Power management, while indispensable for the working of battery-free devices on fragile ambient energy, unfortunately, also entails excessive workloads that consume the scarce harvested energy. Existing efforts aimed at addressing this typically manage to tackle only a fraction of the challenges, leaving power management as a painful Achilles’ heel for battery-free devices. In this paper, we systematically analyze the full-flow of power management and propose μ Man, a painless architecture with no extra workload on battery-free devices. That is, we shift the entire workload of power management from the resource-constrained battery-free devices to the resource-rich gateway. For this goal, we design a near-zero-power sampling-free monitoring mechanism to transparently piggyback the power status of the device directly onto the uplink signal waveform. Based on these real-time statuses, the gateway can take over the required computation and issue the resultant energy allocations back to devices. The design is fully transparent to the devices, and the devices can even remain in deep sleep during the whole process to minimize energy consumption. The experiments show that μ Man can reduce the energy consumption of power management by 97.2%, improve the power efficiency by 53%, and reduce the minimum energy requirements for the device start-up by 5.8 ×.

RF energy harvesting devices operate intermittently due to the dynamic nature of harvested energy. To prevent power outages and ensure reliability, intermittency control needs to track the harvested voltage signals and adjusting the system states (e.g., active or sleep) accordingly. However, tracking the harvested energy involves power-starving operations such as using analog-to-digital converter (ADC) and micro-controller (MCU) to sample to the harvested voltage. In this paper, we propose a novel approach to address this problem by shifting energy tracking from the device to the gateway side. The gateway uses ultra-low power backscatter communication to remotely sense the power harvesting state of the device, and then opportunistically controls device's system states via downlink transmission. The key challenge is that many RF-powered devices like RFID tags already utilize backscatter communication for their existing communication needs. Sensing the energy state via backscatter could lead to collisions with ongoing communications. To address this challenge, we enable the gateway to use existing backscatter communication for energy sensing. We leverage a physical channel attenuation model for backscatter communication, allowing the gateway to infer the power harvesting state of the device using signal strength information from the gateway's transmission and reception. We also leverage the backscatter reply to build the multipath profile that calibrates the power harvesting estimation in multipath-rich scenarios. We instantiate our approach in a commercial RFID system and demonstrate significant improvements of power efficiency.

With the increasing popularity of smart wearable devices, such as earbuds and smart watches, presents new challenges for seamless and secure user authentication due to their limited user interfaces. Conventional biometric methods, including voice, fingerprints, and facial recognition often face issues such as usability limitations, interference from noise, or vulnerability to spoofing attacks. This paper introduces a novel authentication system called BaroAuth, which utilizes the stable and unique Speech-aware Pressure Sequences (SPSs) patterns captured by a miniaturized MEMS barometer embedded in earbuds. The design of BaroAuth hinges on two important observations. First, the production of speech relies on the coordinated movements of articulatory organs, including the tongue, jaw, and soft palate. These organs, through the activity of the temporomandibular joint (TMJ), alter the shape of the ear canal, thereby causing subtle pressure changes that encode the speaker's unique physiological characteristics and articulatory dynamics. Second, SPSs show significant intra-individual consistency and considerable inter-individual variability, which barometers can effectively measure. Meanwhile, we develop the BaroAuth prototype and carry out comprehensive experiments based on it. The experimental findings reveal that BaroAuth demonstrates a mean false-acceptance rate (FAR) of 0.41% and a false-rejection rate (FRR) of 1.23%, respectively, even under complex attack scenarios.

Recent advances in backscatter communication have exhibited great advantages on uplink, both in power consumption and communication performance. However, their downlink tends to lag far behind due to stringent on-device power constraints. This paper presentsSisyphus, a novel communication paradigm designed to empower backscatter devices with LoRa downlink. To achieve this, we propose a novel receiver design for passive coherent demodulation of LoRa. In this design, we creatively couple LoRa’s down-conversion with de-chirping (dc2), leveraging the processing gain brought by chirp spread spectrum (CSS) modulation to boost communication range without the need for additional power supply. Moreover, we exploit the cyclical time-frequency feature intrinsic to LoRa for demodulation, and a low-power analog-digital signal processing circuit with negligible power is devised to replace the existing power-intensive sampling and costly digital computation. We prototype Sisyphus for proof-of-concept, and comprehensive experimental results demonstrate that Sisyphus can achieve significant power savings compared to legacy LoRa receiver while retaining the anti-interference ability of legacy LoRa. We envision that the design of Sisyphus can unlock the potential for broader applications of LoRa-based backscatter devices.

Location-Based Social Networks (LBSNs) offer a rich dataset of user activity at Points-of-Interest (POIs), making next POI recommendation a key task. Traditional algorithms face challenges due to broad searching scopes, affecting recommendation accuracy. Users tend to visit nearby POIs and show temporal concentration in their activities, reflecting personalized spatio-temporal clustering. However, individual user data may be insufficient to capture these clustering effects for personalized recommendations. In this paper, we propose an integrated Personalized Spatio-Temporal Clustering Model (iPCM) for next POI recommendation. The model learns this kind of personalized spatio-temporal clustering effect by using global historical trajectory data in conjunction with user feature embeddings. It integrates the features of personalized spatio-temporal clustering with the user's trajectory, and completes the user's POI recommendation through a Transformer encoding and MLP decoding. To enhance the accuracy of predictions, we add a module of probability adjustment. The experimental results on multiple datasets show that with the help of personalized spatio-temporal clustering, the proposed iPCM is superior to existing methods in various evaluation metrics.

With the rapid development of high-throughput sequencing technologies, multi-omics integration analysis has become a core means to decipher complex disease mechanisms, and graph neural networks (GNNs) have shown significant advantages in multi-omics data fusion due to their strong nonlinear modeling and relational reasoning capabilities. However, such models typically rely on large-scale labeled data for training, while small-sample disease scenarios are prevalent in biomedical research. The limited sample size in these scenarios does not support stable deep model training, severely restricting the application of precision medicine in critical disease fields. Directly constructing GNN models under small-sample settings leads to severe overfitting and training instability, resulting in a significant decline in model generalization performance. In this paper, we propose a Cross-disease knowledge transfer framework for Small-sample Omics analysis with GNNs (CSOG), through a pretraining and parameter-freezing fine-tuning paradigm. To the best of our knowledge, we are the first to explore the GNN cross-disease knowledge transfer framework for small-sample omics analysis. Experimental evaluations on three independent small-sample datasets show that compared with 10 state-of-theart baseline methods, the proposed method achieves significantly improved average classification accuracy and maintains stability across different sample sizes.

Graph computing in cloud-edge collaborative environments faces critical challenges in distributed task processing, particularly in fundamental operations such as subgraph isomorphism that underpins triangle counting applications. In typical architectures where data streams are transmitted from edge collectors to cloud masters, conventional approaches employ reservoir sampling to distribute edge streams among workers for triangle estimation. However, the computational accuracy degradation is caused by cross-domain edge distribution strategies. In this paper, we propose a cloud-edge collaborative framework for distributed triangle counting. We employ spectral clustering analysis to reveal latent domain relationships that guide edges distribution. Our experimental evaluation uses streaming data with global relative error measurement across multiple datasets, demonstrating superior performance over existing algorithms.

Battery-free nodes harvest ambient energy, accelerating large-scale IoT (Internet of Things) deployment. However, sporadic beginnings and ends of power failures impede collaboration, obstructing the execution of complex applications. The prior collaborative protocols have high energy demands and lack scalability. This paper introduces Cupid, a novel scheduling architecture that employs a coordinator device to circumvent the collaborative energy bottleneck, enhancing the scalability of battery-free node collaboration. Cupid employs an efficient crosslayer communication protocol to offload energy-intensive tasks to the coordinator. To reduce latency from non-local execution, we propose a predictive scheduling algorithm based on curve fitting. Additionally, we implement a circuit on the node side for ultra-low-power upload and download capabilities. We implement a prototype and conduct extensive evaluations. Compared to the state-of-the-art, it is the first to achieve intermittent coordination in medium-scale EH-WSNs, reducing latency by 94.56%.

The growing adoption of smart wearable devices (e.g., earbuds and smart watches) poses new challenges for secure and seamless user authentication due to their limited interaction interfaces. Conventional biometric methods, including fingerprints, voice, and facial recognition, often suffer from usability constraints, noise interference, or susceptibility to spoofing attacks. In this paper, we propose BaroAuth, a novel authentication system that utilizes the stable and distinctive patterns of Speech-aware Pressure Sequences (SPSs) captured by miniature MEMS barometers embedded in earbuds. The design of BaroAuth is based on two key observations. First, speech production involves coordinated movements of articulatory organs, such as the jaw, tongue, and soft palate, which reshape the ear canal geometry via the temporomandibular joint (TMJ), generating subtle pressure variations that encode the speaker’s unique articulatory dynamics and physiological traits. Second, SPSs demonstrate strong intra-individual consistency and notable inter-individual variability, which can be effectively captured by barometers. We implement the prototype of BaroAuth and conduct comprehensive experiments on it. Experimental results demonstrate that BaroAuth achieves a mean false-acceptance rate (FAR) and false-rejection rate (FRR) of 1.62% and 1.74%, respectively, even under sophisticated attack scenarios.

Ultrasonic microphone jammers (UMJs) use ultrasonic waves to interfere with concealed microphone recorders, offering significant values in confidential meetings and secret talks. Existing UMJs, however, performs in a short range of typically 2 m, which remains a huge gap to practical applications. The key challenge lies in the fact that the ultrasonic signal will produce audible sounds during its transmission, caused by nonlinear distortion of power amplifiers and loudspeakers in the transmission chain of the UMJ. In this paper, we propose Hedgehog, a room-scale ultrasonic jammer design, which enhances the jamming range through two key methods. First, it corrects nonlinear distortion by modeling the transmission chain and applying digital pre-distortion. Furthermore, it redesigns the jamming signal to improve its effectiveness by not only reducing the signal-to-noise ratio (SNR) but also by suppressing the semantic information in human speech. The experimental results show that Hedgehog achieved a word error rate (WER) of over 95% at 8 meters and over 80% at 10 meters, which is a 4 times increase in jamming distance compared to existing solutions.

The Internet of Things (IoT) can only thrive if IoT sensor nodes can be effortlessly deployed and maintained without compromising their general-purpose nature. However, existing low-power sensor systems fail to strike a balance between these two issues, leaving the widespread of IoT sensor nodes as an open problem. In this paper, we propose LEGO+ as a minimalist yet general-purpose sensing edge-end architecture. Instead of running embedded software on a redundant general-purpose microprocessor, LEGO+ can directly construct the desired control functionality for various IoT sensing applications through hardware-level logic orchestration. To achieve this, we first conduct an in-depth analysis of the underlying unit behaviors within IoT sensor systems and, based on this, abstract a uniform logic orchestration model. Next, to enable sensor nodes to comprehend and execute the generated logic, we devise a hierarchical atomic control circuit with negligible overheads. Finally, we develop a task state prediction scheme to further improve the overall operation efficiency among multiple nodes. We prototype LEGO+ for proof-of-concept and conduct comprehensive experiments, and the results demonstrate that LEGO+ can reduce the overall power consumption of sensor nodes by 86% and enhance task efficiency by 49%, thereby facilitating a wider array of IoT sensing applications.

The deployment of low-power wide-area networks (LPWAN) in urban environments faces a critical challenge with signal blockage caused by dense obstacles like buildings, resulting inblind spotswhere end nodes have difficulty reaching the gateway. This paper proposes LoRaMirror, a reflective smart surface design, to essentially eliminate these blind spots and improve overall communication in urban LoRaWAN. LoRaMirror is different from existing smart surface designs, as it addresses unprecedented challenges posed by LPWAN's unique application scenario of extremely long communication distances, extremely low data transmission rates, and extremely wide coverage. LoRaMirror is prototyped with a 16-antenna multi-layer array and the experimental results show significant performance gains in real world practice.

Large-scale IoT sensor deployment calls for inexpensive, low-power sensor nodes that still perform long-range, large-scale networking at the system level. However, current sensor nodes are constructed according to the 'one-size-fits-all’ embedded design, where the processor and RF transceiver are indispensable but underutilized in low-duty cycles, resulting in overwhelmingly significant unit price and run-time power. In this paper, we propose a novel processor-sharing IoT architecture that converts the vast majority of sensor nodes from embedded computers to low-end RF peripherals. The conventional full-fledged sensor nodes are smashed into the air, and the scattered chips are scaled well with negligible overheads through a virtual I$^{2}$C bus calledRFBus. Specifically, RFBus interface is designed to be backward compatible with the I$^{2}$C bus interface, and thus, RFBus network inherits versatile link layer services transparently from the well-established I$^{2}$C link layer protocol. We design RFBus with joint consideration of system-level performance and deployment costs and evaluate the prototypes both indoors and outdoors. The result indicates that the proposed architecture achieves 6.09 × (indoor) and 6.69 × (outdoor) energy saving and reduces the unit price of sensor nodes by 23.5% (indoor) and 33.5% (outdoor).

In streaming federated learning, where data on each client is received in the form of a data stream, the distribution of data on the clients has a significant impact on the performance of the federated learning model. The continuous influx of streaming data on the clients leads to real-time changes in the local data distribution, which in turn affects the performance of the federated learning model. Furthermore, the heterogeneity in data distribution among clients exacerbates this impact. In this paper, to address these challenges, we propose a Data-aware Probabilistic Client Sampling scheme (DPCS) for selecting appropriate clients to participate in model training in each round of federated learning. DPCS begins with a method for real-time monitoring of local data distributions on the clients. Based on these observations, the central server adopts a probability-based client sampling strategy. Through extensive experimentation, we demonstrate that our client sampling scheme offers higher timeliness and enhances the performance of federated learning compared to traditional methods.

Legacy LoRa receiver adopts a superheterodyne architecture with a runtime power consumption of up to 100mW, resulting in its low-power promise can only be delivered in low duty-cycle mode. This paper presents Sisyphus as an ultra-low-power LoRa receiver, ensuring around-the-clock LoRa availability while extending battery life significantly. To achieve this, we propose a novel receiver design for passive coherent demodulation of LoRa. In this design, we creatively couple LoRa's down-conversion with de-chirping (dc2), leveraging the processing gain brought by chirp spread spectrum (CSS) modulation to boost communication range without the need for additional power supply. Moreover, we exploit the cyclical time-frequency feature intrinsic to LoRa for demodulation, and a low-power analog-digital signal processing circuit with negligible power is devised to replace the existing power-intensive sampling and costly digital computation. We prototype Sisyphus for proof-of-concept, and comprehensive experimental results demonstrate that Sisyphus can achieve significant power savings compared to legacy LoRa receiver while retaining the anti-interference ability of legacy LoRa. We envision that the design of Sisyphus can unlock the potential for broader applications of LoRa.

Large-scale IoT sensor deployment calls for inexpensive, low-power sensor nodes that still perform long-range, large-scale networking at the system level. However, current sensor nodes are constructed according to the `one-size-fits-all' embedded design, where the processor and RF transceiver are indispensable but underutilized in low-duty cycles, resulting in overwhelmingly significant unit price and run-time power. In this paper, we propose a novel processor-sharing IoT architecture that converts the vast majority of sensor nodes from embedded computers to low-end RF peripherals. The conventional full-fledged sensor nodes are smashed into the air, and the scattered chips are scaled well with negligible overheads through a virtual I2C bus called RFBus. Specifically, the RFBus interface is designed to be backward compatible with the I2C bus interface, and thus, the RFBus network inherits versatile link layer services transparently from the well-established I2C link layer protocol. We design the RFBus with a joint consideration of system-level performance and deployment costs and evaluate the prototypes in indoor and outdoor scenarios. The result indicates that the proposed architecture achieves 6.09 x (indoor) and 6.69 x (outdoor) energy saving and reduces the unit price of sensor nodes by 23.5% (indoor) and 33.5% (outdoor).

The rapid development of IoT applications calls for re-configurable IoT devices that can easily extend new functionality on demand. However, in the current architecture, updating chip functions on the end device is highly coupled with the local microprocessor in both hardware and software aspects, leading to inadequate flexibility. In this paper, we propose LEGO, a lightweight architecture with chip-level plug-and-play capabilities for IoT end devices. To achieve this, we first decoupling the control over heterogeneous chips from end devices to the gateway, and design a novel Unified Chip Description Language (UCDL) to access various types of functional chips uniformly. To supporting chips plug-and-play, we design a novel signal converting circuit on end devices to generate all required underlying signals for chip control. We also design a layered instruction orchestrator and hierarchical scheduler to minimize transmission overhead. The results show that our LEGO system can respond to chips plug-and-play within 0.13 seconds, and the lightweight architecture could reduce 49%$\sim$61% of power consumption in practical scenarios compared with traditional IoT end devices that are controlled by a microprocessor. The lightweight and easy-to-deploy features of LEGO makes it helpful to reduce deployment cost, thus conducive to accelerating large-scale applications.

USB flash drives are widely employed for data storage including sensitive personal or business data. Current defense strategies to protect those data mainly focus on preventing data theft when a USB drive plugs into a host computer that is infected with malware. This paper reveals a threat - attackers can produce spy USB flash drives that are able to leak the stored data via covert wireless communication without triggering security defenses on host computers. In this paper, we presentSpyUSB, a USB flash drive implanted with a backscatter-based data theft hardware to demonstrate the threat of covert data theft.SpyUSBcollects data from the physical layer of the communication between the host computer andSpyUSBdevice, which is transparent to the security mechanisms on the host computer.SpyUSBleverages backscatter communication to create a covert wireless channel. Furthermore, we explore the opportunity of covert data theft when theSpyUSBdevice is disconnected from the host computer using a tiny energy reservoir. Our experiment shows thatSpyUSBcan achieve a transmission bandwidth of up to 1,600 kbps. After unplugged from a computer, it can maintain standby for over 6 hours or continuously transmit data for 1.9 hours.

Streaming triangle counting is a critical issue in graph stream mining, with applications in dense subgraph discovery, web mining, anomaly detection, and more. Recent efforts have focused on estimating triangle counts in graph streams, primarily through sampling methods. However, because of limited memory resources for handling high speed streams, traditional sampling methods suffer from reduced sampling rate and thereby performance loss. In this paper, we propose a new compact data structure called uHLL to process edge streams by considering the tradeoff between estimation accuracy and memory efficiency. Furthermore, different from conventional triangle counting algorithms, we solve the estimation of union set cardinality for edge-local triangle count under both centralized and distributed framework, so as to efficiently estimate the global triangle count by a one-pass streaming algorithm. To the best of our knowledge, this is the first implementation of a distributed framework using a compact data structure for streaming triangle counting. We provide theoretical proof of unbiasedness and derive the variance of the union set and global triangle count. We compare our scheme with 11 algorithms, showing that under the same experimental setting, uHLL and distributed uHLL are at least$ 2.3$and$ 1.7$times more accurate than the state-of-the-art, respectively.

RFID (Radio Frequency Identification) computing devices in practical applications often suffer from their poor computing performance in terms of low task throughput (also known as task rate) due to scarce harvested power. For optimizing the task throughput, the basic idea is to choose an optimal processor clock frequency when executing a specific task ($e.g.$, operate sensor) in order to maximize task execution rate. Existing methods are based on the common sense where the frequency and task throughput are directly proportional to each other, meaning that a higher frequency causes a higher task rate. In RFID-scale devices, however, we observe that the relationship between the frequency and task throughput overturns the common sense, in which if the device rises the frequency, the task throughput will increase first and then decrease due to intermittent task execution pattern on such devices. In this paper, we present a systematic task throughput model to explain and formulate the non-monotonic relationship between the frequency and task throughput. Based on the throughput model, we further introduce dynamic optimal frequency scaling (DOFS) to calculate the optimal frequency for task execution and thus optimize the task throughput in the RFID-scale devices. The experimental results show that the task throughput can be improved by 45.8% on average compared to the existing best effort.

Versatile Internet of Things (IoT) applications call for re-configurable IoT devices that can easily extend new functionality on demand. However, the heterogeneity of functional chips brings difficulties in device customization, leading to inadequate flexibility. In this paper, we propose LEGO, a novel architecture for chip-level re-configurable IoT devices that supports plug-and-play with Commercial Off-The-Shelf (COTS) chips. To combat the heterogeneity of functional chips, we first design a novel Unified Chip Description Language (UCDL) with meta-operation and chip specifications to access various types of functional chips uniformly. Then, to achieve chips plug-and-play, we build up a novel platform and shift all chip control logic to the gateway, which makes IoT devices entirely decoupled from specific applications and does not need to make any changes when plugging in new functional chips. Finally, to handle communications overheads, we built up a novel orchestration architecture for gateway instructions, which minimizes instruction transmission frequency in remote chip control. We implement the prototype and conduct extensive evaluations with 100+ types of COTS functional chips. The results show that new functional chips can be automatically accessed by the system within 0.13 seconds after being plugged in, and only bringing 0.53 kb of communication load on average, demonstrating the efficacy of LEGO design.

Designing ultra-low power RFID sensor tags is a major challenge, especially when incorporating a micro-controller (MCU) to operate sensors. While simplifying MCU functionality can reduce power consumption, it has limited effect as the fundamental information transformation is necessary for communication between the RFID reader and the sensor. Unfortunately, information transformation requires baseband sampling and processing, which consumes significant power on passive RFID tags. This paper proposes a novel approach that enables the reader to communicate directly with the sensor, eliminating the need for information transformation of MCU. We address the unique challenges posed by the physical and link layers of the EPC Gen2 protocol and introduce GoodID, a cross-layer design for next-generation RFID sensor tags featuring ultra-low power consumption. We prototype the GoodID tag for proof-of-concept and demonstrate significant power benefits through experimental results.

Accurate and robust specific emitter identification (SEI) is very challenging since distribution shift of signals occurs in cross-session scenario. General domain adaptation (DA) is proposed to alleviate the shift by aligning different signal distributions. However, existing general-DA based SEI methods which focus on the shift in the same session cannot be directly applied to cross-session SEI, since the distribution of signals varies more drastically in different sessions due to the continuously changing hardware imperfections. In this paper, we propose a novel method named adversarial domain adaptation with wasserstein distance (ADAW) to tackle the cross-session SEI. Specifically, to alleviate the severer distribution shift of signals in different sessions, a generative model is applied to map the data of previous session to latter session regardless of the degree of radio frequency fingerprints (RFFs) variations. Then, a wasserstein distance guided adversarial unsupervised domain adaptation (UDA) strategy is introduced to learn common feature representations for signals of different sessions, such that the model trained on the signals of previous session can precisely identify the signals of latter session. Experiments on ADS-B signals of same emitters in three distinct time sessions validate the capability of ADAW for SEI under cross-session and noisy conditions.

Electrocardiogram (ECG) signal based cross-subject emotion recognition methods reduce the influence of individual differences using domain adaptation (DA) techniques. These methods generally assume that the entire unlabeled data of unknown target subjects are available in training phase. However, this assumption does not hold in some practical scenarios where the data of target subjects arrive one by one in an online manner instead of being acquired at a time. Thus, existing DA methods cannot be directly applied in this case since the unknown target data is inaccessible in training phase. To tackle the problem, we propose a novel online cross-subject ECG emotion recognition method leveraging hypergraph-based online transfer learning (HOTL). Specifically, the proposed hypergraph structure is capable of learning the high-order correlation among data, such that the recognition model trained on source subjects can be more effectively generalized to target subjects. Meanwhile, the structure can be easily updated by adding a hyperedge which connects a newly coming sample with the current hypergraph, resulting in further reduce the individual differences in online manner without re-training the model. Consequently, HOTL can effectively deal with the online cross-subject scenario where unknown target ECG data arrive one by one and varying overtime. Extensive experiments conducted on the Amigos dataset validate the superiority of the proposed method.

Differing from tremendous existing works that mainly focus on optimizing backscatter communication, Radio-to-Bus (R2B) communication utilizes backscatter to offload processors from IoT devices to the gateway, achieving processor-free devices of significantly reduced power and hardware cost. However, R2B communication is not suitable for large-scale backscatter networks, since R2B cannot support parallel and long-range communication between the gateway and hundreds of R2B devices. In this article, we present Chipnet, a network that supports hundreds of long-range and concurrent connections between the gateway and multiple processor-free devices. The high-level design of Chipnet includes a parallel frequency-division uplink mechanism that can work on processor-free devices and a processor-free MAC layer protocol that supports gateway to broadcast downlink data and individually manage each processor-free device. This design addresses practical issues facing the processor-free device architecture, such as synchronizing hundreds of processor-free devices, assigning unique channel frequencies to every device, and realizing power-efficient processor-free signal conversion. The results demonstrate that a Chipnet network can achieve a task throughput of 2,400 tasks/s with a latency of 72.23 ms. Compared with the R2B network, Chipnet achieves 3×–5× improvements in network coverage range and two orders of magnitude improvement in both network throughput and network latency.

Distance bounding protocols guarantee a credible distance upper bound between the devices which require the spatial distance as a security parameter to defend Mafia Fraud attacks. However, in RF systems, the realization of distance bounding protocol faces obstacles due to low spectrum efficiency, since the distance bound estimation consumes a significant amount of frequency band in existing schemes. This hinders RF distance bounding from being practically deployed, especially in commonly used ISM bands. In this work, we propose an alternative, spectrum-efficient scheme for RF distance bounding. We build the physical layer as well as a protocol design based on SFCW signal and SFCW ranging. Thus, comparing existing schemes that consume many frequency bands, our scheme frees many spectrum resources. We propose solutions to the unique challenges facing such an SFCW-based scheme design, namely, data communication over unintelligent SFCW signals, and secure synchronization in the SFCW-based challenge-response exchange. We evaluate our scheme via the security analysis and physical layer simulations. The results show (i) its resistance to attacks commonly concerned in distance bounding, (ii) the feasibility of the physical layer design such as accurate ranging and data communication function, and (iii) the communication noise tolerance and the ability of multipath signal discrimination.

Energy consumption of Internet-of-Things end devices is a major constraint that limits their long-term and large-scale deployment. Conventionally, the radios and processors used in these end devices are major power consumption that drains at the level of milliwatts (mWs). However, in recent decades, backscatter communication has dramatically reduced the power consumed by the radios in end devices to microwatts (μWs), and thus the processor remains the major bottleneck for energy optimization.

Over last two decades, Speaker Recognition has primarily been focused on source, system, and prosodic features of the speech. The breath, however, has either been treated as a trivial part of the speech, or considered a noise entity. Our observation reveals that breath is a unique fingerprint of human respiratory system which offers overwhelming results for Speaker Recognition. Moreover, its passive nature, short-duration, fewer occurrences and simple processing results to a light-weight, text-independent and transparent system, which we articulate as BreathID. The breath features are extracted and classified by Mel Frequency Cepstral Coefficients, MFCC, based template matching technique. The verification is performed by a similarity based scheme, whose efficiency competes with classification algorithms. We process a data set collected from 50 users. Our system offers a 0.04 percent False Identification Rate, FIR, for Speaker Identification, and 0.12 percent False Acceptance Rate, FAR, and 0.15 percent False Rejection Rate, FRR, for Speaker Verification. We further evaluate our scheme under various practical modalities, like text in-dependence, replay scenario, users' motion status (sitting and walking), recording equipment (03 smartphones and 02 microphones), recording period (08 months), and bilingual contents (English and Chinese). Though we use Matlab to formulate a fine-grained approach, we foresee breath biometric as a viable security measure for practical realizations.

Heart rate (HR) monitoring using photoplethysmography (PPG) is a promising feature in modern wearable devices. PPG is easily contaminated by motion artifacts (MA), hindering estimation of HR. For quasi-periodic motions, previous works generally focused on a few specific motions, such as walking and fast running. However, they may not work well for many different quasi-periodic motions where MA are very complex. In this paper, a robust HR monitoring scheme for different quasi-periodic motions using wrist-type PPG is proposed, which consists of dictionary learning for signal characteristics learning, human motion recognition for the current motion recognition and dictionary selection, sparse representation-based MA elimination for denoising, and spectral peak tracking for HR-related spectral peak tracking. The proposed scheme is robust to MA caused by different motions and has high accuracy. Experiments on six common quasi-periodic motions showed that the average absolute error of heart rate estimation was 2.40 beat per minute, and also showed that the proposed method is more robust than some state-of-the-art approaches for different motions.

As a result of the limited available energy, radio frequency (RF)-powered devices must be capable of efficiently utilizing scarce energy by planning task execution according to the current harvested energy. However, the energy utilization efficiency is challenging to be improved in RF-powered devices, since sensing the harvested energy consumes a significant amount of energy that should be used for task execution. In this paper, we propose Sentinel, a novel low power method to sense the harvested energy. Sentinel is fully delegated to detect the energy for the device, while the device does not participate in the energy sensing. By this means, the computing overhead of the device is reduced. Sentinel works with low energy consumption, and functions as a trigger to activate the device when, and only when, the energy reaches an expected energy threshold. We also present a lightweight scheme to set the desired thresholds so that Sentinel achieves detecting any expected thresholds. We implement Sentinel by off-the-shelf components and conduct experiments to show that Sentinel consumes only 5.2% of energy overhead of the general energy sensing technique. With Sentinel, we show that the energy utilization efficiency can be improved up to 94.9%, outperforming the best existing works at 64.7% in the WISP platform.

The collaborative filtering (CF) based models are capable of grasping the interaction or correlation of users and items under consideration. However, existing CF-based methods can only grasp single type of relation, such as restricted Boltzmann machine which distinctly seize the correlation of user-user or item-item relation. On the other hand, matrix factorization explicitly captures the interaction between them. To overcome these setbacks in CF-based methods, we propose a novel deep learning method which imitates an effective intelligent recommendation by understanding the users and items beforehand. In the initial stage, corresponding low-dimensional vectors of users and items are learned separately, which embeds the semantic information reflecting the user-user and item-item correlation. During the prediction stage, a feed-forward neural networks is employed to simulate the interaction between user and item, where the corresponding pretrained representational vectors are taken as inputs of the neural networks. Several experiments based on two benchmark datasets (MovieLens 1M and MovieLens 10M) are carried out to verify the effectiveness of the proposed method, and the result shows that our model outperforms previous methods that used feed-forward neural networks by a significant margin and performs very comparably with state-of-the-art methods on both datasets.

Heart rate (HR) monitoring based on Photoplethys-mography (PPG) has drawn increasing attention in modern wearable devices due to its simple hardware implementation and low cost. In this work, we propose a variational mode decomposition(VMD)-based HR estimation method using wrist-type PPG signals during physical exercise. To remove motion artifacts (MA), VMD was first used and then a post-processing method after VMD was proposed to guarantee the robustness of MA removal. The performance of our proposed method was evaluated on two PPG datasets used in 2015 IEEE Signal Processing Cup. The method achieved the average absolute error of 1.45 beat per minute (BPM) on the 12 training sets and 3.19 BPM on the 10 testing sets, confirmed by the experimental results.

Computational Radio Frequency Identification (CRFID) tags operate solely on harvested energy and have emerged as viable platforms for a variety of ubiquitous sensing and computation applications. Due to their battery-less nature, these tags can be permanently deployed in hard-to-reach places where the possibility of tag access is eliminated. In such scenarios, maintaining and upgrading the tag’s firmware becomes infeasible because programming tools, including wired interface and PC-based software, are required to erase, modify, or reprogram the microcontroller unit’s memory. Such limitations necessitate the demand for an over-the-air (OTA) scheme, which can wirelessly reprogram or upgrade the firmware in CRFID tags. In this article, we present R 3 —a reliable OTA reprogramming scheme that is compliant with EPC protocol and requires no hardware upgrade to RFID reader or CRFID tag. We demonstrate our scheme on three platforms, which include both software-defined as well as chip-based CRFID tags, that is, WISP5.1 and Optimized WISP (Opt-WISP), and Spider tag, respectively. The selection also includes both the FLASH- and FRAM-based microcontrollers. We extensively evaluate our scheme in terms of several metrics, including overall system delay, time and energy overhead, and success rate in line with interrogation range. We foresee our endeavor to offer the viability of OTA reprogramming and firmware upgrade for CRFID tokens under practical situations.

We present the design, implementation, and evaluation of an RFID-based cellphone anti-lost and anti-theft measure. The cellphone owner is given an audio-visual alert at the very instant of the lost event, and the cellphone deduces the fact that it is away from its owner and executes the safety measures. This is realized by equipping the cellphone with a low-power RFID Reader and tagging the owner with a passive RFID token to determine a private space around him, which spans within 2-10 feet. We prototype an RFID Reader from discrete components under $60 which can transmit 30 dBm with -78 dBm sensitivity, and can also serve as an educational tool for academic learning. Our system works on automatic, timed, or accelerometer based thresholds. We interface our system with Samsung Galaxy Note2 and develop an Android User Interface. We carry out extensive indoor and outdoor experiments under static and dynamic scenarios to ascertain the Frontal and Angular ranges, energy and power consumption, and memory and computational overheads. Our salient contribution is a twofold probing scheme-a duty cycle approach that economizes battery overhead, mitigates false alarms and scans the tag for multiple times by leveraging the interrogation time and power. We argue that though our design is costly in power budget, it is highly economical on battery energy because of short interrogation cycles. We show that for 17 tag interrogations from 20-24 dBm, our scheme consumes 72.1 to 52.4 percent lower energy than a single Bluetooth device scan. For a fully embedded design, we propose System-on-Chip RFID solutions. We foresee our endeavor as a viable proximity absence detection scheme for short range applications and scenarios.

Smartphones have been widely used with a vast array of sensitive and private information stored on these devices. To secure such information from being leaked, user authentication schemes are necessary. Current password/pattern-based user authentication schemes are vulnerable to shoulder surfing attacks and smudge attacks. In contrast, stroke/gait-based schemes are secure but inconvenient for users to input. In this paper, we propose ShakeIn, a handy user authentication scheme for secure unlocking of a smartphone by simply shaking the phone. With embedded motion sensors, ShakeIn can effectively capture the unique and reliable biometrical features of users about howthey shake. In this way, even if an attacker sees a user shaking his/her phone, the attacker can hardly reproduce the same behavior. Furthermore, by allowing users to customize the way they shake the phone, ShakeIn endows users with the maximum operation flexibility. We implement ShakeIn and conduct both intensive trace-driven simulations and real experiments on 20 volunteers with about 530,555 shaking samples collected over multiple months. The results show that ShakeIn achieves an average equal error rate of 1.2 percent with a small number of shakes using only 35 training samples even in the presence of shoulder-surfing attacks.

Access to accurate relative front-rear distance information between vehicles can be of great interest to drivers as such information can be utilised to improve driving safety. Acquiring such information in urban settings is very challenging due to the high complexity of urban environments. In this paper, we propose a novel scheme, called RUPS, to tackle the relative distance fixing problem. We first investigate pervasive GSM signals and find that the received signal strength indicator (RSSI) of multiple GSM channels measured over a distance has ideal temporal-spatial characteristics for temporary fingerprinting. With this observation, an RUPS-enabled vehicle first perceives the information of its GSM-aware trajectory while moving. Then by exchanging and comparing its own trajectory with that of a neighbouring vehicle, the vehicle can identify common locations overlapped on both trajectories. Finally, the relative distance between this pair of vehicles can be obtained by further comparing their geographical trajectories since that common location. As a result, RUPS is a fully distributed and lightweight scheme, requiring only a minimum hardware deployment, and does not need synchronization between vehicles or any pre-constructed signal maps. Extensive trace-driven simulation results show that RUPS can work stably under complex urban environments and overwhelm the performance of GPS by 2.7 times on average.

Radio Frequency Identification (RFID) is a key emerging technology for supply chain systems. By attaching RFID tags to various products, product-related data can be efficiently indexed, retrieved and shared among multiple participants involved in an RFID-enabled supply chain. The flexible data access property, however, raises security and privacy concerns. In this paper, we target at security and privacy issues in RFID-enabled supply chain systems. We investigate RFID-enabled Third-party Supply chain (RTS) systems and identify several inherent security and efficiency requirements. We further design a Secure RTS system called SRTS, which leverages RFID tags to deliver computation-lightweight crypto-IDs in the RTS system to meet both the security and efficiency requirements. SRTS introduces a Private Verifiable Signature (PVS) scheme to generate computation-lightweight crypto-IDs for product batches, and couples the primitive in RTS system through careful design. We conduct theoretical analysis and experiments to demonstrate the security and efficiency of SRTS.

Participatory regression modeling is a cost-efficient mechanism to establish the relationships among multiple dimensions of sensory data collected from volunteers. Getting an accurate model estimate is challenging for two main reasons. First, with the concern of confidentiality of individual private data, the original data are nearly unavailable; second, low quality data with outliers are inherently embedded in the collected data. In this paper, we propose an innovative scheme, PURE, which can accurately estimate the global regression model without the need for knowing local private data (referred to as blind regression modeling) even when there is a large portion of outliers embedded. The wisdom of PURE is to let individual participants peer judge and further improve the global estimate via negotiations. Meanwhile, during the whole process, all information is exchanged in an aggregated way. By design, PURE is secure and can well protect individual privacy. Furthermore, PURE is a lightweight protocol suitable for mobile devices. Extensive trace-driven simulation results show that PURE can achieve an outstanding accuracy gain of two orders of magnitude even with random outliers near a ratio of 50 percent compared with the state-of-the-art least square estimator.

Mobile advertising in vehicular networks is of great interest with which timely information can be fast spread into the network. Given a limited budget for hiring seed vehicles, how to achieve the maximum advertising coverage within a given period of time is NP-hard. In this paper, we propose an innovative scheme, POST, for mobile advertising in vehicular networks. The POST design is based on two key observations we have found by analyzing three large-scale vehicular traces. First, vehicles demonstrate dynamic sociality in the network; second, such vehicular sociality has strong temporal correlations. With the knowledge, POST uses Markov chains to infer future vehicular sociality and adopts two greedy heuristics to select the most “centric” vehicles as seeds for mobile advertising. Extensive simulations based on three real data sets of taxi and bus traces have been carried out. The results show that POSTcan greatly improve the coverage and the intensity of advertising. For all the three involved data sets, it achieves an average gain of 64 percent comparing with the state-of-art schemes.

We present a physical layer solution to RF distance bounding, which reduces the prover's processing delay virtually to zero by employing the concepts of frequency modulated continuous wave (FMCW) and secondary radars, and equipping it with the capability of cryptographic communication over unintelligent radar signals. To realize, we encode the verifier's challenges in waveform slope of signals while the prover communicates its cryptographic replies in shape of backscatter modulation. For resilience to multipath, the prover introduces analog frequency modulation using the principle of secondary radar. Our protocol is based on pre-commitment distance bounding scheme. Our paper primarily focuses on design intricacies of a realizable system while we briefly present the simulation results. We critically analyze the security of our protocol against attacks most concerned in distance bounding and thoroughly investigate its robustness under noise and multipath environments. A shortcoming of our design is low range resolution at lower spectral bandwidths, which can be foretoken as system tolerance. We foresee our endeavor to present a viable zero-delay RF distance bounding scheme while minimizing the hardware, energy, and computational overhead for passive and semipassive tokens.

With the development of Location Based Services (LBSs), both academic researchers and industries have paid more attention to GPS-less mobile localization on mobile phones. The majority of the existing localization approaches have utilized signal-fingerprint as a metric for location determinations. However, one of the most challenging issues is the problem of uncertain fingerprints for building the fingerprint map, termed as the jigsaw puzzle problem. In this paper, for more accurate fingerprints of the mobile localization, we investigate the changes of Received Signal Strength Indication (RSSI) from the connected cell-towers over time along the mobile users' trajectories, termed as RSSI Time Series (RTS). Thus, we propose an RTS Assisted Localization System (RALS), which is a GPS-less outdoor mobile localization system. For localization, an RTS map is built on the back-end server, which consists of RTS harvested from the mobile phones, by the way of crowd sensing. The jigsaw puzzle problem slows down the map construction solely by the regular unintentional users with short-distance trajectories, and affects its efficiency. To speed up the map construction, we propose employing a few advanced intentional users with additional long-distance trajectories, at a higher cost than the regular user, this is called extra mile. Our extensional experiments verify the effectiveness of our localization system.

In recent past, the rapid developing of mobile internet inspires the widespread use of WiFi (IEEE 802.11) technology. In WiFi, the access control of a terminal to the router remains a significant challenge because the PIN (password) and MAC address are easy to guess and forge. In this paper, we present FastID - a practical system that identifies WiFi terminals in real-time by fingerprinting their clocks. Previous approaches of clock fingerprinting require tens of minutes or even hours for clock data collection, and thus cannot be applied into real-time WiFi terminal identification. Even worse, unstable wireless communications and unknown status of terminals' OSes may further degrade the accuracy of fingerprint computation. In comparison, FastID performs fast clock fingerprinting based on the timestamps carried by terminals' ICMP packets. Moreover, FastID employs simple but efficient techniques to remove outliers of collected clock data and differentiate terminals based on the similarity of their distributions, making it suitable for fast terminals identification. FastID is implemented on an off-the-shelf commercial WiFi router and extensively evaluated based on 10 commodity WiFi terminals. Experimental results show that FastID is able to identify terminals with high accuracy and low cost within several seconds.

With the emergence of smartphones as an essential part of daily life, the demand for user reauthentication has increased manifolds. The effective and widely practiced biometric schemes are based upon the principle of “who you are” which utilizes inherent and unique characteristics of the user. In this context, the behavioral biometrics such as sliding dynamics and pressure intensity make use of on-screen sliding movements to infer the user’s patterns. In this paper, we present Safeguard, an accurate and efficient smartphone user reauthentication (verification) system based upon on-screen finger movements. The computation and processing is performed at back-end which is transparent to the users. The key feature of the proposed system lies in fine-grained on-screen biometric metrics, i.e., sliding dynamics and pressure intensity, which are unique to each user under diverse scenarios. We first implement our scheme through five machine learning approaches and finally select the support vector machine (SVM)-based approach due to its high accuracy. We further analyze Safeguard to be robust against adversary imitation. We validate the efficacy of our approach through implementation on off-the-shelf smartphone followed by practical evaluation under different scenarios. We process a set of more than 50 000 effective samples derived from a raw dataset of over 10 000 slides collected from each of the 60 volunteers over a period of one month. The experimental results show that Safeguard can verify a user with 0.03% false acceptance rate (FAR) and 0.05% false rejection rate (FRR) within 0.3 s with 15 to 20 slides by the user. The FRR of our system adequately meets the European Standard for Access Control Systems, whereas FAR differs by 0.029%. Our future works aim to integrate multitouch sliding movements in existing scheme.

As distance bounding can be leveraged for solving numerous security issues, extensive studies have been carried out for its implementation. Realizing RF distance bounding in battery-less or constrained devices is quite challenging because of inadequate harvested energy and large signal processing delays. We present TIGHT, an RF distance bounding scheme based on Signal Conditioning and Polarization Selection, with which a prover codes and reflects the incident challenges as a polarization function at analog RF at 1 nsec. In addition, our focus lies in designing TIGHT as a full duplex energy optimized system while considering the device synchronization in passive hardware. Security analysis shows that TIGHT is resilient to attacks most concerned in distance bounding. We demonstrate our scheme through prototype implementation and practical evaluation for delay measurements and calculate bit error rate while considering channel interference at ten outdoor and indoor places. Dealing with noise, we estimate the protocol failure, false-acceptance and false-rejection probabilities for TIGHT. Our results show that TIGHT is an effective RF distance bounding approach for passive wireless devices, especially the RFID tokens.

Wormhole attack is one of the severe threats to wireless sensor and ad hoc networks. Most of the existing countermeasures either require specialized hardware or demand high network overheads in order to capture the specific symptoms induced by the wormholes, which in result, limits their applicability. In this paper, we exploit an inevitable symptom of wormholes and present Pworm, a passive wormhole detection and localization system based upon the key observation that a large amount of network traffic will be attracted by the wormholes. The proposed passive and real-time scheme silently observes the variations in network topology to infer the wormhole existence. Our approach relies solely on network routing information and does not necessitate specialized hardware or poses rigorous assumptions on network features. We evaluate our system performance through extensive simulations of 100 to 500 nodes for various network scales and show that Pworm is well suited for false alarms, scalability and time delay.

Batch recall is a practically important problem for most industry manufacturers. The batches of products which contain flawed parts need to be recalled by manufacturers in time to prevent further economic and health loss. Accurate batch recall could be a challenging issue as flawed parts may have already been integrated into a large number of products and distributed to customers. The recent development of Radio Frequency Identification (RFID) provides us a promising opportunity to implement batch recall in an accurate and efficient way. RFID-enabled batch recall provides us the opportunity to further enhance the security of batch recall operation, allowing us to achieve recognition of problematic products, privacy preserving of production pattern, recall authentication and non-repudiation, etc. In this paper, we thoroughly study the security aspects and identify the unique requirements in RFID-enabled batch recall. We propose a practically secure protocol, COLLECTOR, to enable accurate, secure and efficient RFID batch recall.

Mobile advertising in vehicular networks is of great interest with which timely information can be fast spread into the network. Given a limited budget for hiring seed vehicles, how to achieve the maximum advertising coverage within a given period of time is NP-hard. In this paper, we propose an innovative scheme, POST, for mobile advertising in vehicular networks. The POST design is based on two key observations we have found by analyzing three large-scale vehicle traces. First, vehicles demonstrate dynamic sociality in the network; second, such vehicular sociality has strong temporal correlations. With the knowledge, POST uses Markov chains to infer future vehicular sociality and adopts one greedy heuristic to select the most “centric” vehicles as seeds for mobile advertising. Extensive trace-driven simulation results show that POST can greatly improve the coverage and the intensity of advertising.

Recent work has demonstrated that path diversity is an effective way to improve the end-to-end performance of network applications. For every node pair in a full-mesh network with $(n)$ nodes, this paper presents a family of new approaches that efficiently identify an acceptable indirect path that has a similar to or even better performance than the direct path, hence considerably scaling the network at the cost of low per-node traffic overhead. In prior techniques, every node frequently incurs $(O(n^{1.5}))$ traffic overhead to probe the links from itself to all other nodes and to broadcast its probing results to a small set of nodes. In contrast, in our approaches, each node measures its links to only $(O(\sqrt{n}))$ other nodes and transmits the measuring results to $(O(\sqrt{n}))$ other nodes, where the two node sets of size $(O(\sqrt{n}))$ are determined by the partial sampling schemes presented in this paper. Mathematical analyses and trace-driven simulations show that our approaches dramatically reduce the per-node traffic overhead to $(O (n))$ while maintaining an acceptable backup path for each node pair with high probability. More precisely, our approaches, which are based on enhanced and rotational partial sampling schemes, are capable of increasing said probability to about 65 and 85 percent, respectively. For many network applications, this is sufficiently high such that the increased scalability outweighs such a drawback. In addition, it is not desirable to identify an outstanding backup path for every node pair in reality, due to the variable link quality.

Radio Frequency Identification (RFID) technique is gaining increasing popularity in supply chain for the product management. By attaching a tag to each product, a reader can employ an authentication protocol to interrogate the tag's information for verification, which facilitates the automatic processing and monitoring of products in many applications. However, most current solutions cannot be directly used as they cannot balance the tradeoff between the privacy and efficiency for individual parties. In this paper, we design a bidirectional efficiency-privacy transferable (BEST) authentication protocol to address this issue. In a relatively secure domain, BEST works in an efficient manner to authenticate batches of tags with less privacy guarantee. Once the tags flow into open environment, BEST can migrate to provide stronger privacy protection to the tags with moderate efficiency degradation. The analytic result shows that BEST can well adapt to the RFID-enabled supply chain.

In this paper, we analyze on the use of token-controlled public key encryption (TCE)schemes. We argue that for many of the applications in some possibilities of application in financial or legal scenarios, for example, the millionaire's will problem, the `private-opening' commitment, the scheduled payment problem, and the sealed-bid auctions and electronic lotteries problem, in the literature, the use of token-controlled public key encryption on its own, leads to inadequate solutions. We suggest that when considering applications of TCE, it is advisable to pay close attention to the lack of authentication and incorporate defences against the problems highlighted in this paper.

Privacy-Preserving Authentication (PPA) is crucial for Radio Frequency Identifcation (RFID)-enabled applications. Without appropriate formal privacy models, it is difficult for existing PPA schemes to explicitly prove their privacy. Even worse, RFID systems cannot discover potential security flaws that are vulnerable to new attacking patterns. Recently, researchers propose a formal model, termed as Strong Privacy, which strictly requires tags randomly generate their output. Adopting the Strong Privacy model, PPA schemes have to employ brute-force search in tags' authentications, which incurs unacceptable overhead and delay to large-scale RFID systems. Instead of adopting Strong Privacy, most PPA schemes improve the authentication efficiency at the cost of the privacy degradation. Due to the lack of proper formal models, it cannot be theoretically proven that the degraded PPA schemes can achieve acceptable privacy in practical RFID systems. To address these issues, we propose a weak privacy model, Refresh, for designing PPA schemes with high efficiency as well as acceptable privacy. Based on Refresh, we show that many well-known PPA schemes do not provide satisfied privacy protection, even though they achieve relatively high authentication efficiency. We further propose a Light-weight privAcy-preServing authenTication scheme, LAST, which can guarantee the privacy based on the Refresh model and realize O(1) authentication efficiency, simultaneously.

In order to protect privacy, radio frequency identification (RFID) systems employ privacy-preserving authentication (PPA) to allow valid readers to explicitly authenticate their dominated tags without leaking private information. Typically, an RF tag sends an encrypted message to the reader, then the reader searches for the key that can decrypt the cipher to identify the tag. Due to the large-scale deployment of today's RFID systems, the key search scheme for any PPA requires a short response time. Previous designs construct balance-tree based key management structures to accelerate the search speed to 0(logN), where N is the number of tags. Being efficient, such approaches are vulnerable to compromising attacks. By capturing a small number of tags, compromising attackers are able to identify other tags that have not been corrupted. To address this issue, we propose an Anti- Compromising authenticaTION protocol, ACTION, which employs a novel sparse tree architecture, such that the key of every tag is independent from one another. The advantages of this design include: 1) resilience to the compromising attack, 2) reduction of key storage for tags from 0(logN) to 0(1), which is significant for resource critical tag devices, and 3) high search efficiency, which is 0(logN), as good as the best in the previous designs.

Most of the current trust models in peer-to-peer (P2P) systems are identity based, which means that in order for one peer to trust another, it needs to know the other peer's identity. Hence, there exists an inherent tradeoff between trust and anonymity. To the best of our knowledge, there is currently no P2P protocol that provides complete mutual anonymity as well as authentication and trust management. We propose a zero-knowledge authentication scheme called pseudo trust (PT), where each peer, instead of using its real identity, generates an unforgeable and verifiable pseudonym using a one-way hash function. A novel authentication scheme based on zero-knowledge proof is designed so that peers can be authenticated without leaking any sensitive information. With the help of PT, most existing identity-based trust management schemes become applicable in mutual anonymous P2P systems. We analyze the security and the anonymity in PT, and evaluate its performance using trace-driven simulations and a prototype PT-enabled P2P network. The strengths of our design include (1) no need for a centralized trusted party or CA, (2) high scalability and security, (3) low traffic and cryptography processing overheads, and (4) man-in-middle attack resistance.

Most of the current trust models in peer-to-peer (P2P) systems are identity based, which means that in order for one peer to trust another, it needs to know the other peer's identity. Hence, there exists an inherent tradeoff between trust and anonymity. To the best of our knowledge, there is currently no P2P protocol that provides complete mutual anonymity as well as authentication and trust management. We propose a zero-knowledge authentication scheme called pseudo trust (PT), where each peer, instead of using its real identity, generates an unforgeable and verifiable pseudonym using a one-way hash function. A novel authentication scheme based on zero-knowledge proof is designed so peers can be authenticated without leaking any sensitive information. With the help of PT, most existing identity-based trust management schemes become applicable in mutual anonymous P2P systems. We analyze the levels of security and anonymity in PT, and evaluate its performance using trace-driven simulations and a prototype implementation. The strengths of pseudo trust include the lack of need for a centralized trusted party or CA, high scalability and security, low traffic and cryptography processing overheads, and man-in-middle attack resistance. We aim for the pseudo trust design to be included in the P2P trust and anonymity context.

The objective of private authentication for radio frequency identification (RFID) systems is to allow valid readers to explicitly authenticate their dominated tags without leaking tags' private information. To achieve this goal, RFID tags issue encrypted authentication messages to the RFID reader, and the reader searches the key space to locate the tags. Due to the lack of efficient key updating algorithms, previous schemes are vulnerable to many active attacks, especially the compromising attack. In this paper, we propose a strong and lightweight RFID private authentication protocol, SPA. By designing a novel key updating method, we achieve the forward secrecy in SPA with an efficient key search algorithm. We also show that, compared with existing designs, SPA is able to effectively defend against both passive and active attacks, including compromising attacks. Through prototype implementation, we observe that SPA is practical and scalable in current RFID infrastructures