The rapid growth of the Internet of Things (IoT) has extended into marine environments, giving rise to the Internet of Underwater Things (IoUT). Within this context, Underwater Named Data Networking (UNDN) has emerged as a promising paradigm, yet it faces severe challenges such as limited bandwidth, long delays, energy scarcity, etc. We propose an Energy-Balanced Multi-Path Interest Forwarding (EBMIF) strategy for UNDN. The strategy has three parts. First, a depth-based name discovery builds a small set of candidate paths and uses a budget to bound diffusion. Second, each packet piggybacks the sender’s residual-energy ratio, which gives neighbors passive and timely energy visibility without extra control packets. Third, a lexicographic rule selects next hops sequentially by residual energy, depth progress, and path cost. EBMIF is implemented in Aqua-Sim-NG and compared with CBILEM-U, BestRoute, and Dual-Mode Interest Forwarding (DMIF). Experimental results illustrate that the EBMIF strategy demonstrates superior performance in energy balancing, network lifetime optimization, and overall operational efficiency, making it well suited for long-term and stable underwater network deployments.

As space communication technology advances at a rapid pace, integrated networks spanning air, space, land, and sea have demonstrated significant potential applications in various fields. However, traditional Transmission Control Protocol/ Internet Protocol (TCP/IP) may not cope well with the high dynamics of satellite networks. Thus, the Virtual Information Defined Satellite Network (VIDSN) architecture is proposed adopting the virtual node strategy. The Pending Interest Table (PIT) and Forwarding Information Base (FIB) are redesigned accordingly, and FIB table is updated through controller polling. To realize efficient data transmission in dynamic satellite network, an appropriate routing strategy is essential. A novel Multi-layer Satellite Routing (MLSR) algorithm is proposed for satellite IoT networks, which adopts a hierarchical routing control architecture to enable scalable and coordinated routing. By exploiting the hierarchical structure of multi-layer satellite networks, the proposed MLSR algorithm effectively supports dynamic topology and traffic variations. Simulation results demonstrate that MLSR comprehensively outperforms baseline methods.

As a core component of intelligent transportation systems, vehicle localization technology enables accurate positioning, supporting comprehensive insights into traffic flow, vehicle status, and environmental changes. Current vehicle position localization technologies primarily rely on visual sensors and the Global Positioning System, while their performance can be affected by extreme weather conditions and signal stability. However, vehicle-generated sound, as a stable data source unaffected by environmental conditions and free from signal limitations, is often underutilized by existing studies. In this article, we construct a vehicle localization dataset based on sound signals and further propose a combined filtering strategy that integrates adaptive filtering with spectral subtraction filtering, dynamically adjusting the filter parameters to suppress time-correlated noise within the signal. We also remove broadband noise in the frequency domain while preserving high-frequency signal details, offering a significant advantage over existing methods in terms of signal-to-noise ratio improvement. The proposed dataset and filtering strategy are validated using the EfficientNet-1D Fusion model. Experimental results demonstrate that the proposed combined filtering method excels in recognition accuracy and computational efficiency.

Existing deep hashing methods mainly focus on preserving pairwise image similarity or reducing quantization error, often overlooking the discriminative capacity of real-valued features learned by neural networks, which limits retrieval performance. To address these issues, we propose a dual-stream Collaborative Asymmetric Similarity-preserving Hashing (CASpH) deep hashing method that preserves semantic structure across categories while generating discriminative hash codes. Specifically, the Cross Attention Feature Enhancement Block (CAFEB) is designed to mitigate information loss from feature dimensionality reduction during extraction. Furthermore, two asymmetric deep networks are constructed to capture image similarity based on semantic labels. To ensure binary codes in Hamming space retain semantic similarity from the original space, an asymmetric loss is introduced to capture the similarity between binary codes and real-valued features. This asymmetric loss not only enhances retrieval performance but also aids in faster convergence during training. Extensive experiments on three benchmark datasets demonstrate that the CASpH method outperforms other comparative methods.

3D Gaussian Splatting (3DGS) is gaining popularity in fields such as robotics, autonomous driving, and virtual reality, due to its effectiveness and efficiency. Given that some tasks involve high risks, it is crucial to investigate the adversarial robustness of 3DGS and its downstream tasks—a topic that remains largely unexplored. In this study, we introduce a framework, Multi-Parametric Adversarial Manipulation for 3D Gaussian Splatting (MPAM-3DGS), that allows to attack 3DGS and its downstream tasks, such as object detection and classification, by perturbing a specified subset of parameters. Leveraging this framework, we examine the adversarial sensitivity of each 3DGS parameter and propose two strategies to attack multiple parameters based on our observations. To our knowledge, this is the first study to explore the adversarial robustness of 3DGS. Our experimental results demonstrate the effectiveness of our attacks on downstream tasks and the invisibility of perturbations in 3DGS. The code can be found at https://github.com/jiang-wenxiang/MPAM-3DGS.

Transformers have made significant progress in dealing with computer vision tasks. However, most existing Vision Transformers (ViT) typically focus on single-scale information, limiting their capability to model interactions when processing multi-scale features. Moreover, the explicit mapping of continuous real-valued features to discrete hashing codes via a quantization layer is suboptimal for retrieval tasks. To overcome the above problem, we propose a novel deep hashing based on a Cross-scale Transformer named CrossHash, aiming to extract multi-scale features. Furthermore, a relative similarity quantization method is first introduced, which maximizes the similarity between the relative positional representation of continuous codes and the normalized centroids; this method effectively reduces quantization error. It optimizes feature distribution via contrastive learning loss, maximizing the inter-class distance and minimizing the intra-class distance of the learned feature. Extensive experiments on three benchmark datasets demonstrate that the proposed model outperforms other state-of-the-art deep hashing methods. Source code is available https://github.com/wwg1010/CrossHash.

With the proliferation of smartphone application services, the pattern lock remains widely used for authentication. Notably, the risks associated with password entry in public spaces have attracted significant attention from researchers. Various attacks have been explored to steal passwords, but each comes with limitations, such as requiring good lighting conditions, close proximity, pre-deployed devices, or system intrusion. To address these challenges, we propose an attack method called BEyes, which utilizes beamforming feedback information (BFI) to eavesdrop on pattern passwords drawn on smartphone screens. Since BFI is transmitted in clear text and describes the downlink channel state information (CSI), any Wi-Fi 5-enabled device can capture it out of the victim’s view, reducing the likelihood of the attack being detected. To avoid missing critical pattern drawing information, we propose a traffic generation mechanism based on traffic competition, which ensures stable BFI. To mitigate the effects of frequency-selective fading and noise, we apply subcarrier alignment and principal component analysis (PCA) to improve efficiency. Additionally, we introduce a motion-based joint inference model, enabling BEyes to generalize its inference from a few known pattern passwords to unknown ones. Extensive experiments demonstrate that BEyes achieves an accuracy of 89.2% in inferring a 3-line pattern password within the Top-10 attempts.

The symbiotic Internet of Things (IoT) computing paradigm leverages high-speed transmission technologies, such as 6G, to execute large-scale model computations while preserving data privacy, thereby mitigating significant economic losses from data breaches. This paradigm has emerged as a pivotal focus in industrial intelligent manufacturing research. However, within this framework, edge devices must concurrently support both large-scale model computations and high-precision industrial core tasks, creating critical challenges in system efficiency and stability that impede technological advancement. To address these issues, this article introduces a novel Quality of Experience (QoE)-driven heuristic task resource scheduling algorithm that employs Composite Differential Evolution (CoDE) integrated with a tabular Kolmogorov–Arnold Network (KANTab), specifically designed for the comprehensive processes of industrial intelligent manufacturing systems. This methodology enables precise simulation of extensive industrial task requirements and efficient allocation of computational resources under constrained conditions, effectively resolving the resource allocation conflict between large-scale model computation tasks at the edge and primary tasks on terminal devices. We evaluate our approach on a custom-built large-scale task demand dataset from refrigerator manufacturing and demonstrate that it achieves superior overall performance compared to state-of-the-art algorithms.

Adversarial attacks, i.e., generating adversarial perturbations with a small magnitude to deceive deep neural networks, are important for investigating and improving model trustworthiness. Traditionally, the topic was scoped within 2D images without considering 3D multiview information. Benefiting from Neural Radiance Fields (NeRF), one can easily reconstruct a 3D scene with a Multi-Layer Perceptron (MLP) from given 2D views and synthesize photo-realistic renderings of novel vantages. This opens up a door to discussing the possibility of undertaking to attack multiview NeRF network with downstream tasks from different rendering angles, which we denote Neural Radiance Fiels-based multiview adversarial Attack (NeRFail). The goal is, given one scene and a subset of views, to deceive the recognition results of agnostic view angles as well as given views. To do so, we propose a transformation mapping from pixels to 3D points such that our attack generates multiview adversarial perturbations by attacking a subset of images with different views, intending to prevent the downstream classifier from correctly predicting images rendered by NeRF from other views. Experiments show that our multiview adversarial perturbations successfully obfuscate the downstream classifier at both known and unknown views. Notably, when retraining another NeRF on the perturbed training data, we show that the perturbation can be inherited and reproduced. The code can be found at https://github.com/jiang-wenxiang/NeRFail.

Knowledge Graph Completion (KGC) task aims at predicting absent links between entities by leveraging existing information. Conventional neural network (CNN)-based KGC models have gained substantial attention due to their efficacy and computational efficiency. The effectiveness of these models greatly hinges on the fusion methods employed for combining entity and relation embeddings. In this paper, we proposed a novel approach for fusing entity and relation embeddings, ensuring a evener combination of elements in terms of indices. This technique serves as the foundation for a new CNN-based KGC model called CombinE. Experimental results on five datasets show that CombinE achieves better performance than the baseline models.

Neural Radiance Field (NeRF) represents a significant advancement in computer vision, offering implicit neural network-based scene representation and novel view synthesis capabilities. Its applications span diverse fields including robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, etc., some of which are considered high-risk AI applications. However, despite its widespread adoption, the robustness and security of NeRF remain largely unexplored. In this study, we contribute to this area by introducing the Illusory Poisoning Attack against Neural Radiance Fields (IPA-NeRF). This attack involves embedding a hidden backdoor view into NeRF, allowing it to produce predetermined outputs, i.e. illusory, when presented with the specified backdoor view while maintaining normal performance with standard inputs. Our attack is specifically designed to deceive users or downstream models at a particular position while ensuring that any abnormalities in NeRF remain undetectable from other viewpoints. Experimental results demonstrate the effectiveness of our Illusory Poisoning Attack, successfully presenting the desired illusory on the specified viewpoint without impacting other views. Notably, we achieve this attack by introducing small perturbations solely to the training set. The code can be found at https://github.com/jiang-wenxiang/IPA-NeRF.

The surging volume of big data underscores the imperative of integrating heterogeneous datasets into a unified, semantically consistent format. We introduce Pyramid, a comprehensive framework for heterogeneous data integration, addressing schema transformation, feature encoding, entity matching, deduplication, and mapping retrieval. At its core, a hierarchical graph captures relationships across databases, bridging diverse data sources. We employ a bottom-up encoding strategy, factoring in data context, and a top-down matching mechanism, curbing attribute misalignment across entity types. Enhanced by the transformer model and contrastive learning, our approach realizes unsupervised feature synthesis, bolstering integration. Extensive experiments and evaluations validate the broad applicability and superior performance of our method across a variety of heterogeneous datasets.

Recently, cross-modal hashing has attracted much attention in large-scale image retrieval scenarios. However, most existing methods ignore the potential higher-order relationships and label semantic information between heterogeneous modality data. Besides, the imbalanced training samples could bias the learning process in most classes and affect the retrieval performance. To solve the above problems, we proposed a Joint-semantics Multi-Similarity Hashing method for cross-modal retrieval (JMSH). We first construct a joint semantic similarity matrix, which supervises hash learning by integrating multi-modal features and semantic labels. This method generates higher-order semantic features that maintain semantic correlation effectively. Then, we propose a multi-similarity loss based on adaptive margin, which can collect and weight informative pairs efficiently and accurately, thus producing more discriminative hashing code and improving retrieval performance. Extensive experiments on two benchmark datasets show the superiority of JMSH in cross-modal retrieval tasks.

Internet of Underwater Things (IoUT) is applied to ocean research by connecting underwater sensing devices. It has ability to maintain efficient communication even in challenging environments and with limited power resources. However, it hinders the efficient storage and forwarding of huge amounts of underwater data due to the traditional IP architecture. As a future network architecture, underwater named data networking (UNDN) is considered an effective architecture of IoUT. While naive UNDN cannot support active forwarding when dangers occur in underwater environments, which may cause severe consequences. In this article, we propose an event-based communication architecture called push-hybrid UNDN, which can achieve active and timely forwarding when critical events occur. To realize critical data forwarding without waiting for user requests, passively accepted interest is replaced by actively forwarded beacon during the critical events. The push-hybrid UNDN is analyzed and compared with the naive one in transmission performance to highlight the advantages of incorporating push mechanism. Energy consumption of push-hybrid UNDN, naive UNDN, and push-based UNDN is compared in different scenarios to demonstrate the advantages of hybrid design. Simulation results verify that our scheme achieves shorter transmission delay, more stable and higher packet delivery ratio, less traffics, and lower energy consumption than the existing UNDN architectures.

Recently, as fitness has become a popular part of people’s lives, the intention to record fitness processes and assess the standards of fitness movements has grown increasingly keen. However, the existing approaches have some limitations, for example, wearable devices can hinder users’ fitness activities; computer vision–based solutions pose the risk of privacy breach, and so on. Fortunately, we observed that smartspeaker, acoustic-based sensing is a promising method of activity monitoring. In this article, we propose Afitness, an acoustic-based sensing system that enables non-intrusive, passive, and high-precision fitness detection. Afitness has the following three innovations. (i) We utilize pulse compression to generate high-precision motion distance images on commercial devices that can be visually recognized. (ii) We propose a data augmentation algorithm, which also incorporates transfer learning to greatly reduce the pressure of data collection. (iii) We exploit incremental learning techniques that allow Afitness to improve the portability of our system and recognize new actions. Overall, Afitness achieves acoustic signal interpretability and environmental reliability detection.

Compared to feature or decision fusion, hybrid fusion can beneficially improve audio-visual speech recognition accuracy. Existing works are mainly prone to design the multi-modality feature extraction process, interaction, and prediction, neglecting useful information on the multi-modality and the optimal combination of different predicted results. In this paper, we propose a multi-scale hybrid fusion network (MSHF) for mandarin audio-visual speech recognition. Our MSHF consists of a feature extraction subnetwork to exploit the proposed multi-scale feature extraction module (MSFE) to obtain multi-scale features and a hybrid fusion subnetwork to integrate the intrinsic correlation of different modality information, optimizing the weights of prediction results for different modalities to achieve the best classification. We further design a feature recognition module (FRM) for accurate audio-visual speech recognition. We conducted experiments on the CAS-VSR-W1k dataset. The experimental results show that the proposed method outperforms the selected competitive baselines and the state-of-the-art, indicating the superiority of our proposed modules.

Text semantic matching is a core problem in Natural Language Processing (NLP), such as information retrieval and question answering, which is significant in intelligent human-computer interaction. However, most deep neural matching models are driven by external knowledge, which lacks fine-grained feature extraction from the sentences, leading to limited performance improvement. Therefore, this paper focuses on generating sentence semantic representations without external knowledge for sentence pair matching. We propose a Gated Attentive Convolutional Recurrent Neural Network (GACRNN), which incorporates a Gated Convolutional Neural Network (GCNN), Multi-scale Cross-Channel Attention Block (MC2AB), and bidirectional gate recurrent units (BiGRUs). First, a gate mechanism is introduced in the convolutional neural network to control the information interaction to extract multi-scale features from the sentence. Then, a multi-scale cross-channel attention mechanism is utilized to capture the feature dependencies at different scales in the channel dimension to generate expressive sentence representation. Finally, an extensive evaluation is conducted on two open-domain and two restricted-domain datasets. The experiment results show that the proposed model outperforms other baselines in terms of sentence pair semantic matching accuracy.

Compared to relying only on audio information, incorporating visual information improves speech recognition accuracy in noisy environments. Existing works are prone to design specific architecture for feature extraction, neglecting feature enhancement. In this paper, we propose an end-to-end Mandarin audio-visual speech recognition model with a Feature Enhancement Module. Specifically, we design a Feature Enhancement Module (FEM) that uses deconvolution and up-sampling to obtain the twin enhanced data for generating high-resolution feature representation. We further develop the Visual Feature Enhancement Module (Visual FEM) and Audio Feature Enhancement Module (Audio FEM) to enhance feature extraction from both visual data and audio data. We incorporate the proposed modules into the blocks of the Residual Network for accurate audio-visual speech recognition. We conducted experiments on the CAS-VSR-W1k and Chinese Mandarin Lip Reading (CMLR) datasets. The experimental results show that the proposed method outperforms the selected competitive baselines and the state-of-the-art, indicating the superiority of our proposed modules.

Internet of Underwater Things (IoUT) needs to maintain effective communication even under the circumstances of severe environments and limited energy. Named data networking (NDN), a future network architecture, is starting to be used for IoUT as an effective architecture implementation. Despite having a good performance of data transmission, Underwater named data networking (UNDN) nevertheless faces some security risks, such as Denial-of-Service (DoS) brought by interest flooding attacks (IFAs). This article proposes a novel DoS attack, Synergetic DoS (SDoS), which can cause hiding damages to router’s content store (CS), pending interest table (PIT), and forwarding information base (FIB). We not only study the basic synergetic attack model of SDoS but also analyze some possible attack variants. Simulation results illustrate that SDoS entirely invalidates the only IFA detection algorithm in UNDN. Compared to ordinary IFAs, SDoS attacks increase network traffic fourfold. Furthermore, we discover a unique infection problem in UNDN and propose a countermeasure named Trident, which has meticulously designed adaptive threshold,Double Trialfor attacker identification, and a self-proving mechanism based on leaky bucket. Experiment results demonstrate that Trident can detect and resist not only IFAs but also SDoS attacks effectively. Meanwhile, Trident also achieves good defense performance on the variants of SDoS and can take on burst traffic and network congestion robustly.

Deep learning (DL) technologies bring new threats to network security. Website fingerprinting attacks (WFA) using DL models can distinguish victim’s browsing activities protected by anonymity technologies. Unfortunately, traditional countermeasures (website fingerprinting defenses, WFD) fail to preserve privacy against DL models. In this paper, we apply adversarial example technology to implement new WFD with static analyzing (SA) and dynamic perturbation (DP) settings. Although DP setting is close to a real-world scenario, its supervisions are almost unavailable due to the uncertainty of upcoming traffics and the difficulty of dependency analysis over time. SA setting relaxes the real-time constraints in order to implement WFD under a supervised learning perspective. We propose Greedy Injection Attack (GIA), a novel adversarial method for WFD under SA setting based on zero-injection vulnerability test. Furthermore, Sniper is proposed to mitigate the computational cost by using a DL model to approximate zero-injection test. FCNSniper and RNNSniper are designed for SA and DP settings respectively. Experiments show that FCNSniper decreases classification accuracy of the state-of-the-art WFA model by 96.57% with only 2.29% bandwidth overhead. The learned knowledge can be efficiently transferred into RNNSniper. As an indirect adversarial example attack approach, FCNSniper can be well generalized to different target WFA models and datasets without suffering fatal failures from adversarial training.

Cameras onboard autonomous, as a critial component of the sensor system of automatic driving, plays a vital role in perception of driving and road environment. However, in some bad weather or unpredictable situations, the image quality obtained by the in-vehicle sensing camera is not ideal, which will become an extremely unsafe factor for autonomous driving. In order to improve the safety of self-driving vehicles, we proposed a novel high-quality image of invehicle cameras generation approach CE-GAN, a conditional generative adversarial network that attempt to leverage the point cloud data from on-board lidar to compensate the defect of visible image to improve the image quality of on-board cameras. Inspired by the generative adversarial networks, our method establishes an adversarial game between the generator and the discriminator We designed specifically loss function for different reasons for image quality impairment including partially obscured and fogged. Consequently, extensive experiments show that CE-GAN renders better performance in detail texture, compared with conventional Cycle-GAN, pix2pix methods without assistance of LiDAR data.

Realtime foreground/background segmentation based on sequence video was of great significance for autonomous vehicles perception, edge device application and higher level data analysis. A new fast background subtraction method for dynamic objects detection was proposed by using the digital features of the whole block of pixels. The algorithm considered that the change of the current pixel was closely related to the surrounding pixels, took the current pixel and its eight neighboring pixels as a whole block, and used the digital features - average and variance to reflect the pixel level of the block and establish the background model. At the same time, a local remodeling method was proposed, which made the algorithm can process and eliminate ghost quickly. The results based on CDnet2014 dataset showed that our algorithm could adapt to various dynamic objects detection scenarios and initialize fast under the condition of low hardware cost, and provided a good overall performance.

Short-term prediction of fishing effort distributions will guide fishery management in a dynamic way. However, it meets two unique challenges: the randomness of fishers’ behaviors and the diversity of marine meteorology such as sea storms in the short period. This study proposes short-term prediction system of fishing effort distribution by mining a new kind of knowledge: fishing chronology among trawlers. We first define, quantify, and dig out chronological fishing relations among trawlers based on the VMS dataset. Then the system extracts the optimal early bird set from chronological fishing relations, whose current fishing behaviors can serve as indicators of future fishing effort distributions. Based on the knowledge of fishing chronology, we further design a Convolution Neural Network (CNN) to predict the short-term fishing effort distribution, only taking the current fishing behaviors of early birds as input. We evaluate the system performance on the VMS dataset of 1589 trawlers in the East China Sea from October 2015 to April 2017. The system uses the VMS traces in the first half period to calculate fishing chronology among trawlers, to extract early birds, and to train the CNN model. The traces in the last half period is used to evaluate the prediction accuracy. The results confirm a low prediction error ratio of 6.95% across all the weeks only by tracking 19 early birds. More importantly, our prediction system keeps its accuracy during the week of a sea storm in Feb. 8th to 10th, 2017. The application of our system for fishery management is encouraging: tracking only 1% trawlers suffices to predict short-term fishing effort distributions in the near future.

An efficient and low‐cost communication system has great significance in maritime communication, but it faces enormous challenges because of high communication costs, incomplete communication infrastructure, and inefficient routing algorithms. Delay Tolerant Vessel Networks (DTVNs), which can create low‐cost communication opportunities among vessels, have recently attracted considerable attention in the academic community. Most existing maritime ad hoc routing algorithms focus on predicting vessels’ future contacts by mining coarse‐grained social relations or spatial distribution, which has led to poor performance. In this paper, we analyze 3‐year trajectory data of 5123 fishery vessels in the China East Sea. Using entropy theory, we observe that the trajectory of the vessel has strongly spatial‐temporal distribution regularity, especially when previous states were given. To predict accurate future trajectories, we develop a long‐term accurate trajectory prediction model by improving the Bidirectional Long‐Short Term Memory (Bi‐LSTM) model. Based on predicted trajectories and the confident degree of each prediction step, we propose a series of routing algorithms called TPR‐DTVN to achieve efficient communication performance. Finally, we carry out simulation experiments with extensive real data. Compared with existing algorithms, the simulation results show that TPR‐DTVN can achieve a higher delivery ratio with lower cost and transmission delay.

With the development of big data and network technology, the rapidly growing data amount brings a great challenge to the performance optimization of Internet of Marine things (IoMaT). Both cutting down the redundant data communication and ensuring reliable communication of necessary data are the key issues IoMaT facing. Most existing studies focus on periodical and continuous-time data transmission mode of the source nodes once some data collection task starts, not considering decreasing the data transmission frequency efficiently in the source node. This study investigates the essential characteristics of marine data, formulates the attributes and collection characteristics (CC) of marine data, and modeling the customized data transmission scheme (DTS) based on the CC for the sake of avoiding the redundant data transmission and minimize the data transmission frequency in nature. Furthermore, two specific DTSs are present in terms of various attributes and CC of marine data to not only meet the different requirements of marine monitoring, but also reduce the network traffic. The simulation results show that the proposed schemes can effectively reduce the data transmission frequency and the energy consumption, prolong the network lifetime, and improve the data packet delivery ratio in IoMaT.

Multivariate time series analysis is an important problem in data mining because of its widespread applications. With the increase of time series data available for training, implementing deep neural networks in the field of time series analysis is becoming common. Res2Net, a recently proposed backbone, can further improve the state-of-the-art networks as it improves the multi-scale representation ability through connecting different groups of filters. However, Res2Net ignores the correlations of the feature maps and lacks the control on the information interaction process. To address that problem, in this paper, we propose a backbone convolutional neural network based on the thought of gated mechanism and Res2Net, namely Gated Res2Net (GRes2Net), for multivariate time series analysis. The hierarchical residual-like connections are influenced by gates whose values are calculated based on the original feature maps, the previous output feature maps and the next input feature maps thus considering the correlations between the feature maps more effectively. Through the utilization of gated mechanism, the network can control the process of information sending hence can better capture and utilize the both the temporal information and the correlations between the feature maps. We evaluate the GRes2Net on four multivariate time series datasets including two classification datasets and two forecasting datasets. The results demonstrate that GRes2Net have better performances over the state-of-the-art methods thus indicating the superiority.

Due to the harsh environment and energy limitation, maintaining efficient communication is crucial to the lifetime of Underwater Sensor Networks (UWSN). Named Data Networking (NDN), one of future network architectures, begins to be applied to UWSN. Although Underwater Named Data Networking (UNDN) performs well in data transmission, it still faces some security threats, such as the Denial-of-Service (DoS) attacks caused by Interest Flooding Attacks (IFAs). In this paper, we present a new type of DoS attacks, named as Synergetic Denial-of-Service (SDoS). Attackers synergize with each other, taking turns to reply to malicious interests as late as possible. SDoS attacks will damage the Pending Interest Table, Content Store, and Forwarding Information Base in routers with high concealment. Simulation results demonstrate that the SDoS attacks quadruple the increased network traffic compared with normal IFAs and the existing IFA detection algorithm in UNDN is completely invalid to SDoS attacks. In addition, we analyze the infection problem in UNDN and propose a defense method Trident based on carefully designed adaptive threshold, burst traffic detection, and attacker identification. Experiment results illustrate that Trident can effectively detect and resist both SDoS attacks and normal IFAs. Meanwhile, Trident can robustly undertake burst traffic and congestion.

Freehand exercises help improve physical fitness without any requirements on devices, or places (e.g., gyms). Existing fitness assistant systems require wearing smart devices or exercising at specific positions, which compromises the ubiquitous availability of freehand exercises. This work proposes MobiFit, a contactless freehand exercise assistant using just one cellular signal receiver. MobiFit monitors the ubiquitous cellular signals sent by the base station and provides accurate repetition counting, exercise type recognition, and workout quality assessment without any attachments to the human body. To design MobiFit, we first analyze the characteristics of the received cellular signal sequence during freehand exercises through experimental studies. Based on the observation, we construct the analytic model of the received signals. Guided by the analytic model, MobiFit segments out every repetition and rest interval from one exercise session through spectrogram analysis, and extracts low-frequency features from each repetition for type recognition. We have implemented the prototype of MobiFit and collected 22,960 exercise repetitions performed by ten volunteers over six months. The results confirm that MobiFit achieves high counting accuracy of 98.6%, high recognition accuracy of 94.1%, and low repetition duration estimation error within 0.3s. Besides, the experiments show that MobiFit works both indoor and outdoor, and supports multiple users exercising together.

Existing range-based localization schemes for under-water acoustic sensor networks (UASNs) rely on sufficient and accurate distance measurements. However, in practice, ranging packets are inevitably corrupted due to packet collisions and signal noises, resulting in missing and noisy distance measurements and further degrading localization performance significantly. In this paper, we propose a packet corruption tolerant localization algorithm to address this challenge. First, we design an energy-efficient mechanism to gather inter-node distance measurements and form partially observed Square Distance Matrix (SDM). Then, leveraging the intrinsic low-rank structure of SDM, the reconstruction of true SDM is formulated as a Frobenius-norm regularized matrix factorization problem and an improved Newton-Raphson method is designed to solve this problem. Finally, we apply Multi-Dimension Scaling technique to localize all the nodes based on the reconstructed SDM. Simulation results demonstrate that, our proposed algorithm outperforms the benchmark approaches in terms of localization accuracy, coverage and stability.

In mobile crowdsensing (MCS), to select the optimal set of participants for a particular sensing task, the cloud-based MCS platform requires mobile users to submit their bids and their sensing quality data. This can cause privacy breaches. One possible solution is to leverage secure sharing or bidding schemes to protect participants' personal information during selection. However, these schemes suffer from high overheads, poor scalability and more importantly, the group formation has never been studied. To address this issue and to enhance the protection of user privacy, we propose a set of novel privacy-preserving grouping methods, which place participants into small groups over hierarchical edge clouds. By doing this, not only can the participants be hidden in groups, but also the overall privacy-preserving participant selection becomes more scalable. The design goal to minimize the communication cost during secure sharing/bidding within groups, while satisfying each participant's requirement for privacy preservation. For different scenarios and optimization functions, we propose a set of grouping schemes to fulfill this goal. Extensive simulations over both synthetic and real-life datasets illustrate the efficiency of proposed mechanisms.

Under the framework of multi-label classification, the excessive training time restricts the availability of non-linear kernel SVM (Support Vector Machine) classification algorithm on large-scale data sets. To solve this problem, this paper provides a fast multi-label SVM classification algorithm b ased on approximate extreme points (AEMLSVM). Firstly, it utilizes the approximate extreme point technique to obtain representative sets from the training data set. These representative sets not only retain almost all information of the training data set, but also its size is much smaller than that of training data set. After that, SVM is trained on the representative sets. Furthermore, the improved AEMLSVM algorithm (AEMLSVM-DEC) adopts DEC (Different Error Costs) technique to solve the label data imbalanced problem. We have conducted extensive experiments on four large-scale benchmark data sets. The results show that the proposed algorithms can effectively reduce training time, and their classification performance is similar to that of the traditional multi-label SVM algorithm. They outperform other scalable multi-label SVM algorithms in training time and classification performance. By adopting DEC method to solve the label data imbalanced problem, the AEMLSVM-DEC algorithm has a better classification performance than AEMLSVM algorithm.

We present WFID, a passive device-free indoor human identification system with one pair of WiFi signal transmitter and receiver. WFID design is motivated by the observation that PHY layer Channel State Information (CSI) is capable of capturing the frequency diversity of wideband channel, such that the human body curve may be uniquely identified by learning the feature pattern of CSI. Different from many CSI-based techniques focusing on phase shift, we propose a novel feature of subcarrier-amplitude frequency (SAF). Based on this feature, WFID realizes human identification through a linear-kernel SVM. We have implemented a prototype of WFID with a commercial AP and a computer equipped with one Intel 5300 NIC. WFID is evaluated in two typical indoor scenarios. The results confirm that WFID achieves high classification accuracy which is permanent over several days under two typical indoor scenarios, with low computation cost. This reveals the potential for WFID to realize real-time indoor human identification.

This paper combines keystroke dynamics features with recently proposed keystroke acoustic features for user identification and authentication. Traditional keystroke dynamics uses temporal features. This paper explores the discriminative capability of the fusion of temporal features and acoustic features. This paper also explores the influence of filtering keystroke sound which turns out to be harmful for the performance of keystroke acoustic features. We collected a total of 824 samples from 7 subjects for experiments. 46 features including 38 acoustic features are extracted for the proposed user identification and authentication system. C-Support Vector Classification (C-SVC) and one-class Support Vector Machine (1-SVM) are applied for user identification and authentication respectively. Extensive experiments are carried out to verify the proposed system. Our work achieves 92.8% accuracy for user identification. The FRR (False Rejection Rate) and FAR (False Acceptance Rate) are only 12% and 11% for user authentication.

Localization is one of the most fundamental problems in ocean sensor networks. Current localization algorithms mainly focus on how to localize as many sensors as possible given a set of mobile or static anchor nodes and distance measurements. In this paper, we consider the optimization problem, minimum cost localization problem in a 3D ocean sensor network, which aims to localize all underwater sensors using the minimum number of anchor nodes or the minimum travel distance of the ship which deploys and measures the anchors. Given the hardness of 3D localization, we propose a set of greedy methods to pick the anchor set and its visiting sequence. Aiming to minimize the localization errors, we also adopt a confidence-based approach for all proposed methods to deal with noisy ranging measurements and possible flip ambiguity. Our simulation results demonstrate the efficiency of all proposed methods.

Time synchronization is the foundation for collaboration among sensor nodes. Acoustic communication and restricted mobility of sensor nodes are two unique characteristics of underwater sensor networks (UWSNs), negating the effect of terrestrial synchronization schemes. Existing UWSN time synchronization protocols focus on point to point synchronization, incompatible for large-scale networks. Moreover, some of them require special hardwares and deployment conditions. In this paper, we propose MulSync, a scalable synchronization protocol for multi-hop UWSNs. MulSync includes the synchronization communication scheme to exploit acoustic communication nature of broadcast. The skew and offset are estimated by performing linear regression three times over a set of time stamp pairs gathered through message exchange. Three linear regressions are exploited to make full use of the time reference information delivered from multi-source beacons. Simulation results demonstrate that MulSync achieves high accuracy at low message overhead and time cost.

Ocean covers nearly 71% of our planet's surface, yet 95% of the ocean remains unexplored by human being, and wireless sensor networks are envisioned to perform monitoring tasks over the large portion of our world. However, deploying wireless sensor networks on the sea poses many challenges and for maritime surveillance security applications we may need to deploy sensors both on the sea surface and underwater for three-dimensional detection. In this paper, we propose a hybrid ocean sensor networks called Double-head maritime Sensor Networks (DSNs), which combine the advantages of wireless sensor networks and underwater acoustic sensor networks. By leveraging the unique characteristics of DSNs, we design a localization scheme LDSN which is consisted of two algorithms SML and FLA. We first use SML to localize moored anchor nodes as seed nodes. After the underwater sensor networks have been localized, the floating double-head nodes can figure out its instant position via FLA algorithm. We evaluate the scheme by simulations and the results show that the scheme can achieve a high localization accuracy.

High accurate indoor localization and tracking of smart phones is critical to pervasive applications. Most radio-based solutions either exploit some error prone power-distance models or require some labor-intensive process of site survey to construct RSS fingerprint database. This study offers a new perspective to exploit RSS readings by their contrast relationship rather than absolute values, leading to three observations and functions called turn verifying, room distinguishing and entrance discovering. On this basis, we design WaP (WiFi-Assisted Particle filter), an indoor localization and tracking system exploiting particle filters to combine dead reckoning, RSS-based analyzing and knowledge of floor plan together. All the prerequisites of WaP are the floor plan and the coarse locations on which room the APs reside. WaP prototype is realized on off-the-shelf smartphones with limited particle number typically 400, and validated in a college building covering 1362m2. Experiment results show that WaP can achieve average localization error of 0.71m for 100 trajectories by 8 pedestrians.

Limited bandwidth capacity and battery power are the unique characters of Underwater Sensor Networks (UWSNs). First this study introduces a competition scheme based on delay time. This scheme provides a selection method of relay nodes considering the limited bandwidth capacity. Then a heterogeneous nodes distribution strategy is proposed to balance the energy consumption of the whole UWSN. The ratio between the node initial energy of the adjacent annuluses is analyzed in a circular UWSN. Simulation results validate this design.

Lifetime prolonging is one significant research issue in underwater acoustic sensor networks (UASNs). First this paper analyzes the relationship between the receiving energy consumption and the transmission energy consumption in the acoustic communication of UASNs. The routing tree is built up on the factor of optimal transmission range. Then a hybrid data transmission mechanism based on energy level is proposed to balance energy consumption. The mechanism combines one-hop and multi-hop data transmission to underwater sink considering the current energy level of adjacent nodes. An optimal classification number of energy level has been evaluated through theoretical analysis. Our design will help prolong the lifetime of whole UASN. The simulation results of UASN's lifetime and the energy consumption of sensor nodes have proved the efficiency of the Energy-Level-based hybrid Transmission (ELT) mechanism.

Surveillance is a critical problem for harbor protection, border control or the security of commercial facilities. The effective protection of vast near-coast sea surfaces and busy harbor areas from intrusions of unauthorized marine vessels, such as pirates smugglers or, illegal fishermen is particularly challenging. In this paper, we present an innovative solution for ship intrusion detection. Equipped with three-axis accelerometer sensors, we deploy an experimental Wireless Sensor Network (WSN) on the sea's surface to detect ships. Using signal processing techniques and cooperative signal processing, we can detect any passing ships by distinguishing the ship-generated waves from the ocean waves. We design a three-tier intrusion detection system with which we propose to exploit spatial and temporal correlations of an intrusion to increase detection reliability. We conduct evaluations with real data collected in our initial experiments, and provide quantitative analysis of the detection system, such as the successful detection ratio, detection latency, and an estimation of an intruding vessel's velocity.

Surveillance is a vital problem for harbor protection, border control or the security of other commercial facilities. It is particularly challenging to protect the vast near-coast sea surface and busy harbor areas from intrusions of unauthorized marine vessels, such as trespassing boats and ships. In this paper, we present an innovative solution for ship intrusion detection. Equipped with three-axis accelerometer sensors, we deploy an experimental wireless sensor network on the sea surface to detect ships. Using signal processing techniques and cooperative signal processing, we can detect the passing ships by distinguishing the ship-generated waves and the ocean waves. We design an intrusion detection system in which we propose to exploit spatial and temporal correlations of the intrusion to increase detection reliability. We conduct evaluations with real data collected by our initial experiments, and provide quantitative analysis on the detection system, such as the successful detection ratio and the estimation of the intruding ship velocity.

Data aggregation is a crucial technique for energy constrained sensor networks. Previous researches on data aggregation are featured as query-oriented, which only provide partial information of the event happened in the deployment area at the base station. In this paper, we propose an event-oriented data aggregation approach, called EDA. EDA presents the distributed algorithm by exploiting Cloud Membership model of fuzzy logic to aggregate the information of events in the sensor networks. It also presents a distributed algorithm to collect and aggregate the event information. The base station will restore the whole event information when receiving the aggregated packets of event features. EDA can balance the tradeoff between delay, traffic savings, and precision of the restored events. The performance has been evaluated through both theoretical analysis and simulations. We also confirm the performance with the traces of our offshore sensor network testbed (OceanSense).

This paper presents a distributed and robust time slot scheduling algorithm, which is suitable for underwater acoustic sensor network (UASN). The information of nodes' 2-hop neighbors is needed to be collected and then be used to calculate nodes' initial time slot by a distributed algorithm. A maximal independent set is formed by the nodes which were assigned with the same initial slot. Some theorems were proved to reveal that in an interference graph the size of this maximal independent set is at least of the size of the maximum independent set of the nodes. The simulation compares UD-TDMA with other three MAC protocols. The results show that the proposed protocol is effective in the UASN with random deployment, especially in high-density underwater acoustic sensor network.

Duty cycling is an important method for energy constrained sensor networks to prolong its lifetime. Current researches on duty cycling are often based on such assumption that all the sensory coverage should be maintained while some nodes are sleeping. For the applications of target monitoring, however, it is not necessary anymore to keep the whole sensory coverage of the sensor networks. It only needs to make sure that such kinds of nodes are active which can perceive the activities of the monitored targets. This observation brings new challenges to the design of duty cycling for the sensor networks. This paper proposes the novel duty cycling design for the sensor networks of target monitoring, which includes two algorithms of the sleep scheduling algorithm and the gradient query algorithm based on sleep periods. Under the proposed design, most of the sensor nodes can be in sleep, while still keep the functions of target monitoring and target query in the sensor networks. The performance of our design has been evaluated through both theoretical analysis and simulations, which prove the functionality of the proposed design on the reduction of energy consumption.

Underwater sensor networks (UWSN) are widely used in many applications, such as oceanic resource exploration, pollution monitoring, tsunami warnings and mine reconnaissance. In UWSNs, determining the location information of each sensor node is a critical issue, because many services are based on the localization results. In this paper, we introduce a novel underwater localization approach based on directional signals, which are transmitted by an autonomous underwater vehicle (AUV). Our method utilizes directional beacons (UDB) to replace traditional omni-directional localization which provides more accurate and efficient ways to locate the sensors themselves by simple calculations. The advantage of this novel scheme is that the communications between AUV and sensors are not necessary because the AUV broadcasts signals and sensors only need to passively listen to the signals. Since the energy consumption for transmissions in underwater environments is a nontrivial factor, our localization scheme not only supports accurate positioning, but also reduces energy consumption of sensors. We evaluate our scheme by simulations. The results show that our new approach is very precise in a strap area. At the same time, we minimize the number of beacons issued from the AUV.

Wireless sensor networks have been applied to monitor pipeline structural health. In these networks, expensive multi-sinks with energy harvesting modules are deployed along the linear pipeline, and battery powered sensor nodes are deployed between the sinks. One of the main problems in such networks is the unbalance of energy consumption of sensor nodes, which makes the whole monitoring system lose its functionality with only a small percentage of sensor nodes depleted of their energy. In this paper, we propose a distributed sensing data propagation algorithm based on graded residual energy (GRE) of the sensor nodes, in order to achieve balanced energy consumption among sensor nodes. The optimum number of energy grades of GRE has been calculated through theoretical analysis in terms of maximizing network lifetime. The simulation results have shown that GRE can achieve balanced energy consumption between the sensor nodes and at the same time prolong the lifetime of the whole monitoring system.

Existing localization approaches are divided into two groups: range-based and range-free. The range-free schemes often suffer from poor accuracy and low scalability, while the range-based localization approaches heavily depend on extra hardware capabilities or rely on the absolute RSSI (received signal strength indicator) values, far from practical. In this work, we propose a mobile-assisted localization scheme called perpendicular intersection (PI), setting a dedicate tradeoff between range-free and range-based approaches. Instead of directly mapping RSSI values into physical distances, by contrasting RSSI values from the mobile beacon to a sensor node, PI utilizes the geometric relationship of perpendicular intersection to compute node positions. We have implemented the prototype of PI with 100 TelosBmotes. Through comprehensive experiments, we show that PI achieves high accuracy and low overhead, significantly outperforming the existing range-based and the mobile-assisted localization schemes.

Network diagnosis, an essential research topic for traditional networking systems, has not received much attention for wireless sensor networks. Existing sensor debugging tools like sympathy or EmStar rely heavily on an add-in protocol that generates and reports a large amount of status information from individual sen-sor nodes, introducing network overhead to a resource constrained and usually traffic sensitive sensor network. We report in this study our initial attempt at providing a light-weight network diag-nosis mechanism for sensor networks. We propose PAD, a prob-abilistic diagnosis approach for inferring the root causes of ab-normal phenomena. PAD employs a packet marking algorithm for efficiently constructing and dynamically maintaining the inference model. Our approach does not incur additional traffic overhead for collecting desired information. Instead, we introduce a prob-abilistic inference model which encodes internal dependencies among different network elements, for online diagnosis of an operational sensor network system. Such a model is capable of additively reasoning root causes based on passively observed symptoms. We implement the PAD design in our sea monitoring sensor network test-bed and validate its effectiveness. We further evaluate the efficiency and scalability of this design through ex-tensive trace-driven simulations.

Abstract Lifetime prolonging is one of the most significant issues in the research on underwater acoustic sensor networks (UASNs). Unbalanced energy consumption influences greatly the network lifetime. First this study discusses a probability‐based energy balance (PEB) scheme. The sensor nodes report the data to the sink by single‐hop direct transmission (DT) or by multi‐hop transmission (MT) under the probabilities. A centralized probabilities finding algorithm (PFA) can find a set of transmission probabilities to better balance the energy consumption. Then, a sub‐optimal distance (SOD)‐based data transmission scheme is proposed which is a distributed scheme and operates on each sensor node. It optimizes the slice width and selects the relays near the optimum transmission range. Simulations show that the two schemes can save more energy and prolong the network lifetime efficiently. Copyright © 2008 John Wiley & Sons, Ltd.