Pose estimation using visual sensors has become a fundamental component in robotic navigation and autonomous driving systems. Learning-based monocular visual odometry (VO) has attracted substantial attention due to its resilience to camera parameter variations and dynamic environments. Given that camera movement manifests as pixel-level motion across the entire image in optical flow data, capturing both global contextual information and local feature details is crucial for accurate pose estimation. To address this challenge, we propose SwinFVO, a novel self-supervised visual odometry framework that incorporates enhanced motion perception to achieve global spatial dependency modeling with temporal continuity. Leveraging quadrant-based motion characteristics, we perform cross-regional feature interaction through a refined Swin Transformer architecture. Two robust spatiotemporal feature extractors are designed to extend the single-frame-based Swin Transformer to a temporally-aware framework for sequential understanding. Through the exploration of long-range spatial correlations and preservation of temporal consistency, SwinFVO delivers accurate and consistent pose estimation. Extensive experiments across multiple datasets demonstrate the superior performance and generalization capability of SwinFVO in both pose and depth estimation tasks. It achieves competitive results against classical algorithms and outperforms related state-of-the-art (SOTA) methods by up to 20.6% and 72.4% on average translational and rotational evaluations, respectively.

A significant challenge in signal recognition tasks is identifying classes not present in the dataset. Zero-shot learning-based signal recognition addresses the challenge by identifying previously unseen classes in a mixed signal space without supervision. However, most existing methodologies are limited to one-time recognition processes. We propose a zero-shot class incremental learning (ZSCIL) method to achieve continuous unseen classes identification. Our model employs an encoder-decoder architecture and incorporates a triplet loss function to train the classifier, thereby enhancing the model’s ability to recognize mixed signals through a metric learning paradigm. Additionally, we utilize class incremental learning, where the identified unseen signals are stored in a fixed-size buffer with a maximum diversity data replay mechanism. These signals are then used for incremental training. The framework’s effectiveness and generality of our method are demonstrated through a series of experiments on two datasets. For instance, we achieved a significant 14.4% accuracy improvement for seen classes and that of the unseen classes by 4.2% on the DeepSig 2016.04C dataset. To the best of our knowledge, ZSCIL is the first method to implement sustainable identification for unseen classes in the mixed signal space.

Many current visual odometry (VO) methods that utilize deep learning primarily concentrate on the constraints of motion relationships between adjacent frames, neglecting the modeling of temporal correlations within sequence data. Consequently, this paper introduces LoSeVO to effectively capture and model the temporal correlation features present in images. We design a Joint Feature Extraction component that not only performs joint feature extraction on adjacent frames but also extracts features from cross-frame images, which are called feature-guided maps. Then, we apply a Local Consistency Constraint component to the joint features between adjacent frames. It can adaptively constrain adjacent frames at different temporal positions within a sequence using different feature-guided maps. Extensive experiments based on the KITTI and Malaga datasets have shown that, compared to our previous DeepAVO model, LoSeVO can improve pose estimation performance by up to 23% and 9% in translation and rotation estimation, respectively.

Loop Closure Detection (LCD) is an essential part of minimizing drift due to the accumulation of previously pose errors in Simultaneous Localization and Mapping (SLAM). The existing loop detection methods are limited by the changes of external conditions such as illumination, viewpoint and appearance. Previous work has mainly focused on the feature descriptor matching methods, which usually only consider the keypoints themselves. Here, we propose a fusion method GraSS, which uses the Graph Neural Network (GNN) based on visual features, and introduces semantics and depth, so as to enhance the spatial characteristics of the keypoints and the information correlation between them in the graph. Furthermore, a learnable parameter is added when two keypoints share the same semantic labels, their matching scores are increased, mitigating to some extent the issue of mismatch caused by significant differences in external conditions between two keypoints that should ideally be paired. Our findings show that GraSS has better performance than other state-of-the-art LCD methods when facing obvious illumination, appearance changes and slight viewpoint changes.

In the field of autonomous driving, semantic segmentation is crucial for scene understanding. Currently, there are two main methods: camera-based and Lidar-based approaches. To address the issues of Lidar segmentation lacking texture features and image segmentation lacking distance information, this paper proposes a fusion of camera and Lidar to achieve 3D semantic segmentation. The method utilizes a dual-stream encoder-decoder network to process camera images and Lidar point cloud and incorporates a specially designed attention mechanism module for feature fusion. To avoid expensive manual annotation of 3D point clouds, the study also introduces a cross-dataset and cross-modal self-supervised training approach. Experimental results show a 2.4% improvement compared to the Lidar-only mode baseline results on the SemanticKITTI dataset and a 6% improvement on the nuScenes dataset.

Learning-based monocular visual odometry (VO) has recently attracted considerable attention for its robustness to camera parameters and environmental variations. Despite traditional pose graph optimization enhancing pose accuracy, its integration with deep learning may lead to error accumulation due to insufficient motion information exchange. Our method, GraphAVO, concentrates simultaneously on the adjacent and interval co-visibility correspondence to establish a feature and pose graph optimization for pose consistency. We design a graph-assisted Windowed Feature Graph Refinement (WFGR) component to operationalize pose graph optimization for deep feature refinement. The geometric consistency is further constrained by a Cycle Consistency Loss. Additionally, the Cascade Dilated Convolution Fusion (CDCF) component is incorporated to handle different degrees of pixel movement, facilitating the joint detection of slight and distinct motion cues for subsequent feature enhancement. Extensive experiments on the KITTI, Malaga, RobotCar, and self-collected outdoor datasets have demonstrated the promising performance and generalization ability of GraphAVO. It achieves competitive results against classical algorithms and outperforms related state-of-the-art methods by up to 24.4% and 40.1% on average translational and rotational evaluation, respectively.

Unsupervised monocular depth estimation plays an important role in autonomous driving, and has been received considerable research attention in recent years. Nevertheless, numerous existing methods relying on photometric consistency are excessively susceptible to variations in illumination and suffer in the regions with strong reflection. To overcome this limitation, we propose a novel unsupervised depth estimation framework named ColorDepth, which forces the model to explore object semantic to infer depth. Specifically, we extract pixel-level semantic prior clues of objects using the semantic segmentation network. These priors and the original image are then adaptively fused into color data by a learnable parameter for depth estimation. The incorporation of semantics endows our model with the ability to perceive scene structure information. The fused data effectively alleviates the depth ambiguity within the same semantic block, leading to improved consistency and robustness in challenging scenarios. Extensive experiments on the KITTI and Make3D datasets show that our method surpasses the previous state-of-the-art methods even those supervised by additional constraints, and brings significant performance improvement particularly in the regions of high reflection.

Precise positioning is critical in autonomous driving, indoor navigation, and intelligent logistics. With the development of mobile communication technology, indoor base stations (BSs) have been utilized and installed to match indoor signal coverage better. Thus for indoor positioning, cellular signals have emerged as a new option. Ranging through the time of arrival (TOA) is achieved by capturing cellular signals as a signal of opportunity for localization. This approach can only estimate the user equipment (UE) to BS distance when there is just one BS and cannot obtain the two-dimensional (2D) coordinates. In this paper, we combine the fingerprint positioning approach with the signal parameters acquired via cellular signal of opportunity. TOA and one-reflection path are employed as fingerprint features, and 2D localization with a single BS is achieved. A complete signal processing flow is constructed, and the ray tracing method is performed to generate the channel parameters to evaluate the proposed positioning algorithm's performance and match the practical application as closely as feasible. Using the$100\ GHz$carrier frequency that conforms to the sixth generation (6G) communication for simulation, the mean positioning error is$0.312 m$when the fingerprint interval is$0.5 m$. This method provides a feasible solution for 6G indoor positioning scenarios.

This letter proposes a deep-learning-based fingerprinting indoor positioning method, aiming to mitigate catastrophic forgetting in the process of depth transfer learning. Recently indoor positioning methods based on fingerprint have made great development. The deep transfer learning technique has been applied to transfer the positioning network among different scenarios. But catastrophic forgetting is a big challenge during the supervised transfer learning, which results in poor performance of fine-tuned network in source scenario. In order to solve this issue, the proposed method improves the fine-tuning learning procedure according to the importance of network parameters. It can adaptively control the ratio of network parameters by adding regularization factor to loss function. Simulation results show that the proposed method can effectively improve the positioning accuracy in source scenario without reducing the positioning accuracy in target scenario, especially for the transfer learning in multiple scenarios.

Learning-based monocular visual odometry (VO) has lately drawn significant attention for its robustness to camera parameters and environmental variations. Unlike most self-supervised learning-based methods, our approach simultaneously focuses on the adjacent and interval co-visibility correspondence to improve the pose estimation. To handle different pixel displacements, we apply the Multi-scale Feature Fusion component for the full exploration of latent motion features. Besides, the Interval Feature Guided Refinement component is incorporated to adaptively exploit the continuity of camera motions and steer the network for retaining pose consistency in the time domain. Extensive experiments on the KITTI and Malaga datasets have demonstrated the promising performance of our approaches. The proposed method produces competitive results against classic algorithms and outperform state-of-the-art methods by up to 23.9 % and 15.4 % on average translational and rotational evaluation.

Monocular depth estimation is a significant task in computer vision, which can be widely used in Simultaneous Localization and Mapping (SLAM) and navigation. However, the current unsupervised approaches have limitations in global information perception, especially at distant objects and the boundaries of the objects. To overcome this weakness, we propose a global-aware attention model called GlobalDepth for depth estimation, which includes two essential modules: Global Feature Extraction (GFE) and Selective Feature Fusion (SFF). GFE considers the correlation among multiple channels and refines the encoder feature by extending the receptive field of the network. Furthermore, we restructure the skip connection by employing SFF between the low-level and the high-level features in element wise, rather than simply concatenation or addition at the feature level. Our model excavates the key information and enhances the ability of global perception to predict details of the scene. Extensive experimental results demonstrate that our method reduces the absolute relative error by 10.32% compared with other state-of-the-art models on KITTI datasets.

To alleviate the dependence on sensor quality and to reduce the accumulated error in traditional inertial navigation systems, this paper proposes RUPT: a millimeter-wave radar aided pedestrian dead reckoning system with dual foot-mounted inertial measurement units (IMU). RUPT in this paper is a comprehensive data processing procedure which pre-processes both inertial data and millimeter-wave data and fuses them in a complementary way. Extensive experiments have demonstrated that the accuracy of RUPT has been improved by up to 65% over the conventional dual-foot mounted pedestrian tracking system.

Simultaneous Localization and Mapping (SLAM) is used to solve the problem of autonomous localization and navigation of mobile robots in unknown environments. Loop closure detection is a key part of SLAM, which largely determines accuracy and stability of SLAM. In recent years, some experiments have proved that the loop closure detection system based on neural network is superior to the traditional loop closure detection in both accuracy and real-time performance. In this paper, we propose an adaptive real-time loop closure detection (AR-Loop) method based on monocular vision. A pre-trained convolutional neural network (CNN) is used to extract image features. Then features of different layers are concatenated as image descriptors. In addition, the adaptive candidate matching range algorithm and image-to- sequence calibration algorithm are proposed to improve the performance of the algorithm. Extensive experiments have been conducted on several open datasets to validate the performance of AR-Loop. It has been demonstrated that the recall rate is increased by over 18% compared with other state-of-the-art algorithms when the precision is 100%.

Monocular visual odometry (VO) is one of the most practical ways in vehicle autonomous positioning, through which a vehicle can automatically locate itself in a completely unknown environment. Although some existing VO algorithms have proved the superiority, they usually need another precise adjustment to operate well when using a different camera or in different environments. The existing VO methods based on deep learning require few manual calibration, but most of them occupy a tremendous amount of computing resources and cannot realize real-time VO. We propose a highly real-time VO system based on the optical flow and DenseNet structure accompanied with the inertial measurement unit (IMU). It cascade the optical flow network and DenseNet structure to calculate the translation and rotation, using the calculated information and IMU for construction and self- correction of the map. We have verified its computational complexity and performance on the KITTI dataset. The experiments have shown that the proposed system only requires less than 50% computation power than the main stream deep learning VO. It can also achieve 30% higher translation accuracy as well.

Zero Velocity Update (ZUPT) has played a key role in Pedestrian Dead Reckoning (PDR) with inertial measurement units (IMU). However, it is both crucial and difficult to determine ZUPT conditions given complex and varying motion types such as walking, fast walking or running, and different walking habits of distinct people, which have direct and significant impact on the tracking accuracy. In this research we proposed a model based on deep neural networks to determine moments when the ZUPT should be conducted. The proposed model ensures nearly identical performance regardless of different motion types. It has been demonstrated by extensive experiments conducted in three different scenarios that our model can work equally well with different pedestrians and walking patterns, enabling the wide use of PDR in real-world applications.

Inertial navigation is a fundamental method for pervasive indoor tacking and navigation. Although PDR based on inertial navigation can achieve robust indoors and outdoors positioning, the positioning accuracy does not meet the accuracy we need, due to the error divergence of the system. We present ZUPT with Kalman filter, a precise, robust technique tracks well even when presented with very noisy sensor data. Key to our ZUPT is zero velocity detection, the step to determine if the person's foot is in stance phase during walking. We used three different methods to detect zero velocity moments and compare their accuracy. Finally, we found that ZUPT using asymptotic zero velocity detection greatly improved the accuracy of inertial navigation. We believe that such a convergent and high precision approach will improve the application of inertial navigation in indoor positioning.

We address the problem of localization in vehicular ad hoc networks. Our goal is to leverage vehicle communications and smartphone sensors to improve the overall localization performance. Assuming vehicles are equipped with the IEEE 802.11p wireless interfaces, we employ a two-stage Bayesian filter to track the vehicle's position: an unscented Kalman filter for heading estimation using smartphone inertial sensors, and a particle filter that fuses vehicle-to-vehicle signal strength measurements received from mobile anchors whose positions are uncertain, with velocity, GPS position, and map information. Our model leads to a robust localization system and is able to provide useful position information even in the absence of GPS data. We evaluate the algorithm performance using real-world measurements collected from four communicating vehicles in an urban scenario, and considering different combinations of location information sources.

Underground mines are characterized by a network of intersecting tunnels and sharp turns, an environment which is particularly challenging for radiofrequency based positioning systems due to extreme multipath, non-line-of-sight propagation, and poor anchor geometry. Such systems typically require a dense grid of devices to enable 3-D positioning. Moreover, the precise position of each anchor node needs to be precisely surveyed, a particularly challenging task in underground environments. Magneto-inductive (MI) positioning, which provides 3-D position and orientation from a single transmitter and penetrates thick layers of soil and rock without loss, is a more promising approach, but so far has only been investigated in simple point-to-point contexts. In this paper, we develop a novel MI positioning approach to cover an extended underground 3-D space with unknown geometry using a rapidly deployable anchor network. The key to our approach is that the position of only a single anchor needs to be accurately surveyed-the positions of all secondary anchors are determined using an iterative refinement process using measurements obtained from receivers within the network. This avoids the particularly challenging and time-intensive task in an underground environment of accurately surveying the positions of all of the transmitters. We also demonstrate how measurements obtained from multiple transmitters can be fused to improve localization accuracy. We validate the proposed approach in a man-made cave and show that, with a portable system that took 5 min to deploy, we were able to provide accurate through-the-earth location capability to nodes placed along a suite of tunnels.

In this paper we propose a novel algorithm for tracking people in highly dynamic industrial settings, such as construction sites. We observed both short term and long term changes in the environment; people were allowed to walk in different parts of the site on different days, the field of view of fixed cameras changed over time with the addition of walls, whereas radio and magnetic maps proved unstable with the movement of large structures. To make things worse, the uniforms and helmets that people wear for safety make them very hard to distinguish visually, necessitating the use of additional sensor modalities. In order to address these challenges, we designed a positioning system that uses both anonymous and id-linked sensor measurements and explores the use of cross-modality training to deal with environment dynamics. The system is evaluated in a real construction site and is shown to outperform state of the art multi-target tracking algorithms designed to operate in relatively stable environments.

Localization is a research area that, due to its overarching importance as an enabler for higher level services, has attracted a vast amount of research and commercial interest. For the most part, it can be claimed that GPS provides an unparalleled solution for outdoor tracking and navigation. However, the same cannot yet be said about positioning in GPS-denied or challenged environments, such as indoor environments, where obstructions such as floors and walls heavily attenuate or reflect high-frequency radio signals. This has led to a plethora of competing solutions targeted toward a particular application scenario, yielding a fragmented solution landscape. In this paper, we present a fresh approach to 3-D positioning based on the use of very low frequency (kHz) magneto-inductive (MI) fields. The most important property of MI positioning is that obstacles such as walls, floors, and people that heavily impact the performance of competing approaches are largely “transparent” to the quasi-static magnetic fields. MI has a number of challenges to robust operation that distort positions, including the presence of ferrous materials and sensitivity to user rotation. Through signal processing and sensor fusion across multiple system layers, we show how we can overcome these challenges. We showcase its highly accurate 3-D positioning in a number of environments, with positioning accuracy below 0.8 m even in heavily distorted areas.

Inertial tracking and navigation systems have been playing an increasingly important role in indoor tracking and navigation. They have the competitive advantage of leveraging not requiring expensive infrastructure-only existing smart mobile devices with embedded inertial measurement units. When aided with other sources of information, such as radio data from existing WiFi/BLE infrastructure, and environment constraints from floor plans or radio maps, they often report great performance of 0.5-2 m. Given the promising results, what is it that prevents the widespread adoption of this tracking solution? We argue that pedestrian dead reckoning (PDR) techniques are often evaluated in a specific context and are not mature enough to handle variations in user motion, device type, device placement, or environment. They typically use a number of parameters that require careful context-specific tuning, which is labor intensive and requires expert knowledge. In this paper, we propose two novel approaches to address these problems. Our first contribution is a robust PDR algorithm, which is based on general physics principles that underpin human motion and is by design robust to context changes. The second contribution is a novel way of interaction between the PDR and map matching layers based on the principle of lifelong learning. Unlike traditional approaches where information flows unidirectionally from the PDR to the map matching layer, we introduce a feedback loop that can be used to automatically tune the parameters of the PDR algorithm. This is not dissimilar to the way that people improve their navigation skills when they repeatedly visit the same environment. Extensive experiments in multiple sites, with a variety of users, devices, and device placements, show that the combination of a robust PDR with a lifelong learning tracker can achieve submeter accuracy with no user effort for parameter tuning.

In most sensing applications, the measurements generated by sensor networks are noisy and usually annotated with some measure of uncertainty. The question that we address in this paper is how to estimate the accuracy of these uncertain sensor measurements. Existing studies on estimating the accuracy of uncertain measurements in real sensing applications are limited in three ways. First, they tend to be application-specific. Second, they typically employ learning techniques to estimate the parameters of sensor noise models, and ignore alternative state estimation approaches without learning. Third, they do not explore whether exploiting the dynamics of the monitored state can yield significant benefits. We address the above limitations as follows: we define the accuracy estimation problem in a general manner that applies to a broad spectrum of application scenarios. We present a general framework to address this problem, and show that the proposed framework can be implemented in a number of different ways. We evaluate and compare the different implementations in the context of two real sensing scenarios, and discuss how they trade accuracy for computation cost, and how this trade-off largely depends on the user's knowledge of the application scenario.

Indoor tracking and navigation is a fundamental need for pervasive and context-aware smartphone applications. Although indoor maps are becoming increasingly available, there is no practical and reliable indoor map matching solution available at present. We present MapCraft, a novel, robust and responsive technique that is extremely computationally efficient (running in under 10 ms on an Android smartphone), does not require training in different sites, and tracks well even when presented with very noisy sensor data. Key to our approach is expressing the tracking problem as a conditional random field (CRF), a technique which has had great success in areas such as natural language processing. Unlike directed graphical models like Hidden Markov Models, CRFs capture arbitrary constraints that express how well observations support state transitions, given map constraints. In addition, we show how to further improve tracking accuracy, by tuning the parameters of the motion sensing model using an unsupervised EM-style optimization scheme. Extensive experiments in multiple sites show how MapCraft outperforms state-of-the art approaches, demonstrating excellent tracking error and accurate reconstruction of tortuous trajectories with zero training effort. As proof of its robustness, we also demonstrate how it is able to accurately track the position of a user from accelerometer and magnetometer measurements only (i.e., gyroand Wi-Fi-free). We believe that such an energy-efficient approach will enable always-on background localisation, enabling a new era of location-aware applications to be developed.

Indoor wireless systems often operate under non-line-of-sight (NLOS) conditions that can cause ranging errors for location-based applications. As such, these applications could benefit greatly from NLOS identification and mitigation techniques. These techniques have been primarily investigated for ultra-wide band (UWB) systems, but little attention has been paid to WiFi systems, which are far more prevalent in practice. In this study, we address the NLOS identification and mitigation problems using multiple received signal strength (RSS) measurements from WiFi signals. Key to our approach is exploiting several statistical features of the RSS time series, which are shown to be particularly effective. We develop and compare two algorithms based on machine learning and a third based on hypothesis testing to separate LOS/NLOS measurements. Extensive experiments in various indoor environments show that our techniques can distinguish between LOS/NLOS conditions with an accuracy of around 95%. Furthermore, the presented techniques improve distance estimation accuracy by 60% as compared to state-of-the-art NLOS mitigation techniques. Finally, improvements in distance estimation accuracy of 50% are achieved even without environment-specific training data, demonstrating the practicality of our approach to real world implementations.

Pedestrian dead reckoning, especially on smart-phones, is likely to play an increasingly important role in indoor tracking and navigation, due to its low cost and ability to work without any additional infrastructure. A challenge however, is that positioning, both in terms of step detection and heading estimation, must be accurate and reliable, even when the use of the device is so varied in terms of placement (e.g. handheld or in a pocket) or orientation (e.g holding the device in either portrait or landscape mode). Furthermore, the placement can vary over time as a user performs different tasks, such as making a call or carrying the device in a bag. A second challenge is to be able to distinguish between a true step and other periodic motion such as swinging an arm or tapping when the placement and orientation of the device is unknown. If this is not done correctly, then the PDR system typically overestimates the number of steps taken, leading to a significant long term error. We present a fresh approach, robust PDR (R-PDR), based on exploiting how bipedal motion impacts acquired sensor waveforms. Rather than attempting to recognize different placements through sensor data, we instead simply determine whether the motion of one or both legs impact the measurements. In addition, we formulate a set of techniques to accurately estimate the device orientation, which allows us to very accurately (typically over 99%) reject false positives. We demonstrate that regardless of device placement, we are able to detect the number of steps taken with >99.4% accuracy. R-PDR thus addresses the two main limitations facing existing PDR techniques.

With sensor technology gaining maturity and becoming ubiquitous, we are experiencing an unprecedented wealth of sensor data. In most sensing applications, users receive sensor measurements, which are prone to error. As a result, they are often annotated with some measure of uncertainty, such as the distribution variance or a confidence interval, and will be hereafter referred to as probabilistic measurements. The question that we address in this paper is how to estimate the accuracy of these probabilistic measurements, that is, how far they lie from the ground truth of the measured attribute. Existing studies on estimating the accuracy of probabilistic measurements in real sensing applications are limited in three ways. First, they tend to be application-specific. Second, they typically employ learning techniques to estimate the parameters of sensor noise models, and ignore alternative approaches that rely on simple state estimation without learning. Third, they do not explore whether exploiting the dynamics of the monitored state can yield significant benefits in terms of accuracy estimation. In this paper, we address the above limitations as follows: We define the problem of accuracy estimation in a general way that applies to a wide spectrum of application scenarios. We then propose a taxonomy of accuracy estimation techniques, which include both state estimation and parameter learning. These techniques are further subdivided into static and dynamic, depending on whether they exploit knowledge of system dynamics. All different approaches in the taxonomy are then applied and compared with each other in the context of two real sensing applications. We discuss how they trade accuracy for computation cost, and how this tradeoff largely depends on the user's knowledge of the application scenario.

Various applications, such as localisation of persons and objects could benefit greatly from non-line-of-sight (NLOS) identification and mitigation techniques. However, such techniques have been primarily investigated for ultra-wide band (UWB) signals, leaving the area of WiFi signals untouched. In this study, we propose two accurate approaches using only received signal strength (RSS) measurements from WiFi signals to identify NLOS conditions and mitigate the effects. We first explore several features from the RSS which are later demonstrated as very effective in identifying and mitigating NLOS conditions. After that, we develop and compare two major optimization problems based on a machine learning technique and hypothesis testing according to different user requirements and information available. Extensive experiments in various indoor environments have shown that our techniques can not only accurately distinguish between LOS/NLOS conditions, but also mitigate the impact of NLOS conditions as well.

A novel queuing model in this paper is proposed to provide a tool for performance evaluation to IEEE 802.15.4 Medium Access Control (MAC) protocol with sleep mode enabled. IEEE 802.15.4 node with sleep mode enabled behaves in a different way from other queuing models because the node goes to sleep periodically and thus packets arriving in sleep period accumulate in the beginning of the active portion, which makes a heavier load at the beginning than any other time. This model analyzes this behavior by using an embedded discrete-time Markov chain model that includes the slot number in the states to obtain the time-varying queue length. To obtain the service time, we introduce the virtual service time into this model which makes the proposed queuing model different from models in any previously published works. The accuracy of the proposed model is validated by Monte Carlo simulations. The proposed model can accurately evaluate the performance of IEEE 802.15.4.