In recent years, the mobile crowdsensing (MCS) paradigm has enabled a diverse array of emerging sensing applications by harnessing the collective efforts of ubiquitous mobile users, who collaborate to carry out specific sensing tasks using smart devices. However, the majority of existing works concentrate on a single MCS platform, which struggles to accommodate diverse service requirements. Moreover, these existing researches either consider opportunistic users (OUs) or participatory users (PUs) for task execution, which leads to low task coverage or high recruitment costs, while reducing the sensing quality of tasks. Therefore, in this paper, we introduce a multi-platform scenario where OUs and PUs are combined to complement each other. Then, we formulate a multi-platform hybrid user recruitment (MPHUR) problem within the limited platform budget and user time budget and decompose it into two NP-hard subproblems. To maximize the total sensing quality of tasks, we propose a Two-phase cross-platform Hybrid User recruitment Strategy called THUS. In the first phase, we present a greedy-based opportunistic user recruitment algorithm to match the user-task pair iteratively with maximum sensing quality according to the shortage degree of PUs. In the second phase, the MCS platforms assign PUs to complete the tasks that OUs fail to cover based on their residual budget. We propose a multi-task minimum-cost flow algorithm to recruit PUs for the remaining tasks. The extensive experiments are conducted on two real-world datasets to demonstrate the effectiveness of our proposed THUS.

With the rapid advancement of technology, mobile crowdsensing (MCS) has become a key enabler of improved daily life, drawing on its unique advantages. However, MCS systems face significant challenges concerning location privacy leakage, particularly during task allocation. To address the risk of worker location privacy leakage, we propose a location privacy-preserving system for crowdsensing based on secure sorting (LPPCS). LPPCS integrates elliptic curve cryptography (ECC) and secure multi-party computation (SMPC) technologies. By sorting the actual distances between workers and task locations, it achieves precise task assignment while ensuring the confidentiality and integrity of workers’ location data. The system consists of two main parts. The first part introduces a reverse sealed-bid auction mechanism, which reduces the computational costs by scaling down the number of workers participating in subsequent encryption computations. The second part designs a secure sorting protocolSSP. This protocol ensures that each worker only learns the relative sorting of their true distance to the task location and uploads this information to the server for final task assignment. Crucially, workers cannot access information about one another. This not only effectively protects their location privacy but also improves the accuracy of task assignments—all without the need for a trusted server. Finally, we conducted comprehensive evaluations using both simulated and real-world datasets. The results show that LPPCS performs exceptionally well in reducing computational costs, protecting workers’ location privacy, and improving task allocation precision.

Split Federated Learning (SFL) is renowned for its privacy-preserving nature and low computational overhead among decentralized machine learning paradigms. In this framework, clients employ lightweight models to process private data locally and transmit intermediate outputs to a powerful server for further computation. However, SFL is a double-edged sword: while it enables edge computing and enhances privacy, it also introduces intellectual property ambiguity as both clients and the server jointly contribute to training. Existing watermarking techniques fail to protect both sides since no single participant possesses the complete model. To address this, we propose RISE, a Robust model Intellectual property protection scheme using client-Server watermark Embedding for SFL. Specifically, RISE adopts an asymmetric client-server watermarking design: the server embeds feature-based watermarks through a loss regularization term, while clients embed backdoor-based watermarks by injecting predefined trigger samples into private datasets. This co-embedding strategy enables both clients and the server to verify model ownership. Experimental results on standard datasets and multiple network architectures show that RISE achieves over $95\%$ watermark detection rate ($p-value \lt 0.03$) across most settings. It exhibits no mutual interference between client- and server-side watermarks and remains robust against common removal attacks.

The rapid growth of IoT devices has led to an increase in computation-intensive and latency-sensitive tasks, making traditional cloud computing insufficient. Cloud-edge-terminal collaboration can enhance offload efficiency by optimizing processing latency, energy consumption, and price cost. However, in real-world networks, computational results often need to be transmitted to multiple users, increasing the offloading complexity. This paper proposes a three-tier collaborative offloading architecture for interacting users, considering constraints such as service caching and various types of resources. The optimization problem of computation latency and price cost is modeled as a Markov Decision Process. To address the problem, we propose a deep reinforcement learning algorithm based on the soft actor-critic framework. Given the discrete-continuous hybrid action space, the algorithm incorporates a dual-head mechanism. After that, transfer learning is incorporated into the training strategy to improve adaptability in dynamic environments. The simulation results demonstrate that the proposed approach outperforms existing performance, convergence, and adaptability methods.

Edge caching alleviates backhaul pressure and enhances video service quality by deploying video content near user devices. However, the limited storage capacity of edge servers struggles to cope with the exponential growth of video data, challenging the delivery of high-quality video services. While both Device-to-Device (D2D) caching and multi-bitrate video technology are promising solutions to relieve the pressure on edge servers, existing research suffers from a key limitation: studies on multi-bitrate caching are predominantly focused on the edge layer, while D2D caching is often limited to single-bitrate scenarios. This isolation neglects the significant benefits of integrating D2D caching with multi-bitrate technology and fails to develop a cross-layer caching strategy for multi-bitrate videos. To address this limitation, we propose a D2D-enhanced Multi-bitrate video cAching straTEgy (MATE) for cloud-edge-device collaborative networks. We formulate a joint service latency and caching replacement cost optimization problem, which can be modeled as a mixed-integer programming problem. To overcome the coupling between caching strategies at the edge layer and device layer, we employ an alternating iterative optimization approach to decouple the original problem into two subproblems. We design an edge-device double-layer joint caching strategy, i.e., a device-layer caching strategy based on greedy algorithm and Lagrange multipliers, and an edge-layer caching strategy based on multi-agent twin delayed deep deterministic policy gradient algorithm. Extensive simulations are conducted to demonstrate the effectiveness of the proposed MATE.

Few-shot segmentation (FSS) has garnered significant attention. Many recent approaches attempt to introduce the segment anything model (SAM) to handle this task. With the strong generalization ability and rich object-specific extraction ability of the SAM model, such a solution shows great potential in FSS. However, the decoding process of SAM highly relies on accurate and explicit prompts, making previous approaches mainly focus on extracting prompts from the support set, which is insufficient to activate the generalization ability of SAM, and this design is easy to result in a biased decoding process when adapting to the unknown classes. In this work, we propose an unbiased semantic decoding (USD) strategy integrated with SAM, which extracts target information from both the support and query set simultaneously to perform consistent predictions guided by the semantics of the contrastive language-image pretraining (CLIP) model. Specifically, to enhance the unbiased semantic discrimination of SAM, we design two feature enhancement strategies that leverage the semantic alignment capability of CLIP to enrich the original SAM features, mainly including a global supplement at the image level to provide a generalize category indicate with support image and a local guidance at the pixel level to provide a useful target location with query image. Besides, to generate target-focused prompt embeddings, a learnable visual-text target prompt generator (VTPG) is proposed by interacting target text embeddings and clip visual features. Without requiring retraining of the vision foundation models, the features with semantic discrimination draw attention to the target region through the guidance of prompt with rich target information. Experiments on both the PASCAL- $5^{i}$ and COCO- $20^{i}$ show that our proposed method outperforms the existing approaches by a clear margin and achieves new state-of-the-art performances.

Mobile users can offload their tasks to adjacent edge servers to enhance service quality. These servers require suitable reimbursements to cover the operational and energy consumption costs incurred while assisting with offloaded tasks. Although previous studies have examined market mechanisms for multiple users offloading tasks to multiple servers, most of them have not investigated the market mechanism for splittable task offloading, where tasks can be divided into multiple subtasks and offloaded to multiple servers. In this work, we propose a novel edge computing marketplace that focuses on splittable task offloading in multi-user multi-server scenarios with the aim of maximizing social welfare. Designing such a marketplace presents several challenges. First, the problem of task and computing resource division introduced in this context results in a complex solution space, and the division decisions are interdependent. Second, the users and edge servers have conflicting objectives and hidden utility/cost information. To overcome these challenges and achieve socially optimal market operation, we devise an Iterative DoublE Auction (IDEA) mechanism.IDEAemploys a broker to facilitate the interactions between users and edge servers and induces truthful reporting of hidden information through iterative updates to the allocation and pricing rules. Rigorous theoretical analysis and extensive simulations demonstrate the effectiveness of the proposedIDEAmechanism in achieving optimal social performance.

The automated construction of educational knowledge graphs from programming tutorial websites faces significant challenges due to the heterogeneous nature of web content structures and the complex relationships between code snippets and their contextual information. While existing approaches rely heavily on either manual rule crafting or direct large language model (LLM) processing, both methods struggle with cross-platform scalability and extraction accuracy. We present a novel distribution-guided extraction approach that systematically analyzes HTML structural patterns through strategic sampling before deploying LLM-based extraction scripts. This approach significantly reduces manual engineering effort while maintaining high accuracy across diverse platforms. Our method achieves a substantial improvement in code snippet extraction accuracy (from 0.15–0.68 to 0.54–0.91) across major programming tutorial websites including w3schools, MDN, and runoob. The extracted knowledge is integrated into a comprehensive educational graph that powers a question-answering system, achieving faithfulness scores of 0.93 and answer correctness of 0.90. These results demonstrate the effectiveness of combining statistical pattern analysis with LLM-driven extraction for building robust, cross-platform educational knowledge systems.

Few-shot classification models trained with clean samples poorly classify samples from the real world with various scales of noise. To enhance the model for recognizing noisy samples, researchers usually utilize data augmentation or use noisy samples generated by adversarial training for model training. However, existing methods still have problems: (i) The effects of data augmentation on the robustness of the model are limited. (ii) The noise generated by adversarial training usually causes overfitting and reduces the generalization ability of the model, which is very significant for few-shot classification. (iii) Most existing methods cannot adaptively generate appropriate noise. Given the above three points, this paper proposes a noise-robust few-shot classification algorithm, VADA—Variational Adversarial Data Augmentation. Unlike existing methods, VADA utilizes a variational noise generator to generate an adaptive noise distribution according to different samples based on adversarial learning, and optimizes the generator by minimizing the expectation of the empirical risk. Applying VADA during training can make few-shot classification more robust against noisy data, while retaining generalization ability. In this paper, we utilize FEAT and ProtoNet as baseline models, and accuracy is verified on several common few-shot classification datasets, including MiniImageNet, TieredImageNet, and CUB. After training with VADA, the classification accuracy of the models increases for samples with various scales of noise.

Abstract Mobile Crowdsensing (MCS) is considered to be a key emerging example of a smart city, which combines the wisdom of dynamic people with mobile devices to provide distributed, ubiquitous services and applications. In MCS, each worker tends to complete as many tasks as possible within the limited idle time to obtain higher income, while completing a task may require the worker to move to the specific location of the task and perform continuous sensing. Thus the time and location information of each worker is necessary for an efficient task allocation mechanism. However, submitting the time and location information of the workers to the system raises several privacy concerns, making it significant to protect both the temporal and spatial privacy of workers in MCS. In this article, we propose the Task Allocation with Temporal‐Spatial Privacy Protection (TASP) problem, aiming to maximize the total worker income to further improve the workers' motivation in executing tasks and the platform's utility, which is proved to be NP‐hard. We adopt differential privacy technology to introduce Laplace noise into the location and time information of workers, after which we propose the Improved Genetic Algorithm (SPGA) and the Clone‐Enhanced Genetic Algorithm (SPCGA), to solve the TASP problem. Experimental results on two real‐world datasets verify the effectiveness of the proposed SPGA and SPCGA with the required personalized privacy protection.

This paper investigates the joint active transceiver and passive beamforming design to maximize the weighted sum-rate (WSR) of an IRS-aided multi-streams multiuser multiple-input multiple-output broadcast channel (MIMO-BC) downlink transmission system. Due to the coupling of the transceiver parameters, the considered WSR optimization problem is highly non-convex and thus challenging to solve. Different from the normally used methods, such as the weighted minimum mean-square error (WMMSE), we rely on the matrix fractional programming (MFP) theory to derive an effective algorithm to the WSR problem. Specifically, we reformulate the original problem into a tractable one by exploiting the special structure of the objective function, i.e., a MFP which involves a matrix ratio inside a logarithm in the objective function. An alternating optimization (AO) framework is then devised to decompose the reformulated problem into four subproblems, which optimize the introduced auxiliary variable, the transmit beamforming matrix, the receive matrix, and the reflecting beamforming matrix by fixing other variables respectively. Through the matrix quadratic transform, we reformulate the MFP problem as a convex one, and thus obtain the optimal transmit beamforming matrix. By leveraging the optimality conditions for unconstrained optimization problems, the optimal receive beamforming matrix and the introduced auxiliary variable are derived in closed form. For solving the passive beamforming subproblem, we propose an iterative algorithm based on successive convex approximation (SCA). Since the computational complexity of SCA is relatively high, we propose a computationally efficient method based on manifold optimization (MO) to optimize the passive beamforming matrix. Finally, we also consider the robust beamforming design when the system suffers from imperfect CSI. Simulation results demonstrate the effectiveness of the proposed methods.

Backdoor attacks pose significant security risks to deep neural networks (DNNs). These attacks involve models that make intentionally incorrect (and potentially targeted) predictions on poisoned inputs containing carefully crafted triggers, while operating normally with clean inputs. Prior studies have investigated the invisibility of backdoor triggers to improve attack stealthiness. However, they primarily concentrate on achieving invisibility solely in the spatial domain, ignoring the generation of invisible triggers in the frequency and feature domains. This constraint makes the poisoned images vulnerable to detection by recent defense mechanisms. To tackle this problem, we introduce a Triple stealthy BAckdoor attack approach, termed TriBA, which simultaneously ensures the invisibility of triggers in all the spatial, frequency, and feature domains, to achieve desirable attack performance, while ensuring strong stealthiness. Specifically, we initially utilize Wavelet Transform to embed the high-frequency information from the trigger image into the clean image to ensure effective attack performance. Then, to achieve strong stealthiness across both spatial and frequency domains, we integrate Fourier Transform and Cosine Transform to blend the poisoned image and clean image in the frequency domain. Furthermore, TriBA adopts an attack strategy to make the backdoor features similar to clean features in the feature space, which guarantees trigger invisibility in the feature domain while maintaining attack effectiveness. We theoretically prove the effectiveness of this strategy. Finally, TriBA has been comprehensively evaluated on four datasets against popular image classifiers, demonstrating a marked improvement over existing state-of-the-art backdoor attacks in terms of both attack success rate and stealthiness.

Virtual reality (VR) technology, as a latency-sensitive application, can achieve real-time response to enhance the user’s quality of experience (QoE) on edge devices. However, edge servers, unlike internally managed cloud servers, are prone to hardware failures, software abnormalities, and network attacks. Most prior studies have focused on reducing service delay and improving user coverage through service entity (SE) placement, often neglecting the critical impact of edge server malfunctions on user QoE. In this work, we design a stable service entity placement framework that connects users on faulty servers to collaborative edge servers, ensuring seamless task completion. This framework presents two primary challenges: determining the grouping of collaborative edge services and the placement of SEs. To address these challenges, we introduce a heuristic stable service entity placement (H-STEP) scheme. This scheme first determines the grouping of collaborative edge servers using an iterative search algorithm and then places SEs on suitable edge servers via a fast non-dominated sorting genetic placement algorithm. This approach balances stability benefits with total cost, enhancing the system’s economic benefits. We theoretically analyze the performance of H-STEP and derive the performance gap between H-STEP and the optimal scheme. Extensive real-data-driven simulations demonstrate that H-STEP’s performance closely approximates that of the optimal scheme and surpasses existing schemes.

To efficiently execute tasks, computation resource requesters (CRRs) with limited resources can offload their tasks to nearby computation resource providers (CRPs) with spare computing capacity. These CRPs require appropriate incentives to compensate for their incurred costs when helping process the offloaded tasks. Although several mechanisms have been designed to incentivize CRPs, none of them have investigated the incentive mechanism considering price-setting and price-taking CRPs simultaneously. In this work, we propose an incentive framework for task offloading in the edge computing marketplace that includes both price-setting and price-taking CRPs. We model the CRR's interactions with both types of CRPs as a three-stage Stackelberg game to maximize the profit for both the CRR and CRPs. We prove the existence of a unique subgame perfect equilibrium (SPE) of the formulated game and further develop iterative algorithms for the CRR and price-setting CRPs to achieve the equilibrium. Through the designed algorithms, each CRP does not require complete information about the CRR and other CRPs. Extensive simulations demonstrate that offloading tasks to both price-setting and price-taking CRPs achieves higher profits for the CRR and price-setting CRPs compared to offloading tasks solely to price-setting CRPs. Additionally, the obtained SPE can achieve near-optimal social welfare.

Adversarial attacks have challenged the security of deep neural networks (DNNs) recently. The most prominent adversarial attack methods include backdoor attacks, adversarial examples, etc. These attack methods inject triggers or perturbations into images, leading to extremely dangerous security vulnerability in deep learning domain. The various forms of adversarial attacks can contaminate DNNs with their distinct characteristics. The complexity of adversarial attack poses a great challenge to designing a general defense strategy. In this paper, we propose a novel defense method against most of adversarial attacks through Image Decomposition and Reconstruction (IDR). Our method can be applied to poisoned images without the need for internal information about the model or any prior knowledge of the clean/poisoned images. We apply a linear transformation on the poisoned image to destroy the perturbations or triggers and deploy a pre-trained diffusion model to reconstruct the original information. In particular, we propose a novel reverse process that utilizes the consistency of range-null space decomposition to guide the generation of purified images. The decomposition of the range-null space can guarantee the retrieval of image information, which enhances the robustness of our method and contributes to the reliable purification of poisoned images. We assess the effectiveness of our proposed IDR against various prevalent backdoor attacks, adversarial examples and Image-Scaling attack methods. The experimental results highlight the outstanding defensive capabilities of our proposed IDR, demonstrating an exceptionally high defense success rate.

Graph-learning methods, especially graph neural networks (GNNs), have shown remarkable effectiveness in handling non-Euclidean data and have achieved great success in various scenarios. Existing GNNs are primarily based on message-passing schemes, that is, aggregating information from neighboring nodes. However, the diversity and complexity of complex systems from real-world circumstances are not sufficiently taken into account. In these cases, the individual should be treated as an agent, with the ability to perceive their surroundings and interact with other individuals, rather than just be viewed as nodes in existing graph approaches. Additionally, the pairwise interactions used in existing methods also lack the expressiveness for the higher-order complex relations among multiple agents, thus limiting the performance in various tasks. In this work, we propose a Multiagent Hypergraph Force-learning method dubbed MHGForce. First, we formalize the multiagent system (MAS) and illustrate its connection to graph learning. Then, we propose a generalized multiagent hypergraph-learning framework. In this framework, we integrate message-passing and force-based interactions to devise a pluggable method. The method empowers graph approaches to excel in downstream tasks while effectively maintaining structural information in the representations. Experimental results on the Cora, Citeseer, Cora-CA, Zoo, and NTU2012 datasets in node classification demonstrate the effectiveness and generality of our proposed method. We also discuss the characteristics of the MHGForce and explore its role through parametric analysis and visualization. Finally, we give a discussion, conclude our work, and propose future directions.

Backdoor attacks pose serious security threats to deep neural networks (DNNs). Backdoored models make arbitrarily (targeted) incorrect predictions on inputs containing well-designed triggers, while behaving normally on clean inputs. Prior researches have explored the invisibility of backdoor triggers to enhance attack stealthiness. However, most of them only focus on the invisibility in the spatial domain, neglecting the generation of invisible triggers in the frequency domain. This limitation renders the generated poisoned images easily detectable by recent defense methods. To address this issue, we propose a DUal stealthy BAckdoor attack method named DUBA, which simultaneously considers the invisibility of triggers in both the spatial and frequency domains, to achieve desirable attack performance, while ensuring strong stealthiness. Specifically, we first use Wavelet Transform to embed the high-frequency information of the trigger image into the clean image to ensure attack effectiveness. Then, to attain strong stealthiness, we incorporate Fourier Transform and Cosine Transform to mix the poisoned image and clean image in the frequency domain. Moreover, DUBA adopts a novel attack strategy, training the model with weak triggers and attacking with strong triggers to further enhance attack performance and stealthiness. DUBA is evaluated extensively on four datasets against popular image classifiers, showing significant superiority over state-of-the-art backdoor attacks in attack success rate and stealthiness.

Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes. Most previous approaches rely on extracting high-level fea-ture maps from the frozen visual encoder to compute the pixel- wise similarity as a key prior guidance for the decoder. However, such a prior representation suffers from coarse granularity and poor generalization to new classes since these high-level feature maps have obvious category bias. In this work, we propose to replace the visual prior representation with the visual-text alignment capacity to capture more reliable guidance and enhance the model generalization. Specifically, we design two kinds of trainingfree prior information generation strategy that attempts to utilize the semantic alignment capability of the Contrastive Language-Image Pre-training model (CLIP) to locate the target class. Besides, to acquire more accurate prior guidance, we build a high-order relationship of attention maps and utilize it to refine the initial prior information. Experiments on both the PASCAL-5i and COCO-20i datasets show that our method obtains a clearly substantial improvement and reaches the new state-of-the-art performance. The code is available on the project website11https://github.com/vangjin/PI-CLIP.

Backdoor defense is crucial to ensure the safety and robustness of machine learning models when under attack. However, most existing methods specialize in either the detection or removal of backdoors, but seldom both. While few works have addressed both, these methods rely on strong assumptions or entail significant overhead costs, such as the need of task-specific samples for detection and model retraining for removal. Hence, the key challenge is how to reduce overhead and relax unrealistic assumptions. In this work, we propose two Energy-Based BAckdoor defense methods, called EBBA and EBBA+, that can achieve both backdoored model detection and backdoor removal with low overhead. Our contributions are twofold: First, we offer theoretical analysis for our observation that a predefined target label is more likely to occur among the top results for various samples. Inspired by this, we develop an enhanced energy-based technique, called EBBA, to detect backdoored models without task-specific samples (i.e., samples from any tasks). Secondly, we theoretically analyze that after data corruption, the original clean label of a poisoned sample is more likely to be predicted as a top output by the model, a sharp contrast to clean samples. Accordingly, we extend EBBA to develop EBBA+, a new transferred energy approach to efficiently detect poisoned images and remove backdoors without model retraining. Extensive experiments on multiple benchmark datasets demonstrate the superior performance of our methods over baselines in both backdoor detection and removal. Notably, the proposed methods can effectively detect backdoored model and poisoned images as well as remove backdoors at the same time.

Mobile edge computing (MEC) is considered a promising technology to provide low-latency services by keeping computing and other resources physically close to where they are needed. The functions implemented through network function virtualization (NFV) technology in MEC are called virtual network function (VNF) instances, and the deployment and scheduling of VNF instances have always been a hot topic. The deployment refers to deploying instances on the edge servers, while scheduling refers to allocating resources to complete user requests. However, most of the existing works fail to jointly consider the deployment and scheduling of VNF instances, which cannot complete user requests reasonably and efficiently. Besides, the deployment cost can be significantly reduced by making users share the same type of instances instead of assigning one to each user. Therefore, the objective of our article is to investigate the efficient deployment and scheduling of VNF instances that are shared among different users under constraints of user delay and network resources. We first build a VNF instance deployment and scheduling model in MEC networks to study how to minimize cost and maximize network throughput under the constraints of user delay and the computing and storage resources of cloudlets. Then, taking advantage of its sharing feature, we propose a set covering-based efficient deployment and scheduling scheme called SCEDS and evaluate its performance by extensive simulations. The simulation results demonstrate the superiority of our proposed method compared to the existing ones.

Due to the swift advancement of edge computing and mobile crowdsensing (MCS), edge-assisted MCS (EAMCS) has emerged as a promising paradigm, leveraging sensor-embedded mobile devices for the collection and sharing of environmental data. As the sensing scale increases in the modern urban, the application scenario becomes more and more complex, and the budget of users and platform is limited. Therefore, it is indispensable to study the effective task allocation mechanism with considering the multiple budget constraints in the EAMCS system. However, a majority of the existing studies unilaterally focus on either the users’ time budget or the platform’s budget, disregarding the crucial aspect of the users’ energy budget. In this article, we design a joint user movement, sensing, offloading, and computation framework adopting the computation offloading strategy called binary processing strategy. In addition, the multitask data collection with a limited budget (MDCB) problem considering time, energy, and platform budget in EAMCS is formulated, which is proved to be nondeterministic polynomial-hard. In order to maximize the amount of data collected by the users in the MDCB problem, we first verify the submodularity of the objective function, then propose the global maximum data first search algorithm and task sequence-based genetic algorithm to solve the problem. The extensive experiments are conducted on both synthetic and real-world data sets to demonstrate the effectiveness of our proposed schemes.

btaining high signal-to-noise ratio (SNR) databtaining high signal-to-noise ratio (SNR) dataO is significant for the subsequent processing and interpretation of seismic data. In recent years, the convolutional neural network (CNN) has been widely used in seismic data denoising. However, the existing CNN-based method usually has a single receptive field, making it difficult to effectively extract feature maps at different scales. Therefore, we propose a multiscale residual U-shaped CNN (MRUnet) by combining the multiscale structure, residual structure, and skip connection structure to cope with the random noise of the post-stack seismic data. The network can use convolutional kernels of different sizes for feature extraction and transfer these features through more extensive skip connections. We construct a training set using existing seismic data and transfer the trained model to field data for denoising experiments. Experiments on synthetic and field data demonstrate that by training the network, a model that removes the random noise from the post-stack seismic data can be obtained and outperforms the existing ones.

Abstract Deep learning has been widely used in many applications such as face recognition, autonomous driving, etc. However, deep learning models are vulnerable to various adversarial attacks, among which backdoor attack is emerging recently. Most of the existing backdoor attacks use the same trigger or the same trigger generation approach to generate the poisoned samples in the training and testing sets, which is also commonly adopted by many backdoor defense strategies. In this paper, we develop an enhanced backdoor attack (EBA) that aims to reveal the potential flaws of existing backdoor defense methods. We use a low-intensity trigger to embed the backdoor, while a high-intensity trigger to activate it. Furthermore, we propose an enhanced coalescence backdoor attack (ECBA) where multiple low-intensity incipient triggers are designed to train the backdoor model, and then, all incipient triggers are gathered on one sample and enhanced to launch the attack. Experiment results on three popular datasets show that our proposed attacks can achieve high attack success rates while maintaining the model classification accuracy of benign samples. Meanwhile, by hiding the incipient poisoned samples and preventing them from activating the backdoor, the proposed attack exhibits significant stealth and the ability to evade mainstream defense methods during the model training phase.

In recent years, wireless rechargeable sensor networks (WRSNs), as a crucial technology in cyber–physical–social systems (CPSSs), have gradually become a hotspot of research, with the development of wireless energy transmission technology. In previous works, the objective is to maximize the survival rate of sensor nodes. However, in this article, we focus on maintaining more targets. First, it details the charging scheduling problem of maximizing coverage of targets (CoT) in on-demand charging architecture of WRSNs. Also, the problem is formalized as a multiple-objective optimization problem, which aims at maximizing the CoT and the energy efficiency simultaneously. After that, the charging scheduling for maximizing coverage of targets (CSCT) scheme is proposed to achieve the above objectives. Then, the problem is reformulated as a Deadline-TSP problem that is NP-hard. To address this problem, we design an energy predictive model and propose the CSCT with an$n$-path ($n$-CSCT) scheme that has an$O(|\mathcal{N}|^n)$computational complexity. In addition, the resurrection of sensor nodes is considered in this article. Thus, the$n$-CSCT with node resurrection ($n$-CSCT-R) scheme is proposed for this case. Finally, we validate the effectiveness of the proposed schemes via extensive simulations.

Recently, backdoor attacks have become a serious security threat to Deep Neural Networks (DNNs). Backdoor attacks involve embedding a hidden backdoor into a DNN model, compelling it to correctly classify benign images while erroneously classifying images with backdoor triggers as the target label. However, both current backdoor attacks and defenses have their limitations. In backdoor attacks, they are either non-stealthy or vulnerable to well-designed backdoor defense strategies. As for backdoor defenses, they often rely heavily on additional assumptions (such as determined extra clean images) and are not universally applicable, which may become impractical in the face of the latest backdoor attacks. To address the above problems, in this paper, we investigate the backdoor attack and defense strategies from a multi-channel perspective. Specifically, in terms of attacks, we propose a recolorization based attack method (RC-Attack) to generate triggers in color ab channels, which is more stealthy and effective. In terms of defenses, we propose a reconstruction-based defense method (RC-Defense) to reconstruct the color AB channels and lightness channel respectively, thus making the triggers in the reconstructed images ineffective, which is a more practical solution. Extensive experiments are conducted to demonstrate the superior performance of the proposed RC-Attack in terms of effectiveness, stealthiness and defense-resistance, and also to validate the effectiveness of the proposed RC-Defense.

The increasing prevalence of deep neural networks (DNNs) in industrial artificial intelligence systems (IAISs) promotes the development of industrial automation. However, the growing employment of DNNs also exposes them to various attacks. Recent studies have shown that the data preprocessing process of DNNs is vulnerable to image-scaling attack. Such attacks can craft an attack image, which looks like a given source image but becomes a different target image after being scaled to the target size. The attack images generated by existing image-scaling attacks are easily perceivable to the human visual system, significantly degrading the attack's stealthiness. In this paper, we investigate image-scaling attack from the perspective of signal processing. We unearth that the root cause of the weak deceiving effects of existing image-scaling attack images lies in the introduction of additional high-frequency signals during their construction. Thus, we propose an enhanced image-scaling attack (EIS), which employs adversarial images crafted based on the source (“clean”) images as the target images. Those adversarial images preserve the “clean” pixel information of source images, thereby significantly mitigating the emergence of additional high-frequency signals in the attack images. Specifically, we consider three realistic threat models covering deep models' training and inference phases. Correspondingly, we design three strategies tailored to generate adversarial images with vicious patterns. These patterns are subsequently integrated into the attack images, which can mislead a model with target input size after the necessary scaling operation. Extensive experiments validate the superior performance of the proposed image-scaling attack compared to the original one.

Over the last decade, the Internet of Things (IoT) technology has advanced significantly in a variety of fields. As a pivotal application of IoT, intelligent transportation systems (ITS) have harvested great attention from the research community. Radio frequency identification (RFID) which is an essential technology in IoT plays a key role in ITS to identify tagged vehicles. Unknown tag identification which aims at identifying the existing unknown tags is crucial to monitor the newly entering vehicles in the RFID-assisted intelligent transportation systems. However, the COTS (commercial-off-the-shelf) RFID tags that harvest energy from the reader can not support the hash function in reality, which hinders the widespread deployment of hash-enabled unknown tag identification protocols. To conquer this tough issue, we propose two approaches to efficiently identify unknown COTS RFID tags. We first propose a Single-Point Selective unknown tag identification approach called SPS, where an analog hash pattern using the EPC (Electronic Product Code) segments is deployed to exclusively identify unknown tags. An unknown tag will be identified when it selects a singleton slot to reply. To improve the time efficiency of SPS, we further propose a Multi-Point Selective unknown tag identification approach called MPS. In MPS, two techniques of batch identification and batch division are developed to reduce the number of empty slots and avoid tag collisions, respectively. Then the parameters are theoretically analyzed to maximize the identification efficiency. The effectiveness of the proposed approaches is validated via both the simulations and COTS RFID device based experiments.

Network motifs, as a fundamental higher-order structure in large-scale networks, have received significant attention over recent years. Particularly in heterogeneous networks, motifs offer a higher capacity to uncover diverse information compared to homogeneous networks. However, the structural complexity and heterogeneity pose challenges in coding, counting, and profiling heterogeneous motifs. This work addresses these challenges by first introducing a novel heterogeneous motif coding method, adaptable to homogeneous motifs as well. Building upon this coding framework, we then propose GIFT, a heterogeneous network motif counting algorithm. GIFT effectively leverages combined structures of heterogeneous motifs through three key procedures: neighborhood searching, motif combination, and redundant motif filtering. We apply GIFT to count three-order and four-order motifs across eight distinct heterogeneous networks. Subsequently, we profile these detected motifs using four classical motif-based indicators. Experimental results demonstrate that by appropriately selecting motifs tailored to specific networks, heterogeneous motifs emerge as significant features in characterizing the underlying network structure.

Federated learning (FL) is an effective privacy-preserving mechanism that collaboratively trains the global model in a distributed manner by solely sharing model parameters rather than data from local clients, like mobile devices, to a central server. Nevertheless, recent studies have illustrated that FL still suffers from gradient leakage as adversaries try to recover training data by analyzing shared parameters from local clients. To address this issue, differential privacy (DP) is adopted to add noise to the parameters of local models before aggregation occurs on the server. It, however, results in the poor performance of gradient-based interpretability, since some important weights capturing the salient region in feature maps will be perturbed. To overcome this problem, we propose a simple yet effective adaptive gradient protection (AGP) mechanism that selectively adds noisy perturbations to certain channels of each client model that have a relatively small impact on interpretability. We also offer a theoretical analysis of the convergence of FL using our method. The evaluation results on both IID and Non-IID data demonstrate that the proposed AGP can achieve a good trade-off between privacy protection and interpretability in FL. Furthermore, we verify the robustness of the proposed method against two different gradient leakage attacks.

The significance of RFID-based information collection is becoming increasingly visible as more and more sensor-augmented RFID systems are deployed. Tag information collection aims at efficiently and accurately collecting valuable information from target objects attached with RFID tags. Polling-based information collection can effectively avoid response collisions between RFID tags, and it is widely adopted to accurately inventory tags. However, in the traditional polling mode, a polling vector can only be used to query a tag at a time, which is inefficient. In this paper, we design a double polling mode to improve the utilization of polling vectors, which can simultaneously interrogate a pair of tags. Afterwards, several techniques are developed to reduce the polling vector length. Firstly, the Basic Double Polling-based protocol (BDP) employs double indexes to collect information, which greatly reduces the number of polling vectors. Secondly, the Segmented Double Polling-based protocol (SDP) divides the double indexes into several segments to cut the polling vector length down. Thirdly, the Partial Double Polling-based protocol (PDP) replaces the double index with the size of the empty segment between two adjacent non-zero indexes to further reduce the average polling vector length. Finally, the Differential Double Polling-based protocol (DDP) utilizes the size of the empty segment between two double indexes to improve the utilization of polling vectors. After that, extensive theoretical analyses and simulations are conducted, which demonstrate the feasibility and effectiveness of the proposed protocols.

The widespread deployment of Internet of Things (IoT) devices brings more and more computation intensive or delay sensitive tasks, causing a series of challenges to efficient services. Collaborative edge computing is an effective way to solve them, where the tasks will be processed in the devices, edge servers, and cloud server in parallel. However, the above collaborative paradigm requires dense deployment of base stations (BSs) and consumes lots of energy. To address this problem, in this paper, we introduce energy harvesting technology and construct a collaborative edge computing system powered by hybrid energy. Considering the highly variable task execution delay caused by the resource contention and the unstable energy state, we further introduce the Holt Linear Exponential Smoothing Prediction to predict the delay and then propose an Online Server Control schedule called OSC based on Lyapunov optimization to obtain the optimized offloading decision without the knowledge of the future system state. The extensive simulations illustrate that the proposed OSC outperforms other benchmark ones.

In recent years, wireless rechargeable sensor networks (WRSNs) have gained significant attention in the research community due to the current advancements in wireless power transfer technology. In mobile charger scheduling, previous works primarily emphasized the survival rate of sensor nodes. However, the primary task of a WRSN is to monitor targets in a given area. Therefore, the coverage of targets (CoT) maximization should be the primary objective of mobile charger scheduling. In this paper, we shift the focus to the CoT maximization on-demand charging scheduling problem, and formulate it as a multi-objective optimization problem, aiming to simultaneously enhance the average coverage and energy efficiency. We prove that the problem is NP-hard by reformulating it as a Multiple Travelling Salesman Problem with Deadline. We first propose the multiple chargers scheduling scheme for maximizing coverage of targets called MaxCov, which is designed to optimize the charging scheduling process and improve network performance in terms of coverage. Then, we further propose the multiple chargers scheduling scheme based on requests grouping called MaxCov-RG, which can well balance the trade-off between the performance and computational complexity. Finally, we validate the effectiveness of the proposed schemes via extensive simulations.

Recent years have witnessed extensive applications of Deep Neural Networks (DNNs) in various vision tasks. However, DNNs are vulnerable to adversarial images crafted by introducing perturbations into inputs to induce incorrect predictions. Unlike$L_{p}$-norm restricted adversarial attacks, many unrestricted attacks have been proposed by modifying attributes of the image (e.g., edge, color), while the critical components of the image are preserved. However, most existing unrestricted attacks easily introduce unnatural distortions, colors, stains and schemes, in the generated adversarial images. This paper proposes a novel unrestricted attack (named AdvST) to create stylized, natural-looking, and high-transferability adversarial images. The basic idea of AdvST is to embed adversarial perturbations when transferring the style from the reference image onto the original image (i.e., rendering the original image's semantic contents into the reference image's style). To further improve the image quality of generated adversarial images, we refine two kinds of reference images (i.e., photographs and artworks) based on different attractive styles and design two attacks accordingly. For photorealistic attack, we incorporate semantic information obtained from segmentation maps to improve the photo realism of adversarial images. For artistic attack, we propose integrating edge information extracted by the Laplace operator to preserve the structural integrity of the original image. Extensive experimental results validate the superior performance of AdvST in terms of adversarial image quality and black-box transferability compared to benchmark methods.

Link scheduling plays a pivotal role in accommodating stringent reliability and latency requirements. In this paper, we focus on the availability and effectiveness of applying protocol interference model (PIM) under Rayleigh fading model to solve the problem. The motivation is that PIM caters to distributed link scheduling algorithm design, but usually lead to irrationality due to its localization behavior. While Rayleigh fading model can accurately describe the inherent characteristic of wireless signal propagation, but the features of global interference and channel fading make algorithm design more challenging. To be specific, we first remove the effect of channel fading on algorithmic design by establishing the relationship between Rayleigh fading model and non-fading model. We then propose a centralized once link elimination (OLE) algorithm by utilizing local nature of PIM, and achieve its distributed implementation based on the message delivery with time complexity of$O(\Delta _{\max }\ln \Delta _{\max })$, where$\Delta _{\max }$is the maximum number of nodes around a given node inside some range. Furthermore, based on random contention resolution, we design another distributed algorithm to schedule all the links within$O(\Delta ^{3}_{\max }\ln \Delta _{\max })$rounds. Simulations show that the PIM is of great confidence as same as Rayleigh fading model, and the proposed algorithms outperform three popular link scheduling algorithms.

Federated learning (FL) is a distributed machine learning paradigm that addresses the challenges of privacy leakage and data silos by collaboratively training the global model through parameter exchange, rather than data, between the central server and local clients. However, recent researches highlight the vulnerability of FL to gradient leakage attacks where adversaries exploit shared parameters from clients to reconstruct sensitive training data. Differential privacy (DP) effectively mitigates this threat by adding noise to shared parameters, yet introduces a trade-off between privacy and accuracy in FL. To better balance the privacy and accuracy, in this paper we propose a staged noise perturbation strategy, called alternating noise permutation (ANP), from a novel perspective. ANP adds Gaussian-distributed random noise to model parameters during the critical learning period of FL, following DP principles. While in non-critical learning period, ANP alternately permutes the noise during odd and even communication rounds, achieving near mutual cancellation and mitigating the negative impact. Experimental results across three datasets and two neural networks under both independent identical distribution (IID) and NonIID scenarios demonstrate that ANP significantly improves classification accuracy and exhibits robustness against gradient leakage attack, ensuring the effectiveness of FL for secure and accurate collaborative model training.

As one of the significant supporting technologies for mobile virtual reality (MVR), computer vision is latency-sensitive and always requires real-time response and accurate object analysis. However, the limited computational resources of mobile devices lead to high service delay and low analysis quality, resulting in poor quality of service (QoS). By placing the edge service entities (SEs) of the video tasks on the edge server close to the mobile users, a satisfactory QoS can be obtained for MVR systems. Most of the previous works are restricted to optimizing QoS through service placement, while ignoring the key impact of the network access point and video frame resolution selections on QoS. In this paper, we propose an edge service entity placement model, which aims to jointly optimize service delay and analysis quality for MVR systems. Specifically, we design an efficient dynamic edge service entity placement scheme (EDSP-Edge) based on the block coordinate descent theory, which dynamically determines the selection strategies of network access points, service entities and video frame resolutions for users to effectively improve the QoS. We theoretically analyze the performance of EDSP-Edge and get the gap between EDSP-Edge and the optimal performance. Finally, extensive real-data driven simulations are conducted to show that the EDSP-Edge performs close to the optimal scheme and achieves at least 22% performance improvement compared with previous works.

Modern Natural Language Processing (NLP) models expose under-sensitivity towards text rubbish examples. The text rubbish example is the heavily modified input text which is nonsensical to humans but does not change the model’s prediction. Prior work crafts rubbish examples by iteratively deleting words and determining the deletion order with beam search. However, the produced rubbish examples usually cause a reduction in model confidence and sometimes deliver human-readable text. To address these problems, we propose an Annealing Genetic based Preposition Substitution (AGPS) algorithm for text rubbish sample generation with two major merits. Firstly, the AGPS crafts rubbish text examples by substituting input words with meaningless prepositions instead of directly removing them, which brings less degradation to the model’s confidence. Secondly, we design an Annealing Genetic algorithm to optimize the word replacement priority, which allows the Genetic Algorithm (GA) to jump out the local optima with probabilities. This is significant in achieving better objectives, i.e., a high word modification rate and a high model confidence. Experimental results on five popular datasets manifest the superiority of AGPS compared with the baseline and expose the fact: the NLP models can not really understand the semantics of sentences, as they give the same prediction with even higher confidence for the nonsensical preposition sequences.

The social trust assessment can spur extensive applications such as social recommendations, shopping, financial investment strategies, etc, but remain a challenging problem having limited exploration. Such explorations mainly limit their studies to static network topology or simplified dynamic networks, toward the social trust relationship prediction. In contrast, in this paper, we explore the social trust by taking into account the time-varying online social networks whereas the social trust relationship may vary over time. The DTrust, a dynamic graph neural network-based solution, will be proposed for accurate social trust prediction. In particular, DTrust is composed of a static aggregation unit and a dynamic unit, respectively responsible for capturing both the spatial dependence features and temporal dependence features. In the former unit, we stack multiple NNConv layers derived from the edge-conditioned convolution network for capturing the spatial dependence features correlated to the network topology and the observed social relationships. In the latter unit, a gated recurrent unit (GRU) is employed for learning the evolution law of social interaction and social trust relationships. Based on the extracted spatial and temporal features, we then employ a fully connected neural network for learning, able to predict the social trust relationships for both current and future time slots. Extensive experimental results exhibit that our DTrust can outperform the benchmark counterparts on two real-world datasets.

Deep neural networks (DNNs) are gradually becoming the preference for the various vision applications of smart cities. However, their success heavily relies on the access to extensive training data and substantial computational resources, posing challenges in training large-scale models for diverse smart city applications. Consequently, the third-party services and resources are often utilized to train the models, exposing them to the potential backdoor attacks. Despite the escalating threat of such attacks, many existing strategies primarily focus on enhancing the stealthiness and evading defenses, often neglecting practical feasibility in the real-world scenarios. In this paper, we introduce a novel backdoor attack named bokeh based backdoor attack $(B^{3}A)$, which leverages the bokeh effect as the trigger. Once the backdoor is deployed in a vision application model, the model’s malicious behavior can be activated solely by using the captured bokeh images. Specifically, we employ saliency and depth estimation maps to synthesize the bokeh images, effectively serving as the poisoned samples. Moreover, we devise a reference model to impose constraints on the feature representations of the poisoned images, thereby further enhancing their stealthiness in the feature space. Extensive experiments demonstrate the attack effects of $B^{3}A$, even on the bokeh photos taken from Digital Still Cameras (DSC) and smartphones.

With the rich sensing ability and extensive usage of various sensors, mobile crowdsensing (MCS) has become a new paradigm to collect sensing data for various sensing applications. In the modern urban environment, the multisource sensing information and the difference of mobile users make the sensing scenario more and more complex. To improve the applicability of different sensing scenarios, it is necessary to design a heterogeneous user recruitment mechanism for multiple heterogeneous tasks. However, most of the prior works focus on the recruitment of single-type users for homogeneous tasks without considering the heterogeneity of tasks (e.g., spatiotemporal characteristics, sensor requirements, etc.) and users (e.g., personal preferences, carrying sensors, etc). In this article, we propose the problem of heterogeneous user recruitment of multiple heterogeneous tasks (HURoTs) in MCS, with the goal of minimizing the total platform payment and maximizing the task coverage ratio. The HURoT problem is proved to be NP-hard, which is divided into multiple subproblems in different sensing cycles. Moreover, by introducing the user’s utility function, we propose three greedy-based user recruitment algorithms to obtain near-optimal solutions. Extensive experiments are conducted to validate the effectiveness of the proposed schemes.

With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoTs), an increasing number of computation intensive or delay sensitive biomedical data processing and analysis tasks are produced in vehicles, bringing more and more challenges to the biometric monitoring of drivers. Edge computing is a new paradigm to solve these challenges by offloading tasks from the resource-limited vehicles to Edge Servers (ESs) in Road Side Units (RSUs). However, most of the traditional offloading schedules for vehicular networks concentrate on the edge, while some tasks may be too complex for ESs to process. To this end, we consider a collaborative vehicular network in which the cloud, edge and terminal can cooperate with each other to accomplish the tasks. The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge. We further construct the virtual resource pool which can integrate the resource of multiple ESs since some regions may be covered by multiple RSUs. In this paper, we propose a Multi-Scenario offloading schedule for biomedical data processing and analysis in Cloud-Edge-Terminal collaborative vehicular networks called MSCET. The parameters of the proposed MSCET are optimized to maximize the system utility. We also conduct extensive simulations to evaluate the proposed MSCET and the results illustrate that MSCET outperforms other existing schedules.

Recommendation systems have been widely developed and extensively used in various websites and platforms to promote products or services to interested users. However, in quite a few sale scenarios, the platform has the necessity to display users a series of items, which is called package recommendation. There is very little research in this area. This article develops a novel and realistic package recommendation system named package graph attention network (PGAT) based on graph neural network. PGAT integrates users, items, and packages to build a unified heterogeneous graph and treat them as a whole. PGAT incorporates an attention mechanism in the first-order neighborhood aggregation operation, which can differentiate the weight of different neighbor nodes to the center node. By performing graph attention and graph convolution operations on the tripartite graph, PGAT can learn node embeddings more expressively and address the problem of data sparse to a large extent. Extensive experiment results on two real-world datasets validate the outstanding performance of PGAT, which is superior to the state-of-the-art baselines by 0.77%–10.12%.

Convolutional neural network (CNN)-based recommender systems are playing an increasingly significant role in the vigorous development of Industrial Internet of Things, and have made great contributions to analyzing and mining a large amount of data to provide various services for terminal users. However, as the lack of explainability in deep learning, users often have low trust in the system due to their incomprehension of recommendation results. In addition, recommender systems have been facing a serious sparsity problem, and relying only on sparse rating data to learn user preferences and similarities may face malicious recommendation attacks. The abovementioned problems have been hindering the further improvement of recommendation performance. Therefore, in order to effectively alleviate the sparsity problem and meanwhile enhance the trustworthiness, an auxiliary review-based personalized attentional CNN (ARPCNN) is proposed in this article. By applying the proposed personalized word-level attention mechanism and personalized review-level attention mechanism in parallel CNNs, critical words and informative reviews are given high attention weights. Moreover, a user auxiliary network is proposed, which regards the reviews written by kindred spirits who have a trust relationship with the user as auxiliary reviews, and effectively extracts the user’s auxiliary review features, thereby achieving more accurate user modeling to improve the recommendation performance. Extensive experiments are conducted on four real-world datasets, and the results show that the performance of the proposed model is better than that of baselines, which verifies the effectiveness of ARPCNN.

With the progress of few-shot learning, it has been scaled to many domains in the real world which have few labeled data, such as image classification and object detection. Many efforts for data embedding and feature combination have been made by designing a fixed neural architecture that can also be extended to variable and adaptive neural architectures for better performance. Recent works leverage neural architecture search technique to automatically design networks for few-shot learning but it requires vast computation costs and GPU memory requirements. This work introduces EMAS, an efficient method to speed up the searching process for few-shot learning. Specifically, we build a supernet to combine all candidate operations and then adopt gradient-based methods to search. Instead of training the whole supernet, we adopt Gumbel reparameterization technique to sample and activate a small subset of operations. EMAS handles a single path in a novel task adapted with just a few steps and time. A novel task only needs to learn fewer parameters and compute less content. During meta-testing, the task can well adapt to the network architecture although only with a few iterations. Empirical results show that EMAS yields a fair improvement in accuracy on the standard few-shot classification benchmark and is five times smaller in time.

Nowadays, Radio Frequency IDentification (RFID) technology is profoundly affecting all walks of life. Unknown tag identification, as an important service for RFID-enabled applications, aims to exactly collect all EPCs (Electronic Product Code) of unknown tags that are not recorded by the back-end server in the RFID systems. Efficient unknown tag identification is significant to accurately discover the unregistered or newly entering tags in many scenarios, such as warehouse management and retail industry. However, the replies of known tags and the unpredictable behaviors of unknown tags bring serious challenges for accurate and efficient identification of unknown tags. To handle these tough issues, we propose a Compact Unknown Tag identification protocol (CUT) to collect unknown tag EPCs in large-scale RFID systems. Firstly, we introduce a compact indicator vector to simultaneously label unknown tags and deactivate known tags. Then the unknown tags are instructed to reply their EPCs via another compact reply based indicator vector. In each indicator vector, the amount of expected empty and singleton slots is increased to greatly improve the labeling, deactivation and collection efficiency. After that, we validate the effectiveness of proposed CUT protocol by extensive theoretical analyses and simulations. The simulation results demonstrate that CUT protocol outperforms the state-of-the-art one.

The rapid development of Internet of Things (IoTs) has posed many complex problems that are difficult to be solved with traditional techniques/algorithms. Recently, the explosive progress in artificial intelligence, especially machine learning, provides new dimensions to design and develop learning-based solutions for many IoT problems. This workshop provides a forum for academic researchers and industry practitioners to exchange the most recent progress in artificial intelligence applications in Internet of Things.

The recommender system is of great significance to alleviate information overload. The rise of online social networks leads to a promising direction—social recommendation. By injecting the interaction influence among social users, recommendation performance has been further improved. Successful as they are, we argue that most social recommendation methods are still not sufficient to make full use of social network information. Existing solutions typically either considered only the local neighbors or treat neighbors’ information equally, even or both. However, few studies have attempted to solve these social recommendation problems jointly from both the perspective of social depth and social strength. Recently, graph convolutional neural networks have shown great potential in learning graph data by modeling the information propagation and aggregation process. Thus, we propose an attention-based social aggregation neural networks (abbreviated as SAN) model to build a recommendation system. Different from previous work, our proposed SAN model simulates the recursive social aggregation process to spread the global social influence, and simultaneously introduces social attention mechanism to incorporate the heterogeneous influences for better model user embedding. Instead of a shallow linear interaction function, we adopt multi-layer perception to model the complex user–item interaction. Extensive experiments on two real-world datasets show the effectiveness of our proposed model SAN, and further analysis verifies the generalization and flexibility of the model.

In recent years, the appearance of graph convolutional networks (GCNs) provides a new idea for graph structure data processing. Because of that, they can learn excellent user and item embedding by using cooperative signals of high-order neighbors, and the GCNs technique shows great potential in the recommendation. The common problem with the bulk of GCN-based models is that it appears the situation of performance degradation during the stacking of network layers. The recently proposed IMP-GCN alleviates this problem to some extent. It aims to avoid the influence of downside information from high-order propagation on embedding learning. However, we consider that it ignores the multi-interest factor, in which users may have different interests. In this paper, we present a multi-interest GCN(MI-GCN) model for a recommendation, and it conducts high-order graph convolution operations in three sets of subgraphs. Users with similar interests and the corresponding interaction items belong to the identical subgraph. As for the formation of the subgraph, we adopt two varied clustering methods and the user feature to form a subgraph generation mechanism. This mechanism can generate three groups of differential subgraphs to divide users into multi-interest groups and make subgraph division more reasonable. We carry out massive experiments on three real-world datasets, demonstrating the effectiveness of our model. Experimental results confirm that our presented MI-GCN outperforms the state-of-the-art GCN-based recommendation models.

A fundamental issue in multiaccess edge computing (MEC) is efficiently offloading multiple tasks to multiple helper nodes (MTMH), i.e., MEC servers. However, most of the existing decentralized schemes do not consider interuser interference or merely adopt time division multiple access (TDMA) as the multiple access scheme for MTMH in the heterogeneous scenario, leading to a large latency. To address these issues, we propose DOT, a novel Decentralized Offloading of Tasks scheme in orthogonal frequency division multiple access (OFDMA)-based heterogeneous MEC, to minimize the sum cost in terms of energy consumption and delay. Specifically, we first formulate DOT as an optimization problem considering the interuser interference and dynamics in communication and computation resource allocation. Then, considering the huge dimension of potential offloading decisions and conflicting objectives of different users, the total cost of each user is minimized in a distributed manner by modeling the offloading problem as a potential game. The formulated potential game is proved to be an ordinal potential game and thus admits a Nash equilibrium (NE). Further, we develop an offloading algorithm to achieve the NE by exploiting the finite improvement property. Finally, simulation results demonstrate that DOT can achieve a lower cost compared with other baselines.

Recently, Graph Neural Networks (GNNs) have been widely used for fraud detection. GNNs first generate node embedding by aggregating neighboring information under different relations, and then use the final node embedding to detect the node’s suspiciousness. However, traditional GNNs employing only a single type of aggregator fail to capture neighbor information from multiple perspectives and treating different relations equally inevitably weakens the semantic information of heterogeneous graphs. Meanwhile, expressive ability of GNNs is limited by using conventional concatenating or averaging operations to update the center node. Also, camouflaged entities could damage GNN-based models. To handle these problems, a novel heterogeneous GNN model calledMultiple Aggregators and Feature Interactions Network(MAFI) is proposed in this paper to conduct fraud detection tasks. Concretely, multiple types of aggregators are applied on different relations to aggregate neighbor information and aggregator-level attention is utilized to learn the importance of different aggregators. Also, relation-level attention is leveraged to learn the importance of each relation. Besides, conventional update operations are replaced with vector-wise implicit and explicit feature interactions. Moreover, a trainable neighbor sampler is employed to filter camouflaged fraudsters. Comprehensive experiments on two real-world fraud datasets indicate that the proposed MAFI outperforms existing GNN-based fraud detectors.

Region profiles are summaries of characteristics of urban regions. Region profiling is a process to discover the correlations between urban regions. The learned urban profiles can be used to represent and identify regions in supporting downstream tasks, e.g., region traffic status estimation. While some efforts have been made to model urban regions, representation learning with awareness of graph-structured data can improve the existing methods. To do this, we first construct an attribute spatio-temporal graph, in which a node represents a region, an edge represents mobility across regions, and a node attribute represents a region’s point of interest (PoI) distribution. The problem of region profiling is reformulated as a representation learning problem based on attribute spatio-temporal graphs. To solve this problem, we developed URGENT, a spatio-temporal graph learning framework. URGENT is made up of two modules. The graph convolutional neural network is used in the first module to learn spatial dependencies. The second module is an encoding–decoding temporal learning structure with self-attention mechanism. Furthermore, we use the learned representations of regions to estimate region traffic status. Experimental results demonstrate that URGENT outperforms major baselines in estimation accuracy under various settings and produces more meaningful results.

Collaborative teamwork is key to major scientific discoveries. However, the prevalence of collaboration among researchers makes team recognition increasingly challenging. Previous studies have demonstrated that people are more likely to collaborate with individuals they are familiar with. In this work, we employ the definition of familiarity and then propose faMiliarity-based cOllaborative Team recOgnition (MOTO) algorithm to recognize collaborative teams. MOTO calculates the shortest distance matrix within the global collaboration network and the local density of each node. Central team members are initially recognized based on local density. Then, MOTO recognizes the remaining team members by using the familiarity metric and shortest distance matrix. Extensive experiments have been conducted upon a large-scale dataset. The experimental results show that compared with baseline methods, MOTO can recognize the largest number of teams. The teams recognized by the MOTO possess more cohesive team structures and lower team communication costs compared with other methods. MOTO utilizes familiarity in team recognition to identify cohesive academic teams. The recognized teams are in line with real-world collaborative teamwork patterns. Based on team recognition using MOTO, the research team structure and performance are further analyzed for given time periods. The number of teams that consist of members from different institutions increases gradually. Such teams are found to perform better in comparison with those whose members are from the same institution.

Radio frequency identification (RFID) systems have achieved wide applications in various scenarios, such as warehouse management, logistic tracking, smart transportation, etc. Despite the enormous benefits from the RFID systems, the security issues are still of great concern, such as the cloning attacks. In this article, we focus on the detection of probabilistic cloning attacks for the anonymous RFID systems, in which each cloned genuine tag suffers attacks from its clone tags with a certain probability. We propose an efficient detection protocol against the probabilistic cloning attacks in anonymous RFID systems named DAP, which can detect the probabilistic cloning attacks with the required detection reliability$\alpha$if at least one tag is attacked with the probability no less than the threshold$P_T$. The proposed DAP protocol fully utilizes the inconsistency and unreconcilable collision caused by the probabilistic cloning attacks to effectively detect the probabilistic cloning attacks.The parameters are theoretically analyzed to maximize the detection efficiency. The extensive simulations are conducted and the results demonstrate the effectiveness of the proposed DAP protocol.

N OWADAYS, billions of devices are connected to the In-ternet, enabling Internet of Things (IoT) systems widely deployed, such as smart city, smart healthcare and intelligent plant, to capture a great quantity of sensing data. Consequently, the data transmission, processing and analysis in IoT applications bring a great pressure to the central server. Fortunately, distributed intelligence becomes one of the potential solutions. Distributed intelligence can greatly relieve server pressures via plenty of terminal devices, and these devices collaboratively perceive and handle the mass data to improve the reliability, s-calability and security of industrial IoT systems. As future IoT system will embrace more wireless sensors and devices, the high-performance computing, high-bandwidth and low-latency communication are excessively required, many new research opportunities and challenges for distributed intelligence over Internet of things have arisen. To promote the development of distributed intelligence technology, this special section (SS) focuses on various technologies and platforms regarding industrial IoT systems. This special section received nearly 50 submitted manuscripts, out of which 10 of them have been accepted after a rigorous peer review. Each manuscript is reviewed by multiple rounds of review with at least three or four reviewers, the problems to be solved and the innovation of each manuscript are mainly concerned. Then the accepted papers are summarized as follows in details. Considering the joint optimization of the offloading decision and resource allocation under limited resource constraints in collaborative edge computing networks with multiple IIoT devices and MEC servers, an improved differential evolution algorithm [7] is proposed to minimize the weighted sum of cost of energy consumption and time delay, which can effectively reduce the system delay and energy consumption. In order to improve the performance of task scheduling in cloud computing, Attiya et al. [1] propose a novel hybrid swarm intelligence method MRFOSSA, which uses a modified Manta-Ray Foraging Optimizer (MRFO) and the Salp Swarm Algorithm (SSA). MRFOSSA is superior to other methods in terms of makespan time and cloud throughput. The research goal of the paper [5] is to design an intelligent computing offloading strategy for industrial applications in order to optimize costs and mitigate energy losses. Then the paper proposes to combine a fog controller and AI-based learning techniques so that the fog controller can intelligently assign tasks to the most appropriate fog devices and find the appropriate path to the target. Considering the resource utilization efficiency under dynamic overload requests and network states in IIoT, Chen et al. [2] propose DRL-based intelligent SFC orchestration scheme and jointly optimize the VNF deployment and SFC embedment by the improved DDQN algorithm, which can improve the performance of resource utilization rate, execution cost and delay compared with other representative schemes. To solve the problem of resource allocation and energy cost in Internet of Vehicles, Kong et al. [8] design a joint computing and caching framework and formulate the problem as a reinforcement learning problem to minimize the energy cost. On this basis, the optimization algorithm based on DDPG is proposed, which can effectively decrease energy costs. To reduce the query numbers of the object model when constructing adversarial examples, Zhang et al. [10] propose generating adversarial examples with shadow model (GASM), i.e., transfering the query operations to the designed shadow model, which can achieve high attack success rates. Chen et al. [3] revise a Decentralized-Wireless-Federated-Learning algorithm (DWFL) which utilizes the superposition property of the analog scheme. It can solve the problem of single failure, limited bandwidth resource and privacy protection in wireless federated learning algorithm, which can be applied widely in wireless IoT networks. To reduce the resource consumption in CNN-based applications, Jia et al. [6] propose the CNN-based Resource Optimization APProach which utilizes model compression and computation sharing to optimize inner-model and inter-model respectively, and the comparison results show the superior performance in scalability and the decrease of resource cost. In mobile crowdsensing activities, Gao et al. [4] propose a differential Location Privacy-preserving Mechanism based on Trajectory obfuscation (LPMT) to protect the location privacy of mobile users, which includes three operations: stay points extraction, stay points obfuscation and stay points sampling. In order to mimic the task-free bottom-up visual attention process by predicting salient regions on natural images, Umer et al. [9] propose a Pseudo Knowledge Distillation (PKD) model based on knowledge distillation and pseudo labelling technique, which is computationally efficient and suitable for real-time on-device saliency prediction.

Mobile crowdsensing (MCS) is an emerging paradigm that leverages pervasive smart terminals equipped with various embedded sensors to collect sensory data for wide applications. As the sensing scale increases in MCS, the design of efficient task allocation becomes crucial. However, many prior task allocation schemes, which ignore the time for task-performing, are not applicable to the scenario where mobile users with limited time budgets are able to undertake multiple sensing tasks. In this article, we focus on the task allocation in time dependent crowdsensing systems and formulate the time dependent task allocation problem, in which both the sensing duration and the user's sensing capacity are considered. We prove that the task allocation problem is NP-hard and propose an efficient task allocation algorithm called optimized allocation scheme of time-dependent tasks (OPAT), which can maximize the sensing capacity of each mobile user. The extensive simulations are conducted to demonstrate the effectiveness of the proposed OPAT scheme.

Radio frequency identification (RFID) technology has been deployed in various scenarios in recent years. In some practical RFID applications, the items attached with tags can be divided into multiple groups. Thus, the efficient and accurate missing tag detection of each group is critical. Accordingly, this article concentrates on the problem of missing tag detection in the multiple-group RFID systems, after which three distinctive protocols are proposed. First, we propose an aptitudinal multiple-group missing tag detection protocol, which makes full use of the expected singleton slots. Then, an enhanced multiple-group missing tag detection protocol is proposed, which can achieve significant broadcast and response savings. Finally, an accurate and expeditious multiple-group missing tag detection protocol is designed, the detection reliability of which can approximate 100%. The theoretical analysis and extensive simulations are conducted and the results verify that the proposed protocols in this article outperform the other ones.

The precise measuring of vehicle location has been a critical task in enhancing the autonomous driving in terms of intelligent decision making and safe transportation. Internet of Vehicles ( IoV ) is an important infrastructure in support of autonomous driving, allowing real-time road information exchanging and sharing for localizing vehicles. Global positioning System ( GPS ) is widely used in the traditional IoV system. GPS is unable to meet the key application requirements of autonomous driving due to meter level error and signal deterioration. In this article, we propose a novel solution, named Semi-Direct Monocular Visual-Inertial Odometry using Point and Line Features ( SDMPL-VIO ) for precise vehicle localization. Our SDMPL-VIO model takes advantage of a low-cost Inertial Measurement Unit ( IMU ) and monocular camera, using them as the sensor to acquire the surrounding environmental information. Visual-Inertial Odometry ( VIO ), taking into account both point and line features, is proposed, which is able to deal with both weak texture and dynamic environment. We use a semi-direct method to deal with keyframes and non-keyframes, respectively. Dual sliding window mechanisms can effectively fuse point-line and IMU information. To evaluate our SDMPL-VIO system model, we conduct extensive experiments on both an indoor dataset (i.e., EuRoC) and an outdoor dataset (i.e., KITTI) from the real-world applications, respectively. The experimental results show that the accuracy of SDMPL-VIO proposed by us is better than the mainstream VIO system at present. Especially in the weak texture of the datasets, fast-moving datasets, and other challenging datasets, SDMPL-VIO has a relatively high robustness.

Recently recommender systems become more and more significant in the daily life such as event recommendation, content recommendation and commodity recommendation, and so forth. Although the recommender systems based on the generative adversarial network (GAN) are competent, the user trust information is seldom taken into consideration to improve the recommendation accuracy. In this paper, we propose a Trust-Aware GAN with recurrent neural network (RNN) for RECommender systems named TagRec, which makes use of the user trust information for top-N recommendation. In the framework, the discriminative model is a multilayer perceptron to distinguish whether a sample is from the real data or fake data generated by the generative model. The discriminator helps to guide the training of the generative model to make it fit the data distribution of the user trust information. The generative model is a RNN with long short-term memory cells, aiming to confuse the discriminative model by generating samples as similar as possible to the real data. Through the adversarial training between the discriminative and generative models, the user trust information can be fully used to improve the recommendation performance. We conduct extensive experiments on real-word data sets to validate the effectiveness of the TagRec by comparing it with the benchmarks.

Radio-Frequency Identification (RFID) is an emerging technology which has been widely applied in various scenarios, such as tracking, object monitoring, and social networks, etc. Cloning attacks can severely disturb the RFID systems, such as missed detection for the missing tags. Although there are some techniques with physical architecture design or complicated encryption and cryptography proposed to prevent the tags from being cloned, it is difficult to definitely avoid the cloning attack. Therefore, cloning attack detection and identification are critical for the RFID systems. Prior works rely on that each clone tag will reply to the reader when its corresponding genuine tag is queried. In this article, we consider a more general attack model, in which each clone tag replies to the reader's query with a predefined probability, i.e., attack probability. We concentrate on identifying the tags being attacked with the probability no less than a threshold $P_{t}$ with the required identification reliability $\alpha $ . We first propose a basic protocol to Identify the Probabilistic Cloning Attacks with required identification reliability for the large-scale RFID systems called IPCA. Then we propose two enhanced protocols called MS-IPCA and S-IPCA respectively to improve the identification efficiency. We theoretically analyze the parameters of the proposed IPCA, MS-IPCA and S-IPCA protocols to maximize the identification efficiency. Finally we conduct extensive simulations to validate the effectiveness of the proposed protocols.

Incentive mechanisms have been commonly proposed to encourage people to participate in mobile crowdsensing (MCS). However, most of them set unchangeable rewards for sensing tasks, while the inherent inequality and on-demand feature of sensing tasks have been long ignored, especially for location-dependent sensing tasks (LDSTs). In this paper, we focus on location-dependent MCS systems and propose a demand-driven dynamic incentive mechanism that dynamically changes the rewards of sensing tasks at each sensing round in an on-demand way to balance their popularity. A demand indicator is introduced to characterize the demand of each sensing task by considering its deadline, completing progress, and number of potential participants. At each sensing round, we use the Analytic Hierarchy Process (AHP) to calculate the relative demands of all sensing tasks and then determine their rewards accordingly. Moreover, we consider two task selection problem with participatory users and opportunistic users, respectively, and prove that both of them are NP-hard. We propose an optimal dynamic programming based solution for participatory scenario and an optimal backtracking based solution for opportunistic scenario to help each user select tasks while maximizing its profit. Extensive experiments show that the demand-driven dynamic incentive mechanism outperforms existing incentive mechanisms.

Link scheduling plays a key role in the network capacity and the transmission delay. In this paper, we study the problem of maximum link scheduling (MLS), aiming to characterize the maximum number of links that can be successfully scheduled simultaneously under Rayleigh-fading and multiuser interference. After analyzing the minimum distance between successful links in the existing GHW scheduling algorithm, we propose a DLS (Distance-based Link Scheduling) algorithm. Then, the global interference is characterized and bounded by introducing a separation distance between selected links, building on which we propose a distributed version of DLS (denoted by DDLS) that converges to a constant factor of the non-fading optimum within time complexity$O(n\ln n)$, where$n$is the number of links. Furthermore, we study the Shortest Link Scheduling (SLS) problem, which minimizes the number of time slots to successfully schedule each link for at least once. An algorithm for SLS with approximation factor of$O(\ln n)$is obtained by executing DDLS. Extensive simulations show that DDLS greatly outperforms GHW and the other two popular algorithms.

The continuous publication of aggregate statistics over crowd-sourced data to the public has enabled many data mining applications (e.g., real-time traffic analysis). Existing systems usually rely on a trusted server to aggregate the spatio-temporal crowd-sourced data and then apply differential privacy mechanism to perturb the aggregate statistics before publishing to provide strong privacy guarantee. However, the privacy of users will be exposed once the server is hacked or cannot be trusted. In this paper, we study the problem of real-time crowd-sourced statistical data publishing with strong privacy protection under an untrusted server. We propose a novel distributed agent-based privacy-preserving framework, called DADP, that introduces a new level of multiple agents between the users and the untrusted server. Instead of directly uploading the check-in information to the untrusted server, a user can randomly select one agent and upload the check-in information to it with the anonymous connection technology. Each agent aggregates the received crowd-sourced data and perturbs the aggregated statistics locally with Laplace mechanism. The perturbed statistics from all the agents are further combined together to form the entire perturbed statistics for publication. In particular, we propose a distributed budget allocation mechanism and an agent-based dynamic grouping mechanism to realize global w-event ε-differential privacy in a distributed way. We prove that DADP can provide w-event ε-differential privacy for real-time crowd-sourced statistical data publishing under the untrusted server. Extensive experiments on real-world datasets demonstrate the effectiveness of DADP..

With the rich sensing capacity and ubiquitous usage of smartphones, crowdsensing leveraging the power of the crowd of mobile users has become an effective technique to collect data for various sensing applications. Many incentive mechanisms have been proposed to encourage people to participate in crowdsensing. However, most of them set unchangeable rewards for sensing tasks, while the inherent inequality and on-demand feature of sensing tasks have been long ignored, especially for location-dependent sensing tasks. In this paper, we focus on location-dependent crowdsensing systems and propose a demand-based dynamic incentive mechanism that dynamically changes the rewards of sensing tasks at each sensing round in an on-demand way to balance their popularity. A demand indicator is introduced to characterize the demand of each sensing task by considering its deadline, completing progress, and number of potential participants. At each sensing round, we use the Analytic Hierarchy Process to calculate the relative demands of all sensing tasks and then determine their rewards accordingly. Moreover, we prove that the distributed task selection problem with time budget is NP-hard. We propose an optimal dynamic programming based solution and a greedy solution to help each user select tasks while maximizing its profit. Extensive experiments show that the demand-based dynamic incentive mechanism outperforms existing incentive mechanisms.

Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in event recommendation. However, the content-based approaches in existing event recommender systems cannot fully represent the preference of each user on events since most of them focus on exploiting the content information from events' perspective, and the bag-of-words model, commonly used by them, can only capture word frequency but ignore word orders and sentence structure. In this paper, we shift the focus from events' perspective to users' perspective, and propose a Deep User Modeling framework for Event Recommendation (DUMER) to characterize the preference of users by exploiting the contextual information of events that users have attended. Specifically, we utilize convolutional neural network (CNN) with word embedding to deeply capture the contextual information of a user's interested events and build up a user latent model for each user. We then incorporate the user latent model into probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct experiments on the real-world dataset crawled from a typical EBSN, Meetup.com, and the experimental results show that DUMER outperforms the compared benchmarks.

In recent years, many schemes have been proposed to disseminate Emergency Warning Messages (EWMs) in VANETs. However, various problems such as broadcast storm, hidden terminal and connectivity issues still persist to reduce the efficacy of these systems. In this paper, we propose a novel Social Utility-based Dissemination Scheme (SUDS) for Emergency Warning Messages in Vehicular Social Networks (VSNs). We utilize social properties of nodes such as centrality, interests and friendships to mitigate broadcast storm and hidden terminal problems. Furthermore, we employ the hybrid architecture of VSNs to solve connectivity and contact duration-related issues in sparse and high mobility environments. For this purpose, we devise a dual-strategy-based mechanism, where vehicles communicate with each other in a distributed or centralized manner according to the required situation. In order to evaluate the performance of proposed scheme, we have conducted extensive experiments for a highway scenario under varying vehicular density, vehicular speed and distance, in comparison with the state-of-the-art dissemination schemes. Simulation results have demonstrated the superiority of SUDS over the compared protocols in terms of delivery ratio, transmission delay and total number of transmissions. We have also demonstrated the positive effects of hybrid architecture of VSNs on various network parameters.

Radio frequency identification (RFID) systems can be applied to efficiently identify the missing items by attaching them with tags. Prior missing tag identification protocols concentrated on identifying all of the tags. However, there may be some scenarios in which we just care about the key tags instead of all tags, making it inefficient to merely identify the missing key tags due to the interference of replies from the ordinary tags (i.e., nonkey tags). In this paper, we propose to investigate the problem of efficiently and completely identifying the missing key tags for anonymous RFID systems in which the tag privacy is required to be well protected. First, we propose a vector-based missing key tag identification protocol called VEKI. Then we propose an improved protocol called iVEKI, which consists of two phases: 1) ordinary tag deactivation and 2) missing key tag identification. The parameters of the proposed VEKI and iVEKI protocols are theoretically optimized to maximize the time efficiency. Finally, we conduct extensive simulations to evaluate the proposed VEKI and iVEKI protocols and the simulation results illustrate that they outperform other existing protocols in terms of execution time.

Narrowband Internet of Things (NB-IoT) is a new cellular technology introduced by the third generation partnership (3GPP) providing low-power and wide-area coverage for IoT. In this paper, we consider the scenario that NB-IoT is deployed in an heterogeneous network and the quality of the direct link from the NB-IoT user equipment (TIE) to the serving base station (BS) cannot satisfy the quality of service requirement for transmission of vital sensing data. Thereupon, device-to-device (D2D) communication is adopted as a routing extension to NB-IoT systems, and thus, enables two-hop routes between NB-IoT TIE and the serving BS via a set of D2D relays. As the candidate TIE relays work in duty cycle to save energy, we derive a model to select a set of TIE relays and perform opportunistic D2D communication according to a working schedule. Two optimization problems are formulated aiming at achieving optimal expected delivery ratio (EDR) and expected two-hop delay, respectively. Dynamic programming-based algorithms are proposed to solve the optimization problems and obtain the optimal working schedule of the relays. Simulation results demonstrate that our proposed maxEDR and minEED algorithms improves the system performance compared with other state-of-the-art algorithms.

Radio frequency identification (RFID), which promotes the rapid development of Internet of Things (IoT), has been an emerging technology and widely deployed in various applications such as warehouse management, supply chain management, and social networks. In such applications, objects can be efficiently managed by attaching them with low-cost RFID tags and carefully monitoring them. The missing objects, therefore, can be identified by the readers in the RFID system. Most of prior missing tag identification protocols consider the ideal scenario that all the tags' IDs are known to the reader, which ignore that some tags with unknown IDs, called unknown tags, may be present in the system. In this paper, we investigate the problem of efficiently identifying the missing tags with a predefined reliability for large-scale RFID systems with unknown tags. We first propose a basic efficient and reliable missing tag identification protocol called B-ERMI. Then we propose an enhanced protocol called E-ERMI to further improve the efficiency. The parameters of our proposed ERMI protocols are optimized to minimize the execution time. We also conduct extensive simulations to evaluate the proposed ERMI protocols and the simulation results illustrate that the ERMI protocols outperform other existing ones.

Abstract Wormhole attack is a severe attack that can be easily mounted on a wide range of wireless networks without compromising any cryptographic entity or network node. In the wormhole attack, an attacker sniffs packets at one point in the network and tunnels them through the wormhole link to another point. Such kind of attack can deteriorate the localization procedure in wireless sensor networks. In this paper, we first analyze the impacts of the wormhole attack on the localization procedure. Then, we propose a secure localization scheme against the wormhole attacks called SLAW including three phases: wormhole attack detection, neighboring locators differentiation, and secure localization. The main idea of the SLAW is to build a so‐called conflicting set for each locator based on the abnormalities during the message exchanges, which can be used to differentiate the dubious locators to achieve secure localization. We first consider the simplified system model in which there is no packet loss and all the nodes have the same transmission range. We further consider the general system model where the packet loss exists and different types of nodes have different transmission radii. We conduct the simulations to illustrate the effectiveness of the proposed secure localization scheme and compare it with the existing schemes under different network parameters. Copyright © 2014 John Wiley & Sons, Ltd.

Barrier coverage is a critical issue in wireless sensor networks for security applications (e.g., border protection), the performance of which is highly related with locations of sensor nodes. Existing work on barrier coverage mainly assume that sensor nodes have accurate location information, however, little work explores the effects of location errors on barrier coverage. In this paper, we study the barrier coverage problem when sensor nodes have location errors and deploy mobile sensor nodes to improve barrier coverage if the network is not barrier covered after initial deployment. We analyze the relationship between the true distance and the measured distance of two stationary sensor nodes and derive the minimum number of mobile sensor nodes needed to connect them with a guarantee when nodes location errors. Furthermore, we propose a fault tolerant weighted barrier graph, based on which we prove that the minimum number of mobile sensor nodes needed to form barrier coverage with a guarantee is the length of the shortest path on the graph. Simulation results validate the correctness of our analysis.

In traditional Delay Tolerant Networks (DTNs), the nodes can take advantage of intermittent contact opportunities to forward messages. However, in noncooperative DTNs, the nodes may be selfish and reluctant to cooperate with each other in message forwarding. Therefore, it is necessary to motivate the nodes to cooperate with each other in such DTNs. The previously proposed incentive mechanisms have obvious limitations such as the security vulnerability. To resolve such kind of drawbacks, in this paper we propose two credit-based rewarding schemes, called earliest path singular rewarding scheme and earliest path cumulative rewarding scheme respectively, to ensure the nodes truthfully forward the messages. The proposed rewarding schemes are incentive compatible. They also ensure that the payment for each delivered message is upper bounded. Furthermore, the proposed rewarding schemes can prevent selfish nodes from having malicious behaviors. Simulations based on the real trace are conducted to illustrate the effectiveness of the proposed rewarding schemes.

Target tracking is a typical and important application of wireless sensor networks (WSNs). Existing target tracking protocols focus mainly on energy efficiency, and little effort has been put into network management and real-time data routing, which are also very important issues for target tracking. In this paper, we propose a scalable cluster-based target tracking framework, namely the hierarchical prediction strategy (HPS), for energy-efficient and real-time target tracking in large-scale WSNs. HPS organizes sensor nodes into clusters by using suitable clustering protocols which are beneficial for network management and data routing. As a target moves in the network, cluster heads predict the target trajectory using Kalman filter and selectively activate the next round of sensors in advance to keep on tracking the target. The estimated locations of the target are routed to the base station via the backbone composed of the cluster heads. A soft handoff algorithm is proposed in HPS to guarantee smooth tracking of the target when the target moves from one cluster to another. Under the framework of HPS, we design and implement an energy-efficient target tracking system, HierTrack, which consists of 36 sensor motes, a sink node, and a base station. Both simulation and experimental results show the efficiency of our system.

Opportunistic networks are a new evolution of mobile ad hoc networks composed of intermittently connected nodes, in which the routing for the dynamic topology is a challenging issue. In opportunistic networks, the mobile nodes with common interest or close relationship may form into groups and move together, which brings us a good feature to employ when designing the routing protocol for opportunistic networks under resource constraints. Our main idea in this paper is to maximize the message delivery probability in a group-aware opportunistic network under the constraints of bandwidth and buffer space. Embedded this idea, we propose a cooperative routing protocol using the group feature, which includes the cooperative message transfer scheme and the buffer management strategy. In the cooperative message transfer scheme, the limited bandwidth is considered and the message transfer priorities are designed to maximize the delivery probability. In the buffer management strategy, by considering the constraint of buffer space, we propose the cooperative message caching scheme and the dropping order of the messages is designed to minimize the reduced delivery probability. Finally, we conduct the simulations to demonstrate the effectiveness of our proposed routing protocol.

Conventional routing algorithms rely on the existence of persistent end-to-end paths for the delivery of a message to its destination via a predesigned path. However, in a delay tolerant network (DTN), nodes are intermittently connected, and thus the network topology is dynamic in nature, which makes the routing become one of the most challenging problems. A promising solution is to predict the nodes' future contacts based on their contact histories. In this paper, we first propose an expected encounter based routing protocol (EER)which distributes multiple replicas of a message proportionally between two encounters according to their expected encounter values. In case of single replica of a message, EER makes the routing decision by comparing the minimum expected meeting delay to the destination. We further propose a community based routing protocol(CR) which takes advantages of the high contact frequency property of the community. The simulations demonstrate the effectiveness of our proposed routing protocols under different network parameters.

Target tracking is a typical and important application of wireless sensor networks (WSNs). A lot of target tracking protocols have been proposed, whereas few of them are implemented into real systems. In this paper, we develop a real target tracking system, called HieTrack, for energy efficient and real-time target tracking in WSNs. The system relies on the cluster-based network architecture and contends for network cost minimization by selectively activating appropriate senor nodes according to the prediction of target trajectory. The estimated locations of the target can be routed to the base station via the backbone composed by the cluster heads. A graphical user interface (GUI) is also designed complementarily to facilitate users while monitoring the status of the sensor network. The efficiency of our system is validated by experiments.

Wireless sensor networks (WSNs) are often deployed in hostile environments for specific applications from mobile objects monitoring to data collecting. By eavesdropping the sensor nodes' transmissions and tracing the packets' trajectories in the WSNs, an adversary can capture the location of a source or sink eventually. Thus, the location privacy of both source and sink becomes a significant issue in WSNs. Previous research only focuses on the location privacy of the source or sink independently. In this paper, we address the importance of location privacy of both source and sink and propose four schemes to protect them simultaneously. Simulation results illustrate the effectiveness of our proposed schemes.

The wormhole attack is a severe attack that can be easily mounted on a wide range of wireless networks without compromising any cryptographic quantity or network node. In the wormhole attack, an attacker sniffs packets at one point in the network, tunnels the packets through a wired or wireless link to another point. Such kind of attack can cause severe problems in wireless sensor networks, especially deteriorate the routing process and the localization process. In this paper, we propose a secure localization scheme against wormhole attacks, which includes three phases: wormhole attack detection, neighboring locators differentiation and secure localization. The main idea of the proposed secure localization scheme is to build a so-called conflicting set for each locator according to the abnormalities of message exchanges among neighboring locators, which is used to differentiate the dubious locators from valid locators for the secure localization. The simulation results show that the proposed scheme outperforms the existed schemes under different network parameters.

Node localization becomes an important issue in the wireless sensor network as its broad applications in environment monitoring, emergency rescue and battlefield surveillance, etc. Basically, the DV-Hop localization mechanism can work well with the assistance of beacon nodes that have the capability of self-positioning. However, if the network is invaded by a wormhole attack, the attacker can tunnel the packets via the wormhole link to cause severe impacts on the DV-Hop localization process. The distance-vector propagation phase during the DV-Hop localization even aggravates the positioning result, compared to the localization schemes without wormhole attacks. In this paper, we analyze the impacts of wormhole attack on DV-Hop localization scheme. Based on the basic DV-Hop localization process, we propose a label-based secure localization scheme to defend against the wormhole attack. Simulation results demonstrate that our proposed secure localization scheme is capable of detecting the wormhole attack and resisting its adverse impacts with a high probability.