Data-model matching, typically achieved through direct contact, is critical to digital markets. However, when data and models belong to different owners, the direct contact-based form faces some security threats, including data security, privacy disclosure, and model reverse engineering attacks. A natural question emerges: Can effective data-model matching be achieved without direct contact ? Previous methodologies can partially alleviate but not eliminate the necessity of direct contact between data and models, making security and privacy challenges persist throughout the matching process. In this article, our research findings indicate that, despite the essential differences between data and models, both can be represented using topological spaces. Therefore, we establish a unified metric of data complexity and model expressivity from a topological perspective. The unified metric satisfies three conditions toward contactless data-model matching. Then, we develop a contactless matching paradigm, circumventing the necessity for direct contact between data and models and addressing privacy and security concerns. Specifically, we use topological data analysis to generate the data complexity topological descriptors (DCTDs) and use topological simplification to generate the model expressivity topological descriptors (METDs). We compute the matching degree and return the matching result. Through theoretical proof and experimental analysis, we validate the feasibility of the proposed contactless data-model matching paradigm in real-world scenarios.

Advanced persistent threat groups are launching extensive attacks on national critical institutions and key infrastructures. Learning contextual information among entities using graph neural network models based on provenance graphs has demonstrated excellent performance in threat detection. However, due to constraints of transmission costs and privacy concerns, existing threat detection methods typically rely solely on audit logs within their own domains, which contain only part of the attacker's behavior. Attackers have also recognized this, so they brazenly carry out attack attempts and expand their impact. The information asymmetry across multiple domains indeed significantly impacts the efficiency and timeliness of APT detection. This paper introduces a novel cross-domain threat detection (CDTD) scheme based on collaborative graph learning for effective and early APT detection. The core idea of CDTD is to foster mutual benefits by utilizing potential cross-domain general knowledge from heterogeneous data and collaborating on precious threat intelligence, which could mitigate information asymmetry. Experimental results demonstrate that the proposed method achieves state-of-the-art detection capabilities and reduces the workload of security analysts. Additionally, the distributed detection scheme significantly decreases system overheads in terms of network bandwidth, memory usage, and execution time, which shows promising potential for broad applications of the proposed scheme.

With advancements in AI, smart home gyms are becoming increasingly popular for providing fitness assistance in indoor environments. In this research, we propose a layer-by-layer framework, called Wi-Fitness, which bridges video perception with Wi-Fi sensing for smart fitness. At the data preprocessing layer, the singular value decomposition-based channel state information denoising mechanism is leveraged to do the Wi-Fi data calibration. Diverse and high-quality training samples are generated by a random quantization-based data augmentation method. At the bimodal fusion layer, the heterogeneity between the Wi-Fi and video is mitigated by the local attention mechanism and the bimodal feature integration mechanism. For the video modality, the attention-based spatio-temporal graph convolutional network (AST-GCN Net) is proposed to refine spatial information. The spatio-temporal semantic alignment module is proposed to transfer spatial information from video to Wi-Fi and maintain temporal consistency across modalities. The fitness assessment layer provides exercise visualization. The generalization of Wi-Fitness is enhanced by layer-by-layer collaboration. Wi-Fitness demonstrates its effectiveness by achieving an average F1-Score of 92.68% in three typical indoor environments.

Obtaining dedicated fraud detection models is important for financial risk management. Online transaction platforms traditionally rely on local data to accumulate domain knowledge and establish fraud detection models to detect fraud. This naturally makes those online trading platforms with limited data and hardware resources more susceptible to fraudulent transactions. In this article, we propose GMDK, which elegantly integrates credible knowledge generation and collaborative model training on weak transaction platforms. Specifically, we decouple the local model parameters into base and specialized layers to learn different levels of knowledge. Local platforms milk different source models with delicately designed two-factor cues to acquire both cross-domain and credible domain-specific knowledge. To this end, source models first aggregate local base layer parameters of each local model to obtain cross-domain base layers for all local models. Then, distinct semantic distribution prototypes generated by heterogeneous source models on desensitized and sampled local business data are aligned to update local models. We conduct extensive experiments on various real-world online transaction platforms, and the results demonstrate that local models trained with GMDK can achieve state-of-the-art fraud detection accuracy in each target transaction area.

Data sharing facilitates the integration and in-depth exploration of cross-domain data, thereby fostering innovative research and model development. However, privacy leakage emerges as a critical barrier to the sharing and circulating of such data. Existing privacy-preserving technologies face challenges in handling complex scenarios involving multiple participants due to the following reasons: 1) Divergent privacy permission. Data sharing is constrained by various privacy limitations, necessitating the consideration of privacy permissions across different domains, akin to a cross-border process. 2) High collaboration cost. Collaboration among multiple domains to determine the privacy constraint and sharing ways incur additional costs. 3) Large noise magnitude. Traditional privacy techniques to protect the privacy of a single domain using local differential privacy (LDP) may introduce excessive noise, thereby reducing data utility. Drawing inspiration from the cross-border visa issuance process, we present an innovative framework called PriVisa for enabling privacy-preserving data sharing across different domains. It consists of four key modules to overcome the mentioned challenges: the hybrid pattern, optimized sharing path construction, personalized grouping, and LDP-based perturbation. 1) The hybrid pattern for coordination among organizations, considering authentication, privacy constraints, and sharing methods. 2) The optimized sharing path construction using a privacy constraint hierarchy tree to maximize data utility while adhering to privacy requirements. 3) The feature similarity grouping and perturbing mechanism satisfying LDP to protect privacy and optimize data utility. The theoretical and experimental validation confirms PriVisa’s effectiveness in addressing divergent privacy constraints and promoting data utility in cross-domain data sharing.

The expressive power of neural networks describes the ability to represent or approximate complex functions. The number of linear regions is the standard and most natural measure of expressive power. However, a major challenge in utilizing the number of linear regions as a measure of expressive power is the exponential gap between the theoretical upper and lower bounds, which becomes more pronounced as the neural network capacity increases. In this article, we aim to derive a sharp upper bound on piecewise linear neural networks (PLNNs) to bridge this gap. Specifically, we first establish the relationship between tropical polynomials and PLNNs. In the unexpanded tropical polynomials form, we make the proposition that hyperplanes are not all in the general positions, thereby reducing the number of intersecting hyperplanes. We propose a rank-based approach and present the empirical analysis that this approach outperforms previous Zaslavsky's theorem-based methods. In the expanded tropical polynomials form, accounting for limitations in weight initialization and model computational precision, we raise the concept that the values range of each term is bounded. We propose a precision-based approach that transforms the approximate exponential growth of the number of linear regions into polynomial growth with width, which is effective at larger layer widths. Finally, we compare the number of linear regions that can be represented by each hidden layer in both forms and derive a sharp upper bound for PLNNs. Empirical analysis and experimental results provide compelling evidence for the efficacy and feasibility of this sharp upper bound on both simulated experiments and real datasets.

The progress of location-based services has led to severe concerns about location privacy leakage. However, existing methods are still incompetent for efficient and scalable location privacy preservation (LPP). They are often vulnerable to inference attacks with side information, or hard to be implemented due to the high computational complexity. In this paper, we pursue the high protection quality with low computational complexity. We propose ascalableLPP method based on the paradigm of counterfeiting locations. To make fake locations extremely plausible, we forge them by synthesizingartificial impostors. The so-called artificial impostors refer to the synthesized traces that have similar semantic features to the actual traces, e.g., similar transition patterns, and do not contain anyprotected location, i.e., the exact location that needs to be protected. We devise two dedicated techniques: thestation-based synthesis methodand thepopulation-level semantic model. We conduct the experiments on real datasets of two cities (Shanghai of China and Asturias of Spain) to validate the quality of privacy preservation, utility loss, and scalability of the proposed method. Based on these two datasets, the experimental results show that our method achieves the privacy preservation quality of$97.68\%$and$96.24\%$, respectively, and the time spent on building generators is only 144.96 seconds and 136.08 seconds, respectively. The experimental results also show that our method achieves a good trade-off between privacy and utility. Our study would give the research community new insights into improving the practicality of LPP paradigm via counterfeiting locations.

Identifying hidden cybercrimes is a challenging task, as these behaviors are often carefully planned by criminals with counter-surveillance awareness. Existing solutions for cybercrime detection struggle to uncover enough clues to identify hidden criminal behaviors. Malicious behaviors are concealed beneath benign behaviors, and the boundaries between malicious and benign behaviors in the representation space are blurred to evade mainstream deep learning-based security authentication models. We introduce abehaviortomographer (BT) to reconstruct the behavior structure from three slices: agent, event, and attribute slices, enabling more granular detection of hidden cybercrimes. The core idea of BT is to reconstruct interior information about behavior structure from multiple slices, much like computed tomography in modern medicine enables the reconstruction of internal body. It enables the extraction of discriminative information from intricate interior associations between behavioral attributes rather than surface information meticulously crafted by criminals. Our experiments are conducted on two representative cybercrime datasets. Promising experimental results demonstrate that BT outperforms state-of-the-art models on key metrics, achieving around 0.99 AUC-ROC and approximately 0.9 AUC-PR. Moreover, BT notably excels at low false positive rates, showcasing its high effectiveness for real-world applications.

The recommendation of long-tail items has been a persistent issue in recommender system research. The primary reason for this problem is that the model cannot learn better item features due to the lack of interactive record data of tail items, which leads to a decline in the model's recommendation performance. Existing methods transfer the features of the head items to the tail items, thereby ignoring their differences and failing to produce a satisfactory recommendation effect. To address the issue, we propose a novel recommendation model called MetaGA based on metalearning. The MetaGA model obtains initial parameters from head items through metalearning and fine-tunes model parameters during the learning process of tail item features. Additionally, it employs a graph convolutional network and attention mechanism to enhance tail data and reduce the difference between head and tail data. Through the above two steps, the model utilizes the abundant data of the head items to address the problem of sparse data of the tail items, resulting in improved recommendation performance. We conducted extensive experiments on three real-world datasets, and the results demonstrate that our proposed MetaGA model significantly outperforms other state-of-the-art baselines for tail item recommendation.

Centralized training and decentralized execution (CTDE) paradigm is widely employed to address the nonstationary and partial observability in multiagent reinforcement learning (MARL). One of the main challenges that restricts the performance of the CTDE paradigm iscredit assignment.Existing methods cannot sufficiently energize each agent for exploring a broader solution space without compromising performance or factorization complexity. In this article, we propose a self-incentive credit assignment scheme to prioritize individual agent actions based on a novel factorization method called multihead residual value factorization (MRVF) rather than being constrained by the quantity of collective policies. It learns an extra representation of value gradients from the cooperative behaviors and factorizes the residual global joint action value as a monotonic function, which can effectively improve the representability of the value function. Theoretical analysis indicates that our method has stronger representational ability and satisfies the individual-global-max (IGM) condition. Extensive experiments validate that our method achieves significant performance improvement in terms of both the learning speed and stability; particularly, it gains the best performance on twosuper hardmaps of the widely used benchmark StarCraft multiagent challenge (SMAC) while the performances on other scenarios of SMAC are better or as well as the state-of-the-art baseline.

Nowadays, the emergence of online trading greatly facilitates people’s life. Meanwhile, online trading also brings hidden dangers, such as online fraudulent trading. To solve the issue, researchers have proposed many different detection models. However, in actual business scenarios, fraudulent transactions usually only account for a small portion of normal transactions, resulting in extremely imbalanced data. Besides, the concealment of fraud is reflected in that the fraudsters are imitating the normal transactions of users, posing a huge challenge for fraudulent transaction detection modeling. Inspired by generative adversarial networks (GANs), we propose a GAN-based framework to detect online banking fraud on extremely imbalanced data, called BalanceGAN. A fraud detection model is first pretrained using the data generated by the generator and then the model is fine-tuned using transfer learning on real-world datasets, by using this approach to address data imbalances. Compared with the conventional methods for solving imbalanced data, our BalanceGAN can avoid over-fitting of the model relatively, experiments on two real datasets show that our BalanceGAN has more than 10% performance improvement in Precision and Recall.

Anti-fraud engineering for online credit loan (OCL) platforms is getting more challenging due to the developing specialization of gang fraud. Associations are critical features referring to assessing the credibility of loan applications for OCL fraud prediction. State-of-the-art solutions employ graph-based methods to mine hidden associations among loan applications effectively. They perform well based on the information asymmetry which is guaranteed by the huge advantage of platforms over fraudsters in terms of data quantity and quality at their disposal. The inherent difficulty that can be foreseen is thedata isolationcaused by mistrust between multiple platforms and data control legislations for privacy preservation. To maintain the advantage owned by the platforms, we design a privacy-preserving distributed graph learning framework that ensures critical association repairs by merging parameter sharing and data sharing. Specially, we propose theassociation reconstruction mechanism(ARM) that consists of the devised exploration, processing, transmission and utilization schemes to realize data sharing. For parameter sharing, we design a hybrid encryption technique to protect privacy during collaboratively learning graph neural network (GNN) models among different financial client platforms. We conduct the experiments over real-life data from large financial platforms. The results demonstrate the effectiveness and efficiency of our proposed methods.

The possibility for public attributes to disclose private information has caused widespread concern. Traditional privacy-preserving approaches have two limitations: 1) Approaches based on data anonymization or distortion often lead to poor utility-privacy trade-offs, and 2) approaches based on data encryption face heavy computational costs. These problems have prompted calls for an effective privacy-preserving framework that provides adequate privacy guarantees while maintaining good data utility. Inspired by denoising autoencoders, in this paper, we regard the information about privacy attributes contained in the public attributes as a kind of noise and design an ex ante privacy-preserving model called the Mutual Information Autoencoder (MIAE), which reconstructs the loss function of the original autoencoder by combining reconstruction errors and mutual information, and we introduce a trade-off coefficient to achieve utility-privacy trade-offs. To elucidate the superiority of the proposed model, we consider utility-privacy trade-offs with the expected distortion function as a metric of data utility and the joint mutual information as a metric of privacy disclosure, and then, we construct a convex optimization problem with multiple constraints based on rate-distortion theory. From an information theory perspective, we provide a lower bound for privacy disclosure with utility guarantees. Elaborate experiments over a real-world dataset reveal that as the level of expected distortion increases, the achievable bound obtained by MIAE exhibits a trend similar to that of the information-theoretic bound. When the expected distortion surpasses 2.2, the achievable bound obtained by MIAE also converges to 0, and the maximum gap between the achievable bound obtained by MIAE and the information-theoretic bound is no more than 1.4. Compared to existing models, MIAE can provide a tighter achievable bound and achieve good utility-privacy trade-offs.

Graph neural networks (GNNs) are playing exciting roles in the application scenarios where features are hidden in information associations. Fraud prediction of online credit loan services (OCLSs) is such a typical scenario. But it has another rather critical challenge, i.e., the scarcity of data labels. Fortunately, GNNs can also cope with this problem due to their good ability of semi-supervised learning by mining structure and feature information within graphs. Nevertheless, the gain of internal information is often too limited to help GNNs handle the extreme deficiency of labels with high performance beyond the basic requirement of fraud prediction in OCLSs. Therefore, adding labels from the experts, such as manually adding labels through rules, has become a logical practice. However, the existing rule engines for OCLSs have the confliction problem among continuously accumulated rules. To address this issue, we propose a Snorkel-based Semi-Supervised GNN (S3GNN). Under S3GNN, we specially design an upgraded version of the rule engines, called Graph-Oriented Snorkel (GOS), a graph-specific extension of Snorkel, a widely used weakly supervised learning framework, to design rules by subject matter experts (SMEs) and resolve confliction. In particular, in the graph of an anti-fraud scenario, each node pair may have multiple different types of edges, so we propose the Multiple Edge-Types Based Attention mechanism. In general, for the heterogeneous information and multiple relations in the graph, we first obtain the embedding of applicant nodes by aggregating the representation of attribute nodes, and then use the attention mechanism to aggregate neighbor nodes on multiple meta-paths to get ultimate applicant node embedding. We conduct experiments over the real-life data of a large financial platform. The results demonstrate that S3GNN can outperform the state-of-the-art methods, including the method of pilot platform.

Effective Service Function Chains (SFCs) mapping and Virtual Network Functions (VNFs) scheduling are crucial to ensure high-quality service provision for Internet of Things (IoT) tasks. Meeting the varying demands of multiple SFCs poses a significant challenge, particularly when working with the limited resources available in edge computing networks. Most existing working focuses on uniformly mapping and scheduling service requests in a batch processing manner within a given time period, without taking the diversity and priority of VNFs into account. When there is a sudden surge in demand, the issues of VNF queueing waiting and resources imbalance become prominent. To address the mentioned issues, this paper proposes a Deep Reinforcement Learning (DRL)-based VNF cooperative scheduling framework with priority-weighted delay. In light of the urgency of VNFs with higher priorities and the limitations of available resources, we begin by modeling an average queuing delay with priority weight based on the shortest remaining time priority technique. We then formulate a mathematical optimization problem to minimize the modeled delay in VNF scheduling process while providing suitable multidimensional resources in the edge network. Finally, a DRL method with experience replay and target Q-network is designed to effectively obtain the optimal solutions of the optimization problem from experience. The experimental results show that our proposed method outperforms its peers in terms of SFC request acceptance, delay, load balance, and resource utilization.

Traffic prediction acts as a fundamental function in the management and optimization of networks and services. There are emerging requirements for extraordinary traffic prediction, including variable-term traffic series and comprehensive traffic behavior. Compared to ordinary traffic prediction, these demands call for solutions to mine rich information and predict business load under broader conditions. In this work, we propose X-Trafformer, a graph spatiotemporal transformer model, for extraordinary traffic prediction. Unlike conventional techniques that model traffic sequences, we transform traffic sequences into traffic behaviors under generalized objects, where behaviors are initiated by generalized objects and possess specific behavioral attributes. It allows for the prediction of multiple variables in traffic data across different networks and services, leveraging the matching of behavioral attributes among behavior objects and events. X-Trafformer incorporates multiple interrelated graph structures to capture fine-grained attribute spatiotemporal associations and coarse-grained object spatiotemporal distributions, forming the foundation for accurate prediction. Evaluation on real and representative traffic scenarios (communication traffic from Italian Telecom and business traffic from Tmall) demonstrates X-Trafformer's exceptional prediction performance at low computational costs.

The convenience offered by the Internet accelerates the evolution of fraudulent behavior during facilitating the rapid development of online payment services. Fraudsters can change their behavior patterns frequently and at a low cost in the online space, allowing them to evade regulatory oversight. This poses a significant challenge for meticulously trained learning-based security applications for fraud detection and can lead to serious social security risks. Most of them depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. To stay ahead of the rapidly evolving fraud, researchers have been exploring models with low latency and fast response capabilities to effectively combat fraudulent behavior. Unfortunately, the evolving fraud is not only reflected in the drift of their superimposed risk features but also in the openness of their category. The interweaving of open-category and concept-drift accelerates the process of existing security methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by theanchoring effecton the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank.

The rapid growth of electronic commerce brings convenience to modern life but comes with security risks by various cybercrimes in online payment services. Most existing security methods for fraud detection depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. Unfortunately, this paradigm becomes incommensurate with the increasingly complicated and varying fraud patterns due to the untimely and delayed responses in the offline environment. Without sensing the evolution of fraud timely, it is challenging to train and deploy targeted countermeasures. The emerging means of fraud are not only reflected in the openness of their category, but also in the drift of their superimposed risk features. The interweaving of open-category and concept drift accelerates the process of existing methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by the anchoring effect on the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank.

Local differential privacy (LDP) is a prevalent measure of privacy protection as it provides rigorous privacy guarantees and has been widely studied for statistical analysis, especially in frequency estimation. As a representative LDP-enabled frequency estimation algorithm, Google'sRandomized Aggregation Privacy-Preserving Ordinal Response(RAPPOR) has been put into practice. However, it achieves sub-optimal utility due to the following limitations. Firstly, the adoption of the MD5 hash function inevitably results in the hash collision. Secondly, the application of the randomized response technique leads to randomness. To improve the practical effectiveness of RAPPOR and the utility of frequency-based services, we propose an LDP-enabled frequency estimation method called PK-RAPPOR, in which we devise an effective re-encoding hash function (RE-HF) incorporating prior knowledge (PK) about the rough frequency ranking of items. RE-HF divides items into several cohorts based on the PK and generates a unique hash value set for each item. Compared with the original RAPPOR, the hash collision can be eliminated for items from different cohorts, and the effect of randomness can be decreased by the overlapping of items from the same cohorts. We validate our proposed method with theoretical analysis and demonstrate its effectiveness with experiments on both synthetic and real-world datasets.

Gang fraud, the major and primary security issue in online lending services, can be efficiently solved by the data-driven paradigm that is recognized as a promising solution for online lending gang fraud prediction. However, it is challenging that such predictions need to detect evolving and increasingly impalpable fraud patterns based on low-quality data, i.e., very preliminary and coarse applicant information. The technical difficulty mainly stems from two factors: the extremedeficiency of information associationsandweakness of data labels. In this work, we mainly address the challenges by enhancing the utility of associations (i.e.,recovering missing associationsandmining underlying associations) on a knowledge graph. Specifically, we first propose an efficient method of Chinese address disambiguation to recover some critical associations that are broken by the ambiguity of applicant information, e.g., address related information. Then, to mine the implicit associations, we design a novel association representation method, calledAdaptive Connected Component Embedding Simplification Scheme(ACCESS), which can adaptively implement embedding for different connected components depending on their sizes. Finally, we adopt the graph clustering algorithms and devised predicting schemes based on the above enhanced associations to predict gang fraud in the case of weakness of data labels. Moreover, we propose a framework called RMCP by integrating the above techniques, which is consists of four steps:Recovering,Mining,Clustering, andPredicting, for efficiently predicting gang fraud. The good performance is validated by the experiments on a real-world dataset from a commercial lending company. Meanwhile, we provide a visual decision support system namedLongArmsover the RMCP framework.

In data-driven anti-fraud engineering for online payment services, the integration of proper function modules is an effective way to further improve detection performance by overcoming the inability of single-function methods to cope with complex and varied frauds. However, a qualified integration is really inaccessible under multiple demanding requirements, i.e., improving detection performance, ensuring decision explainability, and limiting processing latency and computing consumption. In this work, we propose a qualified integration system, named CAeSaR, that can simultaneously meet all of the above requirements. This satisfactory result is achieved by the cooperation of two innovative techniques. The first is a novel three-way taxonomy of function division, called TRTPT, according to the temporal positions of transactions relative to a reference fraudulent transaction. Based on TRTPT, CAeSaR can introduce three kinds of anti-fraud function modules which collaboratively cover all types of frauds theoretically. The second is an effective integration scheme, called TELSI. It generates the candidate decision strategies by combining the judgments of three function modules by only two simple logical connectives, which essentially ensures the decision explainability. Particularly, TELSI can assign the most effective decision strategy to the corresponding transaction adaptively by a devised stacking-based multi-classification. The advantages of CAeSaR are validated in practice over real-life data from a prestigious bank.

In online lending services, fraud prediction is an especially critical step to control loss risk and improve processing efficiency. Unfortunately, it is definitely challenging since the ex-ante prediction actually needs to be made only based on the most basic information of applicants. This work figures out that the essential difficulty here is the low information density of data associations which contain the useful information for fraud prediction. Accordingly, we propose a novel multi-stage data representation scheme, called AI2Vec (Applicant Information Vectoring), as an information density booster. It can gradually boost information density of associations by simultaneously decreasing the scale of information carriers and increasing the amount of useful information. The qualified performance of our AI2Vec is validated by the experiments over real-life data from a prestigious online lending platform. It can help commonly-used machine learning classifiers outperform the state-of-the-art methods, including the method of pilot platform with manual feature engineering by the subject matter experts.

Locating multiple sources in an unknown environment based on their signal strength is called a multi-source location problem. In recent years, there has been great interest in deploying autonomous devices to solve it. A particle swarm optimizer (PSO) is a widely employed source location method. Yet most work in this field focuses on a flat search space while ignoring height information. An unmanned aerial vehicle (UAV) has a coarser but wider view as it flies higher. Inspired by such facts, this paper focuses on improving the efficiency of locating sources by utilizing height information through UAVs. A novel source location model is designed where their sensing range gradually increases as their flying height rises, but their obtained signal strength fades away. It can be directly deployed to existing PSO-based multi-source location methods and improve their performance, especially in a large-scale environment with sparse sources. UAVs can spontaneously switch their search schemes between a rough search at a higher height and a fine one at a lower height. Experimental results of three PSO-based methods show their significant improvement after deploying our model. Given the same computation resources, its deployment leads to over 30% hike in both location accuracy and speed. This represents a great advance to the field of source location.

Recently, using a particle swarm optimizer (PSO) to guide robots in a source location problem has attracted widespread interest. While being navigated by PSO, robots are easily trapped into U-shape-like concave obstacles such that they move back and forth cyclically and fail to locate a correct source. Existing obstacle avoidance strategies perform well when robots have information about all obstacles. Yet in many real scenes, robots have no prior information. This work proposes a novel PSO based on Tabu Search (PSO-TS) for robots to locate multiple sources. Instead of traditionally setting obstacles as tabu objects, PSO-TS innovatively sets trapping areas as tabu objects such that robots do not need prior knowledge or expensive hardware and much time to obtain obstacle information. The weighted average velocity of a robot is employed to determine if it is stuck inside an obstacle-induced area. If so, a rectangular tabu area is set to push robots out of the area and prevents robots from searching the same area again. The proposed method can be embedded into various source location algorithms to improve their performance. Its obstacle avoidance capability is proved. Finally, experimental results show the algorithmic compatibility, environmental adaptability and obstacle avoidance performance of the proposed method.

For the future open 5G Internet of Vehicles (IoV), due to the flexibility and load sharing, the popular network architecture of IoV proposed by many studies is the mobile-edge computing (MEC) framework combining with software-defined networking (SDN). However, under this architecture, moving vehicles and MEC devices are not like the cloud SDN with strong security protection. Thus, identity verification is an important security issue. We find that if the identity credentials of vehicles and infrastructures are obtained by adversaries (i.e., identity theft), the current cryptography-based authentication methods cannot cope with this problem. In this article, we propose a behavior-based verification method, named Crowd-Learning, by utilizing the idea of crowd in software-defined vehicular networks with a MEC framework. In Crowd-Learning, we design an incentive mechanism to stimulate some MEC infrastructures to provide accurate and appropriate amount of data for future correct behavior estimation. Without knowing the model of the dynamic environment, this incentive mechanism needs to apply reinforcement learning to let MEC infrastructures learn how to send data based on the current state. Our Crowd-Learning method verifies vehicles and reduces the verification latency by estimating the vehicle’s behavior in advance. Meanwhile, it verifies infrastructures during the process of reinforcement learning based on the idea of crowd intelligence. The fake infrastructures and anomalous vehicles expose themselves when learning. In experiments, we use the traffic simulation tool, called simulation of urban mobility (SUMO), to generate extensive vehicle traces and evaluate the performance of the Crowd-Learning verification method. The results show that the Crowd-Learning verification method can ensure high verification accuracy for vehicles and infrastructures with satisfying low verification latency.

In this work, we aim at building a bridge from coarse behavioral data to an effective, quick-response, and robust behavioral model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multidimensional low-quality data, e.g., offline check-ins and online user-generated content (UGC). As an insightful result, we validate that there is a complementary effect among different dimensions of records for modeling users’ behavioral patterns. To deeply exploit such a complementary effect, we propose ajoint(instead offused) model to capture both online and offline features of a user’s composite behavior. We evaluate the proposed joint model by comparing it with typical models and their fused model on two real-world datasets: Foursquare and Yelp. The experimental results show that our model outperforms the existing ones, with the area under the receiver operating characteristic curve (AUC) values 0.956 in Foursquare and 0.947 in Yelp, respectively. Particularly, therecall(true positive rate) can reach up to 65.3% in Foursquare and 72.2% in Yelp with the correspondingdisturbance rate(false-positive rate) below 1%. It is worth mentioning that these performances can be achieved by examining only one composite behavior, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how real-time online identity authentication can be improved via modeling users’ composite behavioral patterns.

The vigorous development of e-commerce breeds cybercrime. Online payment fraud detection, a challenge faced by online service, plays an important role in rapidly evolving e-commerce. Behavior-based methods are recognized as a promising method for online payment fraud detection. However, it is a big challenge to build high-resolution behavioral models by using low-quality behavioral data. In this work, we mainly address this problem from data enhancement for behavioral modeling. We extract fine-grained co-occurrence relationships of transactional attributes by using a knowledge graph. Furthermore, we adopt the heterogeneous network embedding to learn and improve representing comprehensive relationships. Particularly, we explore customized network embedding schemes for different types of behavioral models, such as the population-level models, individual-level models, and generalized-agent-based models. The performance gain of our method is validated by the experiments over the real dataset from a commercial bank. It can help representative behavioral models improve significantly the performance of online banking payment fraud detection. To the best of our knowledge, this is the first work to realize data enhancement for diversified behavior models by implementing network embedding algorithms on attribute-level co-occurrence relationships.

The so-calledbehavioral anomaly detection(BAD) is expected to solve effectively a variety of security issues by detecting the deviances from normal behavioral patterns of protected agents. We propose a new graph-based behavioral modeling paradigm for BAD problem, namedbehavioral identification graph(BIG), which has distinct advantages over existing methods by mining deeply theproperty-level(as an enhancement to theevent-level) associations in behavioral data. Under BIG, the behavioral properties and their co-occurrence associations in behavioral data are modeled as the entities and relationships of graph, respectively; furthermore, behavioral properties and events are both vectorized by a devised event-property composite model, and the behavioral patterns of agents are finally represented as a multidimensional spatial distribution of behavioral properties. Consequently, for a behavior, the intensity of its behavioral anomaly can be transformed into the spatial decentrality of its behavioral agent and properties which contain both fine-grained information between behavioral properties and coarse-grained information between behavioral events. To the best of our knowledge, this is the first work to improve behavioral modeling for anomaly detection by integratinginter(event-level) andintra(property-level) associations of behaviors into a unified graph and space. Our method is validated by four representative security issues, i.e.,fraud detectionin online payment services (by transaction behaviors),intrusion detectionin network communication services (by traffic behaviors),insider threat detectionin organizational information systems (by system behaviors), andcompromise detectionin social networking services (by trajectory behaviors).

With the development of 5G technology, mobile edge computing (MEC) is introduced into the construction of internet of vehicles (IoV). However, the distributed denial of services (DDoS) attacks become a serious problem in IoV under MEC. Although numbers of studies have been done on DDoS detection in common wired or wireless networks, they cannot satisfy the high dynamic requirement and cannot cope with the complex and diverse DDoS attacks in IoV. Fortunately, the data traffic flows in IoV exist potential and predictable space-time regularities. By employing reinforcement learning, we propose a feature adaption reinforcement learning approach based on the space-time flow regularities in IoV for DDoS mitigation, named FAST. In FAST, we elaborately design a combinational action space, and a reward function based on Kalman filter method and historical data traffic flows, which can make FAST to recognize DDoS attacks more quickly and accurately. Then through combining Q-learning and DDQN, FAST can select features and disconnect DDoS attacks adaptively according to the changes of the environment. In experiments, we evaluate the performance of FAST based on Shenzhen taxicab dataset. We simulate and inject DDoS attacks into Shenzhen taxicabs through two DDoS simulation tools named ‘ddosflowgen’ and ‘hping3’. The experimental results show that FAST has a high quality in detecting multiple types of DDoS attacks compared with other detection methods.

Recently, an attention mechanism has been used to help recommender systems grasp user interests more accurately. It focuses on their pivotal interests from a psychology perspective. However, most current studies based on it only focus on part of user interests; they have not mined user preferences thoroughly. To address the above problem, we propose a novel recommendation model: comparative convolutional dynamic multi-attention (CCDMA). This model provides a more accurate approach to represent user and item features and uses multi-attention-based convolutional neural networks to extract user and item latent feature vectors dynamically. The multi-attention mechanism considers both self-attention and cross-attention. Self-attention refers to the internal attention within users and items; cross-attention is the mutual attention between users and items. Moreover, we propose an optimized comparative learning framework that can mine the ternary relationships between one user and a pair of items, focusing on their relative relationship and the internal link between a pair of items. Extensive experiments on several real-world data sets show that the CCDMA model significantly outperforms state-of-the-art baselines in terms of different evaluation metrics.

Anomaly detection relies on individuals' behavior profiling and works by detecting any deviation from the norm. When used for online banking fraud detection, however, it mainly suffers from three disadvantages. First, for an individual, the historical behavior data are often too limited to profile his/her behavior pattern. Second, due to the heterogeneous nature of transaction data, there lacks a uniform treatment of different kinds of attribute values, which becomes a potential barrier for model development and further usage. Third, the transaction data are highly skewed, and it becomes a challenge to utilize the label information effectively. The three disadvantages result in both poor generalization and high false positive rate of anomaly detection, and we propose a ranking metric embedding based multi-contextual behavior profiling (ReMEMBeR) model to battle them effectively. We solve the original fraud detection problem as a pseudo-recommender system problem, where an individual is treated as a pseudo-user, his/her behavior as a pseudo-item, and the label as the corresponding pseudo-rating. With the idea of collaborative filtering, for an individual, information from other similar individuals can be used to establish his/her behavior profile. In order to obtain a uniform treatment of heterogeneous attributes, we turn to an embedding based method to learn both attribute embedding and individuals' behavior profiles within a common latent space simultaneously. To utilize the label information better, our model is designed to fit pseudo-users' correct preference ranking for pseudo-items. By doing so, it explicitly learns to tell the fraudulent from the legitimate. Last but not least, we propose to identify and distinguish individuals under different contexts and further generalize the behavior profiling model to be a multi-contextual one. The proposed model can, thus, integrate the multi-contextual behavior patterns and allow transactions to be examined under the different contexts. Extensive experiments on a real-world online banking transaction dataset demonstrate that our model not only outperforms benchmarks on all metrics but also can be combined with them to achieve even better performance.

Big data can help with providing valuable perceptions into business activities and disclosing the potential benefits. Advances in machine learning and deep learning technologies make it easier to achieve significant performance in a wide range of domains from city planning and marketing analysis to credit evaluation and identity theft detection. However, it still requires great efforts in selecting efficient learning algorithms and precise model parameters that are deemed confidential in the light of experience. Also worth noting is that there is a fundamental gap between impracticable business requirements and the available value of data reflected. The data holder or data service provider may not have a clear understanding of data interference. The solution to these two problems depends on the capability of predicting the data utility in advance, which raises a fundamental question: to what degree is the data utility predictable? In this work, we present a primary analytical framework for information-theoretic bounds of data utility and utilize the current state-of-the-art and representative algorithms to obtain the achievable lower bounds on a real-world data set. The gap between theoretical upper bounds and achievable lower bounds indicates that the achievable lower bounds can still be optimized for performance.

The rapid development of internet finance has caused increasing concern in online payment fraud due to its great threat. It is typical to employ rule systems or machine learning-based techniques to detect frauds. For the most significant features of such fraudulent transactions are exhibited in a sequential form, the sliding time window is a widely-recognized effective tool for this problem. With a sliding time window, features about the transaction characteristics can be extracted, and the latent patterns hidden in transaction records can be captured. However, the adaptive setting of sliding time window is really a big challenge, since the transaction patterns in real-life application scenarios are often too elusive to be captured. As a matter of fact, the practical setting usually needs to be updated and refined with manual intervention regularly. This is time-consuming indeed. In this article, we pursue an adaptive learning approach to detect fraudulent online payment transactions with automatic sliding time windows. Accordingly, we make efforts on optimizing the setting of windows and improving the adaptability. We design an intelligent window, called learning automatic window (LAW). It utilizes the learning automata to learn the proper parameters of time windows and adjust them dynamically and regularly according to the variation and oscillation of fraudulent transaction patterns. By the experiments over a real-world dataset of the online payment service from a commercial bank, we validate the gain of LAW in terms of detection effectiveness and robustness. To the best of our knowledge, this is the first work to make a sliding time window for fraud detection capable of learning its proper size in changing situations.

Location-Based Services (LBS), which answer users’ location-dependent queries to Points of Interest (POI), have become popular along with mobile devices’ widespread use. While benefiting from convenience, users may suffer privacy leak risk, due to publishing sensitive information, i.e., locations, to servers. Previous studies have proposed a number of methods for protecting LBS. Through providing provable privacy protection, Private Information Retrieval (PIR) becomes well-known for its high effectiveness in protecting privacy. However, the PIR-protected LBS must cautiously handle the consequent transmission/computation cost and service error brought by adopting PIR, as both of them impact user experience. Then an important question arises: Can the user experience of PIR-protected LBS be greatly improved? We answer it by presenting a novel interface from PIR to LBS, gaining benefits from road networks. The proposed interface is comprised of two road partition methods fully considering POI’s distribution along roads, and two response-calculating methods to satisfy users’ requirements on driving distance. Since Vehicular Location Based Services contain adequate road network knowledge, we accomplish our work on them. The experimental results on a real dataset validate that our interface can improve PIR-protected LBS greatly in user experience while protecting privacy.

As a matter of fact, it is usually taken for granted that the occurrence of unauthorized behaviors is necessary for the fraud detection in online payment services. However, we seek to break this stereotype in this work. We strive to design an ex-ante anti-fraud method that can work before unauthorized behaviors occur. The feasibility of our solution is supported by the cooperation of a characteristic and a finding in online payment fraud scenarios: The well-recognized characteristic is that online payment frauds are mostly caused by account compromise. Our finding is that account theft is indeed predictable based on users' high-risk behaviors, without relying on the behaviors of thieves. Accordingly, we propose an account risk prediction scheme to realize the ex-ante fraud detection. It takes in an account's historical transaction sequence, and outputs its risk score. The risk score is then used as an early evidence of whether a new transaction is fraudulent or not, before the occurrence of the new transaction. We examine our method on a real-world B2C transaction dataset from a commercial bank. Experimental results show that the ex-ante detection method can prevent more than 80\% of the fraudulent transactions before they actually occur. When the proposed method is combined with an interim detection to form a real-time anti-fraud system, it can detect more than 94\% of fraudulent transactions while maintaining a very low false alarm rate (less than 0.1\%).

Driving safety is a persistent concern for urban dwellers who spend hours driving on road in ordinary daily life. Traditional driving hazard detection solutions heavily rely on onboard sensors (e.g., front and rear radars, cameras) with limited sensing range. In this article, we propose a proactive hazard warning system, called APP, which aims to alert drivers when there are vehicles with dangerous behaviors nearby. To this end, APP incorporates several basic techniques (e.g, tensor decomposition, similarity comparison) to estimate behavioral data of a driver based on sparse sampled GPS trace at first. Then, with the estimated unlabelled data, potential dangerous behaviors of a particular vehicle are identified and recognized with a Gaussian Mixture Model (GMM) based approach. We have implemented and evaluated our system with a dataset collected for 30 days from over 13,676 taxicabs. Our method shows on average 81% accuracy in potential dangerous behavior recognition.

Using large-scale GPS trajectory data to improve taxi services has recently attracted much attention in Internet of Things and smart city communities. In this paper, we use a large-scale GPS trajectory dataset generated by over 12 000 taxis in a period of three months in Shanghai, China, and present an efficient passenger-hunting recommendation framework with the multitask deep learning paradigm. This framework contains two modules: 1) offline training of passenger-hunting recommendation model (OT-PHRM) and 2) online application of passenger-hunting recommendation model (OA-PHRM). The module OT-PHRM mainly includes two deep convolutional neural networks (DCNNs) and uses the multitask learning strategy. The first DCNN realizes the region prediction for picking up passengers, while the second DCNN uses the weight-sharing structure to predict the levels of road congestion and earnings of carrying passengers. In particular, for the input of two DCNNs, we not only consider contextual features of taxi driving, region features and valuable statistical features, but also combine individual features into meaningful ones. In the module OA-PHRM, we propose DL-PHRec, which calculates three prediction values using two trained DCNNs in OT-PHRM in real time, and then recommends a personal ranking-list of regions to each taxi driver according to their scores. The experimental results show the feasibility and effectiveness of our recommendation framework.

The recommender system has recently drawn a lot of attention to the communities of information services and mobile applications. Many deep learning-based recommendation models have been proposed to learn the feature representations from items. However, in Internet of Things (IoT), items' description information are typically heterogeneous and multimodal, posing a challenge to items' representation learning of recommendation models. To address this challenge and to improve the recommendation effectiveness in IoT, a novel multimodal representation learning-based model (MRLM) has been proposed. In MRLM, two closely related modules were trained simultaneously; they are global feature representation learning and multimodal feature representation learning. The former was designed to learn to accurately represent the global features of items and users through simultaneous training on three tasks: 1) triplet metric learning; 2) softmax classification; and 3) microscopic verification. The latter was proposed to refine items' global features and to generate the final multimodal features by using items' multimodal description information. After MRLM converged, items' multimodal features and users' global features could be used to calculate users' preferences on items via cosine similarity. Through extensive experiments on two real-world datasets, MRLM remarkably improved the recommendation effectiveness in IoT.

We aim at exploiting users' coarse behavioral records for identity theft detection in online services. We concentrate on this issue in online social networks (OSNs) that users' behavioral records usually consist of multiple dimensional behavior data. The behavioral records in each dimension are possibly coarse and insufficient for effectively modeling users' behavioral patterns. In this paper, we investigate whether there is a complementary effect among different dimensions of records for modeling users' behavioral patterns. We focus on three typical dimensions of behaviors in OSNs, i.e., offline check-ins, online tip-postings, and social contacts. We devise the dedicated behavior models based on each dimension of data, i.e., users' behavioral projection models. Then, by examining all feasible logical combinations of them, we find the optimal ones for two real-world data sets: Foursquare and Yelp. Notably, we analyze the potential correlation between customized demand and optimal logical fusion scheme. As an insightful result, we find that the correlation is independent of the specific data. This study would give the cybersecurity community new insights into the possibility and methodology to achieve a customized identity theft detection in OSNs by integrating multiple behavioral projection models.

The average travel speed on certain road sections is an important piece of information for the intelligent transportation system. Traditional ways for estimating travel speed usually depend on dedicated sensors or infrastructures, which is financially costly. Alternatively, as an infrastructure-free way, vehicular crowdsensing can be used to collect data including real-time locations and velocities of vehicles, which is quite low cost and effective. This paper aims to produce a fully covered distribution of average travel speeds for road sections both in time and space domains based on vehicular crowdsensing data. However, due to the uneven spatial-temporal distribution of vehicles and the variation of their data-offering intervals, vehicular crowdsensing data are usually coarse grained. This coarseness leads to missing travel speed values of vehicles on some road sections. To handle this problem, we propose an approach that exploits the spatial-temporal causality among travel speeds of road sections by a time-lagged correlation coefficient function. We use a time-lagging factor to quantify the time consumption of vehicles traveling along road sections. Then, we utilize the local stationarity of correlation coefficient to estimate the travel speeds of road sections. Experiments based on real taxi trace data show that the proposed method performs better than some methods in use.

Querying an incident (i.e., an occurrence of seemingly minor importance) from coarse-grained driving log (i.e., GPS trace) has been a daunting task. For example, “Which restaurant did I drive by at exactly 4 pm yesterday?” The question seems very simple but is nontrivial, because the question is semantics-driven while the actual log data are GPS coordinate-based. Especially, the practical GPS log is very sparse and inaccurate for high-speed mobile objects such as vehicles. This paper seeks to answer any fuzzy query over sparse vehicles GPS data. Our system, called iLogBook, achieves these two goals by leveraging tensor technique and latent semantic analysis to high-precision trajectory recovery and similarity matching. We have implemented and evaluated the iLogBook with the GPS data of over 13, 798 taxicabs collected in eight days in Shenzhen, China. Our results show about 97% accuracy in trajectory inference. Moreover, the system handles about 90% daily queries among 10 marked drivers.

As a typical latent factor model, Matrix Factorization (MF) has demonstrated its great effectiveness in recommender systems. Users and items are represented in a shared low-dimensional space so that the user preference can be modeled by linearly combining the item factor vector$V$using the user-specific coefficients$U$. From a generative model perspective,$U$and$V$are drawn from twoindependentGaussian distributions, which is not so faithful to the reality. Items are produced to maximally meet users’ requirements, which makes$U$and$V$strongly correlated. Meanwhile, the linear combination between$U$and$V$forces a bijection (one-to-one mapping), which thereby neglects the mutual correlation between the latent factors. In this paper, we address the upper drawbacks, and propose a new model, named Correlated Matrix Factorization (CMF). Technically, we apply Canonical Correlation Analysis (CCA) to map$U$and$V$into a new semantic space. Besides achieving the optimal fitting on the rating matrix, one component in each vector ($U$or$V$) is also tightly correlated with every single component in the other. We derive efficient inference and learning algorithms based on variational EM methods. The effectiveness of our proposed model is comprehensively verified on four public datasets. Experimental results show that our approach achieves competitive performance on both prediction accuracy and efficiency compared with the current state of the art.

Synthetic graph generators facilitate research in graph algorithms and graph processing systems by providing access to graphs that resemble real social networks while addressing privacy and security concerns. Nevertheless, their practical value lies in their ability to capture important metrics of real graphs, such as degree distribution and clustering properties. Graph generators must also be able to produce such graphs at the scale of real-world industry graphs, that is, hundreds of billions or trillions of edges. In this paper, we propose Darwini, a graph generator that captures a number of core characteristics of real graphs. Importantly, given a source graph, it can reproduce the degree distribution and, unlike existing approaches, the local clustering coefficient distribution. Furthermore, Darwini maintains a number of metrics, such as graph assortativity, eigenvalues, and others. Comparing Darwini with state-of-the-art generative models, we show that it can reproduce these characteristics more accurately. Finally, we provide an open source implementation of Darwini on the vertex-centric Apache Giraph model that can generate synthetic graphs with up to 3 trillion edges.

The Internet of Vehicles (IoV) is a subapplication of the Internet of Things in the automotive field. Large amounts of sensor data require to be transferred in real-time. Most of the routing protocols are specifically targeted to specific situations in IoV. But communication environment of IoV usually changes in the space-time dimension. Unfortunately, the traditional vehicular networks cannot select the optimal routing policy when facing the dynamic environment, due to the lack of abilities of sensing the environment and learning the best strategy. Sensing and learning constitute two key steps of the cognition procedure. Thus, in this paper, we present a software defined cognitive network for IoV (SDCIV), in which reinforcement learning and software defined network technology are considered for IoV to achieve cognitive capability. To the best of our knowledge, this paper is the first one that can give the optimal routing policy adaptively through sensing and learning from the environment of IoV. We perform experiments on a real vehicular dataset to validate the effectiveness and feasibility of the proposed algorithm. Results show that our algorithm achieves better performance than several typical protocols in IoV. We also show the feasibility and effectiveness of our proposed SDCIV.

Discovering and tracking spatiotemporal event patterns have many applications. For example, in a smart-home project, a set of spatiotemporal pattern learning automata are used to monitor a user's repetitive activities, by which the home's automaticity can be promoted while some of his/her burdens can be reduced. Existing algorithms for spatiotemporal event pattern recognition in dynamic noisy environment are based on fixed structure stochastic automata whose state transition function is fixed and predesigned to guarantee their immunity to noise. However, such design is conservative because it needs continuous and identical feedbacks to converge, thus leading to its very low convergence rate. In many real-life applications, such as ambient assisted living, consecutive nonoccurrences of an elder resident's routine activities should be treated with an alert as quickly as possible. On the other hand, no alert should be output even for some occurrences in order to diminish the effects caused by noise. Clearly, confronting a pattern's change, slow speed and low accuracy may degrade a user's life security. This paper proposes a fast and accurate leaning automaton based on variable structure stochastic automata to satisfy the realistic requirements for both speed and accuracy. Bias toward alert is necessary for elder residents while the existing method can only support the bias toward "no alert." This paper introduces a method to allow bias toward alert or no alert to meet a user's specific bias requirement. Experimental results show its better performance than the state-of-the-art methods.

Document network is a kind of intriguing dataset which can provide both topical (textual content) and topological (relational link) information. A key point in modeling such datasets is to discover proper denominators beneath the text and link. Most previous work introduces the assumption that documents closely linked with each other share common latent topics. However, the heterophily (i.e., tendency to link to different others) of nodes is neglected, which is pervasive in social networks. In this paper, we simultaneously incorporate community detection and topic modeling in a unified framework, and appeal to Canonical Correlation Analysis (CCA) to capture the latent semantic correlations between the two heterogeneous factors, community and topic. Despite of the homophily (i.e., tendency to link to similar others) or heterophily, CCA can properly capture the inherent correlations which fit the dataset itself without any prior hypothesis. We also impose auxiliary word embeddings to improve the quality of topics. The effectiveness of our proposed model is comprehensively verified on three different types of datasets which are hyperlinked networks of web pages, social networks of friends, and coauthor networks of publications. Experimental results show that our approach achieves significant improvements compared with the current state of the art.

Link similarity is widely applied in measuring the similarity between such objects as Web pages, scientific papers, and social networks. However, there are some deficiencies in the existing methods to measure it. For example, they cannot handle some semantic-similar contents. Their computation may not lead to accurate results in some cases. This paper presents a novel method to do so. It introduces the semantic similarity to calculate the similarity between two given objects, and overcomes the drawback caused by the fact that the existing methods ignore the semantic information of objects. It also gives a novel computation function to make the computing result of similarity more accurate.

The soaring of social media services has greatly propelled the prevalence of document networks. Rather than a set of plain texts, documents are nodes in graphs. An observable link connects the documents at its two ends, thus it implicitly reflects the semantic association between the document pair. Previous work assumes that only similar documents tend to be connected, which neglects the rich connective patterns in the topological structure. In this paper, we introduce a latent correlation factor to categorize the links into several categories, and each category corresponds to a unique kind of association. By fitting the data, the relational information (e.g., homophily and heterophily) can be comprehensively captured. By resorting to Canonical Correlation Analysis (CCA), we maximize the correlation between all pairs of linked documents. We propose a pure generative model and derive efficient learning algorithms based on the variational EM methods. Experiments on three different datasets demonstrate that the proposed model is competitive and usually better than the state-of-the-art baselines on both topic modeling and link prediction.

Internet information services have been greatly improved profiting from the growing performance of interest mining technology. Visual perceptual behaviours, a new hotspot of mining user's interests, have resulted in great gains in some typical Internet information services, e.g., information retrieval and recommendation. It is validated that combining the subjective visual perceptual behaviours with the objective contents can significantly improve these services' performance. However, the existing methods usually treat the contents and visual perceptual behaviours as two independent parts in the calculating process. The gain of visual perceptual behaviours has not been fully exploited. In this paper, we mainly aim at improving the gain of visual perceptual behaviour for text recommendation, by integrating the objective contents with subjective visual perceptual behaviours. We investigate the correlation between user's reading interests and records of real-time interaction on texts, and then design a real-time visual perceptual behaviour based method for text recommendation, which is able to: (1) build a joint interest model, called ViP-LDA (Visual Perceptual LDA), by integrating the user's visual perceptual behaviours into topic model; (2) make more accurate text recommendation based on ViP-LDA with feedback adjustment. Several experiments on a real data set are implemented to demonstrate the effectiveness of our method.

In this paper we aim at addressing the correlation between two critical factors in mobile social networks (MSNs): the social-relationship networking among users and the spatial mobility pattern of users. Specifically, we investigate the impact of users' spatial distribution on their social relationship formation in MSNs. Based on the geolocation data (check-in records) and social relation data of MSN users, we propose a model, called neighborhood-cardinality-based model (NCBM), to describe this impact by taking into account both the multiple home-points/hotspots property of spatial mobility and the long-tailed social relationship degree distribution of MSN users. We define a fundamental quantity for each user, i.e., the so-called neighborhood cardinality, to measure how many and how often other MSN users visit his nearby area with a given range. The core of NCBM is a principle: The probability that a user, say u, is followed by another user, say v, obeys a power law distribution of the neighborhood cardinality of user u. The proposed formation model is evaluated on two large check-in datasets: Brightkite and Gowalla. Our experimental results indicate that the proposed formation model provides a useful paradigm for capturing the correlation between MSN users' mobility patterns and social relationships.

User behavioral analysis is expected to be a key technique for identity theft detection in the Internet, especially in mobile social networks (MSNs). While traditional methods prefer to use explicit behaviors, a series of behaviors implicit in user's texts can probably provide much more accurate identity. And these implicit behaviors can be digged from texts by LDA. Besides the latent feature in texts, a behavior also include other features (e.g., spatial and temporal features). A joint feature including these features can be a better evidence for identity theft detection. In this paper, we use a probabilistic generative model to detect identity theft in MSNs. We are going to conduct experiments on two real-life datasets: Foursquare and Yelp. A early experiment shows that semantic features achieve better performance than spatial features and we are conducting our main experiment to see a better performance with joint behavioral feature.

Document network is a kind of intriguing dataset which provides both topical (texts) and topological (links) information. Most previous work assumes that documents closely linked with each other share common topics. However, the associations among documents are usually complex, which are not limited to the homophily (i.e., tendency to link to similar others). Actually, the heterophily (i.e., tendency to link to different others) is another pervasive phenomenon in social networks. In this paper, we introduce a new tool, called copula, to separately model the documents and links, so that different copula functions can be applied to capture different correlation patterns. In statistics, a copula is a powerful framework for explicitly modeling the dependence of random variables by separating the marginals and their correlations. Though widely used in Economics, copulas have not been paid enough attention to by researchers in machine learning field. Besides, to further capture the potential associations among the unconnected documents, we apply the tree-averaged copula instead of a single copula function. This improvement makes our model achieve better expressive power, and also more elegant in algebra. We derive efficient EM algorithms to estimate the model parameters, and evaluate the performance of our model on three different datasets. Experimental results show that our approach achieves significant improvements on both topic and link modeling compared with the current state of the art.

Summary Urban traffic condition usually serves as basic information for some intelligent urban applications, for example, intelligent transportation system. The traditional acquisition of such information is often costly because of the dependencies on infrastructures, such as cameras and loop detectors. Crowdsensing, as a new economic paradigm, can be utilized together with vehicular networks to efficiently gather vehicle‐sensed data for estimating the traffic condition. However, it has the problem of being lack of data uploading efficiency and data usage effectiveness. In this paper, we take into account the topology of the road net to deal with these problems. Specifically, we divide the road net intoroad sectionsandjunction areas. Based on this division, we introduce a two‐phased data collection and processing scheme named road topology‐based scheme. It leverages the correlations among adjacent roads. In a junction area, data collected by vehicles are first processed and integrated by a sponsor vehicle to locally calculate traffic condition. Both the selection of the sponsor and the calculation of road condition utilize the road correlation. The sponsor then uploads the local data to a server. By employing the inherent relations among roads, the server processes data and estimates traffic condition for the road sections without vehicular data in a global vision. We conduct experiments based on real vehicle trace data. The results indicate that our design can commendably handle the problems of efficiency and effectiveness in traffic condition evaluation using the vehicular crowdsensing data. Copyright © 2016 John Wiley & Sons, Ltd.

A stochastic point location (SPL) problem aims to find a target parameter on a 1-D line by operating a controlled random walk and receiving information from a stochastic environment (SE). If the target parameter changes randomly, we call the parameter dynamic; otherwise static. SE can be 1) informative (p > 0.5 where p represents the probability for an environment providing a correct suggestion) and 2) deceptive (p <; 0.5). Up till now, hierarchical stochastic searching on the line (HSSL) is the most efficient algorithms to catch static or dynamic parameter in an informative environment, but unable to locate the target parameter in a deceptive environment and to recognize an environment's type (informative or deceptive). This paper presents a novel solution, named symmetrical HSSL, by extending an HSSL binary tree-based search structure to a symmetrical form. By means of this innovative way, the proposed learning mechanism is able to converge to a static or dynamic target parameter in the range of not only 0.6181 <; p <; 1, but also 0 <; p <; 0.382. Finally, the experimental results show that our scheme is efficient and feasible to solve the SPL problem in any SE.

Recently, a number of technologies have been developed to promote vehicular networks. When vehicles are associated with the heterogeneous base stations (e.g., macrocells, picocells, and femtocells), one of the most important problems is to make load balancing among these base stations. Different from common mobile networks, data traffic in vehicular networks can be observed having regularities in the spatial-temporal dimension due to the periodicity of urban traffic flow. By taking advantage of this feature, we propose an online reinforcement learning approach, called ORLA. It is a distributed user association algorithm for network load balancing in vehicular networks. Based on the historical association experiences, ORLA can obtain a good association solution through learning from the dynamic vehicular environment continually. In the long run, the real-time feedback and the regular traffic association patterns both help ORLA cope with the dynamics of network well. In experiments, we use QiangSheng taxi movement to evaluate the performance of ORLA. Our experiments verify that ORLA has higher quality load balancing compared with other popular association methods.

Internet of Vehicles (IoV) has gained considerable attention from industry and academia due to the development of communication technology and smart city. However, a proprietary and closed way of operating hardware in network equipment slows down the progress of new service deployment and extension in IoV. Moreover, the tightly coupled control and data planes in traditional networks significantly increase the complexity and cost of network management. By proposing a novel architecture, i.e., software-defined IoV (SDIV), we adopt a software-defined network (SDN) architecture to address these problems by leveraging its separation of the control plane from the data one and a uniform way to configure heterogeneous switches. However, IoV characteristics introduce some great challenges in rule installation due to the limited size of flow tables at OpenFlow-enabled switches that are the main SDN component. It is necessary to build compact flow tables for IoV scalability. Accordingly, we develop a novel rule installation mechanism to reduce the number of rules for real-time query services in SDIV. We separate the wired data plane from the wireless one and use multicast addresses in the latter. We introduce a destination-driven model in the wired data plane to reduce the number of rules at switches. Experiments with a real data trace show that the developed approach significantly reduces the number of rules without degrading the performance of data transmissions for real-time query services in IoV.

The Fast Fourier Transform (FFT) is one of the most important numerical tools widely used in many scientific and engineering applications. The algorithm performs O(nlogn) operations on n input data points in order to calculate only small number of k large coefficients, while the rest of n - k numbers are zero or negligibly small. The algorithm is clearly inefficient, when n points input data lead to only k <;Z n non-zero coefficients in the transformed domain. MIT in 2012 developed a sparse FFT (sFFT) algorithm that provides a solution to this problem. In this paper, we explore the challenges and propose effective solutions to efficiently port sFFT to massively parallel processors, such as GPUs, using CUDA. GPGPUs are being increasingly adopted as popular HPC platforms because of their tremendous computing power and remarkable cost efficiency. However, sFFT algorithm is a complex and computationally challenging memory-bound algorithm that is not straightforward to be implemented on GPUs. In this paper, we present some of the optimization strategies such as index coalescing, loop splitting, asynchronous data layout transformation, linear time selection algorithm that are required to compute sFFT on such massively parallel architectures. Our CUDA-based sFFT, cusFFT, performs over 10x faster than the state-of-the-art cuFFT library on GPUs and over 28x faster than the parallel FFTW on multicore CPUs.

In SDN (Software-Defined Networks), applications are granted the ability to control and manage networks, such as routing, access control, and load balance, which is considered one of the most significant characters of SDN. However, these concurrent applications are lack of interactions when they operate on overlapping portions of the traffic and introduce a critical challenge of potentially conflicting flow rules (including match fields and actions). When their actions are dropping, forwarding, or diverting to the controller, these kinds of flow rule conflicts are inherent and arduous, and there exist no appropriate compositional methods of resolving them. Under the circumstances, the paper proposes a general model of abstract network states where different applications can own their different network views. When conflicting rules are detected, the model provides new network states for applications by composing original network states and conflicting rules. Therefore, applications do not need to handle underlying conflicting rules and just achieve the latest network states to install flow rules correctly.

In this paper, we aim to model the formation of interest-driven data dissemination in online social networks (OSNs). We focus on a usual type of interest-driven social sessions in OSNs, called Social-InterestCast, under which a user will autonomously determine whether to view the content from his followees depending on his interest. To figure out the formation mechanism of such a Social-InterestCast, we propose a four-layered system model, consisting of physical layer, social layer, content layer, and session layer to model this interestdriven sessions. To the best of our knowledge, this is the first work to model data dissemination in OSNs with the interestdriven characteristics.

A learning automaton (LA) is a powerful tool for reinforcement learning. Its action probability vector plays two roles: 1) deciding when it converges, i.e., total computing budget it has used, and 2) allocating computing budget among actions to identify the optimal one. These two intertwined roles lead to a problem: the computing budget mostly goes to the currently estimated optimal action due to its high action probability regardless whether such budget allocation can help identify the true optimal one or not. This work proposes a new class of LA that avoids the use of its action probability vector for computing budget allocation. Instead we use such vector only to determine if it converges and then employ optimal computing budget allocation to accomplish the allocation of computing budget in a way that maximizes the probability of identifying the true optimal actions. ε-optimality is proven. Simulations verify its advantages over existing algorithms. A learning automaton (LA) represents an important leaning mechanism with applications in automated system design, biological systems, computer vision, and transportation. It updates its action probability vector in accordance with the inputs received from the environment to improve its performance. It acts as an adaptive controller in modeling a process as well as generating appropriate control signals. The existing LAs simply employ heuristics to update their action probability vectors and then use the vectors for ordinal optimization and determining the computing budget size. This work separates ordinal optimization from the action probability vector and introduces optimal computing budget allocation to maximize the probability of selecting the true optimal action. Compared with the state-of-the-art methods in five popular environments, the proposed LA speeds up the learning efficiency ranging from 10.93% to 65.94%.

Radio frequency identification (RFID) technology has been widely adopted in a variety of applications from logistics to access control. Many applications gain benefits from knowing the exact position of an RFID-tagged object. Existing localization algorithms in wireless network, however, can hardly be directly employed due to tag's limited capabilities in terms of energy and memory. In this paper, we propose BackPos, a fine-grained backscatter positioning technique using the commercial off-the-shelf (COTS) RFID products with detected phases. Our studies show that the phase is a stable indicator highly related to tag's position and preserved over frequency or tag orientation, but challenged by its periodicity and tag's diversity. We attempt to infer the distance differences from phases detected by antennas under triangle constraint. Further, hyperbolic positioning using the distance differences is employed to shrink the tag's candidate positions until finding out the real one. In combination with interrogation zone, we finally relax the triangle constraint and allow arbitrary deployment of antennas by sacrificing the feasible region. We implement a prototype of BackPos with COTS RFID products and evaluate this design in various scenarios. The results show that BackPos achieves the mean accuracy of 12:8cm with variance of 3:8 cm.

Document network is a kind of intriguing dataset which can provide both topical (textual content) and topological (relational link) information. A key point in viably modeling such datasets is to discover proper denominators beneath the two different types of data, text and link. Most previous work introduces the assumption that documents closely linked with each other share common latent topics. However, the heterophily (i.e., tendency to link to different others) of nodes is neglected, which is pervasive in social networks. In this paper, we simultaneously incorporate community detection and topic modeling in a unified framework, and appeal to Canonical Correlation Analysis (CCA) to capture the latent semantic correlations between the two heterogeneous latent factors, community and topic. Despite of the homophily (i.e., tendency to link to similar others) or heterophily, CCA can properly capture the inherent correlations which fit the dataset itself without any prior hypothesis. Logistic normal prior is also employed in modeling network to better capture the community correlations. We derive efficient inference and learning algorithms based on variational EM methods. The effectiveness of our proposed model is comprehensively verified on three different types of datasets which are namely hyperlinked networks of web pages, social networks of friends and coauthor networks of publications. Experimental results show that our approach achieves significant improvements on both topic modeling and community detection compared with the current state of the art. Meanwhile, our model is impressive in discovering correlations between extracted topics and communities.

The best design among many can be selected through their accurate performance evaluation. When such evaluation is based on discrete event simulations, the design selection is extremely time-consuming. Ordinal optimization greatly speeds up this process. Optimal Computing-Budget Allocation (OCBA) has further accelerated it. Other kinds of OCBA have been introduced for reaching different goals, for example, to select the optimal subset of designs. However, facing the issue of subset ranking, which is a generalized form from problems selecting the best design or optimal subset, all the existing ones are insufficient. This work develops a new OCBA-based approach to address this subset ranking issue. Through mathematical deduction, its theoretical foundation is laid. Our numerical simulation results reveal that it indeed outperforms all the other existing methods in terms of probability of correct subset ranking and computational efficiency.

RFID has been widely adopted as an effective method for anti-counterfeiting. Legacy systems based on security protocol are either too heavy to be affordable by passive tags or suffering from various protocol-layer attacks, e.g. reverse engineering, cloning, side-channel. In this work, we present a novel anti-counterfeiting system, TagPrint, using COTS RFID tags and readers. Achieving a low-cost and offline genuineness validation utilizing passive tags has been a daunting task. Our system achieves these three goals by leveraging a few of federated tags' fingerprints and geometric relationships. In TagPrint, we exploit a new kind of fingerprint, called phase fingerprint, extracted from the phase value of the backscattered signal, provided by the COTS RFID readers. To further solve the separation challenge, we devise a geometric solution to validate the genuineness. We have implemented a prototype of TagPrint using COTS RFID devices. The system has been tested extensively over 6,000 tags. The results show that our new fingerprint exhibits a good fitness of uniform distribution and the system achieves a surprising Equal Error Rate of 0.1% for anti-counterfeiting.

Urban traffic condition usually serves as a basic information for some intelligent urban applications, e.g., intelligent transportation system. But the acquisition of such information is often costly due to the dependency on equipments such as cameras and loop detectors. Crowdsensing can be utilized to gather vehicle-sensed data for traffic condition estimation. This way of data collection is economic. However, it has the problems of data uploading efficiency and data usage effectiveness. To deal with these problems, in this paper, we take into account the topology of the road net. We divide the road net into Road Sections and Junction Areas. Based on this division, we introduce a two-phased data collection and processing scheme named RTS (Road Topology based Scheme). It leverages the correlations among adjacent roads. In a junction area, data collected by vehicles is first processed and integrated by a sponsor vehicle. This sponsor vehicle will calculate the traffic condition locally. Both the selection of the sponsor and the calculation of the traffic condition utilize the road correlation. The sponsor then uploads the local data to a server. By employing the inherent relations among roads, the server processes data and estimates traffic condition for road sections unreached by vehicular data in a global vision. We conduct extensive experiments based on real vehicle trace data. The results indicate that, our design can commendably handle the problems of efficiency and effectiveness in the vehicular-crowdsensing-data based traffic condition evaluation.

In this paper, we study capacity scaling laws of the deterministic dissemination (DD) in random wireless networks under the generalized physical model (GphyM). This is truly not a new topic. Our motivation to readdress this issue is two-fold: Firstly, we aim to propose a more general result to unify the network capacity for general homogeneous random models by investigating the impacts of different parameters of the system on the network capacity. Secondly, we target to close the open gaps between the upper and the lower bounds on the network capacity in the literature. We derive the general upper bounds on the capacity for the arbitrary case of (λ, nd, ns) by introducing the Poisson Boolean model of continuum percolation, where λ, nd, and ns are the general node density, the number of destinations for each session, and the number of sessions, respectively. We prove that the derived upper bounds are tight according to the existing general lower bounds constructed in the literature.

In this paper, we mainly investigate capacity scaling laws of the mobile ad hoc social networks (MAHSNs)where social networking applications are implemented over the underlying mobile ad hoc networks. We model the real-world mobility pattern of mobile social users by introducing a clustered model that defines two levels of mobility, i.e., Strong mobility and weak mobility, according to the impacts of mobility on the gain of network capacity. To address the formation of social relationships among mobile social users, we adopt a distance and density aware social model called population-distance-based model that comprehensively and practically takes account of the clustering levels of friendship degree and distribution. Under those models, we derive the capacity scaling laws for social-broadcast sessions in MAHSNs. The results provide the exploratory insights into the impacts of users' mobility patterns and the formation of social relationships on the network capacity of MAHSNs.

In this paper, we focus on the cyber-physical system consisting of interdependent physical-resource and computational-resource networks, e.g., smart power grids, automated traffic control system, and wireless sensor and actuator networks, where the physical-resource and computational-resource network are connected and mutually dependent. The failure in physical-resource network might cause failures in computational-resource network, and vice versa. A small failure in either of them could trigger cascade of failures within the entire system. We aim to investigate the issue of cascading failures occur in such system. We propose a typical and practical model by introducing the interdependent complex network. The interdependence between two networks is practically defined as follows: Each node in the computational-resource network has only one support link from the physical-resource network, while each node in physical-resource network is connected to multiple computational nodes. We study the effect of cascading failures using percolation theory and present detailed mathematical analysis of failure propagation in the system. We analyze the robustness of our model caused by random attacks or failures by calculating the size of functioning parts in both networks. Our mathematical analysis proves that there exists a threshold for the proportion of faulty nodes, above which the system collapses. Using extensive simulations, we determine the critical values for different system parameters. Our simulation also shows that, when the proportion of faulty nodes approaching critical value, the size of functioning parts meets a second-order transition. An important observation is that the size of physical-resource and computational-resource networks, and the ratio between their sizes do not affect the system robustness.

Learning automata (LA) are powerful tools for reinforcement learning. A discretized pursuit LA is the most popular one among them. During an iteration its operation consists of three basic phases: 1) selecting the next action; 2) finding the optimal estimated action; and 3) updating the state probability. However, when the number of actions is large, the learning becomes extremely slow because there are too many updates to be made at each iteration. The increased updates are mostly from phases 1 and 3. A new fast discretized pursuit LA with assured ε -optimality is proposed to perform both phases 1 and 3 with the computational complexity independent of the number of actions. Apart from its low computational complexity, it achieves faster convergence speed than the classical one when operating in stationary environments. This paper can promote the applications of LA toward the large-scale-action oriented area that requires efficient reinforcement learning tools with assured ε -optimality, fast convergence speed, and low computational complexity for each iteration.

Screen locking/unlocking is important for modern smart phones to avoid the unintentional operations and secure the personal stuff. Once the phone is locked, the user should take a specific action or provide some secret information to unlock the phone. The existing unlocking approaches can be categorized into four groups: motion, password, pattern, and fingerprint. Existing approaches do not support smart phones well due to the deficiency of security, high cost, and poor usability. We collect 200 users' handwaving actions with their smart phones and discover an appealing observation: the waving pattern of a person is kind of unique, stable and distinguishable. In this paper, we propose OpenSesame, which employs the users' waving patterns for locking/unlocking. The key feature of our system lies in using four fine-grained and statistic features of handwaving to verify users. Moreover, we utilize support vector machine (SVM) for accurate and fast classification. Our technique is robust compatible across different brands of smart phones, without the need of any specialized hardware. Results from comprehensive experiments show that the mean false positive rate of OpenSesame is around 15 percent, while the false negative rate is lower than 8 percent.

Existing methods in RFID systems often employ presence or absence fashion to detect the tags' motions, so they cannot meet motion detection requirement in many applications. Our recent observations suggest that the signal strength backscattered from the tag is hypersensitive to its position, inspiring us to perceive the tag motion through its radio signal strength changes. Motion perception is not trivial and challenged by weak stability of strength in that any other interference or noise may incur significant changes as well, resulting in high false positives. To tackle this issue, we propose to model the strength via the Mixture of Gaussian Model (MoG). The problem is thus converted to foreground segment in computer vision with the help of Strength Image, where the technique of MoG based background subtraction is employed. We then implement a prototype using commercial off-the-shelf products. The evaluation results show that the slightest tag motion (~10 cm) can be precisely perceived, and the accuracy is up to 92.34 percent while the false positive is suppressed under 0.5 percent.

In cyber physical system (CPS), computational resources and physical resources are strongly correlated and mutually dependent. Cascading failures occur between coupled networks, cause the system more fragile than single network. Besides widely used metric giant component, we study small cluster (small component) in interdependent networks after cascading failures occur. We first introduce an overview on how small clusters distribute in various single networks. Then we propose a percolation theory based mathematical method to study how small clusters be affected by the interdependence between two coupled networks. We prove that the upper bounds exist for both the fraction and the number of operating small clusters. Without loss of generality, we apply both synthetic network and real network data in simulation to study small clusters under different interdependence models and network topologies. The extensive simulations highlight our findings: except the giant component, considerable proportion of small clusters exists, with the remaining part fragmenting to very tiny pieces or even massive isolated single vertex; no matter how the two networks are tightly coupled, an upper bound exists for the size of small clusters. We also discover that the interdependent small-world networks generally have the highest fractions of operating small clusters. Three attack strategies are compared: Inter Degree Priority Attack, Intra Degree Priority Attack and Random Attack. We observe that the fraction of functioning small clusters keeps stable and is independent from the attack strategies.

This paper aims to design an efficient data forwarding scheme based on local activity and social similarity(LASS) for mobile social networks (MSNs). Various definitions of social similarity have been proposed as the criterion for relay selection, which results in various forwarding schemes. The appropriateness and practicality of various definitions determine the performances of these forwarding schemes. A popular definition has recently been proven to be more efficient than other existing ones, i.e., the more common interests between two nodes, the larger social similarity between them. In this work, we show that schemes based on such definition ignore the fact that members within the same community, i.e., with the same interest, usually have different levels of local activity, which will result in a low efficiency of data delivery. To address this, in this paper, we design a new data forwarding scheme for MSNs based on community detection in dynamic weighted networks, called Local-Activity and Social-Similarity, taking into account the difference of members' internal activity within each community, i.e., local activity. To the best of our knowledge, the proposed scheme is the first one that utilizes different levels of local activity within communities. Through extensive simulations, we demonstrate that LASS achieves better performance than state-of-the-art protocols.

In this paper, we investigate capacity scaling laws of wireless social networks under the social-based session formation. We model a wireless social network as a three-layered structure, consisting of the physical layer, social layer, and session layer; and we introduce a cross-layer distance & density-aware model, called the population-based formation model, under which: 1) for each node vk, the number of its friends/followers, denoted by qk, follows a Zipf's distribution with degree clustering exponent g; 2) qkanchor points are independently chosen according to a probability distribution with density function proportional to (Ek,X)-β, where Ek;Xis the expected number of nodes (population) within the distance |vk-X| to vk, and β is the clustering exponent of friendship formation; 3) finally, qknodes respectively nearest to those qkanchor points are selected as the friends of vk. We present the general density function of social relationship distribution, with general distribution of physical layer, serving as the basis for studying general capacity of wireless social networks. As the first step of addressing this issue, for the homogeneous physical layer, we derive the social-broadcast capacity under both generalized physical and protocol interference models, taking into account general clustering exponents of both friendship degree and friendship formation in a 2-dimensional parameter space, i.e., (γ,β) ϵ[0,∞)2. Importantly, we notice that the adopted model with homogenous physical layer does not sufficiently reflect the advantages of the population-based formation model in terms of realistic validity and practicability. Accordingly, we introduce a random network model, called the center-clustering random model (CCRM) with node distribution exponent δ ϵ [0, ∞), highlighting the clustering and inhomogeneity property in real-life networks, and discuss how to further derive more general network capacity over 3-dimensional parameter space (δ,γ,β) ϵ [0, ∞)3based on our results over (γ,β) ϵ [0, ∞)2.

Prior work on anti-collision for radio frequency identification (RFID) systems usually schedule adjacent readers to exclusively interrogate tags for avoiding reader collisions. Although such a pattern can effectively deal with collisions, the lack of readers' collaboration wastes numerous time on the scheduling process and dramatically degrades the throughput of identification. Even worse, the tags within the overlapped interrogation regions of adjacent readers (termed as contentious tags), even if the number of such tags is very small, introduce a significant delay to the identification process. In this paper, we propose a new strategy for collision resolution. First, we shelve the collisions and identify the tags that do not involve reader collisions. Second, we perform a joint identification, in which adjacent readers collaboratively identify the contentious tags. In particular, we find that neighboring readers can cause a new type of tag collision, cross-tag-collision, which may impede the joint identification. We propose a protocol stack, named Season, to undertake the tasks in two phases and solve the cross-tag-collision. We conduct extensive simulations and preliminary implementation to demonstrate the efficiency of our scheme. The results show that our scheme can achieve above 6× improvement on the identification throughput in a large-scale dense reader environment.

In this paper, we study two tightly coupled issues, space-crossing community detection and its influence on data forwarding in mobile social networks (MSNs). We propose a communication framework containing the hybrid underlying network with access point (AP) support for data forwarding and the base stations for managing most of control traffic. The concept of physical proximity community can be extended to be one across the geographical space, because APs can facilitate the communication among long-distance nodes. Space-crossing communities are obtained by merging some pairs of physical proximity communities. Based on the space-crossing community, we define two cases of node local activity and use them as the input of inner product similarity measurement. We design a novel data forwarding algorithm Social Attraction and Infrastructure Support (SAIS), which applies similarity attraction to route to neighbor more similar to destination, and infrastructure support phase to route the message to other APs within common connected components. We evaluate our SAIS algorithm on real-life datasets from MIT Reality Mining and University of Illinois Movement (UIM). Results show that space-crossing community plays a positive role in data forwarding in MSNs. Based on this new type of community, SAIS achieves a better performance than existing popular social community-based data forwarding algorithms in practice, including Simbet, Bubble Rap and Nguyen's Routing algorithms.

In this paper, we study two tightly coupled issues: space-crossing community detection and its influence on data forwarding in Mobile Social Networks (MSNs) by taking the hybrid underlying networks with infrastructure support into consideration. The hybrid underlying network is composed of large numbers of mobile users and a small portion of Access Points (APs). Because APs can facilitate the communication among long-distance nodes, the concept of physical proximity community can be extended to be one across the geographical space. In this work, we first investigate a space-crossing community detection method for MSNs. Based on the detection results, we design a novel data forwarding algorithm SAAS (Social Attraction and AP Spreading), and show how to exploit the space-crossing communities to improve the data forwarding efficiency. We evaluate our SAAS algorithm on real-life data from MIT Reality Mining and University of Illinois Movement (UIM). Results show that space-crossing community plays a positive role in data forwarding in MSNs in terms of delivery ratio and delay. Based on this new type of community, SAAS achieves a better performance than existing social community-based data forwarding algorithms in practice, including Bubble Rap and Nguyen's Routing algorithms.

Radio Frequency Identification technology (RFID) is widely used in many applications, such as asset monitoring, e-passport and electronic payment, and is becoming one of the most effective solutions in cyber physical system. Since the identification alone does not provide any guarantee that tag corresponds to genuine identity, authentication of tag information is needed in most RFID systems. Meanwhile, as the number of tags is rapidly growing in recent years, per-tag based methods suffer from severely low efficiency and thus give way to probabilistic batch authentication. Most previous methods, however, share a common drawback from statistical perspective: they fail to explore correlation information, i.e., they do not comprehensively utilize all the information in authentication data structures. In addition, those schemes are not scalable well when multiple tag sets need to be verified simultaneously. In this paper, we propose a fast and efficient batch authentication scheme, Wise Counting (WIC), for large-scale RFID systems. We are the first to formally introduce the general batch authentication problem with multiple tag sets and give counterfeits estimation scheme with high efficiency. By employing a novel hierarchical authentication structure, we show that WIC is able to fast and efficiently authenticate both a single tag set and multiple tag sets in an easy, intuitive way. Through detailed theoretical analysis and extensive simulations, we validate the design of WIC and demonstrate its large superiority over state-of-the art approaches.

In this paper, we model the data dissemination in online social networks (OSNs) and study the scaling laws of traffic load. We propose a three-layered system model to formulate data dissemination sessions for social applications in OSNs. The layered model consists of the physical network layer, social relationship layer, and application session layer. By analyzing mutual relevances among these three layers, we investigate the geographical distribution feature of dissemination sessions in OSNs. Based on this, we derive the traffic load of OSNs under a realistic assumption that every source sustains a data generating rate of constant order. To the best of our knowledge, this is the first work to address the issue of traffic load scaling for OSNs by modeling the social data dissemination from a layered perspective.

A critical function of wireless sensor networks (WSNs) is data gathering. One is often only interested in collecting a specific function of the sensor measurements at a sink node, rather than downloading all the raw data from all the sensors. In this paper, we study the capacity of computing and transporting the specific functions of sensor measurements to the sink node, called aggregation capacity, for WSNs. We focus on random WSNs that can be classified into two types: random extended WSN and random dense WSN. All existing results about aggregation capacity are studied for dense WSNs, including random cases and arbitrary cases, under the protocol model (ProM) or physical model (PhyM). In this paper, we propose the first aggregation capacity scaling laws for random extended WSNs. We point out that unlike random dense WSNs, for random extended WSNs, the assumption made in ProM and PhyM that each successful transmission can sustain a constant rate is over-optimistic and unpractical due to transmit power limitation. We derive the first result on aggregation capacity for random extended WSNs under the generalized physical model. Particularly, we prove that, for the type-sensitive divisible perfectly compressible functions and type-threshold divisible perfectly compressible functions, the aggregation capacities for random extended WSNs with${\mbi {n}}$nodes are of order$\Thetab ({{{({\bf log} {\mbi {n}})}^{ - {\alphab \over {\bf 2}} - {\bf 1}}}})$and$\Theta ({{{{{({\bf log} {\mbi {n}})}^{ - \alphab /{\bf 2}}}} \over {{\bf log}{\bf log} {\mbi {n}}}}})$, respectively, where$\alphab \gt {\bf 2}$denotes the power attenuation exponent in the generalized physical model. Furthermore, we improve the aggregation throughput for general divisible perfectly compressible functions to$\Omegab ({{{({\bf log} {\mbi {n}})}^{ - {\alphab \over {\bf 2}}}}})$by choosing$\Thetab ({\bf log}\; {\mbi {n}})$sensors from a small region (relative to the whole region) as sink nodes.

An update scheme of the state probability vector of actions is critical for learning automata (LA). The most popular is the pursuit scheme that pursues the estimated optimal action and penalizes others. This paper proposes a reverse philosophy that leads to last-position elimination-based learning automata (LELA). The action graded last in terms of the estimated performance is penalized by decreasing its state probability and is eliminated when its state probability becomes zero. All active actions, that is, actions with nonzero state probability, equally share the penalized state probability from the last-position action at each iteration. The proposed LELA is characterized by the relaxed convergence condition for the optimal action, the accelerated step size of the state probability update scheme for the estimated optimal action, and the enriched sampling for the estimated nonoptimal actions. The proof of the ϵ-optimal property for the proposed algorithm is presented. Last-position elimination is a widespread philosophy in the real world and has proved to be also helpful for the update scheme of the learning automaton via the simulations of well-known benchmark environments. In the simulations, two versions of the LELA, using different selection strategies of the last action, are compared with the classical pursuit algorithms Discretized Pursuit Reward-Inaction (DP(RI)) and Discretized Generalized Pursuit Algorithm (DGPA). Simulation results show that the proposed schemes achieve significantly faster convergence and higher accuracy than the classical ones. Specifically, the proposed schemes reduce the interval to find the best parameter for a specific environment in the classical pursuit algorithms. Thus, they can have their parameter tuning easier to perform and can save much more time when applied to a practical case. Furthermore, the convergence curves and the corresponding variance coefficient curves of the contenders are illustrated to characterize their essential differences and verify the analysis results of the proposed algorithms.

In this paper, we focus on the asymptotic capacity and delay, and their tradeoffs in mobile ad hoc networks (MANETs). As we all know, some fixed rate communication models such as the protocol model and the physical model have been studied in the past. However, our work aims to investigate the impact of an adaptive rate communication model on capacity-delay tradeoffs in MANETs under classical mobility models. Specifically, we adopt a well-known adaptive rate model called the generalized physical model (GphyM). The mobility of nodes is characterized by two broad classes of practical mobility models and they are hybrid random walk models and discrete random direction models. The two models generalize many mobility models studied in the literature, including the random walk, i.i.d., Brownian, and random way point models. For each mobility model, we derive the optimal delay for the optimal per-session unicast capacity (that of constant order Θ(1)) under the generalized physical model, depending on the individual parameters of mobility models. In particular, we show that for the i.i.d. model, compared with those under the protocol and physical models, the adaptive feature of link rate under the generalized physical model results in a significant decrease in the optimal delay for the optimal capacity; more precisely, both the optimal capacity and optimal delay can be simultaneously achieved, while there is no improvement for the random way-point model.

With the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and, thus, control the balance of reliability versus monetary costs. Mechanisms and tools that deal with the cost-reliability tradeoffs under this scheme are of great value for users seeking to reduce their costs while maintaining high reliability. In this paper, we propose a set of bidding strategies under several service-level agreement (SLA) constraints. In particular, we aim to minimize the monetary cost and volatility of resource provisioning. Essentially, to derive an optimal bidding strategy, we formulate this problem as a Constrained Markov Decision Process (CMDP). Based on this model, we are able to obtain an optimal randomized bidding strategy through linear programming. Using real Instance price traces and workload models, we compare several adaptive checkpointing schemes in terms of monetary costs and job completion time. We evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence.

Multicore embedded systems are being widely used in telecommunication systems, robotics, medical applications and more.While they offer a high-performance with low-power solution, programming in an efficient way is still a challenge. In order to exploit the capabilities that the hardware offers, software developers are expected to handle many of the low-level details of programming including utilizing DMA, ensuring cache coherency, and inserting synchronization primitives explicitly. The state-of-the-art involves solutions where the software toolchain is too vendor-specific thus tying the software to a particular hardware leaving no room-for portability.

Information propagation in delay tolerant networks (DTN) is difficult due to the lack of continues connectivity. Most of previous work put their focus on the information propagation in static network. In this work, we examine two closely related problems on information propagation in predicable DTN. In particular, we assume that during a certain time period, the interacting process among nodes is known a priori or can be predicted. The first problem is to select a set of initial source nodes, subject to budget constraint, in order to maximize the total weight of nodes that receive the information at the final stage. This problem is well-known influence maximization problem which has been extensively studied for static networks. The second problem we want to study is minimum cost initial set problem, in this problem, we aim to select a set of source nodes with minimum cost such that all the other nodes can receive the information with high probability. We conduct extensive experiments using $10,000$ users from real contact trace.

We study the capacity scaling laws for the cognitive network that consists of the primary hybrid network (PhN) and secondary ad hoc network (SaN). PhN is further comprised of an ad hoc network and a base station-based (BS-based) network. SaN and PhN are overlapping in the same deployment region, operate on the same spectrum, but are independent with each other in terms of communication requirements. The primary users (PUs), i.e., the ad hoc nodes in PhN, have the priority to access the spectrum. The secondary users (SUs), i.e., the ad hoc nodes in SaN, are equipped with cognitive radios, and have the functionalities to sense the idle spectrum and obtain the necessary information of primary nodes in PhN. We assume that PhN adopts one out of three classical types of strategies, i.e., pure ad hoc strategy, BS-based strategy, and hybrid strategy. We aim to directly derive multicast capacity for SaN to unify the unicast and broadcast capacities under two basic principles: 1) The throughput for PhN cannot be undermined in order sense due to the presence of SaN. 2) The protocol adopted by PhN does not alter in the interest of SaN, anyway. Depending on which type of strategy is adopted in PhN, we design the optimal-throughput strategy for SaN. We show that there exists a threshold of the density of SUs according to the density of PUs beyond which it can be proven that: 1) when PhN adopts the pure ad hoc strategy or hybrid strategy, SaN can achieve the multicast capacity of the same order as it is stand-alone; 2) when PhN adopts the BS-based strategy, SaN can asymptotically achieve the multicast capacity of the same order as if PhN were absent, if some specific conditions in terms of relations among the numbers of SUs, PUs, the destinations of each multicast session in SaN, and BSs in PhN hold.

To ensure sustainable operations of wireless sensor networks, environmental energy harvesting has been well recognized as one promising solution for long-term applications. Unlike in battery-powered sensor networks, we are targeting a duty-cycle adjustment to optimize the network performance, e.g., delay minimization, with full harvested energy utilization. In this paper, we introduce a set of duty-cycle adjustment schemes that will minimize cross traffic delay (CTD) in energy-harvesting sensor networks. We first present an offline solution by assuming that the link reliability and traffic distribution are known a priori. Based on the submodular property of the CTD function, we theoretically prove that a simple greedy algorithm can achieve constant approximation. We next propose a class of online algorithms that do not require the knowledge of link reliability and traffic distribution. For each of these algorithms, we give a theoretical bound on the performance. We have evaluated our design with a TelosB-based implementation and experimental results corroborate our theoretical analysis.

Extensive efforts have been made to study the asymptotic capacity, delay, and their tradeoffs for large-scale mobile ad hoc networks, under different mobility models and communication models. Majority results adopt the fixed-rate communication model, such as the protocol model and physical model, and none of them breaks the limitation of tradeoffs: delay/capacity = ω(1) so far, even for the simplest i.i.d. model. In this work, we investigate this problem under the Gaussian channel model, and demonstrate that the delay-capacity tradeoffs can be further improved by designing new two-hop strategy under a general mobility model, called hybrid random walk mobility model (HRWMM). We found that the capacity and delay have several regions, depending on the freedom degree γ ϵ [0, 1] of mobile nodes. Specifically, we show that under the prerequisite of ensuring the optimal per-session capacity, i.e., of order Θ(1): (1) for 0 <; γ ≤ 1, the optimal delay under the Gaussian channel model is smaller than that under the protocol model or physical model; (2) for γ = 0, i.e., ordinary random walk model, it is not larger than the delay under the protocol model or physical model; (3) for γ = 1, i.e., i.i.d. model, the capacity and delay can simultaneously achieve the optimal order, i.e., Θ(1).

We study the asymptotic networking-theoretic multicast capacity bounds for random extended networks (REN) under Gaussian channel model, in which all wireless nodes are individually power-constrained. During the transmission, the power decays along path with attenuation exponent \alpha > 2. In REN, n nodes are randomly distributed in the square region of side length \sqrt{n}. There are n_s randomly and independently chosen multicast sessions. Each multicast session has n_d+1 randomly chosen terminals, including one source and n_d destinations. By effectively combining two types of routing and scheduling strategies, we analyze the asymptotic achievable throughput for all n_s=\omega (1) and n_d. As a special case of our results, we show that for n_s=\Theta (n), the per-session multicast capacity for REN is of order \Theta ({1\over \sqrt{n_d n}}) when n_d=O({n\over ({\log n})^{\alpha +1}} ) and is of order \Theta ({1\over n_d} \cdot (\log n)^{-{\alpha \over 2} }) when n_d=\Omega ({n\over \log n} ).

In this paper, we study multicast capacity for cognitive networks. We consider the cognitive network model consisting of two overlapping ad hoc networks, called the primary ad hoc network (PaN) and secondary ad hoc network (SaN), respectively. PaN and SaN operate on the same space and spectrum. For PaN (or SaN, respectively), we assume that primary (or secondary, respectively) nodes are placed according to a Poisson point process of intensity n (or m, respectively) over a unit square region. We randomly choose n_s (or m_s, respectively) nodes as the sources of multicast sessions in PaN (or SaN, respectively), and for each primary source v^p (or secondary source v^s, respectively), we pick uniformly at random n_d primary nodes (or m_d secondary nodes, respectively) as the destinations of v^p (or v^s, respectively). Above all, we assume that PaN can adopt the optimal protocol in terms of the throughput. Our main work is to design the multicast strategy for SaN by which the optimal throughput can be achieved, without any negative impact on the throughput for PaN in order sense. Depending on n_d and n, we choose the optimal one for PaN from two strategies called percolation strategy and connectivity strategy, respectively. Subsequently, we design the corresponding throughput-optimal strategy for SaN. We derive the regimes in terms of n, n_d, m, and m_d in which the upper bounds on multicast capacities for PaN and SaN can be achieved simultaneously. Unicast and broadcast capacities for the cognitive network can be derived by our results as the special cases by letting n_d=1 (or m_d=1) and n_d=n-1 (or m_d=m-1), respectively, which enhances the generality of this work.

In this work, for a wireless sensor network (WSN) of n randomly placed sensors with node density \lambda \in [1,n], we study the tradeoffs between the aggregation throughput and gathering efficiency. The gathering efficiency refers to the ratio of the number of the sensors whose data have been gathered to the total number of sensors. Specifically, we design two efficient aggregation schemes, called single-hop-length (SHL) scheme and multiple-hop-length (MHL) scheme. By novelly integrating these two schemes, we theoretically prove that our protocol achieves the optimal tradeoffs, and derive the optimal aggregation throughput depending on a given threshold value (lower bound) on gathering efficiency. Particularly, we show that under the MHL scheme, for a practically important set of symmetric functions called divisible perfectly compressible (DPC) functions, including the mean, max, and various kinds of indicator functions, etc., the data from \Theta (n) sensors can be aggregated to the sink at the throughput of a constant order \Theta (1), implying that, our MHL scheme is indeed scalable.

A critical function of wireless sensor networks (WSNs) is data gathering. While, one is often only interested in collecting a relevant function of the sensor measurements at a sink node, rather than downloading all the data from all the sensors. This paper studies the capacity of computing and transporting the specific functions of sensor measurements to the sink node, called aggregation capacity, for WSNs. It focuses on random WSNs that can be classified into two types: random extended WSN and random dense WSN. All existing results about aggregation capacity are studied for dense WSNs, including random cases and arbitrary cases, under the protocol model (ProM) or physical model (PhyM). In this paper, we propose the first aggregation capacity scaling laws for random extended WSNs. We point out that unlike random dense WSNs, for random extended WSNs, the assumption made in ProM and PhyM that each successful transmission can sustain a constant rate is over-optimistic and unpractical due to transmit power limitation.We derive the first result on aggregation capacity for random extended WSNs under the generalized physical model. Particularly, we prove that, for the type-sensitive perfectly compressible functions and type-threshold perfectly compressible functions, the aggregation capacities for random extended WSNs with n nodes are of order Θ ((log n)-β/2-1) and Θ (((log n)-β/2)/(log log n)), respectively, where β >; 2 denotes the power attenuation exponent in the generalized physical model.

We study the general scaling laws of the capacity for random wireless networks under the generalized physical model. The generality of this work is embodied in three dimensions denoted by (λ ∈ [1, n], nd∈ [1, n], ns∈ (1, n]). It means that: (1) We study the random network of a general node density λ ∈ [1, n], rather than only study either random dense network (RDN, λ = n) or random extended network (REN, λ = 1) as in the literature. (2) We focus on the multicast capacity to unify unicast and broadcast capacities by setting the number of destinations for each session as a general value nd∈ [1, n]. (3)We allow the number of sessions changing in the range ns∈ (1, n], rather than assume that ns= Θ(n) as in the literature.We derive the general lower bounds on the capacity for the arbitrary case of (λ, nd, ns). Particularly, we show that for the special cases (λ = 1, nd∈ [1, n], ns= n) and (λ = n, nd∈ [1, n], ns= n), our schemes achieve the highest multicast throughputs proposed in the existing works.

In this work, for a wireless sensor network (WSN) of n randomly placed sensors with node density λ ∈ [1, n], we study the tradeoffs between the aggregation throughput and gathering efficiency. The gathering efficiency refers to the ratio of the number of the sensors whose data have been gathered to the total number of sensors. Specifically, we design two efficient aggregation schemes, called single-hop-length (SLH) scheme and multiple-hop-length (MLH) scheme. By novelly integrating these two schemes, we theoretically prove that our protocol achieves the optimal tradeoffs, and derive the optimal aggregation throughput depending on a given threshold value (lower bound) on gathering efficiency. Particularly, we show that under the MLH scheme, for a practically important set of symmetric functions called perfectly compressible functions, including the mean, max, or various kinds of indicator functions, etc., the data from Θ(n) sensors can be aggregated to the sink at the throughput of a constant order Θ(1), implying that our MLH scheme is indeed scalable.

Prior work on anti-collision for Radio Frequency IDentification (RFID) systems usually schedule adjacent readers to exclusively interrogate tags for avoiding reader collisions. Although such a pattern can effectively deal with collisions, the lack of readers' collaboration wastes numerous time on the scheduling process and dramatically degrades the throughput of identification. Even worse, the tags within the overlapped interrogation regions of adjacent readers (termed as contentious tags), even if the number of such tags is very small, introduce a significant delay to the identification process. In this paper, we propose a new strategy for collision resolution. First, we shelve the collisions and identify the tags that do not involve reader collisions. Second, we perform a joint identification, in which adjacent readers collaboratively identify the contentious tags. In particular, we find that neighboring readers can cause a new type of collisions, cross-tag-collision, which may impede the joint identification. We propose a protocol stack, named Season, to undertake the tasks in two phases and solve the cross-tag-collision. We conduct extensive simulations and preliminary implementation to demonstrate the efficiency of our scheme. The results show that our scheme can achieve above 6 times improvement on the identification throughput in a large-scale dense reader environment.

Radio frequency identification (RFID) is a technology where a reader device can "sense'' the presence of a close by object by reading a tag device attached to the object. To guarantee the coverage quality, multiple RFID readers can be deployed in the given region. In this paper, we consider the problem of activation schedule for readers in a multi-reader environment. In particular, we try to design a schedule for readers to maximize the number of served tags per time-slot while avoiding various interferences. We first develop a centralized algorithm under the assumption that different readers may have different interference and interrogation radius. Next, we propose a novel algorithm which does not need any location information of the readers. Finally, we extend the previous algorithm in distributed manner in order to suit the case where no central entity exists. We conduct extensive simulations to study the performances of our proposed algorithm. And our evaluation results corroborate our theoretical analysis.

We present TelosCAM, a networking system that integrates wireless module nodes (such as TelosB nodes) with legacy surveillance cameras to provide storage-efficient and privacy-aware services of accurate, real time tracking and identifying of the burglar who stole the property. In our system, a property owner will have a wireless module node (called secondary module) attached to the property that s/he wants to protect. The secondary wireless module node will not store any personal information about the owner, nor any specific information about the property to be protected. Each user of the system will also have a unique wireless module node (called primary module) that contains some security information about the user, thus should be privately held by the user and be kept to the user always. Once a tracking process is triggered in privacy preserving manner, the secondary module will start sending out the alarm signal periodically. The alarm signal will be captured by some surveillance wireless module, integrated with existing surveillance cameras. Using the trajectory information provided by the secondary wireless module node, and the videos captured by the surveillance cameras, our system will then automatically pinpoint a burglar (e.g., a person or a car) that is more likely to carry the stolen property. Our extensive evaluation of the system shows that we can find the burglars with surprisingly high accuracy under various experiment settings, with significantly reduced storage-requirement of the legacy video surveillance system. It also can help the police to catch the burglars more efficiently by providing critical images or videos containing the burglars.

A real-time monitor and rescue system must be able to both quickly and reliably detect the event happening in its monitoring region. Furthermore, it is required to fulfill certain rescue mission, e.g., navigate victims to exit through safe path in case of emergency. Current monitor and rescue approaches generally rely on either teleoperated robots, or teams of wireless robots. Typically the robots used in these systems tend to have high cost which make them unpractical in large scale deployment and applications. In this work, we present a realtime monitor and rescue system, TelosW-Bot Net, utilizing integrated networks. The integrated network is an integration of stationary sensor networks and robots: static sensor networks comprised of large numbers of small, simple, and inexpensive wireless sensors, and the robots which can communicate and controlled by sensor nodes. We demonstrate the efficacy of our system in real test bed composed of 46 sensors, which is one of the largest robotic sensor network to our knowledge, providing empirical results.

We study the multicast capacity for hybrid wireless networks consisting of ordinary ad hoc nodes and base stations under Gaussian Channel model, which generalizes both the unicast and broadcast capacities for hybrid wireless networks. Assume that all ordinary ad hoc nodes transmit at a constant power P, and the power decays along the path, with attenuation exponent \alpha >2. The data rate of a transmission is determined by the Signal to Interference plus Noise Ratio (SINR) at the receiver as B \log (1 + {\rm SINR}). The ordinary ad hoc nodes are placed in the square region {\cal A}(a) of area a according to a Poisson point process of intensity n/a. Then, m additional base stations (BSs) acting as the relaying communication gateways are placed regularly in the region {\cal A}(a ), and are connected by a high-bandwidth wired network. Let a=n and a=1, we construct the hybrid extended network (HEN) and hybrid dense network (HDN), respectively. We choose randomly and independently n_s ordinary ad hoc nodes to be the sources of multicast sessions. We assume that each multicast session has n_d randomly chosen terminals. Three broad categories of multicast strategies are proposed. The first one is the hybrid strategy, i.e., the multihop scheme with BS-supported, which further consists of two types of strategies called connectivity strategy and percolation strategy, respectively. The second one is the ordinary ad hoc strategy, i.e., the multihop scheme without any BS-supported. The third one is the classical BS-based strategy under which any communication between two ordinary ad hoc nodes is relayed by some specific BSs. According to the different scenarios in terms of m, n, and n_d, we select the optimal scheme from the three categories of strategies, and derive the achievable multicast throughput based on the optimal decision.

Counting or estimating the number of tags is crucial for RFID system. Researchers have proposed several fast cardinality estimation schemes to estimate the quantity of a batch of tags within a short time frame. Existing estimation schemes scarcely consider the privacy issue. Without effective protection, the adversary can utilize the responding signals to estimate the number of tags as accurate as the valid reader. To address this issue, we propose a novel privacy-preserving estimation scheme, termed as MEAS, which provides an active RF countermeasure against the estimation from invalid readers. MEAS comprises of two components, an Estimation Interference Device (EID) and two well-designed Interference Blanking Estimators (IBE). EID is deployed with the tags to actively generate interfering signals, which introduce sufficiently large estimation errors to invalid or malicious readers. Using a secret interference factor shared with EID, a valid reader can perform accurate estimation via two IBEs. Our theoretical analysis and simulation results show the effectiveness of MEAS. Meanwhile, MEAS can also maintain a high estimation accuracy using IBEs.

In RFID systems, the reader is unable to discriminate concurrently reported IDs of tags from the overlapped signals, and a collision happens. Many algorithms for anticollision are proposed to improve the throughput and reduce the latency for tag identification. Existing anti-collision algorithms mainly employ CRC based collision detection functions for determining whether the collision happens. Generating CRC codes, however, requires complicated computations for both RF tags and readers, and hence incurs non-trivial time consumption, becoming the bottleneck. In this study, we design a Quick Collision Detection (QCD) scheme based on the bitwise complement function plus collision preamble, which significantly reduces the number of gates for computation and facilitates to simplify the IC design of RFID tags. The QCD scheme does not require any modification on upperlevel air protocols, so it can be seamlessly adopted by current anti-collision algorithms. Through comprehensive analysis and simulations, we show that QCD improves the identification efficiency by 40%.

In this work, we present a monitor and rescue system utilizing hybrid networks which is a integration of stationary sensor networks and mobile sensor networks: stationary sensor networks comprised of large numbers of small, simple, and inexpensive wireless sensors, and the mobile sensor network contains a set of mobile sensors (robots). The static sensors in our network have “monitoring” ability, i.e., any activated static sensor can detect the event as long as its sensing range intersects the event region. And the mobile sensors have “moving” and “rescuing” ability, e.g., they can move toward the event region with limited speed and further perform certain rescuing/processing operations on the event. We can consider the event as a hazard, e.g., wild fire, and the mobile sensors as fireman robots. As soon as the fire is detected by the static sensors, the fireman robots are expected to move from its initial location to the hazard region within minimum latency. We define the reaction delay of the system as the delay from the occurrence of event till at least one mobile sensor reaches the event. In order to satisfy certain reaction delay requirement while minimizing the total cost, we propose a number of deployment strategies for the stationary sensor network and mobile sensor network respectively. We further design a random wake-up scheduling for the static sensors for the sake of energy efficiency. Finally, we propose a pure distributed motion strategy for mobile sensors without reliance on localization services such as GPS, focusing on simple algorithms for distributed decision making and information propagation. We demonstrate the efficacy of our system in simulation, providing empirical results.

We study the capacity scaling laws for the cognitive network that consists of the primary hybrid network (PhN) and secondary ad hoc network (SaN). PhN is further comprised of an ad hoc network and a base station based (BS-based) network. SaN and PhN are overlapping in the same deployment region, operate on the same spectrum, but are independent with each other in terms of communication requirements. The primary users (PUs), i.e., the ad hoc nodes in PhN, have the priority to access the spectrum. The secondary users (SUs), i.e., the ad hoc nodes in SaN, are equipped with cognitive radios, and have the functionalities to sense the idle spectrum and obtain the necessary information of primary nodes in PhN. We assume that PhN adopts one out of three classical types of strategies, i.e., pure ad hoc strategy, BS-based strategy, and hybrid strategy. We aim to directly derive multicast capacity for SaN to unify the unicast and broadcast capacity under two basic principles: (1) The throughput for PhN cannot be undermined in order sense due to the presence of SaN. (2) The protocol adopted by PhN does not alter in the interest of SaN, anyway. Depending on which type of strategy is adopted in PhN, we design the optimal-throughput strategy for SaN. We show that there exists a threshold of the density of SUs according to the density of PUs beyond which it can be proven that: (1) when PhN adopts the pure ad hoc strategy or hybrid strategy, SaN can achieve the multicast capacity of the same order as it is stand-alone; (2) when PhN adopts the BS-based strategy, SaN can asymptotically achieve the multicast capacity of the same order as if PhN were absent, if some conditions of the relations among the number of SUs, PUs, the destinations of each multicast sessions in SaN, and the base stations in PhN hold.

Localization problem in wireless sensor networks (WSNs) has been widely studied recently. However, most previous work simply assume that all the nodes stay awake during the localization phase. This assumption clearly overlooks the common scenario that sensor nodes are usually duty-cycled in order to save energy. In this paper we propose a kind of novel DV (distance vector)-based localization algorithm which performs pretty good in duty-cycled network. In order to get a good localization accuracy, the DV-based positioning algorithms need to keep a critical minimum average neighborhood size (CMANS) for every sensor node. However, in the time-varying connectivity (TVC) (this phenomenon results from duty-cycling) network, it is difficult to keep CMANS for every node all the time. We can use CKN sleep scheduling algorithm to tackle this problem. CKN sleep scheduling algorithm can save energy while keeping certain CMANS. We further propose a novel localization algorithm: Spline Function Localization (SFL) algorithm which guarantees high accuracy even under small neighborhood size. Finally, we estimate the performance of our algorithm and compare with several classical DV-based localization algorithms (DVHOP and HCRL (Hop-Count-Ratio based Localization)) in simulation. Experimental results confirm that our algorithm has much higher accuracy under duty-cycled network.

We study the multicast capacity for hybrid wireless networks consisting of ordinary wireless nodes and base stations under Gaussian Channel model, which generalizes both the unicast capacity and broadcast capacity for hybrid wireless networks. We simply consider the hybrid extended network, where the ordinary wireless nodes are placed in the square region An with side-length sqrt n according to a Poisson point process with unit intensity. In addition, $m$ additional base stations (BSs) serving as the relay gateway are placed regularly in the region An and they are connected by a high-bandwidth wired network. Three broad categories of multicast strategies are proposed in this paper. According to the different scenarios in terms of m, n and n_d, we select the optimal scheme from the three categories of strategies, and derive the achievable multicast throughput based on the optimal decision.

We focus on the networking-theoretic multicast capacity for both random extended networks (REN) and random dense networks (RDN) under Gaussian Channel model, when all nodes are individually power-constrained. During the transmission, the power decays along path with the attenuation exponent alpha > 2. In REN and RDN, n nodes are randomly distributed in the square region with side-length radic(n) and 1, respectively. We randomly choose nsnodes as the sources of multicast sessions, and for each source v, we pick uniformly at random ndnodes as the destination nodes. Based on percolation theory, we propose multicast schemes and analyze the achievable throughput by considering all possible values of nsand nd. As a special case of our results, we show that for ns= Theta(n), the per-session multicast capacity of RDN is Theta((1)/(radic(ndn))) when nd= O((n)/((log n)3)) and is Theta((1)/(n)) when nd= Omega((1)/(log n)); the per-session multicast capacity of REN is Theta((1)/radic(ndn)) when nd= O((n)/((log n)alpha+1)) and is Theta((1)/(nd) ldr (log n)-(alpha)/(2)) when nd= Omega((n)/(log n)).

In this paper, we study the capacity of cognitive networks. We focus on the network model consisting of two overlapping ad hoc networks, called the primary ad hoc network (PaN) and secondary ad hoc network (SaN), respectively. PaN and SaN operate on the same space and spectrum. For PaN (or SaN resp.) we assume that primary (or secondary resp.) nodes are placed according to a Poisson point process of intensity n (or m resp.) over a unit square region. We randomly choose ns(or msresp.) nodes as the sources of multicast sessions in PaN (or SaN resp.), and for each primary source vp(or secondary source vs), we pick uniformly at random ndprimary nodes (or mdsecondary nodes) as the destinations of vp(or vs). Above all, we assume that PaN can adopt the optimal protocol in terms of the throughput. Our main work is to design the multicast strategy for SaN by which it can achieve the optimal throughput, without any negative impact on the throughput for PaN in order sense. Specifically, depending on ndand n, we choose the optimal strategy for PaN from two candidates called percolation strategy and connectivity strategy, respectively. Subsequently, we design the corresponding throughput-optimal strategy for SaN. We further derive the regimes for n, nd, m and mdwhere the throughputs for PaN and SaN can simultaneously achieve the upper bound of their capacities asymptotically.

Assume that n wireless nodes are randomly deployed in a square region with side-length a and all nodes have the uniform transmission range r and uniform interference range R = Θ(r). Each node is equipped with φ interfaces. There are C = O(min(nr 2 /a 2, log n)) channels of equal bandwidth W available. We consider a random C (C, g) channel assignment where each node may switch between a preassigned random subset of g channels (with g ≥ φ). In this paper, we study the multicast capacity of such a random wireless network, where for each node vi, we randomly pick k − 1 nodes from the other n−1 nodes as the receivers of the multicast session rooted at node vi. We derive matching asymptotic upper bounds and lower bounds on multicast capacity. We show that the per-flow multicast

We mainly study the achievable multicast throughput (AMT) for homogeneous wireless ad hoc networks under Gaussian channel model. We focus on two typical random networks, i.e., random extended networks (REN) and random dense networks (RDN). In REN and RDN, n nodes are randomly distributed in the square region with side-length n1/2and 1, respectively. We randomly choose nsnodes as the sources of multicast sessions, and for each source v, we pick uniformly at random ndnodes as the destinations. We propose multicast schemes without using percolation theory, and analyze the achievable multicast throughput by taking account of all possible values of nsand nd. As a special case of our results, we show that for ns=Θ(n), under specified conditions.