In open real-world autonomous driving scenarios, challenges such as sensor failure and extreme weather hinder the generalization of current autonomous driving perception models to these unseen domain, due to the domain shifts between the test and training data. As the parameter scale of autonomous driving perception models grows, traditional test-time adaptation (TTA) methods become unstable and often degrade model performance in most scenarios. To address these challenges, this paper proposes two new robust methods to improve the Batch Normalization with TTA for object detection in autonomous driving: (1) We introduce a new LearnableBN layer based on Geometric Confidence Maximization and Entropy Minimization. Specifically, we modify the traditional BN layer by incorporating auxiliary learnable parameters, which enables the BN layer to dynamically update the statistics according to the different input data. (2) We propose a novel semantic-consistency based dual-stage adaptation strategy, which encourages the model to iteratively search for the optimal solution and eliminates unstable samples during the adaptation process. Extensive experiments on the NuScenes-C dataset shows that our method achieves a maximum improvement of about 10\% using BEVFormer as the baseline across six corruption types and three levels of severity.

This paper presents TurboInfer, a system that enables power-efficient LLM inference on mobile devices. The core insight behind TurboInfer is that while LLMs are power-intensive due to their heavy computational demands, the model inference experiences unavoidable GPU stalls caused by tensor preparation for subsequent kernel executions at run-time. These GPU stalls arise from the unique host-controlled execution pipeline tailored to mobile phones and the significant DRAM access contention inherent to the shared memory architecture of mobile System-on-chips (SoCs). With LLM inference requiring hundreds to thousands of kernel executions, these short but frequent GPU stalls accumulate, accounting for over 74% of the token generation latency.

Wi-Fi computational imaging has emerged as a promising paradigm for non-intrusive sensing; however, its practical deployment is severely hindered by dense physical multipath clutter, hardware phase quantization errors, and the limited bandwidth of commercial Wi-Fi. Traditional beamforming-based algorithms often completely lose target focus in complex environments, resulting in severe ghosting artifacts. To overcome these fundamental limitations, we propose RISimg, a novel robust Wi-Fi imaging framework empowered by the spatiotemporal coding of Reconfigurable Intelligent Surface. We design a differential coding strategy to guarantee a well-conditioned and noise-robust sensing matrix, and propose a multi-frequency sparse reconstruction algorithm based on the Least Absolute Shrinkage and Selection Operator. By constructing a large-scale overdetermined system, this physics-driven approach effectively suppresses multipath interference and hardware errors, successfully recovering the basic morphological outlines of complex targets. To further enhance the imaging performance, we propose leveraging a Conditional Diffusion Model to refine the imaging results. By utilizing the reconstruction as a structural prior, a carefully designed Conditional U-Net progressively refines the image through a generative reverse sampling process, restoring high-fidelity continuous boundaries. Extensive evaluations using a hardware prototype built with commercial Wi-Fi devices and a low-cost metasurface demonstrate that the final cascaded CDM achieves an unprecedented Structural Similarity Index Measure of 0.9192 and a Spatial Correlation Coefficient of 0.7336, paving a robust new avenue for Wi-Fi imaging.

Deploying Tiny Deep Learning (TinyDL) on Internet of Things (IoT) devices is gaining popularity. To accommodate the limited memory, recent methods split tensors into fine-grained parts and plan memory offline to minimize its footprint. However, they fail to adapt to dynamic memory, missing the opportunity to utilize temporarily available memory for faster inference. Additionally, existing approaches focus solely on minimizing memory size while neglecting cache usage characteristics, resulting in frequent cache misses and increased latency. In this paper, we propose RAMS, an efficient framework supporting runtime adaptive memory scaling to fully utilize the dynamic memory. We also propose a cache-friendly memory management approach that minimizes cache miss times. RAMS includes an offline planner to minimize the memory footprint essential for inference and an online manager to determine memory sizes and generate layouts for size-controllable tensors based on available memory. RAMS significantly reduces inference latency while maintaining a compact memory footprint. Extensive experiments on commercial devices running RTOS and Android systems demonstrate that, compared to the state-of-the-art methods, RAMS can efficiently reduce latency by up to 1.57× and 1.48× compared to TFLM and TinyTS, respectively using a comparable memory footprint, while reducing power consumption by 67.74% and 15.97%.

With the explosive increase in wireless devices, enabling sensing between incompatible radios has become critically beneficial. Integrating diverse IoT devices enhances sensing accuracy by providing richer data, while utilizing the diverse characteristics of heterogeneous signals meets sensing needs in complex environments. However, most existing wireless sensing methods primarily focus on homogeneous signals, while research on sensing with heterogeneous signals is still in its infancy. In this paper, we proposeCrossSense, a novel Cross-Technology Sensing (CTS) framework that enables sensing between incompatible WiFi and LoRa device.CrossSenserecovers the fine-grained trajectory of a WiFi transmitter based on its emulated LoRa signals. To decompose the motion feature components of WiFi transmitter, we develop a chirp difference vector model that utilizes the energy peak within each chirp window for sensing. We model the relationship between sampling frequency offsets and oscillation frequency offsets among heterogeneous devices to guide the extraction of motion features from the emulated signal. We also propose a greedy-based peak enhancement method to calculate the optimized LoRa phases, minimizing the impact of phase discontinuity caused by cyclic prefix (CP) errors. We implement a prototype ofCrossSenseon the USRP platform. The extensive experiments demonstrate thatCrossSensecan achieve an efficient Cross-Technology Sensing with$2.92cm$distance accuracy and$0.26cm/s$speed accuracy over a$120m$sensing range.

Short-video platforms have grown rapidly by allowing users to browse rich media content through seamless swiping. However, the inherently random nature of swipe behavior creates significant challenges for bandwidth efficiency and playback continuity, often resulting in stalls and unnecessary data transfers. We present OffLoad, a new preloading framework that enhances bandwidth efficiency and playback quality using range-based downloading, which generalizes traditional chunk-based preloading to arbitrary-length segments for finer-grained control. At the core of OffLoad is a two-dimensional watch-time estimation model that jointly captures user preferences and video characteristics. Guided by this estimator, OffLoad introduces a hybrid preloading algorithm that integrates heuristic rules with a learning-based module trained directly on large-scale production data, enabling strong generalization in deployment. Following extensive system-level optimization, OffLoad has been deployed on a commercial short-video platform for more than six months. Our A/B testing results show that OffLoad increases overall user watch-time by 1.1‰, while simultaneously reducing 0.13% rebuffering events and 4.92% of bandwidth consumption.

Millimeter-wave (mmWave) communication technology with high throughput and high reliability attracts much attention in both academic and industrial fields. This technology plays a pivotal role in next-generation communication networks, offering promising solutions for high-speed data transfer. Localization of mobile mmWave communication devices is essential in this context, as it can effectively guide mmWave beam steering, thereby eliminating the need for cumbersome beam alignment processes. However, existing approaches for commercial mmWave communication devices suffer channel state fluctuations and can only work on static devices. To provide accurate localization for mobile mmWave devices, we propose MobiLoc , a neural network-based approach to enhance localization accuracy in mobile scenarios. Our method leverages Channel Frequency Response (CFR) and the angular spectrum for assistance to determine the positions. We first analyze the feasibility of classifying sensing data into different qualities. Then we implement a neural network architecture specifically designed to identify the sensing data with high quality. The effectiveness of our approach is demonstrated through comprehensive experiments conducted on commercial off-the-shelf (COTS) mmWave communication devices. Results show that MobiLoc can increase the localization accuracy significantly and reduce the median angle estimation error of mobile devices to 1.33ˆ with only single items of CFR measurements.

Understanding the traffic scenes and then generating highdefinition (HD) maps present significant challenges in autonomous driving. In this paper, we defined a novel Traffic Topology Scene Graph (T2SG), a unified scene graph explicitly modeling the lane, controlled and guided by different road signals (e.g., right turn), and topology relationships among them, which is always ignored by previous high-definition (HD) mapping methods. For the generation of T2SG, we propose TopoFormer, a novel one- stage Topology Scene Graph TransFormer with two newly-designed layers. Specifically, TopoFormer incorporates a Lane Aggregation Layer (LAL) that leverages the geometric distance among the centerline of lanes to guide the aggregation of global information. Furthermore, we proposed a Counterfactual Intervention Layer (CIL) to model the reasonable road structure (e.g., intersection, straight) among lanes under counterfactual intervention. Then the generated T2SG can provide a more accurate and explainable description of the topological structure in traffic scenes. Experimental results demonstrate that TopoFormer outperforms existing methods on the T2SG generation task, and the generated T2SG significantly enhances traffic topology reasoning in downstream tasks, achieving a state-of-the-art performance of 46.3 OLS on the OpenLane-V2 benchmark. Our source code is available at https://github.com/MICLAB-BUPT/T2SG.

Subsea Internet of Things (IoT) networks have rapidly developed but still suffer the inefficient underwater-toair communication. Due to wireless signals exhibiting different properties in different media, it is difficult to use any single modality of signal for cross-medium communication. In this paper, we propose Exocoetus, a novel passive relay based water-to-air communication system. By taking advantage of the out-of-specification characteristics of the RF switch, Exocoetus can trigger acoustic-to-RF conversion even if the input voltage is below the standard threshold and use a clamp circuit to further maximize the efficiency of the acoustic-to-RF signal conversion. We design a dual-capacitor circuit-based pulse position modulation method to amplify the acoustic signals emitted by powerconstrained underwater nodes, ensuring reliable communication over greater distances. We implement a prototype of Exocoetus and evaluate its performance in the real environment. The results show that Exocoetus can achieve a communication distance of 6 meters above water and$\mathbf{1.25}$meters underwater.

While deep reinforcement learning (DRL)-based congestion control (CC) algorithms outperform traditional TCP methods in dynamic networks, their robustness is challenged by the complex influence caused by state perturbations from competing flows. Existing DRL-based CC models, constrained by their black-box nature, often suffer performance degradation under dynamic network conditions, which may indirectly alter their observations. To address this challenge, we propose Mona, an adversarial attack framework that explores vulnerabilities in existing DRL-based CC schemes while helping improve their robustness. Leveraging a dual-Critic architecture and a global state representation, Mona enables centralized training with an explicit estimation mechanism embedded in the reward function, facilitating targeted attacks without direct access to the victim’s state. Based on this design, Mona can be distributedly deployed to indirectly perturb the victim flow’s observations by injecting a stealthy flow into the bottleneck link, ultimately inducing suboptimal decisions. Experiments demonstrate Mona’s effectiveness across diverse CC models, reducing victim flow throughput by 8.69%–26.67% under various network conditions in both simulated environments and real-world network deployments, and we conduct a preliminary exploration of defense strategies through a minimax-based adversarial training framework that improves model robustness by 20%.

Short video streaming has become a dominant paradigm in digital media, characterized by rapid swiping interactions and diverse media content. A key technical challenge is designing an effective preloading strategy that dynamically selects and prioritizes download tasks from an evolving playlist, balancing Quality of Experience (QoE) and bandwidth efficiency under practical commercial constraints. However, real-world analysis reveals critical limitations of existing approaches: (1) insufficient adaptation of download task sizes to dynamic conditions, and (2) watch-time prediction models that are difficult to deploy reliably at scale. In this paper, we propose DeLoad, a novel preloading framework that addresses these issues by introducing dynamic task sizing and a practical, multi-dimensional watch-time estimation method. Additionally, a Deep Reinforcement Learning (DRL)-enhanced agent is trained to optimize the download range decisions adaptively. Extensive evaluations conducted on an offline testing platform, leveraging massive real-world network data, demonstrate that DeLoad achieves significant improvements in QoE metrics (34.4%-87.4% gain). Furthermore, after deployment on a large-scale commercial short-video platform, DeLoad has increased overall user watch-time by 0.9‰ while simultaneously reducing rebuffering events and 3.76% bandwidth consumption.

In large-scale short-video platforms, CDN resource selection plays a critical role in maintaining users' Quality of Experience (QoE) while controlling escalating traffic costs. To better understand this phenomenon, we conduct in-the-wild network measurements during video playback in a production short-video system. The results reveal that CDNs delivering higher average QoE often come at greater financial cost, yet their connection quality fluctuates even within a single video-underscoring a fundamental and dynamic trade-off between QoE and cost. However, the problem of sustaining high QoE under cost constraints remains insufficiently investigated in the context of CDN selection for short-video streaming. To address this, we propose PIRA, a dynamic resource selection algorithm that optimizes QoE and cost in real-time during video playback. PIRA formally integrating QoE and cost by a mathematical model, and introduce a intra-video control-theoretic CDN resource selection approach which can balance QoE and cost under network dynamics. To reduce the computation overheads, PIRA employs state-space pruning and adaptive parameter adjustment to efficiently solve the high-dimensional optimization problem. In large-scale production experiments involving 450,000 users over two weeks, PIRA outperforms the production baseline, achieving a 2.1% reduction in start-up delay, 15.2% shorter rebuffering time, and 10% lower average unit traffic cost, demonstrating its effectiveness in balancing user experience and financial cost at scale.

In this work, we study how vision-language models (VLMs) can be utilized to enhance the safety for the autonomous driving system, including perception, situational understanding, and path planning. However, existing research has largely overlooked the evaluation of these models in traffic safety-critical driving scenarios. To bridge this gap, we create the benchmark (SafeDrive228K) and propose a new baseline based on VLM with knowledge graph-based retrieval-augmented generation (SafeDriveRAG) for visual question answering (VQA). Specifically, we introduce SafeDrive228K, the first large-scale multimodal question-answering benchmark comprising 228K examples across 18 sub-tasks. This benchmark encompasses a diverse range of traffic safety queries, from traffic accidents and corner cases to common safety knowledge, enabling a thorough assessment of the comprehension and reasoning abilities of the models. Furthermore, we propose a plug-and-play multimodal knowledge graph-based retrieval-augmented generation approach that employs a novel multi-scale subgraph retrieval algorithm for efficient information retrieval. By incorporating traffic safety guidelines collected from the Internet, this framework further enhances the model's capacity to handle safety-critical situations. Finally, we conduct comprehensive evaluations on five mainstream VLMs to assess their reliability in safety-sensitive driving tasks. Experimental results demonstrate that integrating RAG significantly improves performance, achieving a +4.73% gain in Traffic Accidents tasks, +8.79% in Corner Cases tasks and +14.57% in Traffic Safety Commonsense across five mainstream VLMs, underscoring the potential of our proposed benchmark and methodology for advancing research in traffic safety. Our source code and data are available at https://github.com/Lumos0507/SafeDriveRAG.

On-demand video streaming continues to dominate the Internet, posing a formidable challenge in designing efficient adaptive bitrate (ABR) algorithms to enhance user quality-of-experience (QoE), particularly amplified by increasing video resolutions (e.g., from 1080P to 2K, 4K, and even 8K) and dynamic Internet conditions. Through a comprehensive study, we identify a common limitation in both existing throughput-based and hybrid-based ABR algorithms: they rely on coarse-grained network bandwidth estimation, missing detailed and accurate (i.e., millisecond-level) network variations. This often leads to misguided resolution (corresponding to bitrate level) decisions, resulting in unsatisfactory QoE. In this work, we propose SuperABR, a fine-grained throughput-driven ABR solution aimed at achieving the optimal bitrate adaptation. To accomplish this, SuperABR first incorporates a two-stage learning module, generating fine-grained future throughput to provide a near-Oracle network view. SuperABR then uses this fine-grained throughput to accurately calculate the download duration for a video chunk, transforming it into the optimal resolution decision via a custom-designed QoE benefit model. We have implemented SuperABR as a lightweight plug-in interface on a standard DASH framework and evaluate it over extensive real-world network traces. Extensive experiments demonstrate that SuperABR can generate accurate future throughput, resulting in a remarkable$1.21\sim 1.46\times $QoE improvement over classic ABR solutions.

Predicting air quality using multimodal data is crucial to comprehensively capture the diverse factors influencing atmospheric conditions. Therefore, this study introduces a multimodal learning framework that integrates outdoor images with traditional ground-based observations to improve the accuracy and reliability of air quality predictions. However, aligning and fusing these heterogeneous data sources poses a formidable challenge, further exacerbated by pervasive data incompleteness issues in practice. In this article, we propose a novel incomplete multimodal learning approach (iMMAir) to recovery missing data for robust air quality prediction. Specifically, we first design a shallow feature extractor to capture modal-specific features within the latent representation space. Then we develop a conditional diffusion-driven recovery module to mitigate the distribution gap between the recovered and true data. This module further incorporates two conditional constraints of temporal correlation and semantic consistency for effective modal completion. Finally, we reconstruct incomplete modalities and fuse available data using a multimodal transformer network to predict the air quality. To alleviate the modality imbalance problem, we employ an adaptive gradient modulation strategy to adjust the optimization of each modality. Experimental results demonstrate that iMMAir significantly reduces prediction errors, outperforming baseline models by an average of 5.6% and 2.5% in air quality regression and classification tasks. Our source code and data are available at https://github.com/pestasu/IMMAir .

Urban land use, intrinsically linked to people’s daily activities, undergoes continuous evolution, presenting a complex interplay that remains partially understood. To bridge this gap, our study leverages fine-grained human mobility data to predict these changes, adopting a novel approach that conceptualizes “community-level” land use shifts as a regression problem and represents citywide changes through dynamic graphs. We harness recent advancements in graph neural networks (GNNs), which, despite their success in various applications, face challenges in directly predicting land use changes due to the temporal mismatch between the slow evolution of urban land and the immediacy of human mobility data. Our research stands out by introducing a temporal skeleton for dynamic GNNs to synchronize human activity graphs with urban land use changes, a dynamic heterogeneous GNN approach for integrating diverse human activity data to capture essential temporal dependencies, and a novel algorithm powered by causal inference to elucidate the primary factors influencing land use predictions at the community level, all of which contribute to a training process informed by the generated causal graph. Empirically validated on three real-world datasets, our model demonstrates a performance leap over state-of-the-art baselines, marking a pivotal step toward understanding and predicting the dynamics of urban land use.

Real-world time series data is inherently complex, noisy, and exhibits abrupt changes, posing various challenges in data modeling. Given the ubiquity and importance of time-series data, accurately forecasting change points, instead of the overall predictive performance, has become increasingly attractive as it assists in risk mitigation and loss prevention. In this task, we argue that the past and future interactions involving the target points determine the comprehensive structure contributing to abrupt changes. However, traditional left-to-right auto-regressive approaches only consider the historical sequence, resulting in a flawed learning process and limited performance. In this paper, we extend the teacher-student learning and propose a novel S elf-optimizing T eacher and A uto-matching S tudent framework (named ST-AS) to predict change points in time series data. Our framework models change point representations specific to the target points by integrating future knowledge while avoiding data leakage. Specifically, we design a Gumbel-enhanced filter for our self-optimizing teacher, which constructs selected and filtered sub-groups to derive discriminative representations using a positive-unlabeled learning strategy. Given this well-trained teacher, we propose an adaptive pattern matcher for our auto-matching student model, which learns missing information by automatically aligning relevant features. After that, a novel two-stage dual-guided learning process is then designed to mimic teacher’s decision-making behavior and enhance student’s excavate capability. Finally, we conduct extensive experiments on four real-world datasets to demonstrate that our proposed ST-AS exhibits significantly better prediction performance compared to existing state-of-the-art alternatives.

Cross-Technology Communication (CTC) is an emerging technique that enables direct interconnection among incompatible wireless technologies. However, for the downlink from WiFi to multiple IoT technologies, serially emulating and transmitting the data of each IoT technology has extremely low spectrum efficiency. In this paper, we propose WiCast, a parallel CTC that uses IEEE 802.11ax to emulate a composite signal that can be received by commodity BLE, ZigBee, and LoRa devices. By taking advantage of OFDMA in 802.11ax, WiCast uses a single Resource Unit (RU) for parallel CTC and sets other RUs free for high-rate WiFi users. But such a sophisticated composite signal is very easily distorted by emulation imperfections, dynamic channel noises, cyclic prefix, and center frequency offset. We propose a CTC link model that jointly models the emulation errors and channel distortions. Then we carve the emulated signal with elaborate compensations in both time and frequency domains. Based on the proposed CTC scheme, a unified Media Access Control approach is introduced to discover and synchronize the heterogeneous IoT devices. We implement a prototype of WiCast using USRP N210 platform along with commodity ZigBee, BLE, and LoRa devices. The extensive experiments demonstrate WiCast can achieve an efficient parallel transmission with the aggregated goodput up to 390.24kbps.

Current WiFi imaging approaches focus on monitoring dynamic targets to facilitate easy object distinction and capture rich signal reflections for image construction. In static object imaging, massive antenna array or emulated antenna array is often necessary. We proposeWiCamera, a novel WiFi imaging prototype that utilizes vortex electromagnetic waves (VEMWs) to monitor stationary human postures using commodity WiFi, by generating human silhouettes with only$3 \times 3$MIMO. VEMWs possess a helical wavefront with different phase variations, enabling the imaging of stationary objects through different OAM (Orbital Angular Momentum) modes with time-division multiplexing.WiCameraemits three OAM modes waves from WiFi devices and utilizes their phase variations for imaging. By ray tracing the received signals to a target image plane,WiCameragenerates a wavefront image. A generative adversarial network (GAN)-based model is further utilized to refine the wavefront image and create a high-resolution human silhouette. The system's output images are evaluated using metrics such as structural similarity index measure (SSIM) and Szymkiewicz-Simpson coefficient (SSC), comparing them to ground truth images captured by cameras. The evaluation shows thatWiCameraperforms consistently well in various environments and with different users, with an SSIM reaching up to 0.89 and an SSC reaching up to 0.93.

LoRa is widely deployed for various applications. Though the knowledge of the channel occupancy is the prerequisite of many aspects of network management, acquiring the channel occupancy for LoRa is challenging due to the large number of possible channels. In this paper, we propose${\sf LoRadar}$, a novel LoRa channel occupancy acquirer based on cross-channel scanning. Our in-depth study finds that Channel Activity Detection (CAD) in a narrow band can indicate the channel activities of wide bands because they have the same slope in the time-frequency domain. Based on this finding, we design a cross-channel scanning mechanism that infers the channel occupancy states of all the overlapping channels by the distribution of CAD results. We elaborately select and adjust the CAD settings to enhance the distribution features and design a pattern correction method to cope with distribution distortions. We also design a CAD scheduler to deal with the low duty-cycle LoRa operations. We implement${\sf LoRadar}$on commercial LoRa platforms and evaluate its performance in the indoor testbed and two outdoor deployed networks. The experimental results show that${\sf LoRadar}$can achieve a detection accuracy of 0.99 and reduce the acquisition overhead by up to 90%, compared to the traversal-based methods.

Unmanned Aerial Vehicle (UAV) equipped with a gateway shows great potential for data collection in many scenarios, especially for the areas lacking of public network infrastructures. However, our in-field experiments on UAV-assisted LoRa networks show that a large throughput gap exists between the ground-to-air and ground-to-ground transmissions. We find that the misalignment of the radiation direction of transceiver antennas with height difference leads to additional signal strength loss, which is ignored by existing ground-to-ground transmissions. In this paper, we propose a directivity-aware ground-to-air link model called annulus model to quantify the impact of directivity on the ground-to-air link quality. Based on our model, a new ground-to-air channel access scheme for UAV-assisted LoRa networks,PreLoRa, is proposed. By predicting the link quality variations,PreLoRaschedules the transmission periods and adopts optimal transmission configurations for ground nodes to improve the link throughput. We implementPreLoRaon commercial LoRa platforms and extensively evaluate its performance in the wild. Experimental results show thatPreLoRacan significantly improve data collection throughput by up to 65.5% compared to baseline methods.

Remarkable advancements have been made in the field of object detection, and given its widespread application, it is of paramount importance to investigate the robustness of detection models. However, previous methods have primarily focused on models based on Convolutional Neural Networks (CNNs), seriously neglecting the Transformer-based models that develop rapidly but exhibit obvious differences in terms of information processing. Therefore, this paper aims to address this gap by exploring potential attacks arising from the self-attention mechanism inhered in Transformer. Specifically, we propose a novel adversarial attack scenario targeting Transformer-based object detection models, where only objects of specific class fail to be detected, while irrelevant objects remain undisturbed. Therefore, human perception is hard to find errors even with the detector fail. To achieve this goal, we introduce an adversarial patch generation method, termed Class-Specific Adversarial (CSAdv) patches, which simultaneously leverages class probability to attack specific objects and utilizes the output from Transformer decoder structures, Query Output, to protect irrelevant objects. Due to the long-range interactions of Transformer, the adversarial patch does not need to directly cover or closely surround the specific objects. Instead, it achieves remote targeted attacks simply by being placed in the corner of image, which greatly enhances the concealment of patches. Extensive experiments are conducted on various benchmark datasets and Transformer-based baselines, and the experimental results show that CSAdv can effectively mask certain class while keeping other classes as unaffected as far as possible.

With the advancement of mobile transmission and the computational limitations of vehicle terminals, cloud-based autonomous driving has gained significant traction. In order to ensure the quality of video transmission, Adaptive Bitrate (ABR) algorithms are commonly employed. In this paper, we conduct extensive measurements on current ABR algorithms and typical autonomous driving tasks, and we identify two key limitations: (i) existing ABR algorithms commonly apply uniform transmission strategies across diverse driving tasks, and (ii) there is no clear correlation between Quality-of-Experience (QoE) and the performance of autonomous driving tasks, though we reveal specific relationships between them. To overcome these challenges, we propose ATA, a novel task-oriented ABR streaming transmission framework tailored for autonomous driving. ATA enables vehicles to upload streaming optimized for specific autonomous driving task, to optimize the performance of the autonomous driving task in fluctuating network environments. To achieve ATA, we devise a task-oriented ABR strategy, by selecting transmission approaches based on the task requirements. Additionally, we propose an initial bitrate selector, grounded in the Pareto frontier model, to ensure the selection of optimal initial bitrates aligning with current bandwidth conditions. Trace-driven experiments demonstrate that ATA significantly outperforms existing ABR schemes in autonomous driving scenarios. Notably, it achieves improvements in Average Precision (AP) for object detection tasks by 5.06% to 14.25%, reduces Average Orientation Error (AOE) in direction prediction tasks by 7.69% to 15.21%, and enhances overall task performance by 10.27% to 12.55% in multitask scenarios.

Unlike existing studies that focus on intra-channel interference, in this paper, we reveal cross-channel interference when collided chirps with different bandwidths have the same slope in the time-frequency domain. Existing methods are inefficient in resolving this type of interference because the demodulation features they use rely on accurate time-domain distributions including the start and end time of all chirps, which is unavailable for uncompleted chirps within the limited receiving bandwidth. We propose SO-LoRa which utilizes the difference in collided chirps' time-domain distributions to identify the target chirp under interference. SO-LoRa adopts self-dechirp operation that maps the chirp's time-domain distribution to recognizable amplitude change of energy peaks that reflect the difference. However, for real received chirps, the amplitude change is unreliable due to the random phase drift and amplified channel noise. So we propose a phase correction method that uses the model between phase difference and the signal energy. We also design time-domain filtering that suppresses noise before self-dechirp. Finally, to avoid extra false energy peaks generated by self-dechirp confusing the demodulation, we separate chirps which cause peak overlapping into different groups and individ-ually perform self-dechirp. The experiments show that SO-LoRa reduces the Symbol Error Rate (SER) by up to 88.6 % compared with state-of-the-art methods.

Accurate and timely air quality prediction is crucial for cities and individuals to effectively take necessary precautions against potential air pollution. Existing studies typically rely on building prediction models based on large-scale monitoring data, often designed for specific tasks. Recently, pre-trained large language models (LLMs) have achieved significant progress in various time series analysis tasks due to their powerful representation and inference capabilities. However, their application to air quality data with spatio-temporal features remains largely unexplored. In this work, we propose LLMAir, an adaptive reprogramming approach that adapts pre-trained LLMs for air quality prediction. We first construct spatiotemporal tokens based on monitoring stations by integrating value, node, and time embeddings. Next, we design an adaptive semantic-enhanced reprogramming module to compute similarity matching scores between our spatiotemporal tokens and pre-trained word embeddings for alignment. We employ a semantic regulator to generate the optimal length of word prototypes, which serve as prompt prefixes for adaptive reprogramming and guiding the spatiotemporal token embeddings into the frozen LLM. Additionally, we jointly optimize predictive error and alignment loss to train our model. Experimental results demonstrate that LLMAir achieves state-of-the-art performance in air quality prediction and few-shot forecasting across two real-world datasets.

The current deployment of extremely shallow-shared-buffer switches in data center networks has posed challenges to widely adopted ECN-based congestion control algorithms, leading to the issue of ECN failure. Switches may not allocate sufficient buffer space for each port, leading to the possibility that the ECN marking threshold exceeds the buffer limit per port. This results in excessive packet loss during bursts, even before the ECN markings take effect. To address this problem, we propose BBQ, an automatic ECN tuning system based on reinforcement learning. BBQ ensures that the ECN threshold does not exceed the buffer capacity allocated to the port, thus avoiding the ECN failure issue. Besides, BBQ is designed to adapt to switches with varying buffer sizes ensuring generalization. We validate the effectiveness of BBQ through experiments conducted with shallow buffering and high bursts. The results show that BBQ efficiently controls the packet loss rate of incast flows to within 3%, 1.2 times lower than State-of-the-Arts in shallow-buffered environments.

Due to the poor indoor coverage and positioning accuracy, existing indoor GPS positioning systems leverages additional RF infrastructure as relay with known position. However, in practice, learning the relay position requires establishing an additional connection between user and relays, which is user unfriendly and even infeasible. In this paper, we propose GPSWindow, a plug-and-play indoor GPS positioning system without the prior knowledge of the relay position. By attaching optically transparent metasurfaces to windows, GPSWindow focuses the incident signal towards determined direction and provide an indoor continuous GPS signal coverage. We exploit the difference between consecutive satellite measurements and the Doppler shift measurements to recover the satellite-to-user true distance from the measured satellite-metasurface-user distance, and then locate the user using the traditional trilateration positioning method, eliminating the requirement of relay position. We also design an error correction method that leverages IMU on smartphone and the Doppler shift information to enhance GPSWindow in mobile scenarios. Extensive real-world experiments demonstrate that GPSWindow can provide continuous position service and achieve median positioning accuracy of 3.6m in indoor environments.

Despite the prevalence of Crowdsourced Live Video Streaming (CLVS), video viewers still suffer from low QoE particularly under rush hours, as the existing Content Delivery Network (CDN) is not scalable enough to handle the massive concurrent streaming. The rapid emergence of Web 3.0 provides new incentives for revisiting and applying the classical P2P networking in CLVS. However, the highly dynamic joining or leaving behavior of CLVS viewers frequently interrupts the real-time streaming and leads to low QoE, which demands to retrofit P2P. In this work, we bridge the gap by proposing a reliable P2P-assisted CLVS system named Venus, where viewers can share their streaming content smoothly, without video freeze regardless of viewers leaving. To realize Venus, different from the single-flow sharing in previous P2P video streaming, we design a novel multiflow framework with lightweight redundancy encoding, so as to handle the inherently high viewer dynamics. Correspondingly, we introduce a multiflow scheduler to enable QoE adaption concertedly over heterogeneous multiple flows. Real-world evaluation confirms the benefits of decentralized CLVS streaming, with Venus outperforming the state-of-the-art CDN solution by almost totally eliminating the video stall while enhancing the video quality by 10.2%.

The Bluetooth 5.1 specification introduces the Angle of Arrival feature, which significantly enhances its applications in indoor positioning. Given the height of the target, a single BLE (Bluetooth Low Energy) base station can locate the target, thereby reducing deployment costs. However, existing methods often assume that the target's height is known in advance and remains constant, which is not always true in practice. The height can vary significantly due to user posture changes, such as when picking up a phone from a pocket, leading to positioning errors. In this paper, we propose BleHe, a novel indoor positioning system that incorporates height correction to enhance positioning accuracy in scenarios with dynamic height changes. BleHe detects height changes by utilizing on-device IMU sensor and subsequently notifies the base station of any detected height change events. To avoid altering the commodity Bluetooth protocols, rather than directly modifying application data, we create a side channel to delivery the height change information from the device to the base station by adjusting the BLE packet transmission frequency. This method allows the base station to infer the start and end times of height changes and subsequently refine the target's trajectory using a height-aware particle filtering-based positioning correction method that we propose. Experimental results demonstrate that BleHe achieves an average positioning error of 34.4cm, even in scenarios involving posture changes during walking.

In modern network environments, the Multipath QUIC (MPQUIC) protocol significantly enhances data transmission reliability and efficiency by leveraging multiple paths. However, the challenge lies in developing an effective scheduling algorithm that can adapt to dynamic network conditions. Existing heuristic scheduling algorithms are tailored to specific environments, while learning-based algorithms lack the capability for fine-grained scheduling. To address this, we propose SSRL (RL-based Scheduler Switcher), a framework that dynamically switches among heuristic scheduling algorithms based on real-time network condition recognition. SSRL combines the advantages of both heuristic and learning-based algorithms, thereby enhancing MPQUIC's performance by reducing latency and improving bandwidth utilization while consuming less reorder buffer. We also design a scheduler selection model that leverages LSTM and Double DQN, enabling SSRL to understand network conditions better and make more effective scheduling decisions. We implement SSRL using Pytorch and conduct extensive evaluations with the NS3 network emulator. The results show that SSRL increases throughput by 23% and reduces RTT by 10%.

Network feedback, representing the impact of congestion control actions on the network, is crucial for evaluating the advantages of the actions taken. Previous reinforcement learning (RL)-based congestion control algorithms use average performances over fixed periods to measure network feedback, which fails to accurately capture the impact of an action and leads to performance degradation. In this paper, we propose Zygos, which accurately estimates network feedback. This accurate feedback can benefit other RL-based congestion control algorithms. Zygos contains a distribution-based reward correction mechanism that leverages a RL model to generate relevance distributions for the sequence rewards of each state-action pair, and then aggregates the rewards by weighted average. Zygos also adopts metagradient RL to capture network feedback offset patterns, thereby updating the relevance generation model during the training of the congestion control algorithm. Experiments show that the RL congestion control method using Zygos achieves an average 20–30% improvement in throughput and 20% decrease in delay compared to the original method, highlighting substantial enhancements in RL-based congestion control algorithms.

In this paper, we reveal the existence of partially overlapping interference (POI) when multiple devices concurrently transmit in partially overlapping channels. Existing methods proposed for collisions in the same channel cannot achieve online decoding for target packets under POI due to unpredictable in-window distribution of interfering chirp. We instead propose PrLoRa, a novel method to achieve online LoRa decoding under POI. PrLoRa relies on the insight that only the target chirp is complete in the decoding window. Then PrLoRa adopts a novel operation named phase rotation which converts the difference in chirp's integrity to the amplitude change of energy peak after dechirp and Fast Fourier Transform (FFT). For energy peaks generated by the target chirps, their amplitude change is expected. To use phase rotation in decoding the target chirp, we first establish the theoretical model between amplitude changing and phase rotation, which can be used to infer the expected amplitude change of the target peak. In practice, the peak's amplitude suffers from the influence of channel noise, which causes decoding errors. So, we also propose a noise-aware window setting that can adaptively select the suitable window size for phase rotation according to channel noise. Furthermore, we propose the iterative phase rotation to cope with decoding errors caused by interfering chirps with confusing distributions. The Experimental results show that PrLoRa can reduce the SER by up to 0.92 compared with existing state-of-the-art methods.

WiFi imaging is an emerging technology that can overcome camera limitations like occlusion and poor lighting. Imaging static objects without antenna arrays or mobile platforms is challenging. In this paper, we use a metasurface with a single pair of WiFi transceiver to achieve high-resolution WiFi imaging for static objects. We analyze the WiFi multipath propagation model with a metasurface and propose an imaging system. This system employs diverse radiation patterns by directing the WiFi beam reflected from the metasurface to illuminate a target. We utilize an image-guided diffusion model for high resolution imaging. We prototype MAWI with commodity WiFi and evaluate its performance in real environments. Experimental results show MAWI performs well on four typical target shapes.

Existing WiFi sensing systems transmit dedicated high-rate packets for accurate sensing. These “sensing packets” greatly affect the main data communication function of WiFi and significantly counteract the promised benefit of reusing WiFi communication for sensing. In this work, we propose WiImg2.0, a lightweight system which involves machine learning techniques to enable WiFi sensing under low packet rate, pushing WiFi sensing one step towards real-life adoption. The key idea is to convert the WiFi CSI samples into images and employ the Generative Adversarial Network (GAN) for CSI image inpainting, relaxing the requirement of high sample rate for sensing. We first recover the sensing data from the antenna spatial domain and then from the sample time domain. To avoid the large training overhead of GAN, we design a lightweight GAN that leverages samples of only three rates in a fixed window to recover the CSI traces of arbitrary rates and varying duration. Experiments show that with just 25 packets per second, WiImg2.0 is able to increase the recognition accuracy for hand gesture recognition and daily activity tracking from the state-of-the-art 59.1% and 65.9% to 86.7% and 96.4%, respectively.

We show that crowd counting can be formulated as a sequential decision-making (SDM) problem. Inspired by human counting, we evade one-step estimation mostly executed in existing counting models and decompose counting into sequential sub-decision problems. During implementation, a key insight is to interpret sequential counting as a physical process in reality-scale weighing. This analogy allows us to implement a novel "counting scale" termed LibraNet. Our idea is that, by placing a crowd image on the scale, LibraNet (agent) learns to place appropriate weights to match the count: at each step, one weight (action) is chosen from the weight box (the predefined action pool) conditioned on the image features and the placed weights (state) until the pointer (the agent output) informs balance. We investigate two forms of state definition and explore four types of LibraNet implementations under different learning paradigms, including deep Q-network (DQN), actor-critic (AC), imitation learning (IL), and mixed AC+IL. Experiments show that LibraNet indeed mimics scale weighing, that it outperforms or performs comparably against state-of-the-art approaches on five crowd counting benchmarks, that it can be used as a plug-in to improve off-the-shelf counting models, and particularly that it demonstrates remarkable cross-dataset generalization. Code and models are available at https://git.io/libranet.

The rising crowdsourced live video streaming (CLVS) poses great challenges to Internet transport scalability, where a broadcaster’s live video is expected to reach thousands and even millions of viewers in real time. To accommodate such huge concurrent video traffic, the de-facto solution is to employ content delivery network (CDN), which distributes the traffic spatially relative to end viewers, using geographically distributed servers. However, our measurement study over a top operational CLVS platform reveals that CDN is not scalable enough, i.e., it loses efficacy, particularly duringbusy timeand leads to tremendous QoE degradation, e.g., 33.3% video bitrate reduction, in comparison to networkidle time. In this work, we propose Spider, which revives the peer-to-peer (P2P) networking principle to extend the scalability of CLVS system. Beyond traditional P2P for elastic data transmission, Spider retrofits P2P to meet the stringent low-latency requirements of CLVS: proposing a “pair-push” streaming mode to tame the excessive signaling latency; designing a QoE-driven peer pairing algorithm to tackle the Internet path variation and CLVS viewer dynamics. We implement, deploy and evaluate Spider in real-world over 20.9 thousand video sessions. Compared to the de-facto CDN solution, Spider achieves remarkable gains, e.g., video stall rate reductions of 52.57%, video quality gains of 8.22%, and even 66% CDN bandwidth saving. The results validate the feasibility and practicability of embracing P2P for low-latency live video communication for the first time.

Recent work proposes Cross-Technology Communication (CTC) from IEEE 802.11b to LoRa but has a low efficiency due to the extremely asymmetric data rates. In this paper, we propose that emulates LoRa waveform with IEEE 802.11ax. By taking advantage of the OFDMA in 802.11ax, uses only a small Resource Unit (RU) to emulate LoRa chirps and sets other RUs free for high-rate WiFi users. carefully selects the RU and adopts WiFi frame aggregation to emulate the long LoRa frame. We propose a subframe header mapping method to identify and remove invalid symbols caused by irremovable subframe headers in the aggregated frame. We also propose a mode flipping method to solve Cyclic Prefix (CP) errors, based on our finding that different CP modes have different impacts on the LoRa symbol. To cope with channel dynamics, we design an adaptation mechanism to maximize the goodput with a satisfying SER. We further extend to one-to-many transmission scenario by concurrently emulating LoRa chirps in different RUs. We implement a prototype of on the USRP platform and commodity LoRa device. Experiments demonstrate can efficiently transmit complete LoRa frames with the throughput of 40.037kbps and the SER lower than 0.1.

De-authentication attack is one of the major threats to Unmanned Aerial Vehicle (UAV) communication, in which the attacker continuously sends de-authentication frames to disconnect the UAV communication link. Existing defense methods are based on authentication by digital passwords or physical channel features. But they suffer from replay attacks or cannot adapt to the UAV mobility. In this paper, instead of enhancing the in-channel authentication, we leverage the ambient broadcasting signal to establish a low-cost additional channel for authentication. Different from methods using another dedicated secure communication channel to perform an independent authentication, we use the ambient FM radio broadcasting channel and couple the two channels by encoding parasitic bits on the host signals of the broadcasting channel, which is called parasitic coding. To further enhance the security, we propose the FM-based Parasitic Coding Authentication (FPCA) that leverages elaborate host signal processing and vector coding to ensure that the attacker cannot decode our authentication even knowing the FM receiving frequency. We implement FPCA on the embedded UAV platform. The extensive experiments show that FPCA can resist replay attacks and brute force searching, achieving reliable continuous authentication for UAVs.

Personalized federated learning (PFL) is a framework that targets individual models for optimization, providing better privacy and flexibility for clients. However, in challenging intelligent sensing applications, the heterogeneous client’s data distributions make the aggregation of local models in the server unstable or even hard to converge. To deal with the performance degradation caused by the preceding problem, existing PFL methods focus more on how to fine-tune the global model but ignore the impact of the global model fusion algorithm on the results. In this article, we propose a new explainable neural-aware decoupling fusion based PFL framework, p-FedADF , to address the preceding challenges. It contains two carefully designed modules. The local decoupling module, deployed on the client, utilizes the architecture disentangle technique to decouple the feature extractors in the client’s local model into sub-network according to data categories. It obtains the inference process of feature extraction for different categories of data by training. The global aggregation module, deployed on the server, aligns the sub-network positions for multiple clients and implements a fine-grained generic feature extractor aggregation. In addition, we provide a mask encoding scheme to reduce the communication overhead of transmitting the sub-network sets between the server and clients. Our p-FedADF obtains 1.6%, 0.2%, 2.3%, and 4.5% improvement on a real-world dataset and three benchmark datasets, compared to state-of-the-art methods.

With the widespread adoption of deep learning models in wireless sensing, substantial efforts have been made to develop sophisticated models that improve the accuracy and performance of sensing applications. However, the exploration of potential vulnerabilities in deep learning models has been limited, with existing studies primarily focusing on evaluating wireless adversarial performance in communication or sensing alone. Moreover, there is a lack of a comprehensive definition for attack imperceptibility. In this article, we come up with a definition of the wireless attack imperceptibility for both communication and sensing. Our objective is to create an adversarial perturbation capable of degrading WiFi sensing performance while preserving WiFi communication integrity. To achieve this, we propose WiCAM2.0 to reveal the temporal and spatial attention of a deep neural network, capturing the crucial portions of its input. Then, we design a mask to confine adversarial perturbations in the attended parts only, minimizing the impact on WiFi communication. WiCAM2.0 is a general adversarial framework that integrates adversarial methods such as the Fast Gradient Sign Method and Projected Gradient Descent to generate perturbations, capable of initiating both non-targeted and targeted attacks. We carry out experiments on three popular WiFi sensing applications, including human activity recognition, gesture recognition, and user identification. Extensive experiments are conducted on both public datasets and self-collected datasets.

LoRa is widely deploying in aquatic environments to support various Internet of Things applications. However, floating LoRa networks suffer from serious performance degradation due to the polarization loss caused by the swaying antenna. Existing methods that only control the transmission starting from the aligned attitude have limited improvement due to the ignorance of aligned period length. In this article, we propose PolarScheduler , a dynamic transmission control method for floating LoRa networks. PolarScheduler actively controls transmission configurations to match polarization aligned periods. We propose a V-zone model to capture diverse aligned periods under different configurations. We also design a low-cost model establishment method and an efficient optimal configuration searching algorithm to make full use of aligned periods. To deal with packet collisions in a multiple-node environment, we further propose an Attitude-aware Slot-allocation MAC protocol, which avoids both packet collisions and polarization loss. We implement PolarScheduler on commercial LoRa platforms and evaluate its performance in a deployed network. Extensive experiments show that PolarScheduler can improve the packet delivery rate and throughput by up to 20.0% and 15.7%, compared to the state-of-the-art method.

Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) is a novel wireless communication technology to improve the coverage of Low-Power Wide-Area Network (LP-WAN). But our measurement finds that given the same set of sub-channels, different Frequency Hopping Sequence (FHS) can result in a reliability difference of up to 52.6 % in terms of Packet Reception Rate (PRR). The key observation indicates that the reliability of LR-FHSS is significantly influenced by the FHS besides the link quality. Hence, in this paper, we propose DFH that takes both link quality and FHS into consideration to improve the reliability of LR-FHSS. We first propose using the hop Signal-to-noise Ratio (SNR), a new indicator to reflect the quality of the sub-channels and establish the PRR prediction model according to hop SNR and FHS. Based on the model, we design an interleaving-based search algorithm to decide the optimal FHS. We implement and evaluate DFH on the commercial transceivers and SDR-based gateway. The results of experiments in real environments show that DFH can improve the PRR by up to 2.76x, compared to the standard LR-FHSS.

The hierarchical cooperative resource scheduling architecture provides a promising direction for efficient collaborative processing of edge computing under the dynamic and intricate landscape of the Industrial Internet of Things (IIoT). However, existing scheduling algorithms often struggle to effectively capture the intricate information features inherent in hierarchical and collaborative domains, leading to suboptimal solutions. To tackle this challenge, we introduce a novel hierarchical cooperative resource scheduling framework based on Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL). We first leverage hierarchical GNN to facilitate seamless information exchange among internal nodes and adjacent nodes between layers in the hierarchical structure and transform it into node embeddings. These meticulously designed embeddings are then input into the policy model of DRL for the iterative learning process to generate higher-quality solutions by leveraging global feature information. Experiment results unequivocally demonstrate the superiority of our approach over baselines in terms of scheduling performance. Furthermore, our model exhibits robust generalization capabilities across various scenarios.

Cross-Technology Communication (CTC) is an emerging technique that enables direct interconnection among incompatible wireless technologies. However, for the downlink from WiFi to multiple IoT technologies, serially emulating and transmitting the data of each IoT technology has extremely low spectrum efficiency. Recent parallel CTC uses IEEE 802.11g to send emulated ZigBee signal and let the BLE receiver decodes its data from the emulated ZigBee signal with a dedicated codebook. It still has a low spectrum efficiency because IEEE 802.11g exclusively uses the whole channel. Besides, the codebook design hinders the reception on commodity BLE devices. In this paper, we propose WiCast, a parallel CTC that uses IEEE 802.11ax to emulate a composite signal that can be received by commodity BLE, ZigBee, and LoRa devices. By taking advantage of OFDMA in 802.11ax, WiCast uses a single Resource Unit (RU) for parallel CTC and sets other RUs free for high-rate WiFi users. But such a sophisticated composite signal is very easily distorted by emulation imperfections, dynamic channel noises, cyclic prefix, and center frequency offset. We propose a CTC link model that jointly models the emulation errors and channel distortions. Then we carve the emulated signal with elaborate compensations in both time and frequency domains to solve the above distortion problem. We implement a prototype of WiCast on the USRP platform and commodity devices. The extensive experiments demonstrate WiCast can achieve an efficient parallel transmission with the aggregated goodput up to 390.24kbps.

Existing LoRa only supports the concurrency of orthogonal channels but ignores the large number of non-orthogonal channel concurrency opportunities. In this paper, we propose Mc-LoRa that enables LoRa concurrency for non-orthogonal overlapping channels by solving cross-channel collision that happens when chirps with different bandwidths have the same slope in time-frequency domain. Existing single-channel concurrency methods fail to resolve this new collision because the deterministic symbol offset is invalid anymore due to the asymmetric symbol duration. But we find that when wiping a part of collided signals, the amplitude change of target chirp that aligns with the decoding window is predictable, while the collided chirps experience different changes. We accordingly regard the amplitude change ratio before and after wiping as a new decoding feature. We propose a wiper selection method based on our theoretical model to obtain robust features. We also design noise-aware wiper searching and grouping mechanisms to balance the feature accuracy and computing overhead. The experiments show that Mc-LoRa efficiently decodes packets in non-orthogonal overlapping channels and improves the network throughput by up to 3.4× under cross-channel collision, compared with the state-of-the-art single-channel concurrency methods.

In the Industrial Internet of Things (IIoT) environment, time-sensitive tasks require efficient utilization of computing and network resources to ensure timely completion and fast processing. However, using existing scheduling schemes based on software-defined network (SDN), time-sensitive network (TSN), or information technology (IT) can lead to problems such as resource inefficiency, transmission delays, and task timeouts. To overcome these challenges, this paper proposes a collaborative scheduling method that combines SDN for global resource management and TSN for precise resource allocation. Additionally, a control plane algorithm is introduced to adapt task resources. This approach effectively schedules global computing and network resources, enabling timely and efficient task processing. Extensive experiments demonstrate the superiority of this method in terms of task completion rate and processing time compared to other baselines in various network environments.

We present nnPerf, a real-time on-device profiler designed to collect and analyze the DNN model run-time inference latency on mobile platforms. nnPerf demystifies the hidden layers and metrics used for pursuing DNN optimizations and adaptations at the granularity of operators and kernels, ensuring every facet contributing to a DNN model's run-time efficiency is easily accessible to mobile developers via well-defined APIs. With nnPerf, the mobile developers can easily identify the bottleneck in model run-time efficiency and optimize the model architecture to meet system-level objectives (SLO). We implement nnPerf on TFLite framework and evaluate its e2e-, operator-, and kernel-latency profiling accuracy across four mobile platforms. The results show that nnPerf achieves consistently high latency profiling accuracy on both CPU (98.12%) and GPU (99.87%). Our benchmark studies demonstrate that running nnPerf on mobile devices introduces the minimum overhead to model inference, with 0.231% and 0.605% extra inference latency and power consumption. We further run a case study to show how we leverage nnPerf to migrate OFA, a SOTA NAS system, to kernel-oriented model optimization on GPUs.

The rapid development of the Industrial Internet of Things (IIoT) provides a significant opportunity to achieve comprehensive awareness and salient event detection in manufacturing factories. However, because of the limited onboard resources of IIoT terminal devices, it remains a challenging task in the face of the processing requirements of compute-intensive and latency-critical applications. To overcome this challenge, we study the hierarchical collaboration dynamic resource scheduling problem for the IIoT cloud-edge computing model. First, we divide the network into different domains for autonomous management and hierarchical collaboration according to the dynamically available computing resources and transmission delay of edge nodes. Second, we establish a computing model of task data size and resource requirement to maximize the processing benefit of tasks and load balancing between domains, formulate the task-domain Pareto optimality matching problem and the task-node optimal matching problem, which is transformed into the 0-1 Multiple Knapsack Problem (MKP). Third, we develop a hierarchical collaboration dynamic resource scheduling algorithm to solve the above optimal matching problems and set each time slot duration according to the processing rate of the algorithm. Extensive experiments show that our method provides an efficient and reliable scheduling strategy for IIoT in various scenarios with good scalability.

Deep-learning-based (DL-based) face recognition has become an important application in the Internet of Things (IoT) environment. However, recent studies demonstrate that elaborate adversarial examples can mislead the results of DL-based face recognition on mobile and edge devices. Such vulnerability threats the robustness of face recognition systems and causes security issues. Generative adversarial defense methods can reform adversarial examples before input into the face recognition model to improve the accuracy under adversarial attacks. Unfortunately, the existing generative adversarial defense methods cannot completely remove the misleading features of adversarial examples due to the lack of robust encoding ability. In this article, we propose a local consistency generative adversarial network (LC-GAN) framework by adding the constraint of local consistency to force the encoder to mine consistent features in each local area, achieving robust encoding ability consequently. The framework includes three main novel designs. First, we present a patch-wise contrastive learning-based refinement stage with local consistency loss to encode robust identity features from nonsalient areas that are undamaged by adversarial attacks. Second, we use a powerful expert network to guide the training of LC-GAN for eliminating adversarial identity features. Third, we design a multilevel identity loss to enhance the identity preservation ability by unifying the local and global identity features. Experimental results on four widely used face data sets show that LC-GAN outperforms other generative adversarial defense methods.

The unprecedented prosperity of the Industrial Internet of Things (IIoT) promotes the traditional industry transforming into intelligent manufacturing so that the whole production process can be comprehensively controlled to achieve flexible production. Intelligent scheduling, as one of the key enabling techniques, is desired to allocate the production of several machines by an efficient solution with minimum makespan. Existing approaches adopt a fixed search paradigm based on expert knowledge to seek satisfactory solutions. However, considering the varying data distribution and large sized of the practical problems, these methods fail to guarantee the quality of the obtained solution under the real-time requirement. To address this challenge, we formulate the production scheduling problem as a Markov decision process (MDP) and specifically design a job scheduling model made up of a job batching module for the hybrid flow-shop scheduling problem on batch processing machines (HFSP-BPM). Our proposed model consists of an actor network that learns the action under different conditions and a critic network that evaluates the action of the actor. We analyze the convergence of the model under different parameter settings to determine the optimal parameter. Extensive numerical experiments on both publicly available data set and real steel plant production data set demonstrate that the proposed deep reinforcement learning (DRL) approach compared with other baselines, more than 6% average improvements can be observed in many instances.

Emotion recognition, particularly contactless recognition via wireless sensing, has shown its promise in diverse applications. However, the previous works only focus on emotions rather than the person, i.e., the premise is already knowing who the subject is, without considering the issue of identifying subjects. We envision that user identification and emotion recognition together will bring more adaptive and personalized Internet of Things applications, e.g., a smart home system can react to specific emotions of a specific user, independently. In this work, we move forward to investigate the problem of simultaneous user identification, using only physiological indicators embedded in wireless signals reflected off from targets. Toward the objective, in this article, we first carry out a comprehensive measurement study, which validates the feasibility of simultaneous user identification and emotion recognition. Moreover, the measurement also discovers that the key challenge lies in the limitation of artificial features and the substantial emotion feature deviation across different days, which hinders accurate and robust sensing. To resolve the challenge, we design two multiscale neural networks, incorporated with a custom-built feature attention mechanism, so as to obtain rich feature expression and, thus, enhance the important features for accurate recognition. We prototype mmEMO using a commercial off-the-shelf millimeter-wave radar and experimental evaluation shows that mmEMO can achieve 87.68% user identification accuracy and 80.59% emotion recognition accuracy, respectively.

Federated learning, as a distributed machine learning framework, enables clients to conduct model training without transmitting their data to the server, which is used to solve the dilemma of data silos and data privacy. It can work well on clients having similar data characteristics and distribution. However, it has some limitations where the dataset of clients may be different in distribution, quantity, and concept in many application scenarios. Personalized federated learning is a new federated learning paradigm that aims to guarantee client personalized models’ effectiveness when collaborating with the cloud server. Intuitively, providing further facilitated collaborations for the clients with similar data characteristics and distribution can benefit personalized model building. However, due to the invisibility of client data, it is challenging to extract client characteristics and define collaborative relationships among them from a fine-grained view. Moreover, a reasonable collaborative training approach needs to be designed for a distributed server–client framework. In this article, we design a Hierarchical Attention-enhanced Meta-learning Network (HAM) to address this issue. The main advantage of HAM is that it utilizes the meta-learning approach of taking model parameters as features and learns to learn an extra model for each client to analyze similarities according to their local dataset automatically. According to its two-layers framework, HAM can reasonably achieve a tradeoff between clients’ personality and commonality and provides a hybrid model with useful information from all clients. Considering there are two networks (HAM and base network) that need to learn for each client during the federated training process, we then provide an alternative learning approach to train them in an end-to-end fashion. To further clarify the approach, we describe the personalized federated learning settings framework as FedHAM where the HAM network is distributed deployed in each client. Extensive experiments based on two datasets prove that our method outperforms state-of-the-art baselines under different evaluation metrics.

Traffic prediction under various conditions is an important but challenging task. Latest studies have achieved promising results but suffer degraded performance without exception under abnormal conditions (e.g., accidents), as the traffic patterns under abnormal conditions often deviate from the normal seriously. To adapt to both normal and abnormal conditions, we propose theMulti-taskMulti-rangeMulti-subgraphAttentionNetwork (M3AN), a novel deep learning model to explicitly model the impacts of abnormal events for condition-aware traffic prediction. It constructs different subgraphs to model node features to address the abrupt traffic patterns with sparse abnormal event data, and uses an attention mechanism to capture dynamic spatial dependencies. Meanwhile, a multi-task fusion module is built upon a road-segment graph and an intersection graph to enhance the ability of capturing complicated dependencies, together with a multi-range attention module for automatically learning the influences of abnormal events with lower computational complexity. Experimental results on two real-world traffic datasets show that our M3AN outperforms state-of-the-art approaches under both normal and abnormal conditions.

The prosperity of Internet of Things (IoT) brings forth the deployment of large-scale sensing systems such as smart cities. To enable the collaboration tasks among distributed devices, time synchronization is crucial. However, due to the long-range and device heterogeneity, accurate time synchronization for a large-scale IoT network is challenging. Existing GPS or NTP solutions either require an outdoor environment or only have low and unstable accuracy. In this paper, we propose AirSync, a novel synchronization method that leverages the widely existed aircraft signals, ADS-B, to synchronize large-scale IoT networks with nodes even in indoor environments. But ADS-B messages have no time stamp and cannot provide a reference time. We leverage the continuity of aircraft movements to estimate the aircraft traveling time. Then devices that observe common aircraft moving segments can calculate their time offset. To obtain the time skew, we propose a combined aircraft linear regression method. We also design a transitive synchronization for devices that cannot observe common aircraft. Besides, we also design a duty-cycled ADS-B message collection method for resource-limited IoT devices. We implement a prototype of AirSync and evaluate its performance in various real-world environments. The results show that AirSync can obtain the sub-ms accuracy.

While 5G has rolled out since 2019 and exhibited versatile advantages, its performance under high/extreme mobility scenes (e.g., driving, high-speed railway or HSR) remains mysterious. In this work, we carry out a large-scale field-trial campaign, taking >13,000 Km round-trips on HSR moving at 250–350 Km/h, with operational 5G cellular coverage along the railway. Our empirical study reveals that coupling interaction among high mobility, 5G handover characteristics, and applications’ sluggish reaction to handover, results in catastrophic damage to user experience: low TCP bandwidth utilization of 26.6% and glitchy 4K VoD streaming. To solve the problem, we propose an edge-assisted mobility management framework called Octopus. Different from previous works, Octopus aims at a standard-compatible and easy-to-deploy solution, thus we take a new design paradigm of exploiting the edge intelligence on multi-access edge computing (MEC). We realize Octopus as a universal MEC service ready for benefiting any third-party mobile applications. We prototype, deploy, and evaluate Octopus in operational 5G, which demonstrates the significant performance gain across the full-range mobile scenarios, e.g., HSR, driving, and walking.

WiFi-based Long Distance (WiLD) networks are promising to cover the rural and remote regions. But the explosive short-range WiFi deployments result in the long-short coexistence. Due to CSMA is ignorance of propagation delay, its carrier sensing is too short to detect long links, leading to the temporal hidden terminal problem that causes serious performance degradation and even starvation of long links. Existing methods for traditional hidden terminal problem are inefficient to cope with this problem because of the different causes. In this paper, we propose CSMA with Protective Jamming (CSMA/PJ), a new WiLD MAC protocol that solves the temporal hidden terminal problem with the minimized influence on uncontrollable short links. The key is generating protective jamming at the WiLD receiver that is sensible to the short links. By leveraging the asymmetric propagation delay of the WiLD transmitter and receiver, we make the jamming protective rather than destructive. We precisely control the jamming right before the arrivals of WiLD packets to set aside channel time for short links. We implement and evaluate CSMA/PJ on commercial devices. The experimental results show that CSMA/PJ can improve the throughput of the WiLD link by$6\times $and$5 \times $compared with the CSMA/CA and RTS/CTS methods.

Time series forecasting in the sensing system aims to predict future values based on historical records that sensors have collected. Previous works, however, usually focus on improving model structure or algorithm for better performance but the perspective of learning proper numeric representations is overlooked. The inappropriate and coarse numeric representations are not expressive enough to capture the intrinsic characteristics of numbers, which will obviously degrade the prediction performance. In this article, we propose Num2vec, an algorithmic framework to learn numeric representations. Specifically, Num2vec lists three main logic characteristics of numbers: arithmetic, direction, and periodicity. By representing numbers into a transition space, Num2vec can translates numbers agilely to different Internet of Things tasks through selecting the corresponding characteristics. According to such a design, Num2vec enjoys flexible numeric representations to fit different Internet of Things time series tasks. Extensive experiments on four real-world datasets show that the approach achieves the best performance when compared with state-of-the-art baselines.

The massive connection of LoRa brings serious collision interference. Existing collision decoding methods cannot effectively deal with the adjacent collisions that occur when the collided symbols are adjacent in the frequency spectrum. The decoding features relied on by the existing methods will be corrupted by adjacent collisions. To address these issues, we propose Paralign , which is the first LoRa collision decoder supporting decoding LoRa collisions with confusing symbols via parallel alignment. The key enabling technology behind Paralign is tha there is no spectrum leakage with periodic truncation of a chirp. Paralign leverages the precise spectrum obtained by aligning the de-chirped periodic signals from each packet in parallel for spectrum filtering and power filtering. To aggregate correlation peaks in different windows of the same symbol, Paralign matches the peaks of multiple interfering windows to the interested window based on the time offset between collided packets. Moreover, a periodic truncation method is proposed to address the multiple candidate peak problem caused by side lobes of confusing symbols. We evaluate Paralign using USRP N210 in a 20-node network. Experimental results demonstrate that Paralign can significantly improve network throughput, which is over 1.46× higher than state-of-the-art methods.

Adversarial training can defend against universal adversarial perturbation (UAP) by injecting corresponding adversarial samples during training. However, adversarial samples used by existing methods, such as UAP, inevitably include excessive perturbations related to other categories due to its inherent goal of universality. Training with them will cause more erroneous predictions with larger local positive curvature. In this paper, we propose a curvature-aware category adversarial training method to avoid excessive perturbations. We introduce the category-oriented adversarial masks that are synthesized with class distinctive momentum. Besides, we split the min-max optimization loops of adversarial training into two parallel processes to reduce the training cost. Experimental results on CIFAR-10 and ImageNet show that our method achieves better defense accuracy under UAP with less training cost than state-of-the-art baselines.

Adversarial training can boost the robustness of the model by aligning discriminative features between natural and generated adversarial samples. However, the generated adversarial samples tend to have more features derived from changed patterns in other categories along with the training process, which prevents better feature alignment between natural and adversarial samples. Unfortunately, existing adversarial training methods ignore such dynamicity of generated adversarial samples. In this paper, we propose Adversarial Contrastive Decoupling (ACD) to filter the features derived from changed patterns. Specificity, we decouple the changed patterns from adversarial samples and then extract robust representations from remaining features. First, we introduce a decoupling module with a dynamic labeling strategy to explore the dynamicity of generated adversarial samples. Then, we propose a siamese network with contrastive learning mechanism to align remaining robust representations between adversarial and natural samples. Extensive experimental results demonstrate the superior performance of ACD over baselines.

Adaptive bitrate (ABR) algorithm is deployed extensively in commercial video delivery platforms, aiming to ensure users' quality of experience(QoE). Among the majority of existing ABR algorithms, throughput prediction plays a critical role. However, these predictors suffer from neglecting the throughput inconsistency across diverse chunk payloads under the network dynamics, e.g., the actual throughput of downloading a 4K or a 720P chunk is usually different, even when starting from the same moment. In this paper, we propose a payload-aware adaptive algorithm called PAR, which predicts multiple throughput estimations for different target payloads, and utilizes them to make better bitrate adaptation decisions. Trace-driven experiments show that PAR outperforms the existing ABR schemes across diverse network conditions, with the average QoE improvement of 2.66% to 79.43%.

Visual Question Answering (VQA) is a challenging task to answer questions with respect to the image. Most approaches concentrate on utilizing attention networks to focus on crucial objects of the image and key words of the question. However, the attention distribution of these prior attempts tends to lo-cate similar regions, which leads to lack of the ability to derive important entities. To address the issue, we propose a multi-head attention fusion network (MHAFN), which can achieve hierarchical multimodal fusion with various branches to capture the fine-grained and intricate relationship in the perspective of multiple levels: word, region and the interaction of them. Furthermore, it can also capture distinct attention distribution for attending to multiple different visual and textual components that are vital to infer the answer. Extensive ex-periments on the benchmark of VQA-v2 dataset demonstrate that MHAFN significantly outperforms previous methods.

Recent studies show that image retrieval systems are vulnerable to adversarial attacks that adds imperceptible noise to the images. But the imperceptibility of noise limits the attack performance. As a commonly acceptable interference, watermark often appears in images, which can be treated as ‘‘imperceptible’’ but is unexploited. In this paper, we propose a Watermark Attack method on Image Retrieval systems (WAIR). Attacking retrieval systems is challenging due to black-box model, the absence of confidence guide, and attack failures and low efficiency caused by the randomness of traditional evolutionary algorithms. To solve these challenges, we propose a new evolutionary algorithm called Gene Joint Selecting Algorithm (GJSA) that jointly optimize the watermark parameters. We also design the Fitness Record Table (FRT), a new data structure that records the historical attack effect to guide the following evolution and avoid local optimal solutions. Thanks to FRT, WAIR can also reduce the duplicate searching caused by algorithm randomness. The extensive experiments show that WAIR can attack the black-box image retrieval system with a successful rate of 0.806, which is 12.4% higher than the traditional evolutionary algorithm. Moreover, for attacking commercial image retrieval system, we achieve 2$\times$ higher attack success rate on Baidu Image Retrieval API than the existing methods.

The Industrial Internet of Things (IIoT) is deemed a promising direction to drive a new industrial revolution. However, due to the isolation of the existing OT network and IT network, the requirements of low latency, low jitter, and high reliability for transmission and processing of industrial time-sensitive tasks data traffic in IIoT scenarios with strong dynamic and complex topology face a series of non-trivial challenges. In this paper, we propose a hierarchical computing network collaboration architecture for IIoT based on edge/fog computing. Our architecture is built upon the Time-Sensitive Networking (TSN) to flexibly support different requirements of large-scale industrial production applications by constructing the hierarchical computing network collaboration domain, combined with an improved Cyclic Queuing and Forwarding (CQF) scheduling shaper mechanism. We tackle the critical problems of architecture design by presenting three essential components. Moreover, we build and implement our simulation testbed based on Omnet++, and evaluate our design.

LoRa is widely deploying in aquatic environments to support various Internet of Things applications. However, floating LoRa networks suffer from serious performance degradation due to the polarization loss caused by the swaying antenna. Existing methods that only control the transmission starting from the aligned attitude have limited improvement due to the ignorance of aligned period length. In this paper, we propose PolarScheduler, a dynamic transmission control method for floating LoRa networks. PolarScheduler actively controls transmission configurations to match polarization aligned periods. We propose a V-zone model to capture diverse aligned periods under different configurations. We also design a low-cost model establishment method and an efficient optimal configuration searching algorithm to make full use of aligned periods. We implement PolarScheduler on commercial LoRa platforms and evaluate its performance in a deployed network. Extensive experiments show that PolarScheduler can improve the packet delivery rate and throughput by up to 20.0% and 15.7%, compared to the state-of-the-art method.

Cross-Technology Communication (CTC) is an emerging technique that enables direct interconnection among incompatible wireless technologies. Recent work proposes CTC from IEEE 802.11b to LoRa but has a low efficiency due to their extremely asymmetric data rates. In this paper, we propose WiRa that emulates LoRa waveforms with IEEE 802.11ax to achieve an efficient CTC from WiFi to LoRa. By taking advantage of the OFDMA in 802.11ax, WiRa can use only a small Resource Unit (RU) to emulate LoRa chirps and set other RUs free for high-rate WiFi users. WiRa carefully selects the RU to avoid emulation failures and adopts WiFi frame aggregation to emulate the long LoRa frame. We propose a subframe header mapping method to identify and remove invalid symbols caused by irremovable subframe headers in the aggregated frame. We also propose a mode flipping method to solve Cyclic Prefix errors, based on our finding that different CP modes have different and even opposite impacts on the emulation of a specific LoRa symbol. We implement a prototype of WiRa on the USRP platform and commodity LoRa device. The extensive experiments demonstrate WiRa can efficiently transmit complete LoRa frames with the throughput of 40.037kbps and the symbol error rate (SER) lower than 0.1.

LoRa is widely deployed for various applications. Though the knowledge of the channel occupancy is the prerequisite of all aspects of network management, acquiring the channel occupancy for LoRa is challenging due to the large number of channels to be detected. In this paper, we propose LoRadar, a novel LoRa channel occupancy acquirer based on cross-channel scanning. Our in-depth study finds that Channel Activity Detection (CAD) in a narrow band can indicate the channel activities of wide bands because they have the same slope in the time-frequency domain. Based on our finding, we design the cross-channel scanning mechanism that infers the channel occupancy states of all the overlapping channels by the distribution of CAD results. We elaborately select and adjust the CAD settings to enhance the distribution features. We also design the pattern correction method to cope with distribution distortions. We implement LoRadar on commodity LoRa platforms and evaluate its performance on the indoor testbed and the outdoor deployed network. The experimental results show that LoRadar can achieve a detection accuracy of 0.99 and reduce the acquisition overhead by up to 0.90, compared to existing traversal-based methods.

With the popularization of deep learning models in wireless sensing, researchers have made considerable efforts to construct sophisticated models to improve the accuracy of related applications. But very few studies have addressed the potential vulnerabilities of deep models, and existing works evaluate wireless adversarial performance only in communication or sensing. None of them has a comprehensive definition of attack imperceptibility. In this paper, we come up with a definition of the wireless attack imperceptibility for both communication and sensing. Our goal is to craft an adversarial perturbation, which can degrade the performance of WiFi sensing without compromising WiFi communication. To achieve this goal, we propose WiCAM to reveal the temporal and spatial attention of a DNN, capturing the crucial portions of its input. Then we design a mask to limit adversarial perturbation in the attended parts only, and thus the impact of the attack on WiFi communication is minimized. WiCAM is a general adversarial framework that can integrate existing adversarial methods such as FGSM and PGD to generate perturbations. We carry out experiments on three popular WiFi sensing applications, including human activity recognition, gesture recognition, and user identification. Extensive experiments are conducted on both public datasets and self-collected datasets. The results show that when declining the accuracy of a target model below 50%, WiCAM can reduce the impact on communication in terms of BER by up to 77.78% in QAM-64, compared to the common adversarial methods.

WiFi has achieved great success in data communication in the past two decades and WiFi signals are recently further exploited for sensing purposes. Promising progress has been achieved and diverse WiFi sensing applications have been enabled. However, one critical issue which was not paid much attention to and we believe would greatly hinder the real-life adoption of WiFi sensing is that it actually affects WiFi communication. The fundamental reason is that WiFi sensing requires high-frequency signal samples and WiFi data packets can not meet this requirement. Therefore, existing WiFi sensing systems transmit dedicated high-frequency packets (200-2000 packets per second) for sensing and these “sensing packets” greatly affect the main data communication function of WiFi. In this work, we propose WiImg, a lightweight system which involves machine learning techniques to enable WiFi sensing under low packet rate, pushing WiFi sensing one step towards real-life adoption. The key idea is to convert the CSI samples into images and improve the Generative Adversarial Network (GAN) for CSI image inpainting, relaxing the requirement of high sample rate in sensing. To avoid the large training overhead of GAN, we design a lightweight GAN that leverages samples of only three rates to recover the CSI traces of any arbitrary rates. Experiments show that with just 25 packets per second, WiImg is able to increase the recognition accuracy for hand gesture recognition and daily activity tracking from the state-of-the-art 59.1% and 65.9% to 86.7% and 96.4%, respectively.

Automatic modulation classification (AMC) is to identify the modulation type of a received signal, which plays a vital role to ensure the physical-layer security for Internet of Things (IoT) networks. Inspired by the great success of deep learning in pattern recognition, the convolutional neural network (CNN) and recurrent neural network (RNN) are introduced into the AMC. In general, there are two popular data formats used by AMC, which are the in-phase/quadrature (I/Q) representation and amplitude/phase (A/P) representation, respectively. However, most of AMC algorithms aim at structure innovations, while the differences and characteristics of I/Q and A/P are ignored to analyze. In this article, lots of popular AMC algorithms are reproduced and evaluated on the same data set, where the I/Q and A/P are used, respectively, for comparison. Based on the experimental results, it is found that: 1) CNN-RNN-like algorithms using A/P as input data are superior to those using I/Q at high signal-to-noise ratio (SNR), while it has an opposite result in low SNR and 2) the features extracted from I/Q and A/P are complementary to each other. Motivated by the aforementioned findings, a multitask learning-based deep neural network (MLDNN) is proposed, which effectively fuses I/Q and A/P. In addition, the MLDNN also has a novel backbone, which is made up of three blocks to extract discriminative features, and they are CNN block, bidirectional gated recurrent unit (BiGRU) block, and a step attention fusion network (SAFN) block. Different from most of CNN-RNN-like algorithms (i.e., they only use the last step outputs of RNN), all step outputs of BiGRU can be effectively utilized by MLDNN with the help of SAFN. Extensive simulations are conducted to verify that the proposed MLDNN achieves superior performance in the public benchmark.

Millimeter wave (mmWave) sensing promises to enable contactless and high-precision “in-air” gesture-based human–computer interaction (HCI). While previous works have demonstrated its feasibility, they require tedious gesture collecting for person-independent recognition and they operate in an off-line mode without considering practical issues, such as segmenting gesture and recognition latency. In this work, we proposeM-Gesture, a person-independent real-time mmWave gesture recognition solution. We first build a compact gesture model with a custom-designed neural network to distill the unique features underlying each gesture, while suppressing personalized discrepancy across different users without extra collection and retraining. Furthermore, we design a system status transition (SST) to decide when a gesture begins and ends, which enables automatic gesture segmentation and hence real-time recognition. We prototypeM-Gestureon a commodity mmWave sensor and demonstrate its advantages using two practical applications: 1) a contactless music player and 2) camera. Extensive experiments and user studies show thatM-Gesturehas an accuracy of 99% and a short response latency within 25 ms. Moreover, we also collect and release a comprehensive mmWave gesture data set consisting of 54 620 instances from 144 persons, which may have an independent value of facilitating future research.

Multistep service-level passenger flow forecasting is of great value in bus transit systems. This task is faced with great challenges due to complicated and dynamic spatial–temporal dependencies, such as interstation semantic dependencies, interline spatial dependencies, and interservice temporal dependencies, which are not effectively modeled by existing methods. To address these challenges, we propose a spatiotemporal hashing multigraph convolution network, called ST-HMGCN. ST-HMGCN constructs two types of subgraphs from perspectives of physical adjacency and semantic similarity to explicitly capture spatial–temporal dependencies among bus stations/lines, and integrates the interservice temporal correlations to achieve the service-level bus passenger flow forecasting. Moreover, it utilizes the hashing graph convolution to extract the dynamic spatial correlations among graph nodes. Furthermore, a temporal-attention block with residual connections is used to model the nonlinear temporal correlations between different time intervals of each station, which significantly reduces the error propagation among prediction time steps. Finally, we use a large-scale real bus operation data set to conduct an extensive evaluation of ST-HMGCN and 11 state-of-the-art baselines, and further leverage the passenger prediction results of our model to provide crowdedness-aware route recommendation. The experimental results verify the effectiveness of the proposed modeling method and its application value in intelligent transportation.

Fast and accurate plant counting tools affect revolution in modern agriculture. Agricultural practitioners, however, expect the output of the tools to be not only accurate but also explainable. Such explainability often refers to the ability to infer which instance is counted. One intuitive way is to generate a bounding box for each instance. Nevertheless, compared with counting by detection, plant counts can be inferred more directly in the local count framework, while one thing reproaching this paradigm is its poor explainability of output visualization. In particular, we find that the poor explainability becomes a bottleneck limiting the counting performance. To address this, we explore the idea of guided upsampling and background suppression where a novel upsampling operator is proposed to allow count redistribution, and segmentation decoders with different fusion strategies are investigated to suppress background, respectively. By integrating them into our previous counting model TasselNetV2, we introduce TasselNetV3 series: TasselNetV3-Lite and TasselNetV3-Seg. We validate the TasselNetV3 series on three public plant counting data sets and a new unmanned aircraft vehicle (UAV)-based data set, covering maize tassels counting, wheat ears counting, and rice plants counting. Extensive results show that guided upsampling and background suppression not only improve counting performance but also enable explainable visualization. Aside from state-of-the-art performance, we have several interesting observations: 1) a limited-receptive-field counter in most cases outperforms a large-receptive-field one; 2) it is sufficient to generate empirical segmentation masks from dotted annotations; 3) middle fusion is a good choice to integrate foreground–backgrounda prioriknowledge; and 4) decoupling the learning of counting and segmentation matters.

In object counting, objects often exhibit different sizes at different scales, even if they have similar physical sizes in reality. This is particularly true when targeting crowd counting and vehicle counting in intelligent transportation. Failing to model such variations leads to the mismatch between the object size and image scale. To address this problem, existing methods often extract multi-scale features, but they either still generate the single-scale prediction or lack an explicit suppression mechanism to eliminate predictions engendered by inappropriate scales. Our scale analysis manifests that, the single-scale estimation only works well for objects of certain sizes, and a suppression operator is required to isolate the estimation of a specific scale. In this work, we propose a scale-aware counting network termed NSSNet. NSSNet has two key features: it not only i) generates multi-scale predictions but also ii) applies a novel non-scale suppression (NSS) operator to suppress scale-mismatched estimations. NSS is inspired by the widely-used non-maximum suppression (NMS). In contrast to NMS that only reserves the maximum response, NSS filters out those clearly wrong predictions (the remaining predictions may still be from multiple scales). We evaluate NSSNet on four standard crowd and vehicle counting benchmarks and report state-of-the-art performance. We also show the scale adaptability of NSSNet through a controlled multi-scale experiment. Code and pretrained models are available athttps://git.io/nssnet.

Low Power Wide Area Networks (LPWAN) such as Long Range (LoRa) show great potential in emerging aquatic IoT applications. However, our deployment experience shows that the floating LPWAN suffers significant performance degradation, compared to the static terrestrial deployments. Our measurement results reveal the reason behind this is the polarization and directivity of the antenna. The dynamic attitude of a floating node incurs varying signal strength losses, which is ignored by the attitude-oblivious link model adopted in most of the existing methods. When accessing the channel at a misaligned attitude, packet errors can happen. In this paper, we propose an attitude-aware link model that explicitly quantifies the impact of node attitude on link quality. Based on the new model, we proposePolarTracker, a novel channel access method for floating LPWAN.PolarTrackertracks the node attitude alignment state and schedules the transmissions into the aligned periods with better link quality. To support concurrent access of multiple LoRa nodes, an attitude-based slotted-ALOHA protocol is proposed to reduce collision. We implement a prototype ofPolarTrackeron commercial LoRa platforms and extensively evaluate its performance in various real-world environments. The experimental results show thatPolarTrackercan efficiently improve the packet reception ratio by 50.6%, compared with ALOHA in LoRaWAN.

Unreliable labels derived from large-scale dataset prevent neural networks from fully exploring the data. Existing methods of learning with noisy labels primarily take noise-cleaning-based and sample-selection-based methods. However, for numerous studies on account of the above two views, selected samples cannot take full advantage of all data points and cannot represent actual distribution of categories, in particular if label annotation is corrupted. In this paper, we start from a different perspective and propose a robust learning algorithm called DualGraph, which aims to capture structural relations among labels at two different levels with graph neural networks including instance-level and distribution-level relations. Specifically, the instance-level relation utilizes instance similarity characterize sample category, while the distribution-level relation describes instance similarity distribution from each sample to all other samples. Since the distribution-level relation is robust to label noise, our network propagates it as supervised signals to refine instance-level similarity. Combining two level relations, we design an end-to-end training paradigm to counteract noisy labels while generating reliable predictions. We conduct extensive experiments on the noisy CIFAR-10 dataset, CIFAR-100 dataset, and the Clothing1M dataset. The results demonstrate the advantageous performance of the proposed method in comparison to state-of-the-art baselines.

Low Power Wide Area Networks (LPWAN) such as Long Range (LoRa) show great potential in emerging aquatic IoT applications. However, our deployment experience shows that the floating LPWAN suffer significant performance degradation, compared to the static terrestrial deployments. Our measurement results reveal the reason behind this is due to the polarization and directivity of the antenna. The dynamic attitude of a floating node incurs varying signal strength losses, which is ignored by the attitude-oblivious link model adopted in most of the existing methods. When accessing the channel at a misaligned attitude, packet errors can happen. In this paper, we propose an attitude-aware link model that explicitly quantifies the impact of node attitude on link quality. Based on the new model, we propose PolarTracker, a novel channel access method for floating LPWAN. PolarTracker tracks the node attitude alignment state and schedules the transmissions into the aligned periods with better link quality. We implement a prototype of PolarTracker on commercial LoRa platforms and extensively evaluate its performance in various real-world environments. The experimental results show that PolarTracker can efficiently improve the packet reception ratio by 48.8%, compared with ALOHA in LoRaWAN.

Deep learning-based person re-identification (Re-ID) methods have achieved the significant performance of matching person images across camera views, which plays an important role in the construction of the smart city. Recent works of adversarial attacks have explored the serious vulnerability of deep Re-ID systems. However, existing attacks are performed with idealized conditions and ignore the real-world environments, such as occlusion caused by walking habits. In this paper, we propose a two-stage method to perform an occlusion resilient adversarial attack for better evaluation of deep Re-ID systems. Specifically, we construct the occlusion template from the observation and statics of pedestrian walking habits. Then, we design a partition training strategy for a better combination of occluded and exposed adversarial patches. During the training, we introduce contextual loss to penalize the semantic distance of attacked images with the same identity. The extensive experiments on Market1501 demonstrate the performance of our method.

Deep neural networks are easily affected by adversarial patches, causing prediction errors. However, existing adversarial patches are trained with specific model-dataset pairs and only effective for the images with the predetermined size in the dataset. The semantic information of the patch will be distorted when scaling the image, which is a common preprocessing process in practical applications. In this paper, we propose SRA Patch (Scaling-Resilient Adversarial Patch), a new adversarial patch resilient to image scaling. Specifically, we generate the patch in a block-wise way and utilize the superpixel method to resist the loss of semantic information during scaling. Further, we introduce the ensemble model as a Black-Box indicator to address the noise space shrinking issue, which is caused by the small size and the block operation of SRA patch. Finally, we leverage Class Activation Mapping to extract the region with salient features as the final patch to improve the ratio of effective semantic features on the patch to remain during scaling. Extensive experiments have demonstrated that our SRA patch has much stronger attack capability and scaling robustness than existing methods.

The advancements of AI and the exponential growth of sensory data are unlocking a wave of intelligent sensing applications. To overcome the shortcoming of centralized learning and local training, Google proposes federated learning that allows users to collectively reap the benefits of shared models trained from decentralized data. However, directly applying federated learning to intelligent sensing applications faces two deficiencies: 1) omitting personalities of local models and 2) high latency. Aiming at these limitations, in this article, we propose a new framework, Federated Sensing, to enable edge-cloud elastic collaborative learning from decentralized sensory data. We design an elastic local update algorithm that can train the personalized models by setting specific updating weights for each node based on the difference between the global and local model. Our algorithm takes both the global consistency and the personalities of the local models. We further propose an n-softsync model aggregation method that significantly reduces training time by combining the synchronous and asynchronous aggregations. Extensive experiments are conducted on two real-world data sets of air quality from Beijing and Los Angeles. Compared with existing federated learning techniques, our framework improves the model performance at least by 2.61% and 18.8% in two data sets, respectively. Besides, it reduces the cloud idle time to 25.5% of the total time, which verifies the advantages of our method in terms of both model performance and training overhead.

Monitoring the status of urban environmental phenomenon, which provides fundamental sensory information, is of great significance for various field of urban research. In this article, we propose a new framework, environmental signal reconstruction generative adversarial network, for reconstructing high-quality environmental signal via sensory data from sparsely distributed monitoring sites. Our framework is based on the generative adversarial network (GAN), in which a three-layer convolutional neural network (CNN)-based generative model is proposed to learn an end-to-end mapping between low- and high-quality signals and a discriminative model is introduced for quantizing the reconstruction accuracy. Considering the scattered distribution of sensory data, we further propose a metric called impact map for building loss function and guiding the adversarial training. Experiments with real-world air quality data of Beijing demonstrate that our method outperforms the state-of-the-art data inference techniques in terms of signal recovery accuracy.

The Internet of Things (IoT) aims to interconnect and converge the physical world and cyberspace. It represents the future of networking, and leads the wave of the third industrial revolution. As IoT applications evolve, more and more regions lacking infrastructures are also experiencing an urgent need to deploy and connect IoT devices into the network. Low earth orbit satellites, due to its wide coverage and relatively low latency, can provide network access for IoT system. In this article, we propose a low-latency heterogeneous network MAC approach for space-earth integrated IoT network, which can significantly reduce network latency, improve system scalability and balance total system load. Specifically, we design a geographic priority time slot allocation strategy which ensures optimal load and latency, besides, we also design a continuous forward time slot allocation strategy, which guarantees data timeliness while reducing the utilization of satellite resources. Then we design three algorithms based on above strategies, which can effectively reduce network latency and balance the satellite resources. The simulation experimental results are consistent with the theoretical results, which embodies the effectiveness of the algorithm.

Mobile CrowdSensing (MCS) is a new paradigm that leverages pervasive mobile devices to efficiently collect the big sensory data, enabling various large-scale applications. However, people's concerns about the loss of individual privacy seriously hinder the prevalence of MCS applications. Differential privacy is widely focused owing to its rigorous definition and strong privacy guarantee, but the state-of-the-art studies still demonstrate its weakness on correlated data, resulting in compromising individual privacy. In this paper, we investigate the influence of sensing data correlation on differential privacy protection for MCS systems, and explore the perturbation mechanisms from two different perspectives. From a protector's perspective, based on the Bayesian Network to model the probabilistic relationship among sensing data, we use the classical definition of differential privacy to deduce the scale parameter, and present one perturbation mechanism. From an adversary's perspective, based on the Gaussian correlation model to describe the data correlation, we analyze the importance of the maximum correlated group to compute the Bayesian differential privacy leakage, and then provide another perturbation mechanism. Compared with the existing solutions, our mechanisms are applicable to arbitrary aggregate query function, and can avoid introducing too much noise. Moreover, we demonstrate the effectiveness of our mechanisms through extensive simulations.

Mobility models, which reproduce traces with basic crowd mobility patterns, are crucial for realistic mobile network simulation and performance evaluation of the planning strategies used for urban networks (such as transit network, communication network, and crowdsensing network). However, trajectories generated by traditional models are often perceived as not realistic for urban context or lack of scalability and universality. This article presents a data-driven trajectory generating technique, named as TraG, that produces synthetic trajectories with the help of some real-world trajectories. Our technique can automatically extract the context features and statistical mobility features, which characterize the mobility of a specific urban crowd from the input empirical traces and, then, regenerate more trajectories based on demand. Moreover, we also summarize the power-law scale correlation of crowd mobility based on four real-world open datasets, including public bicycle traces in New York City and Washington, D.C., taxicab traces in San Francisco and Shenzhen. Finally, we validate the proposed TraG model via the continuous San Francisco taxicab traces, and the result demonstrates that the trajectories simulated by TraG not only inherit the fundamental statistical features of crowd mobility from real traces, but also reflect the features of urban context.

Bus services play a crucial role in urban transit. It is significant to achieve the fine-grained service-level passenger flow prediction (SPFP), namely to predict the total number of passengers for each service of each bus line passing through each station during the next short-term interval. However, it faces great challenges due to complex factors including inter-station and inter-line spatial dependencies, intra-station and inter-service temporal dependencies, and internal/external influences. To address these challenges, we propose a multitask deep-learning (MDL) approach, calledMDL-SPFP, to jointly predict the arriving bus service flow, line-level on-board passenger flow and line-level boarding/alighting passenger flow by leveraging well-designed deep neural networks calledARM. The MDL framework can mutually reinforce the prediction of each type of flow, and finally integrate the outputs to achieve the fine-grained service-level prediction. The ARM network combines three modules, Attention mechanism, Residual block and Multi-scale convolution, to well capture various complex non-linear spatio-temporal dependencies and influence factors. Extensive experiments based on a large-scale realistic bus operation dataset are conducted to confirm that our MDL-SPFP approach outperforms 10 state-of-the-art baselines, and improves 22.39% accuracy than the best baseline.

Cross-Technology Communication (CTC) is an emerging technique that enables direct interconnection among incompatible wireless technologies. However, CTC channels are inherently asymmetric because of either the one-way nature of emulation or the asymmetric communication range caused by the asymmetric transmission power. In this paper, we focus on establishing symmetric CTC over asymmetric CTC channels. The bottleneck is the short communication range from the low-power and narrow-band technology to the high-power and wide-band technology. To compensate the inevitable distortions, we take advantage of the channel asymmetry and construct chirps in WiFi Channel State Information (CSI) to extend the communication range from ZigBee to WiFi. We build the theoretical model of CSI chirp based CTC and design c-Chirp, a novel CTC from ZigBee to WiFi. Due to channel asymmetry and discreteness, the WiFi receiver can only observe partial and distorted CSI chirps. To cope with this issue, we design a matching based chirp decoding method as well as an adaptation algorithm to reliably decode the symbols. We further extend c-Chirp to one-to-multiple concurrent transmission scenario. The evaluation results show that c-Chirp can achieve a communication range of 60m, which is 6× longer than ZigFi, an existing representative CTC from ZigBee to WiFi.

Cross-Technology Interference (CTI) badly harms the transmission reliability for low-power networks such as ZigBee at 2.4-GHz band. Though promising, channel hopping still faces challenges because the increasingly dense deployment of CTI leaves very few available channels. Selecting a good channel with the least overhead is crucial but challenging. Most of the existing works are heuristic methods that choose a channel far from the current one to avoid adjacent channels that may be correlatively interfered by CTI with a wider bandwidth such as WiFi. However, we observe that the correlated channels influenced by the same CTI source do not necessarily have the same channel qualities and even the opposite state, due to the uneven spectrum power density of CTI. Such channel opportunities are unexplored and wasted. In this article, we propose CoHop, a quantitative correlation-based channel hopping method for low-power wireless networks. We establish a quantitative model that describes the correlation of channel qualities to capture channel opportunities and calculate channel quality without probing, to reduce probing overhead. The probing sequence is optimized based on the Pearson Correlation Coefficient and the prediction-based probing algorithm. We implement CoHop on TinyOS and evaluate its performance in various environments. The experimental results show that CoHop can increase the Packet Reception Ratio by 80%, compared with existing methods.

Monitoring the status of urban environmental phenomena is of great significance for urban research and management. While The monitoring sites are often insufficient and unevenly, interpolation values vary in urban spaces non-linearly. It is difficult to find a method that fulfills the requirements of accuracy, robustness, and flexibility for various types of phenomena. In this paper, we present a new kind of deep learning driven spatial interpolation method which works on the grid data that can be applied on the unevenly distributed sites. To generate better accurate spatial continuous data, we design the S2 attention structure and incorporate it with the GAN to turn it into SI-AGAN which can model spatial dependencies across different regions via sparsely and unevenly distributed sampling. It can directly learn an end-to-end mapping between low- and high-quality environmental signals without in-depth knowledge of the phenomenon. Experiments on two real-world air-pollution datasets demonstrate that our training strategy effectively makes the GAN work for the interpolation of uneven data and our proposed SI-AGAN significantly outperforms previous state-of-the-art spatial interpolation methods.

Cross-Technology Communication (CTC) is an emerging technique that enables the direct communication across incompatible wireless technologies. Without modifying any hardware, CTC establishes mutually sensible side channel by manipulating the packet transmissions and encodes information by constructing transmission patterns in terms of signal strength, packet interval, and etc. However, the transmission patterns are prone to the coexisting interference, leading to the unreliability of CTC. Most of the existing methods deal with the reliability problem by reactive retransmission of the corrupted packets, which incurs large delay. In this paper, we propose RCTC, a rateless-coding based CTC that proactively copes with the unreliability. Since the computation ability of low-power ZigBee nodes is limited, we carefully design the coding combination with proper degree distribution to balance the trade-off between reliability and decoding latency. We also propose a coding adaptation algorithm to adapt to the channel dynamics. We implement a prototype on commercial WiFi and ZigBee platforms. The experiment results show that RCTC can reduce the BER by up to 92.6%, compared to existing CTC methods.

Most existing domain adaptation frameworks were based on the strict assumption that different domains share the same label space, which is too idealized for real-world applications. In this paper, we examine a realistic case of open set domain adaptation with partially shared classes in two domains and consider the negative transfer brought by the unknown class from the target domain. Therefore, how to distinguish the unknown class from the known ones plays a key role. To achieve this goal, we propose a new approach called Open Distribution Control Adaptation Network(OpenDCAN). Inspired by the idea of Out-of-Distribution Detection, our method tries to explicitly model the distribution of the known source samples to differentiate it from unknown class in probabilistic distribution, which is different from existing methods. We use OpenBP, an open set domain adaptation framework, to provide a rough decision boundary between known and unknown classes. Then we further boost the performance by modifying the recently proposed Adaptive Feature Norm algorithm for the open set setting. If the probabilistic distribution of source samples is modeled, a likelihood estimate can easily detect unknown class without requiring any unknown samples in the source domain to get the concept of “unknown”. Experiments on representative benchmark datasets demonstrate the effectiveness of our approach.

Opportunistic forwarding seizes early forwarding opportunities in duty-cycled networks to reduce delay and energy consumption. But increasingly serious Cross-Technology Interference (CTI) significantly counteracts the benefits of opportunistic forwarding. Existing solutions try to reserve the channel for low-power networks by implicit avoidance or explicit coordination but ignore the potential of high-power CTI's superior capability. In this paper, we propose a new paradigm for low-power opportunistic forwarding in CTI environments. Instead of keeping high-power CTI devices silent, we directly involve them into the forwarding, as cross-technology forwarders. We design Portal to solve the challenges of realizing cross-technology opportunistic forwarding. To be transparent to the low-power networks, Portal adopts cross-technology rebroadcasting to enable the fast overhearing and forwarding of cross-technology data. To maximize the performance gain of using heterogeneous forwarders while minimizing the influence on legacy high-power traffic, we propose a post-forwarding forwarder selection and a traffic scheduling method. We also propose a feature-based ACK recognition method and a jamming-based ACK replying mechanism to forward the unreliable ACKs from asymmetric regions. Extensive experiments demonstrate that Portal not only avoids the CTI but also breaks through the existing performance limit.

As a new type of highly collaborative and shared intelligent network between producers and production environments, Industrial Internet of Things (IIOT) has been taken an important part of the fourth industrial revolution. IIOT generates large amounts of sensory data which need to be processed rapidly. However, the cloud-based data processing method consumes a long time and huge network overhead, which further affects the quality of service. On the other hand, the emerging edge computing also cannot process data efficiently because of limited compute and network resource. In this paper, we propose a four-layer network architecture based on SDN for the industrial internet of things scenario. Through effective transmission and computation coupling, the processing response efficiency is improved. We present a three-level computation offloading method to realize the optimization of network delay and power consumption. Theory and experiments show that the method proposed in this paper can effectively reduce the computation power consumption and response time.

Thanks to Industrial Internet of Things (IIoT), traditional industry is transforming to the fine and flexible production. To comprehensively control the dynamic industrial processes that includes marketing and production, accurate productivity is a vital factor that can reduce the idle operation and excessive pressure of the equipment. Due to increasing requirements of flexible control desired by IIoT, the productivity forecast also demands finer granularity. However, due to the neglect of multiple related factors and the ignorance of the multi-frequency characteristics of productivity, existing methods fail to provide accurate fine-grained productivity forecasting service for IIoT. To fill this gap, we propose a multivariate and multi-frequency Long Short-Term Memory model (mmLSTM) to predict the productivity in the granularity of day. mmLSTM takes equipment status and order as new supporting factors and leverages a multivariate LSTM to model their relationship to productivity. mmLSTM also integrate a multi-level wavelet decomposition network to thoroughly capture the multi-frequency features of productivity. We apply the proposed method in a real-world steel factory and conduct a comprehensive evaluation of performance with the productivity data in nearly two years. The result shows that our method can effectively improve the prediction accuracy and granularity of industrial productivity.

Cross-Technology Interference (CTI) badly harms the transmission reliability for low-power networks such as ZigBee at 2.4GHz band. Though promising, channel hopping still faces challenges because the increasingly dense deployment of CTI leaves very few available channels. Selecting a good channel with the least overhead is crucial but challenging. Most of the existing works are heuristic methods that choose a channel far from the current one to avoid adjacent channels that may be correlatively interfered by CTI with a wider bandwidth such as WiFi. However, we observe that the correlated channels influenced by the same CTI source do not necessarily have the same channel qualities and even the opposite state, due to the uneven spectrum power density of CTI. Such channel opportunities are unexplored and wasted. In this paper, we propose CoHop, a quantitative correlation based channel hopping method for low-power wireless networks. We establish a quantitative model that describes the correlation of channel qualities to capture channel opportunities and calculate channel quality without probing, to reduce probing overhead. We implement CoHop on TinyOS and evaluate its performance in various environments. The experimental results show that CoHop can increase the Packet Reception Ratio (PRR) by 80%, compared with existing methods.

Cross-Technology Communication (CTC) is an emerging technique that enables direct interconnection among incompatible wireless technologies. However, CTC channels established by existing methods are inherently asymmetric because of either the one-way nature of emulation in physical-level CTC or the asymmetric communication range caused by the asymmetric transmission power. In this paper, we focus on establishing symmetric CTC over asymmetric CTC channels. The bottleneck is the short communication range from the low-power and narrow-band technology to the high-power and wide-band technology because the asymmetric bandwidth and transmission power lead to serious symbol distortions. To compensate the inevitable distortions, we take advantage of the channel asymmetry and construct chirps in WiFi Channel State Information (CSI) to enhance the patterns used for conveying data. In this way, we can extend the communication range from ZigBee to WiFi. We theoretically build the model of CSI chirp based CTC and design c-Chirp, a novel CTC from ZigBee to WiFi. Due to channel asymmetry and discreteness, the WiFi receiver can only observe partial and distorted CSI chirps. To cope with this issue, we design a matching based chirp decoding method as well as an adaptation algorithm to reliably decode the symbols. We conduct extensive experiments to evaluate c-Chirp. The results show that c-Chirp can achieve a 60m communication range from ZigBee to WiFi, which is 6× longer than ZigFi, an existing representative CTC from ZigBee to WiFi.

The prosperity of Internet of Things (IoT) brings forth the deployment of large-scale sensing systems such as smart cities. The distributed devices upload their local sensing data to the cloud and collaborate to fulfill the large-area tasks such as pollutant diffusion analysis and target tracking. To accomplish the collaboration, time synchronization is crucial. However, due to the long range and device heterogeneity, accurate time synchronization for a large-scale IoT network is challenging. Existing GPS or NTP solutions either require an outdoor environment or only have low and unstable accuracy. In this paper, we propose AirSync, a novel synchronization method that leverages the widely existed aircraft signals, ADS-B, to synchronize large-scale IoT networks with nodes even in indoor environments. But ADS-B messages have no time stamp and cannot provide a reference time. We leverage the continuity of aircraft movements to estimate the aircraft traveling time. Then devices that observe common aircraft moving segments can calculate their time offset. To obtain the time skew, we propose a combined aircraft linear regression method. We also design a transitive synchronization for devices that cannot observe common aircraft. We implement a prototype of AirSync and evaluate its performance in various real-world environments. The results show that AirSync can obtain the sub-ms accuracy.

5G, as a monumental shift in cellular communication technology, holds tremendous potential for spurring innovations across many vertical industries, with its promised multi-Gbps speed, sub-10 ms low latency, and massive connectivity. On the other hand, as 5G has been deployed for only a few months, it is unclear how well and whether 5G can eventually meet its prospects. In this paper, we demystify operational 5G networks through a first-of-its-kind cross-layer measurement study. Our measurement focuses on four major perspectives: (i) Physical layer signal quality, coverage and hand-off performance; (ii) End-to-end throughput and latency; (iii) Quality of experience of 5G's niche applications (e.g., 4K/5.7K panoramic video telephony); (iv) Energy consumption on smartphones. The results reveal that the 5G link itself can approach Gbps throughput, but legacy TCP leads to surprisingly low capacity utilization (< 32%), latency remains too high to support tactile applications and power consumption escalates to 2 - 3x over 4G. Our analysis suggests that the wireline paths, upper-layer protocols, computing and radio hardware architecture need to co-evolve with 5G to form an ecosystem, in order to fully unleash its potential.

Information-centric networking (ICN) is regarded as a promising architecture for Internet of Things (ICN-IoT) and access control is one of the critical problems to enable secure ICN-IoT. This article proposes high efficient access control (HAC), a high efficient access control system for ICN-IoT. Specifically, HAC enables access control via an elaborate designed hierarchical key tree (HKT) mechanism based on the hierarchical naming scheme of ICN. The proposed HKT contains the hierarchical authority information and allow users to locally derive the key according to their needs, thus greatly decreasing the overhead in IoT's many-to-many communication scenario. To ensure the security and efficiency of HKT, we further propose a level-oriented ciphertext policy attribute-based encryption (LOCP-ABE) algorithm such that users can only obtain the authority level according to their attributes, and also utilize the ICN's receiver-driven model and the in-network caching mechanism to speed up the distribution efficiency. Moreover, an attribute-based command verification mechanism is used to improve the efficiency of command verification for resource-constrained and isolated IoT edge. We evaluate the proposed HAC by theoretical security analysis and real-world experiments. The theoretical analysis proves that the proposed HAC is secure and experiment results show that HAC can greatly improve the access control efficiency in ICN-IoT compared with the state of the art.

In this paper, we introduce the idea of blockwise classification to count objects. The current mainstream method for counting objects is to regress the density map or to regress the redundant count map via a deep convolutional neural network (CNN). However, these methods suffer from two critical issues: inaccurately generated regression targets and serious sample imbalances. First, the ground truth density map is generated by convolving the dot map using a Gaussian kernel. Because an inappropriate kernel can cover the background or uncover objects, this approach introduces a form of noise, and therefore results in ambiguities when training the networks. Second, inhomogeneously distributed objects often exist in images, which gives rise to a data collection bias. This leads to a long-tailed distribution of region counts, which is a typical characteristic that occurs with imbalanced samples; therefore, underestimations in high-density regions and overestimations in low-density regions are common. In this paper, we address these two issues within one framework-blockwise count level classification. The intuition behind this idea is that while it may not be possible to provide an exact count of pixels or patches, it is possible to provide a count of a region that falls within a certain interval with high confidence. Our method classifies the count levels of each block produced by nonlinearly quantizing the continuous counts, thus transforming the imbalance of sample patch counts into a class imbalance of count levels. Consequently, an information-entropy-inspired loss can be applied to alleviate this issue. Through ablative studies, we analyze the impact of imbalanced data, Gaussian kernel sizes, quantization errors, and the effectiveness of each module in our method. Without bells and whistles, our method outperforms or performs competitively with other state-of-the-art approaches on seven object-counting benchmarks, including four crowd-counting datasets from ShanghaiTech, WorldExpo'10, UCF-QNRF and UCF_CC_50, one vehicle-counting dataset (TRANCOS), one maize-tassel-counting dataset (MTC), and one challenging sonar fish-counting dataset that we constructed. The results suggest that our framework provides a strong and improved baseline for object counting.

Building an open global sensing layer is critical for the Internet of Things (IoT). In this article, we present a Sensory Data-Centric Networking (SDCN) architecture for inter-networking two main networked sensing systems in IoT—wireless sensor networks and mobile sensing networks. Specifically, the proposed SDCN is a systematic solution including NDNs for sensor nodes in the Zigbee network, NDNm for mobilephones in the Wi-Fi network, and NDNg for gateways. Considering the sensing requirement of IoT, we first design a novel Spatio-Temporal 16 Tree (ST16T) naming scheme associated with the scope-matching method. Based on the naming scheme, we further propose the related discovery methods, network switching mechanism, forwarding, and routing strategies according to the features of large-scale sensing and resource-constrained environment. A proof-of-concept prototype is implemented and further is deployed on our campus (BUPT) and the Great Wall (Shaanxi, China) for Environment Monitoring Project. Several experiments are conducted on the deployed platform. The experimental results show that SDCN outperforms the state-of-the-arts and gains a great performance improvement in terms of energy consumption, data collection efficiency, memory footprint, and time delay.

Google congestion control (GCC) is the de-facto standard for web real-time communications (WebRTC) applications and has been implemented in mainstream browsers including Chrome and Firefox. While GCC is designed to achieve high video bit-rate and low latency simultaneously, we find that GCC's performance is far from satisfactory particularly under good network conditions. In particular, we collect a GCC trace dataset with over 1.18 million sessions from a major crowd-sourced live video streaming service provider. We perform in-depth analytics using the dataset, which shows that the sending video bit-rate unnecessarily experiences frequent rollbacks caused by minor fluctuation of transmission delay. To address the issue, we propose a mechanism called GCC-β, which can distinguish normal network fluctuation from real network congestion, and then adaptively sends appropriate bitrates. We implement GCC-β in the WebRTC framework and evaluate its performance using test-bed experiments. The results show that GCC-β is able to avoid up to 90% unnecessary bitrate rollbacks.

In this paper, we focus on the coverage and capacity analysis for LEO satellite network supporting Internet of Things. We propose a metric of coverage degree to measure average cover level during a return circle of a satellite constellation in different latitudes, and furter consider the spot beam communication process with TDMA as a discrete time queueing process to caculate the number of devices in a unit area, which can be accessed during a period. Finally, we derive the relationship among device density, maximum tolerant delay, and coverage degree of the satellite constellation. The obtained theoretical results are verified by extensive simulation experiments.

Visual counting, a task that predicts the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in [0,+∞) in theory. However, the collected images and labeled count values are limited in reality, which means only a small closed set is observed. Existing methods typically model this task in a regression manner, while they are likely to suffer from an unseen scene with counts out of the scope of the closed set. In fact, counting is decomposable. A dense region can always be divided until the count values of sub-regions are within the previously observed closed set. Inspired by this idea, we propose a simple but effective approach, Spatial Divide-and-Conquer Network (S-DCNet). S-DCNet learns to classify closed-set counts and can generalize to open-set counts via S-DC. S-DCNet is also efficient. To avoid repeatedly computing sub-region convolutional features, S-DC is executed on the feature map instead of on the input image. S-DCNet achieves the state-of-the-art performance on three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QNRF), a vehicle counting dataset (TRANCOS) and a plant counting dataset (MTC). Compared to the previous best methods, S-DCNet brings a 20.2% relative improvement on the ShanghaiTechPart B, 20.9% on the UCF-QNRF, 22.5% on the TRANCOS and 15.1% on the MTC. Code has been made available at: https://github.com/xhp-hust-2018-2011/S-DCNet.

Semantic segmentation is widely used in the industry recently, especially in the field of scene understanding, surveillance and autonomous driving. However, majority of current state-of-the-art algorithms run accompany with high consumption of computation resources. Thus, our work focuses on real-time semantic segmentation which could reduce a large proportion of computation. Traditional methods to speed up segmentation process tend to down sample image. However, down sampling would cause the loss of information. Hence, we propose a real-time edge-based segmentation network (ESNet) that incorporate high-resolution global edge information with low-resolution classification-level semantic information. Our network performs real-time inference on single GPU card on high-resolution Cityscapes dataset.

IoT device are usually built as edge networks in smaller areas, but with the development of technology, more and more areas without infrastructure need to be covered. LEO satellites, due to its full coverage of the Earth and relatively low latency, can provide network access for IoT system. In this paper, we propose a hybrid TDMA MAC protocol LST-MAC for IoT network based on LEO satellite, which can reduce network delay and improve the system scalability. Specifically, we design a geographic priority time slot allocation strategy which can ensure optimal load and delay, besides, we also design a continuous forward time slot allocation strategy, which guarantees real-time data while reducing the utilization of satellite resources. Then we design two algorithms based on both strategies, which can effectively reduce network delay and balance the satellite resources. The simulation experimental results are consistent with the theoretical results, which embodies the effectiveness of the algorithm.

Low Earth Orbit (LEO) satellite networks play an indispensable role in global communications. To cope with the dynamic nature of the LEO networks, a flexible management architecture that can provide low-latency configuration and routing services is desired. Recent advances of Software Defined Networks (SDN) inspire researches using geostationary satellites, GEO, as the controllers to build the SDN solutions for LEO satellite networks. However, GEO-based solutions inevitably face the bottleneck problem because all the routing requests have to be processed and forwarded by a limited number of GEO satellites. In this paper, we propose HTCA, a Hierarchical Terrestrial Controllers Architecture based on SDN that reuses the ground stations instead of dedicated GEO satellites to establish a more scalable control plane. But the limited coverage of a ground station brings about design challenges for consistent network management and seamless routing service. HTCA adopts an online network view integration method to support flexible and consistent management. A load-aware routing method is also designed for HTCA to provide seamless and low-latency routing service. The experiment results demonstrate that HTCA can achieve agile configuration and reduce the instruction update time by 86.87%, compared with the GEO-based solution.

This article proposes IoTCache, a popularity-based caching solution for Internet of Things (IoT). We first build a large popularity dataset that reflects peoples interest and request pattern for IoT data. By analyzing the popularity features of the dataset, we propose the popularity evolving model (PEM) for answering the fundamental problem that what is the popularity pattern of IoT data? Then, we design a data-driven popularity prediction method, which consists of two parts: 1) deep neural network (DNN)-based PEM for generating predicted popularity and 2) statistic-based PEM for dealing with the cold boot problem. Furthermore, we present a popularity-based evicting and prefetching algorithm to address what to cache and when to cache problems. We evaluate IoTCache on two IoT platforms which are on the basis of content delivery network (CDN) and information-centric network (ICN), respectively. The experimental results show that IoTCache can significantly increase the cache hit ratio, and decrease the IoT edge traffic and data latency.

Efficient typing or text-input on mobile devices, such as smartphones and wearables is a long-standing problem, due to the miniature touchscreen on the devices. Recently, touchscreen-free solutions leveraging on acoustic sensing have been proposed, with the advantage of low cost and ubiquitous availability. However, existing solutions usually require people to write in print-style, and more importantly, they are highly vulnerable to environmental change, i.e., they need repetitive training upon slight deviation of writing places or device locations. Therefore, they are far from practical usage. In this paper, we propose a novel acoustic-based text-input system called UbiWriter, which can recognize freestyle handwriting with high ubiquity, i.e., one-time training and writing elsewhere. UbiWriter is built on a new letter recognition principle, which treats the acoustic signal from writing a letter as a complete trajectory, and then distills the recognition feature that is resilient to environmental change. For the actual realization of the principle, we adopt and incorporate a series of techniques, including a feature-preserved fast letter alignment, ${K}$ -nearest neighbor letter classification, and language structure-driven word recognition. We also design and implement an APP with cloud-computing support, in order to facilitate real-time text input. Extensive experimental results demonstrate that UbiWriter outperforms the state-of-the-art under various practical settings.

With the increasing popularity of video delivery among mobile users, the problem of explosive traffic growth becomes more and more serious for the mobile wireless networks. To deal with this problem, in this paper we propose to exploit opportunistic transmission and the idea of crowdsourcing to offload mobile video traffic. Based on this idea, a Quality of VIDEO (QoV) oriented pricing incentive scheme, namely Vbargain, is proposed to stimulate mobile users to deliver video data collaboratively. More specifically, in our scheme, the network is regarded as a Virtual Market, and the video packets are treated as commodities, which are dynamically priced according to their expected marginal gains on the quality of reconstructed video; the process of video delivery is regarded as a sequence of packet transactions which are modeled as two-person cooperative games. Driven by the profit-hunting nature of mobile users, the video data is delivered from the source to its destination. In order to gain deep insight into our scheme, we also analyze the overhead and the influence of start-up capital on the performance of video delivered theoretically, and give its lower bound and upper bound. Our simulation results, based on both the synthetic and real-life traces of mobile users, verify the efficiency of our scheme and analysis.

Sparse sensory data caused by insufficient monitoring sites and their incomplete records becomes the main challenge of fine-grained environment sensing. In this paper, we develop a novel inference framework, named Federated Region- Learning (FRL), for urban environment sensing. The proposed framework inherits the basic idea of federated learning, and also considers the regional characteristics during the distribution of training samples so as to improve the inference accuracy. Moreover, we exploit an edge computing architecture to implement the FRL for improving the computational efficiency. We also apply FRL to PM2.5 monitoring in Beijing. The evaluation shows that our FRL improves computational efficiency nearly 3 times than centralized training mode and increases accuracy by more than 5% compared with normal distributed training.

The goal of this paper is to estimate the population of fishes in sonar images. Compared to natural images, sonar images present substantially different visual characteristics. Fishes in sonar images exhibit unreliable appearance cues, expose under imaging noise, vary significantly in shape and size. These pose great challenges for counting even for a human expert. In Computer Vision, a possible solution to this task is object counting with deep networks. This paradigm is typically formulated as a regression problem. The regression, however, greatly suffers from the issue of sample imbalance caused by fish variations in size and density, leading to underestimates in high-density regions and over-estimates in low-density regions. To address this, we build upon a recent local counts regression network and propose two novel losses to regularize a modified l1 loss with slack constraints. In particular, a challenging sonar fish counting dataset with 537 images and manually labeled dotted annotations is constructed. Experimental results on the dataset justify the effectiveness of our proposition and show improved performance of our method over other state-of-the-art approaches.

Unmanned Aerial Vehicles (UAVs) have been widely used in various applications such as inspection, security surveillance, and aerial photography, in which the cooperation of multiple UAVs is significantly important for better accomplishing complex tasks due to the limited capability for individual UAV s. Task planning is the primary issue for the cooperation of multiple UAV s, and has attracted extensive research interests. However, most research fails to account adequately for limited energy on each UAV, which involves in many factors such as different operations for performing a task and various movement patterns besides the distance and turns that have been commonly considered. By contrast, we conduct a series of experiments to obtain the energy model of UAV s. Furthermore, we focus on the energy-efficient min-max task planning (E2M2TP) problem by considering the heterogeneity of tasks and integrating various energy factors, which is beneficial for balancing the workload and energy consumption among UAV s and thus reducing the number of required UAVs. We show that E2M2TP is NP-hard, and propose an energy-aware variable neighbor search (EVNS) algorithm to iteratively optimize both task allocation and path planning. Extensive simulations are conducted to validate that EVNS outperforms the other state-of-the-art algorithms.

Named Data Networking (NDN), a revolution of the IP architecture, provides an information-centric paradigm to transmit video traffic which occupies most of the Internet traffic. However, the control schemes in the field of video NDN research can easily result in full bottleneck bandwidth at the cost of high video delay and frequent packet loss. This is because the loss-based or transient-queue-delay-based congestion control schemes respond to the signal after the congestion is formed. And NDN multi-source feature can't make the loss and delay-based signals accurately reflect the network situation. In addition, the current video applications need to choose the suitable video rate to stream across the network. The bandwidth estimation scheme of video rate control ignoring multi-source reduces accuracy in NDN. In this paper, we firstly build a max-plus algebra based model to characterize the interest-data transmission process for video consumer and to estimate the NDN bandwidth delay product. The hops (lived time)that data pass through from the producer to the consumer represent the multi-source feature in the model. Then we propose a Time-Lived based congestion and rate control scheme for the NDN video consumer. Extensive experiments have been conducted to evaluate our scheme from the perspective of throughput, delay, and queue length. The results show that our scheme can improve the throughput of the video consumer while reducing the round trip time by shortening the queue length.

LEO Satellites, which can provide Internet access points for IoT devices deployed in areas without infrastructure, will play an important role in the IoT area. In this paper, we propose a joint TDMA MAC protocol SL-MAC for LEO satellite supported IoT, which can reduce network delay and improve the energy efficiency. Specifically, we design a continuous forward time slot allocation strategy, which guarantees real-time data while reducing the utilization of satellite resources. Then we design two algorithms based on the forward time slot allocation strategy, which can effectively reduce network delay. The simulation experimental results are consistent with the theoretical results, which embodies the effectiveness of the algorithm.

Information-centric networking (ICN), as a major branch of the future network architecture, shifts from address-based communication to named content retrieval. The special characteristics of ICN, such as named content, in-network cache and receiver-driven, make the content diffusion process in ICN be different from traditional point-to-point packet transmission. Thus existing transmission analysis theory cannot illustrate the content diffusion process of ICN accurately. Inspired by the epidemic-like spread model, whose infected-recovered process is similar to ICN's cached-evicted process, we propose a content diffusion model (CDM) to illustrate the content diffusion process in ICN. We also evaluate the accuracy of CDM by a lightweight ICN simulator developed. The results show that our proposed CDM can illustrate the content diffusion process in ICN accurately.

The performance of mobile networks is significantly influenced by the mobility patterns of wireless device holders. Human mobility models, which yields synthetic trajectories with essential mobility patterns of crowd, are important for the research and development of mobile networks. However, traditional models are often perceived as not realistic for depicting urban context (such as urban hotspots and direction of crowd flow). This paper proposes a temporal correlation based model that produce large-scale of synthetic trajectories on the basis of a short period of time real human traces. The trajectories generated by our model not only maintain the inherent statistical features of human mobility, but also “learn” the urban context from real traces. For simulating crowd mobility in a city, our model first extract the statistical features and urban context features from the the real traces, then regenerate synthetic trajectories on the basis of temporal correlation of human mobility. We validate our model and findings through three open datasets: taxicabs' traces in San Francisco, public bicycles' traces in Washington D.C. and New York City.

Named Data Networking (NDN), a revolution of the IP architecture, provides an information-centric paradigm to transmit video traffic which occupies most of the Internet traffic. However, the data caching schemes exploited by the current NDN can easily result in the unfair distribution of cache space, which is not suitable for performance isolation when there are multiple network users and the cache space is of high utilization for video applications. In this paper, we propose a max-min utility fairness scheme for the built-in cache allocation of video applications. We propose a computing method which calculates the benefit of all video data cached in the NDN node. Based on the benefit of individual user and the relevance of video data, NDN node evicts the video data which belongs to users with the most benefits. Extensive simulation experiments have been performed to evaluate our scheme in terms of user fairness, average delay and hit ratio. The results show that our scheme can guarantee the fairness for different video Users, meanwhile, reducing the packet round trip times by improving the cache hit ratio.

The scale-invariant feature transform algorithm and its many variants are widely used in feature-based remote sensing image registration. However, it may be difficult to find enough correct correspondences for remote image pairs in some cases that exhibit a significant difference in intensity mapping. In this letter, a new gradient definition is introduced to overcome the difference of image intensity between the remote image pairs. Then, an enhanced feature matching method by combining the position, scale, and orientation of each keypoint is introduced to increase the number of correct correspondences. The proposed algorithm is tested on multispectral and multisensor remote sensing images. The experimental results show that the proposed method improves the matching performance compared with several state-of-the-art methods in terms of the number of correct correspondences and aligning accuracy.

Although many feature-based registration methods have been proposed, automatic image registration is still a challenging task due to the influence of various conditions and uncertain difficulties for remote sensing images. In this letter, a novel image registration method, including two types of feature detectors and a region boundary constraint strategy for matching, is proposed. Two types of features detected by scale-invariant feature transform and Harris operators have advantages of keeping different structural information in the image and increasing the number of keypoints for later matching. Afterward, a region boundary constraint strategy based on the image sketch map is utilized in matching step. This strategy restricts the detected two types of features in their respective structural region and nonstructural region to reduce the incorrect correspondences. Experimental results demonstrate the superiority of our proposed registration algorithm compared with other research works in terms of correct matching number and aligning accuracy.

Recognizing object location by taking a photo with smartphones is useful for many location-based services. However, start-of-the-art technologies for both localization and location recognition have difficulty in achieving satisfactory performance. Moreover, it is a challenging issue to construct and maintain a large-scale image database for existing visual-based location recognition systems. To cope with these issues, we introduce CrowdOLR, a crowdsourcing based object location recognition system, which collects one location image together with various rich sensory data (GPS coordinates, azimuth angle, tilt angle, etc.) as a fingerprint of a location query and matches it to a fingerprint database crowdsourced from users' smartphones. We designed a simple and efficient user action mode and proposed a series of fingerprint extracting, searching, and matching methods, so that CrowdOLR satisfies five desirable properties: high recognition accuracy, user friendliness, quick response, no/little site survey, and timely update. We implemented CrowdOLR and collected 8100 location fingerprints of 162 objects for performance evaluation. Extensive experiments demonstrate that CrowdOLR achieves promising results in various complicated and realistic scenarios.

Urban environmental monitoring, which provides crucial environmental information, is of great importance in various fields of urban research. The rising popularity of smart-phones and vehicles equipped with onboard sensors points out an ubiquitous computing mode for city-scale environmental monitoring, namely “crowdsensing”. This paper studies the problem that how to promote the ability of crowdsensing for fine-grained environmental monitoring. Based on a new metric for measuring the sensing ability of crowdsensing, the resolution of sensing image, we propose a method of generating sensing images via crowdsensing networks and enhance the ability of crowdsensing from a data perspective. To achieve a higher resolution, we not only utilize temporal correlation of mobile sensing nodes but also fuse the sensory data with correlated environmental information. Finally, we conduct numerical simulations to verify the promotion of resolution beyond traditional spatial interpolation-based approach.

With the increasing popularity of video delivery among mobile users, the explosive traffic growth problem becomes more and more serious for the mobile wireless networks. We propose to exploit opportunistic transmission and the idea of crowdsourcing to offload mobile video traffic. Specifically, we propose a QoV (Quality of Video) oriented pricing incentive scheme, namely Vbargain, to stimulate mobile users to deliver video data collaboratively. In our scheme, the video packets are treated as commodities, which are dynamically priced according to their expected marginal gains on the quality of reconstructed video; the process of video delivery is regarded as a sequence of packet transactions which are modeled as two-person cooperative games. Our simulation results, based on both the synthetic and real-life traces of mobile users, verify the efficiency of our scheme.

Wireless Sensor Networks (WSNs), the major technique in the sensing layer of Internet of Things. Because the WSN is extensively regarded as a data-centric network, more and more researchers start to exploit information-centric networking (ICN), a major branch of future network architecture, to design WSNs. In this paper, we propose a collaborative caching strategy for information-centric wireless sensor network (ICN-WSN). The proposed strategy consists of three parts: the node betweenness based cache size adjustment, the data replacement frequency based cache decision, and the content value based cache replacement algorithm. Based on our developed prototype of ICN-WSN, we compared our caching strategy with traditional strategies. Extensive experiments have been performed in terms of energy consumption, hit ratio, average delay and average hops. The results show that our strategy can improve the hit ratio of content caching, and in the meanwhile reduce the average delay, average hops, and energy consumption.

Internet of Things (IoT), as the trend of future networks, begins to be used in many aspects of daily life. It is of great significance to recognize the networking problem behind developing IoT. In this paper, we first analyze and point out the key problem of IoT from the perspective of networking: how to interconnect large-scale heterogeneous network elements and exchange data efficiently. Combining our on-going works, we present some research progresses on three main aspects: 1) the basic model of IoT architecture; 2) the internetworking model; and 3) the sensor-networking mode. Finally, we discuss two remaining challenges in this area.

Named Data Networking (NDN), a revolution of the IP architecture, provides an information-centric routing for designing the unified Internet of Things (IoT) network protocol. However, the data forwarding scheme exploited by the current NDN generates lots of communication overhead and invalid caching hits, which is not suitable for IoT. This is because there exist large amounts of weak network devices with constrained resources in IoT compared to the Internet. In this paper, we first build a caching time model to characterize the caching process for individual packet and estimate the packet's caching time on the next hop node. Based on this model, we propose a cache-aware named-data forwarding scheme for the typical weak devices in IoT-wireless sensor nodes. Extensive simulation experiments have been performed to evaluate our scheme in terms of forwarding number, average delay and valid hit ratio. The results show that our scheme can reduce the packet number during multicast forwarding of router nodes, meanwhile guarantee valid hit ratio of content caching.

This paper proposes a novel approach for automatically controlling stereoscopic camera parameters that specifically addresses challenges in stereo 3D animation production process.Our proposed camera control method produces stereo contents with preferable depth perception and guarantees visual comfort by optimization of camera parameters. We introduce an attention tracking method to calculate convergence plane, avoiding window violation and minimizing visual conflict. Moreover, we derive an smoothing function on convergence plane that reduces depth jump over time. Then, we calculate the inter-axial separation using a perceived depth mapping. We describe how to implement our method on the Maya plug-in and test the stereo effect using professional stereo 3D animation scenes. The experimental results, including a user study, show that our method enhances the stereo effect. Our controller provides automatic camera control that can be helpful in creating comfortable and faster stereo 3D animations.

As one of the future networking architecture, Content Centric Networking (CCN) regards content as a first-class network entity and fits Internet of Things (IoT) applications well. CCN retrieves contents by their unique hierarchical names. However, in IoT, different autonomous networks have their own naming rules. When a mobile node enters a new network environment and interacts with local networks, it can hardly know the exact name of the content that it needs. In this paper, we propose Tag-assisted Content Centric Networking (TCCN) for IoT, which is a more flexible and efficient scheme for the content sharing between mobile node and local IoT network. We design the tables and structures of three basic components of CCN. Based on counting bloom filter, we further design Tag Filter (TF) for interest-content matching and forwarding information lookup. By using TF, Content Store (CS) can quickly check whether coming Interests can be satisfied locally. Moreover, in our designed Tag-based Forwarding Information Base (T-FIB), we change the traditional prefix-faces structure into face-tags structure, which can greatly reduce the size of FIB and the lookup time consumption. We design the Tag-based Caching Strategy (TCS), by which the node can decide whether to cache the coming content according to the TF and a counter threshold. By using TCS, contents with similar set of tags are cached intensively, which benefits the forwarding information.We conduct simulations to evaluate the validity and performance of our proposed methods.

Using insights from biological processes could help to design new optimization techniques for long-standing computational problems. This paper exploits a cellular computing model in the slime mold physarum polycephalum to solve the Steiner tree problem which is an important NP-hard problem in various applications, especially in network design. Inspired by the path-finding and network formation capability of physarum, we develop a new optimization algorithm, named as the physarum optimization, with low complexity and high parallelism. To validate and evaluate our proposed models and algorithm, we further apply the physarum optimization to the minimal exposure problem which is a fundamental problem corresponding to the worst-case coverage in wireless sensor networks. Complexity analysis and simulation results show that our proposed algorithm could achieve good performance with low complexity. Moreover, the core mechanism of our physarum optimization also may provide a useful starting point to develop some practical distributed algorithms for network design.

The rising popularity of smartphones and vehicles equipped with onboard sensors sheds lights on building a city-scale sensing system for urban surveillance. This paper proposes a novel metric, urban resolution, to measure the quality of urban sensing. Urban resolution describes how sensitivity the urban sensing system could achieve for environment monitoring applications. Then, we study the relationship between resolution r and number of sensing nodes s, and reveal the linear growth relationship between √r and √s . Furthermore, by employing a commonly used human/vehicle mobility model, SLAW, we find that the distribution model of urban sensing nodes is able to be described by a truncated Pareto distribution, and derive the complementary cumulative distribution function (CCDF) of urban resolution. The CCDF reveals the radio of the sub-regions which satisfy the required sensing quality to the whole region. Our findings provide valuable insights to infer the urban sensing quality according to the scale of urban sensing system or determine how many smartphone/vehicles needed for participating in urban sensing applications. Finally, based on five real datasets-three human/vehicle trajectory datasets and two environment monitoring datasets, we examine the metric of urban resolution and evaluate the main results in this paper.

Energy efficiency is the critical problem in wireless multi-hop networks for extending network lifetime. Recently, the construction of multicast tree and converging tree has become increasingly important in multi-hop wireless networks. However, the existing topology-based protocols have large control overhead, and can not acquire the approximately optimal tree with considering the energy equilibrium for energy limited node and large-scale networks. In this paper, we solve this problem by an Advanced Distributed Physarum Optimization algorithm. We formulate optimal energy-efficient tree problem based on the cellular computing model in the slime mold physarum polycephalum. Then, we improve convergence rate and accuracy by the novel flux terminal selecting method and deleting edge mechanism, and trade off iterative cost and robustness by combining the central and distributed iterative algorithm. The simulation results demonstrate that the energy-efficient tree constructed by our algorithm achieves the better performance than Directed Diffusion protocol, and the similar performance to Loss-Contracting algorithm in less constructing overhead.

The Minimal Exposure Problem (MEP), which corresponds to the quality of coverage, is a fundamental problem in wireless sensor networks. This paper exploits a biological model of physarum to design a novel biology-inspired optimization algorithm for MEP. We first formulate MEP and the related models, and then convert MEP into the Steiner problem by discretizing the monitoring field to a large-scale weighted grid. Inspired by the path-finding capability of physarum, we develop a biological optimization solution to find the minimal exposure road-network among multiple points of interest, and present a Physarum Optimization Algorithm (POA). Furthermore, POA can be used for solving the general Steiner problem. Extensive simulations demonstrate that our proposed models and algorithm are effective for finding the road-network with minimal exposure and feasible for the Steiner problem.

As a fundamental problem of wireless sensor networks, the minimal exposure path problem corresponding to the sensor network's worst-case coverage plays an important role in the applications for detecting intrusions. However, most existing works about minimal exposure path are based on omnidirectional sensors. In contrast, this paper studies the minimal exposure path problem for two different types of directional sensing models: the binary sector model and directional sensitivity model. For the binary sector model, we construct a special Voronoi diagram, called sector centroids-based Voronoi diagram, to transform the minimal exposure path problem from a continuous geometric problem into a discrete geometric problem. By using the sector centroids-based Voronoi diagram, we develop an approximate algorithm to find the minimal exposure path in the sensors deployment field. For the directional sensitive model, we formulate the minimal exposure path problem by using two sensing intensity functions: all-sensor intensity function and maximum-sensor intensity function and then generate two weighted grids to convert the minimal exposure path problem into two discrete geometric problems. On the basis of the aforementioned weighted grids, we also develop two approximation algorithms to find the minimal exposure path for the directional sensitive model. We conduct extensive experiment simulations to validate and evaluate our proposed models and algorithms. Copyright © 2012 John Wiley & Sons, Ltd.

Content-Centric Networking (CCN) is a newly proposed networking architecture for the future Internet. It has superiorities over today's IP-based architecture in some aspects. On the other hand, the Internet of Things (IoT) is a direction of the future Internet. Due to the existence of large amounts of weak network devices (NDs) with constrained resources, current CCN models that are designed mainly oriented to Super Routers (SRs) in the core networks are not appropriate for the edge networks in IoT. In this paper, we propose an internetworking scheme for weak NDs based on task mapping. In this scheme, weak NDs with constrained resources map the overcapacity tasks (in terms of storing, publishing, and retrieving) to SRs. Then, based on this scheme, we present two specific strategies for NDs as producers and consumers, respectively. Finally, we take cameras as typical NDs to illustrate the process of the communication by using our proposed scheme.

In this paper, we explore the area coverage optimization problem by directional sensors with tunable sensing orientations. We firstly introduce the concept of "sensing centroid", which is the geometric center of a sensing sector to simplify the pending problem. Particularly, we regard "sensing centroid" as artificial fish (AF), and search an optimal solution in the solution space by simulating fish swarm behaviors (such as prey, swarm and follow) with a tendency toward high food consistence. Fully considering that AFs have to satisfy both kinematic constraint and dynamic constraint in the process of motion, we propose a Constrained Artificial Fish-Swarm Algorithm (CAFSA), and discuss the control laws to guide the behaviors of AFs with high convergence speed. Finally, we evaluate the effect of some primary parameters on the performance of our solution through extensive simulations.

Opportunistic sensing is a new paradigm which exploits human-carried or vehicle-mounted sensors to collect data ubiquitously for large-scale urban sensing. Existing work lacks an in-depth investigation on the sensing quality of such sensing systems, which faces two basic problems: 1) how to measure the sensing quality? and 2) how many humans or vehicles are necessary to satisfy the sensing quality requirement of the whole urban area? To solve the first problem, we propose a metric called Inter-Cover Time (ICT) to characterize the opportunity with which a sub region is covered, which reflects the sensing quality directly. According to the empirical measurement studies on real mobility traces of thousands of taxis collected in Beijing and Shanghai, we find that the aggregated ICT Distribution (ICTD) closely resembles a truncated power-law distribution regardless of the size of sub regions and the number of vehicles. We also analyze the reasons behind this particular pattern by the evaluation on four known mobility models. To solve the second problem, we further propose a metric called opportunistic coverage ratio based on the ICTD to characterize the relationship between the sensing quality of an urban area and vehicle number. Our results provide fundamental guidelines on the measurement of sensing quality and network planning for opportunistic urban sensing applications.

In Wireless Multimedia Sensor Networks (WMSNs), data fusion and collaborative in-network processing operations often require effective multimedia synchronization control. Extensive researches have been done in the traditional networks. Most of these works assume that there exists a powerful synchronization controller in the network. However, for WMSNs, the in-network processing of the multimedia content is usually performed based on the resource-constrained sensors. Traditional synchronization control algorithms fail to run on the low-end hardware platform. In this paper, we propose an adaptive synchronization control scheme with multi-level buffer to address this problem. Main contributions of this paper are as follows. (i) To overcome the resource constraint of WMSNs, we perform the synchronization control in a distributed way by combining the sensors in the stream transmission path. (ii) By evaluating the current network state, we adjust the level number of the buffer adaptively to balance the delay and delay jitter. (iii) Through analyzing the buffer state of each level, we propose an efficient packet scheduling scheme to reduce the delay jitter actively. (iv) We implement the proposed scheme and verify its effectiveness in our practical WMSNs platform. The experiment results show that the proposed scheme can maintain the streams synchronization effectively with the low-end sensors.

With rapid improvements and miniaturization in hardware, sensor nodes equipped with acoustic and visual information collection modules promise an unprecedented opportunity for target surveillance applications. This paper investigates a critical task of target surveillance, multi-class classification, in distributed multimedia sensor networks. We first analyze the procedure of target classification utilizing the acoustic and visual information. Then, we propose a binary classification tree based framework for distributed target classification in multimedia sensor networks. The proposed framework includes three main components: Generation of binary classification tree, Division of binary classification tree, and Selection of multimedia sensor nodes. Finally, we conduct an experimental application of target classification and extensive simulations to validate and evaluate our proposed framework and related schemes.

Using insights from biological processes could help to design new optimization techniques for long-standing computational problems. This paper exploits a cellular computing model in the slime mold physarum polycephalum to solve the minimal exposure path problem which is a fundamental problem corresponding to the worst-case coverage in wireless sensor networks. We first formulate the minimal exposure path problem, and then convert it into the shortest path problem by discretizing the monitoring field to a large-scale weighted grid. Inspired by the path-finding capability of physarum, we develop a new optimization algorithm, named as the physarum optimization, for solving the shortest path problem. Our proposed algorithm is with low-complexity and high-parallelism. Moreover, the core mechanism of our physarum optimization is also helpful for designing new graph algorithms and improving routing protocols and topology control in self-organized networks.

Most existing works on sensor coverage mainly concentrate on the full coverage models which ensure that all points in the deployment region are covered at the expense of high complexity and cost. In contrast, the exposure-path prevention does not require full coverage sensor deployment, and instead it only needs the partial coverage, because the exposure paths are prevented as long as no moving objects or phenomena can go through a deployment region without being detected. Towards this end, we focus on the partial coverage by applying the percolation theory to solve the exposure path problem for wireless sensor networks. Specifically, we propose a bond-percolation based scheme by mapping the exposure path problem into a bond percolation model. Using this model, we derive the analytical expressions of critical densities for wireless sensor networks under random sensor deployment.

Nature is an invaluable source of inspiration for long-standing computational problems. Genetic algorithms and ant colony optimization are the well-known successes. Drawing the inspirations from these successful experience, we exploit the computing model in a single-celled organism -- physarum polycephalum, and further design a new heuristic algorithm, Physarum Optimization, to the minimal exposure problem of wireless sensor networks. More importantly, our proposed physarum optimization algorithm can be also applied to the general Steiner tree problem.

Exposure problem, which corresponds to the quality of coverage, is a fundamental problem in wireless sensor networks. In this paper, we exploit a cellular computing model in the physarum for solving the minimal exposure problem. We first use the road-network among all points of interesting (PoIs) in the monitoring filed to formulate the minimal exposure problem, and then convert it into the Steiner tree problem by discretizing the monitoring field to a large-scale weighted grid. Inspired by the path-finding capability of physarum, we develop a new heuristic algorithm, named as the physarum optimization, to solve the Steiner tree problem with low complexity and high parallelism. Extensive simulations demonstrate that our proposed models and algorithm are effective for finding the road-network with minimal exposure.

Mobile sensors are a viable choice for providing monitoring service on a set of Points of Interest (PoIs) in a large sensing field. In some applications, each PoI should be covered periodically (sweep coverage), and the collected data should be delivered to the sink node timely (timely transmission), namely both the sensing and transmission delay constraints for each PoI should be satisfied, which we call as timely sweep coverage. We investigate how to optimize the movement path of one mobile sensor to satisfy the two delay constraints for each PoI, so that the required movement velocity for the mobile sensor is minimized. We consider two cases: 1) all PoIs are placed along a straight line (linear case), and 2) all PoIs are arbitrarily placed on a plane (general 2-D case). Under the linear case, the optimal algorithm is presented. Under the general 2-D case, we prove the problem is NP-hard, and two algorithms, STSP and ITSP, are presented. We prove that the approximation ratio of STSP depends on the ratio between the sensing and transmission delay constraints. The ITSP can improve the solution much especially when the two delay constraints differ greatly for each PoI. Extensive simulation results are provided to evaluate our algorithms.

Probabilistic forwarding methods have been exploited in opportunistic networks to reduce the overhead of epidemic routing. However, most existing methods make all the nodes forward messages with the same probability (i.e., equal scheme), which causes the energy unbalance of nodes. To guarantee the energy balance of nodes and prolong the network lifetime, we design a differentiated scheme, i.e., different nodes are assigned with different forwarding probabilities based on their respective energies. We model the message dissemination based on the differentiated scheme, and formulate two optimization problems: maximize the message deliver probability under the constraint on the total energy consumption, and based on this, maximize the network lifetime under the constraint on the energy consumption of each node. By solving these two optimization problems, we derive the optimal differentiated forwarding probabilities by theoretical analysis. Our simulation results show that our designed differentiated scheme can guarantee the message deliver probability and extend the network lifetime, compared with the equal scheme.

Deriving the proper density to achieve the region coverage for random sensors deployment is a fundamentally important problem in the area of wireless sensor networks. Most existing works on sensor coverage mainly concentrate on the two-dimensional (2D) plane coverage which assume that all the sensors are deployed on an ideal plane. In contrast, sensors are also deployed on the three-dimensional (3D) rolling surfaces in many real applications. Toward this end, we study the coverage problem of wireless sensor networks for the rolling terrains, and derive the expected coverage ratios under the stochastic sensors deployment. According to the different terrain features, we investigate two kinds of terrain coverage problems: the regular terrain coverage problem and the irregular terrain coverage problem. Specifically, we derive the general expression of the expected coverage ratio for an arbitrary surface z=f(x, y) and build two models, cone model and Cos-revolution model, to estimate the expected coverage ratios for regular terrains. For irregular terrains, we propose a digital elevation model (DEM) based method to calculate the expected coverage ratio and design an algorithm to estimate the expected coverage ratio of an interested region by using only the contour map of this region. We also conduct extensive simulations to validate and evaluate our proposed models and schemes.

ABSTRACT Camera sensor networks have recently emerged as a critical research topic. In this paper, we investigate the coverage problem for camera sensor networks. Specially, compared to the coverage problem for target detection which has been intensively studied, this paper studies the coverage problem from the perspective of target localization. We first propose a novel localization‐oriented sensing model based on the perspective projection of the camera sensors. Then, under the random uniform deployment strategy, we analyze how the probability of the localization‐oriented coverage (L‐coverage for short) changes with the sensors number and the parameters of the proposed sensing model. Finally, we conduct extensive simulations to validate our model and theoretical analysis about L‐coverage probability. The obtained results show that our scheme can be effectively applied for practical scenarios. Copyright © 2011 John Wiley & Sons, Ltd.

Multimedia sensor networks (MSNs), which allow capturing acoustic and visual information, provide an unprecedented opportunity for variety of applications. This paper investigates the heterogeneous coverage problem in MSNs, i.e., how many multimedia sensors should be deployed to guarantee that each point in the monitored region is covered by multiple types of heterogeneous sensors. It is different from the coverage problem in conventional homogeneous sensor networks, mainly because that it is based on a heterogeneous sensing model, which is a hybrid of the omni-sensing model and the directional-sensing model. We propose a mathematical model to describe the relationships among the number of multimedia sensors, the sensing radius of the acoustic collection module, the sensing radius and the sensing angle of the visual collection module and the heterogeneous coverage rate. Our simulation results show that deploying our model in practical scenarios is effective.

In this paper, we try to address the difficult problem of detecting humans robustly with low energy consumption in the visual sensor network. The proposed method contains two parts: one is an ESOBS (Enhanced Self-Organizing Background Subtraction) based foreground segmentation module to obtain active areas in the observed area from the visual sensor; the other is a HOG (Histograms of Oriented Gradients) based detection module to detect the appearance shape from the foreground areas. Moreover, we create a large pedestrian dataset according to the specific scene in visual sensor networks. Numerous experiments are conducted. The experimental results show the effectiveness of our method.

Object/event classification is an important aspect in the applications of Wireless Audio Sensor Networks (WASNs). In order to reduce the computational burdens of individual sensors, distributed classification method is adopted, i.e., the individual sensors only perform binary classification and send out a binary decision to the fusion center. However, it brings about a new coverage problem, namely, how to deploy sensors so that all locations in the surveillance region are covered by multiple types of sensors with different sensing radii for collaborative classification. We call it k-class coverage problem. In this paper, we propose two algorithms: straight-forward algorithm and divide-and-conquer algorithm. The first algorithm converts the problem into k-class set cover problem, and the second algorithm converts the problem into solving set 1-cover problem k times. Greedy strategies are used in both of two algorithms. Performance analysis and simulation results are provided for evaluating two algorithms.

In this paper, we investigate the coverage problem from the perspective of target localization for wireless camera sensor networks. We first propose a novel localization-oriented sensing model based on the perspective projection of camera sensors. Based on the sensing model, we propose a new notion of coverage, Localization-oriented coverage (L-coverage for short), by using Bayesian estimation theory. Furthermore, we analyze the relationship between the density of camera sensors and the L-coverage probability under random deployment where camera sensors are deployed according to a 2-dimensional Poisson process. According to the relationship between the density of camera sensors and the L-coverage probability, we derive the density requirements for an expected L-coverage probability. We validate and evaluate our proposed models and schemes by simulations.

The audio surveillance is traditionally performed by using wired microphones. We propose an alternative surveillance system by using audio sensor networks for event classification in our living environment. We first compare two classical acoustic features - the Fast Fourier Transform (FFT) based acoustic features and the Mel-Frequency Cepstral Coefficient (MFCC) based acoustic features, and then, by using the FFT based acoustic features, we present a hierarchical classification approach for distinguishing abnormal or catastrophic events. A distance based decision fusion is used to combine the sensory information collected by the audio sensors. We present the performance analysis on the proposed approaches using a real audio sensor network.

As a fundamental problem of wireless sensor networks, the minimal exposure path problem corresponding to the sensor network's worst-case coverage plays an important role in the applications for detecting intrusions. However, most existing works about minimal exposure path are based on omnidirectional sensors. In contrast, this paper studies the minimal exposure path problem for directional sensor networks. We first develop a directional sensing model: directional sensitivity model. Then, we formulate the minimal exposure path problem by using two sensing intensity functions: all-sensor intensity function and maximum-sensor intensity function, and generate two weighted grids to convert the minimal exposure path problem into two discrete geometric problems. Based on the above weighted grids, we also develop two approximation algorithms to find the minimal exposure path for the directional sensitive model. We conduct extensive simulations to validate and evaluate our proposed models and algorithms.

In wireless sensor networks, most existing works on region coverage mainly concentrate on the omnidirectional sensor based full coverage, which ensures that all points in the sensor-deployed region are covered. In contrast, this paper studies the problem of exposure-path prevention for the region coverage in directional sensor networks. Because the exposure paths are prevented as long as no moving objects or phenomena can go through a sensor-deployed region without being detected, exposure-path prevention does not require full coverage, and instead it only needs the partial coverage. Towards this end, we apply the percolation theory to solve the exposure path problem for directional sensor networks. In particular, we map the exposure path problem into a sector based percolation model, and then derive the bounds of critical density where directional sensors are deployed according to a 2-dimensional Poisson process. Also conducted is a set of extensive simulations to validate and evaluate our developed models and schemes.

Compared to the other types of sensor networks, the wireless camera sensor networks can offer much more comprehensive and accurate information in mobile target tracking applications. We propose a dynamic node collaboration scheme for mobile target tracking in wireless camera sensor networks. Unlike the traditional sensing models, we develop a nonlinear localization-oriented sensing model for camera sensors by taking the perspective projection and the observation noises into account. Based on our sensing model, we apply the sequential Monte Carlo (SMC) technique to estimate the belief state of the target location. In order to implement the SMC based tracking mechanism efficiently, we propose a dynamic node collaboration scheme, which can balance the tradeoff between the quality of tracking and the network cost. Our scheme deploys the dynamic cluster architecture which mainly includes the following two components. First, we design a scheme to elect the cluster heads during the tracking process. Second, we develop an optimization-based algorithm to select an optimal subset of camera sensors as the cluster members for estimating the target location cooperatively. Also conducted is a set of extensive simulations to validate and evaluate our proposed schemes.

Most existing works on sensor coverage mainly concentrate on the full coverage models which ensure that all points in the deployment region are covered at the expense of high complexity and cost. In contrast, the exposure-path prevention does not require full coverage sensor deployment, and instead it only needs the partial coverage, because the exposure paths are prevented as long as no moving objects or phenomena can go through a deployment region without being detected. Towards this end, we focus on the partial coverage by applying the percolation theory to solve the exposure path problem for wireless sensor networks. We propose abond-percolationtheorybased scheme by mapping the exposure path problem into a bond percolation model. Using this model, we derive the critical density where sensors are deployed according to a 2-dimensional Poisson process. We evaluate our proposed model by simulations.

Recently, smart surveillance is becoming a very important application of camera sensor networks. Most face recognition and tracking techniques employed in surveillance systems rely on the assumption of a frontal view of the human face. In order to detect the precise face orientation by cooperation among multiple cameras, we need to guarantee that each point in monitored region be covered by more than one camera. In this paper, we address the problem of Directional if-Coverage (DKC) in camera sensor networks. The DKC problem is different from that in conventional sensor networks due to the two aspects: directionality of sensing model and effective sensing. In particular, we propose a mathematical model to describe the relation among the number of cameras deployed randomly, the range of effective sensing angle, and the rate of directional if-coverage. Finally, we use simulations to show the effectiveness of deploying our model in practical scenarios.

Target localization is an important application in sensor networks. Although the localization problem in acoustic /radar sensor networks has been intensively studied, few consider the problem in camera sensor networks. In this paper, we propose a method to utilize the active cameras to localize the target collaboratively. We describe the perspective projection of target firstly, and then utilize the Gaussian error model to establish a more practical target projection model. On the basis of the target projection model, we present a vision-based localization model, and use Shannon entropy to quantify the localization gain due to the observation of camera. Case study demonstrates the validity and efficiency of our design.

Camera sensor networks have recently emerged as a very critical research topic. Target tracking and localization are important applications in camera sensor networks. In this paper, we investigate the coverage problem from the perspective of target localization in camera sensor network, compare to the coverage problem for target detection has been intensively studied. We first propose a novel localization-oriented sensing model based on the perspective projection of the camera. Then, assuming that the camera sensors are deployed as a random uniform process, we study how the probability of the localization- oriented coverage (L-coverage for short) changes with the number of sensors or some other factors. Finally, we use simulations to validate our theoretical analysis and demonstrate the boundary effect onL-coverage probability. The obtained results show that our model can be effectively deployed in practical scenarios.

Target tracking and localization are important applications in camera sensor networks. Although coverage is a very important research topic in wireless sensor networks, and the coverage problem for target detection has been intensively studied, few considers the coverage problem from the perspective of target localization. In this paper, we investigate the coverage problem from the perspective of target localization for camera sensor networks. We first propose a novel localization-oriented sensing model based on the perspective projection of camera. Then, we propose a new notion of localization-oriented coverage (L-coverage for short). We assume that all camera sensors make the measurements independent of other in the field, and these camera sensors can cooperate to make an accurate estimation for the location of the target. In addition, the relationships among L-coverage, 2-coverage, and the density of camera sensors are also discussed in this paper. The obtained results show that our model can be effectively deployed in many practical scenarios.

Complicated multimedia sensor networks pose several theoretic and technical challenges. These challenges center on cooperative work supported by sensor networks. This paper presents, with the aid of previous works on CSCW, a novel conception - sensor networks supported cooperative work (SNSCW). First, we classify the cooperative work supported by sensor networks into: cooperation between human and sensor nodes, and cooperation among sensor nodes. Then we depict the hierarchical collaboration by speech-action model and layered abstract model (activity-task-cooperation). Moreover, this paper proposes a multi-level C/S architecture for SNSCW system. Finally, we utilize a case $the fire monitoring of intelligent building - to show that our works are effective to cooperative work supported by sensor networks

Application reconfiguration is essential in order to complement the flexibility and adaptability for sensor networks in the environment monitoring domain. In this paper, we first describe an effective environment adaptive application reconfiguration (EAAR) mechanism. Then we present a push-based paradigm to carry out EAAR, where instead of a pull-based paradigm proposed in our prior work. This paradigm suits for clustered sensor networks, which provides a software entity, reconfiguration agent, to perform the process of reconfiguration on cluster head nodes. Reconfiguration agent is a novel concept, and has great potential in providing energy-efficient reconfiguration processing with low latency. A prototype is constructed to show the workflow of reconfiguration agent. Particularly, we design two metrics: execution time and energy consumption, and use the simulation platform to quantitatively measure the performance of different paradigms in the reconfiguration procedure. Experimental results show that push-based paradigm performs better than pull-based one