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In autonomous driving, environmental perception technology often encounters challenges such as false positives, missed detections, and low accuracy, particularly in detecting small objects and complex scenarios. Existing algorithms frequently suffer from issues like feature redundancy, insufficient contextual interaction, and inadequate information fusion, making it difficult to perform multi-task detection and segmentation efficiently. To address these challenges, this paper proposes an end-to-end multi-task environmental perception model named YOLO-Mg, designed to simultaneously perform traffic object detection, lane line detection, and drivable area segmentation. First, a multi-stage gated aggregation network (MogaNet) is employed during the feature extraction process to enhance contextual interaction by improving diversity in the channel dimension, thereby compensating for the limitations of feed-forward neural networks in contextual understanding. Second, to further improve the model's accuracy in detecting objects of various scales, a restructured weighted bidirectional feature pyramid network (BiFPN) is introduced, optimizing cross-level information fusion and enabling the model to handle object detection at different scales more accurately. Finally, the model is equipped with one detection head and two segmentation heads to achieve efficient multi-task environmental perception, ensuring the simultaneous execution of multiple tasks. The experimental results on the BDD100K dataset demonstrate that the model achieves a mean average precision (mAP50) of 81.4% in object detection, an Intersection over Union (IoU) of 28.9% in lane detection, and a mean Intersection over Union (mIoU) of 92.6% in drivable area segmentation. The tests conducted in real-world scenarios show that the model performs effectively, significantly enhancing environmental perception in autonomous driving and laying a solid foundation for safer and more reliable autonomous driving systems. [ABSTRACT FROM AUTHOR]

Background: The use of high-frequency electric welding technology for intestinal end-to-end anastomosis holds significant promise. Past studies have focused on in vitro, and the safety and efficacy of this technology is uncertain, severely limiting the clinical application of this technology. This study investigates the impact of compression pressure, energy dosage, and duration on anastomotic quality using a homemade anastomosis device in both in vitro and in vivo settings. Methods: Two hundred eighty intestines and 5 experimental pigs were used for in vitro and in vivo experiments, respectively. The in vitro experiments were conducted to study the effects of initial pressure (50–400 kpa), voltage (40–60 V), and time (10–20 s) on burst pressure, breaking strength, thermal damage, and histopathological microstructure of the anastomosis. Optimal parameters were then inlaid into a homemade anastomosis and used for in vivo experiments to study the postoperative porcine survival rate and the pathological structure of the tissues at the anastomosis and the characteristics of the collagen fibers. Results: The anastomotic strength was highest when the compression pressure was 250 kPa, the voltage was 60 V, and the time was 15 s. The degree of thermal damage to the surrounding tissues was the lowest. The experimental pigs had no adverse reactions after the operation, and the survival rate was 100%. 30 days after the operation, the surgical site healed well, and the tissues at the anastomosis changed from immediate adhesions to permanent connections. Conclusion: High-frequency electric welding technology has a certain degree of safety and effectiveness. It has the potential to replace the stapler anastomosis in future and become the next generation of new anastomosis device. [ABSTRACT FROM AUTHOR]

We present a 20-year-old patient with subglottic and tracheal stenosis was taken for a tracheal resection and end-to-end anastomosis. The patient's neck was positioned in hyperflexion using chin stitches to minimize tension at the anastomosis. On post-operative period, the patient developed paresthesias in upper and lower extremities associated with motor weakness. Magnetic resonance imaging was performed showing lesions compromising ventral spinal cord at the level of C4–C5 and C6–C7. Chin stitches were removed and neck flexion was reduced. The patient remained in the intensive care unit with vasopressors, physical therapy and intravenous fluid-therapy to maintain mean arterial pressure above 90 mmHg. After 3 weeks, the patient was discharged with no neurologic deficit. There are few cases reported of acute ischemic spinal injury following tracheal reconstruction. If this complication arises, neck posture should be corrected, maintenance of MAP above 90 mmHg and implementation of early physical therapy is key to improve neurologic outcomes. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) aims to conduct fine-grained sentiment analysis, necessitating the extraction of three key components: target entity, aspect category and sentiment polarity. These three components collectively form an integrated ABSA task known as TASD (Target-Aspect-Sentiment jointly Detection). Most of existing approaches on ABSA usually employ Recurrent neural networks(RNNs), Convolutional neural networks(CNNs) or pre-training models such as Bidirectional Encoder Representations from Transformers(BERT). However, they have some common weaknesses. First, most of the existing methods focus on one or two sub-tasks instead of triplet detection, thus they do not establish an end-to-end (training a complex learning system represented by a single model) ABSA model and cannot utilize the relevance of multiple ABSA sub-tasks during training. Second, they cannot achieve accuracy and efficiency simultaneously due to the coupling of context and given aspects. Third, they are poor in recognizing implicit targets. To tackle these limitations, this paper proposes a novel method named the Twin Towers End to End model (TTEE) to solve TASD task. It transforms complex TASD task into a simple end-to-end multi-task framework, simultaneously conducting target and aspect-sentiment detection. It builds twin towers system based on BERT or its updated versions to decouple context and given aspects, which can reduce redundant calculation to improve computational efficiency significantly. It offers great advantage to identify implicit target entity and its associated aspect-sentiment in the context without introducing extra model architecture. Experiments on three public datasets in different domains demonstrate that our approach not only achieves better performance on various evaluation metrics, but also has high efficiency in both training and inference phases, over a wide range of sample size and number of aspect categories. • The T-A-S triplets can be decoded from the end-to-end output of TTEE. • BERT-based model is employed to encode context and aspect-sentiment pairs separately. • TTEE efficiently reduces redundant calculations of embedding process. • TTEE excels in detecting implicit targets within the context. [ABSTRACT FROM AUTHOR]

Traditionally, teaching a human and a Machine Learning (ML) model is quite different, but organized and structured learning has the ability to enable faster and better understanding of the underlying concepts. For example, when humans learn to speak, they first learn how to utter basic phones and then slowly move towards more complex structures such as words and sentences. Motivated by this observation, researchers have started to adapt this approach for training ML models. Since the main concept, the gradual increase in difficulty, resembles the notion of the curriculum in education, the methodology became known as Curriculum Learning (CL). In this work, we design and test new CL approaches to train Automatic Speech Recognition systems, specifically focusing on the so-called end-to-end models. These models consist of a single, large-scale neural network that performs the recognition task, in contrast to the traditional way of having several specialized components focusing on different subtasks (e.g., acoustic and language modeling). We demonstrate that end-to-end models can achieve better performances if they are provided with an organized training set consisting of examples that exhibit an increasing level of difficulty. To impose structure on the training set and to define the notion of an easy example, we explored multiple solutions that use either external, static scoring methods or incorporate feedback from the model itself. In addition, we examined the effect of pacing functions that control how much data is presented to the network during each training epoch. Our proposed curriculum learning strategies were tested on the task of speech recognition on two data sets, one containing spontaneous Finnish speech where volunteers were asked to speak about a given topic, and one containing planned English speech. Empirical results showed that a good curriculum strategy can yield performance improvements and speed-up convergence. After a given number of epochs, our best strategy achieved a 5.6% and 3.4% decrease in terms of test set word error rate for the Finnish and English data sets, respectively. • Curriculum learning can improve the performance of end-to-end ASR systems. • A good curriculum strategy can speed up convergence without impacting performance. • Curriculum learning shows benefits in both low and high resource settings. • An adaptive curriculum leads to faster convergence rates than a static curriculum. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning have contributed greatly to the field of parallel MR imaging, where a reduced amount of k-space data are acquired to accelerate imaging time. In our previous work, we have proposed a deep learning method to reconstruct MR images directly from k-space data acquired with Cartesian trajectories. However, MRI utilizes various non-Cartesian trajectories, such as radial trajectories, with various numbers of multi-channel RF coils according to the purpose of an MRI scan. Thus, it is important for a reconstruction network to efficiently unfold aliasing artifacts due to undersampling and to combine multi-channel k-space data into single-channel data. In this work, a neural network named 'ETER-net' is utilized to reconstruct an MR image directly from k-space data acquired with Cartesian and non-Cartesian trajectories and multi-channel RF coils. In the proposed image reconstruction network, the domain transform network converts k-space data into a rough image, which is then refined in the following network to reconstruct a final image. We also analyze loss functions including adversarial and perceptual losses to improve the network performance. For experiments, we acquired k-space data at a 3T MRI scanner with Cartesian and radial trajectories to show the learning mechanism of the direct mapping relationship between the k-space and the corresponding image by the proposed network and to demonstrate the practical applications. According to our experiments, the proposed method showed satisfactory performance in reconstructing images from undersampled single- or multi-channel k-space data with reduced image artifacts. In conclusion, the proposed method is a deep-learning-based MR reconstruction network, which can be used as a unified solution for parallel MRI, where k-space data are acquired with various scanning trajectories. [ABSTRACT FROM AUTHOR]

We propose a conceptually simple, general framework and end-to-end approach to point cloud completion, entitled PCA-Net. This approach differs from the existing methods in that it does not require a "simple" network, such as multilayer perceptrons (MLPs), to generate a coarse point cloud and then a "complex" network, such as auto-encoders or transformers, to enhance local details. It can directly learn the mapping between missing and complete points, ensuring that the structure of the input missing point cloud remains unchanged while accurately predicting the complete points. This approach follows the minimalist design of U-Net. In the encoder, we encode the point clouds into point cloud blocks by iterative farthest point sampling (IFPS) and k-nearest neighbors and then extract the depth interaction features between the missing point cloud blocks by the attention mechanism. In the decoder, we introduce a new trilinear interpolation method to recover point cloud details, with the help of the coordinate space and feature space of low-resolution point clouds, and missing point cloud information. This paper also proposes a method to generate multi-view missing point cloud data using a 3D point cloud hidden point removal algorithm, so that each 3D point cloud model generates a missing point cloud through eight uniformly distributed camera poses. Experiments validate the effectiveness and superiority of PCA-Net in several challenging point cloud completion tasks, and PCA-Net also shows great versatility and robustness in real-world missing point cloud completion. [ABSTRACT FROM AUTHOR]

Simple Summary: Trunk-boring insects belong to one of the most destructive forest pests. Larvae in some groups are particularly difficult to detect since they make their living in trunks and no obvious sign can be found from outside. To deal with this problem, a new method is presented here, which is the embedding of a vibration probe into the tree trunk to pick up vibrations caused by larvae and the use of a model to distinguish whether the tree is infected. However, this procedure can experience severe interference from environmental noise, which is simultaneously picked up with vibrations. Thus, it is necessary to add a noise suppression process before discrimination. Previous examples have proved that the application of the analysis intended for sounds to boring vibrations is feasible. Therefore, we took advantage of deep learning-based speech enhancement and further improved it to develop a boring vibration enhancement model. The training data used in this research contains boring vibrations recorded within pieces of trunks and noise, which are common for trees' living environment. The experimental results indicate that the enhancement procedure provided by our model substantially increases the accuracy of several well-known classification models, guaranteeing a more practical larvae detection. The larvae of some trunk-boring beetles barely leave traces on the outside of trunks when feeding within, rendering the detection of them rather difficult. One approach to solving this problem involves the use of a probe to pick up boring vibrations inside the trunk and distinguish larvae activity according to the vibrations. Clean boring vibration signals without noise are critical for accurate judgement. Unfortunately, these environments are filled with natural or artificial noise. To address this issue, we constructed a boring vibration enhancement model named VibDenoiser, which makes a significant contribution to this rarely studied domain. This model is built using the technology of deep learning-based speech enhancement. It consists of convolutional encoder and decoder layers with skip connections, and two layers of SRU++ for sequence modeling. The dataset constructed for study is made up of boring vibrations of Agrilus planipennis Fairmaire, 1888 (Coleoptera: Buprestidae) and environmental noise. Our VibDenoiser achieves an improvement of 18.57 in SNR, and it runs in real-time on a laptop CPU. The accuracy of the four classification models increased by a large margin using vibration clips enhanced by our model. The results demonstrate the great enhancement performance of our model, and the contribution of our work to better boring vibration detection. [ABSTRACT FROM AUTHOR]

Around the world, the video surveillance system has gained wide acceptance and astonishing growth due to its broad applications. The surveillance system has become a paramount tool and benchmark for analyzing the harmony and safety of society. Anomaly detection and its associated applications play a key role in the integrity of the system. The aim of anomaly detection is to find rare and sparse occurrences of events from videos. Developing an accurate and time-efficient system is still remains challenging due to the dynamic nature of a nomalies. The deep learning-based end-to-end system with full use of both spatial and temporal features from the input videos is proposed. The model combines the use of 2DCNN and Stacked LSTM to extract frame-level features through an anisotropic Gunnar Farneback Optical Flow algorithm. The system is evaluated on the benchmarked datasets namely UCSD Ped1 and UCSD Ped2, and it achieves an AUC of 95% and 94% respectively. The experimental results indicate that the proposed method is superior to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Rock discontinuities are essential for the mechanical behavior and stability of rock mass. Previous approaches for characterizing discontinuities either rely on limited handcrafted features (point normals, point curvatures, point densities, and so on) or fail to classify discontinuities, making them unsuitable for complex and large-scale scenes. To cope with these problems, an end-to-end point-based deep learning method that can automatically learn rich and high-dimensional features and classify discontinuities was developed in this study. Firstly, a roadcut and part of a natural slope were selected to train the developed network and assess its performance. Subsequently, the trained network was used to classify the remaining part of the slope. Finally, the "Density-Based Scan Algorithm with Noise" (DBSCAN) and principal component analysis (PCA) algorithms were employed to extract individual discontinuities and calculate their orientations. The two cases achieved a global accuracy (GA) of 97.25% and 94.56%, respectively, and a mean intersection over union (MIoU) of 93.77% and 88.66%, respectively, indicating the excellent performance of the network. Meanwhile, the average error in dip angle and dip direction was 0.67° and 3.33°, respectively, proving the characterization ability of the developed method was satisfactory. Furthermore, the presented method exhibits strong robustness and the potential to characterize large-scale rock discontinuities with noise. This method facilitates the application of deep learning in geosciences and provides geologists and geological engineers with a new idea for rock discontinuity characterization. • A point-based deep learning method for discontinuity characterization was developed. • The method can process point cloud directly without handcrafted features. • It can extract rich and high-dimensional features and classify discontinuities. • It has huge advantages in mapping discontinuities in complex and large-scale scenes. [ABSTRACT FROM AUTHOR]

Dialogue system aims to realize the communication for information between machine and human so as to provide personalized service, it has certain research value. In order to follow up the research in related fields timely, this paper reviewed the current research progress. Firstly, it introduced the development and classification of dialogue system. Secondly, it summarized the architecture of task-oriented dialogue system, focusing on module based and the end-to-end based frameworks, then summarized the advantages and disadvantages of the models. Thirdly, it introduced the evaluation methods and application fields of task-oriented dialogue system, including e-commerce, education, medicine and so on. Finally, it analyzed and summarized the problems and challenges faced by the multi-turn task-oriented dialogue system. [ABSTRACT FROM AUTHOR]

Performance of Automatic Speech Recognition (ASR) systems is known to suffer considerable degradation when exposed to Far-Field speech data capture. Consequently, far-field ASR has received considerable attention in recent years. Motivated by our recent work using Curriculum Learning (CL) based strategies to improve Speaker Identification (SID) under noisy and degraded conditions, this study proposes a novel approach to improve far-field ASR using CL based approaches. Specifically, we propose using a CL based approach for training a Bidirectional Long Short Term Memory (BLSTM) based ASR network trained using the Connectionist Temporal Classification (CTC) objective function. We initiate the training with comparatively easier near-field data, and include more diverse (difficult) far-field data progressively in the later stages of training. These proposed approaches are shown to significantly outperform the baseline BLSTM ASR system, and offer relative reductions in WERs of up to +7.3% and +10.1% for the dev and eval sets of the AMI far-field voice capture corpus. [ABSTRACT FROM AUTHOR]

With the many varieties of AI hardware prevailing on the market, it is often hard to decide which one is the most suitable to use but not only with the best performance. As there is an industry-wide trend demand for deep learning deployment, the inference benchmark for the effectiveness of DNN processor becomes important and is of great help to select and optimize AI hardware. To systematically benchmark deep learning deployment platforms, and give more objective and useful metrics comparison. In this paper, an end to end benchmark evaluation system was brought up called IBD, it combined 4 steps include three components with 6 metrics. The performance comparison results are obtained from the chipsets from Qualcomm, HiSilicon, and NVIDIA, which can provide hardware acceleration for AI inference. To comprehensively reflect the current status of the DNN processor deploying performance, we chose six devices from three kinds of deployment scenarios which are cloud, desktop and mobile, ten models from three different kinds of applications with diverse characteristics are selected, and all these models are trained from three major training frameworks. Several important observations were made by using our methodologies. Experimental results showed that workload diversity should focus on the difference came from training frameworks, inference frameworks with specific processors, input size and precision (floating and quantized). [ABSTRACT FROM AUTHOR]

We present an image projection network (IPN), which is a novel end-to-end architecture and can achieve 3D-to-2D image segmentation in optical coherence tomography angiography (OCTA) images. Our key insight is to build a projection learning module (PLM) which uses a unidirectional pooling layer to conduct effective features selection and dimension reduction concurrently. By combining multiple PLMs, the proposed network can input 3D OCTA data, and output 2D segmentation results such as retinal vessel segmentation. It provides a new idea for the quantification of retinal indicators: without retinal layer segmentation and without projection maps. We tested the performance of our network for two crucial retinal image segmentation issues: retinal vessel (RV) segmentation and foveal avascular zone (FAZ) segmentation. The experimental results on 316 OCTA volumes demonstrate that the IPN is an effective implementation of 3D-to-2D segmentation networks, and the uses of multi-modality information and volumetric information make IPN perform better than the baseline methods. [ABSTRACT FROM AUTHOR]

In view of the low resource of endangered languages, the establishment and research of end-to-end speech recognition model can explore new ways for the protection and transmission of endangered languages. This paper combined dynamic bidirectional long short-term memory network and connectionist temporal classification model into an end-to-end speech recognition model. When performing phoneme-level recognition training, the batch size of the data passed into the model could be adaptively adjusted according to the training model, which not only speeded up the convergence but also improved the generalization of the model. By adjusting the hierarchy of the deep neural network and extracting different phonetic features for model comparison, the experimental results show that both the endangered languages-Lyusu and Tujia have good recognition results. [ABSTRACT FROM AUTHOR]

• WRN-PPNet model is proposed firstly to extract features from multimodal MRI brain tumor images. • Different level global prior representation of slices is obtained through pyramid pooling module. • Global prior representation is combined with original inputs for brain tumor segmentation. • The process to segment the brain tumor is fully automatically, end-to-end, robust and fast. Tumor segmentation is of great importance for diagnosis and prognosis of brain cancer in medical field. Because of the noise, inhomogeneous gray, diversity of tissue, bias among modalities, and the fuzzy boundaries between tumor and adjacent tissues in the magnetic resonance imaging (MRI), tumor segmentation is a very difficult task. At present, many of the existing brain tumor segmentation methods are semi-automatic which are troublesome and inconvenient because interventions of raters or specialists are required. In this paper an automatic method, named wide residual network & pyramid pool network (WRN-PPNet), which can automatically segment glioma end to end is put forward. The main idea is described below. Firstly, WRN is used to extract features of multimodal brain tumor slices which are proved to have strong expressive ability. Secondly, the global prior representation with different level obtained by PPNet is stacked on the features from WRN. Finally, the scale recovery module in which the original inputs are fed into the network again is utilized to produce the pixel-level predictions which have the same size with the original inputs. Dice coefficients, sensitivity coefficient and predictive positivity value (PPV) coefficient are used to evaluate the performance of WRN-PPNet quantitatively. The experimental results show that the proposed method has a superior performance compared with the other state-of-the-art methods, and the average Dice, sensitivity and PPV on the randomly selected test data can reach 0.91, 0.94 and 0.89 respectively. [ABSTRACT FROM AUTHOR]

In laparoscopic cholecystectomy (LC), the anatomical variation of gallbladder canal should be noted. Bile duct injury is one of the most serious complications of LC, which might lead to serious complications. This case because of an intraoperative accidental cut to right anterior inferior lobe bile duct which joins to the cystic duct, after confirming by intraoperative magnetic resonance imaging-guided real-time three-dimensional bile duct reconstruction and performing end-to-end anastomosis of the right anterior inferior lobe bile duct and the gallbladder tube under laparoscopy, the patient was discharged 5 days after surgery, was followed up for 4 months and was disease-free. [ABSTRACT FROM AUTHOR]

Vector of Locally Aggregated Descriptor (VLAD) is a very popular feature coding method in image classification and image retrieval. Recently, the original VLAD method is extended to an end-to-end model called NetVLAD. The NetVLAD layer is readily embedded into a deep neural network and can be trained by the back-propagation algorithm. Although the NetVLAD model has achieved noticeable classification results in many image databases, the discrimination embedded in the NetVLAD method is not fully exploited. In this paper, in order to design a more discriminative feature coding network, a novel localized and second-order VLAD Network (LSO-VLADNet) is proposed. First, we design a localized and second-order VLAD coding method. Second, the back propagation functions of all newly designed layers are obtained. Third, the new feature coding method is extended to an end-to-end feature coding network which can be jointly trained with a deep convolutional neural network for visual recognition. Some experiments show that the newly designed network has the significant improvements over the original NetVLAD. Some experimental comparisons of the proposed model and other state-of-art methods will also be given to validate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Airplane detection in remote sensing images remains a challenging problem due to the complexity of backgrounds. In recent years, with the development of deep learning, object detection has also obtained great breakthroughs. For object detection tasks in natural images, such as the PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) VOC (Visual Object Classes) Challenge, the major trend of current development is to use a large amount of labeled classification data to pre-train the deep neural network as a base network, and then use a small amount of annotated detection data to fine-tune the network for detection. In this paper, we use object detection technology based on deep learning for airplane detection in remote sensing images. In addition to using some characteristics of remote sensing images, some new data augmentation techniques have been proposed. We also use transfer learning and adopt a single deep convolutional neural network and limited training samples to implement end-to-end trainable airplane detection. Classification and positioning are no longer divided into multistage tasks; end-to-end detection attempts to combine them for optimization, which ensures an optimal solution for the final stage. In our experiment, we use remote sensing images of airports collected from Google Earth. The experimental results show that the proposed algorithm is highly accurate and meaningful for remote sensing object detection. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for remote heart rate (HR) estimation. Recent studies have proved that blood pumping by the heart is highly correlated to the intense color of face pixels, and surprisingly can be utilized for remote HR estimation. Researchers successfully proposed several methods for this task, but making it work in realistic situations is still a challenging problem in computer vision community. Furthermore, learning to solve such a complex task on a dataset with very limited annotated samples is not reasonable. Consequently, researchers do not prefer to use the deep learning approaches for this problem. In this paper, we propose a simple yet efficient approach to benefit the advantages of the Deep Neural Network (DNN) by simplifying HR estimation from a complex task to learning from very correlated representation to HR. Inspired by previous work, we learn a component called Front-End (FE) to provide a discriminative representation of face videos, afterward a light deep regression auto-encoder as Back-End (BE) is learned to map the FE representation to HR. Regression task on the informative representation is simple and could be learned efficiently on limited training samples. Beside of this, to be more accurate and work well on low-quality videos, two deep encoder–decoder networks are trained to refine the output of FE. We also introduce a challenging dataset (HR-D) to show that our method can efficiently work in realistic conditions. Experimental results on HR-D and MAHNOB datasets confirm that our method could run as a real-time method and estimate the average HR better than state-of-the-art ones. • We propose an effective yet simple remote HR estimation method based on deep learning. • The propsoed method is robust against noise, face movement and low-quality videos. • Two deep networks are adversarially learned to refine the outputs for HR estimation. • We introduce a new challenging data-set named HR-D. • Our results are better than state-of-the-art in terms of complexity and accuracy. [ABSTRACT FROM AUTHOR]

We describe a patient who sustained a penetrating injury to the posterior right lower extremity just above the popliteal region with transection of the superficial femoral artery (SFA) despite minimal evidence of active bleeding. An on-table angiogram identified flow in the SFA followed by the popliteal artery and into the trifurcation of the right lower extremity. Eventually, a second operation revealed transection followed by end-to-end anastomosis of SFA and stabilization of the patient. The findings of this case highlight the need for a high index of suspicion and persistent clinical investigation to identify vascular injuries in the absence of hard signs of vascular trauma. [ABSTRACT FROM AUTHOR]

The integration of wireless sensor networks within the Internet has been possible with the use of the IPv6 over low-power wireless personal area network standard at lower layers and Web services at higher layers. The operating constraints of wireless sensor networks and the implementation of traditional Internet protocols in these networks require an efficient adaptation of specialized low-power application protocols. Under this scope, this article presents the design, implementation, and evaluation of a secure, lightweight, and energy-efficient application protocol based on binary encoding and a RESTful approach to enable Web services in wireless sensor networks. The lightweight and secure protocol for wireless sensor network protocol has been designed to work over transmission control protocol, and it is recommended for applications where strong reliability constraints are imposed or in the configuration, reprogramming, or management of sensor nodes. Results show that lightweight and secure protocol for wireless sensor network is energy efficient regardless the use of legacy transmission control protocol as transport protocol, which brings the possibility of interoperability with existing systems, Internet protocols, and network infrastructure. We believe that lightweight and secure protocol for wireless sensor network will allow the expansion of the Internet of Things at a global scale. [ABSTRACT FROM AUTHOR]