Zero-shot object navigation tasks agents with locating target objects in unseen environments—a core capability of embodied intelligence. While recent vision-language navigation methods leverage Large Language Models (LLMs) for multimodal reasoning, they suffer from two key limitations: (1) semantic misalignment between language-grounded maps and real-world layouts, and (2) inefficiency due to LLMs’ lack of specialization for navigation-specific tasks. To address these challenges, we propose Chain-of-Search (CoS), a novel parameter-efficient framework that enables human-like decision-making via iterative semantic reasoning. First, CoS replaces traditional global maps with an optimal-benefit multi-map construction that continuously balances expected gain and cost throughout the navigation process. Second, we introduce a Parameter-Efficient Intent Aligner (PEIA), trained via a prompt-guided paradigm to align directional decisions with navigation intent. PEIA injects semantic cues into benefit-aware maps, enabling more rational and goal-consistent exploration. Finally, a Reflection-Guided Destination Verifier (RDV) confirms whether the target is reached via language-driven reasoning and corrects potential errors through self-reflection. CoS achieves state-of-the-art performance on HM3D (+2.8% SR) and MP3D (+1.2% SR) without relying on LLMs, demonstrating the effectiveness of lightweight, reasoning-centered navigation.

Augmented Reality (AR) navigation has emerged as a transformative tool for spatial intelligence, enabling users to interactively explore complex environments through wearable and mobile AR devices. However, current AR navigation systems struggle with low indoor localization accuracy, weak semantic understanding, and limited long-term memory, which severely limits their adaptability in dynamic, multi-floor, and large-scale real-world settings. To address these challenges, we present AR-Nav benchmark, a novel dataset with corresponding suite that leverages vision and language for AR navigation. First, to construct this benchmark, we proposed an Augmented Reality Visual-Language Memory Model (AR‑VLM²), which generates structured, semantically rich, and temporally indexed representations for long-term AR navigation. Second, we design a lightweight navigation intent recommending module with hierarchical topological reasoning and language-grounded path planning, called ARN‑Pilot, enabling low-latency and personalized route selection. Third, we introduce a closed-loop AR interaction module that supports real-time multi-modal feedback, dynamic memory updates, and human-in-the-loop query refinement. Extensive experiments in indoor multi-floor and outdoor parking scenarios show that AR-Nav suite significantly outperforms state-of-the-art AR navigation methods.

Foreign object detection (FOD) in railway catenary systems is crucial for ensuring operational safety and preventing catastrophic failures. However, current detection frameworks encounter two significant challenges. First, the infrequency of fault events leads to severe data scarcity, hampering the training and validation of robust detection models. Second, although lightweight networks (e.g., MobileNet, YOLO) achieve compactness by compressing channel factors, they struggle to balance local feature extraction with global dependency modeling. To address these challenges, we propose a solution that includes the following: 1) RailFOD23, a publicly available dataset created using generative AI to mitigate data scarcity; and 2) EPRepSADet, a compact detection framework that utilizes a re-parameterizable bottleneck (Re-bottleneck) and lightweight self-attention (LSA) module for efficient FOD. The Re-bottleneck consolidates multi-branch structures into a single-path representation, whereas LSA facilitates element-wise attention modeling to effectively reduce computational complexity. In addition, the efficient detection head further minimizes model complexity through hierarchical semantic modeling. Extensive experiments demonstrate that EPRepSADet achieves a mean Average Precision (mAP) of 92.5% on the RailFOD23 test set, requiring only 1.7G FLOPs, thus outperforming several state-of-the-art baseline models.

The lack of encryption and authentication mechanisms in USB standards renders USB traffic susceptible to sniffing attacks. This paper presents an initial effort to detect USB bus sniffing through the development of a detection system, SnoopDog. It does not require hardware redesign of USB devices or modifications to the kernel or USB protocol stack. The system utilizes a probe-and-detect strategy. The host PC generates bait traffic with a dummy endpoint address. While benign devices discard this traffic due to the address mismatch, a sniffer captures the data, consequently emitting responsive electromagnetic radiation (EMR). To determine whether a USB device is a sniffer, SnoopDog calculates the correlation between the bait traffic and the responsive EMR signals captured near the target device. A high correlation indicates the presence of a sniffer. Recognizing that sniffer's EMR signals can be weak, we introduce a novel temporal folding scheme to improve the signal-to-noise ratio (SNR). To evaluate the performance, we build a prototype of SnoopDog and conduct comprehensive evaluations under a variety of settings, where SnoopDog delivers a promising detection accuracy with minor system overhead.

Dense video captioning is crucial for enhancing video understanding in daily applications and presents a significant challenge in multimodal analysis. Existing methods often overlook video-to-dynamic-space mapping at varying scales, resulting in captions that lack specificity and remain overly general, failing to capture real-world physical detail. To address this limitation, we propose a multi-granularity Spatio-Temporal Reasoning (STaR) approach, which integrates: (i) efficient global feature integration to model long-term temporal dependencies, (ii) spatial attention mechanisms with position encoding to capture absolute spatial information, and (iii) cross-modal feature fusion to align and unify global, local, and spatial representations. Moreover, we enhance the framework using a Large Language Model (LLM) to improve the richness and naturalness of the generated descriptions. Comparative experiments have been conducted to evaluate the effectiveness of the proposed method on SoccerNet dataset. Experimental results demonstrate that our model effectively enhances localization accuracy and generates captions with superior temporal and spatial detail fidelity. The code is available at https://github.com/bread-555/STaR.

Quantization enables efficient deployment of large language models (LLMs) in resourceconstrained environments by significantly reducing memory and computation costs.While quantized LLMs often maintain performance on perplexity and zero-shot tasks, their impact on truthfulness-whether generating truthful or deceptive responses-remains largely unexplored.In this work, we introduce Truthful-nessEval, a comprehensive evaluation framework for assessing the truthfulness of quantized LLMs across three dimensions: (1) Truthfulness on Logical Reasoning; (2) Truthfulness on Common Sense; and (3) Truthfulness on Imitative Falsehoods.Using this framework, we examine mainstream quantization techniques (ranging from 4-bit to extreme 2-bit) across several open-source LLMs.Surprisingly, we find that while quantized models retain internally truthful representations, they are very susceptible to producing false outputs under misleading prompts.To probe this vulnerability, we test 15 rephrased variants of "honest", "neutral" and "deceptive" prompts and observe that "deceptive" prompts can override truth-consistent behavior, whereas "honest" and "neutral" prompts maintain stable outputs.Further, we reveal that quantized models "know" the truth internally yet still produce false outputs when guided by "deceptive" prompts via layer-wise probing.Our findings provide insights into future designs of trustworthy quantization-aware alignment.Codes and data are available here 1 .

The computational power required for training deep learning models has been skyrocketing in the past decade as they scale with big data, and has become a very expensive and scarce resource. Therefore, distributed training, which can leverage distributed available computational power, is vital for efficient large-scale model training. However, most previous distributed training frameworks like DDP and DeepSpeed are primarily designed for co-located clusters under homogeneous computing and communication conditions, and hence cannot account for geo-distributed clusters with both computing and communication heterogeneity. To address this challenge, we develop a new data parallel based distributed training framework called Co-Adaptive Data Parallelism (C-ADP). First, we consider a data owner and parameter server that distributes data to and coordinates the collaborative learning across all the computing devices. We employ local training and delayed parameter synchronization to reduce communication costs. Second, we formulate a data parallel scheduling optimization problem to minimize the training time by optimizing data distribution. Third, we devise an efficient algorithm to solve this scheduling problem, and formally prove that the obtained solution is optimal in the asymptotic sense. Experiments on the ImageNet100 dataset demonstrate that C-ADP achieves fast convergence in heterogeneous distributed training environments. Compared to Distributed Data Parallel (DDP) and DeepSpeed, C-ADP achieves 21.6 times and 26.3 times improvements in FLOPS, respectively, and a reduction in training time of about 72% and 47%, respectively.

Facial image steganography is crucial for privacy-preserving media transmission. Traditional embedding methods degrade image quality and are vulnerable to steganalysis, while GAN-based non-embedding approaches lack controllability and realism. Diffusion-based methods using textual prompts face two key issues: (1) security risks from interpretable prompts and (2) poor preservation of facial details. This paper presents Featurized Denoising Diffusion Implicit Models (F-DDIM), a novel non-embedding steganography framework. First, F-DDIM replaces explicit textual prompts with implicit image-based encoding, enhancing security. Second, it selectively refines facial regions for natural and high-quality recovery through iterative reconstruction. Third, it enables indistinguishable encryption without secret key sharing via a novel sub-code embedding algorithm. Fourth, a refinement step post-decoding improves the clarity and accuracy of recovered facial image details. Experimental results demonstrate that F-DDIM achieves superior image fidelity and robustness against transmission interference.

3D perception in train operating environments presents significant challenges, as it must ensure both precise distance estimation and computational efficiency to meet stringent braking requirements. To date, existing 3D detection architectures, which employ dense voxel or pillar representations, encounter challenges of computational inefficiency and accuracy degradation when processing large-scale railway Light Detection And Ranging (LiDAR) data. To address this challenge, we propose RailVoxelDet, a railway-optimized 3D detector integrating the Multi-factor Dynamic Voxel Feature Encoder (MDVFE) and efficient backbone. Specifically, MDVFE converts point clouds to 2D sparse voxels, reducing computational complexity. The backbone employs residual bottlenecks with shared full connected layers and sparse convolutions, enhanced by the SimAM-Point module. Additionally, the Feature Query and Matching Module (FQMM) is proposed to establish a bottom-up multi-level feature fusion architecture. Experimental results show RailVoxelDet reaches 71.29% mAP on OSDaR23 and 61.94% mAP on AirR24, with 6.42G FLOPs and a 71.42ms inference time. It outperforms 12 comparison models, delivering state-of-the-art results.

Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising technology for network performance enhancement by intelligently manipulating the propagation environment. In contrast to conventional reflecting-only RIS, the simultaneous transmitting and reflecting RIS (STAR-RIS) extends coverage to 360 degrees through transmission and reflection, offering more flexibility in channel reconfiguration. The application of STAR-RIS in wireless networks holds tremendous potential for significantly improving network connectivity by fostering cascaded paths. However, it remains open on the deployment strategy for STAR-RIS to guarantee network connectivity cost-effectively, especially when considering random blockages. In this paper, we focus on investigating the impact of STAR-RIS on network connectivity in blockage-prone scenarios. Leveraging percolation theory, we derive upper and lower bounds for the critical density of nodes in both no-STAR-RIS and STAR-RIS-aided networks. Our results quantify the impact of STAR-RIS on network connectivity, demonstrating its significant enhancement on network connectivity and providing valuable insights into cost-effective deployment schemes for STAR-RIS.

Recently, federated learning (FL) has been threat-ened by the gradient inversion attack that infers user-private data from shared gradients. To cope with this problem, the differential privacy (DP) technique is widely employed in FL. However, when FL faces the non-independent identically distributed (non-IID) data scenarios, applying DP to protect user data privacy remains inefficient in terms of model accuracy and communication costs. In this paper, inspired by the Mixup data augmentation method, we propose a privacy-preserving FL approach called Fed2VAEs to address this problem. Specifically, we introduce a Mixup Module consisting of two variational autoencoders to remove the private information of user data. To balance the trade-off between data privacy and data utility, from the perspective of mutual information, a learning objective is proposed. We conduct extensive experiments under different non-IID data settings, and the experimental results show that Fed2VAEs can significantly reduce the communication cost and improve model accuracy (up to 8.57%) on the premise of successfully protecting user data privacy.

Sharing genomic databases is critical to the collaborative research in computational biology. A shared database is more informative than specific genome-wide association studies (GWAS) statistics as it enables "do-it-yourself" calculations. Genomic databases involve intellectual efforts from the curator and sensitive information of participants, thus in the course of data sharing, the curator (database owner) should be able to prevent unauthorized redistributions and protect individuals' genomic data privacy. As it becomes increasingly common for a single database be shared with multiple recipients, the shared genomic database should also be robust against collusion attack, where multiple malicious recipients combine their individual copies to forge a pirated one with the hope that none of them can be traced back. The strong correlation among genomic entries also make the shared database vulnerable to attacks that leverage the public correlation models. In this paper, we assess the robustness of shared genomic database under both collusion and correlation threats. To this end, we first develop a novel genomic database fingerprinting scheme, called Gen-Scope. It achieves both copyright protection (by enabling traceability) and privacy preservation (via local differential privacy) for the shared genomic databases. To defend against collusion attacks, we augment Gen-Scope with a powerful traitor tracing technique, i.e., the Tardos codes. Via experiments using a real-world genomic database, we show that Gen-Scope achieves strong fingerprint robustness, e.g., the fingerprint cannot be compromised even if the attacker changes 45% of the entries in its received fingerprinted copy and colluders will be detected with high probability. Additionally, Gen-Scope outperforms the considered baseline methods. Under the same privacy and copyright guarantees, the accuracy of the fingerprinted genomic database obtained by Gen-Scope is around 10% higher than that achieved by the baseline, and in terms of preservations of GWAS statistics, the consistency of variant-phenotype associations can be about 20% higher. Notably, we also empirically show that Gen-Scope can identify at least one of the colluders even if malicious receipts collude after independent correlation attacks.

Federated Learning (FL) has been widely used to facilitate distributed and privacy-preserving machine learning in recent years. Different from centralized training that usually has independent and identically distributed (IID) distribution of all users' data, FL suffers from significant communication cost and model performance degradation due to the non-IID data from individual edge devices. Existing work calibrates the local models using a global anchor or sharing global data. However, these studies either assume that the central server has the global dataset or require participating devices to share raw data, which incurs additional communication costs and privacy concerns. In this paper, we proposeSlaugFL, a novel selective GAN-based data augmentation scheme for communication-efficient edge FL, which selects representative devices to share specific local class prototypes with the central server for GAN model training and improves FL performance with the trained GAN. Specifically, on the server side, we generate diverse labeled candidate data with the help of powerful generative models (the stable diffusion model and ChatGPT). To ensure that the GAN-generated data possesses a similar domain to the devices' local data, we leverage these selected local class prototypes to pick desired GAN training samples from the labeled candidate data. On the device side, we propose a dual-calibration approach consisting of two calibration manners. Concretely, we augment devices' non-IID data with the trained GAN model, where devices utilize the trained GAN model to generate the IID dataset. Thus, the device's local model can be directly calibrated with the augmented data. With the generated IID data, we yield privacy-free (p-f) global class prototypes which can be employed to further calibrate devices' local models. Combining these two calibrations effectively improves devices' local models. Extensive experimental results show thatSlaugFLcan significantly reduce the communication cost (up to 52.49%) while achieving the same accuracy, compared to the state-of-the-art work.

Most terminal devices authenticate users only once at the time of initial login, leaving the terminal unprotected during an active session when the original user leaves it unattended. To address this issue, continuous authentication has been proposed by automatically locking the terminal after a period of inactivity. However, it does not fully eliminate the risk of unauthorized access before the session expires. Recent research has also investigated the feasibility of using physiological and behavioral patterns as biometrics. This study presents a novel two-factor continuous authentication that explores a new form of signal called human-induced electric potential captured by wearables in contact with the user’s body. By analyzing this signal, we can determine the time of user-terminal interactions and compare it with information recorded by the terminal’s OS. If the original user remains on the same terminal, the two-source readings would match. Additionally, the proposed scheme includes an extra layer of protection by extracting terminal’s physical fingerprints from the human-induced electric potential to defend against advanced mimicry attacks. To test the effectiveness of our design, a low-cost wearable prototype is developed. Through extensive experiments, it is found that the proposed scheme has a low error rate of 2.3%, with minimal computational and energy requirements.

Nowadays, face recognition technology has been dramatically boosted by the advances in deep learning and big data fields. However, this also poses grand challenges in protecting personal identity information in intelligent applications of the Internet of Things (IoT). Existing methods based on the$K$-Same algorithm have low effectiveness for protecting personal identity while preserving face attributes. In this article, we propose an attribute-preserving face de-identification framework called Enhanced Embedded AutoEncoders to address this problem. Our framework consists of three parts: 1) a privacy removal network (PRN); 2) a feature selection network; and 3) a privacy evaluation network. The main purpose of our framework is to ensure that the PRN is capable of discarding information involving identity privacy and retaining desired face attributes for certain prediction applications. In order to achieve this goal, the design of the PRN is crucial. Specifically, we employ two different autoencoders, one of which is embedded within the other. Extensive experimental results show that our framework outperforms existing methods by an average of 3.42%–26.22% in terms of data utility under comparable face de-identification performance, which indicates that the proposed framework can not only effectively retain face attributes but also protect personal identity well.

Database fingerprinting is widely adopted to prevent unauthorized data sharing and identify source of data leakages. Although existing schemes are robust against common attacks, their robustness degrades significantly if attackers utilize inherent correlations among database entries. In this paper, we demonstrate the vulnerability of existing schemes by identifying different correlation attacks: column-wise correlation attack, row-wise correlation attack, and their integration. We provide robust fingerprinting against these attacks by developing mitigation techniques, which can work as post-processing steps for any off-the-shelf database fingerprinting schemes and preserve the utility of databases. We investigate the impact of correlation attacks and the performance of mitigation techniques using a real-world database. Our results show (i) high success rates of correlation attacks against existing fingerprinting schemes (e.g., integrated correlation attack can distort 64.8% fingerprint bits by just modifying 14.2% entries in a fingerprinted database), and (ii) high robustness of mitigation techniques (e.g., after mitigation, integrated correlation attack can only distort 3% fingerprint bits). Additionally, the mitigation techniques effectively alleviate correlation attacks even if (i) attackers have access to correlation models directly computed from the original database, while the database owner uses inaccurate correlation models, (ii) or attackers utilizes higher order of correlations than the database owner.

In the wake of the Web 2.0, crowdsourcing has emerged as a promising approach to maintain a flexible workforce for human intelligence tasks. To stimulate worker participation, many reverse auction-based incentive mechanisms have been proposed. Designing auctions that discourage workers from cheating and instead encouraging them to reveal their true cost information has drawn significant attention. However, the existing efforts have been focusing on tackling individual cheating misbehaviors, while the scenarios that workers strategically form collusion coalitions and rig their bids together to manipulate auction outcomes have received little attention. To fill this gap, in this work we develop a$(t,p)$-collusion resistant scheme that ensures no coalition ofweighted cardinality$t$can improve its group utility by coordinating the bids at a probability of$p$. This paper takes into account the unique features of crowdsourcing, such as diverse worker types and reputations, in the design. The proposed scheme can suppress a broad spectrum of collusion strategies. Besides, desirable properties, including$p$-truthfulness and$p$-individual rationality, are also achieved. To provide a comprehensive evaluation, we first analytically prove our scheme's collusion resistance and then experimentally verify our analytical conclusion using a real-world dataset. Our experimental results show that the baseline scheme, where none of the critical properties is guaranteed, costs up to 20.1 times the optimal payment in an ideal case where no collusion exists, while our final scheme is merely 4.9 times the optimal payment.

)-dependent local differential privacy (LDP) for privacy-preserving sharing of correlated data and propose a genomic data sharing mechanism under this privacy definition. We first show that the original definition of LDP is not suitable for genomic data sharing, and then we propose a new mechanism to share genomic data. The proposed mechanism considers the correlations in data during data sharing, eliminates statistically unlikely data values beforehand, and adjusts the probability distributions for each shared data point accordingly. By doing so, we show that we can avoid an attacker from inferring the correct values of the shared data points by utilizing the correlations in the data. By adjusting the probability distributions of the shared states of each data point, we also improve the utility of shared data for the data collector. Furthermore, we develop a greedy algorithm that strategically identifies the processing order of the shared data points with the aim of maximizing the utility of the shared data. Our evaluation results on a real-life genomic dataset show the superiority of the proposed mechanism compared to the randomized response mechanism (a widely used technique to achieve LDP).

MOTIVATION: Database fingerprinting has been widely used to discourage unauthorized redistribution of data by providing means to identify the source of data leakages. However, there is no fingerprinting scheme aiming at achieving liability guarantees when sharing genomic databases. Thus, we are motivated to fill in this gap by devising a vanilla fingerprinting scheme specifically for genomic databases. Moreover, since malicious genomic database recipients may compromise the embedded fingerprint (distort the steganographic marks, i.e. the embedded fingerprint bit-string) by launching effective correlation attacks, which leverage the intrinsic correlations among genomic data (e.g. Mendel's law and linkage disequilibrium), we also augment the vanilla scheme by developing mitigation techniques to achieve robust fingerprinting of genomic databases against correlation attacks. RESULTS: Via experiments using a real-world genomic database, we first show that correlation attacks against fingerprinting schemes for genomic databases are very powerful. In particular, the correlation attacks can distort more than half of the fingerprint bits by causing a small utility loss (e.g. database accuracy and consistency of SNP-phenotype associations measured via P-values). Next, we experimentally show that the correlation attacks can be effectively mitigated by our proposed mitigation techniques. We validate that the attacker can hardly compromise a large portion of the fingerprint bits even if it pays a higher cost in terms of degradation of the database utility. For example, with around 24% loss in accuracy and 20% loss in the consistency of SNP-phenotype associations, the attacker can only distort about 30% fingerprint bits, which is insufficient for it to avoid being accused. We also show that the proposed mitigation techniques also preserve the utility of the shared genomic databases, e.g. the mitigation techniques only lead to around 3% loss in accuracy. AVAILABILITY AND IMPLEMENTATION: https://github.com/xiutianxi/robust-genomic-fp-github.

Nonlinearly constrained nonlinear programming (NLC-NLP) problems arise in various real-world decision-making fields, such as financial engineering, urban planning, supply chain management, and power system control. They are usually large-scale because of having to consider massive variables and constraints. Solving NLC-NLP problems by employing common algorithms (e.g., gradient projection method (GPM)) is usually computationally-expensive, which challenges common organizations in solving large-scale NLC-NLP problems. To address this issue, an option is to adopt cloud computing for help. However, this raises security concerns since real-world NLC-NLP problems may carry sensitive information. Although previous secure outsourcing algorithms try to protect sensitive information, they still let cloud service tenants bear heavy computation burden. In this paper, we develop a practical secure outsourcing algorithm for using the GPM to solve large-scale NLC-NLP problems. To be more prominent, to accelerate computations and avoid possible memory overflowing, we parallelize the developed algorithm. We implement the developed algorithm on the Amazon Elastic Compute Cloud (EC2) and a laptop, and also offer extensive experiment results to show that the developed algorithm can reduce the tenant’s computing time significantly.

Computation offloading is indispensable for mobile edge computing (MEC). It uses edge resources to enable intensive computations and save energy for resource-constrained devices. Existing works generally impose strong assumptions on radio channels and network queue sizes. However, practical MEC systems are subject to various uncertainties rendering these assumptions impractical. In this paper, we investigate the energy-efficient computation offloading problem by relaxing those common assumptions and considering intrinsic uncertainties in the network. Specifically, we minimize the worst-case expected energy consumption of a local device when executing a time-critical application modeled as a directed acyclic graph. We employ the extreme value theory to bound the occurrence probability of uncertain events. To solve the formulated problem, we develop an$\epsilon $-bounded approximation algorithm based on column generation. The proposed algorithm can efficiently identify a feasible solution that is less than$(1+\epsilon)$of the optimal one. We implement the proposed scheme on an Android smartphone and conduct extensive experiments using a real-world application. Experiment results corroborate that it will lead to lower energy consumption for the client device by considering the intrinsic uncertainties during computation offloading. The proposed computation offloading scheme also significantly outperforms other schemes in terms of energy saving.

With the advance in artificial intelligence and high-dimensional data analysis, federated learning (FL) has emerged to allow distributed data providers to collaboratively learn without direct access to local sensitive data. However, limiting access to individual provider’s data inevitably incurs security issues. For instance, backdoor attacks, one of the most popular data poisoning attacks in FL, severely threaten the integrity and utility of the FL system. In particular, backdoor attacks launched by multiple collusive attackers, i.e., distributed backdoor attacks, can achieve high attack success rates and are hard to detect. Existing defensive approaches, like model inspection or model sanitization, often require to access a portion of local training data, which renders them inapplicable to the FL scenarios. Recently, the norm clipping approach is developed to effectively defend against distributed backdoor attacks in FL, which does not rely on local training data. However, we discover that adversaries can still bypass this defense scheme through robust training due to its unchanged norm clipping threshold. In this paper, we propose a novel defense scheme to resist distributed backdoor attacks in FL. Particularly, we first identify that the main reason for the failure of the norm clipping scheme is its fixed threshold in the training process, which cannot capture the dynamic nature of benign local updates during the global model’s convergence. Motivated by it, we devise a novel defense mechanism to dynamically adjust the norm clipping threshold of local updates. Moreover, we provide the convergence analysis of our defense scheme. By evaluating it on four non-IID public datasets, we observe that our defense scheme effectively can resist distributed backdoor attacks and ensure the global model’s convergence. Noticeably, our scheme reduces the attack success rates by 84.23% on average compared with existing defense schemes.

Internet connected mobile devices in 5G and beyond (simply 5G+) systems are penetrating all aspects of people's daily life, transforming the way we conduct business and live. However, this rising trend has also posed unprecedented traffic burden on existing telecommunication infrastructure including cellular systems, consistently causing network congestion. Although additional spectrum resources have been allocated, exponentially increasing traffic tends to always outpace the added capacity. In order to increase the data rate and reduce the latency, 5G+ systems have heavily relied on hyperdensification and higher frequency bands, resulting in dramatically increased interference temperature, and consequently significantly more weak signals (i.e., signals with low Signal-to-Noise-plus-Interference (SINR) ratio). With traditional detection mechanisms, a large number of weak signals will not be detected, and hence be wasted, leading to poor throughput in 5G+ systems.

Database fingerprinting have been widely adopted to prevent unauthorized sharing of data and identify the source of data leakages. Although existing schemes are robust against common attacks, like random bit flipping and subset attack, their robustness degrades significantly if attackers utilize the inherent correlations among database entries. In this paper, we first demonstrate the vulnerability of existing database fingerprinting schemes by identifying different correlation attacks: column-wise correlation attack, row-wise correlation attack, and the integration of them. To provide robust fingerprinting against the identified correlation attacks, we then develop mitigation techniques, which can work as post-processing steps for any off-the-shelf database fingerprinting schemes. The proposed mitigation techniques also preserve the utility of the fingerprinted database considering different utility metrics. We empirically investigate the impact of the identified correlation attacks and the performance of mitigation techniques using real-world relational databases. Our results show (i) high success rates of the identified correlation attacks against existing fingerprinting schemes (e.g., the integrated correlation attack can distort 64.8% fingerprint bits by just modifying 14.2% entries in a fingerprinted database), and (ii) high robustness of the proposed mitigation techniques (e.g., with the mitigation techniques, the integrated correlation attack can only distort 3% fingerprint bits). Furthermore, we show that the proposed mitigation techniques effectively alleviate correlation attacks even if the attacker has access to the correlation models that are directly calculated from the database.

The coronavirus disease 2019 (COVID-19) has rapidly become a significant public health emergency all over the world since it was first identified in Wuhan, China, in December 2019. Until today, massive disease-related data have been collected, both manually and through the Internet of Medical Things (IoMT), which can be potentially used to analyze the spread of the disease. On the other hand, with the help of IoMT, the analysis results of the current status of COVID-19 can be delivered to people in real time to enable situational awareness, which may help mitigate the disease spread in communities. However, current accessible data on COVID-19 are mostly at a macrolevel, such as for each state, county, or metropolitan area. For fine-grained areas, such as for each city, community, or geographical coordinate, COVID-19 data are usually not available, which prevents us from obtaining information on the disease spread in closer neighborhoods around us. To address this problem, in this article, we propose a two-level risk assessment system. In particular, we define a "risk index." Then, we develop a risk assessment model, called MK-DNN, by taking advantage of the multikernel density estimation (MKDE) and deep neural network (DNN). We train MK-DNN at the macrolevel (for each metro area), which subsequently enables us to obtain the risk indices at the microlevel (for each geographic coordinate). Moreover, a heuristic validation method is further designed to help validate the obtained microlevel risk indices. Simulations conducted on real-world data demonstrate the accuracy and validity of our proposed risk assessment system.

The Internet of Things (IoT) integrates information collected from multisources and is able to support various intelligent smart city applications, such as industrial manufacturing, power systems, and mobile healthcare. In the big data era, multisourced data are collected on a daily basis, whereas a large part of the data may be irrelevant, redundant, noisy, or even malicious from a machine learning perspective. Feature selection has been a powerful data cleaning technique to reduce data redundancy and improve system performance in machine learning. Inspired by reinforcement learning that learns from its experience, in this article, we propose a novel efficient deep$Q$-network (DQN)-based feature selection method for multisourced data cleaning. In particular, we model the feature selection problem as a competition between an agent and the environment in dynamic states, which is solved by a DQN. Traditional DQN suffers from high computational complexity and requires a significant amount of time in order to converge in the training process. To tackle these challenges, we develop a space searching algorithm called SS to speed up the training process of the DQN agent. To validate the efficacy and efficiency of the proposed method, we conduct extensive experiments on various types of IoT data. Simulation results show that the proposed DQN-based feature selection algorithms achieve much better performance compared with state-of-the-art methods, and are robust under data poisoning attacks.

Differential privacy has been widely adopted to release continuous- and scalar-valued information on a database without compromising the privacy of individual data records in it. The problem of querying binary- and matrix-valued information on a database in a differentially private manner has rarely been studied. However, binary- and matrix-valued data are ubiquitous in real-world applications, whose privacy concerns may arise under a variety of circumstances. In this paper, we devise an exclusive or (XOR) mechanism that perturbs binary- and matrix-valued query result by conducting an XOR operation on the query result with calibrated noises attributed to a matrix-valued Bernoulli distribution. We first rigorously analyze the privacy and utility guarantee of the proposed XOR mechanism. Then, to generate the parameters in the matrix-valued Bernoulli distribution, we develop a heuristic approach to minimize the expected square query error rate under ϵ -differential privacy constraint. Additionally, to address the intractability of calculating the probability density function (PDF) of this distribution and efficiently generate samples from it, we adapt an Exact Hamiltonian Monte Carlo based sampling scheme. Finally, we experimentally demonstrate the efficacy of the XOR mechanism by considering binary data classification and social network analysis, all in a differentially private manner. Experiment results show that the XOR mechanism notably outperforms other state-of-the-art differentially private methods in terms of utility (such as classification accuracy and F 1 score), and even achieves comparable utility to the non-private mechanisms.

We are now in the big data era. Due to the emerging various systems and applications, such as the Internet of Things, cyber-physical systems, smart cities, smart healthcare, we are able to collect more data than ever before. On the other hand, it makes it very difficult to analyze such massive data in order to advance our science and engineering fields. We note that QR and LU factorizations are two of the most fundamental mathematical tools for data analysis. However, conducting QR or LU factorization of an m×n matrix requires computational complexity ofO(m2n). This incurs a formidable challenge in efficiently analyzing large-scale data sets by normal users or small companies on traditional resource-limited computers. To overcome this limitation, industry and academia propose to employ cloud computing that can offer abundant computing resources. This, however, obviously raises security concerns and hence a lot of users are reluctant to reveal their data to the cloud. To this end, we propose two secure outsourcing algorithms for efficiently performing large-scale QR and LU factorizations, respectively. We implement the proposed algorithms on the Amazon Elastic Compute Cloud (EC2) platform and a laptop. The experiment results show significant time saving for the user.

While traffic modeling and prediction are at the heart of providing high-quality telecommunication services in cellular networks and attract much attention, they have been approved as an extremely challenging task. Due to the diverse network demand of Internet-based apps, the cellular traffic from an individual user can have a wide dynamic range. Most existing methods, on the other hand, model traffic patterns as probabilistic distributions or stochastic processes and impose stringent assumptions over these models. Such assumptions may be beneficial at providing closed-form formula in evaluating prediction performances, but fall short for practice use. In this paper we propose STEP, aspatio-temporal fine-granular user trafficprediction mechanism for cellular networks. A deep graph convolution network, called GCGRN, is constructed. It is a novel combination of the graph convolution network (GCN) and gated recurrent units (GRU), which exploits graph neural network to learn an efficient spatio-temporal model from a user’s massive dataset for traffic prediction. The prototype of STEP has been implemented. Extensive experimental results demonstrate that our model outperforms the state-of-the-art time-series based approaches. Besides, STEP merely incurs mild energy consumption, communication overhead and system resource occupancy to mobile devices. Moreover, NS-3 based simulations validate the efficacy of STEP in reducing session dropping ratio in cellular networks.

Information-centric networking (ICN) as an emerging networking paradigm has recently gained significant attention, due to the improvement of content delivery efficiency. The built-in network storage for caching is a key component in ICN to provide low latency service and reduce high backhaul traffic by caching popular content. However, users' content preference contains individual sensitive characteristics which is distinguishable from others. Therefore, in this work, we propose a data-driven caching revenue maximization problem with the considerations of users' local differential privacy. Specifically, we employ dBitFlip, a local differential privacy (LDP) mechanism, to locally add differential private noise to the users' preference content information. We leverage data-driven approach to predict the content popularity based on the reference distribution constructed by the reported noisy preference content data from users, mathematically present the distance between the noisy reference distribution and the true distribution by the tolerance level, and prove the relationship among the tolerance level, differential privacy budget and the confidence level. We provide feasible solutions to the proposed revenue maximization problem, and conduct simulations to show the effectiveness of the proposed scheme.

With the development of wireless communication and the pervasive Internet of Things, vehicular ad hoc networks (VANETs) have attracted much attention in recent years. As a platform for vehicular communication and management, VANETs require the guarantee of security and efficiency for the resources constrained and unreliable networks. In this article, a data-sharing scheme is proposed for VANETs where both efficient key updating and dynamic property of VANETs are supported. In particular, the symmetric balanced incomplete block design (SBIBD) in combinatorics is introduced to ensure secure and efficient VANETs while the concept of indistinguishability obfuscation is employed to support efficient key updating. In order to simultaneously satisfy the resources constrained and real-time requirements of VANETs, both serial and parallel construction of the SBIBD is implemented by the shift register. Taking advantage of this efficient sequential logical circuit, we eliminate the complicated construction of the SBIBD, thereby, making it effective for the resource-constrained environment. Theoretical and experimental analyses indicate that the proposed scheme is practical for VANETs with high security and efficiency.

Edge computing, an emerging computing paradigm pushing data computing and storing to network edges, enables many applications that require high computing complexity, scalability, and security. In the big data era, one of the most critical applications is multiparty learning or federated learning, which allows different parties to collaborate with each other to obtain better learning models without sharing their own data. However, there are several main concerns about the current multiparty learning systems. First, most existing systems are distributed and need a central server to coordinate the learning process. However, such a central server can easily become a single point of failure and may not be trustworthy. Second, although quite a few schemes have been proposed to study Byzantine attacks, a very common and challenging kind of attack in distributed systems, they generally consider the scenario of learning a global model. However, in fact, all parties in multiparty learning usually have their own local models. The learning methods and security issues, in this case, are not fully explored. In this article, we propose a novel blockchain-empowered decentralized secure multiparty learning system with heterogeneous local models called BEMA. Particularly, we consider two types of Byzantine attacks, and carefully design “off-chain sample mining” and “on-chain mining ” schemes to protect the security of the proposed system. We theoretically prove the system performance bound and resilience under Byzantine attacks. The simulation results show that the proposed system obtains comparable performance with that of conventional distributed systems, and bounded performance in the case of Byzantine attacks.

Community detection is an effective approach to unveil relationships among individuals in online social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties, such as advertisement companies and hospitals, with access to social networks for different purposes, which can easily lead to a privacy breach. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topology and the users' attributes. We show that with additional prior knowledge, community detection can be performed by querying the information of only a fraction of instead of the entire population. In particular, we first propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Then, we propose a parsimonious node affiliation recovery (NAR) algorithm, which is also differentially private, to unveil the community affiliation information of the whole population based on that of the limited number of queried individuals by solving a sparse optimization problem. Through both theoretical analysis and experimental validation using synthetic and real-world social networks, we demonstrate that the proposed DPCD scheme detects social communities under the modest privacy budget. In addition, we show the effectiveness of NAR to perform community detection by querying a limited number of individuals in social networks.

Community detection is an effective approach to unveil social dynamics among individuals in social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties like advertisement companies, hospitals, with access to social networks for different purposes, which can easily lead to privacy breaches. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topologies and the users’ attributes. In particular, we propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Through both theoretical analysis and experimental validation using synthetic and real world social networks, we demonstrate that the proposed DPCD scheme detects social communities under modest privacy budget.

The Magnetic Resonance Imaging (MRI) clinical applications historically have generated large amounts of data, driven by record keeping, data analysis, and regulatory requirements. However, most raw data is not necessarily stored in hard copy form after analysis (like image reconstruction) is completed. In addition, such image analysis, including reconstruction, registration, and perfusion quantification that performed “offline” are computationally-intensive and time-consuming. These limitations make some MRI analysis applications inappropriate for clinical timescale. Driven by the potential to improve the efficiency of MRI analysis and delivery meanwhile reducing the costs, we develop a data streaming analysis framework specifically for Dynamic Contrast-Enhanced (DCE) Liver MRI application in this paper. The proposed framework has two main features. First, the framework transforms the whole image processing from “offline” to “online” to extensively reduce the data saving and transfer time through data streaming architecture. Second, with the design of optimized reconstruction and registration algorithms, as well as the integration of external computing resources, including Graphics Processing Units (GPUs) parallel computing techniques, the streaming framework achieved 180 times speed-up compared with the original protocol. Our in-vivo experiments showed significantly increased speed (Average 7.72 minutes total analysis time compared to 21.6 hours by original protocol) with minor differences in both image quality and perfusion quantification results. This framework allows easy and direct deployment of clinical studies.

The auction mechanism is deemed to be an effective method to address the problem of spectrum scarcity. Numerous spectrum auction mechanisms can alleviate spectrum shortage under the consideration of truthfulness, social welfare maximization and spectrum reusability, while the privacy preservation and preferences of primary/secondary users have not been fully discussed. In this paper, we propose a matching based double auction mechanism for spectrum trading with differential privacy (MastDP) to protect the privacy of buyers/sellers from the untrustworthy auctioneer, other buyers/sellers and other potential parties. Each participant adds distributed differential private noise following Geom(α) distribution to his bid value and encrypts the noisy bid value. The auctioneer can decrypt only the sum of all uploaded noisy bid values and determines the clearing price by using its private key. Based on the clearing price, the matching theory is adopted to maximize the winning participants' revenue while fully considering their preferences and spectrum reuse. Simulation results show that MastDP achieves satisfactory performance in terms of economic properties' privacy preservation and spectrum trading efficiency.

In recent years, generative adversarial network (GAN) has attracted great attention due to its impressive performance and potential numerous applications, such as data augmentation, real-like image synthesis, image compression improvement, etc. The generator in GAN learns the density of the distribution from real data in order to generate high fidelity fake samples from latent space and deceive the discriminator. Despite its advantages, GAN can easily memorize training samples because of the high model complexity of deep neural networks. Thus, training a GAN with sensitive or private data samples may compromise the privacy of training data. To address this privacy issue, we propose a novel \textit{Privacy Preserving Generative Adversarial Network} (PPGAN) that perturbs the objective function of discriminator by injecting Laplace noises based on functional mechanism to guarantee the differential privacy of training data. Since generator training is considered as a post-processing step while guaranteeing differential privacy of discriminator, the trained generator should be differentially private to effectively protect data samples. Through detailed privacy analysis, we theoretically prove that PPGAN can provide such strict differential privacy guarantee. With extensive simulation study on the benchmark dataset MNIST, we show the efficacy of the proposed PPGAN under practical privacy budgets.

The ever-growing proliferation of mobile devices equipped with accelerometers has provided new opportunities to capture the semantic meanings of human activities and improve user experience with behavior-based recommendations, which heavily rely on the accuracy of the recognition of daily human activities. Acceleration-based human activity recognition (HAR) is a challenging problem because each accelerometer records multi-dimensional signals in both spatial and temporal domains that have different attributes for representing different activities or even the same activity. Thus we cannot directly compare these signals with each other, because they are embedded in a non-metric space. In this paper, we present a Personalized Recurrent Neural Network (PerRNN) to dynamically segment and recognize the human activities based on accelerometer data. Enlightened by the idea of spatiotemporal predictive learning, the proposed architecture is capable of memorizing different acceleration signals' appearances and temporal variations in a unified memory pool. We evaluate the performance of the proposed framework on a commonly used dataset, WISDM. Experiment results show that compared with state-of-the-art schemes, our proposed PerRNN system recognizes 6 different human activities with the highest overall accuracy of 96.44%.

Studies find that deep learning models are vulnerable to deliberate adversarial manipulations by attackers. Adversarial training is an effective approach to address this problem. Previous works quantize the input sample space to find appropriate perturbations on the benign samples so as to generate adversarial samples for adversarial training. However, since only the input sample space is quantized with the perturbation space being still continuous, finding the optimal perturbation noise is still a non-convex and computationally expensive problem. Moreover, in this case, the found perturbation noise that will be used to generate an adversarial sample may be strong in the continuous search space, but may become weak after quantization in the input sample space. In this paper, we first develop an Iterative Quantized Local Search (IQLS) algorithm that finds strong perturbation noises by quantizing both the input space and perturbation space. Then, we theoretically analyze and prove the upper bound on the number of iterations needed for the IQLS algorithm, based on which we devise an efficient and effective Quantized Adversarial Training (QAT) scheme. Experiment results on six public datasets show that our proposed scheme outperforms state-of-the-art methods to defend against different adversarial attacks. Particularly, QAT improves the system performance by 14%, 11%, 16% on average on CIFAR-10, SVHN, and CIFAR-100 datasets respectively compared with the existing defense schemes, and reduces the computing time by about 60%.

In the big data era, distributed machine learning emerges as an important learning paradigm to mine large volumes of data by taking advantage of distributed computing resources. In this work, motivated by learning to learn, we propose a meta-learning approach to coordinate the learning process in the master-slave type of distributed systems. Specifically, we utilize a recurrent neural network (RNN) in the parameter server (the master) to learn to aggregate the gradients from the workers (the slaves). We design a coordinatewise preprocessing and postprocessing method to make the neural network based aggregator more robust. Besides, to address the fault tolerance, especially the Byzantine attack, in distributed machine learning systems, we propose an RNN aggregator with additional loss information (ARNN) to improve the system resilience. We conduct extensive experiments to demonstrate the effectiveness of the RNN aggregator, and also show that it can be easily generalized and achieve remarkable performance when transferred to other distributed systems. Moreover, under majoritarian Byzantine attacks, the ARNN aggregator outperforms the Krum, the state-of-art fault tolerance aggregation method, by 43.14%. In addition, our RNN aggregator enables the server to aggregate gradients from variant local models, which significantly improve the scalability of distributed learning.

Big data has become a key basis of innovation and intelligence, potentially making our lives more convenient and bringing new opportunities to the modern society. Towards this goal, a critical underlying task is to solve a series of large-scale fundamental problems. Conducting such large-scale data analytics in a timely manner requires a large amount of computing resources, which may not be available for individuals and small companies in practice. By outsourcing their computations to the cloud, clients can solve such problems in a cost-effective way. However, confidential data stored at the cloud is vulnerable to cyber attacks, and thus needs to be protected. Previous works employ cryptographic techniques like homomorphic encryption, which significantly increase the computational complexity of solving a large-scale problem at the cloud and is impractical for big data applications. For the first time in the literature, we present an efficient secure outsourcing scheme for convex separable programming problems (CSPs). In particular, we first develop efficient matrix and vector transformation schemes only based on arithmetic operations that are computationally indistinguishable both in value and in structure under a chosen-plaintext attack (CPA). Then, we design a secure outsourcing scheme in which the client and the cloud collaboratively solve the transformed problems. The client can efficiently verify the correctness of returned results to prevent any malicious behavior of the cloud. Theoretical correctness and privacy analysis together show that the proposed scheme obtains optimal results and that the cloud cannot learn private information from the client's concealed data. We conduct extensive simulations on Amazon Elastic Cloud Computing (EC2) platform and find that our proposed scheme provides significant time savings to the clients.

Inference using deep neural networks may be outsourced to the cloud due to its high computational cost, which, however, raises security concerns. Particularly, the data involved in deep neural networks can be highly sensitive, such as in medical, financial, commercial applications, and hence should be kept private. Besides, the deep neural network models owned by research institutions or commercial companies are their valuable intellectual properties and can contain proprietary information, which should be protected as well. Moreover, an untrusted cloud service provider may return accurate and even erroneous computing results. To address the above issues, we propose a secure outsourcing framework for deep neural network inference called SecureNets, which can preserve both a user’s data privacy and his/her neural network model privacy, and also verify the computation results returned by the cloud. Specifically, we employ a secure matrix transformation scheme in SecureNets to avoid privacy leakage of the data and the model. Meanwhile, we propose a verification method that can efficiently verify the correctness of cloud computing results. Our simulation results on four- and five-layer deep neural networks demonstrate that SecureNets can reduce the processing runtime by up to $64%$. Compared with CryptoNets, one of the previous schemes, SecureNets can increase the throughput by $104.45%$ while reducing the data transmission size by $69.78%$ per instance.

Unsupervised anomaly detection on multidimensional time series data is a very important problem due to its wide applications in many systems such as cyber-physical systems, the Internet of Things. Some existing works use traditional variational autoencoder (VAE) for anomaly detection. They generally assume a single-modal Gaussian distribution as prior in the data generative procedure. However, because of the intrinsic multimodality in time series data, previous works cannot effectively learn the complex data distribution, and hence cannot make accurate detections. To tackle this challenge, in this paper, we propose a GRU-based Gaussian Mixture VAE system for anomaly detection, called GGM-VAE. In particular, Gated Recurrent Unit (GRU) cells are employed to discover the correlations among time sequences. Then we use Gaussian Mixture priors in the latent space to characterize multimodal data. The proposed detector reports an anomaly when the reconstruction probability is below a certain threshold. We conduct extensive simulations on real world datasets and find that our proposed scheme outperforms the state-of-the-art anomaly detection schemes and achieves up to 5.7% and 7.2% improvements in accuracy and F1 score, respectively, compared with existing methods.

With the onset of the big data era, designing efficient and effective machine learning algorithms to analyze large-scale data is in dire need. In practice, data is typically generated by multiple parties and stored in a geographically distributed manner, which spurs the study of distributed machine learning. Traditional master-worker type of distributed machine learning algorithms assumes a trusted central server and focuses on the privacy issue in linear learning models, while privacy in nonlinear learning models and security issues are not well studied. To address these issues, in this paper, we explore the blockchain technique to propose a decentralized privacy-preserving and secure machine learning system, called LearningChain, by considering a general (linear or nonlinear) learning model and without a trusted central server. Specifically, we design a decentralized Stochastic Gradient Descent (SGD) algorithm to learn a general predictive model over the blockchain. In decentralized SGD, we develop differential privacy based schemes to protect each party’s data privacy, and propose an l-nearest aggregation algorithm to protect the system from potential Byzantine attacks. We also conduct theoretical analysis on the privacy and security of the proposed LearningChain. Finally, we implement LearningChain on Etheurum and demonstrate its efficiency and effectiveness through extensive experiments.

Paradigm-shifting systems such as cyber-physical systems, collect data of high- or ultrahigh- dimensionality tremendously. Detecting outliers in this type of systems provides indicative understanding in wide-ranging domains such as system health monitoring, information security, etc. Previous dimensionality reduction based outlier detection methods suffer from the incapability of well preserving the critical information in the low-dimensional latent space, mainly because they generally assume an isotropic Gaussian distribution as prior and fail to mine the intrinsic multimodality in high dimensional data. Moreover, most of the schemes decouple the model learning process, resulting in suboptimal performance. To tackle these challenges, in this paper, we propose a unified Unsupervised Gaussian Mixture Variational Autoencoder for outlier detection. Specifically, a variational autoencoder firstly trains a generative distribution and extracts reconstruction based features. Then we adopt a deep brief network to estimate the component mixture probabilities by the latent distribution and extracted features, which is further used by the Gaussian mixture model to estimate sample densities with the Expectation-Maximization (EM) algorithm. The inference model is optimized jointly with the variational autoencoder, the deep brief network, and the Gaussian mixture model. Afterwards, the proposed detector identifies outliers when the estimated sample density exceeds a learned threshold. Extensive simulations on six public benchmark datasets show that the proposed framework outperforms state-of-the-art outlier detection schemes and achieves, on average, 27% improvements in F1 score.

Tensor decompositions are very powerful tools for analyzing multi-dimensional multi-modal data. Particularly, CP tensor decomposition is one of the most fundamental tensor decomposition models. However, it is usually computationally expensive to conduct CP tensor decompositions on a large-scale tensor by common algorithms like alternative least squares (ALS). To address this issue, one widely recognized solution is to adopt cloud computing. However, this raises privacy concerns due to the private information carried by a tensor. Previous algorithms for privacy-preserving outsourcing of tensor decompositions and other related computations require heavy communication cost. In this paper, we first develop an efficient tensor transformation scheme to protect the private information carried by elements' values of a tensor. Then we design a privacy-preserving outsourcing algorithm for ALS based CP tensor decompositions. We implement our proposed algorithm on a laptop and Amazon EC2 cloud and offer experiment results to show the sianificant computing time-savings.

Manufacturing systems are rapidly adopting the Internet of Things (IoT) to improve their efficiency and productivity. The IoT equips manufacturing systems with sensing, computing and communications capabilities, which enable real-time monitoring and control of increasingly complex and geographically distributed factory floors. However, the increased used of computer networks in IoT-enabled manufacturing introduces cyber-vulnerabilities that can be exploited by sophisticated adversaries to sabotage manufacturing operations. A particularly serious cyberattack against manufacturing systems is the defect injection (DI) attack. In a DI attack, a compromised machine fabricates objects with deformed geometry, weak material composition, abnormal dimensions, etc., which pose a great risk to safety-critical applications. In this paper, we develop a method to identify compromised machines that launch DI attacks against smart manufacturing systems. Specifically, we first propose a DI attack localization (DIAL) algorithm that uses machines' energy consumption and voltage measurements to identify compromised machines in the system. Our proposed approach only requires modest hardware resources and can be used in large-scale systems. We implement our DIAL algorithm on a real-world advanced manufacturing testbed, and observe that it can successfully locate the compromised machines with a high detection rate.

Information-centric networking (ICN) is developed for the future Internet because of the tremendous increase of content demands in the Internet. In the ICN architecture, in-network storage for caching plays an important role in improving content delivery efficiency, scalability and availability. To enjoy the benefits of caching users' preferable contents without disclosing the users' privacy, in this paper, we aim to integrate local differential privacy (LDP) techniques into data-driven optimization, and propose a novel scheme to allow content provider (CP) to collect the locally differentially private content preferences of a selected group of users, exploit data-driven approach to predict the content popularity, and offer the cache-enabled access points (APs) economic incentives to cache the selected preferable content. Here, optimized local hashing (OLH) is employed to locally add differential private noise to the users' preference content information and the noisy data is sent to the CP. Besides, we leverage data-driven methodology to predict the content popularity according to the constructed reference distribution of the given noisy preference content data from users. We formulate a data-driven caching revenue optimization, provide feasible solutions, and conduct simulations to show the effectiveness of the proposed scheme.

The popularity of smart mobile devices has resulted in the surged growth of mobile data traffic, which makes current cellular communication systems overloaded. To accommodate the data, the current wireless communication system is evolving to a 5G wireless communication system that employs multiple technologies to boost its system capacity. We notice that non-line-of-sight (NLOS) transmission is ubiquitous in wireless communication systems, and is even more common in 5G wireless communication systems due to using millimeter-Wave (mmWave) communications. Previous works employ beamforming techniques to enhance NLOS transmission performance but suffer from the high cost for controlling antennas. In this paper, we propose a dynamic transmission power control scheme for improving NLOS transmission performance. Particularly, we explore the control of UE association with MBS/SBSs and power allocation to maximize UEs' sum-rate under the constraints of transmission power and UEs' quality of service (QoS). To solve this maximization problem, we propose a deep Q- network (DQN) scheme, in which we apply a convolutional neural network (CNN) to estimate the Q-function offline and conduct a deep Q-learning online to find the control strategy. We offer simulation results to show the efficacy of the proposed scheme.

Due to the rapid growth of various systems and applications like cyber-physical systems, smart cities, and e-commerce systems, we have a massive volume of data collected from these systems and applications. We notice that matrix convolution is one of the most fundamental operations on largescale data, such as using it for image registration and object detection. However, performing large-scale matrix convolutions is usually time-consuming, hence hindering a general-purpose computer to conduct large-scale matrix convolutions by its own. Cloud computing allows using an economical way to offload the most expensive computations to the cloud. This, however, obviously raise security concerns. To this end, we proposed an efficient secure outsourcing scheme for large-scale matrix convolutions. Specifically, the user first masks the matrices for protecting the security and sends the masked matrices to the cloud. Then, the cloud conducts matrix convolution and returns the result to the user. Finally, the user recovers the real result from the returned one. Particularly, the matrix convolution is performed in a non- interactive way, hence leading to the very low communication cost. We implement the proposed algorithm on the Amazon Elastic Compute Cloud (EC2) platform and a laptop. The experiment results show significant time saving for the user.

Because of the co-existing of H.264/AVC and high efficiency video coding standard (HEVC) in the coming long period, video transcoding technology has become an essential part of multimedia communication in the field of the Internet of Vehicles (IoV). However, due to the huge computational complexity of re-encoding processes, traditionally cascaded transcoders greatly increase the computing burden of the embedded devices and impact the real-time capability of transportation communication systems. In order to address this problem, a fast transcoding solution is proposed in this paper. First, we exploit the mapping relationship among H.264/AVC decoding information and HEVC coding unit (CU) depth decision and prediction unit (PU) mode decision. Then, a three-output classification model is built for CU depth decision processes, and a two-output classification model is built for PU mode selection processes by using support vector machine method. Finally, the models are applied into the cascaded transcoder to accelerate the re-encoding process. The experimental results show that our proposal averagely achieves up to 53.7% and 52.3% complexity reductions under Lowdelay_P_main and Randomaccess_main configurations, respectively, with the negligible rate-distortion degradation, which show a great potential in improving the transcoding efficiency in the real-time video communication system of IoV.

Solving large-scale sparse linear systems of equations (SLSEs) is one of the most common and fundamental problems in big data, but it is very challenging for resource-limited users. Cloud computing has been proposed as a timely, efficient, and cost-effective way of solving such expensive computing tasks. Nevertheless, one critical concern in cloud computing is data privacy. Specifically, clients’ SLSEs usually contain private information that should remain hidden from the cloud for ethical, legal, or security reasons. Many previous works on secure outsourcing of linear systems of equations (LSEs) have high computational complexity, and do not exploit the sparsity in the LSEs. More importantly, they share a common serious problem, i.e., a huge number of memory I/O operations. This problem has been largely neglected in the past, but in fact is of particular importance and may eventually render those outsourcing schemes impractical. In this paper, we develop an efficient and practical secure outsourcing algorithm for solving large-scale SLSEs, which has low computational and memory I/O complexities and can protect clients’ privacy well. We implement our algorithm on Amazon Elastic Compute Cloud, and find that the proposed algorithm offers significant time savings for the client (up to 74 percent) compared to previous algorithms.

Device-to-device (D2D) communications can potentially alleviate cellular network congestion by utilizing local available links, and have attracted intensive attention recently. Cognitive radio (CR) allows users to opportunistically access unused licensed spectrums. It thus serves as a great candidate technology for D2D communications, but has not been widely employed in cellular networks due to hardware development limitations. In this paper, we propose a new architecture, called cognitive mesh assisted cellular network (CMCN), in which several secondary service providers (SSPs) deploy CR routers to facilitate D2D communications among wireless users. To address the competition among the SSPs, we further construct a secondary spectrum auction market. Although a few works have studied spectrum auctions, most of them are designed for single-hop communications, and it is usually not clear whom a winning user communicates with. Uncertain spectrum availability is not considered in previous schemes either. In this paper, we propose a transmission opportunity auction scheme, called TOA, which can address these problems. Extensive simulations are conducted to validate the efficiency of the CMCN architecture and that of the TOA scheme.

Electric power systems are critical infrastructure and are vulnerable to contingencies including natural disasters, system errors, malicious attacks, etc. These contingencies can affect the world's economy and cause great inconvenience to our daily lives. Therefore, security of power systems has received enormous attention for decades. Recently, the development of the Internet of Things (IoT) enables power systems to support various network functions throughout the generation, transmission, distribution, and consumption of energy with IoT devices (such as sensors, smart meters, etc.). On the other hand, it also incurs many more security threats. Cascading failures, one of the most serious problems in power systems, can result in catastrophic impacts such as massive blackouts. More importantly, it can be taken advantage by malicious attackers to launch physical or cyber attacks on the power system. In this paper, we propose and investigate cascading failure attacks (CFAs) from a stochastic game perspective. In particular, we formulate a zerosum stochastic attack/defense game for CFAs while considering the attack/defense costs, budget constraints, diverse load shedding costs, and dynamic states in the system. Then, we develop a Q-CFA learning algorithm that works efficiently in power systems without any a priori information. We also formally prove that the convergence of the proposed algorithm achieves a Nash equilibrium. Simulation results validate the efficacy and efficiency of the proposed scheme by comparisons with other state-of-the-art approaches.

The ubiquity of smartphones makes the mobile crowdsourcing possible, where the requester (task owner) can crowdsource data from the workers (smartphone users) by using their sensor-rich mobile devices. However, data collection, data aggregation, and data analysis have become challenging problems for a resource constrained requester when data volume is extremely large, i.e., big data. In particular to data analysis, set operations, including intersection, union, and complementation, exist in most big data analysis for filtering redundant data and preprocessing raw data. Facing challenges in terms of limited computation and storage resources, cloud-assisted approaches may serve as a promising way to tackle the big data analysis issue. However, workers may not be willing to participate if the privacy of their sensing data and identity are not well preserved in the untrusted cloud. In this paper, we propose to the use cloud to compute a set operation for the requester, at the same time workers' data privacy and identities privacy are well preserved. Besides, the requester can verify the correctness of set operation results. We also extend our scheme to support data preprocessing, with which invalid data can be excluded before data analysis. By using batch verification and data update methods, the proposed scheme greatly reduces the computational cost. Extensive performance analysis and experiment based on real cloud system have shown both the feasibility and efficiency of our proposed scheme.

The massive amount of data that is being collected by today's society has the potential to advance scientific knowledge and boost innovations. However, people often lack sufficient computing resources to analyze their large-scale data in a cost-effective and timely way. Cloud computing offers access to vast computing resources on an on-demand and pay-per-use basis, which is a practical way for people to analyze their huge data sets. However, since their data contain sensitive information that needs to be kept secret for ethical, security, or legal reasons, many people are reluctant to adopt cloud computing. For the first time in the literature, we propose a secure outsourcing algorithm for large-scale quadratic programs (QPs), which is one of the most fundamental problems in data analysis. Specifically, based on simple linear algebra operations, we design a low-complexity QP transformation that protects the private data in a QP. We show that the transformed QP is computationally indistinguishable under a chosen plaintext attack (CPA), i.e., CPA-secure. We then develop a parallel algorithm to solve the transformed QP at the cloud, and efficiently find the solution to the original QP at the user. We implement the proposed algorithm on the Amazon Elastic Compute Cloud (EC2) and a laptop. We find that our proposed algorithm offers significant time savings for the user and is scalable to the size of the QP.

The database-driven cognitive radio network (CRN) is regarded as a promising way for a better utilization of spectrum resources without introducing the interference to primary users (PUs). However, there are some critical security and privacy issues in database-driven CRNs, which have been rarely discussed before. First of all, in order to retrieve the spectrum available information (SAI) of one's vicinity, an SU's query will inevitably disclose its location information. Second, malicious SUs may query SAI for other locations so as to infer operational patterns of PUs and other SUs. In addition, they can reconstruct the entire SAI of the database and sell it for profit. Therefore, in this paper we aim to guarantee both location privacy of SUs and information security of the database during spectrum query in database-driven CRNs. We first leverage private information retrieval (PIR) techniques to allow the database to find out the SAI regarding a querying SU's location, without learning the query information, i.e., this SU's location. To prevent malicious SUs inferring SAI of other locations, SUs are required to provide location proofs indicating that they are at the places where they claim to be. Theoretical analysis is provided showing that our scheme is privacy-preserving and secure. Experiments are also conducted to evaluate the its efficiency.

Crowdsourcing is a crowd-based outsourcing, where a requester (task owner) can outsource tasks to workers (public crowd). Recently, mobile crowdsourcing, which can leverage workers' data from smartphones for data aggregation and analysis, has attracted much attention. However, when the data volume is getting large, it becomes a difficult problem for a requester to aggregate and analyze the incoming data, especially when the requester is an ordinary smartphone user or a start-up company with limited storage and computation resources. Besides, workers are concerned about their identity and data privacy. To tackle these issues, we introduce a three-party architecture for mobile crowdsourcing, where the cloud is implemented between workers and requesters to ease the storage and computation burden of the resource-limited requester. Identity privacy and data privacy are also achieved. With our scheme, a requester is able to verify the correctness of computation results from the cloud. We also provide several aggregated statistics in our work, together with efficient data update methods. Extensive simulation shows both the feasibility and efficiency of our proposed solution.

The security and privacy threats on e-commerce have attracted intensive attention recently. The explosive growth of online social networks (OSNs) has made them potential new great marketplaces for e-commerce, which, however, raise serious security and privacyconcerns. This is mainly due to the centralized system architecture where the service provider knows all users’ private data and becomes the single point of failure. To this end, we propose a secure and private auction framework, called SPA, for decentralized online social networks (DOSNs). SPA consists of three phases: identity initiation, buyer-seller matching, and private auction. It requires no trust among the participants but can provide security, privacy, authenticity, non-repudiation, and correctness for the auctions. We analyze the computation and communication complexities of the proposed private auction scheme, which are$O(n+K)$for each node where$n$is the number of bidders and$K$is the number of pricing points. In contrast, those of previous auction schemes are$O(nK)$at best. The storage complexity is significantly lower than before as well. Security and privacy of SPA are also analyzed. Extensive experiments are conducted to validate the efficiency of SPA.

The advancement of cloud-computing technologies opens new possibilities to outsource to the third-party cloud the computation-intensive and time-consuming dynamic simulations needed in power grid system research and operations. Outsourcing makes it possible to conduct dynamic simulations much faster and with lower cost than to keep all computations local. On the other hand, outsourcing, however, also gives rise to the risk of information leak, as the outsourced simulation contains sensitive information, such as critical operational parameters and projected states of the power grid. In this paper, a novel secure outsourcing scheme, combining disguising technique and code obfuscation, was proposed to enable efficient outsourcing while preserving the confidentiality of the information. It was shown that our scheme can limit the adversary's capability to obtain the sensitive information in the context of outsourcing of power system dynamic simulations.

Many truthful spectrum auction schemes have been recently proposed to to ensure that the dominant strategy for bidders is to bid truthfully and thus protect the auctioneer's benefits. However, most of them assume the auctioneer is trustful and do not protect bidders' interests. An auctioneer can manipulate the winner's charging price if it knows bidders' bids. Thus, it is critical to protect bids from the auctioneer. Towards this end, we develop a Privacy-Preserving Economic-Robust spectrum auction scheme, namely PPER. Not only does it well protect users' bid privacy, but also guarantees economic-robustness which is another important auction property. Besides, only transmitters but not receivers are considered in most previous spectrum auctions, resulting in many unexpected collisions during transmissions. In this work, we consider interference constraints from transmissions instead of transmitters in spectrum allocation. Extensive privacy analysis and simulation results show the effectiveness and efficiency of our scheme.

Widely deployed mHealth systems enable patients to efficiently collect, aggregate, and report their Personal Health Records (PHRs), and then lower the costs and shorten their response time. The increasing needs of PHR monitoring require the involvement of healthcare companies that provide monitoring programs for analyzing PHRs. Unfortunately, healthcare companies are lack of the computation, storage, and communication capability on supporting millions of patients. To tackle this problem, they seek for the help from the cloud. However, delegating monitoring programs to the cloud may incur serious security and privacy breaches because people have to provide their identity information and PHRs to the public domain. Even worse, the cloud may mistakenly return the incorrect computation results, which will put patients' life in jeopardy. In this paper, we propose a verifiable privacy-preserving monitoring scheme for cloud-assisted mHealth systems. Our scheme allows patients to verify the correctness of computation results from the cloud without revealing their PHRs and identity information. In addition, our advanced schemes offer efficient PHR updates and PHR computations on complex monitoring programs. By detailed performance evaluation, we have shown the security and efficiency of our proposed scheme.

Solving large-scale linear systems of equations (LSEs) is one of the most common and fundamental problems in big data. But such problems are often too expensive to solve for resource-limited users. Cloud computing has been proposed as a timely, efficient, and cost-effective way of solving such computing tasks. Nevertheless, one critical concern in cloud computing is data privacy. To be more prominent, in many cases, clients's LSEs contain private data that should remain hidden from the cloud for ethical, legal, or security reasons. Many previous works on secure outsourcing of LSEs have high computational complexity. More importantly, they share a common serious problem, i.e., a huge number of external memory I/O operations. This problem has been largely neglected in the past, but in fact is of particular importance and may eventually render those outsourcing schemes impractical. In this paper, we develop an efficient and practical secure outsourcing algorithm for solving large-scale LSEs, which has both low computational complexity and low memory I/O complexity and can protect clients' privacy well. We implement our algorithm on a real-world cloud server and a laptop. We find that the proposed algorithm offers significant time savings for the client (up to 65%) compared to previous algorithms.

With the rapid growth of the smart device market, associated security issues become more threatening and diverse than ever before. Due to the limitations of the traditional explicit authentication mechanisms (e.g., Password-based, biometrics), researchers and the industry have been promoting implicit authentication (IA) that does not require explicit user action and potentially enhances user experience to further protect devices from misuse. IA typically leverages various types of behavioral data to deduce a user behavior model for authentication purpose. However, IA systems are still at their infancy and exhibit many limitations, one of which is how to determine the best retraining frequency when updating the user behavior model. Another limitation is how to gracefully degrade user privilege, when authentication fails to identify legitimate users (i.e., False negatives) for a practical IA system. To address the first problem, we propose an algorithm that utilizes Jensen-Shannon (JS)-dis(tance) to determine the optimal retraining frequency. For the second problem, we introduce a dynamic privilege mechanism, again based on JS-dis(tance), to achieve multi-level fine-grained access control. Our simulation results show that the proposed techniques can successfully detect the degradation of accuracy of the user behavior model, as well as automatically determine and adjust to the best retraining frequency. It is also shown that the dynamic privilege-based access control reduces the impact of false negatives on legitimate users and enhances system reliability and user experience compared with the traditional lock-only method in case of authentication failure.

Cellular networks are faced with serious congestions nowadays due to the recent booming growth and popularity of wireless devices and applications. Opportunistically accessing the unused licensed spectrum, cognitive radio can potentially harvest more spectrum resources and enhance the capacity of cellular networks. In this paper, we propose a new multihop cognitive cellular network (MC2N) architecture to facilitate the ever exploding data transmissions in cellular networks. Under the proposed architecture, we then investigate the minimum energy consumption problem by exploring joint frequency allocation, link scheduling, routing, and transmission power control. Specifically, we first formulate a maximum independent set (MIS) based energy consumption optimization problem, which is a non-linear programming problem. Different from most previous work assuming all the MISs are known, finding which is in fact NP-complete, we employ a column generation based approach to circumvent this problem. We develop an ϵ-bounded algorithm, which can obtain a feasible solution that are less than (1 + ϵ) and larger than (1 - ϵ) of the optimal result of MP, and analyzed its computational complexity. We also revisit the minimum energy consumption problem by taking uncertain channel bandwidth into consideration. Simulation results show that we can efficiently find ϵ-bounded approximate results and the optimal result as well.

Due to the emerging various data services, current cellular networks have been experiencing a surge of data traffic and are already overloaded; thus, they are not able to meet the ever exploding traffic demand. In this study, we first introduce a multi-radio multi-channel multi-hop cognitive cellular network (M$^3$C$^2$N) architecture to enhance network throughput. Under the proposed architecture, we then investigate the minimum length scheduling problem by exploring joint frequency allocation, link scheduling, and routing. In particular, we first formulate a maximal independent set based joint scheduling and routing optimization problem called original optimization problem (OOP). It is a mixed integer non-linear programming (MINLP) and generally NP-hard problem. Then, employing a column generation based approach, we develop an$\epsilon$-bounded approximation algorithm which can obtain an$\epsilon$-bounded approximate result of OOP. Noticeably, in fact we do not need to find the maximal independent sets in the proposed algorithm, which are usually assumed to be given in previous works although finding all of them is NP-complete. We also revisit the minimum length scheduling problem by considering uncertain channel availability. Simulation results show that we can efficiently find the$\epsilon$-bounded approximate results and the optimal result as well, i.e., when$\epsilon =0\%$in the algorithm.

In ad hoc networks, selfish nodes deviating from the standard MAC (Medium Access Control) protocol can significantly degrade normal nodes' performance and are usually difficult to detect. In this paper, we propose detection and defense schemes to identify and defend against MAC-layer selfish misbehavior, respectively, in IEEE 802.11 multi-hop ad hoc networks. Specifically, the non-deterministic nature of the IEEE 802.11 MAC protocol imposes great challenges to distinguishing selfish nodes from well-behaved nodes. Most traditional selfish misbehavior detection approaches are for wireless local area networks (WLANs) only. They either rely on a large amount of historical data to perform statistical detection, or employ throughput or delay models that are only valid in WLANs for detection. In contrast, we propose a realtime selfish misbehavior detection scheme for multi-hop ad hoc networks. It requires only several samples, and hence is more efficient and can adapt to channel dynamics more quickly. Then, based on the proposed detection scheme, we design three selfish misbehavior defense schemes against three typical kinds of smart selfish nodes. We find that the smart selfish nodes cannot degrade normal nodes' performance much without getting detected. Extensive simulation results are finally presented to validate the proposed detection and defense schemes.

The explosive growth of Online Social Networks (OSNs) over the past few years has redefined the way people interact with existing friends and especially make new friends. Some works propose to let people become friends if they have similar profile attributes. However, profile matching involves an inherent privacy risk of exposing private profile information to strangers in the cyberspace. The existing solutions to the problem attempt to protect users’ privacy by privately computing the intersection or intersection cardinality of the profile attribute sets of two users. These schemes have some limitations and can still reveal users’ privacy. In this paper, we leverage community structures to redefine the OSN model and propose a realistic asymmetric social proximity measure between two users. Then, based on the proposed asymmetric social proximity, we design three private matching protocols, which provide different privacy levels and can protect users’ privacy better than the previous works. We also analyze the computation and communication cost of these protocols. Finally, we validate our proposed asymmetric proximity measure using real social network data and conduct extensive simulations to evaluate the performance of the proposed protocols in terms of computation cost, communication cost, total running time, and energy consumption. The results show the efficacy of our proposed proximity measure and better performance of our protocols over the state-of-the-art protocols.

Numerous applications of wireless sensor networks (WSNs) in harsh terrains are constrained by the sensors' battery-power and face the difficulties of data collection. In this paper, we propose to exploit wireless power transfer technology to replenish the energy of sensor clusters and develop an efficient data collection scheme for those wireless rechargeable senor clusters deployed in harsh terrains. In view of the harsh terrains, we employ unmanned aerial vehicles (UAVs) to travel to the sites of sensor clusters, collect data, and recharge the sensors in corresponding clusters. With joint consideration of data collection characteristics, wireless power transfer features and travel time, we mathematically formulate the data collection in rechargeable WSNs into an optimization problem with the objective of maximizing data collection utility. Based on the matching theory, we also develop a one side matching algorithm and a greedy algorithm to solve the problem in distributed manner. Through simulations, we show that UAVs are not always matched with nearest sensor clusters, the solution of the proposed greedy algorithm is optimal, and the sensed data can be efficiently collected.

With the ever increasing user adoption of mobile devices like smart phones and tablets, the cellular service providers' energy consumption and cost are fast-growing and have received tremendous attention. How to effectively reduce the energy cost of cellular networks and achieve green communications while satisfying cellular users' rocketing traffic demands has become an urgent and challenging problem. In this paper, we investigate the minimization of the long-term time-averaged expected energy cost of a cellular service provider while guaranteeing the strong stability of the network. We first formulate an offline optimization problem with a joint consideration of flow routing, link scheduling, and energy (i.e., renewable energy resource, energy storage unit, etc.) constraints. Since the formulated problem is a time-coupling stochastic Mixed-Integer Non-Linear Programming (MINLP) problem, it is prohibitively expensive to solve. Then, we reformulate the problem by employing Lyapunov optimization theory. A decomposition based algorithm is developed to solve the problem, which is proved to guarantee the network strong stability. Both the lower and upper bounds on the optimal result of the original problem are derived and proven. Simulation results demonstrate that the obtained lower and upper bounds are very tight, and that the proposed scheme results in noticeable energy cost savings.

Spectrum trading creates more accessing opportunities for secondary users (SUs) and economically benefits the primary users (PUs). However, it is challenging to implement spectrum trading in multi-hop cognitive radio networks (CRNs) due to harsh cognitive radio (CR) requirements on SUs' devices, uncertain spectrum supply from PUs and complex competition relationship among different CR sessions. Unlike the per-user based spectrum trading designs in previous studies, in this paper, we propose a novel session based spectrum trading system, spectrum clouds, in multi-hop CRNs. In spectrum clouds, we introduce a new service provider, secondary service provider (SSP), to facilitate the accessing of SUs without CR capability and harvest uncertain spectrum supply. The SSP also conducts spectrum trading among CR sessions w.r.t. their conflicts and competitions. Leveraging a 3-dimensional (3-D) conflict graph, we mathematically describe the conflicts and competitions among the candidate sessions for spectrum trading. Given the rate requirements and bidding values of candidate trading sessions, we formulate the optimal spectrum trading into the SSP's revenue maximization problem under multiple cross-layer constraints. In view of the NP-hardness of the problem, we develop heuristic algorithms to pursue feasible solutions. Through extensive simulations, we show that the solutions found by the proposed algorithms are close to the optimal one.

Motivated by the privacy issues, curbing the adoption of electronic healthcare systems and the wild success of cloud service models, we propose to build privacy into mobile healthcare systems with the help of the private cloud. Our system offers salient features including efficient key management, privacy-preserving data storage, and retrieval, especially for retrieval at emergencies, and auditability for misusing health data. Specifically, we propose to integrate key management from pseudorandom number generator for unlinkability, a secure indexing method for privacy-preserving keyword search which hides both search and access patterns based on redundancy, and integrate the concept of attribute-based encryption with threshold signing for providing role-based access control with auditability to prevent potential misbehavior, in both normal and emergency cases.

The proliferation of mobile devices has driven the mobile marketing to surge in the past few years. Emerging as a new type of mobile marketing, mobile location-based services (MLBSs) have attracted intense attention recently. Unfortunately, current MLBSs have a lot of limitations and raise many concerns, especially about system security and users' privacy. In this paper, we propose a new location-based rewarding system, called LocaWard, where mobile users can collect location-based tokens from token distributors, and then redeem their gathered tokens at token collectors for beneficial rewards. Tokens act as virtual currency. The token distributors and collectors can be any commercial entities or merchants that wish to attract customers through such a promotion system, such as stores, restaurants, and car rental companies. We develop a security and privacy aware location-based rewarding protocol for the LocaWard system, and prove the completeness and soundness of the protocol. Moreover, we show that the system is resilient to various attacks and mobile users' privacy can be well protected in the meantime. We finally implement the system and conduct extensive experiments to validate the system efficiency in terms of computation, communication, energy consumption, and storage costs.

The rapid growth of wireless devices and services exacerbates the problem of spectrum scarcity in wireless networks. Recently, spectrum auction has emerged as one of the most promising techniques to enhance spectrum utilization and mitigate this problem. Although there exist some works studying spectrum auction, most of them are designed for single-hop communications, and it is usually not clear whom a winning user communicates with. Moreover, most previous auction schemes only focus on satisfying the incentive compatibility property, also called truthfulness, but ignore another two critical properties: individual rationality, and budget balance. Thus, they may not be economic-robust. In this paper, we propose a transmission opportunity auction scheme, called TOA, which can support multi-hop data traffic, ensure economic-robustness, and generate high revenue for the auctioneer. Specifically, in TOA, instead of spectrum bands as in traditional spectrum auction schemes, users bid for transmission opportunities (TOs). A TO is defined as the permit of data transmission on a specific link using a certain band, i.e., a link-band pair. The TOA scheme is composed of three procedures: TO allocation, TO scheduling, and pricing, which are performed sequentially and iteratively until the aforementioned goals are reached. We prove that TOA is economic-robust, and conduct extensive simulations to show its effectiveness and efficiency.

With great advances in mobile devices, e.g., smart phones and tablets, location-based services (LBSs) have recently emerged as a very popular application in mobile networks. However, since LBS service providers require users to report their location information, how to preserve users' location privacy is one of the most challenging problems in LBSs. Most existing approaches either cannot fully protect users' location privacy, or cannot provide accurate LBSs. Many of them also need the help of a trusted third-party, which may not always be available. In this paper, we propose a geometric approach, called n-CD, to provide realtime accurate LBSs while preserving users' location privacy without involving any third-party. Specifically, we first divide a user's region of interest (ROI), which is a disk centered at the user's location, into n equal sectors. Then, we generate n concealing disks (CDs), one for each sector, one by one to collaboratively and fully cover each of the n sectors. We call the area covered by the n CDs the concealing space, which fully contains the user's ROI. After rotating the concealing space with respect to the user's location, we send the rotated centers of the n CDs along with their radii to the service provider, instead of the user's real location and his/her ROI. To investigate the performance of n-CD, we theoretically analyze its privacy level and concealing cost. Extensive simulations are finally conducted to evaluate the efficacy and efficiency of the proposed schemes.

Connectivity of large-scale wireless networks has received considerable attention in the past several years. Different from traditional wireless networks, in Cognitive Radio Ad-hoc Networks (CRAHNs), primary users have spectrum access priority of the licensed bands over secondary users. Therefore, the connectivity of the secondary network is affected by not only the density and transmission power of secondary users, but also the activities of primary users. In addition, the number of licensed bands also has impact on the connectivity of CRAHNs. To capture the dynamic characteristics of opportunistic spectrum access, we introduce the Cognitive Radio Graph Model (CRGM) which takes into account the impact of the number of channels and the activities of primary users. Furthermore, we combine the CRGM with continuum percolation model to study the connectivity in the secondary network. We prove that secondary users can form the percolated network when the density of primary users is below the critical density. Then, the upper bound of the critical density of the primary users in the percolated CRAHNs is derived. Simulation results show that both the number of channels and the activities of primary users greatly impact the connectivity of CRAHNs.

Spectrum trading creates more accessing opportunities for secondary users (SUs) and economically benefits the primary users (PUs). However, it is challenging to implement spectrum trading in multi-hop cognitive radio networks (CRNs) due to harsh cognitive radio (CR) requirements on SUs' devices and complex conflict and competition relationship among different CR sessions. Unlike the per-user based spectrum trading designs in previous studies, in this paper, we propose a novel session based spectrum trading system, spectrum clouds, in multi-hop CRNs. In spectrum clouds, we introduce a new service provider, called secondary service provider (SSP), to harvest the available spectrum bands and facilitate the accessing of SUs without CR capability. The SSP also conducts spectrum trading among CR sessions w.r.t. their conflicts and competitions. Leveraging a 3-dimensional (3-D) conflict graph, we mathematically describe the conflicts and competitions among the candidate sessions for spectrum trading. Given the rate requirements and bidding values of candidate trading sessions, we formulate the optimal spectrum trading into the SSP's revenue maximization problem under multiple cross-layer constraints in multi-hop CRNs. In view of the NP-hardness of the problem, we have also developed heuristic algorithms to pursue feasible solutions. Through extensive simulations, we show that the solutions found by the proposed algorithms are close to the optimal one.

In the U.S., energy theft causes six billion dollar losses to utility companies (UCs) every year. With the smart grid being proposed to modernize current power grids, energy theft may become an even more serious problem since the “smart meters” used in smart grids are vulnerable to more types of attacks compared to traditional mechanical meters. Therefore, it is important to develop efficient and reliable methods to identify illegal users who are committing energy theft. Although some schemes have been proposed for the UCs to detect energy theft in power grids, they all require the users to send their private information, e.g., load files or meter readings at certain times, to the UCs which invades users' privacy and raises serious concerns about privacy, safety, etc. As far as we know, we are the first to investigate the energy theft detection problem considering users' privacy issues. In this paper, we propose to solve in a distributed fashion a linear system of equations (LSE) for the users' “honesty coefficients”, which indicate the users are honest when equal to 1 and are fraudulent when larger than 1. In particular, we develop two distributed privacy-preserving energy theft detection algorithms based on LU decomposition, called LUD and LUPD, respectively, which can identify fraudulent users without invading any user's privacy. Compared to LUD, LUPD requires higher execution time but is stable even in large-size systems. Moreover, the LUD and LUPD algorithms are proposed in the case that users commit energy theft at a constant rate, i.e., with constant honesty coefficients. We also propose adaptive LUD/LUPD algorithms to account for the scenarios where the users have variable honesty coefficients. Extensive simulations are carried out and the results show that the proposed algorithms can efficiently and successfully identify the fraudulent users in the system.

Throughput maximization is a key challenge for wireless applications in cognitive Vehicular Ad-hoc Networks (C-VANETs). As a potential solution, cooperative communications, which may increase link capacity by exploiting spatial diversity, has attracted a lot of attention in recent years. However, if link scheduling is considered, this transmission mode may perform worse than direct transmission in terms of end-to-end throughput. In this paper, we propose a cooperative communication aware link scheduling scheme and investigate the throughput maximization problem in C-VANETs. Regarding the features of cooperative communications and the availability of licensed spectrum, we extend the links into cooperative links/general links, define extended link-band pairs, and form a 3-dimensional (3-D) cooperative conflict graph to characterize the conflict relationship among those pairs. Given all cooperative independent sets in this graph, we mathematically formulate an end-to-end throughput maximization problem and near-optimally solve it by linear programming. Due to the NP-completeness of finding all independent sets, we also develop a heuristic pruning algorithm for cooperative communication aware link scheduling. Our simulation results show that the proposed scheme is effective in increasing end-to-end throughput for the session in C-VANETs.

The essential impediment to apply cognitive radio (CR) technology for efficient spectrum utilization lies in the uncertainty of licensed spectrum supply. In this paper, we propose a novel architecture for spectrum harvesting and sharing, and investigate the joint routing and frequency scheduling problem in multi-hop cognitive radio networks (CRNs) under uncertain spectrum supply. We introduce a new service provider, Secondary Service Provider (SSP), to facilitate the accessing for secondary users (SUs). We model the vacancy of available bands with a series of random variables, and mathematically describe the corresponding frequency scheduling and flow routing constraints. From the SSP's point of view, we characterize the CRN performance with a pair of parameters (α, β), and present an optimization problem to minimize the required network-wide spectrum resource at the (α,β) level. Given that (α, β) level is specified, we obtain a lower bound for the optimization problem and develop a threshold based coarse-grained fixing algorithm for a feasible solution. Simulation results show that (i) for any (α,β) level, the proposed algorithm provides a near-optimal solution to the formulated NP-hard problem, and (ii) the (α,β) based solution is better than the expected bandwidth based one in terms of blocking ratio and spectrum utilization in multi-hop CRNs.

A substantial body of the literature exists addressing the capacity of wireless networks. However, it is commonly assumed that all nodes in the network are identical. The issue of heterogeneity has not been embraced into the discussions. In this paper, we investigate the throughput capacity of heterogeneous wireless networks with general network settings. Specifically, we consider an extended network with n normal nodes and m = nb(0 ≤ b ≤ 1) more powerful helping nodes in a rectangular area with width s(n) and length n/s(n), where s(n) = nwand 0 ≤ w ≤ 1/2. We assume that there are n flows in the network. All the n normal nodes are sources while only randomly chosen nd(0 ≤ d ≤ 1) normal nodes are destinations. We further assume that the n normal nodes are uniformly and independently distributed, while the m helping nodes are either regularly placed or uniformly and independently distributed, resulting in two different kinds of networks called Regular Heterogeneous Wireless Networks and Random Heterogeneous Wireless Networks, respectively. We show that network capacity is determined by the shape of the network area, the number of destination nodes, the number of helping nodes, and the bandwidth of helping nodes. We also find that heterogeneous wireless networks can provide throughput higher in the order sense than traditional homogeneous wireless networks only under certain conditions.

Throughput capacity in mobile ad hoc networks has been studied extensively under many different mobility models. However, most previous research assumes global mobility, and the results show that a constant per-node throughput can be achieved at the cost of very high delay. Thus, we are having a very big gap here, i.e., either low throughput and low delay in static networks or high throughput and high delay in mobile networks. In this paper, employing a practical restricted random mobility model, we try to fill this gap. Specifically, we assume that a network of unit area with n nodes is evenly divided into cells with an area of n^{-2\alpha }, each of which is further evenly divided into squares with an area of n^{-2\beta} (0 \le \alpha \le \beta \le {1\over 2} ). All nodes can only move inside the cell which they are initially distributed in, and at the beginning of each time slot, every node moves from its current square to a uniformly chosen point in a uniformly chosen adjacent square. By proposing a new multihop relay scheme, we present smooth trade-offs between throughput and delay by controlling nodes' mobility. We also consider a network of area n^\gamma (0\le \gamma \le 1) and find that network size does not affect the results obtained before.

Network capacity investigation has been intensive in the past few years. A large body of work on wireless network capacity has appeared in the literature. However, so far most of the effort has been made on two-dimensional (2-D) wireless networks only. With the great development of wireless technologies, wireless networks are envisioned to extend from 2-D space to three-dimensional (3-D) space. In this paper, we investigate the throughput capacity of 3-D regular ad hoc networks (RANETs) and of 3-D nonhomogeneous ad hoc networks (NANETs), respectively, by employing a generalized physical model. In 3-D RANETs, we assume that the nodes are regularly placed, while in 3-D NANETs, we consider that the nodes are distributed according to a general Nonhomogeneous Poisson Process (NPP). We find both lower and upper bounds in both types of networks in a broad power propagation regime, i.e., when the path loss exponent is no less than 2.

A computationally efficient algorithm is presented for locating multiple sources on a two-dimensional grid. The total number of the sources as well as their intensities and locations on the two-dimensional grid are assumed unknown and the intensities of the sources are not necessarily identical. The distribution of the source intensity on the grid, which contains all the information about the sources, is obtained by solving a convex optimization problem. With the source distribution interpreted as a two-dimensional gray-level image, the sources are determined as the centroids of the objects in the image. Given the number of sources and the source locations, the source intensities are estimated by solving a linear least-squares problem.

Spectrum auction is an enabling approach to drastically improving the spectrum utilization to satisfy the ever increasing service demands in wireless networks. However, the behaviors of the untrustworthy auctioneer (i.e., the frauds of the untrustworthy auctioneer and the bid-rigging between the greedy bidders and the insincere auctioneer) pose significant design challenges. In this paper, we propose a secure combinatorial spectrum auction (SCSA) by using homomorphic encryption to deal with the untrustworthy auctioneer. SCSA computes and reveals the results of spectrum auction while the actual bidding values are kept confidential. By taking frequency reuse and interference constraints into consideration, we also incorporate a corresponding procedure to implement the combinatorial spectrum auction. It has been shown that SCSA can effectively thwart the back-room dealing without much performance degradation.

Cognitive Radio (CR) emerges as a promising solution to current unbalanced spectrum utilization. The cognitive ad hoc network can take advantage of dynamic spectrum access and spectrum diversity over wide spectrum. It could achieve higher network capacity compared to traditional ad hoc networks, thus supporting bandwidth-demanding applications. A cognitive radio operates over wide spectrum with unpredictable channel availability. Moreover, the transmission opportunity of a cognitive node is not guaranteed due to the presence of primary users (PUs). These two unique features define new routing problems in cognitive ad hoc networks. To better characterize the unique features of cognitive radio networks, we propose new routing metrics, including accumulated spectrum temperature, highest spectrum temperature, and mixed spectrum temperature to account for the time-varying spectrum availability. The proposed metrics favor the "coolest'' path, or the path with the most balanced and/or the lowest spectrum utilization by the primary users. We also study the computational complexity of the routing algorithm in cognitive ad hoc networks. Experiment results on our USRP-2 testbed show that the proposed metrics are capable of capturing the fluctuation of spectrum availability and suitable for cognitive ad hoc networks.

Wireless cellular networks are large-scale networks in which asymptotic capacity investigation is no longer a cliché. A substantial body of work has been carried out to improve the capacity of cellular networks by introducing ad hoc communications, resulting in the so-called multihop cellular networks. Most of the previous research allows ad hoc transmissions between certain source and destination pairs to alleviate base stations' relay burden. However, since reports show that Internet data traffic is becoming more and more dominant in cellular networks, we explore in this paper the capacity of multihop cellular networks with all traffic going through base stations and ad hoc transmissions only acting as relay. We first investigate the capacity of regular multihop cellular networks where both nodes and base stations are regularly placed. By fully exploiting the link rate variability, we find that multihop cellular networks can have higher per-node throughput than traditional cellular networks by a scaling factor of log2n. Then, for the first time we extend our study to the capacity of heterogeneous multihop cellular networks where nodes are distributed according to a general Inhomogeneous Poisson Process and base stations are randomly placed. We show that under certain conditions multihop cellular networks can also outperform traditional cellular networks by a scaling factor of log2n. Moreover, both throughput-fairness and bandwidth-fairness are considered as fairness constraints for both kinds of networks.

Network capacity investigation has been intensive in the past few years. A large body of work has appeared in the literature. However, so far most of the effort has been made on two-dimensional wireless networks only. With the great development of wireless technologies, wireless networks are envisioned to extend from two-dimensional space to three-dimensional space. In this paper, we investigate for the first time the throughput capacity of 3D regular ad hoc networks (RANETs) and of 3D heterogeneous ad hoc networks (HANETs), respectively, by employing a generalized physical model. In 3D RANETs, we assume that the nodes are regularly placed, while in 3D HANETs, we consider that the nodes are distributed according to a general Nonhomogeneous Poisson Process (NPP). We find both lower and upper bounds in both types of networks in a broad power propagation regime, i.e., when the path loss exponent is no less than 2.

The essential impediment to apply cognitive radio (CR) technology for spectrum utilization improvement lies in the uncertainty of licensed spectrum supply. In this paper, we investigate the joint routing and link scheduling problem of multi-hop CR networks under uncertain spectrum supply. We model the vacancy of licensed bands with a series of random variables, and introduce corresponding scheduling constraints and flow routing constraints for such a network. From a CR network planner/operator's point of view, we characterize the network with a pair of (α, β) parameters, and present a mathematical formulation with the goal of minimizing the required network-wide spectrum resource at the (α, β) level. Given that (α, β) is specified, we derive a lower bound for the optimization problem and develop a threshold based coarse-grained fixing algorithm for a feasible solution. Simulation results show that i) for any (α, β) level, the proposed algorithm provides a near-optimal solution to the formulated NP-hard problem; ii) the (α, β) based solution is better than expected bandwidth based one in terms of blocking ratio as well as spectrum utilization in CR networks..

Considering a disk of unit area with n nodes, we investigate the capacity of wireless networks using directional antennas. First, we study the throughput capacity of random directional networks with multihop relay schemes, and find that the capacity gain compared to random omnidirectional networks is O(log n), which is tighter than previous results. We also show that using directional antennas can significantly reduce power consumption in the networks. Second, for the first time, we explore the throughput capacity of random directional networks with one-hop relay schemes. Interestingly and against our intuition, we find that one-hop instead of multihop delivery schemes can make random directional networks scale. Third, we investigate the trade-offs between transmission range and throughput in random directional networks and show that using larger transmission range can result in higher throughput. Finally, we present a lower bound on the transport capacity of arbitrary directional networks, and find that without side lobe directional antenna gain, arbitrary directional networks can also scale.

Cognitive Radio technology releases the spectrum from shackles of authorized licenses and facilitates the trading of spectrum bands. In the spectrum market, primary service providers (PSPs) set price for the vacant licensed bands of primary users (PUs) and sell them for monetary gains, and the secondary service provider (SSP) can buy the bands and opportunistically use them to satisfy the service demands of secondary users (SUs) when the primary services are not active. However, when there are multiple bands available, the SSP confronts the challenges of how to choose bands and how to split the overall traffic on them considering both his monetary reward and the potential risk, from the unpredictable activities of the primary services, for opportunistic spectrum accessing (OSA). In this paper, we propose a reward and risk based band-mix selection algorithm to address these concerns of the SSP, and help the SSP to make appropriate decisions of traffic splitting over available spectrum band- mix, consisting of both the band belonging to SSP itself and the bands from PSPs. By numerical simulations, we verify our theoretical analysis and show that the proposed spectrum band-mix selection effectively improves the spectrum utilization as well as the satisfactory degree of SUs.

Although capacity has been extensively studied in wireless networks, most of the results are for homogeneous wireless networks where all nodes are assumed identical. In this paper, we investigate the capacity of heterogeneous wireless networks with general network settings. Specifically, we consider a dense network with n normal nodes and m = nb(0wand -1/2d(0 < d < 1) normal nodes are destinations. We further assume the n normal nodes are uniformly and independently distributed, while the m helping nodes are either regularly placed or uniformly and independently distributed, resulting in two different kinds of networks called Regular Heterogeneous Wireless Networks and Random Heterogeneous Wireless Networks, respectively. In this paper, we attempt to find out what a heterogeneous wireless network with general network settings can do by deriving a lower bound on the capacity. We also explore the conditions under which heterogeneous wireless networks can provide throughput higher than traditional homogeneous wireless networks.

Throughput capacity in wireless ad hoc networks has been studied extensively under many different mobility models such as i.i.d. mobility model, Brownian mobility model, random walk model, and so on. Most of these research works assume global mobility, i.e., each node moves around in the whole network, and the results show that a constant per-node throughput can be achieved at the cost of very high expected average end-to-end delay. Thus, we are having a very big gap here, either low throughput and low delay in static networks or high throughput and high delay in mobile networks. In this paper, employing a more practical restricted random mobility model, we try to fill in this gap. Specifically, we assume a network of unit area with n nodes is evenly divided into n2¿cells with an area of n-2¿where 0 ¿ ¿ ¿ 1/2, each of which is further evenly divided into squares with an area of n-2ßwhere 0 ¿ ¿ ¿ ß ¿ 1/2. All nodes can only move inside the cell which they are initially distributed in, and at the beginning of each time slot, every node moves from its current square to a uniformly chosen point in an uniformly chosen adjacent square. Proposing a new multi-hop relay scheme, we present an upper bound and a lower bound on per-node throughput capacity and expected average end-to-end delay, respectively. We finally explicitly show smooth trade-offs between throughput and delay by controlling nodes' mobility.

Considering the constraint brought by mobility and resources, it is important for routing protocols to efficiently deliver data in Intermittently Connected Mobile Network (ICMN). Different from previous works that use the knowledge of previous encounters to predict the future contact, we propose a storagefriendly REgioN-bAsed protocol, namely, RENA, in this paper. Instead of using temporal information, RENA builds routing tables based on regional movement history, which avoids excessive storage for tracking encounter history. We validate the generality of RENA through time-variant community mobility model with parameters extracted from the MIT WLAN trace, and the vehicular network based on 8 bus routes of the city of Helsinki. The comprehensive simulation results show that RENA is not only storage-friendly but also more efficient than the epidemic routing, the restricted replication protocol SNW and the encounter-based protocol RAPID under various conditions.

An optical network is too costly to act as a broadband access network. On the other hand, a pure wireless ad hoc network with n nodes and total bandwidth of W bits per second cannot provide satisfactory broadband services since the pernode throughput diminishes as the number of users goes large. In this paper, we propose a hybrid wireless network, which is an integrated wireless and optical network, as the broadband access network. Specifically, we assume a hybrid wireless network consisting of n randomly distributed normal nodes, and m regularly placed base stations connected via an optical network. A source node transmits to its destination only with the help of normal nodes, i.e., in the ad hoc mode, if the destination can be reached within L (L /spl geq/ 1) hops from the source. Otherwise, the transmission will be carried out in the infrastructure mode, i.e., with the help of base stations. Two transmission modes share the same bandwidth of W bits/sec. We first study the throughput capacity of such a hybrid wireless network, and observe that the throughput capacity greatly depends on the maximum hop count L and the number of base stations m. We show that the throughput capacity of a hybrid wireless network can scale linearly with n only if m = Omega(n), and when we assign all the bandwidth to the infrastructure mode traffics. We then investigate the delay in hybrid wireless networks. We find that the average packet delay can be maintained as low as Theta(1) even when the per-node throughput capacity is Theta(W).

In this paper, we investigate the throughput capacity in wireless hybrid networks with various network topologies and traffic patterns. Specifically, we consider n randomly distributed nodes, out of which there are n source nodes and nd(0b(0w] times [0, n1-w] (0b-1, nd-1}, min {nw-1/radiclog n, nd-1}} bits/sec is achievable by all nodes. We then investigate the throughput capacity when the base stations are uniformly and randomly placed, and their transmission power is as small as that of the normal nodes. We present that each node can achieve a throughput of max{min{nb-1/log n, nd-1}, min {nw-1/radiclog n, nd-1}} bits/sec. In both settings, we observe that only when d > b and d > w, the maximum achievable throughput can be determined by both the number of base stations and the shape of network area. In all the other cases, the maximum achievable throughput is only constrained by the number of destination nodes. Moreover, the results in these two settings are the same except for the case d > b > w, in which the random placement of base stations will cause a degradation factor of log n on the maximum achievable throughput compared to the regular placement. Finally, we also show that our results actually hold for different power propagation models.

Operating on a frequency band occupying several nonoverlapping channels, IEEE 802.11 is now widely used in Wireless Mesh Networks (WMNs). Many multichannel MAC protocols are proposed to improve the spatial reuse in the network under the assumption that the transmissions on nonoverlapping channels do not interfere with each other. Some joint routing and channel assignment algorithms are also designed to increase the network throughput based on the premise that we can switch between different channels freely. Although simulations show that great improvements on network throughput can be observed in both cases, two fundamental questions remain: 1) Can we really use multiple nonoverlapping channels freely in WMNs? 2) If we can, what will be the cost when we switch channels dynamically and frequently? In this paper, by conducting extensive experiments on our testbed, we attempt to answer these questions. We find that in spite of interference between both overlapping and nonoverlapping channels, we can still use multiple channels in mesh networks under certain conditions but with care. We also show that the channel switching cost is actually very significant in WMNs. We recommend not to switch the channels too frequently when designing the channel assignment algorithms, and those channel assignment algorithms selecting one channel for each packet are not really beneficial.

Transmission power control (TPC) has been extensively used not only to save energy, but also to improve the network throughput in wireless ad hoc networks. Among the existing throughput-oriented TPC protocols, many can achieve significant throughput improvement but have to use multiple channels and/or multiple transceivers, and others just require a single channel and a single transceiver but can only have limited throughput enhancement. In this paper, we propose a new adaptive transmission power control protocol, ATPMAC, which can improve the network throughput significantly using a single channel and a single transceiver. Specifically, by controlling the transmission power, ATPMAC can enable several concurrent transmissions without interfering with each other. Moreover, ATPMAC does not introduce any additional signalling overhead. We show by simulations that ATPMAC can improve the network throughput by up to 136% compared to IEEE 802.11 in a random topology.

In this paper, we propose for multi-rate ad hoc networks a cross-layer design using power control, called MRPC. MRPC consists of two parts. First, we propose a multi-rate power controlled MAC protocol, called MRPC-MAC. By carefully controlling the transmission power, it can enable concurrent transmissions, which is otherwise impossible for the IEEE 802.11 standard. Second, we propose a multi-rate power controlled routing protocol, called MRPC-Routing. Different from traditional routing protocols, MRPC-Routing is not intended to find end-to-end paths, rather, it determines the next hop right before transmitting packets at the MAC layer. In this protocol, it uses the effective transport capacity as the routing metric such that short links with high bandwidth are preferred and more concurrent transmissions can be enabled. Having these coupled power controlled MAC protocol and routing protocol, MRPC can greatly improve the spatial reuse and the network throughput. Simulation results also show MRPC-MAC, MRPC-routing, and especially MRPC, can improve the network throughput significantly.

An admission control algorithm should be properly designed to guarantee the quality of service (QoS) in wireless mesh networks (WMNs). Based on channel business ratio, an admission control algorithm (ACA) is proposed to provide QoS for realtime and non-realtime traffic. For realtime traffic, all the nodes on a route make the admission control decision based on the estimation of available bandwidth. For non-realtime traffic, a rate adaption algorithm is proposed to adjust the sending rates of the source nodes to prevent a network from entering a saturated status. Finally, we demonstrate the effectiveness by simulations in NS-2.

In his seminal work, Jon Kleinberg considers a small-world network model consisting of a k-dimensional lattice augmented with shortcuts. Under the assumption that the probability of a shortcut being present between two nodes u and v decays as a power, d(u,v) -\alpha, of the distance d(u,v) between them, Kleinberg shows that decentralized routing scheme such as greedy geographic routing is efficient if alpha=k and that there is no efficient decentralized routing algorithm if alpha\neq k. The results are extended to a continuum model recently, wherein the nodes are distributed as a homogeneous Poisson point process by Franceschetti and Meester, Draief and Ganesh. In our work, we extend the result further to a more realistic model constructed from a nonhomogeneous Poisson point process, wherein each node is connected to all its neighbors within some fixed radius, as well as possessing random shortcuts to more distant nodes. More importantly, we show that in nonhomogeneous cases, the necessary and sufficient condition for greedy geographic routing to be efficient is that the probability of a shortcut being present from node u to v should be inversely proportional to the number of nodes which are closer to u than v is. We also demonstrate some applications of our results to wireless networks.

Recent studies indicate that by improving the spatial reuse ratio the throughput of 802.11 wireless networks can be improved. In this paper, we study the impact of physical carrier sensing and channel rate on the throughput of 802.11 wireless networks with chain topology. Firstly, this paper propose

There are many different channels denned in the IEEE 802.11 standard. However, the performance of WiFi networks still greatly suffers from the interference between users, even if they are using different channels. In this paper, we conduct some theoretical analysis of the interference between two channels, which is further verified by experiments. We show that there is indeed serious interference between two non- overlapping channels if they are close to each other.

Connectivity is a crucial issue in wireless networks. Gupta and Kumar show that with omnidirectional antennas, the critical transmission range for a wireless network to achieve asymptotic connectivity is O(radiclog n/n) if n nodes are uniformly and independently distributed in a disk of unit area. In this paper, we investigate the connectivity problem when directional antennas are used. We find that there also exists a critical transmission range, which corresponds to a critical transmission power. We show that in the same propagation environment, when directional antennas use the optimal antenna pattern, the critical transmission power could be much smaller than that in networks using omnidirectional antennas. Moreover, to achieve asymptotic connectivity, it is known that each node has to have O(log n) neighbors when using omnidirectional antennas. We show that even using the transmission power level at which each node has only O(1) neighbors when using omnidirectional antennas, we can still achieve the asymptotic connectivity with directional antennas.