in improving the capacity at the destination and secondary user's spectrum utilization efficiency.

In this paper, we study the capacity and delay scaling laws for cognitive radio networks (CRN). The primary network consists of n static, randomly and evenly distributed primary users (PUs), which require high throughput with low delay. The secondary network consists of m = O(n1+δ) randomly and evenly distributed cognitive secondary users (SUs) with δ >; 0, which move in different area according to the hierarchical i.i.d. mobility model. The secondary network is carefully designed to support the primary network, so that PUs may achieve near-optimal performance in both capacity and delay aspects. Then by utilizing hierarchical cooperation among SUs with different mobility, a new hierarchical relay algorithm is proposed to improve the delay performance extensively. Under optimal condition, this algorithm may provide Ω(n-δ') per-node throughput and O(nδ") average delay for PU with δ', δ" >; 0, and significantly improve the performance of SU at the same time.

This paper presents an evolution framework of resource allocation by infection among secondary users (SUs) in an OFDMA-based cognitive radio cellular networks. Each primary user (PU) sells his extra sub-channels to SUs in his sensing range to achieve the highest payoff and each SU may come across another in his sensing range to make infection. Two different infection processes among SUs, the infection with and without local knowledge respectively, are considered. We prove the existence and convergence of evolutionary equilibrium (EE) for both cases, and show some interesting properties such as the impact of cheating of SUs in the above infection processes. Besides, we proposed two algorithms for the infection processes to converge to EE in a distributed manner. Simulation results show that the algorithms with local knowledge can equally share the extra resources among SUs efficiently, which is actually the overall optimal solution (OOS). While for the algorithm without local knowledge, we find that though EE exists, OOS cannot always be achieved. Furthermore, we optimize the second algorithm to make EE approximate to OOS.

Cooperative sharing of spectrum among one primary user (PU) and multiple secondary users (SUs) helps to enhance the throughput of the whole system. In this paper, we propose a two-tier game in the sharing of spectrum in which SUs make their decisions on whether to cooperate under replicator dynamics and the PU adjusts its strategy to allocate time slots for the cooperative SUs' transmission. Furthermore, we develop the distributed algorithm to describe the learning process of SUs, and prove that the dynamics can converge to the evolutionary stable strategy (ESS) efficiently, which is actually the optimal strategies of both PU and SUs. Simulation results show that our proposed mechanism converges to ESS automatically, at which point all the SUs will remain their strategies. In addition, it is shown that this mechanism helps the SU to share information and gain higher transmission rate than fully cooperative or non-cooperatively scenario.

Recently, Cognitive network is growing more and more attractive due to its capability of dynamic spectrum usage. When the dynamic spectrum is wideband, a sequential sensing scheme is proposed to sense the spectrum with low system complexity. Conventionally, parameters of sequential sensing are optimized subject to sensing time and detection performance constraints, such as false alarm and miss detection probability. However, when energy consumption is considered, tradeoff involved in sensing parameters design have to be studied. Apart from sensing time, we find that the energy consumption of a cognitive handset is greatly affected by the sampling rate of A/D&D/A convertor, which will affect detection performance as well. For the purpose of figuring out the optimal sequential sensing parameters, we establish Energy Optimization and Detection Optimization problems based on the relationship among energy consumption, sampling rate and detection performance. By exploiting the convexity property of energy consumption function and monotonic decreasing property of detection performance function with respect to sampling rate, the optimal solution of a practical cognitive handset utilizing AD9051(ADC) is obtained. Numerical results are provided to investigate the impact of sampling rate on energy consumption and detection performance. It is shown that the optimal sampling rate will bring up to 50% energy saving and 3 times detection performance improvement.

In this paper, we propose an agent-based spectrum trading model, where an agent can play a third-party role in the spectrum trading process. Providing service to Secondary Users (SUs) with spectrum bought from Primary Users (PUs), the agent can make profits during the process by providing service to secondary users. During each trading period, the agent has to decide how much spectrum it should lease from PUs and what price it should charge SUs. Therefore, the most significant challenge to implement this spectrum trading model is finding the most profitable strategy for agent(s). We address this challenge under two scenarios in which: 1) a single agent and 2) multiple agents. Instead of quantifying SUs' spectrum demand by a deterministic function of price, we take the randomness of secondary users' demand or demand uncertainty into consideration. To the best of our knowledge, this is the first solution to agent-based spectrum trading considering demand uncertainty.

In this paper, we propose an agent-based spectrum trading model, where agent plays a third-party role in the trading process. Providing service to secondary users (SUs) with spectrum bought from primary users (PUs), agent makes profit in the spectrum trading process. We address the challenge of finding the most profitable strategy of agent(s) when spectrum demand is uncertain. We first address this challenge for the secondary network where single agent operates, and extend it to a multiple-agent system. To our best knowledge, this is the first solution to agent-based spectrum trading considering demand uncertainty.

Cognitive Radio is a promising technology to solve the problem of increasing spectrum shortage in wireless communication. It allows secondary users(SUs) to opportunistically access idle spectrum of primary users(PUs) while ensuring collision perceived by PUs under a specific requirement. Testbed is essential to advance the development of cognitive radio networks. This paper presents a real time testbed for the evaluation of the cognitive radio MAC algorithm. The testbed supports the necessary components of cognitive radio including programmable RF transceiver, software-defined MAC layer and adaptive network layer to support multi-PU multi-SU cognitive radio networks. It is much easier to configure and control than the traditional FPGA-based testbed by using system-on-chip(SoC) programmable processors. We present the main features and the implementation details of the testbed in this paper. We also introduce an experiment using the testbed to evaluate the performance of an adaptive CR MAC algorithm proposed by us.

We consider the problem of cooperative spectrum sharing among a primary user (PU) and multiple secondary users (SUs), where the PU selects a proper set of secondary users to serve as the cooperative relays for its transmission. In return, the PU leases portion of channel access time to the selected SUs for their own transmission. The PU decides the portion of channel access time it will leave for the selected SUs (i.e., the cooperative relays), and the cooperative relays decide their respective power level used to help PU's transmission in order to achieve proportional access time to the channel. We assume that the PU and SUs are rational and selfish, i.e., they only aim at maximizing their own utility. As SU's utility is in term of their own transmission rate and the power cost for PU's transmission, so they will choose a proper power level to meet the tradeoff between transmission rate and power cost. PU will choose a proper portion of channel access time for the cooperative relays to attract them to employ higher power level. We formulate the problem as a non-cooperative game between PU and SUs, and prove that the proposed game converges to a unique Stackelberg equilibrium. By employing an iterative updating algorithm, we can achieve the unique equilibrium point.

Cyclostationary feature detection is capable of improving sensing performance in Cognitive Radio systems. The maximum over spectral on cyclostationary domain is called α-domain. α-domain feature detection will greatly reduce computational complexity with respect to prior-knowledge of carrier frequency. To select the threshold for α-domain feature detection under a given noise level and miss detection probability Pf, we need the expression of α-domain noise distribution. In this paper, Generalized Extreme Value (GEV) distribution is introduced and found as the noise distribution on α-domain. Maximum likelihood estimation is applied to estimate the parameters of GEV distribution. Based on GEV distribution, close form of detection threshold λth in terms of false alarm probability Pf is obtained. The validation of α-domain noise distribution is confirmed by the Monte-Carlo simulation.

In this paper, we consider a scenario where multiple secondary users search for idle frequency bands in a spectrum consisting of multiple channels. The state of each channel is modeled as a discrete-time Markov process. In each time slot, one secondary user(SU) chooses one channel to sense and decides whether to use the channel based on the sensing result. When there is only one SU, former work has proved that a myopic policy maximizing the current reward is optimal when the channel state transitions are positively correlated over time. In this paper, we extend the myopic policy to multiple-SU scenario. We propose and analyze two approaches that make use of the myopic policy more efficiently when multiple SUs exist.

In this paper, a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is discussed. An AIC RF front-end sampling structure is proposed requiring only low rate ADCs and few storage units for spectrum sampling. Multiple CRs collect compressed samples through AICs and recover spectrum jointly. A novel joint sparsity model is defined in this scenario, along with a universal recovery algorithm based on S-OMP. Numerical simulations show this algorithm outperforms current existing algorithms under this model and works competently under other existing models.

Spectrum under-utilization is one of the bottlenecks of the development of wireless communication, and dynamic spectrum access (DSA) is envisioned as a novel mechanism to solve the problem of spectrum scarcity. Spectrum auction has been recognized as an effective way to achieve DSA, wherein the primary spectrum owner (PO) acts as an auctioneer who has free channels and is willing to sell them for additional revenue, and the secondary user (SU) acts as a bidder who is willing to buy a channel from POs for its service. In this paper, we adopt a progressive spectrum auction named MAP, which has been proved optimal and incentive compatible in DSA networks with distributed POs and SUs. However, in MAP, the profit of POs is not maximized under the equilibrium point due to the scarcity of SUs' private information known by POs. We propose an information sharing mechanism, in which the POs exchange their local information with each other. We show analytically that, allowing information sharing, each PO is able to learn the private information of SUs and increase its profit accordingly. Long term profit acts as the incentive for information sharing that all the POs automatically reveal the true information when they are aware of this. It is notable that information sharing doesn't affect social optimality. Simulation shows the increase of POs' profits in the sense of long term interests.

Some properties of permutation polynomials over integer rings, mainly the 3ncase, are studied and some conclusions are drawn. A decision theorem for 3ncase and a proof of the MCF property for size pnpermutation polynomial-based interleaver are presented. A design criterion for interleavers based on third degree permutation polynomial is proposed to select the coefficients of the polynomials. Simulation results show that these interleavers can achieve near performance of S-random interleavers, and for short frame size, they outperform S-random interleavers.

A time-domain based feedback reduction algorithm for orthogonal frequency division multiplexing (OFDM) transmitter power adaptation is proposed, which can significantly reduce the feedback amount during the power adaptation comparing with the conventional frequency-domain adaptation schemes. It is found that the size of cluster plays an important role on the adaptation performance, especially for the vehicular environment. According to simulation results, Lagrange interpolation is used to give an explicit curve between the Doppler frequency, cluster size and the adaptation performance, which makes a good trade-off between the resulted BER and the amount of feedback.

We propose a new rate allocation algorithm for closed loop MIMO-OFDM system. The new scheme utilizes ordered statistics of channel matrix's singular value and is significantly simpler than ideal scheme which uses water-filling in both frequency and space domain. The proposed scheme is more flexible in that it does not force the use of large antenna array as "conventional frequency flat constraint" scheme does [J.R.B. Joon Hyun Sung, January 2003]. It also outperforms FFC algorithm under equal hardware complexity.