Decentralized federated learning (DFL) enables collaborative training through peer-to-peer (P2P) communication, removing reliance on a central coordinator and thereby improving fault tolerance and scalability. In practical DFL deployments, clients differ substantially in data distributions, computational resources, and network bandwidth. Consequently, enforcing a single model architecture across all clients is often infeasible or inefficient, motivating model-heterogeneous DFL. Prior heterogeneous approaches typically exchange distilled knowledge (e.g., logits, soft labels, or class prototypes) rather than model weights; however, under label-distribution skew they often improve performance primarily on locally observed (seen) classes and generalize poorly to locally missing (unseen) classes. We propose ProtoGenesis, a model-heterogeneous DFL framework that improves clients' recognition of unseen classes by combining (i) a semantic-preserving autoencoder trained on public data to support privacy-oriented sample reconstruction from compact latent embeddings and (ii) prototype-based regularization to stabilize representation learning. Clients proactively request lowdimensional embeddings and prototype statistics for selected classes from neighboring peers, reconstruct class-consistent samples locally, and augment training without exchanging raw data or full model parameters. We evaluate ProtoGenesis on image and text classification tasks using CIFAR-10, CIFAR-100, and DBpedia under heterogeneous model-mixing and non-IID settings. ProtoGenesis achieves comparable accuracy on seen classes while improving accuracy on unseen classes form near 0% to up to 70%. Privacy analysis shows that sensitive information cannot be reconstructed from shared embeddings. Moreover, ProtoGenesis reduces communication overhead by about 1500 × and demonstrates low inference and communication latency on a real-world Jetson-based DFL system.

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 ×.

Large Language Models (LLMs) have demonstrated exceptional natural language understanding and generation capabilities in many emerging applications. However, providing end users with real-time LLM inference on their mobile devices poses substantial challenges. In this work, we propose a fully distributed LLM inference framework, where spare computational resources across heterogeneous mobile devices are effectively harnessed. Our approach integrates two key components: initial model preloading and layer-wise execution scheduling. The proposed methods tackle two major challenges, optimal parameter preloading without prior knowledge of device availability and real-time inference scheduling under dynamic conditions. We evaluate the performance of our framework in real-world environments, experimental results show that it significantly reduces end-to-end delay by 44%∼83% compared to existing approaches.

Large Language Models (LLMs) have become foundational components in a wide range of applications, including natural language understanding and generation, embodied intelligence, and scientific discovery. As their computational requirements continue to grow, these models are increasingly deployed as cloud-based services, allowing users to access powerful LLMs via the Internet. However, this deployment model introduces a new class of threat: denial-of-service (DoS) attacks via unbounded reasoning, where adversaries craft specially designed inputs that cause the model to enter excessively long or infinite generation loops. These attacks can exhaust backend compute resources, degrading or denying service to legitimate users. To mitigate such risks, many LLM providers adopt a closed-source, black box setting to obscure model internals. In this paper, we propose ThinkTrap, a novel input-space optimization framework for DoS attacks against LLM services even in black-box environments. The core idea of ThinkTrap is to first map discrete tokens into a continuous embedding space, then undertake efficient black-box optimization in a low-dimensional subspace exploiting input sparsity. The goal of this optimization is to identify adversarial prompts that induce extended or non-terminating generation across several state-of-the-art LLMs, achieving DoS with minimal token overhead. We evaluate ThinkTrap across multiple commercial, closed-source LLM services and observe that it can consistently induce abnormally long outputs and noticeable response-side degradation under black-box access. To further quantify the system-level impact of the attack, we conduct controlled experiments on private LLM deployments, where ThinkTrap reduces service throughput to as low as 1% of its original capacity and, in extreme cases, induces complete service failure due to resource exhaustion.

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.

Ensuring stable and high-quality real-time video analytics for computationally constrained mobile agents is essential. However, limited computing resources and network bandwidth present significant challenges in meeting the objective of low response time and high inference accuracy. In this paper, we present DiVE, an edge-assisted video analytics system that utilizes motion vectors calculated by video codec to extract foregrounds and differentially encode frames. DiVE removes rotational components from motion vectors by solving over-determined linear equations and filters noisy motion vectors based on the observation that motion vectors of static objects point to the same point when the ego agent purely translates. To distinguish foregrounds from backgrounds, DiVE estimates the ground based on observations that all foregrounds stand on the ground and motion vectors on static objects at the same height have the same normalized magnitude. DiVE then uses region-growing-based clustering to identify foreground objects. An adaptive bitrate allocation method is applied to optimize accuracy under estimated bandwidth. We implement a prototype and conduct extensive experiments to evaluate the performance of DiVE. The results demonstrate that DiVE can improve detection accuracy by up to 19.0% and reduce response time by up to 56.0% compared with other video analytics schemes in real-world traces.

Computer-vision-based assessment on waste sorting is desired to replace manpower supervision in Shanghai city. Due to the hardness of labeling a multitude of waste images, it is infeasible to train a semantic segmentation model for this purpose directly. In this work, we construct a new dataset consisting of 12, 208 waste images, upon which seed regions (i.e., patches) are annotated and classified into 21 categories in a crowdsourcing fashion. To obtain pixel-level labels to train an effective segmentation model, we propose a weakly-supervised waste image pseudo label generation scheme, called WISNet. Specifically, we train a cohesive feature extractor with contrastive prototype learning, incorporating an unsupervised classification pretext task to help the extractor focus on more discriminative regions even with the same category. Furthermore, we propose an effective iterative patch expansion method to generate accurate pixel-level pseudo labels. Given these generated pseudo labels, a few-shot segmentation model can be trained to segment waste images. We implement and deploy WISNet in real-world scenarios and conduct intensive experiments. Results show that WISNet can achieve a state-of-the-art 40.2% final segmentation mIoU on our waste benchmark, outperforming all other baselines and demonstrating its efficacy. The dataset and code will be publicly available at: https://github.com/shifan-Z/WISNet

Controllable scene generation could reduce the cost of diverse data collection substantially for autonomous driving. Prior works formulate the traffic layout generation as predictive progress, either by denoising entire sequences at once or by iteratively predicting the next frame. However, full sequence denoising hinders online reaction, while the latter's short-sighted next-frame prediction lacks precise goal-state guidance. Further, the learned model struggles to generate complex or challenging scenarios due to a large number of safe and ordinal driving behaviors from open datasets. To overcome these, we introduce Nexus, a decoupled scene generation framework that improves reactivity and goal conditioning by simulating both ordinal and challenging scenarios from fine-grained tokens with independent noise states. At the core of the decoupled pipeline is the integration of a partial noise-masking training strategy and a noise-aware schedule that ensures timely environmental updates throughout the denoising process. To complement challenging scenario generation, we collect a dataset consisting of complex corner cases. It covers 540 hours of simulated data, including high-risk interactions such as cut-in, sudden braking, and collision. Nexus achieves superior generation realism while preserving reactivity and goal orientation, with a 40% reduction in displacement error. We further demonstrate that Nexus improves closed-loop planning by 20% through data augmentation and showcase its capability in safety-critical data generation.

Inertial measurement units (IMUs), have been prevalently used in a wide range of mobile perception applications such as activity recognition and user authentication, where a large amount of labelled data are normally required to train a satisfactory model. However, it is difficult to label micro-activities in massive IMU data due to the hardness of understanding raw IMU data and the lack of ground truth. In this paper, we propose a novel fine-grained user perception approach, called Saga, which only needs a small amount of labelled IMU data to achieve stunning user perception accuracy. The core idea of Saga is to first pre-train a backbone feature extraction model, utilizing the rich semantic information of different levels embedded in the massive unlabelled IMU data. Meanwhile, for a specific downstream user perception application, Bayesian Optimization is employed to determine the optimal weights for pre-training tasks involving different semantic levels. We implement Saga on five typical mobile phones and evaluate Saga on three typical tasks on three IMU datasets. Results show that when only using about 100 training samples per class, Saga can achieve over 90% accuracy of the full-fledged model trained on over ten thousands training samples with no additional system overhead.

Decentralized Federated Learning (DFL) removes the central server in traditional Centralized FL, eliminating performance and security bottlenecks while improving scalability for large-scale cross-device federated scenarios. In these scenarios, devices are heterogeneous in computation, storage, and data distribution, requiring personalized models. A common strategy for personalized DFL is for each device to collaborate with others having similar data distributions to train a federated model and then perform local pruning. However, in the absence of a central server, along with privacy concerns and the limited inference capabilities of low-end devices, challenges emerge in terms of communication, computation, and model convergence. This paper presents an efficient personalized DFL method, named PRISAM, based on BN (Batch Normalization) masks. Each device adaptively adjusts its BN mask, enabling effective structured pruning with minimal performance loss. By exchanging BN masks, communication overhead for similarity comparison is reduced by a factor of 100,000, comparing to exchanging an entire model, while computational costs are also significantly lowered. Extensive experiments show that PRISAM significantly improves personalized model performance on three datasets across two models, outperforming state-of-the-art methods, while greatly reducing computational and communication overhead.

To extend the receptive field of a mobile agent in complex scenarios, it is essential for multiple agents to cooperate with each other. However, it is challenging to achieve comprehensive 3D perception at the minimal computational and communication costs. In this paper, we propose CoPe, a lightweight and efficient collaborative 3D perception scheme for mobile agents. The main idea of CoPe is for an ego agent to query the most helpful information from its neighboring agents through a lightweight network attention mechanism. To this end, at each agent, we first leverage Singular Value Decomposition (SVD) to decompose a full-size point cloud feature into components. Meanwhile, with the novel self-attention and cross-attention algorithms, we respectively select the key component of an ego agent that best represent the point cloud of the ego agent as a query, and valuable components of each helper agent that are most relevant to the query as the answer. After feature reconstruction and aggregation, an ego agent can have a comprehensive understanding about the scene and make accurate predictions on downstream tasks. CoPe is lightweight and can be easily implemented on mobile devices. Results of extensive experiments conducted on both real-world and simulation datasets demonstrate that CoPe can achieve superior 3D object detection accuracy while significantly reducing the incurred computational and communication costs.

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.

The city perception problem which means to estimate the future information from historical data is of great importance for smart city applications. In this paper, by analyzing two metropolitan scale sensory datasets, we find that city perception data demonstrate strong spatial-temporal correlation. Based on this observation, we propose an innovative deep learning method, called DeepSeer, which can accurately estimate and predict signals at a metropolitan scale with short-term noisy sensory data. To this end, DeepSeer first reconstructs the original short-term data using multi-channel singular spectrum analysis (MSSA) to obtain sufficient training data. Second, because the metropolitan scale data would introduce an unacceptable computational complexity, DeepSeer only selects the sites that are spatially most relevant for prediction and uses a extended Treestructure Long Short-Term Memory (Tree-LSTM) to extract and fuse the features of different sites. We conduct extensive trace-driven simulations on both datasets. The average prediction accuracy of DeepSeer for both datasets can reach 90.75% and$\mathbf{9 1. 1 9 \%}$, respectively. The experiment results demonstrate the efficacy of the DeepSeer design.

With the rapid advancement of generative forgery technologies, the detection of multi-modal deepfake audio-video content has become an urgent demand in cyber security and forensic analysis. However, detecting audio-video deepfakes remains challenging: forgery traces are often subtle, dispersed, and highly resolution-dependent; existing multimodal methods rely on simple concatenation or shallow interactions, leading to insufficient exploitation of cross-modal consistency. To address these issues, we propose a Cross-level Multi-modal Fusion (CLMF) framework that progressively integrates audio cues into visual representations through cross-level attention, adaptively enhancing complementary information while suppressing redundancy. In addition, we design an Adaptive Audio Feature Enhancement module (AAFE) to highlight subtle frequencydomain artifacts often masked by noise, and a Multi-scale Visual Feature Enhancement module (MVFE) to capture both local texture inconsistencies and global structural distortions. These components jointly achieve robust and consistent cross-modal alignment of forgery traces, leading to significant improvements in deepfake detection performance. On the FakeAVCeleb benchmark, AURORA achieves an accuracy (ACC) of 94.32% and an area under the curve (AUC) of 93.66%, demonstrating superior performance.

A wide range of user perception applications leverage inertial measurement unit (IMU) data for online prediction. However, restricted by the non-i.i.d. nature of IMU data collected from mobile devices, most systems work well only in a controlled setting (e.g., for a specific user in particular postures), limiting application scenarios. To achieve uncontrolled online prediction on mobile devices, referred to as the flexible user perception (FUP) problem, is attractive but hard. In this paper, we propose a novel scheme, called Prism, which can obtain high FUP accuracy on mobile devices. The core of Prism is to discover task-aware domains embedded in IMU dataset, and to train a domain-aware model on each identified domain. To this end, we design an expectation-maximization (EM) algorithm to estimate latent domains with respect to the specific downstream perception task. Finally, the best-fit model can be automatically selected for use by comparing the test sample and all identified domains in the feature space. We implement Prism on various mobile devices and conduct extensive experiments. Results demonstrate that Prism can achieve the best FUP performance with a low latency.

Smart contracts on the open network (TON) have become vital in Internet of Things (IoT) applications due to their low latency and high scalability. However, the unique architectural features of TON introduce specialized vulnerabilities that existing tools fail to address comprehensively. In this letter, we propose a novel defect detection framework that combines large language models (LLMs) for automated defect discovery with a locatable call graph for precise and efficient code analysis. Our method identifies four new types of TON-specific defects: 1) Ignore Errors Mode Usage; 2) Premature Acceptance; 3) Pseudo Deletion; and 4) Improper Jetton Refund. Evaluated on 1640 real-world smart contracts written in FunC and Tact, the framework uncovers 669 defects, with an average of one defect every 2.45 code segments. The detection achieves an average F1 score of 99.75% for FunC and 100% for Tact contracts. Additionally, our approach demonstrates lightweight computational overhead, consuming only 12.6 MB of memory and achieving a mean response time of 0.05 s. These results highlight the accuracy, efficiency, and practicality of our framework for securing TON-based smart contracts in IoT ecosystems.

3D face recognition systems are frequently susceptible to spoofing attacks, with 3D face presentation attacks being particularly notorious. Attackers commonly exploit 3D scanning and printing techniques to generate masks of target individuals, a method proven successful in various real-world scenarios. A defining characteristic of these attacks involves acquiring 3D face models via 3D scanning, a process that is notably more expensive and cumbersome compared to obtaining 2D photos. In this work, we introduce DREAM, a novel method for recovering 3D face models from a single 2D image. Specifically, our approach adopts a black-box strategy, reconstructing sufficient depth information to compromise target recognition models—such as face identification and authentication systems—by merely accessing their output and the corresponding RGB photo. Our key insight is that achieving successful attacks doesn’t necessitate restoring the precise ground-truth depth values; instead, it only requires recovering the essential features that are salient to the target model’s decision-making process. We evaluate DREAM’s effectiveness using four public 3D face datasets. Experimental results indicate that DREAM achieves a 94% success rate on face authentication models, even in cross-dataset testing. For face identification models, the success rate is 36%. Building upon DREAM, we further propose DREAM-3D, which leverages a 3D GAN to reconstruct depth images for deceiving 3D face recognition systems. Additionally, we evaluate DREAM-3D’s effectiveness on two datastes. Experimental results indicate that DREAM-3D achieves attack success rates exceeding 90% and approximately 50% against different models.

Detecting 3D objects from a monocular camera in mobile applications, such as on a vehicle, drone, or robot, is a crucial but challenging task. The monocular vision's near-far disparity and the camera's constantly changing position make it difficult to achieve high accuracy, especially for distant objects. In this paper, we propose a new Mono3D framework named MoGDE, which takes inspiration from the observation that an object's depth can be inferred from the ground's depth underneath it. MoGDE estimates the corresponding ground depth of an image and utilizes this information to guide Mono3D. We use a pose detection network to estimate the camera's orientation and construct a feature map that represents pixel-level ground depth based on the 3D-to-2D perspective geometry. To further improve Mono3D with the estimated ground depth, we design an RGB-D feature fusion network based on transformer architecture. The long-range self-attention mechanism is utilized to identify ground-contacting points and pin the corresponding ground depth to the image feature map. We evaluate MoGDE on the KITTI dataset, and the results show that it significantly improves the accuracy and robustness of Mono3D for both near and far objects. MoGDE outperforms state-of-the-art methods and ranks first among the pure image-based methods on the KITTI 3D benchmark.

Voice assistants, increasingly integrated into wearable devices with limited human-computer interaction modalities, are susceptible to voice spoofing attacks. Such attacks exploit pre-recorded or synthesized voice commands to trick the assistants into executing actions unauthorized by legitimate users. In this work, we propose GyroTalk, a novel approach extracts individual and reliable features from speech movement sequences of users, using built-in gyroscopes in wearables, to differentiate between legitimate users and malicious attackers. GyroTalk is inspired by two critical insights. First, speech, as a highly intricate motor task, necessitates the synchronized coordination of multiple respiratory, laryngeal, lingual and mandibular muscles. These collective muscle movements propagate throughout the body, providing unique movement signatures. Second, the distinctive speech movement sequences of individual speakers, essential for generating specific words, can be grabbed by embedded IMU of wearables. We conduct a comprehensive evaluation of GyroTalk across various COTS Android devices, including smart phones, watches and glasses. Our experimental results demonstrate that GyroTalk can achieve a mean FAR of 2.23% and a FRR of 2.48%, even in the face of complicated voice spoofing attacks.

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

Emerging Artificial Intelligence of Things (AIoT) applications desire online prediction using deep neural network (DNN) models on mobile devices. However, due to the movement of devices,unfamiliartest samples constantly appear, significantly affecting the prediction accuracy of a pre-trained DNN. In addition, unstable network connection calls for local model inference. In this paper, we propose a light-weight scheme, calledAnole, to cope with the local DNN model inference on mobile devices. The core idea of Anole is to first establish an army of compact DNN models, and then adaptively select the model fitting the current test sample best for online inference. The key is to automatically identifymodel-friendlyscenes for training scene-specific DNN models. To this end, we design a weakly-supervised scene representation learning algorithm by combining both human heuristics and feature similarity in separating scenes. Moreover, we further train a model classifier to predict the best-fit scene-specific DNN model for each test sample. We implement Anole on different types of mobile devices and conduct extensive trace-driven and real-world experiments based on unmanned aerial vehicles (UAVs). The results demonstrate that Anole outwits the method of using a versatile large DNN in terms of prediction accuracy (4.5% higher), response time (33.1% faster) and power consumption (45.1% lower).

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).

Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario, which is vital for driving safety related applications, yet existing literature suffers from the expensive pose label acquisition and the deficiency to gen-eralize to new data distributions. In this paper, we propose EYOC, an unsupervised distant point cloud registration method that adapts to new point cloud distributions on the fly, requiring no global pose labels. The core idea of EYOC is to train a feature extractor in a progressive fashion, where in each round, the feature extractor, trained with near point cloud pairs, can label slightly farther point cloud pairs, enabling self-supervision on such far point cloud pairs. This process continues until the derived ex-tractor can be used to register distant point clouds. Par-ticularly, to enable high-fidelity correspondence label gen-eration, we devise an effective spatial filtering scheme to select the most representative correspondences to register a point cloud pair, and then utilize the aligned point clouds to discover more correct correspondences. Experiments show that EYOC can achieve comparable performance with state-of-the-art supervised methods at a lower training cost. Moreover, it outwits supervised methods regarding generalization performance on new data distributions.

Parameter-efficient fine-tuning (PEFT) on pre-trained models is a new paradigm for model training, where the majority parameters of a pre-trained model are frozen, left only a small number of unfrozen (or additional) parameters to be tuned. PEFT demonstrates its effectiveness in fitting the downstream tasks, while introducing a new surface for backdoor attacks. In this paper, we design a novel backdoor attack towards the Vision Transformer (ViT) PEFT, called Bad-Tuning. To mislead the target pre-trained model, Bad-Tuning first purposefully tailors the trigger for the frozen portion of the model, and then backdoors the unfrozen part by injecting the trigger into fine-tuning samples of PEFT. The main challenges are two-fold. First, backdooring PEFT should be highly efficient with a few backdoored samples. Second, the trigger should be hardly noticed by human beings as well as backdoor scanners. To deal with the challenges, Bad-Tuning optimizes the trigger by learning CLS sequences which represent rich deep semantics of samples, and introduces color loss to evaluate the invisibility of triggers. Extensive experiments on different datasets demonstrate the effectiveness, efficiency and invisibility of Bad-Tuning under both white-box and gray-box scenarios. Bad-Tuning achieves an average attack success rate (ASR) of over 99.9% even only 0.1% backdoored samples are injected. Moreover, Bad-Tuning outperforms the SOTA backdoor attacks on both ASR and invisibility (SSIM).

Emerging Artificial Intelligence of Things (AIoT) applications desire online prediction using deep neural network (DNN) models on mobile devices. However, due to the movement of devices, unfamiliar test samples constantly appear, significantly affecting the prediction accuracy of a pre-trained DNN. In addition, unstable network connection calls for local model inference. In this paper, we propose a light-weight scheme, called Anole, to cope with the local DNN model inference on mobile devices. The core idea of Anole is to first establish an army of compact DNN models, and then adaptively select the model fitting the current test sample best for online inference. The key is to automatically identify model-friendly scenes for training scene-specific DNN models. To this end, we design a weakly-supervised scene representation learning algorithm by combining both human heuristics and feature similarity in separating scenes. Moreover, we further train a model classifier to predict the best-fit scene-specific DNN model for each test sample. We implement Anole on different types of mobile devices and conduct extensive trace-driven and real-world experiments based on unmanned aerial vehicles (UAV s). The results demonstrate that Anole outwits the method of using a versatile large DNN in terms of prediction accuracy (4.5 % higher), response time (33.1 % faster) and power consumption (45.1 % lower).

Federated learning is a mechanism that allows participating clients to train locally with their own data in order to receive rewards, thus avoiding the transfer of data to a central server and protecting users’ privacy. However, some “lazy” clients may adopt the strategy of fabricating false model local updates in an attempt to “free-riding” without actually contributing real data or consuming local computational resources. To address this issue, we propose FLoomChecker, an integrity detection scheme for federated learning training models. The scheme combines the techniques of trusted execution environments and Bloom filters to efficiently identify clients that do not train honestly by committing and proving. We conducted experimental evaluations of FLoomChecker, examining three main aspects: query time, build time, and memory footprint in trusted execution environment (TEE). The experimental results demonstrate the effectiveness of our scheme, and its performance improves as the number of local training rounds increases.

Video analytics is the killer workload in edge computing, which involves the scheduler’s complex decisions to balance analysis performance (latency and accuracy) and resource consumption (network, computation, and energy). Traditional schedulers address this as a single-objective optimization problem with fixed weights, unable to precisely capture unknown system preferences due to intricate pricing rules across various service levels and resource costs, consequently leading to suboptimal system benefit like monetary gain. In this paper, we propose a Bayesian optimization-driven multi-objective scheduler, PaMO, that can proactively explore the system pricing preference by pairwise comparing outcome vectors of all objectives. Moreover, PaMO designs a heuristic scheduling algorithm with a zero-delay jitter guarantee to avoid performance degradation caused by resource contention and uses a revised Bayesian optimization algorithm to make video configuration and scheduling decisions. Experiments on real video analytics workloads show that PaMO can achieve up to 53.9% benefit gain compared to state-of-the-art scheduling methods.

Point cloud registration (PCR) can significantly extend the visual field and enhance the point density on distant objects, thereby improving driving safety. However, it is very challenging for vehicles to perform online registration between long-range point clouds. In this paper, we propose an online long-range PCR scheme in VANETs, called LoRaPCR, where vehicles achieve long-range registration through multi-hop short-range highly-accurate registrations. Given the NP-hardness of the problem, a heuristic algorithm is developed to determine best registration paths while leveraging the reuse of registration results to reduce computation costs. Moreover, we utilize an optimized dynamic programming algorithm to determine the transmission routes while minimizing the communication overhead. Results of extensive simulations demonstrate that LoRaPCR can achieve high PCR accuracy with low relative translation and rotation errors of 0.55 meters and 1.43°, respectively, at a distance of over 100 meters, and reduce the computation overhead by more than 50% compared to the state-of-the-art method.

Decentralized and open features of federated learning provides opportunities for malicious participants to inject stealthy trojan functionality into deep learning models collusively. A successful trojan attack is desired to be effective, precise and imperceptible, which generally requires priori knowledge such as aggregation rules, tight cooperation between attackers, e.g. sharing data distributions, and the use of inconspicuous triggers. However, in realistic, attackers are typically lack of the knowledge and hardly to fully cooperate (for privacy and efficiency reasons), and out of scope triggers are easy to be detected by scanners. We propose FedTrojan, a zero-knowledge federated trojan attack. Each attacker independently trains a quasi-trojaned local model with a self-select trigger. The model behaves normally on both regular and trojaned inputs. When local models are aggregated on the server side, the corresponding quasi-trojans will be assembled into a complete trojan which can be activated by the global trigger. We choose existing benign features rather than artificial patches as hidden local triggers to guarantee imperceptibility, and introduce catalytic features to eliminate the impact of local trojan triggers on behaviors of local/global models. Extensive experiments show that the performance of FedTrojan is significantly better than that of existing trojan attacks under both the classic FedAvg and Byzantine-robust aggregation rules.

Federated Learning (FL) enables multiple participants to collaboratively train a globally shared model without the need of explicit data sharing. However, prior research indicates that local model updates released during the federated training may also jeopardize privacy of participants. To address this issue, local differential privacy (LDP) mechanism has been applied to FL systems. LDP provides privacy protection with rigorous mathematical proof by introducing random perturbations, e.g., Gaussian noise, to the released updates, however excessive noise compromises the utility of the updates. In this paper, we propose a novel Correlation-aware Adaptive LDP mechanism, Fed-CAD, for FL, which reduces the required scale of noise by leveraging the temporal correlation between consecutive local model updates belonging to the same participant, without increasing the privacy budgets (risks). We theoretically prove that Fed-CAD satisfies (ε, δ)-LDP as long as the difference between local models is smaller than the differential bound, and analyze the noise variance, a metric of utility. We implement Fed-CAD on image classification FL tasks. Experimental results demonstrate that Fed-CAD significantly outperforms the one-shot LDP baseline.

Adhesive adversarial patches have been common used in attacks against the computer vision task of monocular depth estimation (MDE). Compared to physical patches permanently attached to target objects, optical projection patches show great flexibility and have gained wide research attention. However, applying digital patches for direct projection may lead to partial blurring or omission of details in the captured patches, attributed to high information density, surface depth discrepancies, and non-uniform pixel distribution. To address these challenges, in this work we introduce DepthCloak, an adversarial optical patch designed to interfere with the MDE of vehicles. To this end, we first simplify the patch to a gray pattern because the projected ''black-and-white light'' has strong robustness to ambient light. We propose a generative adversarial network (GAN) based approach to simulate projections and deduce a projectable list. Then, we employ neighborhood averaging to fill sparse depth values, compress all depth values into a reduced dynamic range via nonlinear mapping, and use these values to adjust the Gaussian blur radius as weight parameters, thereby simulating depth variation effects. Finally, by integrating Moiré pattern and applying style transfer techniques, we customize adversarial patches featuring regularly arranged characteristics. We deploy DepthCloak in real driving scenarios, and extensive experiments demonstrate that DepthCloak can achieve an attack success rate of over 80% in the physical world.

3D face recognition is subject to frequent spoofing attacks, in which 3D face presentation attack is one of the most notorious attacks. The attacker takes advantages of 3D scanning and printing techniques to generate masks of targets, which has found success in numerous real-life examples. The salient feature in such attacks is to obtain 3D face models through 3D scanning, though relatively more expensive and inconvenient when comparing with 2D photos. In this work, we propose a new method, DREAM, to recover 3D face models from single 2D image. Specifically, we adopt a black-box approach, which recovers 'sufficient' depths to defeat target recognition models (e.g., face identification and face authentication models) by accessing its output and the corresponding RGB photo. The key observation is that it is not necessary to restore the true value of depths, but only need to recover the essential features relevant to the target model. We used four public 3D face datasets to verify the effectiveness of DREAM. The experimental results show that DREAM can achieve a success rate of 94% on face authentication model, even in cross-dataset testing, and a success rate of 36% on face identification model.

Controllable synthetic data generation can substantially lower the annotation cost of training data. Prior works use diffusion models to generate driving images conditioned on the 3D object layout. However, those models are trained on small-scale datasets like nuScenes, which lack appearance and layout diversity. Moreover, overfitting often happens, where the trained models can only generate images based on the layout data from the validation set of the same dataset. In this work, we introduce a simulator-conditioned scene generation framework called SimGen that can learn to generate diverse driving scenes by mixing data from the simulator and the real world. It uses a novel cascade diffusion pipeline to address challenging sim-to-real gaps and multi-condition conflicts. A driving video dataset DIVA is collected to enhance the generative diversity of SimGen, which contains over 147.5 hours of real-world driving videos from 73 locations worldwide and simulated driving data from the MetaDrive simulator. SimGen achieves superior generation quality and diversity while preserving controllability based on the text prompt and the layout pulled from a simulator. We further demonstrate the improvements brought by SimGen for synthetic data augmentation on the BEV detection and segmentation task and showcase its capability in safety-critical data generation.

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).

Studies have proven that applying patch stickers generated through adversarial training to target objects can effectively deceive classifiers or target detectors. These ’Print-and-paste’ adversarial attacks however have three shortcomings. First, touching the target object physically is required, which may be infeasible in practice. Second, stickers might be taken as evidence to identify attackers. Third, the attack effect decreases significantly in poor light, especially at long distances. To overcome above limitations, we introduce OptiCloak, a car vanishing attack, which fools the Object Detector (OD) of a vision-based autonomous driving systems with transient projection pattern. We establish three digital-to-physical mapping models to compensate the distortions caused by perspective deformation, double image and partial light reflection in real-world. Furthermore, to avoid adversarial functionality degeneration caused by the loss of patch details in long-range attacks, we utilize MeanShift Filtering to constrain the ’resolution’ of pixels in a patch during training. We propose a gradient-free patch updating approach, which utilizes ZO-AdaMM to approximate gradients and model parameters through confidence scores of OD, making OptiCloak can work well in both white-box and black-box scenarios. We deploy OptiCloak in real-world driving scenarios, and the extensive experimental results demonstrate that OptiCloak achieves similar Attack Success Rates (ASRs) as printed patches in bright environments, while significantly improving the attack performance in gloomy environments. This effect is validated across all settings, including different angles, imaging devices, and film transparency rates. In black-box settings, the average ASR can reach 71%, with a maximum attack distance of approximately 10m.

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.

Video analytics at mobile edge servers offers significant benefits like reduced response time and enhanced privacy. However, guaranteeing various quality-of-service (QoS) requirements of dynamic video analysis requests on heterogeneous edge devices remains challenging. In this paper, we propose a scalable online video analytics scheme, called Novas, which automatically makes precise service configuration adjustments upon constant video content changes. Specifically, Novas leverages the filtered confidence sum and a two-window t-test to online detect accuracy fluctuations without ground truth information. In such cases, Novas efficiently estimates the performance of all potential service configurations through a singular value decomposition (SVD)-based collaborative filtering method. Finally, given the NP-hardness of the optimal scheduling problem, a heuristic scheduling strategy that maximizes the minimum remaining resources is devised to schedule the most suitable configurations to servers for execution. We evaluate the effectiveness of Novas through extensive hybrid experiments conducted on a dedicated testbed. Results show that Novas can achieve a substantial over 27$\times$improvement in satisfying the accuracy requirements compared with existing methods adopting fixed configurations, while ensuring latency requirements. Moreover, Novas improves the goodput of the system by an average of 37.86% compared to existing state-of-the-art scheduling solutions.

Continuous authentication is viewed to be increasingly important for mobile devices, which store a wide range of private data and sensitive information of users. Traditional continuous authentication methods need user inputs (e.g. typing, sliding). In this work, we present MAUTH, a zero-effect continuous authentication scheme for mobile devices. With the built-in motion sensors on commercial off-the-shelf (COTS) devices, MAUTH can continuously extract, classify and verify the unique tremor features of users on how their body intrinsically shakes during the normal use of such devices. As a result, it is extremely difficult if not impossible to reproduce the same set of tremors as individuals differ in their muscle development. We implement MAUTH as a software on Android-based smartphones, which demonstrates that MAUTH is light-weight and unobtrusive to its users. We conduct extensive real-world experiments and trace-driven simulations in controlled and uncontrolled environments on 21 volunteers. The results show that MAUTH is difficult to counterfeit and achieves a low average false positive rate (FPR) of 6.73% under real-world spoofing attacks. Moreover, MAUTH is comfortable to use and can achieve a low average false negative rate (FNR) of 2.2% during uncontrolled and continuous usage of devices, leveraging isolation-forest-based classifiers trained with only 40 training samples.

Efficient one-hop multicasting (OHM) of high-volume sensor data plays a pivotal role in the success of cooperative autonomous driving applications. Although millimeter-Wave (mmWave) bands demonstrate huge potential for high- bandwidth OHM data transmission, the challenge lies in enabling individual vehicles to locate and communicate with suitable neighbors in a fully distributed and highly dynamic scenario. This paper introduces mmV2V, a fully distributed OHM scheme designed for vehicular networks, comprising three tightly integrated protocols. Initially, synchronized vehicles perform a probabilistic neighbor discovery procedure, wherein randomly divided transmitters (or receivers) clockwise scan (or listen to) the surroundings in synchronization with heterogeneous Tx (or Rx) beams. This approach facilitates the identification of the vast majority of neighbors within a few repeated rounds. Subsequently, vehicles engage in negotiations with their neighbors to establish an optimal communication schedule in evenly distributed slots. Finally, matched pairs of neighboring vehicles commence high data rate transmissions using refined beams. We implement a prototype testbed to validate the feasibility of the main components of mmV2V. Extensive simulations based on generated and real-world traffic traces are conducted and the results demonstrate that mmV2V consistently achieves a high completion ratio in demanding OHM tasks across various traffic conditions.

Point cloud registration (PCR) can significantly extend the visual field and enhance the point density on distant objects, thereby improving driving safety. However, it is very challenging for vehicles to perform online registration between long-range point clouds. In this paper, we propose an online long-range PCR scheme in VANETs, called LoRaPCR, where vehicles achieve long-range registration through multi-hop short-range highly-accurate registrations. Given the NP-hardness of the problem, a heuristic algorithm is developed to determine best registration paths while leveraging the reuse of registration results to reduce computation costs. Moreover, we utilize an optimized dynamic programming algorithm to determine the transmission routes while minimizing the communication overhead. To the best of our knowledge, LoRaPCR is the first solution to achieve multi-vehicle point cloud long-range registration. Results of extensive experiments demonstrate that LoRaPCR can achieve high PCR accuracy with low relative translation and rotation errors of 0.55 meters and 1.43${}^{\circ }$, respectively, at a distance of over 100 meters, and reduce the computation overhead by more than 50% compared to the state-of-the-art method.

Stream processing systems commonly work with auto-scaling to ensure resource efficiency and quality of service (QoS). Existing auto-scaling solutions lack accuracy in resource allocation because they rely on static QoS-resource models that fail to account for high workload variability and use indirect metrics with much distractive information. Moreover, different types of QoS metrics present different characteristics and thus need individual auto-scaling methods. In this paper, we propose a versatile auto-scaling solution for operator-level parallelism configuration, called AuTraScale+, to meet the throughput, processing-time latency, and event-time latency targets. AuTraScale+ follows the Bayesian optimization framework to make scaling decisions. First, it uses the Gaussian process model to eliminate the negative influence of uncertain factors on the performance model accuracy. Second, it leverages the expected improvement-based (EI-based) acquisition function to search and recommend the optimal configuration quickly. Besides, to make a more accurate scaling decision when the new model is not ready, AuTraScale+ proposes a transfer learning algorithm to estimate the benefits of all configurations at a new rate based on existing models and then recommend the optimal one. We implement and evaluate AuTraScale+ on the Flink platform. The experimental results on three representative workloads demonstrate that compared with the state-of-the-art methods, AuTraScale+ can reduce 66.6% and 36.7% resource consumption, respectively, in the scale-down and scale-up scenarios while achieving their throughput and processing-time latency targets. Compared with other methods of optimizing event-time latency, AuTraScale+ saves 26.9% of resources on average.

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.

Mobile monocular 3D object detection (Mono3D) (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Existing transformer-based offline Mono3D models adopt grid-based vision tokens, which is suboptimal when using coarse tokens due to the limited available computational power. In this paper, we propose an online Mono3D framework, called MonoATT, which leverages a novel vision transformer with heterogeneous tokens of varying shapes and sizes to facilitate mobile Mono3D. The core idea of MonoATT is to adaptively assign finer tokens to areas of more significance before utilizing a transformer to enhance Mono3D. To this end, we first use prior knowledge to design a scoring network for selecting the most important areas of the image, and then propose a token clustering and merging network with an attention mechanism to gradually merge tokens around the selected areas in multiple stages. Finally, a pixel-level feature map is reconstructed from heterogeneous tokens before employing a SOTA Mono3D detector as the underlying detection core. Experiment results on the real-world KITTI dataset demonstrate that MonoATT can effectively improve the Mono3D accuracy for both near and far objects and guarantee low latency. MonoATT yields the best performance compared with the state-of-the-art methods by a large margin and is ranked number one on the KITTI 3D benchmark.

Registration of distant outdoor LiDAR point clouds is crucial to extending the 3D vision of collaborative autonomous vehicles, and yet is challenging due to small overlapping area and a huge disparity between observed point densities. In this paper, we propose Group-wise Contrastive Learning (GCL) scheme to extract density-invariant geometric features to register distant outdoor LiDAR point clouds. We mark through theoretical analysis and experiments that, contrastive positives should be independent and identically distributed (i.i.d.), in order to train density-invariant feature extractors. We propose upon the conclusion a simple yet effective training scheme to force the feature of multiple point clouds in the same spatial location (referred to as positive groups) to be similar, which naturally avoids the sampling bias introduced by a pair of point clouds to conform with the i.i.d. principle. The resulting fully-convolutional feature extractor is more powerful and density-invariant than state-of-the-art methods, improving the registration recall of distant scenarios on KITTI and nuScenes benchmarks by 40.9% and 26.9%, respectively. Code is available at https://github.com/liuQuan98/GCL.

Check-in data widely published in mobile social networks (MSNs) pose serious privacy threats to users. Existing inference attack methods show that pairwise social relationship could be estimated by analyzing check-in user records. However, the efficacy of such attacks heavily depends on the density of check-in data, which is often not present in practice. In this work, we propose a new inference attack scheme, which for the first time can effectively reveal hidden social friendship in both the real world and in cyberspace among users with only sparse check-in data. Our attack method enjoys two salient features. First, it requires no prior knowledge about social connections, instead it estimates users' social proximity by exploiting both physical presence and social proximities. Second, our attack scheme can automatically learn representative features based on the significance of various check-in records, rather than relying on heuristic features. We conduct extensive trace-driven simulations, and the results demonstrate that our inference attack method can improve the efficacy of the state-of-the-art learning-based schemes up to 40%. Moreover, our proposed attack method is also robust against common data obfuscation mechanisms.

For many driving safety applications, it is of great importance to accurately register LiDAR point clouds generated on distant moving vehicles. However, such point clouds have extremely different point density and sensor perspective on the same object, making registration on such point clouds very hard. In this paper, we propose a novel feature extraction framework, called APR, for online distant point cloud registration. Specifically, APR leverages an autoencoder design, where the autoencoder reconstructs a denser aggregated point cloud with several frames instead of the original single input point cloud. Our design forces the encoder to extract features with rich local geometry information based on one single input point cloud. Such features are then used for online distant point cloud registration. We conduct extensive experiments against state-of-the-art (SOTA) feature extractors on KITTI and nuScenes datasets. Results show that APR outperforms all other extractors by a large margin, increasing average registration recall of SOTA extractors by 7.1% on LoKITTI and 4.6% on LoNuScenes. Code is available at https://github.com/liuQuan98/APR.

Voice interaction, as an emerging human-computer interaction method, has gained great popularity, especially on smart devices. However, due to the open nature of voice signals, voice interaction may cause privacy leakage. In this paper, we propose a novel scheme, calledSeVI, to protect voice interaction from being deliberately or unintentionally eavesdropped. SeVI actively generates jamming noise of superior characteristics, while a user is performing voice interaction with his/her device, so that attackers cannot obtain the voice contents of the user. Meanwhile, the device leverages the prior knowledge of the generated noise to adaptively cancel received noise, even when the device usage environment is changing due to movement, so that the user voice interactions are unaffected. SeVI relies on only normal microphone and speakers and can be implemented as light-weight software. We have implemented SeVI on a commercial off-the-shelf (COTS) smartphone and conducted extensive real-world experiments. The results demonstrate that SeVI can defend both online eavesdropping attacks and offline digital signal processing (DSP) analysis attacks.

Monitoring of human vital signs such as respiration and heart rates is crucial in detecting medical problems. In this paper, we propose TreMo, a continuous vital sign monitoring scheme based on subtle intrinsic muscular tremors measured on commercial off-the-shelf (COTS) mobile devices (e.g., smartphones and smartwatches). With the built-in motion sensors on such devices, TreMo continuously recognizes different types of user behaviors and analyzes the spectrum of subtle tremors of users on how their body intrinsically shakes during the normal use of such devices, using cross fast Fourier transform (CFFT) to remove random noises in the frequency domain. We implement TreMo as a software on Android-based smartphones, which demonstrates that TreMo is light-weight and unobtrusive to its users. We conduct extensive real-world experiments on 21 volunteers. The results show that the mean absolute error for respiration rate and for heart rate are 0.21 Breaths Per Minute (BPM) and 0.55 Beats Per Minute (BPM), respectively.

One-hop multicasting (OHM) of high-volume sensor data is essential for cooperative autonomous driving applications. While millimeter-Wave (mmWave) bands can be utilized for high-bandwidth OHM data transmission, it is very challenging for individual vehicles to find and communicate with a proper neighbor in a fully distributed and highly dynamic scenario. In this paper, we propose a fully distributed OHM scheme in vehicular networks, called mmV2V, which consists of three highly integrated protocols. Specifically, synchronized vehicles first conduct a probabilistic neighbor discovery procedure, in which randomly divided transmitters (or receivers) clockwise scan (or listen to) the surroundings in pace with heterogeneous Tx (or Rx) beams. In this way, the vast majority of neighbors can be identified in a few repeated rounds. Furthermore, vehicles negotiate with each of their neighbors about the optimal communication schedule in evenly distributed slots. Finally, each agreed pair of neighboring vehicles start high data rate transmissions with refined beams. We conduct extensive simulations and the results demonstrate that mmV2V can achieve a high completion ratio in rigid OHM tasks under various traffic conditions.

Online garbage collection scheduling is demanding for large cities to reduce the increasing operational costs. However, the garbage collection problem is NP-complete, making the problem intractable when the number of garbage sites is large. In this paper, we first intensively investigate the garbage collection problem and derive insightful theoretical guidance for decomposing a large-scale garbage collection problem. We then propose an agglomerative hierarchical clustering algorithm, called Pie, for online large-scale garbage collection scheduling, where the original problem can be equivalently decomposed into a set of small-scale tractable sub-problems. We implement Pie which has a $O(n^{2})$ complexity and adopt LKH-3, the state-of the-art CVRP algorithm, as the underlying algorithm to solve sub-problems obtained by Pie. We conduct extensive trace-driven simulations on 11 real-world datasets. The results show that Pie can effectively reduce both the overall collection cost and the running time, demonstrating the efficacy of the Pie algorithm. Index Terms--Large-scale garbage collection problem, capacitated vehicle routing problem, agglomerative hierarchical clustering algorithm.

Monocular 3D object detection (Mono3D) in mobile settings (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Due to the near-far disparity phenomenon of monocular vision and the ever-changing camera pose, it is hard to acquire high detection accuracy, especially for far objects. Inspired by the insight that the depth of an object can be well determined according to the depth of the ground where it stands, in this paper, we propose a novel Mono3D framework, called MoGDE, which constantly estimates the corresponding ground depth of an image and then utilizes the estimated ground depth information to guide Mono3D. To this end, we utilize a pose detection network to estimate the pose of the camera and then construct a feature map portraying pixel-level ground depth according to the 3D-to-2D perspective geometry. Moreover, to improve Mono3D with the estimated ground depth, we design an RGB-D feature fusion network based on the transformer structure, where the long-range self-attention mechanism is utilized to effectively identify ground-contacting points and pin the corresponding ground depth to the image feature map. We conduct extensive experiments on the real-world KITTI dataset. The results demonstrate that MoGDE can effectively improve the Mono3D accuracy and robustness for both near and far objects. MoGDE yields the best performance compared with the state-of-the-art methods by a large margin and is ranked number one on the KITTI 3D benchmark.

Voice assistants are popular to wearable devices with limited input and output capabilities, however vulnerable to voice attacks, which cheat a voice assistant by playing forged voice commands without user awareness. In this paper, we propose VOGUE, which captures unique yet stable pattern of speech movement sequences of speakers with embedded gyroscope in wearable devices, to distinguish between registered legal user and malicious attackers (human or machines). The design of VOGUE is based on two key observations. First, speech, as a type of highly complex motor task, inherently requires coordinated actions of many orofacial, laryngeal, pharyngeal, and respiratory muscles, and the collective movements of muscles propagate to distant body segments. Second, to generate a certain word, the speech movement sequence of a speaker is known to be distinctive, and can be captured by inertial sensors. We implement VOGUE on three kinds of COTS android devices including smart glasses, watches and phones, and conduct comprehensive evaluation on the performances. Experimental results show that VOGUE achieves a mean false-acceptance rate (FAR) and false- rejection rate (FRR) of 2.23% and 2.48%, respectively, even under sophisticated voice impersonation attacks.

Localizing RFID-tagged objects by a mobile robot plays an important role in many Internet of Things (IoT) applications. Existing RFID localization systems are infeasible, since they either demand bulky RFID infrastructures or cannot achieve sufficient localization accuracy. In this article, a portable localization (POLO) system is developed for a mobile robot to locate RFID-tagged objects. POLO consists of an RFID reader, a tag array, and a lightweight receiver. The reader is used for interrogating the RFID tag on an object. The tag array is designed to reflect the RFID signal from an object into multipath signals. The receiver captures such signals and estimates their multipath channel coefficients by a tag-array-assisted channel estimation (TCE) mechanism. Such channel coefficients are further exploited to determine the object’s direction by a spatial smoothing direction estimation (SSDE) algorithm. To resist the impact of multipath reflections from surroundings, more spatial information is exploited by placing the tag elements densely and collecting the channel coefficients during the robot’s movement. Based on the object’s direction, POLO guides the robot to approach the object. When the object is in proximity, its 3-D location is finally determined by a near-range positioning (NRP) algorithm. Moreover, POLO is designed to be compatible with commercial RFID systems. POLO is prototyped and evaluated via extensive experiments. Results show that the average angular error is within 1 degree when the object is in the far range (2–6 m), and the average location error is within 6 cm while the object is in the near range (~1 m).

Monocular 3D object detection is an important task in autonomous driving. It can be easily intractable where there exists ego-car pose change w.r.t. ground plane. This is common due to the slight fluctuation of road smoothness and slope. Due to the lack of insight in industrial application, existing methods on open datasets neglect the camera pose information, which inevitably results in the detector being susceptible to camera extrinsic parameters. The perturbation of objects is very popular in most autonomous driving cases for industrial products. To this end, we propose a novel method to capture camera pose to formulate the detector free from extrinsic perturbation. Specifically, the proposed framework predicts camera extrinsic parameters by detecting vanishing point and horizon change. A converter is designed to rectify perturbative features in the latent space. By doing so, our 3D detector works independent of the extrinsic parameter variations and produces accurate results in realistic cases, e.g., potholed and uneven roads, where almost all existing monocular detectors fail to handle. Experiments demonstrate our method yields the best performance compared with the other state-of-the-arts by a large margin on both KITTI 3D and nuScenes datasets.

Acquiring the geographical distribution of neighbors can support more adaptive media access control (MAC) protocols and other safety applications in Vehicular ad hoc network (VANETs). However, it is very challenging for each vehicle to estimate its own neighbor distribution in a fully distributed setting. In this paper, we propose an online distributed neighbor distribution estimation scheme, called PeerProbe, in which vehicles collaborate with each other to probe their own neighborhood via simultaneous symbol-level wireless communication. An adaptive compressive sensing algorithm is developed to recover a neighbor distribution based on a small number of random probes with non-negligible noise. Moreover, the needed number of probes adapts to the sparseness of the distribution. We implement a prototype system to verify the feasibility of PeerProbe in various typical vehicular channel conditions. We further conduct extensive simulations and the results demonstrate that PeerProbe is lightweight and can accurately recover highly dynamic neighbor distributions in critical channel conditions.

Monocular 3D object detection is an important task in autonomous driving. It can be easily intractable where there exists ego-car pose change w.r.t. ground plane. This is common due to the slight fluctuation of road smoothness and slope. Due to the lack of insight in industrial application, existing methods on open datasets neglect the cam-era pose information, which inevitably results in the detector being susceptible to camera extrinsic parameters. The perturbation of objects is very popular in most autonomous driving cases for industrial products. To this end, we propose a novel method to capture camera pose to formulate the detector free from extrinsic perturbation. Specifically, the proposed framework predicts camera extrinsic parameters by detecting vanishing point and horizon change. A converter is designed to rectify perturbative features in the latent space. By doing so, our 3D detector works independent of the extrinsic parameter variations and produces accurate results in realistic cases, e.g., potholed and uneven roads, where almost all existing monocular detectors fail to handle. Experiments demonstrate our method yields the best performance compared with the other state-of-the-arts by a large margin on both KITTI 3D and nuScenes datasets.

Online semantic segmentation on a time series of point cloud frames is an essential task in autonomous driving. Existing models focus on single-frame segmentation, which cannot achieve satisfactory segmentation accuracy and offer unstably flicker among frames. In this paper, we propose a light-weight semantic segmentation framework for largescale point cloud series, called TempNet, which can improve both the accuracy and the stability of existing semantic segmentation models by combining a novel frame aggregation scheme. To be computational cost-efficient, feature extraction and aggregation are only conducted on a small portion of key frames via a temporal feature aggregation (TFA) network using an attentional pooling mechanism, and such enhanced features are propagated to the intermediate non-key frames. To avoid information loss from non-key frames, a partial feature update (PFU) network is designed to partially update the propagated features with the local features extracted on a non-key frame if a large disparity between the two is quickly assessed. As a result, consistent and information-rich features can be obtained for each frame. We implement TempNet on five state-of-the-art (SOTA) point cloud segmentation models and conduct extensive experiments on the SemanticKITTI dataset. Results demonstrate that TempNet outperforms SOTA competitors by wide margins with little extra computational cost.

Acquiring the geographical distribution of neighbors can support more adaptive media access control (MAC) protocols and other safety applications in Vehicular ad hoc network (VANETs). However, it is very challenging for each vehicle to estimate its own neighbor distribution in a fully distributed setting. In this paper, we propose an online distributed neighbor distribution estimation scheme, called PeerProbe, in which vehicles collaborate with each other to probe their own neighborhood via simultaneous symbol-level wireless communication. An adaptive compressive sensing algorithm is developed to recover a neighbor distribution based on a small number of random probes with non-negligible noise. Moreover, the needed number of probes adapts to the sparseness of the distribution. We conduct extensive simulations and the results demonstrate that PeerProbe is lightweight and can accurately recover highly dynamic neighbor distributions in critical channel conditions.

In many Internet-of-Things (IoT) applications, various RFID-tagged objects need to be localized by mobile robots. Existing RFID localization systems are infeasible, since they either demand bulky RFID infrastructures or cannot achieve sufficient localization accuracy. In this paper, a portable localization (POLO) system is developed for a mobile robot to locate RFID-tagged objects. Besides a single RFID reader on board, POLO is distinguished with a tag array and a lightweight receiver. The tag array is designed to reflect the RFID signal from an object into multi-path signals. The receiver captures such signals and estimates their multi-path channel coefficients by a tag-array-assisted channel estimation (TCE) mechanism. Such channel coefficients are further exploited to determine the object's direction by a spatial smoothing direction estimation (SSDE) algorithm. Based on the object's direction, POLO guides the robot to approach the object. When the object is in proximity, its 2D location is finally determined by a near-range positioning (NRP) algorithm. POLO is prototyped and evaluated via extensive experiments. Results show that the average angular error is within 1.6 degrees when the object is in the far-range (2~6 m), and the average location error is within 5 cm while the object is in the near-range (~1 m).

Connected vehicles have been considered as an effective solution to enhance driving safety as they can be well aware of nearby environments by exchanging safety beacons periodically. However, under dynamic traffic conditions, especially for dense-vehicle scenarios, the naive beaconing scheme where vehicles broadcast beacons at a fixed rate with a fixed transmission power can cause severe channel congestion and thus degrade the beaconing reliability. In this paper, by considering the kinematic status and beaconing rate together, we study the rear-end collision risk and define a danger coefficient ρ to capture the danger threat of each vehicle being in the rear-end collision. In specific, we propose a fully distributed adaptive beacon control scheme, called ABC, which makes each vehicle actively adopt a minimal but sufficient beaconing rate to avoid the rear-end collision in dense scenarios based on individually estimated ρ. With ABC, vehicles can broadcast at the maximum beaconing rate when the channel medium resource is enough and meanwhile keep identifying whether the channel is congested. Once a congestion event is detected, an NP-hard distributed beacon rate adaptation (DBRA) problem is solved with a greedy heuristic algorithm, in which a vehicle with a higher ρ is assigned with a higher beaconing rate while keeping the total required beaconing demand lower than the channel capacity. We prove the heuristic algorithm's close proximity to the optimal result and thoroughly analyze the communication overhead of ABC scheme. By using Simulation of Urban MObility (SUMO)-generated vehicular traces, we conduct extensive simulations to demonstrate the efficacy of our proposed ABC scheme. Simulation results show that vehicles can adapt beaconing rates according to the driving safety demand, and the beaconing reliability can be guaranteed even under high-dense vehicle scenarios.

As mobile devices play increasingly important roles in our daily lives, it is of great significance to protect personal mobile devices from being lost. Noticing the trend that one person normally carries more than one mobile device, we propose an innovative scheme, calledCoSafe, to detect device loss by verifying the motion consistency between a pair of devices. The rationale is that the vibrations perceived on devices carried by the same person should be tightly coupled whereas a lost device would show distinct mobility characteristics from others. Specifically, CoSafe compares the mobility consistency between a pair of devices on three levels, where coarse features (i.e., the mobility state and motion periodicity) are first compared to give fast response and more complex comparison on subtle feature (i.e., the relative phase) is conducted only when needed. In this way, CoSafe can instantly respond and introduce very low computation and communication costs. We implement CoSafe on a Commercial-Off-The-Shelf Android smartphone and a smartwatch, and conduct both trace-driven simulations and real-world experiments to evaluate the performance of CoSafe. The results show that CoSafe achieves a mean false negative ratio and false positive ratio of 1.46 and 3.12 percent, respectively, even under sophisticated stealing attacks.

Finding a small object (e.g., earbuds, keys or a wallet) in an indoor environment (e.g., in a house or an office) can be frustrating. In this paper, we propose an innovative system, calledHyperEar, to localize such an object using only a single smartphone, based on enhanced time-difference-of-arrival (TDoA) measurements over acoustic signals issued from the object. One major challenge is the hardware limitations of a Commercial-Off-The-Shelf (COTS) phone with a short separation between the two microphones and the low sampling rate of such microphones. HyperEar enhances the accuracy of TDoA measurements by virtually increasing distances between microphones through sliding the phone in the air. HyperEar requires no communication for synchronization between the phone and the object and is a low-cost and easy-to-use system. We evaluate the performance of HyperEar via extensive experiments in various indoor conditions and the results demonstrate that, for an object of 7 m away, HyperEar can achieve a mean localization accuracy of about 15 cm when the object in normal indoor environments.

Voice interaction, as an emerging human-computer interaction method, has gained great popularity, especially on smart devices. However, due to the open nature of voice signals, voice interaction may cause privacy leakage. In this paper, we propose a novel scheme, called SeVI, to protect voice interaction from being deliberately or unintentionally eavesdropped. SeVI actively generates jamming noise of superior characteristics, while a user is performing voice interaction with his/her device, so that attackers cannot obtain the voice contents of the user. Mean-while, the device leverages the prior knowledge of the generated noise to adaptively cancel received noise, even when the device usage environment is changing due to movement, so that the user voice interactions are unaffected. SeVI relies on only normal microphone and speakers and can be implemented as light-weight software. We have implemented SeVI on a commercial off-the- shelf (COTS) smartphone and conducted extensive real-world experiments. The results demonstrate that SeVI can defend both online eavesdropping attacks and offline digital signal processing (DSP) analysis attacks.

The IEEE 802.11p-based dedicated short range communication (DSRC) is essential to enhance driving safety and improve road efficiency by enabling rapid cooperative message exchanging. However, there is a lack of good understanding on the DSRC performance in urban environments for vehicle-to-vehicle (V2V) communications, which impedes its reliable and efficient application. In this paper, we first conduct intensive data analytics on V2V performance, based on a large amount of real-world DSRC communications trace collected in Shanghai city, and obtain several key insights as follows. First, among many context factors, the non-line-of-sight (NLoS) link condition is the major factor degrading V2V performance. Second, the durations of line-of-sight (LoS) and NLoS transmission conditions follow power law distributions, which indicate that the probability of experiencing long LoS/NLoS conditions both could be high. Third, the packet inter-reception (PIR) time distribution follows an exponential distribution in the LoS conditions but a power law in the NLoS conditions, which means that the consecutive packet reception failures rarely appear in the LoS conditions but can constantly appear in the NLoS conditions. Based on these findings, we propose a context-aware reliable beaconing scheme, called CoBe, to enhance the broadcast reliability for safety applications. The CoBe is a fully distributed scheme, in which a vehicle first detects the link condition with each of its neighbors by machine learning algorithms, then exchanges such link condition information with its neighbors, and finally selects the minimal number of helper vehicles to rebroadcast its beacons to those neighbors in bad link condition. To analyze and evaluate the CoBe performance, a two-state Markov chain is devised to model beaconing behaviors. The extensive trace-driven simulations are conducted to demonstrate the efficacy of CoBe.

In mobile crowd sensing (MCS) applications, a public model of a system is expected to be derived from observations collected by mobile device users, through regression modeling. For example, a model describing the relationship between running speed, heart rate, height, and weight of runner can be constructed using MCS data collected from wristbands. Unique features of MCS data bring regression new challenges. First, observations are error-prone and private, making it of great difficulty to derive an accurate model without acquiring raw data. Second, observations are nonstationary and opportunistically, calling for an adaptive model updating mechanism. Last, mobile devices are resource-constrained, posing an urgent demand for lightweight regression. We propose an adaptive and blind regression scheme. The core idea is first to select an optimal `safe' subset of observations locally stored over all participants, such that the inconsistency between the subset and the corresponding regression model is minimized, and as many observations as possible are included. Then, based on the resulted regression model, more observations are checked and selected to refine the model. With observations constantly coming, newly selected `safe' observations are used to make the model updated adaptively. To preserve data privacy, one-time pad masking and blocking scheme are integrated.

Finding a small object (e.g., keys or a wallet) in an indoor environment (e.g., in a house or an office) can be frustrating. In this paper, we propose an innovative system, called HyperEar, to localize such an object using only one single smartphone, based on enhanced time-difference-of-arrival (TDoA) measurements over acoustic signals issued from the object. One major challenge is the hardware limitations of a Commercial-Off-The-Shelf (COTS) phone with a short separation between the two microphones and the low sampling rate of such microphones. HyperEar enhances the accuracy of TDoA measurements by virtually increasing distances between microphones through sliding the phone in the air. HyperEar requires no communication for synchronization between the phone and the object and is a low-cost and easy-to-use system. We evaluate the performance of HyperEar via extensive experiments in various indoor conditions and the results demonstrate that, for an object of 7m away, HyperEar can achieve a mean localization accuracy of about 15cm when the object in normal indoor environments.

Relative direction estimation among neighboring vehicles in urban environment is essential to a wide variety of driving safety applications. To obtain accurate direction information solely from vehicle-to-vehicle (V2V) communications is desirable but very challenging due to the highly dynamic vehicular environments. In this paper, we propose an online vehicular AoA estimation scheme, called DeepAoA, based on a deep learning method. More specifically, Channel state information (CSI) is estimated from a set of synchronized receiving radios by a receiver vehicle. By taking the CSI phase difference between a pair of such radios, CSI phase errors in baseband can be effectively eliminated, which makes CSI phase difference a compelling feature to represent the direction of incident radio frequency (RF) signals and the dynamic channel characteristics. A convolutional neural network (CNN) model is then trained with labeled samples of CSI phase difference. We implement a prototype of DeepAoA receiver using four synchronized USRPs with their antennas in uniform circular array (UCA) configuration for full field of view. We collect real-world CSI trace and conduct trace-driven simulations. DeepAoA can achieve AoA estimation errors of less than 3 degrees with a 98% confidence interval with four antennas. The results demonstrate the efficacy of DeepAoA.

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

Smartphones with dual cameras are increasingly popular due to the need of supporting 3D vision. The depth information is critical for 3D vision. However, the two cameras on a smartphone are too close to accurately estimate the depth information especially for objects beyond two meters. In this paper, we propose an innovative system, called HyperSight, to estimate the depth information of objects using a dual camera smartphone. HyperSight realizes a virtual longbaseline stereo vision rig by having a user to move the phone in the air. The phone movement is continuously tracked and estimated using the short-baseline dual camera seeing nearby objects. We implement HyperSight as software on a Commercial-Off-The-Shelf (COTS) smartphone and conduct real-world experiments. The results show that when measuring feature-rich objects at a distance of five meters, HyperSight achieves a mean depth error of 6cm, which is up to 10× and 18× improvement in the accuracy compared with the stereo vision system using the native dual cameras and the Measure app based on ARKit 1 on mobile devices, respectively.

Mobile crowd sensing (MCS) data have unique features, i.e., private, error-prone, non-stationary and opportunistically generated, and collected by resource-constrained mobile devices, which bring the regression task new challenges. First, it is of great difficulty to derive an accurate model without acquiring raw data. Second, adaptive model updating mechanism is urgently needed. Last, regression schemes should be lightweight. In this paper, we propose a blind regression scheme, called Sieve, in MCS settings. The core idea is first to let the server help in coordinating the selection of a small `clean' subset of observations locally stored over all volunteers. Based on the `clean' subset, an initial model can be established among volunteers in a distributed fashion. With observations constantly coming, instead of model re-estimating from the scratch, newly selected `clean' observations are used to update the established model. Sieve preserves data privacy by only exchanging aggregated information. With the incremental model updating strategy, it also minimizes the communication and computation overhead of mobile devices. Extensive trace-driven simulations are conducted and the results demonstrate the efficacy of the Sieve design.

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.

In mobile crowd sensing (MCS) applications, a public model of a system or phenomenon is expected to be derived from sensory data, i.e., observations, collected by mobile device users, through regression modeling. Unique features of MCS data bring the regression task new challenges. First, observations are error-prone and private, making it of great difficulty to derive an accurate model without acquiring raw data. Second, observations are non-stationary and opportunistically generated, calling for an evolutionary model updating mechanism. Last, mobile devices are resource-constrained, posing an urgent demand for lightweight regression schemes. In this paper, we propose an evolutionary blind regression scheme, called Lotus, in MCS settings. The core idea is first to select a 'maximum- safe-subset' of observations locally stored over all participants, which refers to finding a subset containing half of observations, such that the corresponding regression model has a minimum value of residual sum of squares. It implies the inconsistency between observations in the subset is minimized. Since such a maximum-safe- subset selection problem is NP-hard, a distributed greedy hill- climbing algorithm is proposed. Then, based on the resulted regression model, more observations are checked. Selected ones will be used to refine the model. With observations constantly coming, newly selected 'safe' observations are used to make the model evolved. To preserve data privacy, a one-time pad masking mechanism, and a blocking scheme are integrated into the process of regression estimation. Intensive theoretical analysis and extensive trace driven simulations are conducted and the results demonstrate the efficacy of the Lotus design.

Vehicular ad hoc network (VANET) has been widely recognized as a promising solution to enhance driving safety, by keeping vehicles well aware of the nearby environment through frequent beacon message exchanging. Due to the dynamic of transportation traffic, especially for those scenarios where the density of vehicles is high, the naive beaconing scheme where vehicles send beacon messages at a fixed rate with a fixed transmission power can cause severe channel congestion. In this paper, we investigate the risk of rear-end collision model and define a danger coefficient ρ to characterize the danger threat of each vehicle being in a rear-end collision. We then propose a fully-distributed beacon congestion control scheme, referred to as ABC, which guarantees each vehicle to actively adapt a minimal but sufficient beacon rate to avoid a rear-end collision based on individual estimates of ρ. In essence, ABC adopts a TDMA-based MAC protocol and solves a NP-hard optimal distributed beacon rate adapting (DBRA) problem with a greedy heuristic algorithm, in which a vehicle with a higher ρ will be assigned with a higher beacon rate while keeping the total required beacon demand lower than the channel capacity. We conduct extensive simulations to demonstrate the efficiency of ABC design in different traffic density and a large variety of underlying road topologies.

WiFi technology has gained a wide prevalence for not only wireless communication but also pervasive sensing. A wide variety of emerging applications leverage accurate measurements of the Channel State Information (CSI) information obtained from commodity WiFi devices. Due to hardware imperfection of commodity WiFi devices, the frequency response of internal signal processing circuit is mixed with the real channel frequency response in passband, which makes deriving accurate channel frequency response from CSI measurements a challenging task. In this paper, we identify non-negligible non-linear CSI phase errors and report that IQ imbalance is the root source of non-linear CSI phase errors. We conduct intensive analysis on the characteristics of such non-linear errors and find that such errors are prevalent among various WiFi devices. Furthermore, they are rather stable along time and the received signal strength indication (RSSI) but sensitive to frequency bands used between a transmission pair. Based on these key observations, we propose new methods to compensate both non-linear and linear CSI phase errors. We demonstrate the efficacy of the proposed methods by applying them in CSI splicing and indoor distance ranging. Results of extensive real-world experiments indicate that accurate CSI phase measurements can significantly improve the performance of splicing and the stability of the derived power delay profiles (PDPs). Moreover, the estimated distance errors are reduced by 5.7 times on average comparing to the state-of-the-art schemes.

WiFi technology has gained a wide prevalence for not only wireless communication but also pervasive sensing. A wide variety of emerging applications leverage accurate measurements of the Channel State Information (CSI) information obtained from commodity WiFi devices. Due to hardware imperfection of commodity WiFi devices, the frequency response of internal signal processing circuit is mixed with the real channel frequency response in passband, which makes deriving accurate channel frequency response from CSI measurements a challenging task. In this paper, we identify non-negligible non-linear CSI phase errors and report that IQ imbalance is the root source of non-linear CSI phase errors. We conduct intensive analysis on the characteristics of such non-linear errors and find that such errors are prevalent among various WiFi devices. Furthermore, they are rather stable along time and the received signal strength indication (RSSI) but sensitive to frequency bands used between a transmission pair. Based on these key observations, we propose new calibration methods to compensate both non-linear and linear CSI phase errors. We demonstrate the efficacy of the proposed methods by applying them in CSI splicing. Results of extensive real-world experiments indicate that accurate CSI phase measurements can significantly improve the performance of splicing and the stability of the derived power delay profiles (PDPs).

IEEE 802.11p based Dedicated Short Range Communication (DSRC) has been considered as a promising wireless technology for enhancing road safety and efficiency. However, there is lack of deep understanding about how IEEE 802.11p performs for vehicle-to- vehicle (V2V) communications in urban environments. In this demo paper, we first introduce the statistical analysis results on V2V communication performance, based on a large volume of real-world measurement trace collected in Shanghai city. With the key insights observed in our experiments, we propose a novel scheme to synthesize V2V communication traces which can be of great value for VANET-related research, such as network protocol design and simulations.

With the development of distributed energy generators and storages, the sustainability of a microgrid comprised of multiple electricity users is significantly increased. Maximizing the efficiency of generated renewable energy is vital to running a sustainable microgrid as it indicates reducing the usage of thermal electricity purchased from the macrogrid. To this end, the excessive renewable energy of a user should be shared with others who are short of energy. Unfortunately, coordinating the transfers of renewable energy among the users in a microgrid is particularly difficult, given the rational nature of users, the stochastic nature of renewable energy and the dynamic nature of energy demand of each user. In this paper, we consider the coupled problem of maximizing the renewable energy efficiency of a sustainable microgrid as well as stimulating rational users to share excessive renewable energy. We propose a near-optimal scheduling algorithm, which determines the amounts of renewable energy transferred among users in an online fashion. We also design an efficient pricing mechanism for the trade of energy among users based on double auction. We rigorously prove that our online scheduling algorithm is approximately optimal and the pricing mechanism guarantees the property of individual rationality of users. Comprehensive simulation results demonstrate the efficacy of our online algorithm and incentive mechanism.

With the proliferation of smartphones, participatory sensing using smartphones provides unprecedented opportunities for collecting enormous sensing data. There are two crucial requirements in participatory sensing, fair task allocation and energy efficiency, which are particularly challenging given high combinatorial complexity, trade-off between energy efficiency and fairness, and dynamic and unpredictable task arrivals. In this paper, we present a novel fair energy-efficient allocation framework whose objective is characterized by min–max aggregate sensing time. We rigorously prove that optimizing the min–max aggregate sensing time is NP hard even when the tasks are assumed as a priori. We consider two allocation models: offline allocation and online allocation. For the offline allocation model, we design an efficient approximation algorithm with the approximation ratio of 2−1m⁠, where m is the number of member smartphones in the system. For the online allocation model, we propose two algorithms: greedy algorithm and Robin-Hood algorithm, which achieve the competitive ratio of at most m and m+1⁠, respectively. The results demonstrate that the approximation algorithm reduces over 81% total sensing time, the online greedy algorithm and Robin-Hood algorithms reduce the total sensing time 73% and 37.5%, respectively. The offline approximation algorithm and online greedy algorithm achieve much better min–max fairness compared to other algorithms.

Accessing to timely and accurate road condition information, especially about dangerous potholes is of great importance to the public and the government. In this paper, we propose a novel scheme, called P3, which utilizes smartphones placed in normal vehicles to sense and estimate the profiles of potholes on urban surface roads. In particular, a P3-enabled smartphone can actively learn the knowledge about the suspension system of the host vehicle without any human intervention and adopts a one degree-offreedom (DOF) vibration model to infer the depth and length of pothole while the vehicle is hitting the pothole. Furthermore, P3 shows the potential to derive more accurate results by aggregating individual estimates. In essence, P3 is light-weighted and robust to various conditions such as poor light, bad weather, and different vehicle types. We have implemented a prototype system to prove the practical feasibility of P3. The results of extensive experiments based on real trace demonstrate the efficacy of the P3 design. On average, P3 can achieve low depth and length estimation error rates of 13 and 16 percent, respectively.

Mobile crowdsensing is a novel paradigm to collect sensing data and extract useful information about regions of interest. It widely employs incentive mechanisms to recruit a number of mobile users to fulfill coverage requirement in the interested regions. In practice, sensing service providers face a pressing optimization problem: How to maximize the valuation of the covered interested regions under a limited budget? However, the relation between two important factors, i.e., Coverage Maximization and Budget Feasibility, has not been fully studied in existing incentive mechanisms for mobile crowdsensing. Furthermore, the existing approaches on coverage maximization in sensor networks can work, when mobile users are rational and selfish. In this paper, we present the first in-depth study on the coverage problem for incentive-compatible mobile crowdsensing, and propose BEACON, which is a Budget fEAsible and strategy-proof incentive mechanism for weighted COverage maximizatioN in mobile crowdsensing. BEACON employs a novel monotonic and computationally tractable approximation algorithm for sensing task allocation, and adopts a newly designed proportional share rule based compensation determination scheme to guarantee strategy-proofness and budget feasibility. Our theoretical analysis shows that BEACON can achieve strategy-proofness, budget feasibility, and a constant-factor approximation. We deploy a noise map crowdsensing system to capture the noise level in a selected campus, and evaluate the system performance of BEACON on the collected sensory data. Our evaluation results demonstrate the efficacy of BEACON.

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.

With more and more trajectory traces available, conducting analysis and mining on those trajectories can obtain valuable information. Although the published traces are often made anonymous by substituting the true identities of mobile nodes with random identifiers, the privacy concern remains. In this paper, we propose a new de-anonymization attack based on the movement pattern of moving objects. Since moving objects are open to observe in public spaces, an attacker can easily learn information about a victim's movement either through direct observations or from third parties. After collecting a few trajectory segments of a mobile object, the movement pattern of the victim can be extracted, using an improved TF-IDF method. By comparing the movement pattern of the victim with those extracted from historical anonymous traces, it is possible to identify the victim from the anonymous traces. We conduct extensive trace-driven simulations and the results demonstrate that the attacker is able to de-anonymize anonymous trajectories with high probability.

To support mobile users in contributing sensing data for making urban management decisions, in ShanghaiGrid, unified data gathering operations are to be performed. For citywide coverage, public vehicles accept data from surrounding users and hand over to computing center through wireless base stations (BSs) deployed in the city. Meanwhile, several among the vehicles are hired as relays, which assist gathering from others with multicopy and multihop forwarding towards the BSs. However, the budget shared by deploying BSs and hiring relays is limited. We explore how to decide BS deployment and relay-based forwarding for efficient gathering under the budget. The challenge lies in the great uncertainty about collection opportunities of candidate locations and vehicles in future gathering processes. In this paper, we present an empirical approach for the problem. To tackle the challenges, we characterize collection performance as function of temporal data paths towards each candidate, and formulate the problem as a multiobjective optimization problem. To solve it, we reveal regular relations between the candidates and estimate expected importance of them with large set of real vehicular traces; and develop an algorithmic framework for BS deployment and corresponding forwarding strategy. Extensive trace-driven simulations demonstrate the efficacy of the approach.

Acquiring accurate location information of vehicles is of great importance. Global Positioning System (GPS) has been widely deployed and used to be the most convenient solution to outdoor localization. As more and more infrastructure such as elevated roads, tunnels and tall buildings is built, however, the ever-increasing complexity of urban environments makes vehicle localization especially in those urban canyons a new challenging problem. In this paper, we propose a novel scheme, called UPS, to tackle urban vehicle localization problem. Inspired by the observation from empirical study that the Received Signal Strength Indication (RSSI) values of cellular signals (e.g., GSM) perceived over a distance have ideal temporal-spatial characteristics for fingerprinting, UPS refines the location accuracy of a moving vehicle by matching its cellular-aware trajectory, which is an association between consecutive geographical positions and the corresponding wide-band GSM RSSI values, with a pre-constructed map. Moreover, UPS leverages large mobility of vehicles to construct large-scale maps. We implement a prototype system to validate the feasibility of the UPS design. We conduct extensive real-world experiments and results show that UPS can work stably in various urban settings and achieve an accuracy of 4.2 meters on average and 5.3 meters with a 90% precision.

With the development of renewable energy generators and electricity storages, microgrids become a promising technology of the smart grid. Maximizing the usage of renewable energy is vital to running a microgrid as it indicates reduction of the usage of thermal electricity purchased from the macrogrid. To this end, the excessive renewable energy of a user should be transferred to other users who need energy. Unfortunately, coordinating the transfers of renewable energy among the users in the microgrid is particularly difficult due to the stochastic nature of renewable energy, and the dynamic energy demand of each user. In this paper, we consider the problem of maximizing the long-term renewable energy usage by exchanging excessive renewable energy among users in a microgrid. We propose an online control algorithm which determines the amounts of renewable energy transferred among users in an online fashion. We rigorously prove that our online control algorithm is approximately optimal. We have conducted comprehensive simulation results that demonstrate the efficacy of our online algorithm.

WiFi technology has gained a wide prevalence for not only wireless communication but also pervasive sensing. A wide variety of emerging applications leverage accurate measurements of the Channel State Information (CSI) information exposed by commodity WiFi devices. Due to hardware imperfection of commodity WiFi devices, the frequency response of internal signal processing circuit is mixed with the real channel frequency response in passband, which makes deriving accurate channel frequency response from CSI measurements a challenging task. In this paper, we conduct an extensive empirical studies on CSI measurements and identify a non-negligible non-linear CSI phase error, which cannot be compensated by existing calibration strategies targeted at linear CSI phase errors. We conduct intensive analysis on the properties of such non-linear CSI phase errors and find that such errors are prevalent among various WiFi devices. Furthermore, they are stable along time and for different time-of-flight but related to the received signal strength indication (RSSI) of the received signal, the band frequency and the specific radios used between a transmission pair. Based on these key observations, we infer that the IQ imbalance issue in the direct-down-conversion architecture of commodity WiFi devices is the root source of the non-linear CSI phase errors. Our findings are essential to CSI-based applications and call for new practical strategies to remedy non-linear phase errors.

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.

IEEE 802.11p based Dedicated Short Range Communication (DSRC) has been considered as a promising wireless technology for enhancing transportation safety and traffic efficiency. However, with limited literature available, there is lack of understanding about how IEEE 802.11p performs for vehicleto-vehicle (V2V) communications in urban environments. In this paper, we conduct intensive statistical analysis on V2V communication performance, based on the empirical measurement data collected from off-the-shelf IEEE 802.11p-compatible onboard units (OBUs). We have several key insights as follows. First, both line-of- sight (LoS) and non-line-of-sight (NLoS) durations follow power law distributions, which implies that the probability of having long LoS/NLoS conditions can be relatively high. Second, the packet inter-reception (PIR) time distribution follows an exponential distribution in LoS conditions but a power law in NLoS conditions. In contrast, the packet inter-loss (PIL) time distribution in LoS condition follows a power law but an exponential in NLoS condition. Third, the overall PIR time distribution is a mix of exponential distribution and power law distribution. The presented results provide solid ground to validate models, tune VANET simulators and improve communication strategies.

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.

Stimulating both service users and service providers is of paramount importance to mobile crowdsourcing. A few incentive mechanisms have been proposed, but all of them have focused only on one-sided interactions either among service users or among service providers. For the first time, to the best of our knowledge, we investigate the important two-sided online interactions among service users and service providers in mobile crowdsourcing. We model such interactions as online double auctions, explicitly taking the dynamic nature of both users and providers into account We propose a general framework for the design of truthful online double auctions for dynamic mobile crowdsourcing. The framework is expressive and can work with different price schedules. We propose price-ranked online double auctions with four price schedules to implement the framework, which are suitable for different scenarios. With theoretical analysis and extensive simulations we demonstrate that the proposed auctions are strategy-proof, individual rational, and ensure budget balance.

Acquiring instant walking distance is desirable in indoor localization and map construction. However, due to the blackout of Global Positioning System (GPS) in indoor settings, to accurately estimate the indoor walking distance with minimum hardware requirement is very challenging. In this paper, we propose a lightweight scheme, called DiSen, to range the instant walking distance of smartphone users. After analysing the extensive walking trace data, we find that people have rather consistent walking behaviour even though they may change their walking speeds in different situations. Furthermore, the relationship between stride length and step frequency while walking can be well estimated using non-linear sigmoid model. Inspired by such insights, we first design a stride segmenting method to obtain reliable and accurate step frequency information from raw accelerometer readings. We then train a sigmoid model using acceleration and GPS information collected when a user walks in outdoor conditions and finally apply the model to indoor walking distance ranging. Real-world experiment results show that, in different walking speeds, DiSen can reach average distance estimation accuracy of 96%.

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.

3G technology has stimulated a wide variety of high-bandwidth applications on smartphones, such as video streaming and content-rich web browsing. Although having those applications mobile is quite appealing, high data rate transmission also poses huge demand for power. It has been revealed that the tail effect in 3G radio operation results in significant energy drain on smartphones. Recent fast dormancy technique can be utilized to remove tails but, without care, can degrade user experience. In this paper, we propose a novel scheme SmartCut, which effectively mitigates the tail effect of radio usage in 3G networks with little side-effect on user experience. The core idea of SmartCut is to utilize the temporal correlation of packet arrivals to predict upcoming packets, based on which unnecessary high-power-state tails of radio are cut out leveraging the Fast Dormancy mechanism. Both prototype experiment and extensive trace-driven simulation results demonstrate the efficacy of SmartCut design. On average, SmartCut can save up to 43 percent network energy while having little side-effect to user experience.

Touch-screen technique has gained the large popularity in human-screen interaction with modern smartphones. Due to the limited size of equipped screens, scrolling operations are indispensable in order to display the content of interest on screen. While power consumption caused by hardware and software installed within smartphones is well studied, the energy cost made by human-screen interaction such as scrolling remains unknown. In this paper, we analyze the impact of scrolling operations to the power consumption of smartphones, finding that the state-of-art strategy of smartphones in responding a scrolling operation is to always use the highest frame rate which arouses huge computation burden and can contribute nearly 50 percent to the total power consumption of smartphones. In recognizing this significance, we further propose a novel system, energy-efficient engine (E3), which automatically tracks the scrolling speed and adaptively adjusts the frame rate according to user preference. The goal of E3is to guarantee the user experience and minimize the energy consumption caused by scrolling at the same time. Extensive experiment results demonstrate the efficiency of E3design. On average, E3can save up to 60 percent of the energy consumed by CPU and 35 percent of the overall energy consumption.

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.

Advances in low-power wireless communications and micro-electronics make a great impact on a transportation system and pervasive deployment of road-side units (RSU) is promising to provide drive-thru Internet to vehicular users anytime and anywhere. Downloading data packets from the RSU, however, is not always reliable because of high mobility of vehicles and high contention among vehicular users. Using inter-vehicle communication, cooperative downloading can maximize the amount of data packets downloaded per user request. In this paper, we focus on effective data downloading for realtime applications (e.g., video streaming, online game) where each user request is prioritized by the delivery deadline. We propose a cooperative downloading algorithm, namely MMCD, which maximizes the amount of data packets downloaded from the RSU while minimizing delivery delay of each user request. The performance of MMCD is evaluated by extensive simulations and results demonstrate that our algorithm can reduce mean delivery delay while gaining downloading throughput as high as that of a state-of-the-art method.

Stimulating participation from smartphone users is of paramount importance to mobile crowd sourcing systems and applications. A few incentive mechanisms have been proposed, but most of them have made the impractical assumption that smartphones remain static in the system and sensing tasks are known in advance. The existing mechanisms fail when being applied to the realistic scenario where smartphones dynamically arrive to the system and sensing tasks are submitted at random. It is particularly challenging to design an incentive mechanism for such a mobile crowd sourcing system, given dynamic smartphones, uncertain arrivals of tasks, strategic behaviors, and private information of smartphones. We propose two truthful auction mechanisms for two different cases of mobile crowd sourcing with dynamic smartphones. For the offline case, we design an optimal truthful mechanism with an optimal task allocation algorithm of polynomial-time computation complexity of O (n+γ)3, where n is the number of smartphones and γ is the number of sensing tasks. For the online case, we design a near-optimal truthful mechanism with an online task allocation algorithm that achieves a constant competitive ratio of 1:2. Rigorous theoretical analysis and extensive simulations have been performed, and the results demonstrate the proposed auction mechanisms achieve truthfulness, individual rationality, computational efficiency, and low overpayment.

With the popularity of intelligent mobile devices, enormous urban information has been generated and required by the public. In response, ShanghaiGrid (SG) aims to providing abundant information services to the public. With fixed schedule and urban-wide coverage, an appealing service in SG is to provide free message delivery service to the public using buses, which allows mobile device users to send messages to locations of interest via buses. The main challenge in realizing this service is to provide efficient routing scheme with privacy preservation under highly dynamic urban traffic condition. In this paper, we present an innovative scheme BusCast to tackle this problem. In BusCast, buses can pick up and forward personal messages to their destination locations in a store-carry-forward fashion. For each message, BusCast conservatively associates a routing graph rather than a fixed routing path with the message in order to adapt the dynamic of urban traffic. Meanwhile, the privacy information about the user and the message destination is concealed from both intermediate relay buses and outside adversaries. Both rigorous privacy analysis and extensive trace-driven simulations demonstrate the efficacy of BusCast scheme.

Acquiring instant vehicle speed is desirable and a corner stone to many important vehicular applications. This paper utilizes smartphone sensors to estimate the vehicle speed, especially when GPS is unavailable or inaccurate in urban environments. In particular, we estimate the vehicle speed by integrating the accelerometer's readings over time and find the acceleration errors can lead to large deviations between the estimated speed and the real one. Further analysis shows that the changes of acceleration errors are very small over time which can be corrected at some points, called reference points, where the true vehicle speed is known. Recognizing this observation, we propose an accurate vehicle speed estimation system, SenSpeed, which senses natural driving conditions in urban environments including making turns, stopping and passing through uneven road surfaces, to derive reference points and further eliminates the speed estimation deviations caused by acceleration errors. Extensive experiments demonstrate that SenSpeed is accurate and robust in real driving environments. On average, the real-time speed estimation error on local road is 1.32mph, and the offline speed estimation error is as low as 0.75mph. Whereas the average error of GPS is 3.1mph and 2.8mph respectively.

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.

With the proliferation of smartphones, participatory sensing using smartphones provides unprecedented opportunities for collecting enormous sensing data. There are two crucial requirements in participatory sensing, fair task allocation and energy efficiency, which are particularly challenging given high combinatorial complexity, tradeoff between energy efficiency and fairness, and dynamic and unpredictable task arrivals. In this paper, we present a novel fair energy-efficient allocation framework whose objective is characterized by min-max aggregate sensing time. We rigorously prove that optimizing the min-max aggregate sensing time is NP hard even when the tasks are assumed as a priori. We consider two allocation models: offline allocation and online allocation. For the offline allocation model, we design an efficient approximation algorithm with the approximation ratio of 2 - 1/m, where m is the number of member smartphones in the system. For the online allocation model, we propose a greedy online algorithm which achieves a competitive ratio of at most m. The results demonstrate that the approximation algorithm reduces over 81% total sensing time, the greedy online algorithm reduces more than 73% total sensing time, and both algorithms achieve over 3x better min-max fairness.

Traffic load is often unevenly distributed among the access points (APs) in enterprise WLANs. Such load imbalance results in sub-optimal network throughput and unfair bandwidth allocation among users. In this paper, we collect real traces from over twelve thousand WiFi users in Shanghai Jiao Tong University. Through intensive data analysis, we find that user behavior like leaving together may cause significant AP load imbalance problem. We also observe from the trace that users with similar application usage have the potential to leave together. Inspired by those observations, we propose an innovative scheme, Social-aware AP Selection Scheme(S3), which can actively learn the sociality information among users trained with their history application profiles and elegantly assign users based on the obtained knowledge. Both real prototype implementation and simulation results show that S3 is feasible and can achieve 41.2% balancing performance gain on average.

Given the unique characteristics of vehicular networks, specifically, frequent communication unavailability and short encounter time, packet replication has been commonly used to facilitate data delivery. Replication enables multiple copies of the same packet to be forwarded towards the destination, which increases the chance of delivery to a target destination. However, this is achieved at the expense of consuming extra already scarce bandwidth resource in vehicular networks. Therefore, it is crucial to investigate the fundamental problem of exploiting constrained network capacity with packet replication. We make the first attempt in this work to address this challenging problem. We first conduct extensive empirical analysis using three large datasets of real vehicle GPS traces. We show that a replication scheme that either underestimates or overestimates the network capacity results in poor delivery performance. Based on the observation, we propose a Capacity-Constrained Replication scheme or CCR for data delivery in vehicular networks. The key idea is to explore the residual capacity for packet replication. We introduce an analytical model for characterizing the relationship among the number of replicated copies of a packet, replication limit and queue length. Based on this insight, we derive the rule for adaptive adjustment towards the optimal replication strategy. We then design a distributed algorithm to dictate how each vehicle can adaptively determine its replication strategy subject to the current network capacity. Extensive simulations based on real vehicle GPS traces show that our proposed CCR can significantly improve delivery ratio comparing with the state-of-the-art algorithms.

3G technology has stimulated a wide variety of high-bandwidth applications on smartphones, such as video streaming and content-rich web browsing. Although having those applications mobile is quite appealing, high data rate transmission also poses huge demand for power. It has been revealed that the tail effect in 3G radio operation results in significant energy drain on smartphones. Recent fast dormancy technique can be utilized to remove tails but, without care, can degrades user experience. In this paper, we propose a novel scheme SmartCut, which effectively mitigates the tail effect of radio usage in 3G networks with little side-effect on user experience. The core idea of SmartCut is to utilize the temporal correlation of packet arrivals to predict upcoming data, based on which unnecessary high-power-state tails of radio are cut out leveraging the Fast Dormancy mechanism. Extensive trace-driven simulation results demonstrate the efficacy of SmartCut design. On average, SmartCut can save up to 56.57% energy on average while having little side-effect to user experience.

Vehicular networks consist of highly mobile vehicles communications, where connectivity is intermittent. Due to the distributed and highly dynamic nature of vehicular network, to minimize the end-to-end delay and the network traffic at the same time in data forwarding is very hard. Heuristic algorithms utilizing either contact-level or social-level scale of vehicular mobility have only one-sided view of the network and therefore are not optimal. In this paper, by analyzing three large sets of Global Positioning System (GPS) trace of more than ten thousand public vehicles, we find that pairwise contacts have strong temporal correlation. Furthermore, the contact graph of vehicles presents complex structure when aggregating the underlying contacts. In understanding the impact of both levels of mobility to the data forwarding, we propose an innovative scheme, named ZOOM, for fast opportunistic forwarding in vehicular networks, which automatically choose the most appropriate mobility information when deciding next data-relays in order to minimize the end-to-end delay while reducing the network traffic. Extensive trace-driven simulations demonstrate the efficacy of ZOOM design. On average, ZOOM can improve 30% performance gain comparing to the state-of-art algorithms.

Touch-screen technique has gained the large popularity in human-screen interaction with modern smartphones. Due to the limited size of equipped screens, scrolling operations are indispensable in order to display the content of interest on screen. While power consumption caused by hardware and software installed within smartphones is well studied, the energy cost made by human-screen interaction such as scrolling remains unknown. In this paper, we analyze the impact of scrolling operations to the power consumption of smartphones, finding that the state-of-art strategy of smartphones in responding a scrolling operation is to always use the highest frame rate which arouses huge computation burden and can contribute nearly 50% to the total power consumption of smartphones. In recognizing this significance, we further propose a novel system, Energy-Efficient Engine(E3), which automatically tracks the scrolling speed and adaptively adjusts the frame rate according to individual user preference. The goal of E3 is to guarantee the user experience and minimize the energy consumption caused by scrolling at the same time. Extensive experiment results demonstrate the efficiency of E3 design. On average, E3 can save up to 58% of the energy consumed by CPU and 34% of the overall energy consumption.

This paper studies delay-bounded service discovery in urban Vehicular Ad-hoc Networks (VANETs), which refers to locating resources and services (e.g., local sensor data and multimedia content) distributed on individual vehicles in the network within a certain delay bound. To facilitate the discovery process, a set of vehicles, called service directories (SDs), can be selected to store the index information of all the resources in the network. Selecting an optimal SD set with minimal size while satisfying the users' requirement of a bounded query response delay is very difficult due to the disruptive nature of VANETs. In this paper, we formulate the Delay Bounded Service Directory Selection (DB-Sel) problem as an optimization problem that minimizes the number of SDs under the delay bound constraint. We prove theoretically that the DB-Sel problem is NP-Complete even when the future positions of vehicles are known a priori. We observe and prove that the number of vehicles encountered by arbitrarily selected SDs within a given delay follows a normal distribution. We also find the contact probabilities among the vehicles exhibit strong temporal correlation. With these observations, we develop a heuristic algorithm which iteratively selects the best candidate according to the normal distribution property and the historical contact probability. We prove that our algorithms have a guaranteed performance approximation ratio compared to the optimal solution. Extensive trace-driven simulation results demonstrate that our algorithm can guarantee the required query delay and select SD sets 20% smaller than those selected by alternative algorithms.

Traffic estimation is crucial to a number of tasks such as traffic management and road engineering. We propose an approach for metropolitan-scale traffic estimation with probe vehicles that periodically send location and speed updates to a monitoring center. In our approach, we use the flow speed on a road link within a time slot to indicate the traffic condition of the road segment at the given time slot, which is approximated by the average value of probe speeds. By analyzing a large data set of two-year probe data collected from a fleet of around 4,000 taxis in Shanghai, China, we find that a set of probe data may contain a lot of spatiotemporal vacancies over both time and space. This raises a serious missing data problem for road traffic estimation, which results from the naturally uneven distribution of probe vehicles over both time and space. Through empirical study based on the data set of real probe data using principal component analysis (PCA), we have observed that there are hidden structures within the traffic conditions of a road network. Inspired by this observation, we propose a compressive sensing-based algorithm for solving the missing data problem, which exploits the hidden structures for computing estimates for road traffic conditions. Different from existing approaches, our algorithm does not rely on complicated traffic models, which usually require costly training with field study and large data sets. With extensive experiments based on the data set of real probe data, we demonstrate that our proposed algorithm performs significantly better than other completing algorithms, including KNN and MSSA. Surprisingly, our algorithm can achieve an estimate error of as low as 20 percent even when more than 80 percent of probe data are missing.

Well-balanced workload among wireless access points (APs) in a wireless local-area network (WLAN) can improve the user experience for accessing the Internet. Most load balancing solutions in WLANs focuses on the optimization of AP operations, assuming that the arrivals and departures of users are independent. However, through the analysis of AP usage based on a real WLAN trace of one-month collected at the Shanghai Jiao Tong University (SJTU), we find that such an assumption does not hold. In fact, due to users' social activities which is particularly time for enterprise environments, they tend to arrive or leave in unison, which would disruptively affect the load balance among APs. In this paper, we propose a novel AP allocation scheme to tackle the load balancing problem in WLANs, taking into account the social relationships of users. In this scheme, users with intense social relationships are assigned to different APs so that jointly departure of those users would have minor impact on the load balance of APs. Given that the problem of allocating an AP for each user so that the average of the sums of social relation intensity between any pair of users in each AP is NP-complete, we propose an online greedy algorithm. Extensive trace-driven simulations demonstrate the efficacy of our scheme. Comparing to the state-of-the-art method, we can achieve about 64.7 percent balancing performance gain on average during peak hours in workdays.

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.

Traffic sensing is crucial to a number of tasks such as traffic management and city road network engineering. We build a traffic sensing system with probe vehicles for metropolitan scale traffic sensing. Each probe vehicle senses its instant speed and position periodically and sensory data of probe vehicles can be aggregated for traffic sensing. However, there is a critical issue that the sensory data contain spatiotemporal vacancies with no reports. This is a result of the naturally uneven distribution of probe vehicles in both spatial and temporal dimensions since they move at their own wills. This paper proposes a new approach based on compressive sensing to large-scale traffic sensing in urban areas. We mine the extensive real trace datasets of taxies in an urban environment with principal component analysis and reveal the existence of hidden structures with sensory traffic data that underpins the compressive sensing approach. By exploiting the hidden structures, an efficient algorithm is proposed for finding the best estimate traffic condition matrix by minimizing the rank of the estimate matrix. With extensive trace-driven experiments, we demonstrate that the proposed algorithm outperforms a number of alternative algorithms. Surprisingly, we show that our algorithm can achieve an estimation error of as low as 20% even when more than 80% of sensory data are not present.

Inter-contact times (ICTs) between moving vehicles are one of the key metrics in vehicular networks, and they are also central to forwarding algorithms and the end-to-end delay. Recent study on the tail distribution of ICTs based on theoretical mobility models and empirical trace data shows that the delay between two consecutive contact opportunities drops exponentially. While theoretical results facilitate problem analysis, how to design practical opportunistic forwarding protocols in vehicular networks, where messages are delivered in carry-and-forward fashion, is still unclear. In this paper, we study three large sets of Global Positioning System (GPS) traces of more than ten thousand public vehicles, collected from Shanghai and Shenzhen, two metropolises in China. By mining the temporal correlation and the evolution of ICTs between each pair of vehicles, we use higher order Markov chains to characterize urban vehicular mobility patterns, which adapt as ICTs between vehicles continuously get updated. Then, the next hop for message forwarding is determined based on the previous ICTs. With our message forwarding strategy, it can dramatically increase delivery ratio (up to 80%) and reduce end-to-end delay (up to 50%) while generating similar network traffic comparing to current strategies based on the delivery probability or the expected delay.

Providing real-time traffic information in metropolises is desired since it can not only facilitate the traffic management but also save the time of travelers on road as well as the vehicle fuel consumption which is crucial in low-carbon society. However, to obtain the traffic information is extremely difficult due to the high cost of deploying a tremendously large number of sensors on every road segments or intersections. Recently, the ShanghaiGrid (SG) project presents an innovative cost-efficient way to address this issue by deploying traffic sensors on several thousands mobile taxies. Traffic condition information perception from these sensory data is very challenging because individual taxi reports are error-prone and sparse in terms of temporal and spatial distribution. In this paper, we use a data aggregation approach to overcome the aforementioned challenge, i.e., the ”error-prone” problem and ”sparse” problem. We first extensively study the characteristics of the measurement data from over 3000 operational taxies in Shanghai City. Utilizing the spatial correlation of traffic conditions, we propose a correlation based traffic estimation algorithm to successfully expand the coverage of taxi sensors. Our experimental result demonstrates the significance of the proposed algorithm by providing the traffic information at any time and any location in Shanghai City.

Intercontact time between moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs) and central to forwarding algorithms and the end-to-end delay. Due to prohibitive costs, little work has conducted experimental study on intercontact time in urban vehicular environments. In this paper, we carry out an extensive experiment involving thousands of operational taxies in Shanghai city. Studying the taxi trace data on the frequency and duration of transfer opportunities between taxies, we observe that the tail distribution of the intercontact time, that is, the time gap separating two contacts of the same pair of taxies, exhibits an exponential decay, over a large range of timescale. This observation is in sharp contrast to recent empirical data studies based on human mobility, in which the distribution of the intercontact time obeys a power law. By analyzing a simplified mobility model that captures the effect of hot areas in the city, we rigorously prove that common traffic influxes, where large volume of traffic converges, play a major role in generating the exponential tail of the intercontact time. Our results thus provide fundamental guidelines on design of new vehicular mobility models in urban scenarios, new data forwarding protocols and their performance analysis.

Inter-contact time between moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs) and central to forwarding algorithms and the end-to-end delay. Due to prohibitive costs, little work has conducted experimental study on inter-contact time in urban vehicular environments. In this paper, we carry out an extensive experiment involving thousands of operational taxies in Shanghai city. Studying the taxi trace data on the frequency and duration of transfer opportunities between taxies, we observe that the tail distribution of the inter-contact time, that is the time gap separating two contacts of the same pair of taxies, exhibits a light tail such as one of an exponential distribution, over a large range of timescale. This observation is in sharp contrast to recent empirical data studies based on human mobility, in which the distribution of the inter-contact time obeys a power law. By performing a least squares fit, we establish an exponential model that can accurately depict the tail behavior of the inter-contact time in VANETs. Our results thus provide fundamental guidelines on design of new vehicular mobility models in urban scenarios, new data forwarding protocols and their performance analysis.

Travel route analysis and prediction are essential for the success of many applications in Vehicular Ad-hoc Networks (VANETs). Yet it is quit challenging to make accuracy route prediction for general vehicles in urban settings due to several practical issues such as very complicated traffic networks, the highly dynamic real-time traffic conditions and their interaction with drivers' route selections. In this paper, we undertake a systematic study on the vehicular route prediction in urban environments where the traffic conditions on complicated road networks keep changing from time to time. Inspired by the observation that a vehicle often has its own route selection flavor when traversing between its sources and destinations, we define a mobility pattern as a consecutive series of road segment selections that exhibit frequent appearance along all the itineraries of the vehicle. We further leverage Variable-order Markov Models (VMMs) to mine mobility patterns from the real taxi GPS trace data collected in Shanghai. In addition, considering the tremendous impact of dynamic traffic conditions to the accuracy of route prediction, we deploy multiple VMMs differentiating different traffic conditions in daytime. Our extensive trace-driven simulation results show that notable patterns can be mined from routes of common vehicles though they usually have no constraints when selecting routes. Given a specific taxi, around 40% next road segments are predictable using our model with a confidence weight of 60%. With multiple VMMs a high route prediction accuracy is achievable from the real traffic trace.

Intelligent transportation systems have become increasingly important for the public transportation in Shanghai. In response, Shanghai Grid (SG) aims to provide abundant intelligent transportation services to improve the traffic condition. A challenging service in SG is to estimate the real-time traffic condition on surface streets. In this paper, we present an innovative approach SEER to tackle this problem. In SEER, we deploy a cost-effective system of taxi traffic sensors. These taxi sensory data are found to be noisy and very lossy in both time and space. By intensively mining the spatio-temporal correlations along with the evolution of traffic condition, SEER provides wealthy knowledge to setup statistical models for inferring traffic condition when they cannot be directly calculated. As an example, we demonstrate utilizing multichannel singular spectrum analysis (MSSA) to iteratively produce estimates of traffic condition in a metropolitan scale. The optimal window width of MSSA is determined with the basic periodicity found in traffic condition. Moreover, we minimize the number of channels required by MSSA to estimate traffic condition at any location. Given a desired estimation granularity, we optimize the MSSA parameters to minimize the estimation error.

Intelligent transportation systems have become increasingly important for the public transportation in Shanghai. In response, ShanghaiGrid (SG) project aims to provide abundant intelligent transportation services to improve the traffic condition. A challenging service in SG is to accurately locate the positions of moving vehicles in real time. In this paper, we present an innovative scheme, hierarchical exponential region organization (HERO), to tackle this problem. In SG, the location information of individual vehicles is actively logged in local nodes which are distributed throughout the city. For each vehicle, HERO dynamically maintains an advantageous hierarchy on the overlay network of local nodes to conservatively update the location information only in nearby nodes. By bounding the maximum number of hops the query is routed, HERO guarantees to meet the real-time constraint associated with each vehicle. A small-scale prototype system implementation and extensive simulations based on the real road network and trace data of vehicle movements from Shanghai demonstrate the efficacy of HERO.

Intelligent transportation systems have become increasingly important for the public transportation in Shanghai. In response, ShanghaiGrid (SG) aims to provide abundant intelligent transportation services to improve the traffic condition. A challenging service in SG is to accurately locate the positions of moving vehicles in real time. In this paper we present an innovative scheme HERO to tackle this problem. In SG, the location information of individual vehicles is actively logged in local nodes which are distributed throughout the city. For each vehicle, HERO dynamically maintains an advantageous hierarchy on the overlay network of local nodes to conservatively update the location information only in nearby nodes. By bounding the maximum number of hops the query is routed, HERO guarantees to meet the real-time constraint associated with each vehicle. Extensive simulations based on the real road network and trace data of vehicle movements from Shanghai demonstrate the efficacy of HERO.

Intelligent transportation systems have become increasingly important for the public transportation in Shanghai. In response, ShanghaiGrid aims to provide abundant intelligent transportation services to improve the traffic condition. A fundamental service in ShanghaiGrid is to locate the nearest desirable vehicles for users. In this paper we propose an innovative protocol ANTS to locate a desirable vehicle close to the querying user. The protocol finely mimics the efficient searching strategy adopted by a lost desert ant in searching for its nest. Taking query locality into account, ANTS can retrieve the nearest vehicles satisfying the query with high probability but incurs small query latency and modest network traffic. ANTS is a fully distributed and robust protocol and therefore has good scalability. Extensive simulations based on the real road network and the trace data of vehicle movements in Shanghai demonstrate the efficacy of ANTS.

Simulation environments and approaches for evaluating real-time of IP traceback in different network scenarios and attacking patterns are very important. A comparison among some of the most promising PPM (Probabilistic Packet Marking) schemes is presented with several metrics, including the received packet number required for reconstructing the attacking path, computation complexity and false positive etc. We constructe a simulation environment via extending ns2, setting attacking topology and traffic, which can be used to evaluate and compare the effectiveness of different PPM schemes. The simulation approach also can be used to test the performing effects of different PPM schemes in large-scale DDoS attacks. Based on the simulation and evaluation results, several improvable aspects of PPM are proposed, which can increase real-time of IP traceback efficiently.