Recent advances in spatial transcriptomics have enabled the simultaneous measurement of gene expression profiles and spatial location information, offering a more comprehensive and in-depth view for studying the tissue microenvironment. Spatial domain identification is a crucial step in analyzing spatial transcriptomics. However, current methods have poor accuracy and visualization because they lack self-adaptability to different tissue data, and moreover, they cannot effectively extract spatial location information. To address these issues, we propose an adaptive graph contrastive learning framework based on multi-head graph attention networks (GATCL) for spatial domain identification. Specifically, we design a data augmentation module to mask and shuffle the pre-processed gene expression data to generate more differentiated negative samples. In addition, we construct the multi-head graph attention networks (MHGAT) to encode gene expression profiles and spatial location information. More importantly, we design an adaptive graph contrastive learning model that works both with positive and negative samples from spatial transcriptomics. We introduce the attention pooling mechanism to dynamically and adaptively aggregate the spots' neighborhood information, and to improve the model's generalization ability for different spatial transcriptomics data. Furthermore, we design a discriminator that adds spectral normalization to bilinear functions. Experimental results on DLPFC, breast cancer, and mouse somatosensory cortex datasets demonstrate that the average Adjusted Rand Index (ARI) scores are 0.5746, 0.6182, and 0.6496, respectively, significantly outperforming baseline methods. More importantly, GATCL provides a more detailed visualization of different spatial transcriptomics data.

Recent advances in spatial transcriptomics have enabled the integration of gene expression profiles with precise spatial coordinates, which have facilitated the exploration of tumor occurrence and development mechanisms, as well as the development of more effective targeted and immunotherapy approaches for tumor treatment. Deciphering cell type represents a critical challenge in spatial transcriptomics research. Existing methods are limited by the pervasive “dropout” events in spatial transcriptomics, hindering their ability to fully capture the relationship between spatial location and gene expression, thereby compromising the performance of cell type deconvolution. To address these limitations, we propose a spatial-aware masked graph transformer-diffusion model (SAMGTD) for enhanced cell type deconvolution in spatial transcriptomics. For spatial transcriptomics, the masked graph transformer model is designed to adaptively capture complex dependencies between spatial locations and gene expression. It employs a masking strategy that guides the model to focus on important local information during training, while the multi-head attention mechanism captures global context. More importantly, the spatial diffusion model is constructed to achieve the dual enhancement of spatial transcriptomics, including denoising and data imputation. It incorporates the multi-head attention mechanism and residual blocks, effectively addressing the “dropout” issue commonly encountered in spatial transcriptomics. For scRNA-seq, we construct a variational autoencoder to reduce noise interference while preserving key gene expression information. Finally, we construct a spatial-aware contrastive learning model to integrate scRNA-seq and spatial transcriptomics for cell type deconvolution. Experiments conducted on three datasets demonstrate that SAMGTD outperforms baseline methods.

Modern deep neural network (DNN) and large language model (LLM) applications integrate multiple models into inference pipelines with stringent latency requirements for customized tasks. To mitigate extensive request timeouts caused by accumulation, systems for inference pipelines commonly drop a subset of requests so the remaining ones can satisfy latency constraints. Since it is commonly believed that request dropping adversely affects goodput, existing systems only drop requests when they have to, which we call reactive dropping. However, this reactive policy can not maintain high goodput, as it neither makes timely dropping decisions nor identifies the proper set of requests to drop, leading to issues of dropping requests too late or dropping the wrong set of requests.

Massive MIMO has emerged as a cornerstone technology for 5G-Advanced and future 6G networks, yet its practical deployment remains limited by hardware cost and power consumption. Switch-based architectures, which share a small number of RF chains among many antenna elements, provide a scalable alternative, but create a new bottleneck: only a subset of antennas is observable at any given moment, leaving the channel state of the remaining elements unknown. Lacking this information prevents the system from exploiting advanced physical-layer functions such as digital beamforming or multi-stream MIMO. In this paper, we present ARGUS, a generative channel reconstruction framework that infers the CSI of unobserved antennas from partial observations. The key idea is that all antenna responses are governed by the same underlying wireless propagation environment, enabling the task to be formulated as a generative inference problem. We employ a variational autoencoder to capture the latent spatial structure and reconstruct unobserved channels through sampling. Extensive experiments show that our reconstructed CSI incurs less than 2.5% achievable rate loss, and real-world measurements demonstrate a more than 90% antenna-selection match rate, confirming the practicality of the proposed approach.

The blockchain-based federated learning framework has garnered widespread attention to ensure data privacy and trustworthy computing in IoT devices. Improving the accuracy and scalability are paramount for the increasing demands of IoT tasks. However, existing solutions, such as SIFL and ChainFL, utilize traditional single-chain architecture and fixed models, which exhibit limitations in terms of model generalizability and scalability. To overcome the above problems, this paper proposesFedShard, an FL framework that integrates a layered dual-track incentive mechanism and a privacy knowledge distillation module.When implementingFedShard, we address two technical challenges: (1) to ensure efficient training among the numerous clients in the sharding architecture, we propose a layered dual-track incentive mechanism that provides both long- and short-term rewards; and (2) to enhance the framework’s convergence and privacy in sharding-based FL, we design a knowledge distillation module that incorporates local differential privacy. Furthermore, we propose a bucket-based hash ring to manage clients, enabling the framework to adapt to dynamic network environments. To validate the framework’s generalizability and scalability, we implementFedShardon a 48-core high-performance server using Fabric to conduct both on-chain and off-chain experiments. Our comprehensive experiments, comparingFedShardwith SIFL, PEFL, and ChainFL, reveal that our solution outperforms the state-of-the-art methods by achieving a notable 29% increase in accuracy and a 22% improvement in throughput.

This paper presents ArmNet, a mobile sensing system for capturing the trajectory of the wrist using measurements from wrist-worn IMU devices, specifically targeting robust interaction within Internet of Things (IoT) ecosystems. Unlike existing solutions that directly map IMU data to joint positions, ArmNet integrates physical kinematic constraints with neural network modeling. This hybrid approach is crucial for resource-constrained IoT devices where computational overhead and sensor limitations are primary concerns. Specifically, kinematic priors capture spatial dependencies between the elbow and wrist, generating physics-guided intermediate features that reduce the solution space. These features, together with raw IMU data, are fed into a recurrent neural network to learn joint displacement vectors, which are then integrated into continuous trajectories. Extensive experiments show that ArmNet achieves robust and accurate arm tracking, generalizing well across users and motion patterns, thereby enabling a new modality for seamless human-computer interaction in smart environments.

Facial Expression Recognition (FER) serves as a foundational sensory interface for Human-Centric IoT, supporting applications such as smart healthcare monitoring and affective intelligent environments. However, real-world performance is often hindered by theSemantic Gap, where models confuse visually similar expressions that arise from fundamentally different physiological muscle movements. To bridge this gap, we propose the Physical-Semantic-Aware Multimodal Framework (PSM-FER), which introduces 3D Blendshape (BS) coefficients as explicit physical priors to encode high-level muscle motion semantics. Our framework utilizes two synergistic pathways:Direct Physical Gating(DPG) for robust feature modulation andSemantic-Guided Spatial Attention(SGSA) for anatomical spatial recalibration. Additionally, an auxiliary physical regression task enforces anatomical consistency by regularizing the latent features to follow underlying biomechanical laws. Extensive experiments on the RAF-DB dataset demonstrate that PSM-FER achieves an accuracy of 92.37%, establishing a robust and interpretable foundation for affective sensing in complex IoT ecosystems.

In an era marked by technological innovation, visual applications have become ubiquitous in everyday life. Harnessing the power of computer vision, these applications process and interpret video data from edge cameras, facilitating tasks such as object detection and vehicle counting. Yet, implementing complex deep learning models on cameras with limited computational capacity poses significant challenges. Furthermore, the bandwidth constraints and fluctuating nature of wide-area networks present substantial difficulties for video analysis systems dependent on cloud computing. This paper first characterizes the relationship between different parameter combinations (such as frame rate and resolution) and video analysis accuracy through offline analysis. It proposes a video stream analysis configuration selection scheme, SPStream, for slowly changing scenes, and a configuration file switching strategy, SPStream+, for rapidly changing scenes. These strategies use idle resources at the camera edge end to select the optimal configuration in real-time, adjust video encoding quality, and dynamically switch configuration files based on the changing states of object motion. Finally, a real-time video stream analysis system for vehicle counting and pedestrian detection suitable for both scenarios is designed, which saves bandwidth to the greatest extent while meeting the accuracy requirements of users and achieving high accuracy of video analysis.

Packet sampling is crucial for managing datacenter networks, serving fault diagnosis, traffic measurement, and intrusion detection functions. However, traditional sampling techniques, such as those based on sketches or ports, either lack packet–level granularity or provide insufficient visibility, leading to functional performance degradation. Recent research has employed the software-defined networking (SDN) model to enable flow-based packet sampling. However, these approaches often introduce substantial control and computation overhead, limiting their scalability. This paper presents Viper, a novel priority-based, high-visibility per-flow packet sampling mechanism tailored to address these challenges. Specifically, Viper leverages existing priority-based traffic scheduling mechanisms to prioritize shorter flows over longer ones. Then, a logical centralized controller orchestrates sampling policies for packets of different priorities. In-depth analysis indicates that the orchestration performed by the controller significantly impacts Viper’s performance. Consequently, we model this process as a nonlinear optimization problem, seeking to maximize the utility of sampling. Then, we propose an online primal–dual interior–point algorithm to address this optimization problem and prove the algorithm’s convergence, optimality, and efficiency. Experimental results show that Viper increases visibility by 3.83% to 8.3%, with negligible control overhead and a substantial reduction in sampling load by at least 20.51%.

Ensuring atomic execution of cross-shard transactions is a fundamental challenge for sharded blockchains, particularly in scenarios demand coordination across multiple shards. However, existing solutions either rely on on-chain coordination, leading to high communication overhead, or leverage secure hardware for off-chain execution, imposing strong trust assumptions and reducing general applicability. To this end, we propose RollShard, a sharded blockchain that integrates stateless off-chain mechanism to efficiently process multi-shard transactions (MSTs). In RollShard, each MST is abstracted into a transaction DAG by the Sequencer Shard to ensure the authenticity of the transaction content and the correctness of its execution order. Batched MSTs are dispatched to off-chain executors, each of which simulates transaction logic using a virtual zero-state model integrate with a hierarchical state-delta tree (HSDT). The HSDT employs a Merkle Sum tree to precisely capture batched MSTs’ impact on per-shard account states. Based on the HSDT, the executor generates the zero-knowledge proof to attest the correctness of each shard’s state changes and global value conservation. The resulting net state deltas are then optimistically committed to the relevant shards without cross-shard coordination, reducing intra-shard coordination. We design a game-theoretic incentive mechanism to ensure rational behavior of off-chain executors, showing that honest execution forms a Nash equilibrium under collateral staking. Experimental results based on a prototype deployed in a local area network demonstrate that ROLLSHARDsignificantly outperforms two baseline coordination models proposed in ByShard, namely the Linear and Distributed designs. Specifically, under high workload, RollShard improves throughput by 44.9% and 158%, and reduces cross-shard latency by 38.9% and 42.1%, compared to the Linear and Distributed models, respectively.

The rapidly expanding blockchain ecosystem faces a critical interoperability crisis—evidenced by siloed total value locked and bridge losses. Existing interoperability solutions are trapped in a paradigm dilemma: centralized relays offer performance but introduce single points of failure, while decentralized relay-chains provide security but suffer from poor scalability and rigidity. To overcome these limitations, we introduce theHybrid Relay Architecture, a new decentralized interoperability paradigm that reconciles the efficiency of centralized relays with the security guarantees of decentralized systems via dynamic role stratification and probabilistic trust coordination. We instantiate this paradigm asCelestial, which realizes hybrid relaying through (1) a unified interchain data unit for coordination, (2) a geo-aware topology that enables adaptive role assignment, and (3) a proof of cross-chain transmission protocol that incentivizes honest node participation. Implemented in 12K+ LoC and evaluated across 90+ nodes spanning three continents,Celestialachieves 3,340 TPS with sub-600ms P99 latency, recovers from faults 6.4× faster than state-of-the-art systems, and reduces verification cost by 63% under adaptive attacks.

The lack of actionable guidance is a fundamental limitation in Action Quality Assessment (AQA), as traditional methods provide overall scores without offering specific insights for improvement. Moreover, existing interpretable approaches often rely on expensive supervised spatial annotations or yield noisy, unsigned saliency maps. To address these challenges, we propose Learning Interpretability Based Supervoxels (LIBS), a novel framework for generating instructional feedback. Distinguishing itself from fully supervised methods, LIBS employs an unsupervised soft-clustering mechanism to segment videos into coherent supervoxels without requiring pixel-level mask annotations. This allows for scalable, fine-grained spatio-temporal analysis while preserving action continuity. Furthermore, we introduce a sensitivity propensity analysis to quantify the contribution of each supervoxel. Unlike traditional attribution methods, this mechanism explicitly decomposes the quality score into positive (strengths) and negative (flaws) components, enabling the system to decode abstract scores into concrete, actionable instructions. Experimental validation across multiple datasets demonstrates that LIBS achieves superior interpretability and efficiency compared to state-of-the-art baselines, marking an improvement from diagnostic to instructional AQA applications.

The dynamic nature of tasks and environments in Internet of Things (IoT) systems require deep learning models to continuously retrain on evolving data to ensure their effectiveness. Existing continual learning (CL) methods aim to mitigate catastrophic forgetting, where the model loses knowledge of previous tasks when learning new ones. However, these methods often ignore the memory resource competition caused by the parallel execution of multiple applications, which limits the realworld IoT application of CL in resource-constrained edge devices. In this article, we propose EDCL, a novel approach that enhances the training efficiency and model accuracy of CL methods while ensuring the uninterrupted operation of high-priority inference programs. Specifically, we first implement a custom batch sampler that can dynamically load batches and measure the memory usage and training time recorded via offline profiling. In the online stage, by monitoring the resource consumption of high-priority programs, EDCL can dynamically select batch policies that meet resource constraints and facilitate efficient training. Additionally, we propose an adaptive hierarchical buffer swap method to enhance the model’s ability to retain previously learned knowledge and mitigate forgetting. Extensive experiments show that EDCL effectively balances training efficiency and model accuracy while preventing high-priority inference programs from failing due to memory contention, demonstrating promising performance compared to baselines.

Wi-Fi device-free tracking enables the inference of user behaviors without physical contact, which is crucial for intelligent indoor location-based services. Nevertheless, the practical implementation of current tracking systems is constrained by several critical limitations: 1) The low-quality sensing signals in complex scenarios lead to increased tracking errors; 2) Existing methods inadequately adjust to dynamic environments, necessitating additional data collection or retraining processes. To address these challenges, this paper introduces EOC-Tracking, a device-free Wi-Fi tracking system that dynamically incorporates environmental information. Our key innovation involves leveraging obstacles to correct illogical users' trajectories and facilitate adjustment to varying environments. This significantly improves the accuracy of the follow-up in complex and changing environments. The EOC-Tracking system is built upon three fundamental design principles: 1) A lightweight dual-branch neural network architecture that effectively fuses environmental data with Wi-Fi signal characteristics; 2) An autonomous map updating mechanism that facilitates real-time adaptation to environmental layout modifications without human intervention; 3) A sophisticated data-driven, phased training paradigm that optimizes the model's ability to learn and apply obstacle constraints. We implement EOC-Tracking using commercial Wi-Fi devices and deploy it on low-power embedded systems such as the MCU. Experimental results demonstrate that EOC-Tracking can reduce tracking errors by at most 49.48% compared to datadriven methods and 62.21% compared to model-based methods in various complex scenarios.

Millimeter-wave (mmWave) 5G-V2X communications face significant challenges in real-time beam alignment within high-mobility vehicular networks. While environmentaware beam prediction methods mitigate channel estimation overhead, their efficacy is severely compromised by modality data loss stemming from lighting variations, adverse weather, or sensor failures. To address this issue, we propose a Cross-modal Losstolerant Beam Prediction model (CLBP). CLBP robustly fuses RGB camera and LiDAR data, employing a novel cross-modal attention mechanism to achieve resilient feature alignment across these heterogeneous modalities. Furthermore, a Branch Features Dynamic Fusion (BFDF) module adaptively reweights modality features, suppressing noise from degraded inputs and promoting effective information propagation to enhance resilience. To facilitate realistic evaluation, we introduce a Data-Conditioned Missingness Mechanism (DCMM), which augments the DeepSense 6G V2V dataset with meticulously simulated sensor failure scenarios. Experimental results demonstrate CLBP's superior performance, achieving 94.48% Top-5 beam prediction accuracy even under 10% modality loss, and a 29% reduction in average power loss compared to baseline methods. These findings demonstrate CLBP's significant robustness in dynamic vehicular environments and its capacity to maintain consistent, high-performance beam prediction despite challenging data imperfections.

Blockchain-based Federated Learning (BCFL) has attracted considerable attention in the intelligent IoT domain for its privacy-preserving and decentralized characteristics. Depending on their applicable scenarios, BCFL frameworks are categorized into two types: synchronous and asynchronous. However, synchronous BCFL struggles with low efficiency in heterogeneous IoT environments, while asynchronous BCFL suffers from slow convergence speed. In additional, Both BCFL incur significant resource consumption from blockchain consensus mechanisms which is unrelated to federated learning tasks, leading to resource wastage and poor scalability, making them unsuitable for large-scale IoT networks. To address these challenges, we propose CoCFL, a novel BCFL framework utilizing multi-chain collaboration. CoCFL introduces two lightweight sub-chains: PoCFL-CChain and PC-CChain, based on different FL strategy. PoCFL-CChain uses a synchronous FL strategy for learning devices with similar performance to generate high-accuracy models, while PC-CChain adopts an asynchronous strategy for heterogeneous devices, which can improving training efficiency. CoCFL assigns devices to suitable sub-chains based on their performance to carry out FL tasks and aggregates the sub-chain models into a global model. This multi-chain collaboration strategy enhances model accuracy and convergence speed and significantly improves the scalability of BCFL. In additional, the consensus mechanisms in CoCFL sub-chains not only maintain the blockchain ledger but also handle FL-related tasks such as detecting poisoning attacks, assigning roles, and distributing incentives. This design not only improving the efficiency of BCFL, but also enhances learning security and ensuring fair incentives. Experiments show that CoCFL improves learning accuracy by 6% and efficiency by 18% over existing BCFL frameworks. It also demonstrates excellent scalability, with time consumption liner decreasing as sub-chains increase, and can withstand up to 40% of poisoning attacks while ensuring fair incentives.

Byzantine fault-tolerant (BFT) consensus protocols are the core components of blockchain. In the process of improving the performance of BFT protocols, existing work faces the following three problems: 1) the binary dilemma between the leader’s performance bottleneck in star-based linear communication and compromised resilience in tree-based sublinear communication; 2) two- or three-round protocols restrict the phase number of one proposal, thereby limiting the number of concurrent proposals and causing high latency. 3) The fixed timeout makes the protocol sensitive to varying network delays. Therefore, this paper proposesCrackle, the first sector-based pipelined BFT protocol with a sublinear communication complexity, for a throughput improvement of consensus protocol with max resilience of$(\mathcal {N}\textrm {-} 1)/3$. We propose a sector-based communication mode to disseminate messages from the leader to a subset of replicas in each phase to accelerate consensus and split the traditional two-round protocol into$2\mathsf {\kappa }$phases to increase the basic pipeline scale. We refine the timer strategy so that the timeout$\Delta $is adjusted with the proposal submission to cope with the changing network environment. We then address two technical challenges: 1) to ensure Quorum Certificate (QC) validation, we design a$\mathit {voteMap} s$field within each block, and verify QC by the signature aggregation of$\mathit {voteMap} s$in continuous$\mathsf {\kappa }$phases; and 2) to achieve pipeline decoupling among shorter phases, we propose a vote-appending mechanism that relaxes the conditions for the leader to send new proposals. We provide comprehensive theoretical proof of the correctness ofCrackle, including safety and$\mathit{liveness}$. Moreover, we implementCracklebased on a public BFT framework and deploy it on 64 cloud servers. Real experimental results reveal that ourCrackleprotocol achieves up to 10.36x higher throughput and can dynamically adapt to network delay compared with state-of-the-art BFT protocols such as Kauri and Hotstuff.

To enable parallel transaction processing in blockchain systems, recent consensus protocols have adopted directed acyclic graph (DAG) structures where DAG is used to organize and parallelize the blocks. Unfortunately, these protocols suffer from high communication overhead. Our experiment on the state-of-the-art Graded DAG[12] reveals that dissemination of transaction and consensus vote messages account for the majority of network traffic. We analyze that the overall overhead is O (N2) per replica and O (N3) for the entire system, where N is the number of replicas, and note that existing approaches have not succeeded in reducing this overhead.

Large language models (LLMs) achieve state-of-the-art performance in many NLP tasks but incur prohibitive memory-access and compute costs when processing very long contexts due to linearly growing KV Cache. Existing static sparsification methods rely on fixed heuristics, while dynamic schemes incur substantial runtime overhead. To address this trade-off, we propose SmartCache, a sparse inference system that exploits two-dimensional KV Cache similarity across adjacent decoding iterations and neighboring layers. SmartCache combines a similarity-driven dual-path selection algorithm, which adaptively reuses TopK KV entries from both the previous iteration and the preceding layer with a rolling-array cache index manager that reduces index storage complexity from$O(L \cdot k)$to$O(k)$. We analyze the layer and iterative sparse patterns of KV Cache in long context LLM decoding and show that SmartCache maintains semantic consistency while drastically reducing redundant computation and memory traffic. Extensive experiments on Llama-3-8B-Instruct-Gradient-1048k, Qwen2.5-7B-Instruct-1M, and glm-4-9b-chat-1m across four long-context benchmarks report up to$\mathbf{3 0. 5 \%}$end-to-end latency reduction and$\mathbf{1 5} \boldsymbol{\%} \mathbf{- 2 3 \%}$average latency reduction, with inference accuracy degradation constrained within 2% and occasional slight improvements. These results indicate that SmartCache offers a practical, high-accuracy solution for scalable long-sequence LLM inference.

Modern LLM decoding has the drawbacks of high cost and slow speed, and speculative decoding has been shown to be an effective solution to this problem. However, the inference latency still poses a significant challenge to maintaining service level objectives (SLOs) in systems that employ multiple draft models for speculative decoding. The verification phase in such systems if reliant on tree attention often constitutes a bottleneck especially when draft sequences lack common prefixes and substantially underutilizes GPU parallelism while increasing end-to-end latency. We introduce SuperSpec, an end-to-end speculative decoding system designed to co-optimize verification, sampling and draft generation. SuperSpec integrates three pivotal innovations: an Efficient Batch Verifier, which substitutes treebased flattening with batch parallel validation and layer-wise KV Cache replication; a Global Optimal Sampler, which assesses all candidate sequences within a batch to ascertain the longest valid path, thereby circumventing the local optima frequently encountered in tree-based rejection sampling; and a Dynamic Adaptive Multi-Drafter, which dynamically modulates the speculative length (K) for each drafter predicated on real-time idleness metrics and acceptance rates. Empirical evaluations of Qwen2.5-72B and the OPT-66B on various datasets show that SuperSpec improves average acceptance rate by 6.4% to 30.2%, and the end-to-end inference acceleration ratio by 7.12% to 62.06%, when compared to the state-of-the-art tree-based speculative decoding system SpecInfer. These improvements were achieved without compromising the quality of text generation, making SuperSpec an effective solution for accelerating LLM inference.

End-to-end semantic communication paradigms demonstrate substantial potential in reducing network load and compressing data redundancy. However, their inherent openness introduces significant security risks, such as unauthorized access that enables attackers to camouflage themselves among legitimate users. Moreover, legitimate users may exploit input-output data pairs to conduct model stealing attacks. Existing defense strategies generally lack user access control mechanisms and fail to provide targeted countermeasures against model inversion attacks from internal users. To address this gap, we propose KGSC-SAT, a Key-Gated Semantic Communication framework enhanced by Steganography Adversarial Training for Secure Transmission. The framework employs a key-based feature modulation method to identify authorized users, while adversarial steganography training facilitates deep feature-level masking. Experimental results demonstrate that KGSC-SAT effectively mitigates both unauthorized access and insider model inversion threats, while delivering reliable communication performance.

Multimodal Large Language Models (MLLMs), while powerful, pose significant privacy risks by memorizing and potentially exposing sensitive information linked to individuals' visual appearances. Existing machine unlearning techniques, developed primarily for text-based models, are ill-equipped to handle the deeply entangled nature of visual and semantic knowledge. To address this challenge, we introduce GAIA-UL, a novel three-stage framework that performs Surgical Unlearning of visual knowledge. Our approach first conducts a Causal Hotspot Diagnosis, using gradient-based analysis to precisely identify influential parameters within the visual-semantic pathway. Second, it performs a Targeted Adapter Intervention, surgically injecting lightweight, trainable adapters only at these hotspots while freezing the base model. Finally, it employs Semantically Orthogonal Fine-tuning, a novel objective that forces the model's internal representation of a target face to become orthogonal to embeddings of associated sensitive concepts, thereby erasing the link at a deep representational level. Extensive experiments on the MLLMU-Bench benchmark demonstrate that GAIA-UL significantly outperforms existing baselines, achieving superior visual knowledge ablation while robustly preserving general model utility and text-only knowledge.

Advances in deep neural networks (DNNs) have significantly contributed to the development of real-time video processing applications. Efficient scheduling of DNN workloads in cloud-hosted inference systems is crucial to minimizing serving costs while meeting application latency constraints. However, existing systems suffer from excessive module latency during request dispatching, low execution throughput during module scheduling, and wasted latency budget during latency splitting for multi-DNN applications, which undermines their capability to minimize the serving cost. In this paper, we design a DNN inference system called Harpagon, which minimizes the serving cost under latency constraints with a three-level design. It first maximizes the batch collection rate with a batch-aware request dispatch policy to minimize the module latency. It then maximizes the module throughput with multi-tuple configurations and proper amount of dummy requests. It also carefully splits the end-to-end latency into per-module latency budget to minimize the total serving cost for multi-DNN applications. Evaluation shows that Harpagon outperforms the state of the art by 1.49 to 2.37 times in serving cost while satisfying the latency objectives. Additionally, compared to the optimal solution using brute force search, Harpagon derives the lower bound of serving cost for 91.5% workloads with millisecond level runtime.

Due to the exponential growth of blockchain ledger sizes, federated-storage which enables multiple devices to jointly store data, has emerged as a promising solution for secure data storage in edge computing. However, how to achieve verifiable queries in such decentralized storage remains underexplored. Existing broadcast-based query methods lack a verification mechanism for query results, making it impossible to ensure their correctness and completeness. Meanwhile, authenticated data structure based (ADS-based) query strategies are constrained by the full ledger data and cannot provide verifiable query services for users in a federated-storage environment. To this end, this paper takes the lead to propose EVQ, a verifiable blockchain keyword query scheme tailored for federated-storage edge computing. We propose a split keyword-based ADS as the core structure of our framework which ensures that users can verify the correctness and completeness of query results while alleviating storage pressure of edge devices. Specifically, the proposed ADS is constructed through a two-phase process: top-bottom keyword index tree construction and bottomtop RSA accumulator integration. Splitting the ADS based on keywords enables distributed data storage and the generation of corresponding ADS for the stored data. To reduce the query costs incurred by edge devices during query processing, we formulate the Keyword Allocation Optimization (KAO) problem and propose a gain-ratio-based keyword allocation mechanism to determine the splitting scheme of the ADS. The experiments are conducted based on the Foursquare dataset, which contains approximately 18 months of global check-in data collected from Foursquare. The experimental results show that, compared to the merkle tree strategy that also integrates the RSA accumulator, our EVQ improves query performance by 24.77 x.

Blockchain, with its immutability and decentralization, drives innovation in finance and supply chain, but the growing data volume makes storing complete ledger replicas impractical for users, especially in the resource-constrained Internet of Thing (IoT) scenarios. Existing solutions focus on nodes storing only a partial ledger to alleviate storage burdens. Nonetheless, these approaches prioritize storage optimization by minimizing the query cost and lack control over storage cost. Furthermore, these approaches overlook the relationships between network users, thus failing to fully measure the future query cost. Thus, this article proposes BSSN, a blockchain storage technology based on social networks. The combined use of storage cost and query cost is introduced for the first time to formulate the node allocation optimization (NAO) problem, and the multipopulation genetic ant colony (MGAC) algorithm will be employed to derive node allocation strategies. Specifically, we address three technical challenges: 1) to predict the transactions that nodes will participate in the future, we employ the social ties to obtain the access frequencies among users; 2) to strike a balance between the storage cost and query cost, we jointly model the two costs as a multiobjective optimization problem to formulate the NAO problem; and 3) to solve the NP-hard NAO problem, we use the MGAC algorithm, where the storage and query populations collaboratively search for solutions based on four operations. Extensive experiments indicate that compared with existing work, BSSN can reduce the average query cost to 67% with its adjustable storage cost, ensuring a balanced data storage among users.

Sign Language recognition is practically important in various scenarios such as smart home, medical rehabilitation, and intelligent industry. Compared with wireless sensing and computer vision methods, data glove-based methods have gained a plenty of attention, because they can perform well even in the environments with multi-path noise or visual occlusion. However, existing data glove-based methods usually require complex calibration and laborious dataset collection, and suffer from accumulated error. To address these challenges, we introduce a robust sign language recognition system with cross-domain generation, called SmartGlove, the first approach to achieve robust sign language recognition. To avoid complex calibration process, we propose a customized feature set that can enable user-insensitive and unintentional system calibration. To avoid the labor cost in training data collection, we propose a cross-domain data transformation technique to generate training data in target domain. To eliminate the accumulated error of sentence recognition, we utilize a context-based calibration method considering correlation among adjacent words. We implement SmartGlove with COTS devices, and extensive experiments reveal that SmartGlove achieves accuracy exceeding 97.11% for 30 sign language words, with an average recognition time of 47 milliseconds per word. Furthermore, the system recognizes 30 common sign language sentences with accuracy of 97.17%.

Automated manufacturing is the cornerstone of the Industrial Internet of Things (IIoT) ecosystem, where vibration monitoring technology is a critical tool for maintaining industrial machinery. The prevailing approach mostly employs inertial measurement units (IMUs), lasers, and cameras, each demonstrating deployment constraints. In recent years, millimeter-wave (mmWave) radar has shown high vibration measurement performance, but it faces challenges in accurately localizing vibrating objects and determining observation points. This study introduces a new system called VibCamera, which leverages the mmWave vibration measurement technology with computer vision (CV) algorithms for vibration monitoring. With the positional assistant of CV semantic segmentation, the radar can accurately determine sufficient observation points, thereby achieving precise measurement with high directionality. VibCamera includes two camera modes, RGB-only and RGB+depth, and solves two technical challenges: 1) integrating multimodal information for vibration target localization and 2) extracting high-quality vibration signals in interference environments. VibCamera provides more consistent and precise outcomes without the need for physical contact. The experimental results indicate that the RGB-only mode has amplitude and frequency errors below$27.04 \; \mu \rm m$and 0.22 Hz, respectively, with a 90% probability, and the RGB+depth mode has errors below$23.72 \; \mu \rm m$and 0.21 Hz.

Amid the accelerating pace of global digital transformation, the Industrial Internet of Things (IIoT) has progressively emerged as a vital force in promoting industrial upgrading and economic restructuring. The proliferation of IIoT devices has augmented the complexity of security management, making the deployment of intrusion traffic detection solutions imperative. Existing solutions for network traffic classification have certain limitations. This paper presents GRID, a Graph-based Robust Intrusion Detection solution for IIoT, encompassing two main modules: the Hierarchical Traffic Graph Constructor (HTGC) and the Cascaded Graph Attention Network (CGATN). The HTGC exploits the inherent packet-flow-conversation hierarchy of traffic data to construct the graph structure and fuse packet-level and behavioral features. The CGATN addresses the issues faced by conventional multi-layer Graph Neural Networks (GNNs) and employs contrastive representation learning during training to enhance the robustness of the solution. GRID demonstrates significant advantages compared to state-of-the-art solutions. The experimental results in both closed-world and open-world scenarios reveal an average increase of 3.09% in classification accuracy, 0.23% in balanced accuracy, and 10.03% in Matthews correlation coefficient.

Sharding has become a significant approach to enhance blockchain scalability. However, existing sharding techniques applied in IoT scenarios suffer from transaction congestion due to imbalanced distribution of transactions across shards, which hinders intra-shard transaction processing capacity. To overcome the above problems, this paper proposes HydraChain for IoT scenarios, the first multi-agent reinforcement learning based sharding blockchain system with account graph relationships, for a throughput improvement of shards under realtime load balancing. Agents collaborate by sharing information and jointly optimizing decisions, enhancing the accuracy and efficiency of the decision-making process. We first construct a sharding blockchain environment integrated with an embedded graph encoder. Concurrently, we propose a SG-MAPPO multiagent model with decoder, which enables agents to cooperatively learn to optimize account allocation strategies based on real-time shard load and global system information. When implementing HydraChain, we address two technical challenges: (i) to extract granular behavioral features from accounts with diverse and time-varying patterns, we design a graph data encoder, which constructs a graph network based on transactional relationship; and (ii) to ensure real-time load balancing under the constraints of dynamic transaction patterns, we propose a multi-agent model (SG-MAPPO), which matches graph encoding features within the environment. Our approach leverages the ability of multi-agent model to collaborate and adapt to the changing environment, enabling efficient resource allocation and improved system performance. Moreover, we implement HydraChain and conduct experiments on a high-performance server equipped with 48 cores and 125GB of memory. Our comprehensive experiments, comparing HydraChain with DQN-Based, SAC-Based and SPRING, reveal that our solution outperforms state-of-theart solutions by achieving a notable 22% increase in transaction throughput and a 5.2% reduction in workload imbalance across shards.

The application scenarios of drone collaboration are rapidly expanding, such as the low-altitude economy and wildfire protection. Blockchain-based drone collaboration requires a consensus mechanism to ensure efficient and secure consistency among large-scale distributed nodes. However, the existing consensus mechanism has problems with poor fault tolerance of topology and rigid proposal concurrency. To this end, this paper proposes AirBFT, an efficient and robust consensus mechanism for large-scale drone collaboration. First, this paper designs a new four-layer network topology, using upper-member and lower-member communication, while ensuring the maximum 1/3 resilience and fanout of √N. Secondly, this paper proposes a dynamic pipelining algorithm to adjust the parallelism of proposals according to the real-time network status. Finally, this paper proposes a committee sampling technology based on the EigenTrust algorithm to reduce the impact of the malicious behavior of Byzantine nodes. Experiments based on the public consensus framework show that compared with Kauri and HotStuff, the proposed AirBFT reduces transaction confirmation delay by 58%, the throughput is increased by 1.9 times, and it can ensure efficient operation with a 1/3 Byzantine node ratio.

The Wi-Fi sensing technique holds great promise for future smart homes, thanks to the widespread use of Wi-Fi devices. With this technique, we can deduce the behavior of the target based on the channel state information (CSI), which is obtained during Wi-Fi communication. However, existing Wi-Fi sensing technologies are not compatible with standard communication technologies. This is because Wi-Fi sensing usually relies on capturing CSI from high-frequency communication packets, whereas regular IoT communication does not consistently maintain such high communication rates. To achieve precise sensing even with a low packet rate, we introduce LowDetrack, an indoor human detection and tracking system at ultra-low packet rates with Wi-Fi. In particular, we utilize compressed sensing to supplement missing data compared to existing systems that rely on linear interpolation or neural networks. To detect and track the target, our insights are twofold: 1) We combine compressed sensing and Fresnel zone to a theoretical model for accurately obtaining the reflection path change rate, which can be converted into the actual velocity of the target; 2) We investigate the mapping relationship between the dynamic frequency composition ratios in different links, which can provide navigation for velocity direction and correct direction recognition errors. We implement LowDetrack on commercial off-the-shelf Wi-Fi and realize human detection and tracking, where the median tracking error is 0.76m at the packet rate of 25 Hz.

Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks, largely due to their substantial model size. However, this also results in significant GPU memory demands during inference. To address these challenges on hardware with limited GPU memory, existing approaches employ offloading techniques that offload unused tensors to CPU memory, thereby reducing GPU memory usage. Since offloading involves data transfer between GPU and CPU, it introduces transfer overhead. To mitigate this, prior works typically overlap data transfer with GPU computation using a fixed pipelining strategy applied uniformly across all inference iterations, referred to asstaticoffloading. However, static offloading policies fail to maximize inference throughput because they cannot adapt to the dynamically changing transfer overhead during the inference process, leading to increasing GPU idleness and reduced inference throughput.We propose that offloading policies should beadaptiveto the varying transfer overhead across inference iterations to maximize inference throughput. To this end, we design and implement an adaptive offloading-based inference system called TightLLM with two key innovations. First, its key-value (KV) distributor employs atrade-compute-for-transferstrategy to address growing transfer overhead by dynamically recomputing portions of the KV cache, effectively overlapping data transfer with computation and minimizing GPU idleness. Second, TightLLM’s weight loader slices model weights and distributes the loading processacross multiple batches, amortizing the excessive weight loading overhead and significantly improving throughput. Evaluation across various combinations of GPU hardware and LLM models shows that TightLLM achieves 1.3 to 23 times higher throughput during the decoding phase and 1.2 to 22 times higher throughput in the prefill phase compared to state-of-the-art offloading systems. Due to the higher throughput in prefill and decoding phases, TightLLM can reduce the completion time for large-scale tasks, which involve processing and generating a substantial number of tokens, by 59.6% to 94.9%.

Federated Learning (FL) faces challenges due to straggler clients that impede timely parameter uploads, potentially leading to suboptimal global model performance. Existing approaches using synchronous and asynchronous communication suffer from long waiting times or convergence issues. We propose Fed-OGD, a novel asynchronous FL method addressing the straggler problem through gradient orthogonalization. Our approach innovatively frames the straggler issue using catastrophic forgetting theory, viewing stragglers as instances of the global model “forgetting” to aggregate their parameters. Fed-OGD introduces an Orthogonal Gradient Descent (OGD) technique that caches straggler gradients and orthogonalizes the difference between these and current active client gradients. By projecting active gradients onto straggler orthogonal bases and subtracting the resulting components, we obtain orthogonalized gradients guiding the model towards optimality. We provide theoretical convergence guarantees and demonstrate Fed-OGD’s effectiveness through extensive experiments. Our method achieves state-of-the-art performance across multiple datasets among SOTA FL baselines, with notable improvements in non-IID (non-Independent and identically distributed) scenarios: there are few main categories with many samples while other categories hold few samples in a client. Fed-OGD achieves that 16.66% increase in accuracy on CIFAR-10, and significant gains on CIFAR-100 (5.37%), Tiny-ImageNet (38.51%), and AG_NEWS (16.30%).

Blockchain offers distinct advantages in terms of data credibility and provenance certification, and its fusion with Internet of Things (IoT) technology holds great promise. Nevertheless, IoT environments are marked by extensive node networks and intricate communication patterns, especially the sensing environment. The conventional blockchain consensus mechanism, hampered by its heavy reliance on computing resources and communication bandwidth, faces difficulties in ensuring seamless data exchange among IoT edge servers. The issues encountered by state-of-the-art Byzantine Fault Tolerance (BFT) consensus include: (i) high communication complexity between nodes; and (ii) the detrimental impact of Byzantine behavior on system performance. To overcome the above problems, we propose the lightweight blockchain consensus called AntB, firstly introducing the concept of sampling into the consensus and significantly reducing the number of participating consensus nodes from$N$to$n$, which lowers the consensus complexity to$\mathbf{2\cdot O(n)+O(N)}$. We design a dynamic reputation mechanism so that Byzantine nodes cannot control the sampling set to affect the activity of the consensus in the long term. When implementing AntB, we address three significant technical challenges: (i) to determine the optimal sample size, we propose a sampling calculation method based on statistical confidence intervals, where the sample size is primarily determined by the chosen confidence level and margin of error; (ii) to prevent Byzantine behavior, we devise a weighted random sampling mechanism utilizing reputation coefficients based on edge servers’ behaviors; and (iii) to maintain consensus activity and consistency after sampling, we propose the consensus mechanism for partial sampling and global verification to avert potential issues. We implement AntB and conduct performance evaluations in a server with 32 cores and 64GB of memory. The evaluation results indicate that, the more nodes participating in the process of consensus, the better the performance of AntB will be. Especially, compared to HotStuff, AntB has a 24.94% higher success rate and Transactions Per Second (TPS) can improve by 102.10% when the number of nodes is 300.

Cross-chain technology facilitates the interoperability among isolated blockchains, where users can transfer and exchange coins. While the heterogeneity between Turing-complete (TC) blockchains like Ethereum and non-Turing-complete (NTC) blockchains like Bitcoin presents a significant challenge for cross-chain transactions. Payment Channel Hubs (PCHs) offer a promising solution for enabling TC-NTC cross-chain payments with high throughput and low confirmation delays. However, existing schemes still face two key challenges: (i) significant computation and communication overhead for variable-amount payment, and (ii) limited unlinkability, i.e., vulnerable to the abort attack. This paper proposes EC2P, the first TC-NTC cross-chain PCH that achieves variable-amount payment unlinkability while resisting the abort attack and minimizing reliance on non-interactive zero-knowledge (NIZK) proofs. EC2P introduces two protocols: the NTC-to-TC and TC-to-NTC payment protocols. The NTC-to-TC payment protocol replaces the traditional puzzle-promise and puzzle-solve paradigm with a semi-blind approach, where only one side is blinded and the blinded side’s interactions are eliminated. This achieves unlinkability and resists the abort attack without NIZK. The TC-to-NTC payment protocol enhances the paradigm by utilizing Turing-complete functionality to constrain the inability to carry out an abort attack. Through rigorous security analysis, we show that EC2P is secure and variable-amount payment unlinkable while resisting the abort attack. We implement EC2P on Ethereum and Bitcoin test networks. Our evaluation demonstrates that EC2P outperforms both in terms of communication and computation overhead and reduces communication costs by 3 orders of magnitude compared to existing variable-amount methods.

In recent years, sharding technology has been frequently applied in blockchain systems to increase scalability. However, when new shards are added, the system may result in significant overhead in terms of computing and networking since the data allocation approach is incompatible with dynamic changes in shards. Currently, S-Store, the state-of-the-art sharding solution built on the account model, has a high re-computing latency when growing shard numbers and an unbalanced sharded data distribution after growth. To address these issues, this paper presents Tangram, an efficient and balanced dynamic storage extension approach for sharding blockchain systems. Tangram reduces system extension overhead and latency while ensuring a balanced shard distribution. In implementing Tangram, we tackle three main technical challenges as follows. (1) Designing a novel state tree structure for the storage and maintenance of sharding state data. We introduce the Jump Merkle Tree (JMT) based on the Merkle Tree, which integrates node migration and orderliness. (2) Presenting a protocol to be compatible with dynamic shard scenarios. We devise a shard addition protocol to improve system extension availability and decrease shard extension delay. (3) Proposing an approach to guarantee system longevity after extension. We first devise algorithms for the state tree to eradicate invalid states after system expansion. Furthermore, we introduce a shard reduction protocol to enhance system storage extension support in complex scenarios, such as cleaning up inactive states to avoid bloating the state tree. We conduct extensive experiments to evaluate the performance of Tangram. Experiment results demonstrate that Tangram outperforms existing solutions, showing reduced latency and superior data balance. When compared to the state-of-the-art sharding storage solution, Tangram decreases the transaction execute time by up to 87.84%, the state data migration by more than approximately 74%, and achieves up to 7.63x improvement in the standard deviation of sharding data balance.

The rise of Machine Learning as a Service (MLaaS) has driven the demand for complex and customized real-time inference tasks, often requiring cascading multiple deep neural network (DNN) models into inference pipelines. However, these pipelines pose significant challenges due to scheduling complexity, particularly in maintaining strict latency service level objectives (SLOs). Existing systems serve pipelines with model-independent scheduling policies, which ignore the unique workload characteristics introduced by model cascading in the inference pipeline, leading to SLO violations and resource inefficiencies. In this paper, we propose that the serving system should exploit the model-cascading nature and inter-model workload dependency of the inference pipeline to ensure strict latency SLO cost-effectively. Based on this, we design and implementSLOpt, a serving system optimized for real-time inference pipelines with a three-stage co-design of workload estimation, resource provisioning, and request execution.SLOptproposes cascade workload estimation and ahead-of-time tuning, which together address the challenge of cascade blocking and head-of-line blocking in workload estimation and resource provisioning.SLOptfurther implements an adaptive batch drop policy to mitigate latency amplification issues within the pipeline. These innovations enableSLOptto reduce the 99th percentile latency (P99 latency) by 1.4 to 2.5 times compared to the state of the arts while lowering serving costs by up to 29%. Moreover, to achieve comparable P99 latency,SLOptrequires up to 70% less cost than existing systems. Extensive evaluations on a 64-GPU cluster demonstrateSLOpt’s effectiveness in meeting strict P99 latency SLOs under diverse real-world workloads.

Given privacy and network load concerns, employing on-device multimodal neural networks (MNNs) for IoT data is a growing trend. However, the high computational demands of MNNs clash with limited on-device resources. MNNs involve input and model redundancies during inference, wasting resources to process redundant input components and run excess model parameters. Model Redundancy Elimination (MRE) reduces redundant parameters but cannot bypass inference for unnecessary input components. Input Redundancy Elimination (IRE) skips inference for redundant input components but cannot reduce computation for the remaining parts. MRE and IRE independently fail to meet the diverse computational needs of multimodal inference. To address these issues, we aim to combine the advantages of MRE and IRE to achieve a more efficient inference. We propose anadaptivemultilevelredundancyelimination framework (AMRE), which supports both IRE and MRE.AMREfirst establishes a collaborative inference mechanism for IRE and MRE. We then propose a multifunctional, lightweight policy model that adaptively controls the inference logic for each instance. Moreover, a three-stage training method is proposed to ensure the performance of collaborative inference inAMRE. We validateAMREin three scenarios, achieving up to 52.91% lower latency, 56.79% lower energy cost, and a slight accuracy gain compared to state-of-the-art baselines.

Non-intrusive hand tracking with mmWave radar technology is important in various Human-Computer Interaction (HCI) scenarios. However, existing mmWave-based solutions require users to be stationary and restrict a fixed hand motion area, which limits application flexibility and user experience. This paper proposes a novel mmWave-basedMobileHandTracking (MHTrack) system, which tracks user's hand gestures during walking. MHTrack focuses on tracking bothabsolutehand trajectory in the global coordinate system andrelativehand trajectory to the body. Specifically, we propose a wake-up mechanism for hand motion capture, in which hand point cloud can be recognized even under body interference and noise. We propose a hand tracking strategy named local spatial update, which overcomes the sparsity and instability of point clouds, to obtain absolute hand trajectory. Subsequently, we propose a hand anchor correction method to suppress anchor offset and remove the impact of body movement from absolute hand trajectory, thereby obtaining relative hand trajectory. As a case study, we project the relative hand trajectory onto a 2D image and feed it into a gesture recognition model to recognize the gestures. We conduct extensive experiments to evaluate the performance of MHTrack. Results demonstrate a 3D hand trajectory tracking error of$3.6cm$in an area of$3.2m\times 4.8m$and a gesture recognition accuracy of$99\%$with 30 gesture classes.

Liquid sensing in ubiquitous contexts plays an essential role in various scenarios. Recently, some wireless sensing systems have been proposed for liquid identification. However, existing works usually require specific equipment or capture the signals penetrating a target, limiting the deployability of liquid sensing. In large-scale scenarios, multiple devices are usually required to expand the coverage area due to the RFID reader antenna's reading range limitation. To enlarge the sensing range and make the liquid sensing method can be adopted in real moving scenarios, in this paper, we presentMobileLiquidIDentification (MLiquID), a liquid sensing system that can recognize the type of liquid in a mobile manner with commercial off-the-shelf (COTS) RFID devices. This mobile process leads to continuous variation in location, so the major challenge in this paper is how to extract signal features from the superimposed information of movement and material. The key insight is to regard movement as an opportunity to acquire data from different perspectives instead of a challenge to hinder feature extraction. We construct a Phase-RSS model by analyzing the influence of moving and liquid on the phase and RSS signals. First, we propose a method to calculate the distance from the tag to the reader antenna. Second, we explore an identification method to identify liquid type by extracting signal features Phase-RSS coefficient$C_{P-R}$and Maximum Response Distance (MRD). Experimental results demonstrate an average accuracy of 96.80% in identifying 10 common liquids, which shows the great potential of MLiquID for mobile liquid sensing.

As an advanced technology for non-contact sensing, mmWave radar enables fine-grained measurement of a wide variety of user behaviors. While creating intelligence and convenience, it also concerns behavioral privacy and security, as radar signals contain a wealth of behavioral information. Existing solutions are either incapable of customizable privacy protections or cannot cope with multi-person scenarios. This paper presents aMulti-user behavioral privacyFilter, MuFilter, a data masking system centered on the idea of dimensional signal interference. It determines the sensing signatures that need to be preserved or interfered with based on the sensing services that users want to enable and disable, thereby making targeted tampering on the radar signal. On this basis, we introduce the multi-person tracking technology to allow MuFilter to determine the number of users in unknown scenarios. Moreover, a subspace tampering technique is proposed to ensure that each tampering only affects the target user and not other users, thus supporting personalized privacy protection for multiple users. Experiments show that MuFilter can interfere with targeted behavioral signatures with a 100% success rate, while the degree of impact on other users’ signatures ranges from 0% to 3.85%.

Location-based services and identification hold promise for future smart home applications. Through them, we can provide customized services for specific users in current locations. Recent studies have demonstrated that Wi-Fi signals can be leveraged to achieve device-free tracking and gait recognition. Despite their good performance, these two technologies are not effectively integrated for the following reasons: First, the device-free tracking method might yield tracking results that conflict with human gait. Second, extracting gait features relies on knowing or accurately estimating the user's trajectory. Consequently, gait recognition and tracking are inherently linked, but there has been no effective approach to integrate these two techniques. In this paper, we present STAGR, a system capable ofSimultaneousTrackingAndGaitRecognition. The main contribution of our technique is that we establish a theoretical model that reveals how to transform path-dependent spectra into path-independent spectra directly. Specifically, we conduct a preliminary study to demonstrate the need for simultaneous tracking and gait recognition. Second, we propose a novel method to extract path-independent gait features, which can significantly save execution time compared with the learning-based method. Third, we design a polar-coordinate filtering method to retain the gait features while correcting the trajectory. We implement a prototype STAGR system and conduct extensive experiments to verify the proposed mechanism. The experimental results show that we can realize simultaneous tracking and gait recognition. The median tracking error is$ 0.45m$, while the recognition accuracy is 95.3% for 6 users.

Wi-Fi contact-free sensing systems have attracted widespread attention due to their ubiquity and convenience. The integrated sensing and communication (ISAC) technology utilizes off-the-shelf Wi-Fi communication signals for sensing, which further promotes the deployment of intelligent sensing applications. However, current Wi-Fi sensing systems often require prolonged and unnecessary communication between transceivers, and brief communication interruptions will lead to significant performance degradation. This paper proposes Baton, the first system capable of accurately tracking targets even under severe Wi-Fi feature deficiencies. To be specific, we explore the relevance of the Wi-Fi feature matrix from both horizontal and vertical dimensions. The horizontal dimension reveals feature correlation across different Wi-Fi links, while the vertical dimension reveals feature correlation among different time slots. Based on the above principle, we propose the Simultaneous Tracking And Predicting (STAP) algorithm, which enables the seamless transfer of Wi-Fi features over time and across different links, akin to passing a baton. We implement the system on commercial devices, and the experimental results show that our system outperforms existing solutions with a median tracking error of 0.46m, even when the communication duty cycle is as low as 20.00%. Compared with the state-of-the-art, our system reduces the tracking error by 79.19% in scenarios with severe Wi-Fi feature deficiencies.

Accurate geographical information of nodes is crucial for network applications. However, many existing positioning algorithms face challenges in achieving efficient, accurate, and robust performance when applied to irregular networks with holes or obstacles. Therefore, we introduce a new algorithm, named Network Division Positioning (NDP), to tackle this issue. In NDP, we use a similarity function to derive the distance between neighboring nodes and explore routing paths concurrently, facilitating efficient distance measurement. Next, we analyze measurement errors between landmark nodes to define a threshold that filters out incorrect distances, ensuring measuring and positioning accuracy. To enhance robustness, we first identify collinearity issues by examining the positional relationship between unpositioned nodes and their nearest landmark. Subsequently, we addressed the poor positioning results and built the subnetwork utilizing the nearest landmark node and its associated measurement distance, seeking the most accurate and robust estimated position within this subnetwork. The simulation results demonstrate that NDP outperforms state-of-the-art algorithms in terms of efficiency, accuracy, and robustness when dealing with various irregular networks. Specifically, NDP enhances positioning accuracy by at least 40.82% in terms of the median.

Asynchronous Complete Secret Sharing (ACSS) in Byzantine fault-tolerant systems has become one of the essential building blocks in multiple threshold cryptosystems. However, current ACSS schemes scale poorly due to high communication costs, which are quadratic in the number of participants n. In this paper, we propose a new scheme ALCES to reduce such communication costs from O(n2) to O(cn) with a negligible probability of failure ${e^{ - \frac{c}{{18}}}}$, while guaranteeing completeness and agreement properties. The key point of ALCES is to sample c parties to construct a committee, which then verifies and distributes the encrypted shares to other parties. Additionally, we introduce a new mechanism, referred to as secret labels in ALCES, by encoding the information of labels in polynomial coefficients. This mechanism allows an arbitrary string to act as the label, binding it to a specific secret while efficiently ensuring security and privacy with minimal communication cost. Experimental results show that our technique reduces the overall communication cost in a single sharing process by 66% and 83% for very large quantities, such as 4096 and 8192 parties, respectively, when compared with prior work.

Phishing scams represent a significant criminal activity on Ethereum, driving the need for effective detection methods. The methods based on graph neural networks(GNNs) make significant breakthroughs due to their ability to model complex transaction networks. However, existing approaches often overlook the heterogeneity of Ethereum’s transaction graph during neighbor nodes aggregation. These methods typically focus on low-order neighbors, disregarding high-order ones, which limits their overall performance. To this end, we propose the High- and Low-order Transaction Aggregation Graph Network(HLTAG), which separately aggregates high- and low-order features for more effective feature representation. Specifically, we utilize biased random walk to aggregate low-order neighbors. We employ path aggregation to handle high-order neighbors. To mitigate the influence of noise and redundant information from high-order neighbors, we introduce a combination of attention decay, node similarity, and path attention mechanism, which dynamically adjust the aggregation weights. Extensive experiments demonstrate that HLTAG (94.4% Recall and 89.3% AUC) outperforms the state-of-the-art approaches in detecting Ethereum phishing scams, and exhibits significant advantages in large-scale scenarios.

With the rapid expansion of blockchain data, the demand for data exchange between chains has grown significantly. Authenticated queries have become one of the crucial methods for retrieving on-chain data due to their efficient performance and ability to ensure data security. However, existing intra-chain query approaches either face substantial maintenance overhead or exhibit low query efficiency, when dealing with the explosive growth of data in cross-chain scenarios; while current cross-chain query approaches suffer from issues including limited query types and poor scalability. To this end, this paper takes the lead to propose a novel generalized framework named CubeChain which provides various query types for intra- and cross-chain authenticated queries. We propose a highly scalable authenticated data structure (ADS) named Cube as the core structure of our framework which excels in achieving high performance while minimizing maintenance overhead by establishing data bridges between vertexes. When implementing CubeChain, we address two challenges: (i) implementing lightweight verification while supporting various query types by using a two-layer hashing structure, and (ii) further improving the query efficiency by suppressing vertexes. We substantiate the superior performance of Cube in terms of query efficiency, update overhead, and scalability through theoretical analysis. Finally, we implement the CubeChain framework based on the open-source Fabric v2.2. Real experiments with YCSB benchmark demonstrate that, compared with the state-of-the-art Bs+tree-based ADSs in MSTDB and SEBDB, our query performance improved by 23.75x in intra-chain scenarios and 10.72x in cross-chain scenarios, while maintaining a 30% reduction of cross-chain query load.

One notable drawback of traditional Federated Learning (FL) is its susceptibility to single point of failures. In recent years, Blockchain-based Federated Learning (BCFL) has been proposed as an effective solution to address this issue. However, existing BCFL frameworks face challenges in heterogeneous IoT scenarios. The heterogeneity of IoT devices poses challenges to the adaptation of blockchain consensus. The integration of blockchain imposes constraints on the learning scalability of systems, making it challenging to accommodate a large number of heterogeneous IoT devices. On the other hand, current blockchain consensus fail to sufficiently measure the contributions and destructions among heterogeneous devices in terms of learning quality, leading to low learning security and insufficient incentive fairness. To overcome the limitations of prior art, this paper introduces CoCFL, a novel blockchain-based federated learning framework based on multi-chain collaborative model. CoCFL enhances learning scalability by adopting a multi-chain asynchronous collaboration approach that partitions both learning and communication granularity of the system. Within each sub chain, CoCFL introduces a lightweight, secure and incentive-fair blockchain-based federated learning consensus, called Proof of Contribution to FL (PoCFL). In PoCFL, partic-ipants' contributions to the learning and the consensus process form the basis for delegating consensus responsibility and dis-tributing rewards. Furthermore, we introduce a novel malicious model detection algorithm into PoCFL, called the Trustee Nearest Algorithm. Through Trustee Nearest, PoCFL effectively mitigates poisoning attacks. Experimental results demonstrate that CoCFL exhibits better learning scalability compared to traditional FL and and avdanced BCFL frameworks in the same scenarios and can effectively withstand poisoning attacks initiated by at least 40% of malicious participants. Moreover, CoCFL demonstrated good incentive fairness during the learning process.

An innovative architecture called execute-order-validate (EOV) has been proposed by Hyperledger Fabric that enables concurrent processing of transactions. However, the architecture suffers from issues such as excessive invalid transactions and serialization limitations in scenarios with high transaction conflicts, which restrict its applicability in real-time and high-performance settings. To address the aforementioned limitations, we propose ParFabric to enhance the EOV architecture. Firstly, we analyze four essential characteristics required for the transaction reordering algorithm within this architecture. We propose a heuristic dynamic reordering algorithm to reduce the number of invalid transactions. This is achieved through real-time identification and early abortion of transactions based on weighted pre-ordering and the construction of a transaction conflict graph. Secondly, leveraging the transaction conflict graph, we introduce a novel optimal block packing strategy based on transaction dependencies. This strategy replaces the total transaction order with partial order, enabling parallel validation and commit at the block level, thereby leading to increased system throughput while reducing transaction latency. Experimental results indicate that, ParFabric demonstrates excellent performance in terms of vertical scaling of peers. Additionally, at the same infrastructure cost, ParFabric provides 2.2x and 1.6x higher throughput than FabricPlusPlus and FabricSharp in high-conflict scenarios.

Despite considerable advancements in specialized hardware, the majority of IoT edge devices still rely on CPUs. The burgeoning number of IoT users amplifies the challenges associated with performing multiple Deep Neural Network inferences on these resource-limited, CPU-only edge devices. Existing strategies, including model compression, hardware acceleration, and model partitioning, often involve a trade-off in inference accuracy, are unsuitable due to hardware specificity, or lead to inefficient resource utilization. In response to these challenges, this paper introduces L-PIC (Latency Minimized Parallel Inference on CPU)—a framework expressly devised to optimize resource allocation, decrease inference latency, and maintain result accuracy on CPU-only edge devices. A series of comprehensive experiments have verified the superior efficiency and effectiveness of the L-PIC framework in comparison to the state-of-the-art method. Remarkably, compared to the state-of-the-art method, L-PIC can reduce the inference latency of multi-DNN by an average of approximately 30% across all tested scenarios.

Blockchain systems widely employ Byzantine fault-tolerant (BFT) protocols to ensure consistency. Improving BFT protocols’ throughput is crucial for large-scale blockchain systems. Frontier protocols face crucial problems: (i) the binary dilemma between leader bottleneck in star-based linear communication and compromised resilience in tree-based sublinear communication; and (ii) 2- or 3-round protocols restrict the phase number of one proposal, thereby limiting the scalability and parallelism of the pipeline. To overcome the above problems, this paper proposes Crackle, the first sector-based pipelined BFT protocol with a sublinear communication complexity, for a throughput improvement of consensus protocol with max resilience of (N-1)/3. We propose a sector-based communication mode to disseminate messages from the leader to a subset of replicas in each phase to accelerate consensus and split the traditional two-round protocol into 2κ phases to increase the basic pipeline scale. When implementing Crackle, we address two technical challenges: (i) to ensure Quorum Certificate (QC) validation during continuous κ phases, we design a voteMap field within each block, and verify QC by the aggregation of continuous κ voteMaps; and (ii) to achieve pipeline decoupling among shorter phases, we propose a vote-appending mechanism that accelerates the leader’s transition to the next phase. We provide comprehensive theoretical proof of the correctness of Crackle, including safety and liveness. Moreover, we implement Crackle based on a public BFT framework and deploy it on 64 cloud servers. Real experimental results reveal that Crackle achieves up to 10.36x higher throughput compared with state-of-the-art BFT protocols such as Kauri and Hotstuff.

With the wide application of multi-modal fusion sensing in scenarios such as autonomous driving and human-computer interaction, the privacy security and communication burden caused by massive data uploading need to be solved urgently. Federated Learning (FL) has received significant attention as a privacy-preserving distributed machine learning paradigm. Recent Multi-Modal Federated Learning (MMFL) focuses on addressing modal heterogeneity to enhance accuracy and speed up convergence. However, it overlooks the huge communication overhead in updating complex multi-modal network models, especially in edge environments with limited bandwidth. At the same time, the state-of-the-art communication-efficient FL methods are not customized to the MMFL characteristics. In this paper, we propose the Adaptive Quantization framework for Multi-modal Federated Learning (AQMFL). AQMFL implements decision-level multi-modal fusion locally by using parallel training and model ensemble, supporting its adaptation to modal heterogeneity and flexible deployment. AQMFL can adaptively allocate the number of quantization levels of gradient according to the modal contribution and the heterogeneous communication ability of nodes, which speeds up the system convergence and achieves a better balance between accuracy and communication efficiency. Compared with the classical baselines, AQMFL can reduce the total communication overhead by up to 50.47% and the total training time by up to 52.11% while maintaining the accuracy.

The ubiquity of acoustic devices and the fine-grained sensing of acoustic signals have made acoustic device tracking a popular option. We propose to expand the use of commercial devices with microphones as an extension of the audio system to support intelligent applications, such as VR/AR. This paper introduces a novel 6D acoustic pose estimation system. To realize device-based pose estimation, most existing systems deploy multiple speakers. However, due to limited inaudible bandwidth, concurrent transmissions with multiple speakers pose challenges in balancing resolution and frame rate. To address this problem, we design 2×Track, a band multiplexing signal model that doubles the availability of limited bandwidth by utilizing a unique encoding strategy for concurrent transmissions. We also propose solutions to enhance signal feature estimation and implement a 6DoF pose tracking scheme tailored for distributed systems. The prototype is deployed on a typical circular microphone array, and experimental results show that 2×Track achieves a median position and orientation error of 7.6mm and 4.1°, respectively, in a 4-speaker setup. Our extended applications on commercial devices also showcase the versatility of our system, particularly in face orientation detection, air mouse and drone tracking.

This paper introduces PPT, a pragmatic transport that achieves comparable performance to proactive transports while maintaining good deployability as reactive transports. Our key idea is to run a low-priority control loop to leverage the available bandwidth left by the reactive transports. The main challenge is to send just enough packets to improve performance without harming the primary control loop. We combine two unconventional techniques: an intermittent loop initialization and an exponential window decrease, enabling us to dynamically identify and fill the spare bandwidth. We further complement PPT's design with a buffer-aware flow scheduling scheme to optimize the average FCT of small flows without prior knowledge of flow size information. We have implemented a PPT prototype in the Linux kernel with ~400 lines of code and demonstrated that compared to Homa, it delivers up to 46.3% lower overall average FCT and even 25%/55.5% lower average/tail FCT of small flows in an Memcached workload.

Despite existing parallel chain systems improving blockchain throughput by allowing concurrent blocks to be appended, challenges such as the excessive number of waiting blocks before confirmation and the inconsistency between block generation order and global confirmation sequence still persist. To address these challenges, we propose CVchain, a parallel chain system with a cross-voting mechanism. Blocks from other subchains are incorporated into the consistency determination of the main chain, reducing the probability of confirmation errors. Our global sorting mechanism leverages both real-time height information and the implicit temporal order contained in voting to improve the accuracy of block ordering. Furthermore, our voting mechanism randomly splits the mining power of the system, preventing targeted attacks on the specific subchain and defending against liveness attacks. We prove the safety and liveness properties of CVchain. We demonstrated its performance with a prototype implementation and large-scale experiments involving 200 nodes across 10 cloud servers in a distributed network environment. The results indicate that CVchain achieves a latency reduction of approximately 32.3% at a confirmation error probability of 0.01 while maintaining throughput levels comparable to OHIE. Additionally, it provides enhanced transaction ordering services.

With the prevalence of various sensors and smart devices in people’s daily lives, numerous types of information are being sensed. While using such information provides critical and convenient services, we are gradually exposing every piece of our behavior and activities. Researchers are aware of the privacy risks and have been working on preserving privacy while sensing human activities. This survey reviews existing studies on privacy-preserving human activity sensing. We first introduce the sensors and captured private information related to human activities. We then propose a taxonomy to structure the methods for preserving private information from two aspects: individual and collaborative activity sensing. For each of the two aspects, the methods are classified into three levels: signal, algorithm, and system. Finally, we discuss the open challenges and provide future directions.

The rapid proliferation of Internet of Things (IoT) devices has brought about unprecedented convenience to people’s daily lives. However, this growth has also created opportunities for hackers to launch large-scale botnet attacks using these devices. As a result, it is critical to deploy real-time traffic classifiers on edge gateways to detect network intrusions and improve near-source protection capabilities. To this end, we propose iDetector, a novel real-time intrusion detection solution for IoT networks that is simple in structure and easy to reproduce. iDetector samples network conversations in real-time using a sliding sampling window and generates traffic samples that integrate multiple features. This allows the samples to accurately capture the patterns of each type of traffic. We propose the nonlinear feature transformation (NFT) algorithm based on the prior distribution of traffic features to increase the information entropy of the samples and thereby improve the classification performance. To enable deployment on edge gateways, we propose EdgeNet, a lightweight deep neural network model that utilizes depthwise separable convolution and self-attention mechanism to enhance classification performance while reducing the number of model parameters. Experimental evaluations show that our solution outperforms state-of-the-art deep learning-based solutions in terms of classification performance and has faster classification speed on resource-constrained edge gateways.

Sleep posture recognition is practically important in various scenarios such as sleep healthcare, bedridden patient care, and chronic disease diagnosis. With concerns of user privacy preserving, we prefer the wireless sensing methods to computer vision methods when dealing with sleep posture recognition. However, the existing wireless sensing methods suffer from at least one of the following major limitations: (i) difficult to deploy in practice; (ii) few posture categories; (iii) insufficient accuracy; (iv) poor generalization ability. In this paper, we use commercial-off-the-shelf (COTS) mmWave radar to implement a sleep posture recognition system called PosMonitor. When designing the PosMonitor system, we need to address the following challenging issues. First, we propose an angle purification method based on multi-frame joint analysis to alleviate the sparsity and instability of the point cloud. Then, we endow the point cloud with respiratory features to enhance its representation of the sleep posture. Further, to make the system applicable to different users, we extract relative respiratory features by normalization to overcome individual differences. Extensive experimental results show that our PosMonitor system can achieve 98% accuracy on average in recognizing 6 typical sleep postures and has good reliability across different conditions.

As the demand for the Internet of Things (IoT) grows, it becomes crucial to possess systems capable of detecting any data leakage used for authentication. Within IoT camera systems based on facial bio-metric recognition, there is a risk of Deepfake Bypassed Facial Feature Authentication due to the widespread use of deepfake video technologies, such as DeepFaceLive and expression manipulation. Traditional Face Anti-Spoofing Detection techniques may struggle to detect real-time deepfake videos within IoT contexts. Moreover, constrained by the scale of Face Anti-Spoofing Detection datasets, current detection models primarily focus on recognizing the entire face in videos, neglecting the inter-component correlations of facial features. However, our investigation indicates that different parts of the face have varying impacts on deepfake detection. To address this issue, we segment the face into several regions within video frames and explore the relationships between these regions. Our approach involves constructing feature graphs that represent such correlations, aiming to leverage the relationships between facial regions and the temporal characteristics of real-time facial manipulation videos for use in live facial detection cameras. Initially, features for each facial region are extracted via Convolutional Neural Networks (CNNs). Subsequently, with these features as vertices and their correlations as edges, a feature graph of the entire video is constructed. Ultimately, a Graph Neural Network (GNN) is employed to determine whether the video has been tampered with. Experiments conducted on several publicly accessible datasets demonstrate that our proposed method outperforms other state-of-the-art Face Anti-Spoofing Detection techniques in most scenarios. Thus, the aforementioned advanced Graph Neural Network model exhibits exceptional performance in real-time deepfake detection tailored for live facial detection cameras.

Wireless signal analytics in IoT systems can enable various promising wireless sensing applications such as localization, anomaly detection, and human activity recognition. As a matter of fact, there are significant correlations in terms of dimension, spatial and temporal aspects among wireless signals from multiple sensors. However, none of the wireless sensing research currently in use directly incorporates or exploits the signal correlations. Therefore, there is still substantial scope for improvement in regards to accuracy and robustness. We are introducing a novel framework called Signal Correlation Learning (SCL). This framework utilizes a directed graph to explicitly represent the signal correlation across various wireless sensors. We use signal embedding to depict the correlation features of a multi-dimensional sensor that arise from a multi-sensor system. Then, we perform Kullback-Leibler (KL) divergence on embedding vectors of any pair of sensors in the system to construct a subgraph at a given time point, which can measure the spatial signal correlation of sensors. Subsequently, several subgraphs spanning a specific time frame are fused into a coherent universal graph based on the small-world theory. This universal graph represents the three types of signal correlation simultaneously. A signal correlation aggregation structure is utilized to extract the features from the universal graph. These features can be used to address target sensing problems. We implement SCL in real RFID, Bluetooth, WIFI, and Zigbee systems, and evaluate its performance in three common wireless sensing problems including localization, anomaly detection, and human activity recognition. Extensive experiments demonstrate that our SCL framework significantly outperforms state-of-the-art wireless sensing algorithms by increasing$80\%\sim 190\%$in terms of accuracy, and by increasing$160\%\sim 220\%$in terms of robustness.

Sharding is an important technology that utilizes group parallelism to enhance the scalability and performance of blockchain. However, the existing solutions use a historical transaction-based approach to reallocate shards, which cannot handle temporary overload and incurs additional overhead during the reallocation process. To this end, this paper proposes LMChain, an efficient load-migratable beacon-based sharding blockchain system. The primary goal of LMChain is to eliminate reliance on historical transactions and achieve the high performance. Specifically, we redesign the state maintenance data structure in Beacon Shard to effectively manage all account states at the shard level. Then, we innovatively propose a load-migratable transaction processing protocol built upon the new data structure. To mitigate read-write conflicts during the selection of migration transactions, we adopt a novel graph partitioning scheme. We also adopt a relay-based method to handle cross-shard transactions and resolve inter-shard state read-write conflicts. We implement the LMChain prototype and conducted experiments in a real network environment comprising 17 cloud servers. Experimental results show that, compared with state-of-the-art solutions, LMChain effectively reduces the average transaction wait latency of overloaded transactions by 30% to 48% in different cases within 16 transaction shards, while improving throughput by 3% to 10%.

Multi-dimensional performance evaluation is crucial for blockchain systems as it enables appropriate blockchain choosing for a given scenario and helps to pinpoint the bottleneck module of a blockchain system to optimize its performance. However, the existing evaluation frameworks for blockchain suffer from low system generality, inefficient workload execution, and incomprehensible evaluation metrics. In order to overcome their limitations, we design and implement the Generalized and Fine-grained Blockchain Evaluation (GFBE) framework. Specifically, we abstract 3 types of Universal Evaluation Interface (UEI) via the dynamic proxying approach to enable generalized evaluation of heterogeneous blockchain systems. Through the design of Lua-based workloads plugin with high flexibility and reusability, GFBE improves the efficiency of workload execution. To achieve comprehensive measurement, we define 15 key performance metrics across hierarchical layers of blockchain architecture. We also implement and deploy GFBE on 16 machines each with 8 CPUs and 16GB RAM, and evaluate three open-source blockchain systems namely Ethereum, ChainMaker, and Haihe smart chain. The experimental results demonstrate that GFBE efficiently and accurately measure 15 key performance metrics such as Contract Execution Efficiency at the contract layer, Consensus Agreement Time Ratio at the consensus layer, and State Query Time at the data layer. Compared with state-of-the-art frameworks such as BLOCKBENCH, Log-based, and Caliper, GFBE distinguishes itself as the only framework that encompasses the appealing features of universal interface, reusable workload, and all-layer metrics.

The ubiquitous Internet-of-Things (IoT) devices generate vast amounts of multimodal data, and the deep multimodal fusion network (DMFN) is a promising technology for processing multimodal data. Deploying DMFNs locally on embedded IoT devices is a profitable way to provide privacy-preserving and robust sensing services. However, the current compression methods suffer from the following limitations: First, they are designed based on unimodal networks or specific model structures. Hence, it is hard to extend these methods to diverse DMFNs; Second, existing works never relate their efforts to disparate computational demands of multimodal data and modalities. Easy samples and redundant modalities consume the same computational resources as powerful modalities and complex samples. We propose anAdaptiveCompressionFramework (ACF) for DMFNs to address those challenges. It enables input-dependent runtime compression locally on resource-constrained embedded devices. Specifically, we propose an offline model transformation module to upgrade the static network with two kinds of dynamic components to support online structural adjustment. Then we design a lightweight policy network to generate multi-granularity and data-dependent compression strategies for different model parts. Finally, we evaluate ACF on four DMFNs across three embedded platforms. Compared with the best results of the existing schemes, ACF obtains up to 2.61× latency reduction and 2.30× energy consumption reduction, with up to 3.57% accuracy improvement.

Due to the contradiction between limited bandwidth and huge transmission parameters, federated Learning (FL) has been an ongoing challenge to reduce the model parameters that need to be transmitted to server in clients for fast transmission. Existing works that attempt to reduce the amount of transmitted parameters have limitations: 1) the reduced number of parameters is not significant; 2) the performance of the global model is limited. In this paper, we propose a novel method called Fed-KGF that significantly reduces the amount of model parameters while improving the global model performance. Our goal is to reduce those transmitted parameters by reducing the number of convolution kernels. Specifically, we construct an incomplete model with a few representative convolution kernels, and propose Kernel Generation Function (KGF) to generate other convolution kernels to render the incomplete model to be a complete one. We discard those generated kernels after training local models, and solely transmit those representative kernels during training, thereby significantly reducing the transmitted parameters. Furthermore, there is a client-drift in the traditional FL because of the averaging method, which hurts the global model performance. We innovatively select one or few modules from all client models in a permutation way, and only aggregate the uploaded modules rather than averaging all modules to reduce client-drift, thus improving the global model performance and further reducing the transmitted parameters. Experimental results on both non-Independent and Identically Distributed (non-IID) and IID scenarios for image classification and object detection tasks demonstrate that our Fed-KGF outperforms SOTA FL models.

Fine-grained recognition of human manipulation activities on objects is crucial in the era of human-computer-object integration. However, there is a lack of solutions for simultaneous recognition of human identity, manipulation activities (including drawing and rotation), and manipulated objects. Therefore, we propose an RF-Camera system that combines RFID and computer vision techniques to address this challenge in multi-person and multi-object scenarios. In RF-Camera, we employ a skeleton-assisted method to extract facial images of target individuals, enabling precise recognition of their identities. To identify manipulation activities, we analyze the 3D hand trajectory and fingertip vector angle, differentiating drawing and rotation manipulation activities. Additionally, we model target person?s hand movements to predict phase data of the target tag, enabling the determination of person-object relationships. Implementing RF-Camera using COTS RFID and Kinect devices involves overcoming challenges such as extracting effective data from noisy streams, predicting virtual phase data considering hand-tag offset, and ensuring high tag reading rates in tag-dense scenarios. We conducted experiments involving six participants performing object manipulation activities, including drawing letters/symbols and rotating movements. Extensive experimental results show that RF-Camera achieves over 90% accuracy in recognizing person identity, manipulation activities, and person-object matching in most conditions.

The smart speakers benefit from the ability to localize and identify users. Specifically, we can analyze the user's habits from the historical trajectory to provide better voice-based services. However, current voice localization method requires the user to actively issue voice commands, which makes smart speakers unable to track and identify silent users most of the time. This paper introducesWSTrack+, a system that combinesWi-Fi andSound to track human movement and recognize gait patterns. In particular, current smart speakers naturally support both Wi-Fi and acoustic functions. As a result, we are able to construct the system using just one router and a smart speaker, which is a more promising approach compared to existing systems that rely on multiple routers for sensing. To track and identify the silent user, our insights are twofold: 1) the smart speakers can hear the sound of the user's footstep, and then extract which direction the user is in; 2) we can extract the reflected path change rate from the Wi-Fi signals, and the acoustic signal can help us convert the path change rate into the actual user's velocity. Our implementation and evaluation on commodity devices demonstrate thatWSTrack+can realize simultaneous tracking and gait recognition, where the median tracking error is$0.34m$and the recognition accuracy is 88.6% for 12 users.

The evolution of Wi-Fi to next-generation 802.11bf demonstrates the potential of device-free Wi-Fi sensing applications, where we can remotely infer the behaviors of users without bringing into physical contact with them. Among these sensing applications, Wi-Fi tracking is critical to provide location based services. Recent Wi-Fi tracking systems can be cataloged into model-based and data-based approaches: (1) the model-based approach is to build the mathematical tracking model. However, this method is sensitive to environmental noise, and spends more execution time; (2) the data-based approach is to train a neural network. However, this method requires a lot of efforts to collect training dataset, and cannot handle all types of trajectories well. To resolve these issues, we propose theNNE-Tracking, a Neural Network Enhanced tracking framework. The core design principle ofNNE-Trackingis as follows: we improve the tracking accuracy based on the data-based approach, and utilize the model-based approach to supervise whether the neural network is already working well. Moreover, we also design a framework to estimate unknown parameters of the tracking model, so that the system can automatically generate the Wi-Fi map. We take the Wi-Fi passive tracking as a specific example to explain how to applyNNE-Trackingin practical applications. Experimental results demonstrate that our design can reduce 59.4% ∼ 85.3% tracking errors while significantly saving execution time. As for deployment costs, we can automatically infer the Wi-Fi map without manual calibration; As for stability, when we repeat the training process with different hidden layers and random seeds, the tracking standard deviation of these neural networks is only 1.4cm.

A wide range of scenarios, such as warehousing, and smart manufacturing, have used RFID mobile robots for the localization of tagged objects. The state-of-the-art RFID-robot based localization works are based on the premise of stable speed. However, in reality this assumption can hardly be guaranteed because Commercial-Off-The-Shelf (COTS) robots typically have inconsistent moving speeds, and a small speed inconsistency will cause a large localization error. To this end, we propose a Speed Inconsistency-Immune approach to mobile RFID robot Localization (SILoc) system, which accurately locates targets when the robot moving speed varies or is even unknown. We propose an optimized unwrapping method to maximize the use of data, and a lightweight algorithm to calculate the locations in both 2D and 3D spaces. By utilizing the characteristics of tag-antenna distance and combining the phase data from multiple antennas, SILoc can effectively eliminate the side effects of speed inconsistency. To increase the flexibility, we further optimize the system and propose SILoc$+$, which enables the system to achieve localization with part of the data, keeping speed inconsistency-immune. Extensive experiments demonstrate that SILoc and SILoc$+$can achieve a centimeter-level localization accuracy in the scenario with an inconsistent or unknown robot moving speed.

Sharding technology has become crucial for enhancing the scalability of blockchains owing to the rapid extension of blockchain data. However, data migration and state reconstruction may cause a high overhead when a new shard is added. Existing solutions have high latency when expanding, and the balance of the state data between shards is poor after extension. To this end, this paper proposes an Effective and Balanced Storage Extension (EBSE) approach for sharding blockchain systems. EBSE can reduce the overhead and latency of the system extension, while ensuring a balance between shards after extension. When implementing the EBSE, we address the following three challenges. 1) To design a data structure that incorporates allocation principles, we designed a Jump Merkle Tree (JMT) based on the Merkle Tree prototype, incorporating node migration and orderliness. 2) To design additional rules that ensure the integrity of the state tree during shard addition, we designed a shard addition protocol to coordinate and standardize the behavior of each shard during the extension process. 3) To ensure the sustainability of the system after extension, we designed state tree addition and cleaning algorithms to remove the invalid information after the system extension. Extensive experiments are conducted to evaluate the performance of the proposed approach. The experimental results show that the EBSE outperforms the existing solutions in terms of balance and latency. Compared with the state-of-the-art sharding storage, EBSE effectively reduces the shard addition latency by 60% and achieves 4× superior shard data balance.

Recently, asynchronous byzantine fault tolerance (BFT) consensus has made progress in linearized communication complexity, allowing nodes to broadcast a small number of transactions at their own pace in networks with variable bandwidth. However, this means that network propagation delay poses a more severe bottleneck to the system compared to transmission delay affected by block size and bandwidth. Particularly in networks with high propagation delays, nodes need to wait more for messages to propagate through the network, which severely hinders system performance. We propose Swarm, an asynchronous BFT protocol that is suitable for networks with tight bandwidth and propagation delay. The new protocol structure supports highly concurrent propagation-delay-intensive and propagationdelay-tolerant stages by separating the control and data layers and ensures that nodes achieve an ordered and consistent log for parallel blocks. We also design a group broadcasting strategy that allows nodes to not broadcast proposals from other nodes without compromising safety and liveness while reducing communication cost. Importantly, our work is orthogonal to existing improvements based on communication complexity and bandwidth and does not sacrifice existing results. We built a complete system prototype and conducted a thorough evaluation. The results show that Swarm performs better in networks with higher propagation delays, achieving 2-5x TPS and reducing latency by 50% compared to existing asynchronous protocols for different scales.

Edge computing is migrating services from remote clouds to the network edge, where a vast amount of data is also flowing into edge nodes. In this context, the Edge Data-Sharing System (EDSS) enhances service quality by enabling edge nodes to cooperate. However, the EDSS is suitable for precise search and faces the existing high overhead when many users retrieve similar data. To solve the obstacle, this paper proposes a similarity-based edge storage system, SESS, which leverages the software-defined edge network to realize efficient storage and retrieval of similarity data. We first design RealminHash, a core module of SESS, for efficient signature and and index for each data. Then, SESS calculates the storage strategy based on the similarity between data. Importantly, SESS adjusts this strategy using periodic network information to ensure load balancing. Experimental results demonstrate that SESS realizes the nearest-neighbor storage while maintaining load balancing. SESS outperforms the well-known k-means and spectral clustering methods in terms of accuracy and latency and supports millisecond similar queries.

As a diagnostic method of equipment operational status, vibration monitoring plays a significant role in industrial systems. It is necessary to monitor multiple equipment components simultaneously, due to their different vibration modes. Previous solutions either work in an invasive manner or face challenges in object localization and results correspondence. Therefore, we propose VibCamera, a vibration monitoring system that combines mmWave radar and computer vision technology. We propose an expand-shrink method to optimize object detection results of computer vision and combine camera localization results to extract mmWave signals. Additionally, we employ mmWave data recombination and respective fitting methods to calculate the vibration characteristics for each point accurately. The experiment shows that after fusing visual information, the target detection accuracy is improved to 94.8%, and the cluster point efficiency is improved by 23.3%. Furthermore, amplitude and frequency measurement errors are reduced to 29.1μm and 0.08Hz, respectively.

Byzantine fault-tolerant (BFT) consensus ensures system consistency in the presence of malicious replicas and is widely adopted in blockchain systems. To enhance scalability and throughput, recent advancements incorporate multiple leaders into BFT consensus. However, employing multiple leaders results in significant resource wastage in terms of storage, bandwidth, and CPU usage, attributable to transaction redundancy. Conversely, to eliminate duplication, the resilience in Byzantine settings is compromised. To bridge this gap, we propose PeterHofe, a novel ring-based collaborative transaction packaging method, aiming to maintain resource efficiency and minimize Byzantine leader influence, thereby reducing transaction latency and improving system robustness. PeterHofe extends the concept of partitioning transaction hash space into buckets, establishing many-to-many mappings between replicas and buckets to diminish Byzantine replica control. When implementing PeterHofe, we address the following two challenges. 1) To improve resistance to Byzantine censorship, we design a permutation-based ring structure with accompanying correctness proofs and mathematical analyses; 2) To further reduce transaction duplication, we introduce a Prophecy-Implementation mechanism with analyzed malicious behaviors. We implement PeterHofe on top of the latest and representative work, Narwhal and Tusk. Experimental results demonstrate that PeterHofe can achieve low resource waste and high system robustness simultaneously. Specifically, PeterHofe's resource waste rate is near 5~17% in general cases, which is a 20-fold reduction compared to the Random-based Strategy; compared with the state-of-the-art Hash-based Partitioning Strategy, the proportion of maliciously controlled transactions is reduced by at least 66%, leading to a latency decrease of up to 75%.

Social Internet of Things (SIoT), a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things), paving the way for the next generation of Internet of Things. However, due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation. Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes. Moreover, the underlying structure of SIoT is usually complex, including both the heterogeneous graph structure and pairwise trust relationships, which renders hard to preserve the properties of SIoT trust during information propagation. To address these aforementioned problems, we propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT. Specifically, we first extract useful knowledge from users’ comment behaviors and external structured triples related to object descriptions, in order to gain a deeper insight into the semantics of users and objects. Furthermore, we introduce a discriminative convolutional layer that utilizes heterogeneous graph structure, node semantics, and augmented trust relationships to learn node embeddings from the perspective of a user as a trustor or a trustee, effectively capturing multi-aspect properties of SIoT trust during information propagation. Finally, a trust prediction layer is developed to estimate the trust relationships between pairwise nodes. Extensive experiments on three public datasets illustrate the superior performance of KGTrust over state-of-the-art methods.

Device-free Wi-Fi tracking has become essential for ubiquitous wireless sensing. However, current device-free Wi-Fi tracking systems suffer from two limitations: First, abnormal signals interfere with tracking performance when the user walks across the direct link of the transceivers and second, the tracking error based on the velocity integral accumulates over time. This article proposes CrossTrack, the first device-free cross-link tracking system with commodity Wi-Fi. Our inspiration is to regard the cross-link behavior as an opportunity to correct the trajectory instead of disturbing noise like previous work. Our approach involves three main steps. First, we devise a metric that is capable of detecting cross-link behavior. Second, we propose a new theoretical model that identifies the cross-link position as a landmark. Third, we develop a path revision technique that utilizes this landmark to optimize the trajectory. The technique innovation of this article is to reveal the theoretical approach to transform cross-link interference into optimization in device-free tracking for the first time. We implement CrossTrack based on commercial Wi-Fi devices and conduct comprehensive experiments. Our results show that CrossTrack can reduce tracking errors by 48.75%, and the median tracking error is 0.41 m.

Owing to the attractive properties of decentralization, unforgeability, transparency, and traceability, blockchain is increasingly being used in various scenarios such as supply chain and public services, where massive Spatial-Temporal-Keywords (STK) transactions need to be packaged. However, due to the multi-dimensionality and randomness of STK transactions, existing solutions fail to enable queries in a verifiable and efficient way for blockchains storing multidimensional transactions. To this end, this article takes the first step to propose an authenticated and efficient query approach in hybrid blockchain systems consisting of on-chain and off-chain parts. We first design a data structure named MRK-Tree in the block body, which organizes STK transactions for efficient nodes pruning of both kNN and range queries. Then we propose an improved block header, which improves the efficient pruning of blocks on the basis of ensuring the authentication of query results. Also, we design a cross-block searching algorithm named Efficient Block Pruning (EBP) and intra-block searching algorithms named Authenticated kNN/Range Query (AKQ/ARQ) to accelerate authenticated queries for multiple MRK-Trees in the hybrid blockchain systems. Authentication mechanisms are proposed to ensure the soundness and completeness of query results. Rigorous security analysis validates the practicability of the proposed approach. We build a blockchain prototype to comprehensively evaluate the performance of proposed query schemes. Extensive evaluation results with real datasets reveal that our approach can ensure authenticated queries, meanwhile improving the time efficiency by up to 36.45x and space efficiency by up to 4 orders of magnitude compared with the well-known benchmark query schemes.

Spatial Crowdsourcing (SC) has been adopted in various applications such as Gigwalk and Uber, where a platform takes location-based tasks (e.g., picking up passengers) from a requester and selects suitable workers to perform them. In most existing works, the platform selects workers based on the requester and worker information, which suffers from serious privacy issues. Some works have considered privacy issues, but they still suffer from either of two limitations: (i) Privacy of the requester and worker cannot be protected simultaneously; (ii) Third-party trusted entities are usually required. Motivated by this, we focus on protecting the privacy of both the requester and the worker without third-party entities while selecting workers. We use randomized response, a widely recognized and prevalent privacy model achieving Local Differential Privacy (LDP), to jointly protect the privacy of workers’ locations and charges based on the location-charge correlation. For the requester, we present a novel mechanism called randomized matrix multiplication to hide the real task locations. More importantly, we prove that the worker selection based on the protected information is non-submodular and NP-hard, which cannot be addressed in polynomial time. To this end, we present an approximate algorithm to solve the problem efficiently, of which the effectiveness is measured by the approximation ratio, i.e., the ratio of the optimal solution to the approximate solution. Finally, simulations based on real-world datasets illustrate that our worker selection outperforms the state-of-the-art method on both privacy protection and worker selection.

Wi-Fi CSI-based indoor localization systems can realize decimeter-level localization accuracy. However, these systems require that the location and antenna array orientation of Wi-Fi Access Point (AP) are known in advance, which makes it impractical for large-scale deployment. In this paper, we present MapFi, which can realize autonomous mapping of Wi-Fi infrastructure without labor-intensive site survey. To this end, we focus on addressing three problems. First, as there will be diverse layouts of devices and antennas with respective to numerous and heterogeneous Wi-Fi APs, we propose a general method to estimate AoA and generate the Wi-Fi map. Second, while the existing systems can provide a promising median localization accuracy, tail performance is usually far worse. Consequently, we develop a revision method to reduce tail errors. Third, when deployed in large-scale indoor environment, obstacles and long-distance communication might incur failed CSI collection. Therefore, we segment Wi-Fi APs into groups and finally merge these groups to generate the global Wi-Fi map. We conduct experiments in different scenarios to verify the proposed methods. The experimental results show that we can realize the$80\%$localization error within$1.15m$and$0.74m$in office room and open space respectively, which is as accurate as localization systems requiring known Wi-Fi map.

Major manufacturers and retailers are increasingly using RFID systems in supply-chain scenarios, where theft of goods during transport typically causes significant economic losses for the consumer. This paper studies how to achieve time-efficient and secure integrity authentication problems in RFID systems. We start with a straightforward solution called SecAuth, which uses a secure identity stored on reserved memory to authenticate tags in a secure way. We then propose a time efficient KTAuth protocol, which design a verification chain mechanism to efficiently verify a small set of key tags using limited on-tag memory. We point out that the limitation of KTAuth is that it takes too much overhead to write a large block of data to tag memory, which leads to the proposed group selection mechanism. The KTAuth with group selection (KTAuth-GS) enables you to select key tags with a single select command, which helps to quickly check the existence of key tags and reduces the data writes on the tags. Experiments and simulation results demonstrate that the proposed KTAuth-GS can defend against counterfeiting attacks by providing more reliable results and reducing the execution time by as much as a factor of 5 when compared with a baseline tag identification protocol.

Mobile robot-assisted book inventory such as book identification and book order detection has become increasingly popular in smart library, replacing the manual book inventory which is time-consuming and error-prone. The existing systems are either computer vision (CV)-based or RFID-based, however several limitations are inevitable. CV-based systems may not be able to identify books effectively due to low accuracy of detecting texts on book spine. RFID tags attached to books can be used to identify a book uniquely. However, in high tag density scenarios such as library, tag coupling effects of adjacent tags may seriously affect the accuracy of tag reading. To overcome these limitations, this paper presents a novel RFID and CV fusion system for Book Inventory using mobile robot (RC-BI). RFID and CV are first used individually to obtain book order, then the information will be fused by the sequence based matching algorithm to remove ambiguity and improve overall accuracy. Specifically, we address three technical challenges. We design a deep neural network (DNN) model with multiple inputs and mixed data to filter out interference of RFID tags on other tiers, and propose a video information extracting schema to extract book spine information accurately, and use strong link to align and match RFID- and CV-based timestamp vs. book-name sequences to avoid errors during fusion. Extensive experiments indicate that our system achieves an average accuracy of 98.4% for tier filtering and an average accuracy of 98.9% for book order, significantly outperforming the state-of-the-arts.

This paper studies the problem of 6D pose estimation, which is practically important in various application scenarios such as robotic-based object grasping, obstacle avoidance in autonomous driving scene, and object integration in mixed reality. However, existing methods suffer from at least one of the five major limitations: dependence on object identification, complex deployment, difficulty in data collection, low accuracy, and incomplete estimation. To overcome the above limitations, this paper proposes an RC6D system, which is the first to estimate 6D poses by fusing RFID and Computer Vision (CV) data with multi-modal deep learning techniques. In RC6D, we first detect 2D keypoints through a deep learning approach. We then propose a novel RFID-CV fusion neural network to predict the depth of the scene, and use the estimated depth information to expand the 2D keypoints to 3D keypoints. Finally, we model the coordinate correspondences between the detected 2D-3D keypoints, which is applied to estimate the 6D pose of the target object. When implementing RC6D, we mainly address the following three technical challenges. (i) To predict 6D poses without using the CAD model, we propose a network architecture for monocular depth estimation. (ii) To train the neural network for 6D pose estimation without time-consuming 6D labeling, we use an unsupervised learning algorithm based on 2D-3D point pair matching. (iii) To detect the subject of the object without identification, we leverage optical flow to restrict the object and RFID to directly obtain its information. The experimental results show that the localization error of RC6D is less than 10 cm with a probability higher than 90.64% and its orientation estimation error is less than 10° with a probability higher than 79.63%. Hence, the proposed RC6D system performs much better than the state-of-the-art related solutions.

The consensus mechanism plays a pivotal role in guaranteeing the security and consistency of blockchain systems and substantially affects system performance. However, an increasing number of blockchain nodes degrade the consensus performance dramatically because of the high communication complexity in traditional consensus mechanisms. In this paper, we propose NS-consensus, a secure node-sampling blockchain consensus mechanism reducing the communication complexity significantly. The key novelty lies in the sampling of blockchain nodes so that the leader only needs to interact with the sampling nodes in each consensus epoch. However, NS-consensus imposes two challenges in determining an optimal sample size and denying malicious proposals. To address the challenges, we determine the sample size under the constraints of a confidence level and a margin of error to enhance communication efficiency without compromising system security. Furthermore, we design a mechanism to enable the leader to interact with all blockchain nodes in the last consensus phase, ensuring the denial of malicious proposals. The extensive experimental results indicate that NS-consensus outperforms the state-of-the-art with up to 175.1% higher system throughput and 79.9% lower time overhead in the sampling phases.

With the wide deployment of RFID in various scenarios such as warehouse management, freight transportation, and manufacturing, tag authentication is increasingly important due to the threat of counterfeit tags. Recent physical-layer authentication approaches have demonstrated that the subtle differences in the hardware features offer a unique fingerprint to authenticate a tag. Although the state-of-the-art approaches are effective in laboratory environments, they are difficult for practical deployment because they either require complex analysis of the raw signal propagation or restrict the geometrical positions of the tags. In this paper, we propose an RFID tag authentication based on frequency- and orientation-related phase fingerprints, called FopPrint, which does not require raw signal analysis or complex geometric relationship. FopPrint uses the phase values of tags at different frequencies and orientations to construct feature matrices as physical-layer fingerprints and uses a pair of adjacent tags as identifiers of each object. FopPrint can effectively eliminate the influence of environmental factors by using the feature matrix constructed by the phase difference. We implement a prototype of FopPrint using Commercial-Off-The-Shelf (COTS) RFID devices. Extensive experimental results show that FopPrint achieves high authentication accuracy of 94% in various experimental settings.

Radio-frequency identification (RFID) electronic license plate (RF-ELP) has been widely used to enable various automatic vehicle identification applications. Endowing RF-ELP with mobile vehicle sensing capabilities, such as localization and speed measurement is of practical importance, yet there is no solution on the shelf. Moreover, the position information is essential for accurate speed measurement, while the related RFID-based vehicular localization and indoor mobile localization methods suffer from at least one of the following major limitations: 1) difficult to deploy in practice; 2) requiring moving speed in advance; 3) only working for indoor-speed vehicles; and 4) not well compatible to frequency-hopping mechanism. To overcome the above limitations, this article proposes an RF-ELP-based mobile vehicle sensing (RESensing) system. RESensing conducts a new signal phase collection strategy to ensure the phase coupling in road-speed cases and converts phases of each interrogation to the relative speed to make it immune to frequency hopping and interinterrogation phase fluctuation. Then, the speed measurement and longitudinal localization are simultaneously performed by solving a nonlinear optimization model. Furthermore, the propagation model and antenna radiation pattern are investigated to facilitate the received signal strength index (RSSI)-based accurate lane-level lateral localization. To our knowledge, RESensing is the first RF-ELP-based speed measurement and localization system for mobile vehicles. The performance of RESensing is evaluated by real experiments under specifications of GB/T 37987 and EPC C1G2, which shows that RESensing achieves the mean speed error ratio of 4.34%, the longitudinal localization error of submeter level, and the lane estimation accuracy of nearly 100%.

The rapid development of the blockchain leads to a blowout of on-chain transactions, contracts, and currencies, which will further accelerate the increase of data. The existing blockchain systems typically support exact transaction queries, which, however, cannot satisfy the QoS requirements with intelligent adjustment in the blockchain systems. To this end, this paper takes the first step to define and address the practically important problem of transaction cardinality estimation for QoS-adjustable intelligent blockchain systems. We first establish a mathematical relationship between the bit string and transaction cardinality. Thus, we can leverage the number of leading 1s of the obtained bit string to estimate the transaction cardinality. We then improve the block header and body with a corresponding search algorithm to access bit strings in blocks. We also propose an estimation protocol with intelligent adjustable QoS to support accuracy-guaranteed and efficiency-optimized estimation. Finally, we design an authentication scheme and guarantee the reliability of our protocol through rigorous theoretical derivation. When achieving the transaction cardinality estimation in blockchain, two technical challenges need to be addressed. (i) To ensure efficient, verifiable, and overhead-saving bit string accessing mechanism in blockchain, we propose the Merkle Cardinality Tree (MCT) and target block filtering mechanism based on Bloom Filter (BF) in off-chain and improve on-chain block header by joining the abstract of MCT and BF. (ii) To improve estimation efficiency while guaranteeing accuracy requirements in hybrid blockchain scheme, we propose a Dynamic One-round Sampling-based cardinality Estimation (DOSE) protocol and integrate BF-DOSE to intelligently accelerate estimation. We build MCT in Ethereum and store the MCT Root in the block header for estimation authentication. Extensive experiments reveal that our BF-DOSE protocol can well satisfy various accuracy and efficiency requirements of QoS-adjustable intelligent blockchain systems, and is one to two orders of magnitude faster compared with benchmark schemes.

Mobile image recognition services, which provide people with image recognition services through the cameras of mobile devices, are revolutionizing our lives. However, most existing cloud/edge-based approaches suffer from two major limitations, (i) Low recognition accuracy and high network bandwidth pressure, and (ii) Not easy to extract features based on currently available resources of mobile devices. In this paper, we propose a resource-aware feature extraction framework for mobile image recognition services. The proposed framework consists of discriminative feature extraction (DFE) and NestDFE algorithms. The DFE algorithm can generate an extractor${{\mathbf E}}$to extract discriminative features from the image data set on the edge server and images on mobile devices. Thus, the proposed framework can achieve higher recognition accuracy and require mobile devices to upload less feature data to the edge server. The NestDFE algorithm generates a single multi-capacity extractor that acts as a series of sub-extractors and enables mobile devices to dynamically select sub-extractors. Experimental results show that the proposed framework improves recognition accuracy by about 23 percent and reduces network traffic by about 76 percent compared with existing approaches.

Tag identification is a critical operation in large-scale RFID applications. Typically, in the RFID-enabled warehouse, the reader needs to execute tag identifications to obtain the inventory information of numerous tagged items. The existing schemes usually divide the time frame into multiple slots and map each tag to one of them for replying its identity. This imposes serious tag collisions because two or more tags may be mapped to the same slot and their responses corrupt with each other. When tag collision happens, all the collided tags cannot be identified by the reader, which significantly increases the identification delay. To overcome the collision problem in the identification process, this paper proposes a Group Tag Identification (GTI) framework to identify grouped tags in both singleton and collision slots. The key novelty of GTI is in leveraging tag-correlation to identify grouped tags in the collision slots without any extra transmission overhead. The main challenge of this work is to overcome the communication and architectural limitations of RFID systems in the context of building ID and slot-correlation between tags. Extensive simulations show that GTI significantly reduces the identification delay by up to 40 percent when compared with the state-of-the-art dynamical frame slotted aloha schemes.

Eye tracking is playing an important role in many applications including human-computer interaction and behavior study. However, the existing approaches have at least one of the following limitations: (i) dedicated devices such as infrared camera and eye-tracker are required; (ii) complex calibration process is involved; (iii) substantial computing resources are consumed; (iv) users suffer from the risk of privacy leakage. To address the above limitations, we propose a H eatmap-based E ye T racking (HETrack) system. One of the key challenges in our system is to design a lightweight model for fine-grained tracking when the computing resources of device is limited. Also, it is necessary to protect user privacy in such a system. To address the above challenging issues, the proposed system consists of the following processes. First, when users randomly look at the screen of the device, HETrack obtains the raw image containing facial information. Then, we design a neural network model and train it with federated learning. The model can map the image to heatmap that implies the possibility of the user’s gaze position on the screen. Finally, HETrack can intercept the real-time video stream into frames, and employ the trained model to generate the heatmap of current frame for gaze estimation. We implement HETrack based on a Commercial-Off-The-Shelf (COTS) camera and conduct extensive experiments to evaluate its performance. Our HETrack system only requires once calibration; whereas, the state-of-the-art work proposed by Google requires 3~5 times calibration on average. Unlike previous approaches that transmit raw image data to a central server, in our HETrack system, only parameters are transmitted, thereby well protecting the user’s privacy. Experimental results demonstrate that the average distance error of estimated gaze point is 3cm, which is compatible with the state-of-the-art methods.

Localization of target tagged objects on the shelf is of great significance in RFID-enabled warehousing scenarios. Compared with the RFID localization systems that use fixed reader antennas or mobile RFID-robot, the portable reader-based methods are much more cost-effective. Hence, this paper focuses on reader-portable RFID localization. However, the existing reader-portable localization systems suffer from the following limitations: (i) reader antenna is required to pass by the target tags. Thus, the tags in the corner can never be located; (ii) many reference tags need to be deployed on the shelf in advance, which considerably increases the manpower; (iii) specialized antenna is required, which limits the promotion potential. To this end, this paper proposes a Waving action-driven RFID Localization (WRL) system, which enables tag localization with a portable camera-augmented reader. In the WRL system, a user only needs to wave the camera-augmented reader before locating the target tags. Specifically, we first use a classical camera pose estimation method named PnP to recover the antenna’s movement trajectory in a pixel coordinate system. Then, WRL constructs a gridded hologram, in which camera data and RFID phase data are jointly used to calculate a probability for each grid. Intuitively, the higher probability a grid has, the more possible the target tag lies in the corresponding grid. Based on this idea, WRL calculates the target tag’s location on the shelf. We use the Commercial-Off-The-Shelf (COTS) RFID and camera devices to implement the WRL system. Extensive experiments have been conducted, and the results demonstrate that the mean localization error of WRL is less than 20cm with a confidence of about 95%.

Major manufacturers and retailers are increasingly using RFID systems in supply-chain scenarios, where theft of goods during transport typically causes significant economic losses for the consumer. Recent sample-based authentication methods attempt to use a small set of random sample tags to authenticate the integrity of the entire tag population, which significantly reduces the authentication time at the expense of slightly reduced reliability. The problem is that it still incurs extensive initialization overhead when writing the authentication information to all of the tags. This paper presents KTAuth, a lightweight integrity authentication approach to efficiently and reliably detect missing tags and counterfeit tags caused by stolen attacks. The competitive advantage of KTAuth is that it only requires writing the authentication information to a small set of deterministic key tags, offering a significant reduction in initialization costs. In addition, KTAuth strictly follows the C1G2 specifications and thus can be deployed on Commercial-Off-The-Shelf RFID systems. Furthermore, KTAuth proposes a novel authentication chain mechanism to verify the integrity of tags exclusively based on data stored on them. To evaluate the feasibility and deployability of KTAuth, we implemented a small-scale prototype system using mainstream RFID devices. Using the parameters achieved from the real experiments, we also conducted extensive simulations to evaluate the performance of KTAuth in large-scale RFID systems.

Mobile RFID robots have been increasingly used in warehousing and intelligent manufacturing scenarios to pinpoint the locations of tagged objects. The accuracy of state-of-the-art RFID robot localization systems depends much on the stability of robot moving speed. However, in reality this assumption can hardly be guaranteed because a Commercial-Off-The-Shelf (COTS) robot typically has an inconsistent moving speed, and a small speed inconsistency will cause a large localization error. To this end, we propose a Speed Inconsistency-Immune approach to mobile RFID robot Localization (SILoc) system, which can accurately locate RFID tagged targets when the robot moving speed varies or is even unknown. SILoc employs multiple antennas fixed on the mobile robot to collect the phase data of target tags. We propose an optimized unwrapping method to maximize the use of the phase data, and a lightweight algorithm to calculate the locations in both 2D and 3D spaces based on the unwrapped phase profile. By utilizing the characteristics of tag-antenna distance and combining the phase data from multiple antennas, SILoc can effectively eliminate the side effects of moving speed inconsistency. Extensive experimental results demonstrate that SILoc can achieve a centimeter-level localization accuracy in the scenario with an inconsistent or unknown robot moving speed.

Mobile Edge Computing (MEC) is a promising cloud-network convergence paradigm which provides computational resources close to end devices at the network edge. There exist multiple Edge Infrastructure Providers (EIPs) in MEC which independently manage edges and provide services to customers. Due to the exponentially increasing data generated by end devices, it is almost impossible for a single EIP to accommodate offloaded data. Moreover, when considering that multiple EIPs provide services through federation, an urgent challenge is how to ensure the sustainability of federation. Most of the existing work improves the service provision capabilities of MEC by optimizing service placement without considering the existence of multiple EIPs. In this paper, we design the horizontal collaboration of edge federation, which integrates all edges of all EIPs. First, we model the service placement problem as a programming problem, towards the goal of maximizing social welfare. Then, we propose two dynamic pricing methods for EIPs to determine typical price for customers and insourcing price for other EIPs. The evaluation results based on two real-world data sets demonstrate that our proposed service placement model can increase the total gain of EIPs by up to 24.5% with a decrease of 35.5% in total delay.

Recognition of human-object interactions is practically important in various human-centric sensing scenarios such as smart supermarket, factory, and home. This paper proposes an RF-Camera system by fusing RFID and Computer Vision (CV) techniques, which is the first work to recognize the human gestural interactions with physical objects in multi-subject and multi-object scenarios. In RF-Camera, we first propose a dimension reduction method to transform the subject's 3D hand trajectory captured by depth camera to a 2D image, using which the subject's gesture can be recognized. We also propose a method to extract the facial image of target subject from an image that may contain irrelevant subjects, thereby further recognizing his/her identity. Finally, we model the physical movements of the held object's tag and further predict the tag phase data, by comparing which with real phase data of each tag human-object matching can be discovered. When implementing RF-Camera, three technical challenges need to be addressed. (i) To remove noisy data corresponding to irrelevant actions from raw sensing data, we propose a state transition diagram to determine the boundary of effective data. (ii) To predict phase data of the held target tag with unknown hand-tag offset, we quantify target tag trajectory by adding a variable hand-tag vector to captured hand trajectory. (iii) To ensure high reading rates of target tags in tag-dense scenarios, we propose a CV-assisted RFID scheduling method, in which analytics on CV data can help schedule RFID readings. We conduct extensive experiments to evaluate the performance of RF-Camera. Experimental results demonstrate that RF-Camera can recognize the gestural actions, human identity and human-object matching with an average accuracy higher than 90% in most cases.

With the growing amount of data, the demand of big data storage significantly increases. Through the cloud center, data providers can conveniently share data stored in the center with others. However, one practically important problem in big data storage is privacy. During the sharing process, data is encrypted to be confidential and anonymous. Such operation can protect privacy from being leaked out. To satisfy the practical conditions, data tranmission with multi receivers is also considered. Furthermore, this paper proposes the notion of pre-authentication for the first time, i.e., only users with certain attributes that have already been authenticated can participate in the data transmission. The pre-authentication mechanism combines the advantages of proxy conditional re-encryption multi-sharing mechanism with the attribute-based authentication technique, thus achieving attributes authentication before re-encryption, and ensuring the security of the attributes and data. Finally this paper proves that the system can resist several attacks and the proposed pre-authentication mechanism could significantly enhance the system security level.

This paper studies the problem of tag localization using RFID-augmented robots, which is practically important for promising warehousing applications, e.g., automatic item fetching and misplacement detection. Existing RFID localization systems suffer from one or more of following limitations: requiring specialized devices; only 2D localization is enabled; having blind zone for mobile localization; low scalability. In this paper, we use Commercial Off-The-Shelf (COTS) robot and RFID devices to implement a Mobile RF-robot Localization (MRL) system. Specifically, when the RFID-augmented robot moves along the straight aisle in a warehouse, the reader keeps reading the target tag via two vertically deployed antennas ( Z1 and Z2) and returns the tag phase data with timestamps to the server. We take three points in the phase profile of antenna Z1 and leverage the spatial and temporal changes inherent in this phase triad to construct an equation set. By solving it, we achieve the location of target tag relative to the trajectory of antenna Z1. Based on different phase triads, we can have candidate locations of the target tag with different accuracy. Then, we propose theoretical analysis to quantify the deviation of each localization result. A fine-grained localization result can be achieved by assigning larger weights to the localization results with smaller deviations. Similarly, we can also calculate the relative location of target tag with respect to the trajectory of antenna Z2. Leveraging the geometric relationships among target tag and antenna trajectories, we eventually calculate the location of target tag in 3D space. We perform various experiments to evaluate the performance of the MRL system and results show that the proposed MRL system can achieve high accuracy in both 2D and 3D localization.

Mobile Edge Computing (MEC) can facilitate various important image retrieval applications for mobile users by offloading partial computation tasks from resource-limited mobile devices to edge servers. However, existing related works suffer from two major limitations. (i) High network bandwidth cost: they need to extract numerous features from the image and upload these feature data to the cloud server. (ii) Lowretrieval accuracy: they separate the feature extraction processes from the image data set in the cloud server, thus unable to provide effective features for accurate image retrieval. In this paper, we propose a cloud-guided feature extraction approach for mobile image retrieval. In the proposed approach, the cloud server first leverages the relationships among labeled images in the data set to learn a projection matrix P. Then, it uses the matrix P to extract discriminative features from the image data set and form a low-dimensional feature data set. Following that, the cloud server sends the matrix P to the edge server and uses it to multiply the image χ. The result PTχ, i.e., image features, is uploaded to the cloud server to find the label of the image with the most similar multiplying result. The label is regarded as the retrieval result and returned to the mobile user. In the cloud-guided feature extraction approach, the matrix P can extract a small number of effective image features, which not only reduces network traffic but also improves retrieval accuracy. We have implemented a prototype system to validate the proposed approach and evaluate its performance by conducting extensive experiments using a real MEC environment and data set. The experimental results show that the proposed approach reduces the network traffic by nearly 93 percent and improves the retrieval accuracy by nearly 6.9 percent compared with the state-of-the-art image retrieval approaches in MEC.

In recent years, there is an increasing demand for indoor localization services with the aim to locate people and objects inside buildings. However, localization accuracy is susceptible to inaccurate and high variant sensor measurements due to the unpredictable fluctuations of received wireless signals and the sensitivity of hardware devices. To address this issue, in this paper, we establish a new Bluetooth indoor localization system, whose architecture can be basically decomposed into two parts: the internet-of-things (IoT) framework and the localization module. Concretely, the IoT platform uses the state-of-the-art light weight Spring Boot microservice framework consisting of multi-layer structure. In the localization module, it follows the general process of trilateration but significantly distinguished from it. A set of measures are adopted to strengthen the system’s robustness when obtained measurements cannot be fully trusted. Specifically, in the first place, rather than using conventional propagation model to predict the distance between Bluetooth transmitter and receiver, we design a bran-new LSTM-based distance estimator which can better depict the nonlinearity of attenuation characteristics of radio signal. Moreover, we also employ a series of self-adaptive mechanisms, including elastic radius intersecting, multiple weighted centroid localization and self-adaptive Kalman tracking, to make the system robust against inaccurate measurements and unpredictable sudden variation of received wireless signal. A bunch of tests are conducted in both ideal lab environment and Alibaba’s large-scale warehouse, and experimental results show our indoor localization system outperforms the state-of-the-art benchmarks by a large margin in both localization accuracy and stability.

Individual activity recognition is crucial for Human-Computer Interaction (HCI) applications, especially in multi-person scenarios. Current approaches, based on wearable sensors or wireless signals (e.g., WiFi and RFID), however, are often focused on single person scenario only, due to the limitation of existing wireless sensing technologies. In order to address the issue, we design a DEeper Exercise Monitoring system, called DEEM, in which we introduce computer vision techniques to facilitate RFID devices to provide exercise estimation support, as well as identifying the users and the objects users hold. We implement this design with COTS Kinect camera and RFID devices in a smart gym application. To the best of our knowledge, it is the first system for estimating multiple people behavior in a complicated gym environment. We conduct extensive experiments to evaluate the performance of the DEEM system. The experimental results show that the matching accuracy can reach 95%, and the exercise estimation accuracy can reach 94% on average.

Visitor counting can be attractive to various applications, like business management and marketing investigation. Recently, many studies have employed wireless signals to achieve visitor counting without people's active participation and privacy intrusion. However, existing systems mainly count the overall visitors inside a certain area, which fails to provide the fine-grained information of the coming and leaving visitor flow. Unlike previous studies, this article proposes to count the in-and-out visitors to monitor visiting frequency and population, which can be applied for many indoor places, such as shops and restaurants. Therefore, we present the first WiFi-based in-and-out visitor counting system, Door-Monitor, which obtains the direction (enter or exit) and the number of visitors passing by the door. The WiFi signals enable us to count the visitors in a low-cost and nonintrusive way, and it can tell the exact number of visitors even when multiple persons pass by the door simultaneously. To detect the visitors' passing direction, we show that the patterns in the phase difference series can indicate the entering and exiting passing directions by analyzing the effects of the passing behavior on the signal's phase information. To count the passing visitors, we perform a short time Fourier transformation on the phase difference series to generate the spectrogram, on which the convolutional neural network is applied for building a counting model. The experimental results show that the average accuracies of passing direction detection and visitor counting are 95.2% and 94.5%, respectively.

This paper studies the practically important problem of data collection for sensor-augmented RFID systems. However, existing RFID data collection protocols suffer from two common limitations: execution time is naturally in proportion to the number of tags, thus they cannot satisfy time-stringent application scenarios; none of them is complaint with the C1G2 standard, thus they cannot be implemented using Commercial-Off-The-Shelf (COTS) RFID tags. To overcome these two limitations, this paper proposes the Geographical correlation-based RF-data Collection (GRC) protocol. GRC is fast because it is able to approximately capture the sensing data of all tags by only actually gathering data from a small set of sampled tags. This is based on the observation from the real-world data set that sensing data has a strong geographical correlation, i.e., data gathered from nearby RFID tags has similar values. In GRC, we use a greedy approach to find the minimum sampling tag set to cover the whole monitoring region such that each un-sampled tag has at least one sampled tag nearby. Then, RFID reader runs the Framed Slotted Aloha (FSA) protocol specified in C1G2 standard to collect sensing data from the sampled tags. For each un-sampled tag, we approximate its sensing data by calculating weight-average of the data collected from its nearby sampled tags, where a faraway sampled tag should be given a small weight, and vice versa. Compared with existing RFID data collection schemes, the advantages of GRC are two-fold: (1) Extensive simulation results demonstrate that the time cost of our GRC scheme is only 1/28~1/3 of the state-of-the-art data collection scheme; (2) GRC is totally complaint with C1G2 standard, thus it can be easily deployed on the COTS RFID tags.

Tag inventory is one of the most fundamental tasks for RFID systems. However, the Framed Slotted Aloha (FSA) protocol specified in the C1G2 standard is of low time-efficiency, because it needs to collect all tags in the system. To improve time-efficiency, research communities proposed a batch of sampling-based approaches, in which the reader only needs to collect a small set of sampled tags instead of all. Although time-efficiency has been improved, existing sampling-based approaches still have two common limitations. First, all tags in the system are assumed to have the same sampling probability. It is unfair that tags attached to differential items (e.g., different values) have the same chance to be sampled and collected. Second, all existing sampling-based approaches stay in theory level and cannot be deployed on Commercial Off-The-Shelf (COTS) RFID devices, because the C1G2 standard does not support the sampling function at all. To deal with the above two limitations, this paper studies the new problem of differential tag sampling-letting each RFID tag be identified with a given sampling probability. In this paper, we use the COTS RFID devices including Impinj Speedway R420 reader and Monza 4QT tags to implement the Differential Tag Sampling (DTS) operation. Then, we apply probabilistic analytics on the collected tag data to address some practically important problems such as Multi-category Tag Cardinality Estimation (MTCE), and Value-based Missing Tag Detection (VMTD). Although the analytics results are not 100 percent accurate, the deviation in the results can be controlled below a small threshold and DTS can significantly improve the time-efficiency. DTS can be easily deployed on the COTS RFID systems, because it is totally compliant with the C1G2 standard. Extensive experiments demonstrate that DTS is able to let each tag take the given sampling probability to be sampled and identified. Moreover, the proposed DTS protocol can significantly reduce the execution time of MTCE and VMTD by nearly 70 percent than the FSA protocol.

Unknown RFID tags appear when tagged items are not scanned before being moved into a warehouse, which can even cause serious security issues. This paper studies the practically important problem of unknown tag detection. Existing solutions either require low-cost tags to perform complex operations or beget a long detection time. To this end, we propose the Collision-Seeking Detection (CSD) protocol, in which the server finds out a collision-seed to make massive known tags hash-collide in the last $N$ slots of a time frame with size $f$ . Thus, all the leading ${f-N}$ pre-empty slots become useful for detection of unknown tags. A challenging issue is that, computation cost for finding the collision-seed is very huge. Hence, we propose a supplementary protocol called Balanced Group Partition (BGP), which divides tag population into $n$ small groups. The group number $n$ is able to trade off between communication cost and computation cost. We also give theoretical analysis to investigate the parameters to ensure the required detection accuracy. The major advantages of our CSD+BGP are two-fold: (i) it only requires tags to perform lightweight operations, which are widely used in classical framed slotted Aloha algorithms. Thus, it is more suitable for low-cost tags; (ii) it is more time-efficient to detect the unknown tags. Simulation results reveal that CSD+BGP can ensure the required detection accuracy, meanwhile achieving $1.7\times $ speedup in the single-reader scenarios and $3.9\times $ speedup in the multi-reader scenarios than the state-of-the-art detection protocol.

Human respiration state is an important indicator to reflect health conditions. Recent advances in wireless human sensing have enabled device-free respiration monitoring using narrow-band wireless signals, which, however, fail to map the estimated respiration states to multiple persons. In this paper, we present Multi-Breath, a UWB-based system to achieve separate respiration monitoring for multiple persons. The UWB radar can accurately measure the travelling distance of the signals, which helps to separate the signals affected by different persons and map the detected respiration patterns to the corresponding persons with the location information. However, the radar signal time series of each person are quite noisy due to the multi-path effects caused by the respiration movements of other persons, making it difficult to accurately estimate the respiration state. To overcome this challenge, we propose to transform the UWB radar signal matrices of different persons as separate RGB images to reveal the respiration pattern of each individual. Then, the image processing operations, including image smoothing, edge detection, dilation and erosion, are applied to identify the breathing cycles. Finally, the respiration state, including the respiration rate and the presence of apnea, is estimated via blob detection and calibration. Extensive experiments show that the mean absolute error on respiration rate estimation is 0.3 - 0.6 bpm, and the percentage of missed and false detected apnea is 3% - 7%.

Sleep monitoring plays an important role in many medical applications, including SIDS prevention, care of patients with pressure ulcers, and assistance to patients with sleep apnea, where studies have shown that autonomous and continuous monitoring of sleep postures provides useful information for lowering health risk. Existing systems are designed based on electrocardiogram, cameras and pressure sensors, which are expensive to deploy, intrusive to privacy, or uncomfortable to use. This paper presents TagSheet, the first sleep monitoring system based on passive RFID tags, which provides a convenient, non-intrusive, and comfortable way of monitoring the sleeping postures. It does not require attaching any tag directly to a patient’s body. Tags are taped under a bed sheet. With a combination of hierarchical recognition, image processing and polynomial fitting, the proposed system identifies body postures based on the observed variation caused by the patient body to the backscattered signals from tags. The system does not require any personalized data training, making it plug-n-play in use. One additional advantage is that the system can also estimate the patient’s respiration rate. This is particularly helpful in assisting patients with sleep apnea. We have implemented a prototype system, and experiments show that the system performs posture identification with an accuracy up to 96.7% and in the meantime it measures the respiration rate with a small error of about 0.7 bpm (breath per minute).

High write throughput plays a vital role in improving the time efficiency of RFID-enabled applications, such as password update and over-the-air programming. In this paper, we make a fresh attempt to study the under-investigated problem of group write and discuss how to improve the write throughput in the commodity RFID system. By conducting extensive experiments, we reveal that the select operation specified by the C1G2 standard takes up the large overhead of a write cycle, which is the key factor in determining the write efficiency. Based on this finding, we propose an efficient write bundling (WB) scheme that bundles multiple writes up and executes them together in a burst mode, which greatly reduces the number of selects and thereby amplifies the write throughput. In WB, a carefully designed bit vector is assigned to each tag for connecting multiple tags into a run, such that a single select command is able to pick these tags concurrently. Besides, WB integrates multiple select commands into one by running a series of logic operations on the inventoried flags, which are used to indicate whether or not a tag is active. We implement WB in a commodity RFID system, with no need of any software modifications or hardware argument. Extensive experiment results show that WB is able to reduce the number of selects by 87.5% and produce a $2\times$ write amplification, compared with the C1G2-compatible exclusive write.

This paper studies the important sensory data collection problem in the sensor-augmented RFID systems, which is to quickly and accurately collect sensory data from a predefined set of target tags with the coexistence of unexpected tags. The existing RFID data collection schemes suffer from either low time-efficiency due to tag-collisions or serious data corruption issue due to interference of unexpected tags. To overcome these limitations, we propose the hierarchical-hashing data collection (HDC) protocol, which can not only significantly improve the utilization of RFID wireless communication channel by establishing bijective mapping between k target tags and the first k slots in time frame, but also effectively filter out the serious interference of unexpected tags. Although HDC has attractive advantages, the theoretical analysis reveals that the computation cost involved in it is as huge as O(k2k), where k is normally large in practice. By making some modifications to the basic HDC protocol, we propose the multi-framed hierarchical-hashing data collection (MHDC) protocol to effectively reduce the involved computation complexity. Unlike HDC that only issues a single time frame, MHDC uses multiple time frames to collaboratively collect sensory data from the k target tags. It can be understood as that a big computation task is disintegrated into multiple small pieces and then shared by multiple time frames. As a result, the computation cost involved in MHDC is reduced to O(k2n), where n ≪ k is the expected number of target tags that each time frame handles. Theoretical analysis is given to jointly consider the communication cost and computation cost thereby maximizing the overall time-efficiency of MHDC. Extensive simulation results reveal that the proposed MHDC protocol can correctly collect all sensory data and is always about more than 2× faster than the state-of-the-art RFID sensory data collection protocols.

This paper studies the practically important problem of range query for sensor-augmented RFID systems, which is to classify the target tags according to the ranges specified by the user. The existing RFID protocols that seem to address this problem suffer from either low time-efficiency or the information corruption issue. To overcome their limitations, we first propose a basic classification protocol called Range Query (RQ), in which each tag pseudo-randomly chooses a slot from the time frame and uses the ON-OFF Keying modulation to reply its range identifier. Then, RQ employs a collaborative decoding method to extract the tag range information from singleton and even collision slots. The numerical results reveal that the number of queried ranges significantly affects the performance of RQ. To optimize the number of queried ranges, we further propose the Partition&Mergence (PM) approach that consists of two steps, i.e., top-down partitioning and bottom-up merging. Sufficient theoretical analyses are proposed to optimize the involved parameters, thereby minimizing the time cost of RQ+PM or minimizing its energy cost. We can trade off between time cost and energy cost by adjusting the related parameters. The prominent advantages of the RQ+PM protocol over previous protocols are two-fold: (i) it is able to make use of the collision slots, which are treated as useless in previous protocols. Thus, frame utilization can be significantly improved; (ii) it is immune to the interference from unexpected tags, and does not suffer information corruption issue. We use USRP and WISP tags to conduct a set of experiments, which demonstrate the feasibility of RQ+PM. Extensive simulation results reveal that RQ+PM can ensure 100% query accuracy, and reduce the time cost as much as 40% when comparing with the state-of-the-art protocols.

With the increasing computation and storage capabilities of mobile devices, the concept of fog computing was proposed to tackle the high communication delay inherent in cloud computing, and also improve the security to some extent. This paper concerns with the privacy issue inherent in the sustainable fog computing platform. However, there is no universal solution to the privacy problem in fog computing due to the device heterogeneity. In this paper, we proposed a differential privacy-based query model for sustainable fog computing supported data center. We designed a method that can quantify the quality of privacy preserving through rigorous mathematical proof. The proposed method uses the query model to capture the structure information of the sustainable fog computing supported data center, and the datasets for the query result are mapped to real vectors. Then, we implemented the differential privacy preserving by injecting Laplacian noise. The experiment results demonstrated that the proposed method can effectively resist various popular privacy attacks, and achieve relatively high data utility under the premise of better privacy preserving.

People counting provides valuable information on population mobility and human dynamics, which plays a critical role for intelligent crowd control and retail management. Recently, people counting has been achieved via radio-frequency signals as human presence can influence the propagation of wireless signals, from which the information of the moving crowd can be extracted. However, most of the existing studies using wireless signals only apply to the scenario when people keep moving all the time. Besides, they require labour-intensive training phase for building the counting model. In the Wi-Count system, we take another approach, which is to count the people passing by the doorway with COTS WiFi devices. It can not only detect the passing direction, but also identify the number of people even when multiple persons pass by concurrently without regulating passing behavior and pre-trained counting model. The passing direction is recognized by modeling the effects of the bi-directional passing behavior on the phase difference of WiFi signals. In addition, the number of passing people is obtained through an enhanced signal separation algorithm for providing precise counting result. Extensive experiments show the average accuracy on passing direction detection and passing people counting are about 95% and 92% respectively.

This paper takes the first step in studying the problem of range query for sensor-augmented RFID systems, which is to classify the target tags according to the range of tag information. The related schemes that seem to address this problem suffer from either low time-efficiency or the information corruption issue. To overcome their limitations, we first propose a basic classification protocol called Range Query (RQ), in which each tag pseudo-randomly chooses a slot from the time frame and uses the ON-OFF Keying modulation to reply its range identifier. Then, RQ employs a collaborative decoding method to extract the tag information range from even collision slots. The numerical results reveal that the number of queried ranges significantly affects the performance of RQ. To optimize the number of queried ranges, we further propose the Partition&Mergence (PM) approach that consists of two steps, i.e., top-down partitioning and bottom-up merging. Sufficient theoretical analyses are proposed to optimize the involved parameters, thereby minimizing the time cost of RQ+PM. The prominent advantages of RQ+PM over previous schemes are two-fold: (i) it is able to make use of the collision slots, which are treated as useless in the previous schemes; (ii) it is immune to the interference from unexpected tags. We use the USRP and WISP tags to conduct a set of experiments, which demonstrate the feasibility of RQ+PM. Moreover, extensive simulation results reveal that RQ+PM can ensure 100% query accuracy, meanwhile reducing the time cost as much as 40% comparing with the existing schemes.

The widely used RFID systems are vulnerable to the denial-of-service (DoS) attacks launched by malicious blocker tags. This paper studies how to quickly and completely identify the valid RFID tags that are blocked. The existing work that can seemingly address this problem suffers from either low time-efficiency or serious false positives. This paper proposes a hybrid approach that consists of two complementary component protocols, namelyAloha Filtering(AF) andPoll&Listen(PL).AFis fast but inaccurate, whilePLis accurate but slow. Taking the merit of each protocol, our hybrid approach is to first repeat the fastAFfor multiple rounds to quickly filter out the target tags that are definitely not blocked. Then, on the size-reduced remaining set that just contains a small number of suspicious tags, we invoke the accuratePLto verify the intactness of each suspicious tag with 100 percent confidence. We optimize the round count ofAFthat trades off between the time costs ofAFandPLto minimize the total time ofAF+PL. As required in the optimization process, we need to know the size of the blocked tag set and that of the unknown tag set, which, however, are not known in advance. To estimate these two set sizes, we propose a supplementary protocol calledSimultaneous Estimation of the Blocked tag size and the Unknown tag size(SEBU). The key advantages of our approach over the prior art are four-fold. First, unlike the detection protocol that just discovers the existence of blocking attacks, our approach exactly identifies all the blocked target tags. Second, our approach is compliant with the C1G2 standard, and does not require any modifications to be made to the commercial RFID tags. It only needs to be installed on readers as a software module. Third, our approach does not involve any false positives. Finally, our approach significantly reduces the execution time when compared with the state-of-the-art schemes that can completely identify the blocked tags.

With the ubiquitous deployment of the mobile devices with increasingly better communication and computation capabilities, an emerging model called spatial crowdsourcing is proposed to solve the problem of unstructured big data by publishing location-based tasks to participating workers. However, massive spatial data generated by spatial crowdsourcing entails a critical challenge that the system has to guarantee quality control of crowdsourcing. This paper first studies a practical problem of task assignment, namely reliability aware spatial crowdsourcing (RA-SC), which takes the constrained tasks and numerous dynamic workers into consideration. Specifically, the worker confidence is introduced to reflect the completion reliability of the assigned task. Our RA-SC problem is to perform task assignments such that the reliability under budget constraints is maximized. Then, we reveal the typical property of the proposed problem, and design an effective strategy to achieve a high reliability of the task assignment. Besides the theoretical analysis, extensive experimental results also demonstrate that the proposed strategy is stable and effective for spatial crowdsourcing.

Summary In wireless sensor networks, a lot of applications need the sensed information be transmitted to the sink node within a predefined time threshold. So end‐to‐end delay is an important performance metric in wireless sensor networks. Opportunistic routing protocols have been proposed to reduce the waiting delay. In the duty cycle networks, increasing the duty cycle ratio can also reduce the end‐to‐end delay. However, this method will consume more energy. It is obvious that there exists a trade‐off between delay and energy consumption. So adjusting the duty cycle ratio of each node can investigate this trade‐off. To the best of our knowledge, no existing work takes both of end‐to‐end delay and energy efficiency into consideration in the opportunistic routing networks. In this paper, we want to minimize the whole energy consumption while guaranteeing the expected end‐to‐end delay between the source nodes and the sink node is below the given threshold. To deal with this problem, we propose a dynamic duty cycle scheme which can significantly reduce the energy consumption and guarantee the expected end‐to‐end delay demand in the opportunistic routing network. To be specific, firstly, we formulate a new metric with the wake‐up time slots as the variable to measure the end‐to‐end delay. Secondly, for simplifying the complex problem, we decompose it into a set of single‐hop delay guarantee problems. Feedback controller has been used to solve the problem. We also analyze the influence of the multiple receivers in the same forwarding set. Finally, we conduct extensive simulations to evaluate the performance of the proposed algorithm. The experimental results reveal that our scheme can guarantee the delay requirement, meanwhile, significantly reduce the energy consumption compared with prior schemes.

For large-scale RFID systems, this paper studies the practically important problem of target tag information collection, which aims at collecting information from a specific set of target tags instead of all. However, the existing solutions are of low time-efficiency because of two reasons. First, the serious collisions among tags due to hashing randomness seriously reduce the frame utilization, whose upper bound is just 36.8 percent. Second, they cannot efficiently distinguish the target tags from the non-target tags and thus inevitably collect a lot of irrelevant information on non-target tags, which further deteriorates the effective utilization of the time frame. To overcome the above two drawbacks, this paper proposes the minimal Perfect hashing-based Information Collection (PIC) protocol, which first leverages lightweight indicator vectors to establish a one-to-one mapping between target tags and slots, thereby improving the frame utilization to nearly 100 percent; and then uses the novel data structure called Minimal Perfect Hashing based Filter (MPHF) to filter out the non-target tags, thereby preventing them from interfering with the process of collecting information from target tags. Sufficient theoretical analyses are also presented in this paper to minimize the execution time of the proposed PIC protocol. Extensive simulations are conducted to compare the proposed PIC protocol with prior works side-by-side. The simulation results demonstrate that PIC significantly outperforms the state-of-the-art protocols in terms of time-efficiency.

For categorized RFID systems, this paper studies the practically important problem of top-k queries, which is to find the top-k smallest and (or) the top-k largest categories, as well as the sizes of such categories. In this paper, we propose a Top-k Query (TKQ) protocol and two supplementary techniques called segmented perfect hashing (SPH) and switching to framed slotted aloha (STA) for optimizing TKQ. First, TKQ lets each tag choose a time slot to respond to the reader with a single-one geometric string using the ON-OFF Keying modulation. TKQ leverages the length of continuous leading 1 s in the combined signal to estimate the corresponding category size. TKQ can quickly eliminate most categories whose sizes are significantly different from the top-k boundary, and only needs to perform accurate estimation on a limited number of categories that may be within the top-k set. We conduct rigorous analysis to guarantee the predefined accuracy constraints on the query results. Second, to alleviate the low frame utilization of TKQ, we propose the SPH scheme, which improves its average frame utilization from 36.8% to nearly 100% by establishing a bijective mapping between tag categories and slots. To minimize the overall time cost, we optimize the key parameter that trades off between communication cost and computation cost. Third, we observed from the simulation traces that TKQ+SPH pays most execution time on querying a small number of remaining categories whose sizes are close to the top-k boundary, which sometimes even exceeds the time cost for precisely identifying these remaining tags. Motivated by this observation, we propose the STA scheme to dynamically determine when we should terminate TKQ+SPH and switch to use FSA to finish the rest of top-k query. Experimental results show that TKQ+SPH+STA not only achieves the required accuracy constraints, but also achieves several times faster speed than the existing protocols.

This paper concerns the practically important problem of multi-category radio frequency identification (RFID) estimation: given a set of RFID tags, we want to quickly and accurately estimate the number of tags in each category. However, almost all the existing RFID estimation protocols are dedicated to the estimation problem on a single set, regardless of tag categories. A feasible solution is to separately execute the existing estimation protocols on each category. The execution time of such a serial solution is proportional to the number of categories, and cannot satisfy the delay-stringent application scenarios. Simultaneous RIFD estimation over multiple categories is desirable, and hence, this paper proposes an approach called simultaneous estimation for multi-category RFID systems (SEM). SEM exploits the Manchester-coding mechanism, which is supported by the ISO 18000-6 RFID standard, to decode the combined signals, thereby simultaneously obtaining the reply status of tags from each category. As a result, multiple bit vectors are decoded from just one physical slotted frame. Built on our SEM, many existing excellent estimation protocols can be used to estimate the tag cardinality of each category in a simultaneous manner. To ensure the predefined accuracy, we calculate the variance of the estimate in one round, as well as the variance of the average estimate in multiple rounds. To find the optimal frame size, we propose an efficient binary search-based algorithm. To address significant variance in category sizes, we propose an adaptive partitioning (AP) strategy to group categories of similar sizes together and execute the estimation protocol for each group separately. Compared with the existing protocols, our approach is much faster, meanwhile satisfying the predefined estimation accuracy. For example, with 20 categories, the proposed SEM+AP is about seven times faster than prior estimation schemes. Moreover, our approach is the only one whose normalized estimation time (i.e., time per category) decreases as the number of categories increases.

With the increasing popularization of radio frequency identification (RFID) technology in the retail and logistics industry, RFID privacy concern has attracted much attention, because a tag responds to queries from readers no matter they are authorized or not. An effective solution is to use a commercially available blocker tag that behaves as if a set of tags with known blocking IDs are present. However, the use of blocker tags makes the classical RFID estimation problem much more challenging, as some genuine tag IDs are covered by the blocker tag and some are not. In this paper, we propose RFID estimation scheme with blocker tags (REB), the first RFID estimation scheme with the presence of blocker tags. REB uses the framed slotted Aloha protocol specified in the EPC C1G2 standard. For each round of the Aloha protocol, REB first executes the protocol on the genuine tags and the blocker tag, and then virtually executes the protocol on the known blocking IDs using the same Aloha protocol parameters. REB conducts statistical inference from the two sets of responses and estimates the number of genuine tags. Rigorous theoretical analysis of parameter settings is proposed to guarantee the required estimation accuracy, meanwhile minimizing the time cost and energy cost of REB. We also reveal a fundamental tradeoff between the time cost and energy cost of REB, which can be flexibly adjusted by the users according to the practical requirements. Extensive experimental results reveal that REB significantly outperforms the state-of-the-art identification protocols in terms of both time efficiency and energy efficiency.

In large-scale radio frequency identification (RFID)-enabled applications, we sometimes only pay attention to a small set of key tags, instead of all. This paper studies the problem of key tag population tracking, which aims at estimating how many key tags in a given set exist in the current RFID system and how many of them are absent. Previous work is slow to solve this problem due to the serious interference replies from a large number of ordinary (i.e., non-key) tags. However, time-efficiency is a crucial metric to the studied key tag tracking problem. In this paper, we propose a singleton slot-based estimator, which is time-efficient, because the RFID reader only needs to observe the status change of expected singleton slots corresponding to key tags instead of the whole time frame. In practice, the ratio of key tags to all current tags is small, because key members are usually rare. As a result, even when the whole time frame is long, the number of expected singleton slots is limited and the running of our protocol is very fast. To obtain good scalability in large-scale RFID systems, we exploit the sampling idea in the estimation process. A rigorous theoretical analysis shows that the proposed protocol can provide guaranteed estimation accuracy to end users. Extensive simulation results demonstrate that our scheme outperforms the prior protocols by significantly reducing the time cost.

This paper studies the practically important problem of top-k queries, which is to find the top k largest categories and their corresponding sizes. In this paper, we propose a Top-k Query (TKQ) protocol and a technique that we call Segmented Perfect Hashing (SPH) for optimizing TKQ. Specifically, TKQ is based on the framed slotted Aloha protocol. Each tag responds to the reader with a Single-One Geometric (SOG) string using the ON-OFF Keying modulation. TKQ leverages the length of continuous leading 1s in the combined signal to estimate the corresponding category size. TKQ can quickly eliminate the sufficiently small categories, and only needs to focus on a limited number of large-size categories that require more accurate estimation. We conduct rigorous analysis to guarantee the predefined accuracy constraints. To further improve time-efficiency, we propose the SPH scheme, which improves the average frame utilization of TKQ from 36.8% to nearly 100% by establishing a bijective mapping between tag categories and slots. To minimize the overall time cost, we optimize the key parameter that trades off between communication cost and computation cost. Experimental results show that our TKQ+SPH protocol not only achieves the required accuracy constraints, but also achieves a 2.6~7x faster speed than the existing protocols.

This paper studies the problem of data collection in sensor-augmented RFID networks: how to quickly obtain the error-bounded data from sensor-augmented RFID tags. Existing data collection protocols require each tag to transmit the sensor data to the reader through a low-rate channel. However, in large-scale RFID system, they take too long time and block other time-sensitive operations. By exploring the correlation of sensor data, our Sampling-based Information Collection (SIC) protocol significantly reduces the number of responding tags. Specifically, SIC obtains an error bound based on the estimation model by using some randomly-sampled data. The error bound is expected to maximize the number of data within it. These data can be seen as a cluster and be approximated by one value within the error bound. Then, SIC only needs to collect the data of out this cluster, thereby significantly reducing the data transmission. It minimizes the execution time by optimizing the sample size and estimating the number of tags out of the error bound. We conduct extensive simulations to evaluate the performance of SIC and compare it with three major related work. The results demonstrate that SIC is 1 to 10 times faster than the state-of-the-art solution.

In Wireless Sensor Networks (WSNs), end-to-end delay is an important metric because the sensed information is necessary to be transmitted to the sink node within a predefined time threshold. Therefore, opportunistic routing protocols are proposed to reduce the end-to-end delay. As a matter of fact, increasing the number of wake-up slots will certainly reduce the transmission delay, however, also consumes more energy. Hence, it is interesting to control the number of wake-up slots to investigate the trade-off between the end-to-end delay and the energy-efficiency. To the best of our knowledge, no existing work takes both of end-to-end delay and energy-efficiency into consideration in the opportunistic routing networks. Therefore, this paper studies how to minimize the energy-consumption while guaranteeing that the expected end-to-end delay is below a given threshold. To solve this problem, we propose an energy-based Dynamic Duty Cycle Scheme(D2CS) in opportunistic routing network. Specifically, we first present an analytical model to measure the expected end-to-end delay. Then, we decompose the studied problem into a set of single-hop delay guarantee problems and using the feedback controller to approximate the optimal solution. Finally, extensive simulations are conducted to evaluate the performance of the proposed D2CS algorithm. The experimental results reveal that our D2CS can guarantee the delay requirement, meanwhile, significantly reduce the energy consumption compared with prior schemes.

The widely used RFID tags impose serious privacy concerns as a tag responds to queries from readers no matter they are authorized or not. The common solution is to use a commercially available blocker tag which behaves as if a set of tags with known blocking IDs are present. The use of blocker tags makes RFID estimation much more challenging as some genuine tag IDs are covered by the blocker tag and some are not. In this paper, we propose REB, the first RFID estimation scheme with the presence of blocker tags. REB uses the framed slotted Aloha protocol specified in the C1G2 standard. For each round of the Aloha protocol, REB first executes the protocol on the genuine tags and the blocker tag, and then virtually executes the protocol on the known blocking IDs using the same Aloha protocol parameters. The basic idea of REB is to conduct statistically inference from the two sets of responses and estimate the number of genuine tags. We conduct extensive simulations to evaluate the performance of REB, in terms of time-efficiency and estimation reliability. The experimental results reveal that our REB scheme runs tens of times faster than the fastest identification protocol with the same accuracy requirement.

Radio Frequency Identification (RFID) technology has been widely used in inventory management in many scenarios, e.g., warehouses, retail stores, hospitals, etc. This paper investigates a challenging problem of complete identification of missing tags in large-scale RFID systems. Although this problem has attracted extensive attention from academy and industry, the existing work can hardly satisfy the stringent real-time requirements. In this paper, a Slot Filter-based Missing Tag Identification (SFMTI) protocol is proposed to reconcile some expected collision slots into singleton slots and filter out the expected empty slots as well as the unreconcilable collision slots, thereby achieving the improved time-efficiency. The theoretical analysis is conducted to minimize the execution time of the proposed SFMTI. We then propose a cost-effective method to extend SFMTI to the multi-reader scenarios. The extensive simulation experiments and performance results demonstrate that the proposed SFMTI protocol outperforms the most promising Iterative ID-free Protocol (IIP) by reducing nearly 45% of the required execution time, and is just within a factor of 1.18 from the lower bound of the minimum execution time.

Unknown RFID tags appear when the unread tagged objects are moved in or tagged objects are misplaced. This paper studies the practically important problem of unknown tag detection while taking both time-efficiency and energy-efficiency of battery-powered active tags into consideration. We first propose a Sampling Bloom Filter which generalizes the standard Bloom Filter. Using the new filtering technique, we propose the Sampling Bloom Filter-based Unknown tag Detection Protocol (SBF-UDP), whose detection accuracy is tunable by the end users. We present the theoretical analysis to minimize the time and energy costs. SBF-UDP can be tuned to either the time-saving mode or the energy-saving mode, according to the specific requirements. Extensive simulations are conducted to evaluate the performance of the proposed protocol. The experimental results show that SBF-UDP considerably outperforms the previous related protocols in terms of both time-efficiency and energy-efficiency. For example, when 3 or more unknown tags appear in the RFID system with 30000 known tags, the proposed SBF-UDP is able to successfully report the existence of unknown tags with a confidence more than 99%. While our protocol runs 9 times faster than the fastest existing scheme and reducing the energy consumption by more than 80%.

RFID is an emerging technology that provides timely and high-value information to inventory management and object tracking, in which areas that identifying unknown tags completely is crucial. From prior researches in this area, one of the pending problem involves processing redundant time frames due to unknown tag collisions. In this paper, we propose a time-efficient unknown tag identification protocol based on coded filtering vector technique. This vector is able to efficiently separate unknown tags from known tags. It reduces the unknown-known tag collisions as well as the required time frame length. The proposed protocol can achieve the minimal execution time theoretically. And further simulations demonstrate that it performs much better than existing work by decreasing 30% of the total execution time on average.

In RFID-enabled applications, we may pay more attention to key tags instead of all tags. This paper studies the problem of key tag counting, which aims at estimating how many key tags in a given set exist in the current RFID system. Previous work is slow to solve this new problem because of the serious interference replies from the large number of ordinary (i.e., Nonkey) tags. However, time-efficiency is an important metric for the fast tag cardinality estimation in a large-scale RFID system. In this paper, we propose a singleton slot-based estimator, which is time-efficient because the RFID reader only needs to observe the status change of expected singleton slots of key tags instead of the whole time frame. In practice, the ratio of key tags to all current tags is small for "key" members should be rare. As a result, even when the whole time frame is long, the expected singleton slot number is limited and the running of our protocol is fast to achieve estimation accuracy. Rigorous theoretical analysis shows that the proposed protocol can provide guaranteed estimation accuracy to end users. We conduct simulations and implement a prototype of our protocol to verify its efficiency and deployability.

Owing to its superior properties, such as fast identification and relatively long interrogating range over barcode systems, Radio Frequency Identification (RFID) technology has promising application prospects in inventory management. This paper studies the problem of complete identification of missing RFID tag, which is important in practice. Time efficiency is the key performance metric of missing tag identification. However, the existing protocols are ineffective in terms of execution time and can hardly satisfy the requirements of realtime applications. In this paper, a Multi-hashing based Missing Tag Identification (MMTI) protocol is proposed, which achieves better time efficiency by improving the utilization of the time frame used for identification. Specifically, the reader recursively sends bitmaps that reflect the current slot occupation state to guide the slot selection of the next hashing process, thereby changing more empty or collision slots to the expected singleton slots. We investigate the optimal parameter settings to maximize the performance of the MMTI protocol. Furthermore, we discuss the case of channel error and propose the countermeasures to make the MMTI workable in the scenarios with imperfect communication channels. Extensive simulation experiments are conducted to evaluate the performance of MMTI, and the results demonstrate that this new protocol significantly outperforms other related protocols reported in the current literature.

Owing to its attractive features such as fast identification and relatively long interrogating range over the classical barcode systems, radio-frequency identification (RFID) technology possesses a promising prospect in many practical applications such as inventory control and supply chain management. However, unknown tags appear in RFID systems when the tagged objects are misplaced or unregistered tagged objects are moved in, which often causes huge economic losses. This paper addresses an important and challenging problem of unknown tag identification in large-scale RFID systems. The existing protocols leverage the Aloha-like schemes to distinguish the unknown tags from known tags at the slot level, which are of low time-efficiency, and thus can hardly satisfy the delay-sensitive applications. To fill in this gap, two filtering-based protocols (at the bit level) are proposed in this paper to address the problem of unknown tag identification efficiently. Theoretical analysis of the protocol parameters is performed to minimize the execution time of the proposed protocols. Extensive simulation experiments are conducted to evaluate the performance of the protocols. The results demonstrate that the proposed protocols significantly outperform the currently most promising protocols.

Wireless sensor networks (WSNs) receive a lot of attention because of their great potential in monitoring the physical or environmental conditions of military, industry, and civilian. Moreover, mobile cloud computing (MCC) is widely focused, as they can greatly alleviate the hardware limit of mobile devices as well as enable a lot of new mobile applications. All these make the integration of WSNs and MCC a very hot research topic. In this paper, we first observe a context non-awareness issue between mobile user and WSNs, which affects the mobile user obtaining the desirable data when integrating WSNs and MCC. Then focusing on solving the context non-awareness issue to provide desirable data to mobile users, we propose a novel framework for integrating WSNs and MCC. The proposed framework performs data recommendation, data prediction as well as data traffic monitoring in the cloud to obtain the data feature information required by the mobile users and potential status of WSNs. Then these user data feature information and potential WSNs status information are utilized to optimize the deployment of WSNs and check the status of WSNs. This could in turn offer the desirable data to the mobile users. Extensive evaluations also validate the effectiveness of the proposed framework.

Radio Frequency Identification (RFID) technology has been widely applied in many scenarios such as inventory control, supply chain management due to its superior properties including fast identification and relatively long interrogating range over barcode systems. It is critical to efficiently identify the unknown tags because these tags can appear when new tagged objects are moved in or wrongly placed. The state-of-the-art Basic Unknown tag Identification Protocol-with Collision-Fresh slot paring (BUIP-CF) protocol can first deactivate all the known tags and then collect all the unknown tags. However, BUIP-CF protocol investigates an ALOHA-like technique and causes too many tag responses, which results in low efficiency. This paper proposes a Fast Unknown tag Identification (FUI) protocol which investigates an indicator vector to label the unknown tags with a given accuracy and removes the time-consuming tag responses in the deactivation phase. FUI also adopts the classical Enhanced Dynamic Framed Slotted ALOHA (EDFSA) protocol to collect the labeled unknown tags. We then investigate the optimal parameter settings to maximize the performance of the proposed FUI protocol. Extensive simulation experiments are conducted to evaluate the performance of the proposed FUI protocol and the experimental results show that it considerably outperforms the state-of-the-art protocol.

Radio Frequency Identification (RFID) technology is widely used in the the retail, warehouse and supply chain management. However, unknown RFID tags appear when the unregistered tagged objects are moved in or tagged objects are misplaced, which leads to huge economic losses (e.g., misplaced chilled food in a warehouse may quickly decay). This paper studies the practically important problem of unknown tag detection. To the best of our knowledge, this is the first piece of work taking both time-efficiency and energy-efficiency into consideration, where the energy-efficiency is very important when the battery-powered active tags are used. This paper proposes two efficient protocols to address the problem of unknown tag detection. Specifically, the Basic Unknown Tag Detection (B-UTD) protocol leverages a cost-effective filter vector to detect the unknown tags, based on which we then propose a Sampling based Unknown Tag Detection (SUTD) protocol by adopting the well-known sampling idea. We present theoretical analysis to optimize the performance of the proposed protocols. Extensive simulations are conducted to evaluate the performance of the proposed protocols. And the experimental results show that the proposed S-UTD protocol considerably outperforms the most related protocol by reducing more than 90% of the required execution time and energy consumption.