This paper focuses on the basic structure of Chinese characters: semantic-phonetic compound characters. This paper takes advantage of this feature of Chinese characters and innovatively proposes a Chinese speech-processing method based on character shape. We use the association between the character shape and pronunciation of Chinese characters to construct a new character stroke-based dataset. We use two neural network structures, RNN and Transformer, to verify our proposed Chinese speech processing method.It is proved through experiments that the method improves the performance of Mandarin ASR(Automatic Speech Recognition) by about 2% and AEC(ASR Error Correction) by about 1%. Theoretically, this method applies to all Chinese speech-processing algorithms based on the attention mechanism.

The utilization of multimodal cues enhances the effectiveness of specific cognitive tasks in audio-visual learning. However, on the one hand, designing a unified model for multimodal learning poses challenges due to the presence of information redundancy and modality noise. On the other hand, existing multimodal models face limitations in handling the modality-missing inference. In this work, we propose a Latent Modality Interaction with mutual information Maximization (LMIM) model architecture for multimodal learning, which effectively integrates multimodal cues by learning essential modality information and reducing the redundant information. We employ a group of latent tokens as pivots to filter out noise and redundancy across different modalities. Simultaneously, mutual information maximization and distribution alignment are utilized to preserve task-related information through multimodal fusion. Furthermore, a random modality masking training strategy is employed to mitigate potential over-reliance on dominant modality. Extensive experiments demonstrate that our model achieves significant improvement over current competitive baselines on two datasets, including UCF51 and Kinetics-Sounds datasets.

Audio-visual speech recognition (AVSR) aims to enhance the robustness of an automatic speech recognition (ASR) systems by incorporating visual information from lip movements, especially in challenging noisy environments. Nevertheless, most current approaches either involve training from scratch or fully finetuning a pre-trained model, both of which incur significant computational costs and are often impractical for large-scale speech foundation models. This gap highlights the need for more efficient methods to leverage visual and acoustic information in AVSR tasks. To address this challenge, we propose AVWhisper, a parameter-efficient model that integrates visual and acoustic representations by injecting visual features from the AV-HuBERT encoder into the pre-trained Whisper model. Our approach leverages the existing attention mechanisms in Whisper to facilitate cross-modal interaction and integrates auxiliary visual information through lightweight adapters based on Low-Rank Adaptation (LoRA) and prompt-based techniques. Furthermore, a two-phase training strategy is adopted to effectively handle cross-domain differences and visual information injection problems respectively. Extensive experiments on the LRS3-TED dataset demonstrate that AVWhisper consistently outperforms state-of-the-art methods across various noise conditions, offering a more efficient and scalable solution for audio-visual speech recognition.

Dataset Distillation (DD) compresses large datasets into compact representations while preserving performance, offering substantial benefits for Federated Learning (FL). However, using distilled datasets introduces new security vulnerabilities, as adversaries can easily embed backdoors into the distilled data. In this paper, we extend existing backdoor attack strategies in DD to the Federated Learning context (DD-FL) and empirically demonstrate their effectiveness. To address these threats, we propose Fed3D, the first defense algorithm specifically designed for DD-FL. Fed3D incorporates a dual-layer defense mechanism, combining intra-client diversity detection with inter-client clustering based on reconstructed feature representations. Comprehensive experiments show that Fed3D effectively reduces attack success rates (<1.5%) while maintaining the performance of distilled datasets (<1.1%). These results establish Fed3D as a robust and promising solution for mitigating backdoor attacks in DD-FL systems.

Dataset Condensation (DC) aims to distill a large original dataset into a compact synthetic counterpart while preserving its utility. Although most research on DC focuses on improving performance, its security aspects remain underexplored. DC is inherently vulnerable to backdoor attacks, where malicious triggers embedded in the original data set become imperceptible, yet remain highly effective after condensation. This paper introduces a novel backdoor attack framework tailored for DC, which iteratively refines triggers by leveraging DC-specific characteristics. Based on distinct alignment strategies, this paper proposes two attack variants, DCA-MIN and DCA-MAX, and incorporates regularization terms to enhance trigger stealthiness. To counter these threats, we propose DC-Judge, a detection defense mechanism that identifies backdoors by reconstructing sample-level features and analyzing intra-class dispersion. Experimental results highlight the superior effectiveness and stealth of our proposed attack compared to existing methods, as well as DC-Judge's robustness in detecting backdoor-contaminated condensed datasets.

Existing multimodal models typically assume the availability of all modalities, leading to significant performance degradation when certain modalities are missing. Recent methods have introduced prompt learning to adapt pretrained models to incomplete data, achieving remarkable performance when the missing cases are consistent during training and inference. However, these methods rely heavily on distribution consistency and fail to compensate for missing modalities, limiting their ability to generalize to unseen missing cases. To address this issue, we propose Memory-Driven Prompt Learning, a framework that adaptively compensates for missing modalities through prompt learning. The compensation strategies are achieved by two types of prompts: generative prompts and shared prompts. Generative prompts retrieve semantically similar samples from a predefined prompt memory that stores modality-specific semantic information, while shared prompts leverage available modalities to provide cross-modal compensation. Extensive experiments demonstrate the effectiveness of the proposed model, achieving significant improvements across diverse missing-modality scenarios, with average performance increasing from 34.76% to 40.40% on MM-IMDb, 62.71% to 77.06% on Food101, and 60.40% to 62.77% on Hateful Memes. The code is available at https://github.com/zhao-yh20/MemPrompt.

CLIP has been widely adopted in affective computing for its strong vision-language representation capabilities. However, it fails to accurately distinguish visually similar yet label-distinct facial expressions. This limitation is rooted in CLIP's encoding paradigm and large-scale contrastive pretraining, which bias the model toward focusing primarily on globally salient visual features and aligning them with broad semantic concepts. Such alignment overlooks subtle facial variations and induces representational shortcuts, where emotionally distinct categories are projected into overlapping regions of the shared semantic space. This semantic entanglement severely compromises the model's ability to preserve emotional separability. We propose LES-CLIP, a Lightweight and Emotion-Sensitive framework that adapts CLIP for precise discrimination of similar emotions. LES-CLIP achieves fine-grained emotional sensitivity using only simple text prompts and facial images. It introduces three novel components: 1) an Emotion-Sensitive Adaptive Mixture-of-Experts, which pre-adapts representations for subtle expression discrimination; 2) a Prompt-Guided Emotion Discrimination module that activates CLIP's visual sensitivity to fine-grained facial cues; and 3) a LES hybrid loss that guides contrastive learning toward accurate emotion-label alignment. Extensive experiments demonstrate that LES-CLIP achieves state-of-the-art performance, reaching 70.18% on the 8-class AffectNet dataset. Moreover, it converges faster and requires significantly fewer parameters.

Dataset condensation aims to synthesize compact yet informative datasets that retain the training efficacy of full-scale data, offering substantial gains in efficiency. Recent studies reveal that the condensation process can be vulnerable to backdoor attacks, where malicious triggers are injected into the condensation dataset, manipulating model behavior during inference. While prior approaches have made progress in balancing attack success rate and clean test accuracy, they often fall short in preserving stealthiness, especially in concealing the visual artifacts of condensed data or the perturbations introduced during inference. To address this challenge, we introduce \textsc{Sneakdoor}, which enhances stealthiness without compromising attack effectiveness. \textsc{Sneakdoor} exploits the inherent vulnerability of class decision boundaries and incorporates a generative module that constructs input-aware triggers aligned with local feature geometry, thereby minimizing detectability. This joint design enables the attack to remain imperceptible to both human inspection and statistical detection. Extensive experiments across multiple datasets demonstrate that \textsc{Sneakdoor} achieves a compelling balance among attack success rate, clean test accuracy, and stealthiness, substantially improving the invisibility of both the synthetic data and triggered samples while maintaining high attack efficacy. The code is available at \url{https://github.com/XJTU-AI-Lab/SneakDoor}.

Most Personalized Federated Learning (PFL) algorithms merge the model parameters of each client with other (similar or generic) model parameters to optimize the personalized model (PM). However, the merged model parameters in these algorithms may fit low relevance data, thereby limiting the performance of PM. In this paper, we generate similar data for each client through the collaboration of a generic model (GM) on the server, rather than merging model parameters. To train a generator capable of generating data for all classes on the server without real data, we employ the GM as the discriminator in adversarial training with the generator. Additionally, we introduce a similarity assessment metric, which allows for the assessment of the similarity between local data and data from other classes. Nevertheless, the presence of non-IID data among clients can weaken the performance of the GM, consequently impacting the training of the generator and similarity assessment. To address this issue, we design a directive mechanism so that GM can be optimized during adversarial training without the need for additional training. The experimental results validate the superiority of our algorithm over state-of-the-art algorithms in terms of accuracy, loss, and convergence speed.

Falls pose a significant risk to the health and wellbeing of older adults, driving the development of various fall detection systems. Existing solutions have explored wearable and vision sensors, while non-invasive RF-based approaches have raised a growing interest due to their convenience and privacy considerations. Despite major advancements in RF-based passive estimation, current approaches still face challenges in handling complex real-world scenarios. They often lack the ability to generalize to new domains (i.e., people, position, environment), and struggle to accurately detect and localize a fallen person in the presence of unknown activities from nearby objects (e.g., pet animal and robot vacuum cleaner) or persons. To address these challenges, we present mm-Fall, a novel mmWave-based non-invasive fall detection system that utilizes Range-Angle (RA) energy maps to separate and localize multiple moving targets, and further accurately estimate their states. Unlike previous approaches, mm-Fall is capable of working with new domains and effectively distinguishing falls from non-fall motions that may appear similar. Additionally, it performs well in challenging conditions, such as poor lighting and occluded scenarios. Our design of mm-Fall is evaluated in 13 environments with over 16 individuals performing 24+ types of motions. The results demonstrate an impressive average recall of 0.969 and precision of 0.996 in detecting falls, whether involving single or multiple moving targets simultaneously. The code and dataset will be made publicly available.

Conventional Federated Domain Adaptation (FDA) approaches usually demand an abundance of assumptions, which makes them significantly less feasible for real-world situations and introduces security hazards. This paper relaxes the assumptions from previous FDAs and studies a more practical scenario named Universal Federated Domain Adaptation (UFDA). It only requires the black-box model and the label set information of each source domain, while the label sets of different source domains could be inconsistent, and the target-domain label set is totally blind. Towards a more effective solution for our newly proposed UFDA scenario, we propose a corresponding methodology called Hot-Learning with Contrastive Label Disambiguation (HCLD). It particularly tackles UFDA's domain shifts and category gaps problems by using one-hot outputs from the black-box models of various source domains. Moreover, to better distinguish the shared and unknown classes, we further present a cluster-level strategy named Mutual-Voting Decision (MVD) to extract robust consensus knowledge across peer classes from both source and target domains. Extensive experiments on three benchmark datasets demonstrate that our method achieves comparable performance for our UFDA scenario with much fewer assumptions, compared to previous methodologies with comprehensive additional assumptions.

In multi-modal emotion recognition, previous studies focus on obtaining more distinguishable unimodal features and expanding complementary information across modalities. However, a considerable amount of latent emotional information is neglected. It leads to insufficient intra-modal representations and a one-sided perspective on inter-modal relationship learning. To address these challenges, we propose a novel framework named MRFER, which explores strategies to reduce the loss of emotional information. It models robust unimodal features through a multi-path feature extractor and captures more comprehensive inter-modal relationships through a text-guided dual attention fusion module. Systematic evaluation covers generalization and overall performance, showcasing MRFER’s advancement beyond existing state-of-the-art approaches.

Parameter-Efficient Fine Tuning (PEFT) has been demonstrated to be effective and efficient for transferring foundation models to downstream tasks. Transferring pretrained uni-modal models to multi-modal downstream tasks helps alleviate substantial computational costs for retraining multi-modal models. However, existing approaches primarily focus on multi-modal fusion, while neglecting the modal-specific fine-tuning, which is also crucial for multi-modal tasks. To this end, we propose parameter-efficient Collaborative Prompt Learning (CoPL) to fine-tune both uni-modal and multi-modal features. Specifically, the collaborative prompts consist of modal-specific prompts and modal-interaction prompts. The modal-specific prompts are tailored for fine-tuning each modality, while the modal-interaction prompts are customized to explore inter-modality association. Furthermore, prompt bank-based mutual coupling is introduced to extract instance-level features, further enhancing the model's generalization ability. Extensive experimental results demonstrate that our approach achieves comparable or higher performance on various audio-visual downstream tasks while utilizing approximately 1% extra trainable parameters.

This paper introduces a pioneering framework for music representation learning, which harnesses knowledge graph embeddings to enrich genre classification. Leveraging metadata from publicly available datasets like FMA and OpenMIC-2018, the constructed knowledge graph delineates intricate relationships among genres, artists, and instruments, offering valuable insights for genre representation. Within this framework, we propose two models tailored for distinct genre classification scenarios: fixed-set genre classification and open-set genre classification. These models exploit the knowledge graph to unveil correlations among different genres and integrate this knowledge into the audio representation. Notably, our approach is the first to merge audio data with high-level knowledge for music genre classification. Experimental results demonstrate that our proposed methods outperform state-of-the-art approaches, achieving an average genre classification accuracy of 68.07% on the FMA-medium dataset and 42.4% for open-set classification on the FMA-large dataset.

Recent defense approaches against targeted model poisoning attacks aim to prevent specific prediction failures in federated learning (FL). However, these defenses remain susceptible to targeted collusion attacks, particularly under conditions of high proportions of malicious clients and attack density. To address these vulnerabilities, we propose RoseAgg, which dynamically identifies a plausible clean ingredient from local updates and leverages it to constrain the influence of poisoned updates. Firstly, RoseAgg recognizes and confines common characteristics found in poisoned updates, such as scaled-up magnitudes or similar directional contributions. Furthermore, RoseAgg dynamically extracts a plausible clean ingredient using a dimension-reduction method. This clean ingredient becomes the foundation for the server to bootstrap credit scores for each local update, ensuring the dominance of benign updates over poisoned ones. Ultimately, the server computes a weighted average of local updates based on credit scores, generating a global update for refining the global model. Comprehensive evaluations on four benchmark datasets showcase RoseAgg’s effectiveness against seven advanced attacks. The code is available athttps://github.com/SleepedCat/RoseAgg.

RFID shows great potentials to build useful sensing applications. However, current RFID sensing can obtain mainly a single-dimensional sensing measurement from each reader-to-tag query, such as phase, RSS, etc. This is sufficient to fulfill the designs that are bound to the tag’s movement, e.g., the localization of tags. However, it imposes inevitable uncertainty on many sensing tasks relying on the features extracted from the RFID signals. These traditional sensing measurements limit the fidelity of RFID sensing fundamentally and prevent its broader usage in more sophisticated sensing scenarios. This paper presents RF-Wise to push the limit of RFID-based sensing, motivated by an insightful observation to customize RFID signals. RF-Wise can enrich the existing single-dimensional feature measure to a channel state information (CSI)-like measure with up to 150-dimensional samples across different frequencies concurrently. More importantly, RF-Wise is a software solution atop the standard EPC Gen2 protocol without using any extra hardware. It requires only one tag for sensing and works within the ISM band. RF-Wise, so far as we know, is the first system of such a kind. Extensive experiments show that RF-Wise does not impact underlying RFID communications, while by using the features extracted by RF-Wise, applications’ sensing performance can be improved remarkably. The source codes of RF-Wise are available at https://cui-zhao.github.io/RF-WISE/.

In this paper, we propose KGenre, a knowledge-embedded music representation learning framework for improved genre classification. We construct the knowledge graph from the metadata in the open-source FMA-medium and OpenMIC-2018 datasets, with no extra information/effort required. KGenre then mines the correlation between different genres from the knowledge graph and embeds such correlation in audio representation. To our knowledge, KGenre is the first method fusing the audio with high-level knowledge for music genre classification. Experimental results demonstrate the embedded knowledge can effectively enhance the audio feature representation, and the genre classification performance surpasses the state-of-the-art methods.

The detection of human objects can be crucial for various real-world applications, such as surveillance and autonomous driving. However, traditional vision-based approaches suffer from limitations such as low lighting conditions, occlusions, and privacy concerns. To overcome these limitations, we propose a novel automatic object detection system, called mmYodar, which utilizes millimeter-wave (mmWave) radar signals. Our system collects mmWave signals and calculates a 3D point cloud, which is transformed into a radar image for easier visualization and analysis. To improve the system's human profiling capability, we expand the corresponding points in the image with color based on the radar angle resolution. Then, a designed deep mutual learning framework is employed to detect human objects from the expanded image. Experimental results show that mmYodar achieves nearly real-time detection with an average precision of 90.35% in various scenarios, including indoor and outdoor environments, various lighting conditions, and in the presence of occlusions. These results demonstrate the effectiveness of using mmWave radar signals for reliable and accurate human object detection. Our code and dataset are available at https:llgithub.comlbrave20005lmmYodar.

Radio frequency identification (RFID)-assisted management systems have been widely applied in warehousing, logistics, retailing, etc. In these scenarios, RFID-aided applications, e.g., object tracking and human behavior sensing, rely on a high-efficiency tag reading to realize accurate analyses and timely responses. However, serious tag collisions in those large-scale RFID systems will inevitably lead to significant decreases in the tag reading rates. To meet the strict timeliness requirements of those practical applications, we aim to treat the individual reading rate for each item tag differently and focus more attention on those user-interactive ones. However, due to unpredictable user behaviors, it is impractical to infer the user-interactive tags in advance. In addition, keeping focusing on them for continuous monitoring despite user movements and multipath-prevalent environments is also challenging. To solve these problems, we propose Spotlight, the first concurrent rate-adaptive reading system in passive RFIDs. Spotlight screens the ID-agnostic user-interactive tags by proposing a multichannel feature for narrow-band RFID systems without any hardware or protocol modification and achieves rate-adaptive reading by implementing real-time MU-MIMO beamforming. Substantial experiments with 1000+ COTS RFID tags exhibit that Spotlight outperforms the commercial reader by$2.7\times $and the SDR-based reader by$6.12\times $. In addition, Spotlight first proposes the online parallel decoding method to realize concurrency among multiple users, which breaks the commercial protocol’s throughput ceiling (37%) and achieves up to 59% throughputs.

Federated domain adaptation (FDA) is an effective method for performing learning tasks over distributed networks, which well improves data privacy and portability in unsupervised multi-source domain adaptation (UMDA) tasks. Despite the impressive gains achieved, two common limitations exist in current FDA works. First, most previous studies require access to the model parameters or gradient details of each source party. However, the raw source data can be reconstructed from the model gradients or parameters, which may leak individual information. Second, these works assume that different parties share an identical network architecture, which is impractical and not desirable for low- or high-resource target users. To address these issues, in this work, we propose a more practical UMDA setting, called Federated Multi-source Domain Adaptation on Black-box Models (B2FDA), where all data are stored locally and only the input-output interface of the source model is available. To tackle B2FDA, we propose an effective method, termed Co2-Learning with Multi-Domain Attention (Co-MDA). Experiments on multiple benchmark datasets demonstrate the effectiveness of our proposed method. Notably, Co-MDA performs comparably with traditional UMDA methods where the source data or the trained model are fully available.

Online Event-based social networks (EBSNs), such as Meetup and Whova, which provide platforms for users to publish, arrange and participate in events, have become increasingly popular. A major challenge for managing EBSNs is to generate the most satisfactory event arrangement, i.e. events are scheduled at the reasonable time to attract maximum number of participants. Existing approaches usually focus on assigning a set of events organized by the same group to time intervals, but ignore the competitive relationships among different event organizers, which will lead to event time allocations unacceptable to organizers. Thus, a more intelligent EBSNs platform that allocates social events properly in a global view (i.e. the perspective of platform) is desired. In this paper, we first formally define the problem of Platform-oriented Event Time Allocation (PETA), which contains two parts: the prediction of event feasible time period and the event time allocation. Unfortunately, we find that the PETA problem is NP-hard due to the global conflict constraints on events. Thus, we propose design a greedy algorithm and two approximation algorithms to solve the PETA problem. Finally, we conduct extensive experiments on both real and synthetic datasets to test the effectiveness and efficiency of the proposed algorithms.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring ‘multi-path-free’ signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost – it requires no extra device. We implement CPIX and study three major RFID sensing applications: tag localization, device calibration and human behavior sensing. CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work. It also significantly improves the quality of device calibration and human behaviour sensing.

Online Event-based social networks (EBSNs), such as Meetup and Whova, which provide platforms for users to publish, arrange and participate in events, have become increasingly popular. A major challenge for managing EBSNs is to generate the most satisfactory event arrangement. Existing approaches usually focus on assigning a set of events organized to time intervals, but ignore the competitive relationships among different event organizers, which will lead to event time allocations unacceptable to organizers. Thus, a more intelligent EBSNs platform that allocates social events properly in a global view (i.e. the perspective of platform) is desired. In this work, we first formally define the problem of Platform-oriented Event Time Allocation (PETA), which contains two parts: the prediction of event feasible time period and the event time allocation. We propose a method to calculate event feasible time period based on event time prediction, and design a greedy algorithm and two approximation algorithms to solve the PETA problem. Extensive experiments on both real and synthetic datasets demonstrate that the proposed algorithms have high effectiveness and efficiency.

RFID shows great potentials to build useful sensing applications. However, current RFID sensing can obtain mainly a single-dimensional sensing measurement from each reader-to-tag query, such as phase, RSS, etc. This is sufficient to fulfill the designs that are bounded to the tag’s own movement, e.g., the localization of tags. However, it imposes inevitable uncertainty to many sensing tasks relying on the features extracted from the RFID signals, which limits the fidelity of RFID sensing fundamentally and prevents its broader usage in more sophisticated sensing scenarios. This paper presents RF-Wise to push the limit of the RFID-based sensing, motivated by an insightful observation to customize RFID signals. RF-Wise can enrich the existing single-dimensional feature measure to a channel state information (CSI)-like measure with up to 150 dimensional samples across different frequencies concurrently. More importantly, RF-Wise is a software solution atop the standard EPC Gen2 protocol without using any extra hardware, requires only one tag for sensing and works within the ISM band. RF-Wise, so far as we know, is the first system of such a kind. Extensive experiments show that RF-Wise does not impact underlying RFID communications, while by using the features extracted by RF-Wise, applications’ sensing performance can be improved remarkably. The source codes of RF-Wise are available at https://cui-zhao.github.io/RF-WISE/.

We study a new problem, refined localization, in this article. Refined localization calculates the location of an object in high precision, given that the object is in a relatively small region such as the surface of a table. Refined localization is useful in many cyber–physical systems such as industrial autonomous robots. Existing vision-based approaches suffer from several disadvantages, including good lighting conditions, line of sight, prelearning process, and high computation overhead. Also, vision-based approaches cannot differentiate objects with similar colors and shapes. This article presents a new refined localization system, called Trio, which uses passive radio frequency identification (RFID) tags for low cost and easy deployment. Trio utilizes RF interference for tag localization by modeling the equivalent circuits of coupled tags. We implement our prototype using commercial off-the-shelf RFID reader and tags. Extensive experiment results demonstrate that Trio effectively achieves high accuracy of refined localization, i.e., < 1 cm errors for several types of main stream tags.

Driven by the Internet of Things (IoT), many device-free crowd density estimation techniques can roughly estimate the crowd density based on the relationship between the dynamic crowd and the variation of wireless signals. However, they cannot distinguish the path information of different persons in a fine-grained manner. In this article, we propose Wisual, a channel state information (CSI)-based device-free crowd density estimation framework and can visualize the distribution of people. The major challenge of Wisual is how to extract proper quantifiable indexes to distinguish the path information of multiple targets and maximize the resolution of crowd density estimation. To address this challenge, Wisual first presents a method for estimating the frequency of the CSI propagation path (FoC) for the moving persons and constructs a joint multifeature parameter (JMFP) spectrum matrix with the other two parameters. Then the multitarget spectrum matrix is put into a proposed deep-learning model called CSI stream 3-D convolutional neural networks (CS-3DCNNs) for implementing crowd density estimation and the target path information differentiation. Finally, Wi-Fi imaging is implemented based on the 2-D-MUSIC algorithm, which shows the approximate distribution situation of indoor persons through the spectrograms. The experimental results in typical real-world scenes demonstrate that Wisual can forecast the crowd density with 98% precision and accurately display the frequency spectra of moving persons. Besides, the results also prove the superior effectiveness, scalability, and generalizability of the proposed framework.

Traffic speed inference enables many applications that are essential for everyday life. Most traffic-prediction approaches assume that a constant number of sensors are deployed on the roads, whether they are either stationary loop detectors or vehicles equipped with Global Positioning System (GPS) tracking devices. The static nature of those fixtures limits their ability to adapt to scenarios that are more dynamic. Rather than relying on several fixed sensors to detect changes and infer traffic, we use crowdsourcing to judiciously select individuals and then make predictions. Our solution consists of three core components: dynamic seed selection, regional cluster building and ensemble traffic prediction. In the first phase, we employ Efficient Transition Probability (ETP) to evaluate candidate seed sets. Road clusters are then formed using hierarchical clustering that is tweaked by a dynamic programming technique. This method assesses the eccentricity of every cluster to bond every road within each cluster more closely. Subsequently, we develop an ensemble-learning strategy in conjunction with Lasso regression to forecast traffic. The strength of our ensemble approach is its ability to manage absent seeds, a condition that has never been investigated, to our knowledge. Substantial experimental evaluation indicates that our claim of dynamic updates is valid and effective. Our solution outperforms the state-of-the-art techniques by a wide margin, in terms of prediction accuracy.

As the ultra high frequency (UHF) passive radio frequency identification (RFID) technology becomes increasingly deployed, it faces an array of new security attacks. In this paper, we consider a type of attack in which a malicious RFID reader could arbitrarily access the tags, e.g., retrieve or modify IDs or other data in the memory, via standard commands. To deal with this type of attack, we propose a physical-layer tag protection framework, namely Arbitrator2.0, that involves two operating mode, i.e., one is to passively listen on RF channels and identify unauthorized readers, the other is working as normal reader to access tag information but resilient to one-antenna eavesdropper. Our solution does not need to modify RFID tags or the underlying communication standards. In this study, we have implemented a prototype Arbitrator2.0 over the universal software radio peripheral (USRP) platform, and conducted extensive experiments to evaluate its performance. The results show that Arbitrator2.0 can effectively diminish the unauthorized access attacks and prevent eavesdropping.

With the rapid development of the Internet of Things (IoT) technology, the position information of indoor people has become an indispensable factor in most fields. Most existing indoor positioning schemes require people to keep moving to detect significant variance of the signal as the location feature. Hence, this paper proposes a passive indoor positioning system based on commodity Wi-Fi called Wisite, which can implement indoor multipath signal measurement and static person positioning modeling. The biggest challenge is how to detect the dynamic features in the reflection path of the static person to achieve target path matching. To address this issue, Wisite proposes a MUSIC expectation-maximization (MEM) joint parameter estimation algorithm to estimate and enhance the indoor multipath parameters. Then, a dynamic path matching model based on signal change enhancement (SCE) is proposed to enhance the signal changes caused by human activities, which can amplify the weak signal changes introduced by human respiration when a person is in a static state. Finally, the multipath geometric positioning model is used to calculate the person's position. We implement Wisite using commercial off-the-shelf (COTS) IEEE 802.11n devices and evaluate its performance via extensive experiments in typical real-world scenes. The results show that Wisite outperforms the comparison approaches in estimating accuracy and effectiveness with the average indoor positioning error is less than 0.65cm.

Security over mobile Internet-of-Things (IoT) devices is critical due to the open nature of distributed wireless communication. To efficiently establish a secure connection between two communication parties, a fast mobile key extraction protocol, KEEP, is proposed. KEEP fastly generates similar bit sequences from two communication parties’ measurements of channel-state information (CSI) of different subcarriers. Then, a distributed “verification-recombination” mechanism is introduced to generate the same encryption key from bit sequences without the public-key authentication, digital signature, or key distribution center of the other party. We implemented real-world experiments using commercial off-the-shelf 802.11n devices to evaluate the performance of KEEP in various scenarios. Theoretical analysis and experimental verification show that KEEP is more secure, effective, and reliable than the state-of-the-art methods.

Presents corrections to the acknowledgement section for the above named article.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring `multi-path-free' signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost - it requires no extra device. We implement CPIX and study two major RFID sensing applications: tag localization and human behavior sensing. CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work. It also significantly improves the quality of human behaviour sensing.

Passive Radio Frequency Identification (RFID) tags have been widely applied in many applications, such as logistics, retailing, and warehousing. In many situations, the order of objects is more important than their absolute locations. However, state-of-art ordering methods need a continuing movement of tags and readers, which limit the application domain and scalability. In this paper, we propose a 2-dimension ordering approach for passive tags that requires no device movement. Instead, our method utilizes signal changes caused by arbitrary movement of human beings around tags, who carry no device for horizontal dimension ordering. Hence, our method is called Human Movement based Ordering (HMO). The basic idea of HMO is that when people pass between the reader antenna and tags, the received signal strength will change. By observing the time-series RSS changes of tags, HMO can obtain the order of tags along with a specific horizontal direction. For vertical dimension, we employ a linear programming method that is tolerant of tiny errors in practice. We implement HMO with commodity off-the-shelf RFID devices. The experimental results show that HMO can achieve up to 88.71 and 90.86 percent average accuracies in the signal-and multi-person cases, respectively.

We provide the first solution to an important question, “how a physical-layer authentication method can defend against signal replay attacks”. It was believed that if an attacker can replay the exact same reply signal of a legitimate authentication object (such as an RFID tag), any physical-layer authentication method will fail. This paper presents Hu-Fu, the first physical layer RFID authentication protocol that is resilient to the major attacks including tag counterfeiting, signal replay, signal compensation, and brute-force feature reply. Hu-Fu is built on two fundamental ideas, namely inductive coupling of two tags and signal randomization. Hu-Fu does not require any hardware or protocol modification on COTS passive tags and can be implemented with COTS devices. We implement a prototype of Hu-Fu and demonstrate that it is accurate and robust to device diversity and environmental changes, including locations, distance, and temperature. Hu-Fu provides a new direction of battery-free/low-power device authentication that enables numerous IoT applications.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring `multipath-free' signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost - it requires no extra device. We implement CPIX and evaluate its effectiveness on improving the performance on tag localization. The results show that CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work.

Smart environments and security systems require automatic detection of human behaviors including approaching to or departing from an object. Existing human motion detection systems usually require human beings to carry special devices, which limits their applications. In this paper, we present a system called APID to detect hand approaching behaviors by analyzing backscatter communication signals from a passive RFID tag on the object. APID does not require human beings to carry any device. The idea is based on the influence of hand movements to the vibration of backscattered tag signals. APID is compatible with commodity off-the-shelf devices and the EPCglobal Class-1 Generation-2 protocol. In APID, a commercial RFID reader continuously queries tags through emitting RF signals and tags simply respond with their IDs. A USRP monitor passively analyzes the communication signals and reports the approach and departure behaviors. We have implemented the APID system for both single-object and multi-object scenarios. Extensive evaluations demonstrate that APID can achieve high detection accuracy in both scenarios.

In this paper, we propose a system called R# to estimate the number of human objects using passive RFID tags but without attaching anything to human objects. The idea is based on our observation that the more human objects are present, the higher the variation in the RSS values of the tag backscattered RF signals. Thus, based on the received RF signals, the reader can estimate the number of human objects. R# includes an RFID reader and some (say 20) passive tags, which are deployed in the region that we want to monitor the number of human objects, such as the region in front of a supermarket shelf. The RFID reader periodically emits RF signals to identify all tags and the tags simply respond with their IDs via EPCglobal Class 1 Generation 2 protocol. We implemented R# using commercial Impinj H47 passive RFID tags and Impinj reader model R420. We conducted experiments in a simulated picking aisle area of the supermarket environment. The experimental results show that R# can achieve high estimation accuracy (more than 90 percent with up to ten human objects).

Smart packaging adds sensing abilities to traditional packages. This paper investigates the possibility of using RF signals to test the internal status of packages and detect abnormal internal changes. Towards this goal, we design and implement a nondestructive package testing and verification system using commodity passive RFID systems, called Echoscope. Echoscope extracts unique features from the backscatter signals penetrating the internal space of a package and compares them with the previously collected features during the check-in phase. The use of backscatter signals guarantees that there is no difference in RF sources and the features reflecting the internal status will not be affected. Compared to other nondestructive testing methods such as X-ray and ultrasound, Echoscope is much cheaper and provides ubiquitous usage. Our experiments in practical environments show that Echoscope can achieve very high accuracy and is very sensitive to various types abnormal changes.

We provide the first solution to an important question, "how a physical-layer authentication method can defend against signal replay attacks''. It was believed that if an attacker can replay the exact same reply signal of a legitimate authentication object (such as an RFID tag), any physical-layer authentication method will fail. This paper presents Hu-Fu, the first physical layer RFID authentication protocol that is resilient to the major attacks including tag counterfeiting, signal replay, signal compensation, and brute-force feature reply. Hu-Fu is built on two fundamental ideas, namely inductive coupling of two tags and signal randomization. Hu-Fu does not require any hardware or protocol modification on COTS passive tags and can be implemented with COTS devices. We implement a prototype of Hu-Fu and demonstrate that it is accurate and robust to device diversity and environmental changes, including locations, distance, and temperature. Hu-Fu provides a new direction of battery-free/low-power device authentication that enables numerous IoT applications.

Object recognition is widely used in vision computing for various applications. Traditional CPU and application specific integrated circuit for vision computing cannot provide high performance and enough flexibility, which limit the use of vision systems. In this paper, a hierarchical and parallel pipelined heterogeneous chip for object recognition is proposed to achieve high flexibility, high performance, and area efficiency. In addition, a reformulation of 3D position estimation is proposed. The method uses single precision to achieve the short computing time and accuracy requirement. The hardware resource is small. Application-specific components, such as connected component information extractor and information extraction accelerator, are designed for high performance. Reconfiguration processors and application-specific instruction set processor are introduced to improve flexibility. These components are connected to hierarchical parallel buses. The chip is fabricated in 180-nm CMOS technology and occupies 72.25 mm2with 1.09M bits on-chip memory. It delivers 204 GOPS + 665M FLOPS operations. The results show that this hierarchical and parallel pipelined heterogeneous chip is suitable for embedded vision systems.

An important function of smart environments is the ubiquitous access of computing devices. In public areas such as hospitals, libraries, and airports, people may want to interact with nearby computing systems to get information, such as directions to a hospital room, locations of books, and flight departure/arrival information. Touch screen based displays and kiosks, which are commonly used today, may incur extra hardware cost or even possible germ and bacteria infection. This work provides a new solution: users can make queries and inputs by performing in-air handwriting to an array of passive RFID tags, named RFIPad. This input method does not require human hands to carry any device and hence is convenient for applications in public areas. Besides the mobile and contactless property, this system is a cost-efficient extension to current RFID systems: an existing reader can monitor multiple RFIPads while performing its regular applications such as identification and tracking. We implement a prototype of RFIPad using commercial off-the-shelf UHF RFID devices. Experimental results show that RFIPad achieves >91% accuracy in recognizing basic touch-screen operations and English letters.

Passive Radio Frequency Identification (RFID) tags have been widely applied in many applications, such as logistics, retailing, and warehousing. In many situations the relative locations of objects are more important than their absolute locations. However, state-of-art relative localization methods need continuing movement of tags and readers, which limit the application domain and scalability. In this paper, we propose a relative localization approach for passive tags that requires no device movement. Instead, our method utilizes signal changes caused by arbitrary movement of human beings around tags, who carry no device. Hence our method is called Human Movement based Relative Localization (HMRL). The basic idea of HMRL is that when people pass between reader antenna and tags, the received signal strength will change. By observing the time-series RSS changes of tags, HMRL can obtain the order of tags along a specific horizontal direction. HMRL can also get the order of tags in a vertical direction using hyperbolic positioning. We implement HMRL with commodity off-the-shelf RFID devices. The experimental results show that HMRL achieves high accuracy for relative localization of passive tags.

With rapid development and popularity, smartphones have been of importance in our daily life. Despite of its convenience in communication and computing, smartphones also lead potential security threats to users. Existing methods on smartphones for protecting user's privacy mainly depend on password or fingerprint based authentication. Most smartphone passwords are very simple and easy to guess or crack, and fingerprinting requires extra hardware and hence increases the price of smartphones. In this paper, we present a smartphone-based identity authentication method based on user's lip motion characteristics, called SALM, which can be used as an additional authentication with password. SALM extracts the feature of lip movements as the authentication token, which is unique for each user. We implement SALM using off-the-shelf smartphones and evaluate its performance via extensive experiments. The results show that the overall accuracy of user authentication using SALM (without password) is higher than 96%.

Fog cover is generally present in outdoor scenes, which limits the potential for efficient information extraction from images. In this paper, the goal of the developed algorithm is to obtain an optimal transmission map as well as to remove hazes from a single input image. To solve the problem, we meticulously analyze the optical model and recast the initial transmission map under an additional boundary prior. For better preservation of the results, the difference-structure-preservation dictionary could be learned, such that the local consistency features of the transmission map could be well preserved after coefficient shrinkage. Experimental results show that the method preserves the natural appearance of the image.

Regular free-weight exercise helps to strengthen natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes for activity sensing, recognition, and counting, etc.. However, none of them have incorporated three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system provides an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1) since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other. 2) Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of the activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities, and provide valuable feedbacks for activity alignment.

Haze removal is useful in computational photography and computer vision applications. Although many haze removal algorithms have been proposed, their computational efficiency requires improvement. A real-time haze removal method is presented in this paper. The method is based on the concept of a dark channel prior. To enhance the haze removal performance, an approximate method to estimate the atmospheric light and transmission is employed. For embedded system applications, a hardware architecture to perform real-time haze removal is proposed. The hardware can achieve 116 MHz on Stratix FPGA. The simulation results indicate that the hardware is highly efficient and performs well. It obtains good image recovery results and satisfies the real-time requirement even for large images.

Device-to-device communication is important to emerging mobile applications such as Internet of Things and mobile social networks. Authentication and key agreement among multiple legitimate devices is the important first step to build a secure communication channel. Existing solutions put the devices into physical proximity and use the common radio environment as a proof of identities and the common secret to agree on a same key. However they experience very slow secret bit generation rate and high errors, requiring several minutes to build a 256-bit key. In this work, we design and implement an authentication and key agreement protocol for mobile devices, called The Dancing Signals (TDS), being extremely fast and error-free. TDS uses channel state information (CSI) as the common secret among legitimate devices. It guarantees that only devices in a close physical proximity can agree on a key and any device outside a certain distance gets nothing about the key. Compared with existing solutions, TDS is very fast and robust, supports group key agreement, and can effectively defend against predictable channel attacks. We implement TDS using commodity off-the-shelf 802.11n devices and evaluate its performance via extensive experiments. Results show that TDS only takes a couple of seconds to make devices agree on a 256-bit secret key with high entropy.

Smart environments and security systems require automatic detection of human behaviors including approaching to or departing from an object. Existing human motion detection systems usually require human beings to carry special devices, which limits their applications. In this paper, we present a system called APID to detect arm reaching by analyzing backscatter communication signals from a passive RFID tag on the object. APID does not require human beings to carry any device. The idea is based on the influence of human movements to the vibration of backscattered tag signals. APID is compatible with commodity off-the-shelf devices and the EPCglobal Class-1 Generation-2 protocol. In APID an commercial RFID reader continuously queries tags through emitting RF signals and tags simply respond with their IDs. A USRP monitor passively analyzes the communication signals and reports the approach and departure behaviors. We have implemented the APID system for both single-object and multi-object scenarios in both horizontal and vertical deployment modes. The experimental results show that APID can achieve high detection accuracy.

Smart packaging adds sensing abilities to traditional packages. This paper investigates the possibility of using RF signals to test the internal status of packages and detect abnormal internal changes. Towards this goal, we design and implement a nondestructive package testing and verification system using commodity passive RFID systems, called Echoscope. Echoscope extracts unique features from the backscatter signals penetrating the internal space of a package and compares them with the previously collected features during the check-in phase. The use of backscatter signals guarantees that there is no difference in RF sources and the features reflecting the internal status will not be affected. Compared to other nondestructive testing methods such as X-ray and ultrasound, Echoscope is much cheaper and provides ubiquitous usage. Our experiments in practical environments show that Echoscope can achieve very high accuracy and is very sensitive to various types abnormal changes.

Identifying the object that attracts human visual attention is an essential function for automatic services in smart environments. However, existing solutions can compute the gaze direction without providing the distance to the target. In addition, most of them rely on special devices or infrastructure support. This paper explores the possibility of using a smartphone to detect the visual attention of a user. By applying the proposed VADS system, acquiring the location of the intended object only requires one simple action: gazing at the intended object and holding up the smartphone so that the object as well as user's face can be simultaneously captured by the front and rear cameras. We extend the current advances of computer vision to develop efficient algorithms to obtain the distance between the camera and user, the user's gaze direction, and the object's direction from camera. The object's location can then be computed by solving a trigonometric problem. VADS has been prototyped on commercial off-the-shelf (COTS) devices. Extensive evaluation results show that VADS achieves low error (about 1.5° in angle and 0.15m in distance for objects within 12m) as well as short latency. We believe that VADS enables a large variety of applications in smart environments.

Multi-user MIMO (MU-MIMO) is proposed in 802.11ac to achieve more than 3x faster than 802.11n. In the real world no-one gets close to theoretical speeds. The primary reason for this anomaly are the various overheads of channel access and channel state information (CSI) feedback. In order to achieve concurrent data transmission, (CSI) feedback from users is required. However, this overhead can easily overwhelm the actual channel time spent on data transmission in large-scale network. Moreover, due to spontaneous uplink traffic, which makes the problem even more challenging.

Activity recognition plays an important role in human-computer interactions. Recently, Channel State Information (CSI), known as a fine-grained information capturing the properties of WiFi signal propagation, has been widely used for activity recognition in a device-free pattern. Since CSI is much sensitive to ambient changes, CSI can be used as fingerprints as human activities. However, existing approaches require tremendous overhead in the model training and suffer from failures due to environmental interferences. In this paper, we propose HAR, a CSI based human activity recognition system. HAR investigates the CSI intra-correlation structure (termed as topics) of different human activities. We leverage an unsupervised machine learning method, namely topic model, to extract action characters. Compared to prior works, HAR only requests minor manual intervention, significantly reducing manpower costs in the model training. We implement HAR using commodity WiFi devices to evaluate its performance under different environment settings. The results show that the extracted features are stable to different devices and volunteers, facilitating HAR to achieving an average matching accuracy, i.e., > 90%.

High-quality and cost-efficient image upscaling design is very important for many real-time video processing applications, especially when the display panel resolution reaches ultrahigh definition. Compared with New Edge-Directed Interpolation (NEDI) based implicit edge directional upscaling, explicit methods require less computational resource and more easily reach real-time performance, especially when the required image definition and upscaling ratio are very high. Nevertheless, the investigation of applications of explicit methods in video processing systems remains largely missing arguably because it is commonly believed that explicit edge-directed interpolation tends to introduce unexpected artifacts because of inaccurate detection and hence its image quality is relatively poor. This paper proposes an explicit edge-directed adaptive interpolation method that leverages more sophisticated edge detection and orientation estimation algorithms to avoid misinterpolation, thereby providing similar or even better image quality than those with implicit methods. Targeting the real-time 4K video display system, the proposed edge-directed image upscaling algorithm is further implemented with an efficient very-large-scale integration (VLSI) architecture. The experimental results demonstrate that the proposed interpolation algorithm outperforms previous explicit and implicit edge-directed methods in both objective and subjective tests. The presented VLSI implementation further demonstrates that the maximum output video sequence of the proposed interpolation method can reach 4k × 2k@60 Hz with a reasonable hardware cost.

With the rapid development of mobile devices and crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. Specifically, the spatial crowdsourcing refers to sending location-based requests to workers, based on their current positions. In this paper, we consider a spatial crowdsourcing scenario, in which each worker has a set of qualified skills, whereas each spatial task (e.g., repairing a house, decorating a room, and performing entertainment shows for a ceremony) is time-constrained, under the budget constraint, and required a set of skills. Under this scenario, we will study an important problem, namelymulti-skill spatial crowdsourcing(MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers’ benefits are maximized under the budget constraint. We prove that the MS-SC problem is NP-hard and intractable. Therefore, we propose three effective heuristic approaches, including greedy,$g$-divide-and-conquer and cost-model-based adaptive algorithms to get worker-and-task assignments. Through extensive experiments, we demonstrate the efficiency and effectiveness of our MS-SC processing approaches on both real and synthetic data sets.

Different from online shopping, in-store shopping has few ways to collect the customer behaviors before purchase. In this paper, we present the design and implementation of an on-site Customer Behavior IDentification system based on passive RFID tags, named CBID. By collecting and analyzing wireless signal features, CBID can detect and track tag movements and further infer corresponding customer behaviors. We model three main objectives of behavior identification by concrete problems and solve them using novel protocols and algorithms. The design innovations of this work include a Doppler effect based protocol to detect tag movements, an accurate Doppler frequency estimation algorithm, an image-based human count estimation protocol and a tag clustering algorithm using cosine similarity. We have implemented a prototype of CBID in which all components are built by off-the-shelf devices. We have deployed CBID in real environments and conducted extensive experiments to demonstrate the accuracy and efficiency of CBID in customer behavior identification.

Device-free object tracking provides a promising solution for many localization and tracking systems to monitor non-cooperative objects, such as intruders, which do not carry any transceiver. However, existing device-free solutions mainly use special sensors or active RFID tags, which are much more expensive compared to passive tags. In this paper, we propose a novel motion detection and tracking method using passive RFID tags, named Twins. The method leverages a newly observed phenomenon called critical state caused by interference among passive tags. We contribute to both theory and practice of this phenomenon by presenting a new interference model that precisely explains it and using extensive experiments to validate it. We design a practical Twins based intrusion detection system and implement a real prototype by commercial off-the-shelf RFID reader and tags. Experimental results show that Twins is effective in detecting the moving object, with very low location errors of 0.75 m in average (with a deployment spacing of 0.6 m).

Physical-layer identification utilizes unique features of wireless devices as their fingerprints, providing authenticity and security guarantee. Prior physical-layer identification techniques on radio frequency identification (RFID) tags require nongeneric equipments and are not fully compatible with existing standards. In this paper, we propose a novel physical-layer identification system, GenePrint, for UHF passive tags. The GenePrint prototype system is implemented by a commercial reader, a USRP-based monitor, and off-the-shelf UHF passive tags. Our solution is generic and completely compatible with the existing standard, EPCglobal C1G2 specification. GenePrint leverages the internal similarity among pulses of tags' RN16 preamble signals to extract a hardware feature as the fingerprint. We conduct extensive experiments on over 10 000 RN16 preamble signals from 150 off-the-shelf RFID tags. The results show that GenePrint achieves a high identification accuracy of 99.68% +. The feature extraction of GenePrint is resilient to various malicious attacks, such as the feature replay attack.

Efficient and accurate tracking of device-free objects is critical for anti-intrusion systems. Prior solutions for device-free object tracking are mainly based on costly sensing infrastructures, resulting in barriers to practical applications. In this paper, we propose an accurate and efficient motion detection system, named EMoD, to track device-free objects based on cheap passive RFID tags. EMoD is the first RFID system that can estimate the moving direction as well as the current location of a device-free object by measuring critical power variation sequences of passive tags. Compared with previous solutions, the unique advantage of EMoD, i.e., the capability to estimate moving directions, enables object tracking using a much sparser tag deployment. We contribute to both theory and practice of this phenomenon by presenting the interference model that precisely explains it and using extensive experiments to validate it. We design a practical EMoD based intrusion detection system and implement a prototype by commercial off-the-shelf (COTS) RFID reader and tags. The real-world experiments results show that EMoD is effective in tracking the trajectory of moving object in various environments.

Indoor localization is of importance for many applications. Crowdsourcing individual users' measurements can provide accurate localization without costly site-survey. However, crowdsourcing based approaches suffer from the cold start problem, in which at the beginning of system deployment, there are insufficient users to contribute their measurements, resulting in inaccurate and time-inefficient localization. In this paper, we propose a hybrid indoor localization method to solve such problem, called ACIL. We first employ the inertial navigation technique to localize some core positions or paths. To tackle the inaccuracy problem, we propose an effective method that utilizes the channel state information (CSI) of wireless signals for accurate distance estimation. This method is based on a new observation: there is a ripple-like fading pattern in wireless signals upon moving objects. Leveraging this observation, our system is capable of calculating the distance of human's movement and his/her direction. We also propose a graph-matching algorithm to setup the correlation between the trajectory and floor map. With those extra obtained location information, the impact of cold start issue will be significantly mitigated, while the LBS can be guaranteed with high localization accuracy. Extensive experiments show that the effectiveness in the human localization and movement detection. Extensive experiments validate the great performance of our protocol in case of various human locations and diverse channel conditions.

In this paper, we propose a system called R# to estimate the number of human objects using passive RFID tags but without attaching anything to human objects. The idea is based on our observation that the more human objects are present, the higher the variance in the RSS values of the tag backscattered RF signal. Thus, based on the received RF signal, the reader can estimate the number of human objects. R# includes an RFID reader and some (say 20) passive tags, which are deployed in the region that we want to monitor the number of human objects, such as the region in front of a painting. The RFID reader periodically emits RF signal to identify all tags and the tags simply respond with their IDs via C1G2 standard protocols. We implemented R# using commercial Impinj H47 passive RFID tags and Impinj reader model R420. We conducted experiments in a simulated picking aisle area of the supermarket environment. The experimental results show that R# can achieve high estimation accuracy (more than 90%).

Regular free-weight exercise helps to strengthen the body's natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes (e.g., WiFi and Blue tooth) for activity sensing, recognition and countingetc.. However, none of them have incorporate three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system responds to these demands, providing an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1): since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other and serves as a reliable signature for each activity. 2): the Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of each performed activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities and users, and provide valuable feedbacks for activity alignment.

With the rapid development of mobile devices and the crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community, specifically, spatial crowdsourcing refers to sending a location-based request to workers according to their positions. In this paper, we consider an important spatial crowdsourcing problem, namely reliable diversity-based spatial crowdsourcing (RDB-SC), in which spatial tasks (such as taking videos/photos of a landmark or firework shows, and checking whether or not parking spaces are available) are time-constrained, and workers are moving towards some directions. Our RDB-SC problem is to assign workers to spatial tasks such that the completion reliability and the spatial/temporal diversities of spatial tasks are maximized. We prove that the RDB-SC problem is NP-hard and intractable. Thus, we propose three effective approximation approaches, including greedy, sampling, and divide-and-conquer algorithms. In order to improve the efficiency, we also design an effective cost-model-based index, which can dynamically maintain moving workers and spatial tasks with low cost, and efficiently facilitate the retrieval of RDB-SC answers. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic datasets.

Although fingerprint based localization is promising for indoor applications, its accuracy still remains a huge challenge. Most of existing approaches rely on the Radio Signal Strength (RSS) to generate fingerprints. However, merely using RSS is unable to accurately localize objects since such an one-dimensional fingerprint will be seriously influenced by the interference and multi-path effect in the indoor environment. In this paper, we propose a new localization approach based on multidimensional Wi-Fi fingerprint. Instead of only using RSS to construct fingerprint, we employ RSS, transmitted power, and channel information to construct an integrated fingerprint. The extended fingerprint enables fine-grained localization and tracking services. We also deign a cosine similarity based matching algorithm and enhanced particle filter mechanism to achieve accurate localization and tracking. Extensive experiment and implementation results show that the new fingerprint and proposed algorithms can achieve an accuracy within two meters in 90% of testing points, while demonstrating a good adaptability to complex indoor environments.

Radio Frequency Identification (RFID) techniques are widely used in many ubiquitous applications. The most important usage of RFID techniques is to read the tags within a reader's interrogation area such that the objects attached with those tags can be identified. In real practice, it is common that a tag is physically in the interrogation range, but cannot be read by the reader, due to the multipath effect and other interference. This phenomenon, namely the hidden tag problem, is a big challenge to achieve high identification rate. To address this problem, most prior works depend on empirical or measurement-based methods to tune the transmission power for readers. Such a case-by-case solution is impractical for generic implementation. In this paper, we theoretically and experimentally explore the reasons why hidden tag problem occurs. To alleviate its impact, we propose a unified and measurable model, PAL, to formulate this problem and its impact. Different from previous works, our solution is generic and fully compatible with existing EPC C1G2 protocol. The analysis and measurement based on our model can help to design and deploy RFID systems with high identification rate.

Device-free based object tracking provides a promising solution for many localization and tracking systems to monitor non-cooperative objects which do not carry any transceiver such as intruders. However, existing device-free solutions mainly use sensors and active RFID tags, which are much more expensive compared to passive tags. In this paper, we propose a novel motion detection and tracking method using passive RFID tags, named Twins. The method leverages a phenomenon called critical state caused by interference among passive tags. We theoretically explain this phenomenon via an interference model and conduct extensive experiment to validate it. We design a practical Twins based intrusion detection system and implement a real prototype with commercial off-the-shelf reader and tags. Experimental results show that Twins is effective in detecting the moving object, with low location errors of 0.75m in average.

Crowd counting, which count or accurately estimate the number of human beings within a region, is critical in many applications, such as guided tour, crowd control and marketing research and analysis. A crowd counting solution should be scalable and be minimally intrusive (i.e., device-free) to users. Image-based solutions are device-free, but cannot work well in a dim or dark environment. Non-image based solutions usually require every human being carrying device, and are inaccurate and unreliable in practice. In this paper, we present FCC, a device-Free Crowd Counting approach based on Channel State Information (CSI). Our design is motivated by our observation that CSI is highly sensitive to environment variation, like a frog eye. We theoretically discuss the relationship between the number of moving people and the variation of wireless channel state. A major challenge in our design of FCC is to find a stable monotonic function to characterize the relationship between the crowd number and various features of CSI. To this end, we propose a metric, the Percentage of nonzero Elements (PEM), in the dilated CSI Matrix. The monotonic relationship can be explicitly formulated by the Grey Verhulst Model, which is used for crowd counting without a labor-intensive site survey. We implement FCC using off-the-shelf IEEE 802.11n devices and evaluate its performance via extensive experiments in typical real-world scenarios. Our results demonstrate that FCC outperforms the state-of-art approaches with much better accuracy, scalability and reliability.

Device to device (D2D) communication is expected to become a promising technology of the next-generation wireless communication systems. Security issues have become technical barriers of D2D communication due to its “open-air” nature and lack of centralized control. Generating symmetric keys individually on different communication parties without key exchange or distribution is desirable but challenging. Recent work has proposed to extract keys from the measurement of physical layer random variations of a wireless channel, e.g., the channel state information (CSI) from orthogonal frequency-division multiplexing (OFDM). Existing CSI-based key extraction methods usually use the measurement results of individual subcarriers. However, our real world experiment results show that CSI measurements from near-by subcarriers have strong correlations and a generated key may have a large proportion of repeated bit segments. Hence attackers may crack the key in a relatively short time and hence reduce the security level of the generated keys. In this work, we propose a fast secret key extraction protocol, called KEEP. KEEP uses a validation-recombination mechanism to obtain consistent secret keys from CSI measurements of all subcarriers. It achieves high security level of the keys and fast key-generation rate. We implement KEEP using off-the-shelf 802.11n devices and evaluate its performance via extensive experiments. Both theoretical analysis and experimental results demonstrate that KEEP is safer and more effective than the state-of-the-art approaches.

Locating objects labeled with RFID tags is an important issue which should be addressed in many applications, such as warehouse management, goods management in supermarket and finding of lost objects. Some existing works use large numbers of reference tags which involve lots of manpower to deploy them. Others achieve high accuracy, but rely on sophisticated equipments which are hardly available in large scale to the industry. This work exploits the radiation pattern of existing directional panel antenna which is steerable and derives angle-of-arrival (AoA) information from the energy reflected by the target tag when the antenna is rotating. We use Commercial Off-The-Shelf (COTS) equipments and get median position accuracy of 29cm in our preliminary experiment.

Physical-layer identification utilizes unique features of wireless devices as their fingerprints, providing authenticity and security guarantee. Prior physical-layer identification techniques on RFID tags require non-generic equipments and are not fully compatible with existing standards. In this paper, we propose a novel physical-layer identification system, GenePrint, for UHF passive tags. The GenePrint prototype system is implemented by a commercial reader, a USRP-based monitor, and off-the-shelf UHF passive tags. Our solution is generic and completely compatible with the existing standard, EPCglobal C1G2 specification. GenePrint leverages the internal similarity among the pulses of tags' RN16 preamble signals to extract a hardware feature as the fingerprint. We conduct extensive experiments on over 10,000 RN16 preamble signals from 150 off-the-shelf RFID tags. The results show that GenePrint achieves a high identification accuracy of 99.68%+. The feature extraction of GenePrint is resilient to various malicious attacks, such as the feature replay attack.

Comparing to well protected data frames, Wi-Fi management frames (MFs) are extremely vulnerable to various attacks. Since MFs are transmitted without encryption or authentication, attackers can easily launch various attacks by forging the MFs. In a collaborative environment with many Wi-Fi sniffers, such attacks can be easily detected by sensing the anomaly RSS changes. However, it is quite difficult to identify these spoofing attacks without assistance from other nodes. By exploiting some unique characteristics (e.g., rapid spatial decorrelation, independence of Txpower, and much richer dimensions) of 802.11n Channel State Information (CSI), we design and implement CSITE, a prototype system to authenticate the Wi-Fi management frames on PHY layer merely by one station. Our system CSITE, built upon off-the-shelf hardware, achieves precise spoofing detection without collaboration and in-advance fingerprint. Several novel techniques are designed to address the challenges caused by user mobility and channel dynamics. To verify the performances of our solution, we conduct extensive evaluations in various scenarios. Our test results show that our design significantly outperforms the RSS-based method. We observe about 8 times improvement by CSITE over RSS-based method on the falsely accepted attacking frames.

Cold chain logistics is of great importance for transporting temperature and vibration sensitive products. However, fine-grained surveillance remains challenging in cold chain logistics, due to the lack of multi-dimensional information that reflects the status of monitored objects. In this paper, we propose a multi-dimensional information sensing surveillance framework, named MISS, to timely detect abnormal events that occur in cold chain logistics. The sensed information, including temperature and acceleration etc., can be integrated to provide accurate detection on the abnormal events. By adopting minimum entropy and AVC algorithms, we can classify various status in cold chain logistics. We further perform real implementations and evaluations on a prototype, and examine the effectiveness of MISS.

Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location-based service. Fingerprint-based approach is one of most popular and inexpensive solution. In terms of constructing the fingerprint database, there have to be a synchronized measurement for both indoor space(eg by labor-intensive site-survey or sensor-based crowd sensing) and fingerprint space, by this means the fingerprints database is established. It is the indoor space measurement hinders the usability of fingerprint-based localization system. In this work, we propose a sensor-free crowds ensing indoor localization scheme, protocol. The main contribution of our protocol is that we don't need indoor space measurement. Floor plan and RSS samples temporal sequence is the only requirement. The core of our method is a graph matching based manifold alignment process, which automatically finds the best correspondence between floor plan and wireless fingerprint transition structure. With no more need of indoor space measurement, the system deployment complexity and cost are significantly reduced. We implement our protocol at AP-end and deploy it in a 2000m^2 office environment. The evaluation has shown that our protocol can handle complex environment mapping and achieve high localization & tracking accuracy.

Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. Our real-world experience from GreenOrbs, a sensor network system deployed in a forest, shows that localization in the wild remains very challenging due to various interfering factors. In this paper, we propose CDL, a Combined and Differentiated Localization approach for localization that exploits the strength of range-free approaches and range-based approaches using received signal strength indicator (RSSI). A critical observation is that ranging quality greatly impacts the overall localization accuracy. To achieve a better ranging quality, our method CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware calibration. We have implemented and evaluated CDL by extensive real-world experiments in GreenOrbs and large-scale simulations. Our experimental and simulation results demonstrate that CDL outperforms current state-of-art localization approaches with a more accurate and consistent performance. For example, the average location error using CDL in GreenOrbs system is 2.9 m, while the previous best method SISR has an average error of 4.6 m.

In urban vehicular networks, where privacy, especially the location privacy of anonymous vehicles is highly concerned, anonymous verification of vehicles is indispensable. Consequently, an attacker who succeeds in forging multiple hostile identifies can easily launch a Sybil attack, gaining a disproportionately large influence. In this paper, we propose a novel Sybil attack detection mechanism, Footprint, using the trajectories of vehicles for identification while still preserving their location privacy. More specifically, when a vehicle approaches a road-side unit (RSU), it actively demands an authorized message from the RSU as the proof of the appearance time at this RSU. We design a location-hidden authorized message generation scheme for two objectives: first, RSU signatures on messages are signer ambiguous so that the RSU location information is concealed from the resulted authorized message; second, two authorized messages signed by the same RSU within the same given period of time (temporarily linkable) are recognizable so that they can be used for identification. With the temporal limitation on the linkability of two authorized messages, authorized messages used for long-term identification are prohibited. With this scheme, vehicles can generate a location-hidden trajectory for location-privacy-preserved identification by collecting a consecutive series of authorized messages. Utilizing social relationship among trajectories according to the similarity definition of two trajectories, Footprint can recognize and therefore dismiss “communities” of Sybil trajectories. Rigorous security analysis and extensive trace-driven simulations demonstrate the efficacy of Footprint.

Due to its great scalability, fast read access, low leakage power, and nonvolatility, magnetic random access memory (MRAM) appears to be a promising memory technology for on-chip cache memory in microprocessors. However, the write-to-MRAM process is relatively slow and results in high dynamic power consumption. Such inherent disadvantages of MRAM make researchers easily conclude that MRAM can only be used for low-level caches (e.g., L2 or L3 cache), where cache memories are less frequently accessed and slow write to MRAM can be more easily compensated using simple architectural techniques. By developing a hybrid cache architecture, this paper attempts to show that, with appropriate architecture design, MRAM can also be used in L1 cache to improve both the energy efficiency and soft error immunity. The basic idea is to supplement the MRAM L1 cache with several small SRAM buffers, which can substantially mitigate the performance degradation and dynamic energy overhead induced by MRAM write operations. Moreover, the proposed hybrid cache architecture is also an efficient solution to protect cache memory from radiation-induced soft errors, as MRAM is inherently invulnerable to emissive particles. Simulation results show that, with only less than 2% performance degradation, the proposed design approach can reduce the power consumption by up to 76.1% on average compared with the traditional SRAM L1 cache. In addition, the architectural vulnerability factor of L1 data cache is reduced from 28.3% to as low as 0.5%.

In spite of the remarkable efforts the community put to build the sensor systems, an essential question still remains unclear at the system level, motivating us to explore the answer from a point of real-world deployment view. Does the wireless sensor network really scale? We present findings from a large scale operating sensor network system, GreenOrbs, with up to 330 nodes deployed in the forest. We instrument such an operating network throughout the protocol stack and present observations across layers in the network. Based on our findings from the system measurement, we propose and make initial efforts to validate three conjectures that give potential guidelines for future designs of large scale sensor networks. (1) A small portion of nodes bottlenecks the entire network, and most of the existing network indicators may not accurately capture them. (2) The network dynamics mainly come from the inherent concurrency of network operations instead of environment changes. (3) The environment, although the dynamics are not as significant as we assumed, has an unpredictable impact on the sensor network. We suggest that an event-based routing structure can be trained optimal and thus better adapt to the wild environment when building a large scale sensor network.

Event detection is a typical application of wireless sensor networks. The existing approaches of event detection usually employ certain event models that are constructed with prior domain knowledge. The resulting event detection processes appear to be cost-inefficient, which require either intensive data exchanges among neighboring nodes or caching large columns of history data. In this paper, we focus on the issue of locating event in wireless sensor network without locations. This involves two tasks, namely detecting an event and identifying an area in the network where the event occurs. Motivated by the real system, we propose a model-free approach for event detection called CCD (Coding Cost based event Detection). Coding cost is a metric that quantifies the diversity of a set of sensor readings. Incorporated into the inherent data collection mechanism, CCD passively constructs a gradient map of coding cost throughout the network. An event is then detected where a change point of gradient appears and identifies the event pattern automatically. CCD is fully distributed and does not incur apparent communication overhead. We implement CCD and evaluate its performance with extensive experiments and simulations. The results demonstrate that CCD is accurate, scalable, and applicable to a variety of sensor networks.

Although GPS is deemed as ubiquitous outdoor localization technology, we are still far from a similar technology for indoor environments. Though a number of techniques are proposed for indoor localization, they are separated efforts that are way from a real ubiquitous localization system. Our real-world experience from InSpace, a pervasive computing system with wireless devices to provide intelligent services to users, shows that locating mobile users remains very challenging due to various interfering factors. We analyze real traces of mobile phones carried by users and find that mobile users exhibit temporal-spatial stability and neighborhood relativity. Motivated by this observation, we develop a Mobile Boundary Localization approach, MBL, to exploit the associated information to locate mobile users. This localization approach uses different treatment in different conditions and lets each mobile phone try to estimate its possible location range. We have implemented and evaluated MBL by extensive real-world experiments in InSpace and simulations. The results demonstrate that MBL significantly outperforms state-of-the-art localization approaches with more accurate, efficient, and consistent performance.

How to reasonably allocate and schedule resources of wireless sensor system to maximum its potential capability has been an important area for research. In this paper, we focus on the Optimal Sampling Frequency Assignment (OSFA) in a real-time wireless sensor networks (RTWSN). An appropriate OSFA should both guarantee a good quality of real-time service and efficiently utilize the limited network resources as well. We propose a distributed optimization algorithm, called Lazy Schema (LySa), to obtain the optimal sampling rates of source nodes with low cost. The central idea is that redundancy reporting and constant adjust step size result in the excessive communicate overhead in iteration process. LySa adopts self-adaptive reporting rates and dynamically adjusts step size to reduce the traffic cost and accelerate the convergence of optimal solution. We have evaluated LySa together with related mainstream algorithms. The results demonstrate that LySa outperforms current state-of-art approaches in terms of low cost, high efficiency and scalability in RTWSN.

Estimation of crowd distribution is critical to various applications. Although most researches have provided solutions based on images and videos technologies, the high costs for deploying and an over-dependence on the bright light restrict its scope of application. In this paper, we use wireless sensor networks (WSNs) originally to make up for the lack of camera. Our approach is an iterative process which contains two phases in each time slot. In detection step, we divide the crowd density into different levels according to the RSSI data obtained by WSNs using K-means algorithm. In calibration step, we eliminate the noises and other deviations estimation based on the spatial-temporal correlation of crowd distribution. In addition, we have implemented and evaluated our algorithm by extensive real-world experiments using 16 sensor nodes and large-scale simulations. The results show that our algorithm has an accurate, efficient, and consistent performance.

In this paper, we study the spectrum assignment problem for wireless access networks. We assume that each secondary user will bid a certain value for exclusive usage of some spectrum channels for a certain time period or for a certain time duration. A secondary user may also require the exclusive usage of a subset of channels, or require the exclusive usage of a certain number of channels. Thus, several versions of problems are formulated under various different assumptions. For the majority of problems, we design PTAS or efficient constant-approximation algorithms such that overall profit is maximized. Here, the profit is defined as the total bids of all satisfied secondary users. As a side product of our algorithms, we are able to show that a previously studied Scheduling Split Interval Problem (SSIP) [CHECK END OF SENTENCE], in which each job is composed of t intervals, cannot be approximated within O(t^{1-\epsilon }) for any small \epsilon >0 unless {\rm NP}={\rm ZPP}. Opportunistic spectrum usage, although a promising technology, could suffer from the selfish behavior of secondary users. In order to improve opportunistic spectrum usage, we then propose to combine the game theory with wireless modeling. We show how to design a truthful mechanism based on all of these algorithms such that the best strategy of each secondary user to maximize its own profit is to truthfully report its actual bid.

In wireless sensor and actor networks (WSANs), a set of static sensor nodes and a set of (mobile) actor nodes form a network that performs distributed sensing and actuation tasks. In [1], Abbasi et al. presented DARA, a Distributed Actor Recovery Algorithm, which restores the connectivity of the interactor network by efficiently relocating some mobile actors when failure of an actor happens. To restore 1 and 2-connectivity of the network, two algorithms are developed in [1]. Their basic idea is to find the smallest set of actors that needs to be repositioned to restore the required level of connectivity, with the objective to minimize the movement overhead of relocation. Here, we show that the algorithms proposed in [1] will not work smoothly in all scenarios as claimed and give counterexamples for some algorithms and theorems proposed in [1]. We then present a general actor relocation problem and propose methods that will work correctly for several subsets of the problems. Specifically, our method does result in an optimum movement strategy with minimum movement overhead for the problems studied in [1].

The benefits of using mobile sink to prolong sensor network lifetime have been well recognized. However, few provably theoretical results remain are developed due to the complexity caused by time-dependent network topology. In this work, we investigate the optimum routing strategy for the static sensor network. We further propose a number of motion stratifies for the mobile sink(s) to gather real time data from static sensor network, with the objective to maximize the network lifetime. Specially, we consider a more realistic model where the moving speed and path for mobile sinks are constrained. Our extensive experiments show that our scheme can significantly prolong entire network lifetime and reduce delivery delay.

Correct and effective instruction pre-fetch strategy is key technique to avoid instruction misses. Unfortunately, branch direction correctness and the accuracy of instruction pre-fetch is not very good, and the utilization ratio of memory bandwidth is relative low, all of these mentioned reasons are the main factors leading to instruction miss. This paper proposes an adaptive and selective instruction active push mechanism for multi-core architecture, called ASIAP. On one hand, request number of invalid instruction pre-fetch is decreased and precise instruction pre-fetch is carried on; on the other hand, part of non-sequential type requests are responded preferentially by a specific instruction active push unit adaptively and selectively. Simulation result indicates that, in double-core configuration, relative to three other strategies, Next_Line, Target_Line and Wrong_Path, the accuracy of ASIAP improves average 22.59%, 11.84% and 8.85% respectively. Relative to Next_Line, the reduction of L1 I-Cache miss ranges from 17.7% to 33.5%, average 26.08%.

Multi-Core architecture is the development trend of microprocessor architecture, and the “Memory Wall” is the chief obstacle to promote the processor performance. This paper analyzes the key factors affecting performance of memory system in shared L2 cache multi-core on a chip architecture, and believes that cache pollution caused by the speculative execution of memory reference instructions in predictive path may affect the performance of processor seriously. This paper proposes a cache pollution filtration technique based on confidence estimation, called FCPC. FCPC proceeds the dynamic appraisal using the confidence estimation mechanism to condition branch, and adds two tag bits CET (confidence estimation tag) and AHT(accessing hint tag) for each cache data line. According to the appraisal result, the memory accessing instructions in predictive path and their returned data in L2 cache are marked as high or low confidence separately. Then, when cache replacing operations are processed, the pollution data in shared L2 cache can be swept out preferentially according to CET and AHT tag. So, the efficiency of cache space is increased. Simulation result indicates that, in dual-core configuration, the FCPC strategy can promote the IPC performance effectively, ranges from 0.18%-4.86%, 1.91% averagely. Simultaneously, the miss rate of L2 Cache can be reduced, ranges from 0.65%-5.76%, average 2.57%.

Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. The real-world experience from GreenOrbs, a sensor network system in the forest, shows that localization in the wild remains very challenging due to various interfering factors. In this paper we propose CDL, a Combined and Differentiated Localization approach. The central idea is that ranging quality is the key that determines the overall localization accuracy. In its unremitting pursuit of better ranging quality, CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware calibration. We have implemented CDL and evaluated it by extensive experiments and simulations. The results demonstrate that CDL outperforms current state-of-art approaches with better accuracy, efficiency and consistent performance.

Localization is a crucial service for various applications in wireless sensor networks (WSNs). Although most researches assume stationary nodes, sensor mobility can enrich the application scenarios. Existing dynamic localization approaches require high seed density or incur a large communication overhead. In order to address these problems, we propose an efficient rang-free localization algorithm, EUL, which utilizes the relationship between neighboring nodes to estimate their possible location boundaries. Our algorithm not only allows all the nodes to remain static or move freely but also reduces the dependence on seeds, which achieves a uniform energy distribution to address the excessive energy drain around seeds and lengthen the network lifetime. We have evaluated EUL together with other major dynamic localization approaches. Simulation results show that EUL outperforms existing approaches in terms of accuracy under many different mobility conditions.

Wireless sensor networks have become a promising technology in monitoring physical world. In many applications with wireless sensor networks, it is essential to understand how well an interested area is monitored (covered) by sensors. The traditional way of evaluating sensor coverage requires that every point in the field should be monitored and the sensor network should be connected to transmit messages to a processing center (sink). Such a requirement is too strong to be financially practical in many scenarios. In this study, we address another type of coverage problem, sweep coverage, when we utilize mobile nodes as supplementary in a sparse and probably disconnected sensor network. Different from previous coverage problem, we focus on retrieving data from dynamic Points of Interest (POIs), where a sensor network does not necessarily have fixed data rendezvous points as POIs. Instead, any sensor node within the network could become a POI. We first analyze the relationship among information access delay, information access probability, and the number of required mobile nodes. We then design a distributed algorithm based on a virtual 3D map of local gradient information to guide the movement of mobile nodes to achieve sweep coverage on dynamic POIs. Using the analytical results as the guideline for setting the system parameters, we examine the performance of our algorithm compared with existing approaches.

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

Efficient aggregation of data collected by sensors is crucial for a successful application of wireless sensor networks (WSNs). Both minimizing the energy cost and reducing the time duration (or called latency) of data aggregation have been extensively studied for WSNs. Algorithms with theoretical performance guarantees are only known under the protocol interference model, or graph-based interference models generally. In this paper, we study the problem of designing time efficient aggregation algorithm under the physical interference model. To the best of our knowledge, no algorithms with theoretical performance guarantees are known for this problem in the literature. We propose an efficient algorithm that produces a data aggregation tree and a collision-free aggregation schedule. We theoretically prove that the latency of our aggregation schedule is bounded by O(R+Δ) time-slots. Here R is the network radius and Δ is the maximum node degree in the communication graph of the original network. In addition, we derive the lower-bound of latency for any aggregation scheduling algorithm under the physical interference model. We show that the latency achieved by our algorithm asymptotically matches the lower-bound for random wireless networks. Our extensive simulation results corroborate our theoretical analysis.

Radio frequency identification (RFID) technologies are on their highway to pervasive usage. However privacy protection is still an important problem since RFID tags attached to items are so cost constrained. Privacy preserving authentication approaches are proposed to authenticate tags without private information leaking. Previously designed approaches based on synchronization seeks O(1) complexity. While these synchronization based methods are efficient in normal case, they have weak points when desynchronized. When maliciously scanned, information stored in tag and reader goes farther and farther away from each other. An adversary can utilize this point to track a tag. We propose an enhanced synchronization approach for RFID private authentication, ESP, to solve this problem. ESP can eliminate the problem caused by desynchronization attack and help detecting replay attack. Analysis shows that ESP enhances privacy protection while still maintaining the authentication efficiency.

Privacy protection is increasingly important during authentications in Radio Frequency Identification (RFID) systems. In order to achieve high-speed authentication in large-scale RFID systems, researchers propose tree-based approaches, in which any pair of tags share a number of key components. Such designs, being efficient, often fail to achieve forward secrecy and resistance to attacks, such as compromising and desynchronization. Indeed, these attacks may still take effect even after a tag successfully finishes the authentication and key-updating procedure. To address the issue, we propose a lightweight RFID private authentication protocol, RWP, based on the random walk concept. RWP also provides the forward security and temporal resistance to the tracking attack. The analysis results show that RWP effectively enhances the security protection for RFID private authentication, and increases the authentication efficiency from O(logN) to O(1).

Motivated by the needs of precise forest inventory and real-time surveillance for ecosystem management, in this paper we present GreenOrbs [2], a wireless sensor network system and its application for canopy closure estimates. Both the hardware and software designs of GreenOrbs are tailored for sensing in wild environments without human supervision, including a firm weatherproof enclosure of sensor motes and a light-weight mechanism for node state monitoring and data collection. By incorporating a pre-deployment training process as well as a distributed calibration method, the estimates of canopy closure stay accurate and consistent against uncertain sensory data and dynamic environments. We have implemented a prototype system of GreenOrbs and carried out multiple rounds of deployments. The evaluation results demonstrate that GreenOrbs outperforms the conventional approaches for canopy closure estimates. Some early experiences are reported in this paper.

In this paper, we address the problem of joint channel assignment, link scheduling, and routing for throughput optimization in wireless networks with multi-radios and multi-channels. We mathematically formulate this problem by taking into account the interference, the number of available radios the set of usable channels, and other resource constraints at nodes. We also consider the possible combining of several consecutive channels into one so that a network interface card (NIC) can use the channel with larger range of frequencies and thus improve the channel capacity. Furthermore, we consider several interference models and assume a general yet practical network model in which two nodes may stillnotcommunicate directly even if one is within the transmission range of the other. We designed efficient algorithm for throughput (or fairness) optimization by finding flow routing, scheduling of transmissions, and dynamic channel assignment and combining. We show that the performance, fairness and throughput, achieved by our method is within a constant factor of the optimum. Our model also can deal with the situation when each node will charge a certain amount for relaying data to a neighboring node and each flow has a budget constraint. Our extensive evaluation shows that our algorithm can effectively exploit the number of channels and radios. In addition, it shows that combining multiple channels and assigning them to a single user at some time slots indeed increases the maximum throughput of the system compared to assigning a single channel.

Recent work in building data-centric sensor networks treats a sensor network as a pool of information. Sensor nodes generate, store, and retrieve information as both data producers and consumers. The intensive demand of data exchange within the network leads previous approaches with sensor-to-sink transmission model inefficient. In-network data storage schemes, such as geographical hash table (GHT) and double-rulings, have been accordingly proposed to structure the information storage among the network so as to facilitate the consumers to efficiently discover and retrieve data. Under those approaches, however, each sensor node needs to publish and retrieve data from different routes calculated every time, introducing unnecessary computation and communication overhead. This paper proposes anew approach that stores and queries data through bouncing tracks. Sensors are able to publish replica of generated data along their bouncing tracks and successfully retrieve data from other sensors along the same tracks, which largely simplifies data exchange and improves efficiency. The strengths of this design also include distance-bounded data retrieval. We conducted extensive simulations and the results show that this approach outperforms existing designs, including rumor routing and double-rulings, in terms of communication efficiency and cost.

The capacity of a wireless network has been widely studied in the literature, including the capacity for unicast and the capacity for broadcast. In this paper, we studied the capacity of a wireless network for broadcast. Previous studies on broadcast capacity either assume that all links in the wireless network has the same channel capacity, or assume that the transmission ranges of a wireless node can be arbitrarily large. In this paper we derive analytical upper bounds and lower bounds on broadcast capacity of a wireless network when all nodes in the network has the same bounded transmission power P and all nodes are placed in a square of side-length a. When the fixed data rate channel is used (each node can send W bits/second to nodes within its transmission range if no interference happened), we prove that the broadcast capacity is Θ(W) under the physical interference model. When the Gaussian channel capacity is used, we show that the total broadcast capacity is only Θ((α√log n/n)−βwhen α√log n/n → ∞. When a α√log n/n → O(1), we show that the broadcast capacity is Θ(1). We also generalize our results to multicast capacity for physical interference model.

No abstract available.

By using personal information in a pervasive computing environment, context-aware applications can provide appropriate services for people. This personal information is often involved in personal privacy. In order to protect personal privacy concerns about personal information, privacy role is proposed to control access personal information. We also construct an information system about the privacy decision of personal information disclosure based on people's interaction history. In the initial period of personal information disclosure, the privacy decision is made by people and the information system is constructed based on the decision data. Then privacy disclosure policies are extracted from this information system using rough set theory. According to deducing from the privacy disclosure policies and people's context information, the contextaware application is assigned to an adequate privacy role. It reduces the distraction of privacy decision for people. A case study further shows the proposed method is effective. Finally, it provides about the overload performance of privacy role analysis personaengine.

In pervasive environment, context-aware service provider can use personal information to customize the adequate services for end users. Personal information is people's privacy concern. Therefore, people need privacy control methods. In this paper, we analyzed privacy control from two aspects: personal privacy model about information disclosure and the function of context-aware service provider. According to the analysis, we designed the components about context-aware privacy control in order to minimize the burden of personal privacy decision about personal information disclosure. The simulation experiment shows that it is possible method for the proposed privacy protection mechanism. Finally, we also proposed conceptual context-aware architecture to control personal information disclosure.

According to the characteristics of the operational behavior and runtime state of application sever, the resource consumption time series are observed and modeled by fuzzy wavelet network (FWN) with fuzzy logic inference and learning capability. The objective is to model the extracted data series of systematic performance parameters to predict software aging in application server. The dimensionality of input variables of FWN is reduced by principal components analysis (PCA), and the structure and parameters of FWN are optimized with adaptive genetic algorithm (GA). Judging by the model, we can get the aging threshold before application server failed and preventively maintenance the application server before systematic parameter value reaches the threshold. The experiments are carried out to validate the efficiency of the proposed model and show that the aging prediction model based on FWN with adaptive genetic algorithm is superior to the neural network (NN) model and wavelet network (WN) model in the aspects of convergence rate and prediction precision

Content protection is now becoming more and more important for digital rights management (DRM), which involves rights embedding, identification, rights validation digital multimedia resource is popular in the real world, how to protect multimedia content from be violated without rights control is an important thing especially to resist copy-spread kind violation. In this paper, an novel approach for multimedia rights management is proposed based on partial encryption method, which can control multimedia resource played in a rights-constraint environment, which can protect multimedia resource from being copying and spreading, and in the authorization usage environment, the protected multimedia is properly played as normal, however once the resource is beyond the authorization environment, the protected resource will not be played correctly. Experiments showed our proposed partial encryption approach was efficient with real-time quality of service, which was suitable for online multimedia streaming in content delivery network.