Automated anomaly detection using unmanned aerial vehicles (UAVs) is critical for wind turbine inspection, yet its reliability is often compromised by cluttered backgrounds and dynamic environmental changes. To address these challenges, we propose a unified Depth-Guided Continual Adaptation Framework that operates in two complementary modes. In the routine operation mode, a Depth-Guided Foreground Segmentation (DTF) module leverages geometric priors to suppress background interference and isolate the blade region, while an Enhanced Attention Fusion Module (EAFM) employs asymmetric cross-attention to recalibrate RGB features using depth cues for more robust anomaly representation. In the maintenance mode, we further introduce a GRPO-based memory evolution strategy for adaptation under concept drift. Specifically, the routine detector remains unsupervised, whereas maintenance adopts a safety-constrained update scheme with a small historical anchor set rather than assuming a fully label-free adaptation process. This design enables the model to incorporate informative target-domain samples while preserving historical defect sensitivity. Experiments on the Blade0 dataset and the DTU benchmark demonstrate strong perception performance (F1-score 0.880, AUROC 0.925). In cross-domain adaptation, the proposed maintenance strategy improves target-domain F1 from 0.807 to 0.920 without degrading source-domain F1, showing its effectiveness for reliable long-term wind turbine inspection.

The integration of the global navigation satellite system (GNSS), odometer, and inertial navigation system (INS) holds significant potentials for achieving high-precision vehicle localization. However, GNSS is vulnerable to obstructions and jamming, and the odometer is unreliable in harsh road conditions. These factors can lead to cumulative positioning errors in GNSS-denied environments. To address these issues, a novel multi-source information fusion based vehicle localization method that integrates an onboard binocular camera, an INS, and smart road studs—Internet of Things (IoT) devices extensively used for road safety and data collection in intelligent transportation systems— is introduced. We construct a position measurement model directly in the camera coordinate system through an enhanced You Only Look Once 8th version (YOLOv8) algorithm for smart road stud detection, combined with binocular vision measurement and position transformations. Additionally, we propose a method to enhance vehicle localization accuracy by integrating vehicle speed without relying on additional hardware speed sensors. The vehicle’s speed is estimated from the image sequences captured by the onboard camera using a deep neural network (DNN), named Speed-Net. The final navigation results are produced by fusing the smart road stud aided positioning information, the estimated vehicle speed, and INS data through an error-state extended Kalman filter (ESEKF). Real-world experiments demonstrate the effectiveness of the proposed Smart road stud (SRS)/Velocity/INS integrated vehicle localization method.

Simultaneous Localization and Mapping (SLAM) has emerged as a cornerstone technology in intelligent transportation systems (ITS) and autonomous robots, finding widespread applications in various scenarios and multiple platforms autonomous driving tasks. However, in environments with weak textures and motion blur, achieving efficient and robust visual-inertial SLAM remains challenging. Current research utilizes line features to enhance SLAM performance in such environments, but incurs difficulties in line extraction, structured scene representation, and computational overheads involved in joint optimization. To address these challenges, this paper introduces a novel SLAM framework with both high pose estimation accuracy and high back-end computational efficiency. Firstly, an intelligent spatial line integration method is proposed to effectively reduce data redundancy in joint optimization by leveraging the remarkable stability of long line segments in structured scenes, thereby enhancing the spatial consistency of structural lines. Secondly, to combat low-light and high-speed motion environments, an optical flow tracking accuracy verification method is designed to bolster the system’s tracking performance and robustness in complex scenarios. Finally, to relieve the substantial computational overhead arising from high-dimensional optimization parameters in bundle adjustment (BA), a multi-mechanism marginalization strategy is presented to enhance the accuracy and computational efficiency of BA, while also preventing scale explosion. Comparative evaluations against state-of-the-art algorithms on both the EuRoC MAV and TUM VI benchmark datasets demonstrate that the proposed framework significantly improves localization accuracy and joint optimization efficiency.

Camera-equipped unmanned aerial vehicles (UAVs) are extensively applied in various surveillance tasks, including building inspection, surface reconstruction, and so on, which often require comprehensive and efficient three-dimensional (3-D) object visual coverage. Due to limited onboard storage, great efforts had been devoted to achieving full coverage with minimal costs in terms of the size of a viewpoint set for taking pictures and the total energy consumptions for flight. However, existing studies usually adopted greedy strategies to generate every viewpoint set without considering the requirement of subsequent path planning, and simply calculate the flight distance of a UAV as an approximate metric for its energy consumption without considering the kinematics constraints and flight motion difference of the UAV. As a result, the final solution may far deviate from the global optimal solution. To this end, this paper establishes a tightly coupled optimization framework to jointly minimize viewpoint set size and energy consumption in UAV visual coverage tasks, which comprises two sequential subtasks: generating a viewpoint set with minimal size and inherent path sequences and planning energy-efficient trajectories between viewpoints. To address them, firstly, a path-aware viewpoint set optimization strategy is developed by leveraging overlapping field of view (FOV) gains between viewpoints for path guidance and adopting a novel tree-based search algorithm that balances global exploration and local convergence. Subsequently, a high-fidelity energy optimization scheme is proposed by fusing an energy consumption model with the UAV position-posture coupled control and trajectory smoothing, and devising a customized iterative solver. Extensive simulation results demonstrated that the proposed hierarchical framework generates smaller viewpoint sets with better overlapping FOVs. In addition, energy-efficient path planning significantly reduces UAV energy consumption by up to 89.84% compared to conventional distance-optimized path planning, simultaneously decreasing task execution time.

WiFi probes are crucial tools in applications such as crowd analysis and traffic monitoring. Due to indiscriminate sniffing of WiFi packets in the environment, probes generate a large amount of irrelevant data, increasing server-side processing overhead and presenting challenges for efficient data collection and real-time information processing. Moreover, a single wired transmission mode significantly limits the application scope and deployment efficiency of probes. In response, this paper presents a lightweight WiFi probe system combining a dual transmission mode with device classification capability. First, the system hardware, composed of a single STM32 and multiple ESP8266 modules, enables both wired and wireless data transmission modes through embedded programming. Second, a lightweight logistic and decision fusion model (LLDFM) is proposed, utilizing a time window and device library dynamic management mechanism to achieve real-time classification of fixed/temporary WiFi devices, which can effectively distinguish and discard application-irrelevant fixed device data. Finally, a server web platform was developed with real-time data sniffing, storage, multi-probe monitoring, and management functions to evaluate the system's practical performance. Results from extensive real-world experiments demonstrate an average fault-free operational time of over 168 hours, a fixed/temporary WiFi device classification accuracy of 97.19 %, and the ability to filter 30.45 % of irrelevant data, reducing the corresponding data transmission volume. These findings confirm the proposed system's reliability, efficiency, and superiority.

The wind turbine blade represents a pivotal element within the context of wind power generation systems, with its performance and reliability exerting a direct influence on the overall efficiency and safety of the system. The identification of defects in wind turbine blades is of considerable economic and safety significance. The existing YOLOv8 target detector is still inadequate in terms of loss function in the process of detecting minor defects in wind turbine blades, which affects the learning efficiency and model performance. Therefore, a new loss function is proposed, which is realised by combining scale sensitivity and cascade structure. The proposed method, implemented in the YOLOv8 detector, markedly enhances both the accuracy of detection and the computational efficiency. Moreover, wind turbine blade defect detection is typically implemented in edge devices with constrained performance. To address this issue, a lightweight convolution network is introduced, reducing the number of parameters by 60% to 1.12M, thereby achieving a balance between performance and efficiency. The experimental results on the DTU and the datasets constructed by our own research team demonstrate improvements in detection accuracy by 2.28% and 1.8%, respectively.

Recent progress in crowd counting has underscored its potential across diverse real-world applications. Nevertheless, the majority of existing approaches remain constrained by reliance on unimodal data, thereby limiting both robustness and generalizability. To address these challenges, we present a novel virtual simulation framework for generating synchronized multi-modal synthetic datasets. The proposed framework supports automated data generation with high-fidelity ground-truth annotations and provides programmatic control over environmental conditions and crowd dynamics. Through comprehensive experiments employing pre-training and fine-tuning strategies, we demonstrate that the synthetic datasets produced by our framework substantially improve model performance and generalization in crowd counting tasks. The work contributes a scalable and reproducible solution to the problem of data scarcity in multi-modal crowd counting research.

Permissioned blockchains have found widespread adoption across diverse scenarios, ensuring data authenticity and integrity. However, transaction conflicts, as an inherent performance challenge in permissioned blockchains, can significantly decrease system throughput and thus degrade its Quality of Service (QoS) under substantial transaction contention. Existing approaches mitigate conflicts typically by either aborting or blocking transactions in advance, encountering two main issues: (i) resource wastage due to transaction failure and (ii) performance degradation, particularly under large block sizes or high transaction contention. In this paper, we propose PreFabric, a novel permissioned blockchain framework that guarantees high throughput by resolving the transaction conflict problem. We first conduct a comprehensive analysis of the transaction scenarios preceding simulation execution of the endorsing phase in the blockchain system to identify potential conflict-causing situations. Then, we devise an key-locking method to prevent transaction conflicts and propose concurrency control strategies based on dependency analysis, encompassing a transaction merging mechanism, an key-renaming mechanism and concurrent validating mechanisms, to improve system throughput. The experimental results demonstrate the superior performance of our method over state-of-the-art methods, with 2.1× higher effective throughput and 0.48× lower latency.

Collaborative inference between large cloud-hosted models and small edge-deployed models offers a promising solution for balancing the accuracy and efficiency of ML-based applications in vehicular networks. Selecting the appropriate models and their hosting nodes for performing various inference tasks plays a crucial role in collaborative inference in vehicular networks. However, existing solutions, primarily based on deep reinforcement learning (DRL), suffer critical limitations, including delayed and suboptimal decisions on model and node selection in dynamic environments. To address these challenges, we propose a dynamic model and node selection strategy for a collaborative inference framework, grounded in active inference theory. Our strategy dynamically aligns task requirements with model capabilities and node capacities by considering factors such as vehicular mobility, latency constraints, task complexity, and model accuracy. Additionally, when significant drops in inference accuracy are detected, we fine-tune and update the models deployed on both the edge and cloud, ensuring reliable and up-to-date inference. By leveraging active inference to minimize free energy through Bayesian belief updates, our framework reduces average latency by 23.2%, lowers task failure rates by 67%, and achieves superior load balancing compared to existing methods. It also demonstrates robust dynamic performance with a 5.1% failure rate under 200% traffic surges, and its hybrid update strategy maintains 85.4% accuracy after 72 hours, effectively addressing the complex and dynamic conditions of vehicular networks.

Effective detection of surface defects on wind turbine blades contributes to the continuous andrgetble operation of wind power equipment. It also facilitates the management and maintenance of equipment components. Wind farms contain a large number of wind turbines and surface defects vary in type and location, making the inspection process extremely complicated. In response to these issues, this paper proposes a wind turbine blade surface defect detection model named WTBFlaw-YOLO. The model is based on an improved version of YOLOv11. First, we designed a spatial-to-depth convolutional neural network block (SPD-Conv). We replaced every convolution stride and pooling layer in the convolutional neural network with SPD-Conv. This design improves WTBFlaw-YOLO’s ability to detect blade surface defects in low-resolution wind turbine images. Secondly, we designed a high-level screening feature pyramid network (HS-FPN) in the neck section. We achieved multi-level feature fusion with HS-FPN. This fusion enables WTBFlaw-YOLO to filter targets of various sizes and enhances its ability to express features at different scales. Finally, we designed a dedicated detection head for small defect target detection (SDTDH) in the head section’s multi-head detector. We used SDTDH to improve the detection precision of WTBFlaw-YOLO for micro defects on wind turbine blade surfaces. Experimental results demonstrate that WTBFlaw-YOLO achieves 89.7% mean average precision (using mAP50 as an example) on the public dataset (wind turbine-v2). The model outperforms other models. We conducted multiple ablation experiments. The experiments confirm the effectiveness and robustness of the WTBFlaw-YOLO model.

In recent years, crowd counting techniques based on channel state information (CSI) have received increasing attention. However, many existing WiFi-based methods directly input CSI magnitude or phase into traditional models such as convolutional neural networks (CNNs), which have limited ability to extract features, and the input data are sensitive to noise interference, thus limiting the effectiveness of crowd counting in high-interference situations. To overcome these drawbacks, we introduce the interference-resistant crowd counting system VICount, which uses time-frequency analysis to reflect the movement of people as a spectrogram. To further utilize the global temporal information, we produce a sequence of spectrograms to be input into the spatio-temporal converter network VICount, in order to effectively merge the features in the time, spatial and frequency domains. Extensive offline experiments show that VICount outperforms the SOTA method with an accuracy of up to 85% on the count task of 0-10 people. In addition, it achieves the accuracy of 74% presence detection in an online public dataset1and 61% cross domain recognition without training the target domain data.

Real-time awareness of crowd counts and distribution is vital for applications like crowd management, traffic control, and urban planning. Compared to vision-based methods, passive Wi-Fi sensing-based approaches offer advantages, such as lower deployment costs, broader coverage, and greater scalability, and thus have become prevalent for fine-grained crowd analysis, e.g., estimating mobile device locations and subsequently inferring crowd density maps (CDMs). Therein, substantial localization errors are often incurred due to significant measurement noises, and even worse, the sporadic active scanning introduces sparsity in localization-based density maps (DMs), degrading the accuracy of resulting crowd analysis. To address these challenges, Wi-FDM, a passive Wi-Fi sensing-based framework for fine-grained CDM estimation, is proposed. Specifically, to mitigate the considerable localization errors, two models based on convolutional neural networks are employed to capture the relationship between received signal strength fingerprints and various locations more effectively; to alleviate the sparsity in localization-based DMs, deep compressed sensing networks are developed to reconstruct fine-grained CDMs by recover missing spatial details; to efficiently transfer the CDM reconstruction capabilities developed on semi-synthetic datasets to diverse real-world scenarios, learnable linear and convolutional residual blocks were designed to establish correlations across environments, addressing variations in scene characteristics and improving model generalization. Experimental results on real-world datasets demonstrate Wi-FDM not only surpasses state-of-the-art methods by 41.8% and 29.3% in crowd counts and distribution estimation but is also applicable to both indoor and outdoor scenes, demonstrating its potential for crowd management and analysis.

The use of Unmanned Aerial Vehicles (UAVs) to perceive surface images and other information of complex three-dimensional (3-D) structures is a key component in infrastructure inspection tasks, such as for wind turbines and bridges. As a result, UAV path planning for 3-D structure coverage has attracted significant attention from researchers. However, existing studies primarily focus on minimizing the viewpoint connection path length under the full coverage condition, neglecting UAV flight constraints, which leads to a significant discrepancy between the planned path and the actual UAV flight trajectory. This paper presents an efficient UAV path-planning method for 3-D structure coverage using an improved ant colony optimization (ACO) algorithm. A multi-constraint UAV flight-trajectory-length minimization problem is formulated to guarantee full coverage of 3-D surfaces under kinematic limits, turning-angle and attitude-rotation constraints, thereby enhancing path consistency and smoothness. A cost function based on Minimum Snap trajectory planning replaces simple waypoint-connection length to more accurately reflect actual flight effort. A beta distribution-driven ant state transition rule and an adaptive pheromone evaporation strategy mitigate the tendency of traditional ACO to stall in local optima, balancing exploration and exploitation for rapid and reliable convergence. Simulation results demonstrate that the method proposed in this paper outperforms existing typical 3-D structure coverage path-planning methods, validating its feasibility and effectiveness.

T-cell acute lymphoblastic leukemia (T-ALL) is a malignant neoplastic disease. Accurate identification of core genes helps to explore the pathogenesis of T-ALL and develop relevant targeted drugs. In this paper, we first screened the RNA-seq, CTCF ChIP-seq and DNA methylation datasets of T-ALL for intersecting differentially expressed genes (DEGs) using bioinformatics software. As such, candidate genes screening is transformed into a semi-supervised classification problem given all known T-ALL related genes. It is clear that there are no labelled negative samples but a few labelled positive samples in the dataset, motivating us to employ the PU bagging method based on Positive-unlabeled (PU) learning to discover the T-ALL related candidate genes, in which a multi-layer perceptron (MLP) classifier was employed to accomplish the classification task. On this ground, a protein-protein interaction (PPI) network was built with the candidate genes, and core genes were screened. Finally, the core genes were subjected to GO and KEGG functional enrichment analysis, search of CTD and review of relevant literature to validate the proposed method. The validation results showed that the proposed method is able to effectively predict T-ALL related core genes, and all core genes have the potential to become candidates in T-ALL biomarker studies in the future.

Smart road studs have been extensively deployed as road safety and data collection devices. Accurate and reliable detection of smart road studs and its further integration into the perception and control modules of connected and autonomous vehicles (CAVs) undoubtedly benefit road boundary detection, localization of CAVs and augument the safety of CAVs’ driving. This work investigates real-time, accurate and reliable detection of smart road studs, which is a challenging task for CAVs because existing methods fail to achieve accurate and real-time smart road stud detection, especially in harsh road environment. To address these challenges, we first build a real-world smart road stud dataset, and then propose and validate a lightweight and efficient smart road stud detection model based on the you only look once 8th version (YOLOv8), called SRS-YOLO. First, a Squeeze-and-Excitation (SE) attention module is used to improve the coarse-to-fine (C2F) module to differentiate the channel importance of feature maps and improve the detection accuracy of smart road studs. Second, a novel downsampling module (DownS) that integrates the average pooling and the max pooling is designed to reduce the number of parameters and minimize information loss during the downsampling process. Third, the loss function is replaced with the Normalized Wasserstein Distance (NWD) loss to alleviate the sensitivity to location deviations when computing the loss for small targets. The experimental results demonstrate that the proposed SRS-YOLO outperforms other state-of-the-art methods, and achieves a 87.92% mean average precision at a real-time speed of 78 frames/s. Our dataset is available at:https://github.com/wky-xidian/smart-road-stud-dataset.

Multi-rotor unmanned aerial vehicles (UAVs) have been widely employed in various sensing tasks, e.g., environmental monitoring and disaster rescuing, many of which often require full coverage of terrestrial regions by UAVs. Efforts have been devoted to minimizing one of two objectives, i.e., energy consumptions and time costs of UAVs fulfilling such tasks, whereas it is still challenging to jointly optimize both objectives due to their complicated interdependent relationship. Therefore, this paper deals with the tasks of sensing terrestrial regions with multiple UAVs, and focuses on the three-dimensional (3-D) coverage problem by formulating a multi-objective optimization problem of jointly minimizing both objectives. Specifically, in order to optimize energy consumption effectively, an advanced closed-form energy consumption model for multi-rotor UAVs is developed based on a rigorous theoretical analysis by introducing the influences of torque and acceleration, which are often ignored by existing heuristic models. Moreover, considering the NP-hardness of the problem, an innovative swarm intelligence optimization framework is established by leveraging a multitasking learning pattern to exploit cross-task knowledge transfer and adopting an improved multi-objective salp swarm algorithm. Therein, two novel operators, i.e., a variable characteristic-guided hybrid solution initialization operator and a large-scale search-space-oriented multi-mechanism solution update operator, are designed to handle continuous, discrete and even high-dimensional variables involved. Real-world experiments validate the proposed energy model due to the reduction of power consumption estimation error by up to 59% compared to baselines, and besides, extensive simulations demonstrate that the proposed algorithm significantly outperforms the benchmarks in terms of both energy consumptions and time costs.

Identifying essential genes in an organism is crucial for bioinformatics and genomics research. In this paper, we propose a new computational method for gene similarity prediction, named GT-KGCN, which combines the advantages of the Graph Transformer (GT), Graph Convolutional Network (GCN), and Kolmogorov-Arnold Network (KAN). Specifically, GT-KGCN utilizes graph transformers for feature embedding and multi-head attention aggregation, GCN for hierarchical aggregation of information from neighboring nodes, and KAN for optimizing feature learning through learnable activation functions. Model inputs include Protein-Protein Interaction (PPI) networks, gene expression profiles, subcellular localization, and orthologs information. We evaluated the performance of GT-KGCN on two different PPI network databases (BioGRID and STRING) using multi-omics datasets of Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens. The experimental results show that GT-KGCN achieves a prediction accuracy of 99.12% for Escherichia coli, 98.47% for Saccharomyces cerevisiae, 92.22% for Drosophila melanogaster, and 92.30% for Homo sapiens. The GT-KGCN model outperforms traditional shallow machine learning methods and other deep learning methods in essential gene prediction. The ablation study verifies the positive effects of multi-omics datasets as well as different convolutional layers on the prediction performance of the model, with gene expression data showing the most significant performance improvement and demonstrating the model’s superiority.

Bluetooth 5 employs Constant Tone Extension (CTE) signals to simulate arrival times of a common signal at multiple antennas, but incurs limited accuracy due to inevitable frequency offsets caused by Gaussian frequency shift keying (GFSK) and time offsets caused by antenna switching. To address this issue, this paper proposes SCRN, an efficient Bluetooth 5 Angle of Arrival(AoA) estimation scheme that combines spectrogram analysis with deep learning co-design. SCRN leverages neural networks to generate a super-resolution spectrogram, effectively eliminating frequency offsets and recovering the true underlying frequency. Subsequently, it utilizes a lightweight convolutional recurrent network to encode the input features into a high-dimensional latent space, so as to alleviate the effect of time offsets. Through extensive experiments conducted in practical indoor settings, the proposed scheme achieves an AoA estimation error of only 1.3 degrees, surpassing traditional algorithms by over 90%.

Angle of Arrival (AoA) estimation based on Constant Tone Extension (CTE) signals in Bluetooth 5 suffers from carrier frequency offsets (CFO) arising from the carrier frequency mismatch between the receiver and the transmitter, extra noises coming from switching frequencies when sampling CTE signals, as well as the phase folding problem due to the usage of mod2π in mapping the increasing phase values in estimation models into [0, 2π). To address these issues, a generalized phase model involving the CFO compensation and the time offset is established, and an adaptive gradient descent optimization algorithm based on the real sampling mechanism is proposed to solve the phase folding problem efficiently while obtaining high-precision AoA estimation. Experimental results show that our method reduces the average estimation error by at least 10% in outdoor scenes and 61% in indoor scenes compared to the conventional methods.

Popular vision-based crowd counting methods suffer from huge costs, limited coverage and high complexity, making it difficult to be applied for large surveillance areas, while emerging WiFi-based methods which are suitable for large surveillance areas incur limited accuracy due to the sparsity and randomness of WiFi sniffing data. Considering the fact that the variations of audio data are spatial-temporally correlated with crowd fluctuations, this paper proposes to fuse audio and WiFi sniffing data for crowd counting by developing a Cross-modal Multi-level Perception Network, termed CMPN. The CMPN can not only extract crowd features from the bimodal data to leverage the temporally continuity for compensating sparse WiFi sniffing data, but also mine the correlation of intra- and inter-modality crowd features for accurate crowd counting. Extensive experiments are conducted in a real campus with the surveillance area of about 4000m2, and demonstrate that the CMPN can achieve the mean absolute error of 5.88, resulting in a 22.12% reduction compared to the state-of-the-art WiFi-only method.

The conventional approach of choosing the best route to carry network traffic in wireless multi-hop networks does not maximize the overall network throughput and can lead to short-term instabilities in network state with dire consequences. To date, wireless network route selection considers mainly network or link metrics, always picking the best links, thus channeling all packets through a subset of all available links. This leaves weaker links under-utilized although such links can in fact be used to carry smaller packets or packets with less stringent requirements and free up bandwidth on the better links for larger packets or traffic with higher service requirements. As network traffic volume and heterogeneity increase in future networks, we need to maximize the usage of available network bandwidth and distribute the network traffic load. We combine network link metrics and packet attributes to determine the successful packet transmission probability, and then use this outcome to pick suitable links to forward the packet, which is not necessarily the link with the best metric. To validate the efficacy of our proposed approach in routing performance and energy efficiency, we applied it in routing for wireless multi-hop networks. More importantly, we are able to spread the traffic across nodes in the network, thus achieving better network load-balancing and higher network resource utilization.

Abstract In comparison with capturing channel state information (CSI) measurements via a laptop or desktop, using a smartphone to collect CSI measurements incurs the restriction of working with a single access point and significant signal distortions, resulting in limited information for smartphone localization. Therefore, this paper intends to leverage as much available localization information as possible by ($1$) shifting the WiFi frequency from $2.4$ to $5$GHz; ($2$) calibrating the noisy CSI measurements and ($3$) fusing both amplitudes and phases of the CSI measurements, so as to enhance localization accuracy. Specifically, we first filter out distorted CSI measurements based on their distribution characteristics, then apply the advanced uniform manifold approximation and projection method to refine the mapping relations from a high-dimensional fingerprint space to a low-dimensional location space, and design a location fusion algorithm based on the continuous feature scaling model, which is able to distinguish two locations with similar fingerprints. Extensive experimental results show that the localization accuracy of the proposed approach outperforms the state-of-the-art counterparts by at least $15.5$ and $18.7\%$ using two off-the-shelf smartphones.

For Unmanned Aerial Vehicles (UAVs) with Tiny Machine Learning (TML), there is mutual exclusivity between the energy consumption for flight and the energy consumption to support their computation and processing. IoUAVs integrated with TML systems often consume substantial amounts of energy during flights, particularly when engaged in extended coverage and surveillance missions. The energy consumption of a UAV with TML performing long, wide-area coverage patrols and monitoring missions in complex areas is significant for the flight itself, and the energy required for the TML to perform calculations and processing is not guaranteed. Therefore, to better support TML computations, this study optimizes flight paths to reduce the energy consumption of UAVs while ensuring coverage. Specifically, in this study, the use of concave point elimination algorithms, enhanced convex decomposition algorithms, and determination of flight direction significantly reduced the frequency of UAV turns. The computational cost of obtaining a complete path is reduced by merging the subconvex regions and the weighted minimum traversal of the graph. This novel bidirectional forwarding path coverage path-planning (BFP-CPP) algorithm maximizes the reduction in the number of turns, reduces energy consumption, and achieves global coverage. The simulation experimental results show that compared with the existing methods without concave point elimination, the BFP-CPP algorithm can effectively reduce the number of subregions, minimize the number of drone turns, and lower energy consumption.

As a promising indoor localization technology, WiFi fingerprint-based localization encounters many issues that need to be addressed urgently, such as high-overhead fingerprint map construction, device heterogeneity among either mobile devices or access points (APs), etc. In this article, we present CBWF: a lightweight circular boundary -based WiFi fingerprinting localization system that is able to provide low-overhead, device calibration-free accurate indoor localization. CBWF achieves this by dividing a localization area into multiple subregions, and then leveraging the relation between the received signal strength (RSS) vectors from two different APs as fingerprints for localization. The key idea behind CBWF is that a superior division mechanism is attained to divide the localization area. Specifically, we propose the circle boundary mechanism to better approximate the real boundary of subregions, compared with the widely used linear boundary mechanism, and then sufficiently exploit the theoretical characteristics behind this novel mechanism. Extensive simulation and real-world experiments show that our lightweight system outperforms state-of-the-art approaches. Specifically, in a 40 m$\times 17$m real scenario with only 20 reference points (RPs) and 11 APs, CBWF achieves an average localization accuracy of 2.95 and 4.15 m for two different mobile devices, respectively. Our codes are available at:https://github.com/dadadaray/circular-boundary.

Crowdsourcing dramatically benefits WiFi fingerprinting localization in reducing the costs of collecting received signal strength (RSS) data during offline site survey and has gained much attention in the literature. This article proposes a deep-learning-based indoor positioning system (IPS), termed SeqIPS, to sufficiently exploit the available information in the crowdsourced sequential RSS data and inertial measurement unit (IMU) data. However, there exist the following three challenges: 1) the relatively large label noises of crowdsourced RSS data; 2) the unavailability of the labels of crowdsourced IMU data; and 3) the incorporation of the knowledge in IMU data into the localization model using RSS measurement as inputs during online localization. To this end, a co-teaching network is developed to effectively extract spatiotemporal features from sequential RSS data and meanwhile alleviate the influence of label noises. Also, a novel loss function involving IMU data is defined to impose spatial penalties, so as to further refine the localization model. Moreover, a domain adaptation module is included to effectively label the crowdsourced IMU data. Extensive experiments are conducted in a real scenario and show that SeqIPS can achieve an average localization error of 3.37 m, outperforming both traditional methods and recent deep-learning-based methods by 18.4% at least. In summary, the novelty of SeqIPS is that extra crowdsourced IMU data is exploited to refine the localization model during offline training, while only sequential RSS data is required as inputs during online localization, such that the system accuracy, simplicity, and costs are reasonably balanced.

Visual odometry (VO) is a critical component of simultaneous localization and mapping (SLAM) with extensive applications in robot navigation and beyond. However, prevalent VO methods often underperform in intricate environments with dynamic textures, insufficient lighting, and rapid rotational movements, primarily due to constrained feature selection and inadequate image structure comprehension. To address these challenges, this paper proposes a novel VO framework, termed Multi-Attention Saliency Direct Sparse Odometry (MAS-DSO). Specifically, MAS-DSO significantly bolsters performance and robustness through accurate recognition of visually salient regions and deep understanding of image structures. With regard to the problem of limited feature selection, we propose a Saliency Transformer Generative Adversarial Network (STRGAN) based on a multi-attention mechanism, narrowing the feature selection scope and enhancing its accuracy. Addressing the issue of limited understanding of image structure, we introduce a robust method for gradient computation to accurately determine the gradient values of features. Building on this, we have designed a dynamic gradient weight adjustment strategy that takes into account both the gradient magnitude and local image structure, thereby achieving precise gradient weight distribution. Comprehensive quantitative evaluations on the ICL-NUIM and TUM monoVO datasets reveal that MAS-DSO not only outperforms SalientDSO, DSO, and ORB-SLAM in performance metrics but also significantly surpasses other methods in saliency prediction performance and mapping quality. In conclusion, MAS-DSO not only augments feature selection efficiency but also enhances the processing prowess for diverse images in complex settings.

Cooperative multiple unmanned aerial vehicles (UAVs) have been widely exploited in various applications, including data collection, forest monitoring, edge computing, and so on. Due to limited onboard storage and expensive hardware costs, reducing both the energy consumption and the number of UAVs is critical for these multi-UAV applications. However, existing studies primarily revolved around energy minimization in two-dimensional (2-D) scenarios, given a sufficient but fixed number of UAVs, and most of them considered specific application scenarios, resulting in poor generality. In contrast, this paper defines a generalized application scenario, in which multiple ground nodes (GNs) are accessed by multiple UAVs in three-dimensional (3-D) scenarios, and aims to minimize the energy consumption by employing the necessary (or equivalently minimum) number of UAVs and formulating a mix-integer nonconvex problem. To this end, this paper decomposes the problem into two subproblems: energy consumption minimization for a single UAV consecutively accessing any two GNs and energy-efficient multi-UAV GN-accessing path planning employing the minimum number of UAVs. The first subproblem is solved by applying the successive convex approximation (SCA) technique and the path discretization method, while the second subproblem is addressed by designing a three-stage approximation framework based on modified particle swarm optimization (MPSO) and greedy path assignment (GPA). Comprehensive simulations demonstrate the superior performance of the proposed method in terms of optimality and efficiency compared to several other counterparts.

Regarding the passive WiFi sensing based crowd analysis, this paper first theoretically investigates its limitations, and then proposes a deep learning based scheme targeted for returning fine-grained crowd states in large surveillance areas. To this end, three key challenges are coped with: to relieve the influences of the randomness and sparsity induced by passive WiFi sensing, an attention-based deep convolutional autoencoder model is designed to recover accurate crowd density maps in a way similar to image reconstruction; to combat the anonymity caused by MAC randomization, following the identification of local high-density crowds (LHDCs) with the density clustering algorithm, i.e. DM-DBSCAN, a bidirectional convolutional LSTM based model is employed to infer LHDC speeds; to overcome the absence of passive WiFi sensing datasets for model training, three semi-synthetic datasets are produced by emulating passive WiFi sensing with practical pedestrian tracking datasets. Extensive experiments confirm that, the proposed scheme significantly outperforms existing WiFi-based methods in terms of crowd density estimation and provides superior crowd speed estimation. More importantly, the scheme can also produce consistent crowd states on a real-world dataset, revealing that it has the ability to support accurate, visualized and real-time crowd monitoring in large surveillance areas.

Crowd counting aims to estimate the number of individuals in targeted areas. However, mainstream vision-based methods suffer from limited coverage and difficulty in multi-camera collaboration, which limits their scalability, whereas emerging WiFi-based methods can only obtain coarse results due to signal randomness. To overcome the inherent limitations of unimodal approaches and effectively exploit the advantage of multi-modal approaches, this paper presents an innovative WiFi and video-fused multi-modal paradigm by leveraging a heterogeneous dual-attentional network, which jointly models the intra- and inter-modality relationships of global WiFi measurements and local videos to achieve accurate and stable large-scale crowd counting. First, a flexible hybrid sensing network is constructed to capture synchronized multi-modal measurements characterizing the same crowd at different scales and perspectives; second, differential preprocessing, heterogeneous feature extractors, and self-attention mechanisms are sequentially utilized to extract and optimize modality-independent and crowd-related features; third, the cross-attention mechanism is employed to deeply fuse and generalize the matching relationships of two modalities. Extensive real-world experiments demonstrate that our method can significantly reduce the error by 26.2%, improve the stability by 48.43%, and achieve the accuracy of about 88% in large-scale crowd counting when including the videos from two cameras, compared to the best WiFi unimodal baseline.

Proteins are among the most essential molecules in the living body and are irreplaceable in various biological processes that sustain life. Predicting their functions is essential for understanding the molecular mechanisms of cellular life activities. With the widespread use of second-generation highthroughput sequencing technologies, more and more protein sequence data are being rapidly sequenced and shared. The rapid accumulation and update of these data provide a more substantial and diverse data foundation for protein function prediction research. Traditisonal biological experimental methods are the most reliable way to determine protein function. However, relying solely on manual experimental methods to test unknown protein functions individually suffers from a high workload and long lead time. Therefore, a deep learning-based protein function prediction model called DeepLA was proposed in this paper. Firstly, the model encodes protein sequences in One-hot and uses the Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST) algorithm to perform a comparative search to obtain a Position-Specific Scoring Matrix (PSSM) containing protein evolutionary information. Next, the protein feature vectors were fed into a multi-channel model consisting of a convolutional neural network, a bidirectional long- and short-term memory network, and a self-attentive mechanism for feature extraction to achieve the protein function prediction task. The results showed that DeepLA exhibited good performance in the Molecular Function(MF) and Biological Process (BP) categories with values of 0.556 and 0.488 on the publicly available dataset CAFA3, respectively, which were 5.8% and 8.0% higher than other models.

Recent research on opportunistic network routing protocols has found that the efficiency of message forwarding varies with the selection of next-hop nodes, so it is of great significance to select the appropriate next- hop nodes for message forwarding. This paper proposes a Node Importance Algorithm Based on Social Properties of Nodes (NI). The importance evaluation model is established by using the social attributes of node centrality and relevance, and the importance forwarding algorithm is constructed according to this model. The algorithm first calculates the node importance and selects the node with higher importance as the next hop forwarding node based on this model. The experimental simulation results show that the algorithm can select more appropriate next-hop nodes to complete message forwarding, so as to improve the message delivery rate of the network.

As a kind of mobile traffic offloading technology, opportunistic triage downloads and distributes data through seed nodes. Therefore, how to efficiently and accurately select suitable seed nodes becomes a key problem of opportunistic streaming technology. To address the problem of insufficient research on seed node content coverage in existing studies, this paper combines the characteristics of opportunity networks and the solution idea of the influence maximization problem and proposes the evaluation model of node influence for the first time. Then proposes a seed node selection algorithm (SNSNI) based on node influence on this basis. Experimental results show that the set of seed nodes selected by SNSNI algorithm can obtain smaller average message transmission delay and more covered nodes in the triage scenario compared with the random algorithm, and fewer seed nodes are required to achieve the same effect.

Nowadays, global navigation satellite systems (GNSS) have been widely used for outdoor pedestrian positioning services. However, GNSS faces challenges such as high energy consumption and limited performance due to urban canyons. Therefore, we propose a novel outdoor positioning scheme by fusing WiFi, GPS, and pedestrian dead reckoning (PDR) using fuzzy logic, aiming to achieve energy-efficient localization through the tradeoff between accuracy and energy consumption. Specifically, we analyze the factors affecting the positioning accuracy and energy consumption of fusion positioning and obtain the coarse metrics of positioning accuracy and energy consumption. Then, we design a fuzzy inference system to intelligently schedule the absolute positioning methods (WiFi, GPS, or their combination) based on metrics such as PDR error, the remaining energy of the smartphone, the grid size of the WiFi fingerprint database, and GPS accuracy. Extensive experimental results demonstrate that the proposed positioning scheme reduces energy consumption by 28.2% compared to the Kalman filter-based WiFi and PDR fusion method, achieving a dynamic balance between accuracy and energy consumption.

Wireless sensor network (WSN) has many applications, such as, military scenarios, habitat monitoring and home security. In recent years, with the advancement of energy harvesting (EH) technology, nodes can obtain available energy from the surrounding environment for their own use, thus extending their lifetimes. Under these conditions, research aimed at improving the WSN lifecycle has further shifted towards improving the performance of the network, albeit subject to unique energy harvesting constraints. This paper proposes an energy prediction algorithm for the devices and an Energy and Density Adaptive Clustering (EDAC) protocol to improve network throughput and transmission ratio for EH-powered WSNs. Based on the EH characteristics, we first employed Convolutional Neural Network (CNN) and Bidirectional Long-Short Term Memory (Bi-LSTM) algorithm for energy prediction, then we divide the energy of the sensor nodes into three levels: low, medium, and high energy levels. At high energy levels, nodes can be selected as cluster head nodes, while at low energy levels, nodes must sleep and charge. EDAC first uses the K-Means clustering algorithm to dynamically cluster the surviving nodes in each round and sets a threshold to partition the clustering density. On this basis, a new adaptive cluster head election formula is proposed for cluster head election based on the energy levels of nodes, the predicted energy of the next stage, and the density of clusters. In the stable communication stage of the network, we introduce a "backup cluster head" to temporarily forward the remaining data packets within the cluster when the current cluster head expires. Our simulation results show that our algorithm significantly improves throughput and data transfer rate compared to the traditional and improved clustering protocols.

The positioning of wild animals usually requires long-distance, high-precision, low-energy positioning technology, and most of the existing methods use GPS, RFID, etc., either high power consumption, high cost, or small ranging range, low accuracy. This paper comprehensively considers the needs of out-door scene positioning, and combines the characteristics of LoRa technology and drones to solve the problem that it is difficult to achieve the best cost, energy consumption and positioning error in outdoor positioning. A sequential localization algorithm for two scenarios of single / multiple LoRa terminals is proposed. Based on the Cramer-Rao lower bound theory, the Unmanned Air Vehicle(UAV) trajectory is optimized to minimize the LoRa terminal positioning error at extremely low cost and energy consumption. The simulation results show that the proposed method effectively improves the accuracy of positioning LoRa terminals, and provides theoretical guidance for the deployment of single UAV as mobile base station in positioning.

Online public transit ridership information is helpful to enhance the service quality of urban public transportation and the travel experiences of passengers. Passive WiFi sensing collects WiFi probe (request) frames sent by nearby mobile devices in a non-intrusive manner, and can thus be employed to monitor ridership. Compared with the existing non-WiFi based approaches, passive WiFi sensing based approaches demonstrate the advantages of limited interferences, large coverage, low costs and lightweight calculations. More recently, although some dedicated passive WiFi sensing based methods have been proposed in an offline mode, due to sniffing opportunistically, unknown dynamic transmission boundary, MAC randomization and the difficulty in online feature extraction, how to utilize limited sensing data to provide accurate online ridership information is still challenging. To this end, an innovative public transit ridership monitoring system built upon a customized WiFi sniffer and an online ridership estimation algorithm is developed. In the algorithm, a convolutional neural network (CNN) module and a bidirectional long short-term memory (BiLSTM) neural network module are first adopted to find correlations among inputs and capture the bidirectional time-series features, respectively; furthermore, an attention module is incorporated to determine the importance of an input sequence at different times. Real-world experiments are carried out on 8 buses corresponding to 4 bus routes in Hohhot, China. The evaluation results show that the proposed algorithm outperforms the other 4 online algorithms and the state-of-the-art offline algorithm.

Great efforts have been devoted to solving the crowd counting problem based on vision or other fine-grained measurements. Popular vision and WiFi channel state information based approaches, though are able to achieve relatively high accuracy, suffer from limited scalability. In contrast, passive WiFi sensing-based approaches are capable of supporting large surveillance areas, but often rely on certain global linear or approximately linear regression models, which cannot accurately capture the complex mapping relationship between WiFi sensing data and the corresponding crowd count, especially in a large surveillance area during a long period of time. This paper addresses the issue from the following three aspects. Firstly, in order to combat with these coarse-grained regression models, the large surveillance is partitioned into grids, such that either a local linear model or other implicit local models can be built with respect to each grid. Secondly, sequential WiFi spatial-temporal matrix (SWSTM) is defined in alignment with grids to encode the spatial-temporal information of crowds based on passive WiFi localization and a sliding time window mechanism. Thirdly, the spatial-temporal correlations among crowd features of different grids are mined to better regress such local models by using a recurrent neural network (RNN) with SWSTMs as inputs. Extensive experiments are conducted in a real campus road network with an area of about$4000m^{2}$, and demonstrate that the proposed method significantly reduces the counting error rate from 22.54% to 13.44% compared to several state-of-the-art methods.

In sparse opportunistic networks with social at-tributes and limited cache space, routing algorithms based on long-term and stable social relations have low message delivery rates in a short period of time, and do not consider the impact of delivered message copies on communication efficiency. To solve the problem of low delivery rates, the paper integrates social attributes and space-time constrained characteristics and proposes social closeness and spatio-temporal interaction degree based on historical information. Then we design a routing algorithm (SC-STID) based on these two metrics. SC-STID takes into account the social closeness and spatio-temporal interaction of nodes to select relay nodes comprehensively and uses the delivered message deletion mechanism to remove the copies of delivered messages from the network. Experimental data shows that SC-STID improves the success rate of message delivery and reduces network resource consumption compared to existing routing algorithms (Prophet, Epidemic, ORRF) in a sparse opportunistic network of nodes with social attributes.

Traffic flow prediction plays an important role in intelligent traffic management. In order to solve the problem that the prediction model has low accuracy in traffic flow prediction when the amount of data is small. Considering the chaotic nature of the traffic flow time series data, the phase space reconstruction method is adopted to process the data, and then the SVR model is used to predict the processed data. The two parameters of phase space reconstruction, including embedding dimension and delay time, have a great impact on the final prediction accuracy. In this paper, the improved KNN method is used to select the parameters of phase space reconstruction and construct the KNN-GA-SVR model. Compared with the CC-GA-SVR model of phase space parameter selection based on C-C method, this model improves the prediction accuracy, is effective and feasible for the prediction of short-term traffic flow, and has strong applicability.

In an opportunistic network with social attributes, the selection of relay nodes is mainly done according to community division. In the social opportunity network, community partition is the key to reducing data forwarding delay and improving the success rate of data delivery. To solve the problem that existing opportunistic network routing algorithms do not analyze the impact of overlap and transitivity of community on opportunistic routing, a 2-hop routing strategy for overlapping communities based on social intimacy(TOCSI) is proposed. The algorithm first introduces the overlapping community division method PercoMCV to reasonably divide the node community structure and designs the opportunity route based on the result of the community division. Experiments show that compared with Epidemic, Prophet, and ORRF algorithms, TOCSI can effectively improve the success rate of message delivery and reduce the average data forwarding delay and routing overhead.

Angle of arrival (AoA) and angle of departure (AoD) are introduced in Bluetooth 5 to better support indoor localization and tracking. However, the performance of AoA estimation using Bluetooth 5 is not thoroughly understood at present. In this paper, a Cramér-Rao lower bound (CRLB) model, taking into account Constant Tone Extension (CTE) signals firstly adopted by Bluethooth 5, is proposed to theoretically analyze its performance given different uniform antenna arrays, such as linear, rectangular and circular arrays, and on these grounds, the effects of the number of antennas, inter antenna distance, incident angle, and CTE parameters on AoA estimation are carefully investigated. A simulation analysis is carried out and a comparison between different types of antenna arrays is reported as well.

The energy consumed by a node in the opportunity network to forward messages determines its service time and survival period. In order to solve the problem of energy consumption imbalance, the paper proposes an energy equalization opportunity network routing algorithm (EOGH) based on game theory and historical similarity. The algorithm selects a suitable relay node to forward the message based on the remaining energy of the node and its encounter probability with the destination node. Simulation results show that EOGH can get high delivery rate with the energy balance consumption and prolongs the network lifetime.

The paper proposes an Opportunistic Network Routing Strategy Based on Relay Node Collaboration (BRCR), which solves the problem of low effectiveness of data forwarding caused by existing algorithms that ignore the social nature of node movement. The paper introduces a hybrid data forwarding service collection consisting of a cluster of fixed relay nodes and mobile relay nodes, which a relatively stable communication link is established between the source and target nodes. The experimental results show that BRCR proposed in this paper improves the efficiency of data forwarding as compared to classical routing algorithms.

Abstract Step counting is not only the key component of pedometers (which is a fundamental service on smartphones), but is also closely related to a range of applications, including motion monitoring, behavior recognition, indoor positioning and navigation. Due to the limited battery capacity of current smartphones, it is of great value to reduce the energy consumption of such a popular service. Therefore, this paper focuses on the energy efficiency of step-counting algorithms. First of all, we formulate a theoretical error model based on the well-known auto-correlation coefficient step-counting (ACSC) algorithm, so as to analyze the factors affecting step-counting accuracy. And then, in light of this model and an adaptive sampling strategy, we propose a novel energy-efficient step-counting algorithm by adaptively substituting the computationally intensive auto-correlation with simple mean absolute deviation. On these grounds, an Android pedometer is implemented. Two individual experiments are carried out and verify both the theoretical error model and the proposed algorithm. It is shown that our algorithm outperforms two famous counterparts, i.e. the original ACSC algorithm and peak detection step-counting algorithm, in terms of both accuracy and energy efficiency.

Passive WiFi localization refers to determining the location of WiFi-enabled mobile devices by deploying dedicated WiFi access points to sniff WiFi packets transmitted by these mobile devices and measure the corresponding received signal strengths (RSSs) for the use in localization. However, most existing studies fail to consider the effect of multiple channels where WiFi packets are transmitted and sniffed. The problem is further exacerbated by device heterogeneity occurring across various mobile devices. In this article, we present a unified deep neural network (DNN)-based solution, termed DHCLoc, to address these two challenges. To be specific, a Cramer–Rao lower bound (CRLB)-based analysis reveals that utilizing multichannel information will benefit localization, motivating us to include channel information into DHCLoc. Moreover, a novel maximum likelihood estimation (MLE)-based localization framework is introduced by incorporating a new variable to characterize the RSS measurement offsets caused by device heterogeneity, inspiring us to apply adversarial training to adopt such offsets against device heterogeneity. Extensive experiments using two real-world data sets are conducted, and show that, in comparison with several existing methods, DHCLoc can improve the localization accuracy by at least 25.2% and 25.8%, respectively.

Path inference aims to reveal missing paths given a few number of GPS samples associated with a moving object by exploiting the topology of road network and statistical information of historical GPS trajectories, and plays a vital role in data preprocessing of location based information services. But, in practice path inference severely suffers from the data sparsity as well as the randomness of drivers path selection behaviors. In this paper, we propose a novel Bayesian path inference model subject to spatiotemporal constraints by taking into account the drivers path selection behaviors. To be specific, the problem of path inference is cast as the problem of searching K most probable candidate paths according to the joint posterior selection probabilities of candidate paths. When estimating model parameters, we use the frequency of each road segment in the historical GPS trajectories instead of that of road segment transfers to mitigate the influence of data sparsity. In addition, both spatiotemporal constraints and probability thresholds are introduced to narrow the search space, which significantly improves the time efficiency. The experiments are conducted using practical data and show that the proposed model is significantly superior to three existing popular models. When the GPS sampling interval varies from 1 minute to 5 minutes, the accuracy of the proposed method is 0.94, 0.91, 0.86, 0.80 and 0.74, and the Jaccard similarity 0.89, 0.85, 0.83, 0.80 and 0.75 respectively, the average improvement in accuracy rises from 3.68% to 18.69% and that in the Jaccard similarity from 4.56% to 18.42%.

With the increasing demand on Indoor Location-Based Services (ILBS), various positioning technologies had emerged in the past decades, and WiFi-based approach is one of the most promising ones. However, the existing WiFi localization methods fail to take into account the disparate influence of packets transmitted in different channels so as to inhibit the further improvement of localization accuracy. Therefore, we present CADNN: a Channel Adaptive WiFi localization method based on Deep Neural Network (DNN). Specifically, a comprehensive analysis on signal attenuations in different channels along with error analysis based on Cramer-Rao Lower Bound (CRLB) is conducted in theory. Then, the channel set splitting scheme for practical localization to leverage multi-channel features is proposed. Finally, we design a localization framework using multi-objective regression DNN to adapt Received Signal Strength (RSS) measurements from different channel sets. The results from real-world experiments confirm the effectiveness of channel adaptive and show that CADNN can improve localization accuracy by at least 25.3% and 19.5% respectively on the two datasets and it can serve thousands users within one second.

In the field of Wi-Fi-based fingerprint positioning, the relationship between localization accuracy and AP deployment location has always been a research hotspot. Obviously, Location of AP deployment affects network coverage and localization accuracy. At present, most optimization deployment algorithms are based on search algorithms, which have the problems of slow convergence speed and weak global optimization performance. A virtual force algorithm (VFA) is one of the most effective algorithms to solve coverage performance in dynamic deployment. In this paper, a virtual force-based AP optimization deployment algorithm (VFA-FD) is proposed, which introduces fingerprint difference at the reference point as a force to guide the AP movement, combined with the global fingerprint difference to evaluate the deployment plan, to meet the coverage and maximize fingerprint difference of the AP deployment plan. The Motley-Keenan model, which is more in line with the actual environment, is used to represent the propagation of the radio by obstacles in the indoor environment. Based on the above, a simulation-based deployment optimization software was developed using GeoTools to present the simulation effect in the form of GUI. And compared with other three algorithms, it proves the effectiveness of the algorithm proposed in this paper.

In public places, even if pedestrians do not have their mobile devices connected with any WiFi access point (AP), WiFi probe requests will be broadcast, so that WiFi sniffers can be employed to crowdsource these WiFi probe packets for use. This paper tackles the problem of exploiting the passive WiFi sensing approach for pedestrian flow analysis. To be specific, a passive WiFi sensing model is first established based on a probabilistic analysis of interactions between WiFi sniffers and the moving pedestrian flow, capturing the main factors affecting pedestrian flow characteristics. On that basis, a sequential filtering algorithm is proposed based on the Rao-Blackwellized particle filter (RBPF) to produce simultaneous and efficient estimates of the pedestrian flow speed and pedestrian number utilizing the real-time sniffing results. In order to validate this study, an experimental pedestrian surveillance system using WiFi sniffers is deployed at the transfer channel of a metro station in Guangzhou, China. Extensive experiments are conducted to verify the passive sensing model, and confirm the effectiveness and advantages of the proposed algorithm. The pedestrian flow estimation not only helps to improve the safety and facility management and customer services, but also paves the way for introducing other novel applications.

In order to improve the performance of the indoor localization system, the fusion of multi-source data is a common approach. For example, one can improve the WiFi localization accuracy on smartphones by combining pedestrian dead reckoning (PDR) results obtained through inertial sensors embedded in smartphones. Though obvious improvement in localization can be achieved, the existing methods do not sufficiently exploit the advantages of two data sources. To be specific, the existing studies directly fuse WiFi localization results and PDR results at a high level, i.e., the final coordinates of the WiFi localization system integrate with the final coordinates of PDR by certain algorithms, but ignores their relationship at a low level, i.e, the heading of the PDR , not its location results, is improved by the help of the WiFi localization. In addition, it is acknowledged that the pedestrian heading is the major source determining the performance of PDR. Therefore, this paper proposes to design a novel pedestrian heading estimation by fusing PDR and WiFi at a low level. Different from the traditional method, which employs a magnetometer to eliminate the drifts of a gyroscope, the method utilizes only the gyroscope of a smartphone for the heading estimation and relies on the WiFi localization trajectory in the fusion to compensate for the drift errors of the gyroscope-based heading estimation. In our algorithm, firstly, a pedestrian's activities trajectory is segmented into several straight paths with the help of the gyroscope of a smartphone. Secondly, the WiFi fingerprint localization coordinates falling into the time window of each straight path are fitted by the least-squares linear regression method. Lastly, the deviations of the gyroscope heading estimation of the smartphone when pedestrians walk in a straight direction are mitigated using the fitting slope obtained by the WiFi localization. Extensive experimental results demonstrate that our proposed algorithm can efficiently estimate the heading of pedestrians, and effectively reduce the cumulative errors of the gyroscope-based heading estimation using smartphones. In our experiments, the average error of the heading for pedestrians in 294 steps was reduced from 24.3 degrees to 1.22 degrees. Not requiring a magnetometer, our algorithm can reduce the drift errors of the heading estimation of pedestrians, achieve the deeper fusion of multi-source data in the fusion of WiFi and PDR, and potentially improves the endurance of smartphones.

Energy Harvesting Wireless Sensor Networks (EHWSNs) have been widely studied in recent years. In solar charged EH-WSNs, the Sun illumination changes with the changes of environment, in consequence the collected energy of the sensor node is unstable, especially in rainy day, windy day or the angle of the solar panel changes. Therefore, the reasonable assignment of energy in EH-WSNs becomes critical important. In This paper, based on the solar energy charateristics, we propose a priority task scheduling algorithm that suitable for EH-WSNs, that is, the transmission method and order of collected data are determined according to task priority and the remaining energy of the node. The simulation results show that the priority task scheduling algorithm guarantees the fairness of node energy distribution, the timeliness of sending urgent tasks and the high processing rate of common tasks when the energy provided by the environment is small.

WiFi fingerprint-based localization is one of the most attractive and promising techniques targeted for indoor localization, and has attained much attention in the past decades. In addition to improving localization accuracy, various efforts have been devoted to efficiently building a radio map which is normally tedious and laborious. Therefore, this paper proposes an efficient approach for building compact radio maps based on compressed multivariate kernel density estimation (CMKDE), in the sense that only a few received signal strength (RSS) measurements are required and the resulting radio maps are far less than the sizes of traditional radio maps. Extensive experiments are carried out in a real scenario of nearly 1000 m2during several working days, and a comparison is made with two existing popular solutions including the Gaussian process regression (GPR) and another approach based on kernel density. It is shown that the proposed method outperforms its counterparts in terms of both robustness and accuracy.

With the rapid development of the Internet and combined with outsourcing, a new paradigm - crowdsourcing which shines brilliantly as a new labor mode. However, the existing pricing strategies for crowdsourcing tasks have several undesirable problems, e.g., no universal pricing model, not meeting the multiple requirements of users, pricing rely too much on decision makers, etc., which bring an unreasonable allocation of task rewards so as to make the pricing results subjective and uncontrollable. Therefore, this paper proposes a reverse pricing mechanism based on VCG and quality all-pay for collaborative crowdsourcing (VCG-QCP). The actual crowdsourcing scenario is considered with VCG mechanism, and the concept of quality all-pay is introduced to evaluate the work quality of workers who might perform the task. Then a general reverse pricing model is established by mathematical modeling, and the pricing algorithm is designed based on this model. Simulations show that the proposed method can achieve higher algorithm efficiency, higher task completion quality, a reasonable balance of benefits between employers and workers, and ensuring the truthfulness of workers' bidding.

Motivated by the network tomography, in this paper, we present a novel methodology to estimate link travel time distributions (TTDs) using end-to-end (E2E) measurements detected by the limited traffic detectors at or near the road intersections. As it is not necessary to monitor the traffic in each link, the proposed estimator can be readily implemented in real life. The technical contributions of this paper are as follows: First, we employ the kernel density estimator (KDE) to model link travel times instead of parametric models, e.g., Gaussian distribution. It is able to capture the dynamic of link travel times that vary with the change of road conditions. The model parameters are estimated with the proposed C-shortest path algorithm, K-means-based algorithm, as well as expectation maximization (EM) algorithm. Second, to reduce the complexity of parameter estimation, we further propose a Q-opt and an X-means -based algorithm. Finally, we validate our proposed method using a dataset consisting of 3.0e +07 GPS trajectories collected by the taxicabs in Xi'an, China. With the metrics of Kullback Leibler and Kolmogorov-Smirnov test, the experimental results show that the link TTDs obtained from our proposed model are in excellent agreement with the empirical distributions, provided that ~70% of the intersections are equipped with traffic detectors.

Nowadays, almost every smartphone is equipped with various inertial sensors, such as accelerometer, gyroscope, magnetometer and so on, which makes it possible to implement Pedestrian Dead Reckoning (PDR) on smartphones to assist in pedestrian positioning and tracking. However, in order to improve the accuracy of heading estimation involved in PDR, it is common to fuse the measurements of inertial sensors, which results in an inevitable increase in energy consumption of smartphones, thus weakening the endurance capacity of smartphones. In this paper, we present an energy efficient heading estimation algorithm based on Kalman filter with commercial off-the-shelf smartphones. To be specific, this algorithm makes use of the relatively accurate measurements of the gyroscope in the short time and the relatively stable measurements of the magnetometer in the long term, then reduces the sampling frequency of magnetometer and accelerometer to the one-sixth of that of gyroscope, finally fuses the asynchronous inertial measurements based on Kalman filter to produce heading estimate so as to save energy consumption without significantly sacrificing heading estimation accuracy. Extensive experiments are conducted and show that our proposed algorithm reduces the energy consumption by as much as 49.52% on average compared to the standard Kalman filter based algorithm, whereas achieving the similar accuracy of heading estimation.

The proliferation of mobile devices, including smartphones and tablets, has been enabling new possibilities for inferring information about the positions, behavior and activities of the users carrying these devices. For instance, by leveraging the WiFi probes sent out by mobile devices in public spaces (such as shopping malls, metro stations, etc.), even if pedestrians do not have their mobile devices to be associated with any WiFi access point (AP), it is attractive to conduct pedestrian analysis in a passive sensing approach to facilitate the efficient management of public infrastructures as well as convenient customer services. This paper considers the problem of pedestrian flow analysis by implementing a pedestrian surveillance system in the transfer channel of a metro station in Guangzhou China. Firstly, a fingerprint database is generated through a Gaussian process regression (GPR) approach. On these grounds, a pedestrian number estimation method based on linear regression is presented by making use of the fingerprint-based localization method to refine the number of mobile devices residing in the surveillance area, and a pedestrian velocity estimation method is proposed based on particle filter and the inverse distance weighted (IDW) method. According to the dataset obtained in real scenarios, the effectiveness and advantages of the proposed two methods are confirmed.

In this paper, a novel scheme is reported to adapt radio maps to environmental dynamics in an online fashion by combining crowdsourcing and gaussian process regression (GPR). Specifically, a Marginalized Particle Gaussian Process (MPGP) is adopted to recursively fuse crowdsourced fingerprints with an existing offline radio map. The advantages of the proposed scheme lie in the efficiency and scalability in comparison with the traditional approaches. Extensive experiments are carried out in a real scenario of nearly 1000 m2during five months, and a comparison is made with several existing popular solutions. It is shown that the proposed scheme outperforms its counterparts in terms of both robustness and accuracy.

Fingerprint-based localization relies on an accurate and up-to-date radio map, which is however cumbersome to obtain. In this paper, a novel scheme is proposed to online adapt radio maps to environmental dynamics by using low-cost crowdsourced received signal strength (RSS) measurements. To be specific, a coarse-grained radio map is initially established in the offline phase utilizing the standard Gaussian process regression (GPR) given a limited number of fingerprints (i.e., RSS measurements with location labels), and further can be recursively refined in the online phase given crowdsourced RSS measurements with their noisy location labels obtained through the existing radio map. Differently from existing GPR-based approaches, the proposed scheme adopts extended GPR to alleviate the model inaccuracy induced by such noisy location labels, and then presents a marginalized particle extended Gaussian process (MPEG) to recursively filter the radio map. In addition, pedestrian dead reckoning (PDR) is leveraged to calibrate such noisy location labels. Extensive experiments are carried out in a real scenario with area of nearly 1000 m2during a five-month period of time, and a thorough comparison with several existing approaches indicates that the proposed scheme gradually improves the localization accuracy on average by as much as 31.2%, while the counterparts result in fluctuant localization performance and improve the localization accuracy on average by 13.3%.

Currently, WiFi (or IEEE 802.11) enabled infrastructures and devices have become ubiquitous, which makes it promising to provide indoor positioning services via WiFi signals. However, most existing studies on its performance are carried out based on simulations and experiments, and it is still challenging to mathematically characterize the localization error. Therefore, in this paper, the Cramer-Rao lower bound (CRLB) for the localization error by using WiFi signals is established and enables us to carry out a theoretical analysis on the fundamentals of WiFi based localization. Extensive experiments based on the well-known WiFi fingerprint-based localization are then carried out and confirm the correctness of the proposed CRLB model as well as the analysis.

Acquiring the knowledge of WiFi access point (AP) locations not only plays a vital role in various WiFi related applications, such WiFi-based indoor localization, the deployment of new WiFi APs, and so on, but also contributes to the emergence of novel applications. Most existing studies assume the well-known lognormal shadowing model, which only reflects large-scale fading in WiFi signal propagations but ignores small-scale fading induced by pervasive multipath effects. In this paper, we tackle the problem of AP localization based on the Rayleigh lognormal model which characterizes the influence of both large-scale and small-scale fading. Provided that a participant holding a smartphone is walking along a path and the smartphone automatically and continuously collects received signal strength (RSS) measurements from a target AP at known positions, particle filtering is applied to sequentially narrow the scope of possible locations of as well as the propagation parameters of the wireless signals emitted by the target AP, and the weighted mean of all candidate locations is returned as its final location estimate. Extensive experiments are carried out in typical indoor and outdoor scenarios, and reveal that the proposed method outperforms the solutions based on the lognormal model by 14.13%-70.38%.

Radio maps play a vital role in fingerprint-based indoor positioning systems (IPSs) in terms of the localization accuracy and computational overheads. Most existing studies either directly eliminate redundant APs or adopt unsupervised dimension reduction methods, say principal component analysis (PCA), to obtain a low-dimension representation of fingerprints, which consumes less storage and computational overheads. In this paper, we propose to reduce the dimensions of radio maps based on the Gaussian Process Manifold Kernel Dimension Reduction (GPMKDR) which is a supervised dimension reduction technique in comparison with the well known PCA-based method. Specifically, GPMKDR is employed to find a nonlinear and optimal embedding into the received signal strength (RSS) sample space during the offline phase, such that any RSS sample vector obtained in the online localization phase can be projected onto the optimal subspace with a lower dimension, with the result that the fingerprint-based localization can be efficiently realized based on a low-dimension radio map. Experiments show that the nonlinear GPMKDR-based method significantly improves the localization performance in comparison with the PCA-based method.

Accurate Location Based Service (LBS) is one of the fundamental but crucial services in the era of Internet of Things (IoT). WiFi fingerprinting-based Indoor Positioning System (IPS) has become the most promising solution for indoor LBS. However, the offline calibrated received signal strength (RSS) radio map is unable to provide consistent LBS with high localization accuracy under various environmental dynamics. To address this issue, we propose TKL-WinSMS as a systematic strategy, which is able to realize robust and adaptive indoor localization in dynamic indoor environments. We developed a WiFi-based Non-intrusive Sensing and Monitoring System (WinSMS) that enables WiFi routers as online reference points by extracting real-time RSS readings among them. With these online data and labeled source data from the offline calibrated radio map, we further combine the RSS readings from target mobile devices as unlabeled target data, to design a robust localization model using an emerging transfer learning algorithm, namely transfer kernel learning (TKL). It is able to learn a domain-invariant kernel by directly matching the source and target distributions in the reproducing kernel Hilbert space instead of the raw noisy signal space. The resultant kernel can be used as input for the SVR training procedure. In this manner, the trained localization model can inherit the information from online phase to adaptively enhance the offline calibrated radio map. Extensive experiments were conducted and demonstrated that the proposed TKL- WinSMS is able to improve the localization accuracy by at least 26% compared with existing solutions under various environmental interferences.

Pedestrian dead reckoning (PDR) is a promising complementary technique to balance the requirements on both accuracy and costs in outdoor and indoor positioning systems. In this paper, we propose a unified framework to comprehensively tackle the three sub problems involved in PDR, including step detection and counting, heading estimation and step length estimation, based on sequentially rotating the device (reference) frame to the Earth (reference) frame through sensor fusion. To be specific, a robust step detection and counting algorithm is devised according to vertical angular velocities and turns out to be tolerant of various smartphone placements; then, a zero velocity update (ZUPT) based algorithm is leveraged to calibrate the measurements in the Earth frame; on these grounds, the heading and step length are further estimated by exploiting the cyclic features of walking. A thorough and extensive experimental analysis is conducted and confirms the effectiveness and advantages of the proposed PDR framework as well as the corresponding algorithms.

Existing WiFi fingerprinting-based Indoor Positioning System (IPS) suffers from the vulnerability of environmental dynamics. To address this issue, we propose TKL-WinSMS as a systematic strategy, which is able to realize robust and adaptive localization in dynamic indoor environments. We developed a WiFi-based Non-intrusive Sensing and Monitoring System (WinSMS) that enables COTS WiFi routers as online reference points by extracting real-time RSS readings among them. With these online data and labeled source data from the offline calibrated radio map, we further combine the RSS readings from target mobile devices as unlabeled target data, to design a robust localization model using an emerging transfer learning algorithm, namely transfer kernel learning (TKL). It can learn a domain-invariant kernel by directly matching the source and target distributions in the reproducing kernel Hilbert space instead of the raw noisy signal space. By leveraging the resultant kernel as input for the SVR training, the trained localization model can inherit the information from online phase to adaptively enhance the offline calibrated radio map. Extensive experimental results verify the superiority of TKL-WinSMS in terms of localization accuracy compared with existing solutions in dynamic indoor environments.

Most existing indoor positioning systems (IPSs) adopt the WiFi fingerprinting technique to localize WiFi enabled mobile devices. In this paper, we improve the WiFi fingerprinting based IPS by efficiently combining the universal Kriging (UK) interpolation method, K nearest neighbor (KNN) and naive Bayes classifier (NBC). Specially, the proposed IPS takes into account the comprehensive features of received signal strengths (RSSs) by adopting the UK method and area partitioning, and further mitigates the boundary effect (which deteriorates the localization accuracy when dealing with mobile devices near the boundary) by virtually augmenting the space boundary. Finally, NBC and weighted KNN (WKNN) are integrated to work with the interpolated fingerprint database. Extensive experiments are carried out in our lab, and show that the proposed IPS with 28 observation points is able to achieve the average positioning error of 1.265m, which is less by 46.6% than the counterparts of the traditional IPS with 28 observation points and is even comparable to the traditional IPS with 112 observation points.

The explosive proliferation of mobile devices and the popularity of social networks have spurred extensive demands on Location Based Services (LBSs) in recent decades. The IEEE 802.11 (WiFi) based Indoor Positioning Systems (IPSs) are gaining popularity because of the wide and ubiquitous availability of WiFi infrastructures in indoor environments. Most of IPSs are adopting the fingerprinting approach to mitigate pervasive indoor multipath effects. However, the heterogeneity of mobile devices significantly degrades the localization performance of the fingerprinting approach. In this paper, we apply the Procrustes analysis method to transform the WiFi received signal strengths (RSSs) to a new type of standard location fingerprints which are tolerant of the heterogeneity of various devices. Then, a robust indoor positioning algorithm based on the standardized location fingerprints and the weighted k nearest neighbor (WKN-N) method is proposed. Extensive experiments are carried out and show that the standardized location fingerprints and the proposed positioning system address the device heterogeneity issue satisfactorily.

Indoor positioning system (IPS) has become one of the most attractive research fields due to the increasing demands on location-based services (LBSs) in indoor environments. Various IPSs have been developed under different circumstances, and most of them adopt the fingerprinting technique to mitigate pervasive indoor multipath effects. However, the performance of the fingerprinting technique severely suffers from device heterogeneity existing across commercial off-the-shelf mobile devices (e.g., smart phones, tablet computers, etc.) and indoor environmental changes (e.g., the number, distribution and activities of people, the placement of furniture, etc.). In this paper, we transform the received signal strength (RSS) to a standardized location fingerprint based on the Procrustes analysis, and introduce a similarity metric, termed signal tendency index (STI), for matching standardized fingerprints. An analysis of the capability of the proposed STI to handle device heterogeneity and environmental changes is presented. We further develop a robust and precise IPS by integrating the merits of both the STI and weighted extreme learning machine (WELM). Finally, extensive experiments are carried out and a performance comparison with existing solutions verifies the superiority of the proposed IPS in terms of robustness to device heterogeneity.

This study proposes a novel shape matching algorithm through exploiting shape contexts. The contributions of the proposed algorithm are twofold: (i) a new framework is presented to deal with the shape matching problem based on shape contexts, but differently from existing methods, the authors exploit a polynomial fitting‐based feature point extraction method as a preprocessing step, so as to enhance the performance of the shape contexts‐based descriptor; (ii) the authors design a voting classification method based on the chi‐square statistical measure to evaluate the matching results. The experimental results show that this method is able to achieve high performance, even if shapes of testing objects suffer from translation, rotation and scaling.

This paper focuses on the problem of source localization using time-difference-of-arrival (TDOA) measurements in both 2-D and 3-D spaces. Different from existing studies where the variance of TDOA measurement noises is assumed to be independent of the associated source-to-sensor distances, we consider the more realistic model where the variance is a function of the source-to-sensor distances, which dramatically complicates TDOA-based source localization. After formulating the distance-dependent noise model, we prove that using the extra information about the source location in the functional variance improves the estimation accuracy of TDOA-based source localization, but contributes little under a sufficiently small noise level. Further, we theoretically analyze the problem of optimal sensor placement, and derive the necessary and sufficient conditions for optimizing localization performance under different circumstances. Then, a localization scheme based on the iteratively reweighted generalized least squares (IRGLS) method is proposed to efficiently exploit the extra source location information. Finally, a simulation analysis confirms our theoretical studies, and shows that the performance of the proposed localization scheme is comparable to the Cramer-Rao lower bound (CRLB) given moderate TDOA measurement noises.

ABSTRACT Distance estimation is vital for localization and many other applications in wireless sensor networks. In this paper, we develop a method that employs a maximum‐likelihood estimator to estimate distances between a pair of neighboring nodes in a static wireless sensor network using their local connectivity information, namely the numbers of their common and non‐common one‐hop neighbors. We present the distance estimation method under a generic channel model, including the unit disk (communication) model and the more realistic log‐normal (shadowing) model as special cases. Under the log‐normal model, we investigate the impact of the log‐normal model uncertainty; we numerically evaluate the bias and standard deviation associated with our method, which show that for long distances our method outperforms the method based on received signal strength; and we provide a Cramér–Rao lower bound analysis for the problem of estimating distances via connectivity and derive helpful guidelines for implementing our method. Finally, on implementing the proposed method on the basis of measurement data from a realistic environment and applying it in connectivity‐based sensor localization, the advantages of the proposed method are confirmed. Copyright © 2012 John Wiley & Sons, Ltd.

This paper tackles the problem of boundary detection by proposing a simple, distributed and connectivity-based algorithm. Our algorithm examines the 2-hop iso-contour of each node, and outperforms existing algorithms examining iso-contours. Specifically, the proposed algorithm makes a rough decision on a suspected boundary node by examining its 2-hop iso-contour, and then refines the decision based on a heuristic operation, which significantly reduces the size of the suspected boundary node set. More importantly, boundary cycles corresponding to inner and outer boundaries are identified and provide valuable knowledge to various applications. A thorough evaluation shows that our algorithm is applicable to dense as well as sparse WSNs.

In this paper, we analyze the performance limit of sensor localization from a novel perspective. We consider distance-based single-hop sensor localization with noisy distance measurements by time of arrival (TOA). Differently from the existing studies, the anchors are assumed to be randomly deployed, with the result that the trace of the associated Cramer-Rao Lower Bound (CRLB) matrix becomes a random variable. We adopt this random variable as a scalar metric for the performance limit and then focus on its statistical attributes. By the Central Limit Theorems for U-statistics, we show that as the number of anchors goes to infinity, this scalar metric converges to a random variable which is an affine transformation of a chi-square random variable of degree 2. In addition, we provide the quantitative relationship among the mean, the standard deviation, the number of anchors, parameters of communication channels and the distribution of the anchors. Extensive simulations are carried out to confirm the theoretical results. On the one hand, our study reveals some fundamental features of sensor localization; on the other hand, the conclusions we draw can in turn guide us in the design of wireless sensor networks.

Distance estimation is of great importance for localization and a variety of applications in wireless sensor networks. In this paper, we develop a simple and efficient method for estimating distances between any pairs of neighboring nodes in static wireless sensor networks based on their local connectivity information, namely the numbers of their common one-hop neighbors and non-common one-hop neighbors. The proposed method involves two steps: estimating an intermediate parameter through a Maximum-Likelihood Estimator (MLE) and then mapping this estimate to the associated distance estimate. In the first instance, we present the method by assuming that signal transmission satisfies the ideal unit disk model but then we expand it to the more realistic log-normal shadowing model. Finally, simulation results show that localization algorithms using the distance estimates produced by this method can deliver superior performances in most cases in comparison with the corresponding connectivity-based localization algorithms.

Error propagation is a greatly complicated problem arising in multihop sensor localization. In this paper, we focus on how certain key factors in a sensor network affect error propagation for a restricted range-based localization scenario and obtain the significant conclusion that localization errors measured by the Mean Squared Error are propagated at the rate of the cube of the minimal hop count to anchors. A simulation analysis based on actual localization processes and the Cramér-Rao Lower Bound verifies this result.

Location information for sensors in wireless sensor networks (WSNs) is essential to many tasks. In the presence of noise, locations must be estimated and thus the errors are unavoidable. Moreover, the errors can propagate (i.e. increase) as sensors progressively more distant from anchors are localized. Understanding the rules governing error propagation is quite helpful to deploying WSNs and improving performances of localization systems. In this paper, we investigate error propagation measured by the Cramer-Rao Lower Bound (CRLB) in a type of regular 1-Dimensional WSNs whose Fisher Information Matrices are symmetric band Toeplitz matrices. Approximate analytic formulas for the CRLBs in the regular and almost regular WSNs are derived, and properties of error propagation are also obtained. In addition, we derive a magic number relating to the number of range measurements, which indicates a turning point as to system localization accuracies.