Path selection and planning are crucial for autonomous mobile robots (AMRs) to navigate efficiently and avoid obstacles. Traditional methods rely on analytical search to identify the shortest distance. However, Reinforcement learning enhances performance by optimizing a sequence of actions efficiently. It is an iterative approach used for computational sequence modeling and dynamic programming. RL received sensory input from the environment in the form of observation or state. The agent interpreted every reward or penalty through trial-and-error interaction. Policy maximizes the rewards and selects the optimal action among all possible actions. A challenging problem in traditional reinforcement learning is environment generalization for dynamic systems. Q-learning faces challenges in dynamic environments because it relies on rewards or penalties based on the entire sequence of actions from the start to the end state. This approach often fails to produce optimal results when the environment changes unexpectedly due to state transitions, iterations, or blocked routes. Such limitations make Q-learning less effective for dynamic path planning. To overcome these challenges, this study focuses on optimizing reward functions for efficient navigation in RL-based path planning, aiming to enhance navigation efficiency and obstacle avoidance. The proposed method evaluates the shortest decision path by considering total steps, counted steps, and discount rates in dynamic environments. By implementing this RL with an optimized reward mechanism, the study analyzes state reward values across different environments, and it evaluates the effect on state-action pair-based Q-Learning and neural networks using Deep Q-Learning algorithms. Here, results demonstrate that the optimized reward function effectively decreases the number of iterations and episodes while achieving a 30% to 70% reduction in overall trajectory distance. These results highlight the effectiveness of reward-based reinforcement learning, demonstrating its potential to improve path optimization, learning rate, episode completion, and decision accuracy in intelligent navigation systems. Q-learning-based reinforcement learning becomes more effective by combining multiple agents and utilizing decision-making techniques such as federated and transfer learning on larger maps to ensure convergence.

The growing use of Internet of Medical Things (IoMT) systems demands accurate sentiment analysis, nuanced emotion tracking, and seamless integration of multimodal data. Current models often struggle when handling heterogeneous sources and evolving emotional patterns. To address these issues, this study proposes QED-Net—a quantum-inspired deep learning architecture designed specifically for IoMT environments. It introduces four modular components. The quantum-driven sentiment amplification (QSA) enhances contextual sentiment interpretation. The temporal emotion evolution graph (TEEG) captures the shifting nature of emotional states over time. The hyperdimensional quantum tensor fusion (HD-QTF) supports synchronized integration of diverse modalities. Finally, the emotion-to-medical ontology encoder (EMOE) translates emotional cues into actionable clinical signals. These components operate both independently and in synergy, allowing for flexible deployment in real-world scenarios. Simulations conducted on benchmark datasets confirm the model’s effectiveness, with QED-Net achieving 93.2% precision in sentiment detection, 92.3% in emotion tracking, and 91.6% robustness in multimodal fusion.

In comparison to the lending data submitted by banks to credit bureaus under the traditional credit scoring paradigm, alternative credit data (such as social media activities and e-commerce consumption records) has increasingly demonstrated its significance in enhancing the accuracy of credit scores and addressing the issue of credit-invisible individuals in recent years. However, credit scoring model based on alternative credit data typically necessitates large-scale data circulation and may involve sensitive information, thereby raising concerns related to data security, user privacy, and data rights. Traditional cryptographic methods often encounter limitations in functionality, efficiency, flexibility, and traceability when addressing these issues. This article initially proposes a novel credit data sharing framework based on an alternative data cloud platform. Subsequently, based on this framework, a blockchain-based privacy-preserving alternative credit data sharing scheme is constructed. This scheme achieves efficient, privacy-preserving, and wildcard-supported attribute-based encryption (ABE) scheme through inner product operations, and implements a “two-level” access control by designing a keyword search mechanism in conjunction with the aforementioned scheme. Furthermore, a hybrid encryption mechanism is introduced to further enhance efficiency and security under high-frequency access scenarios. Security analysis and rigorous formal security reductions have been conducted to demonstrate the security of the proposed scheme. Comparative experimental results also indicate that the proposed scheme exhibits significant advantages in practicality compared with related schemes.

Abstract Due to the characteristics of vehicular ad hoc networks, the increased mobility of nodes and the inconsistency of wireless communication connections pose significant challenges for routing. As a result, researchers find it to be a fascinating topic to study. Furthermore, since these networks are vulnerable to various assaults, providing an authentication method between the source and destination nodes is crucial. How to route in such networks more efficiently, taking into account node mobility characteristics and accompanying massive historical data, is still a matter of discussion. Fuzzy logic‐based Trusted Routing Protocol for vehicular cloud networks (FTRP) is proposed in this study that determines the secure path for data dissemination. Fuzzy Logic determines the node candidacy value and selects or rejects a path accordingly. The cloud assigns a confidence score to each vehicle based on the data it collects from nodes after each interaction. Our study identifies the secure path on the basis of trust along with factors such as speed, closeness to other nodes, signal strength and distance from the neighbouring nodes. Simulations of the novel protocol demonstrate that it can keep the packet delivery ratio high with little overhead and low delay. FTRP has significant implications for deploying Vehicular Cloud Networks using electric vehicle technologies in smart cities. The routing data is collected with the help of Internet of Technology (IOT) sensors. The information is transmitted between vehicles using IOT gateways.

ABSTRACT Medical image segmentation, the process of precisely delineating regions of interest (e.g. organs, lesions, cells) within medical images, is a pivotal technique in medical image analysis. It finds widespread application in computer‐aided diagnosis, surgical planning, radiation therapy, and pathological analysis, thus playing a crucial role in enabling precision medicine and enhancing the quality of clinical care. Traditional medical image segmentation methods often rely on hand‐crafted features and rule‐based approaches, which struggle to handle the inherent complexity and variability of medical imagery, leading to limitations in segmentation accuracy and robustness. Recently, deep learning methodologies, driven by their powerful capabilities in automatic feature learning and non‐linear modelling, have overcome the limitations of traditional methods and achieved significant advancements in the field of medical image segmentation. This review provides a comprehensive overview and summary of recent progress in deep learning‐based medical image segmentation, with a particular focus on fully supervised learning paradigms leveraging convolutional neural networks, transformers, and the segment anything model. We delve into the underlying principles, network architectures, advantages, and limitations of these approaches. Furthermore, we systematically compare their performance across diverse imaging modalities, anatomical structures, and pathological targets. We also present a curated compilation of commonly used datasets, evaluation metrics, and loss functions relevant to medical image segmentation. Finally, we discuss future research directions and potential challenges, offering insights into the evolving landscape of this critical field.

Adaptive HCI for education refers to design and development computer interfaces that can dynamically adjust and tailor themselves to individual users’ needs, preferences, and abilities. The goal of...

The rapid growth of the Internet of Things (IoT) has led to revolutionary innovations in many fields; however, it has also resulted in significant security and privacy issues due to the resource limitations and distributed nature of IoT networks. Traditional cryptographic techniques or machine learning-based anomaly detection systems do not jointly provide data privacy and resilience to threats in real time. The existing methods, such as Homomorphic Encryption (HE), offer a high computation cost for performing encryption. Furthermore, Long Short-Term Memory (LSTM) networks can predict an anomaly profile instead of performing encryption. To address these shortcomings, this paper proposes NeuroCrypt. This new hybrid system combines Fully Homomorphic Encryption (FHE) with LSTM-based encrypted anomaly detection and supplements it with blockchain-based dynamic key management and multi-factor authentication. The architecture targets edge and fog computing settings using, among other techniques, ciphertext packing, model quantisation, and parallelised encrypted operations. The performance of the proposed framework has been evaluated on a real dataset. The results show that the accuracy in the proposed framework is 99.2% compared to existing techniques such as HE-based DNN, FL-based models, and LSTM IDS. Conclusively, NeuroCrypt provides a privacy-preserving, effective, and scalable solution to real-time threat abatement in IoT networks.

Artificial intelligence and machine learning have been transforming the health care industry in many areas such as disease diagnosis with medical imaging, surgical robots, and maximizing hospital efficiency. The Healthcare service market utilizing Artificial Intelligence is expected to reach 45.2 billion U. S. Dollars by 2026 from its current valuation, off $4.9 billion. Diabetic Retinopathy (DR) is a disease that results from complications of type one and Type two diabetes and affects patients' eyes. Diabetic retinopathy, if remains unaddressed, is one of the most serious complications of diabetes, resulting in permanent blindness. The disease has been affecting the lives of 347 million people worldwide. The paper aims to propose a residual network-based deep learning framework for the detection of diabetic retinopathy. The accuracy of our approach is 83% whereas the precision value for checking the absence of DR is 95%.

The cloud-edge computing model has been expected to play a revolutionary role in promoting the quality of future generation large-scale Internet of Things (IoT) services. However, security and privacy in data sharing remain crucial issues hindering the success of cloud-edge IoT services. While some solutions based on attribute-based encryption (ABE) have been proposed to address these issues, they still face practical challenges such as attribute privacy leakage, resource-constrained devices, dynamic user groups, inflexible and inefficient service response. To address these challenges, this paper proposes a privacy-preserving fine-grained data sharing scheme with dynamic service (PF2DS), which implements access control by calculating the inner product between an attribute vector and an access vector. PF2DS is also capable of providing dynamic user group services through an efficient and indirect user revocation mechanism that periodically updates the key-embedded leaf nodes. Building on PF2DS, edge-assisted PF2DS (EPF2DS) delegates most of the operations to the edge device, which facilitates the performance of resource-constrained IoT devices. EPF2DS also supports efficient and asynchronous keyword search over the ciphertexts stored in the cloud. We demonstrate the security by the rigorous security proof. Both theoretical comparisons and experimental simulations demonstrate the practicality and superiority of our schemes over existing works.

Abstract This work addresses the rolling element bearing (REB) fault classification problem by tackling the issue of identifying the appropriate parameters for the extreme learning machine (ELM) and enhancing its effectiveness. This study introduces a memetic algorithm (MA) to identify the optimal ELM parameter set for compact ELM architecture alongside better ELM performance. The goal of using MA is to investigate the promising solution space and systematically exploit the facts in the viable solution space. In the proposed method, the local search method is proposed along with link‐based and node‐based genetic operators to provide a tight ELM structure. A vibration data set simulated from the bearing of rotating machinery has been used to assess the performance of the optimized ELM with the REB fault categorization problem. The complexity involved in choosing a promising feature set is eliminated because the vibration data has been transformed into kurtograms to reflect the input of the model. The experimental results demonstrate that MA efficiently optimizes the ELM to improve the fault classification accuracy by around 99.0% and reduces the requirement of hidden nodes by 17.0% for both data sets. As a result, the proposed scheme is demonstrated to be a practically acceptable and well‐organized solution that offers a compact ELM architecture in comparison to the state‐of‐the‐art methods for the fault classification problem.

The vehicle-road cooperative perception needs high accuracy and real-time automatic background filtering to separate background objects from foreground objects in complex traffic scenes. Reducing the influence of foreground objects to improve accuracy, and introducing a new framework to improve real-time performance are two main challenges in automatic background filtering. This paper proposes an Automatic Background Filtering method with innovative Frame Selection and Background Matrix Extraction modules (ABF-FSBME) to address these challenges. Firstly, a new space division method with equal hitting probability is proposed to divide the 3D point cloud formed by roadside Light Detection and Ranging (LiDAR), which can reduce the influence of slight LiDAR vibrations. Secondly, the terminal-edge-cloud framework is introduced to balance delay-constrained tasks and computation-intensive tasks in automatic background filtering. Thirdly, a variance-based frame selection strategy with a sliding window mechanism is proposed to select candidate frames with fewer foreground objects. This strategy can reduce the influence of foreground objects in a coarse-grained way. Meanwhile, a new background matrix extraction method is proposed to construct the background matrix. This method can further reduce the influence of foreground objects in a fine-grained way. Finally, based on the extracted background matrix from a cloud server, the edge server can filter the raw frame in real-time. The experimental results show that the proposed ABF-FSBME method has better accuracy than other methods in error rate and integrity rate. Besides, the proposed ABF-FSBME can complete fame filtering within 10ms, and has almost no network delay, so it can satisfy the real-time requirement.

As increasingly prevalent technologies in autonomous driving, 5G and the Internet of Things (IoT), Internet of autonomous vehicle (IoAV) technology is recognized as a technique that is capable of disruptively changing the way people travel and greatly improving the travel experience. In the IoAV scenarios, information dissemination is inseparable from the interaction between autonomous vehicles and smart infrastructure. However, existing efforts rarely focus on the secrecy, authenticity of interactive data and flexible one-to-many communication between autonomous vehicles. In this paper, we propose a tamper-resistant broadcasting (TRBS) scheme for secure communication, which handles the inefficiencies and insecurity of existing identity-based broadcast signcryption solutions. Not only can our TRBS protect communication data from being illegally accessed, forged, or tampered with by malicious vehicles, but it can also enable efficient and flexible secure information dissemination between autonomous vehicles. We also exhibit strict security proofs and experimental evaluations to demonstrate our TRBS is secure and efficient for real-world applications.

The heterogeneous nature of the Industrial Internet of Thing (IIoT) has a considerable impact on the development of an effective Intrusion Detection System (IDS). The proliferation of linked devices results in multiple inputs from industrial sensors. IDS faces challenges in analyzing the features of the traffic and identifying anonymous behavior. Due to the unavailability of a comprehensive feature mapping method, the present IDS solutions are non-usable to identify zero-day vulnerabilities. In this paper, we introduce the first comprehensive IDS framework that combines an efficient feature-mapping technique and cascading model to solve the above-mentioned problems. We call our proposed solution deeP learnIG model intrusioN detection in indUStrial internet-of things (PIGNUS). PIGNUS integrates Auto Encoders (AE) to select optimal features and Cascade Forward Back Propagation Neural Network (CFBPNN) for classification and attack detection. The cascading model uses interconnected links from the initial layer to the output layer and determines the normal and abnormal behavior patterns and produces a perfect classification. We execute a set of experiments on five popular IIoT datasets: gas pipeline, water storage tank, NSLKDD+, UNSW-NB15, and X-IIoTID. We compare PIGNUS to the state-of-the-art models in terms of accuracy, False Positive Ratio (FPR), precision, and recall. The results show that PIGNUS provides more than 95% accuracy, which is 25% better on average than the existing models. In the other parameters, PIGNUS shows 20% improved FPR, 10% better recall, and 10% better in precision. Overall, PIGNUS proves its efficiency as an IDS solution for IIoTs. Thus, PIGNUS is an efficient solution for IIoTs.

Abstract Medical cyber‐physical systems (CPS) provide the possibility for real‐time health monitoring of patients and flexible diagnostic services based on expert systems by collaboratively integrating and connecting various physical devices including sensors, terminals, and cloud infrastructure. However, the ubiquitous security threats in cyberspace have raised concerns about data security and user privacy. Although related works propose to protect data security and user privacy with cryptographic protocols, their heavy computational and storage overheads incur performance and battery life challenges for resource‐constrained devices in the healthcare CPS. This article proposes an energy‐saving and privacy‐preserving data sharing (ESPPDS) scheme to address the challenge. ESPPDS inherits the anonymous fine‐grained access control from attribute‐based encryption (ABE) while protecting data integrity and supporting efficient user revocation. We also eliminate the repetitive computations of ciphertext components by utilizing the online/ offline encryption technology, and design a subtle and secure trick to delegate the decryption operations to the edge device, thereby reducing the computational overheads of the resource‐constrained devices. We then show the security proof, and discuss the construction in the untrusted/ compromised server setting. The comparison and experiment indicate that ESPPDS is practical and more efficient than related schemes.

Currently, malware attacks pose a high risk to compromise the security of Android-IoT apps. These threats have the potential to steal critical information, causing economic, social, and financial harm. Because of their constant availability on the network, Android apps are easily attacked by URL-based traffic. In this paper, an Android malware classification and detection approach using deep and broad URL feature mining is proposed. This study entails the development of a novel traffic data preprocessing and transformation method that can detect malicious apps using network traffic analysis. The encrypted URL-based traffic is mined to decrypt the transmitted data. To extract the sequenced features, the N-gram analysis method is used, and afterward, the singular value decomposition (SVD) method is utilized to reduce the features while preserving the actual semantics. The latent features are extracted using the latent semantic analysis tool. Finally, CNN-LSTM, a multi-view deep learning approach, is designed for effective malware classification and detection.

The Internet of Vehicles (IoV) is expected to play a revolutionary role in improving users’ driving experience and urban traffic governance. By widely absorbing emerging technologies including cloud computing, the future IoV evolution is leading towards providing more flexible and diversified data services. However, the publicly accessible IoV environment arouses the user’s concerns about the leakage of data and personal privacy. Despite some cryptographic solutions have been proposed, they still raise challenges on privacy, efficiency and usability. To cope with these challenges, this paper first presents an efficient scheme PH-ABE-DS, which attains the full policy hiding by implementing the access control with the inner product. Besides, we design an efficient indirect revocation mechanism, to enable the cloud and users to update the ciphertext and user secret key with slight storage and computational overheads. On this basis, we then present the EA-PH-ABE-DS scheme, by resorting to edge computing, it further reduces the overheads of resource-constrained devices. We design a deployment model for EA-PH-ABE-DS in IoV to discuss its usability. Rigorous security proof and security properties analysis show that our proposal is secure and reliable. Finally, through detailed comparisons on theoretical and experimental, both our two schemes show their superiority over the latest related works in terms of functionality and performance. The simulation evaluates and demonstrates the practicality of our solutions in practical IoT scenarios.

The increasing number of Unmanned Aerial Vehicles (UAVs) in the low-altitude airspace and the increasing complexity of the work environment present new challenges for ensuring airspace security, especially the effective conflict detection and resolution (CD&R) of UAVs. In the era of the sixth generation (6G) technology, there is an improvement in communication speed and capacity in comparison with the traditional communication technologies, which contributes to forming a UAV Internet of Things (IoTs) through remote intelligent control platform and improve the effect of CD&R. In this paper, we innovatively develop a cooperative CD&R method in the UAV IoT environment considering UAV relative motion relationships and UAV priorities. Using this method in 6G environment, the real-time and reactive conflict-free paths for UAVs can be generated. The developed method has the advantage of smaller calculation and needs fewer UAVs to take maneuvers than the CD&R methods based on traditional Artificial Potential Field (APF). To verify the effectiveness of CD&R methods, a safety assessment method (evaluate from both conflict feature and network structure perspectives) is also proposed. A Monte Carlo Simulation with ``clone mechanism'' is designed to incorporate the effect of CD&R systems. Three cases of distributed CD&R protocols are simulated and compared. The simulations with different parameter settings are also discussed. Quantitative simulation experiments show that the safety effect of CD&R proposed in this paper is improved a lot due to the improved APF and the UAV priority determination. Meanwhile, the safety assessment method is demonstrated to be feasible for evaluating the safety of CD&R systems.

As an increasingly prevalent technology in intelligent autonomous transportation systems, autonomous vehicle platoon has been indicated the ability to significantly reduce fuel consumption as well as heighten highway safety and throughput. However, existing efforts rarely focus on protecting data confidentiality and authenticity in autonomous vehicle platoons. How to ensure secure and high-fidelity platoon-level communication is still in its infancy. This paper makes the first attempt for efficient and secure communication across autonomous vehicle platoons. Specifically, we presentPDSM-FC, the first privacy-preserving data share mechanism with flexible cross-domain authorization over distinctive platoons. The key insight ofPDSM-FCis the design of a new ciphertext conversion technique, which allows a ciphertext to be easily converted into another type of ciphertext, facilitating efficient access by all entities holding the legitimate authorization. As a result,PDSM-FCcan achieve high-fidelity data communication between two unique platoons in ciphertext, so as to complete specific tasks including platoon integration. Rigorous security analysis shows thatPDSM-FCis secure against various attacks such as collusion, forgery and chosen-plaintext attacks. Moreover, theoretical evaluation and extensive experiments demonstrate the practicability ofPDSM-FCin terms of functionality, storage and computation overheads.

Abstract Emergency material allocation and scheduling is a combination optimization problem, which is essentially a Non‐deterministic Polynomial (NP) problem. Aiming at the problems such as slow convergence, easy prematurely falling into local optimum, and parameter constraints to solve high‐dimensional and multi‐modal combination optimization problems, this article proposes an adaptive weighted dynamic differential evolution (AWDDE) algorithm. The algorithm uses a chaotic mapping strategy to initialize the population. By weighting the standard differential evolution (DE) mutation strategy, a new weighted mutation operator is proposed. The scaling factor and cross probability can be adaptively adjusted. A disturbance operator is introduced to randomly generate the perturbation mutation and to accelerate the premature individuals to jump out of the local optimum. The algorithm is applied to the problem of emergency material allocation and scheduling, and a two‐stage emergency material allocation and scheduling model is established. Compared with the standard DE algorithm and the chaos adaptive particle swarm algorithm, the results show that the AWDDE algorithm has the characteristics of stronger global optimization ability and faster convergence speed compared with other optimization algorithms, which provide assistance for smart cities research, including smart city services, applications, case studies, and policymaking considerations for emergency management.

Software similarity in different programming codes is a rapidly evolving field because of its numerous applications in software development, software cloning, software plagiarism, and software forensics. Currently, software researchers and developers search cross-language open-source repositories for similar applications for a variety of reasons, such as reusing programming code, analyzing different implementations, and looking for a better application. However, it is a challenging task because each programming language has a unique syntax and semantic structure. In this paper, a novel tool called Cross-Language Software Similarity (CroLSSim) is designed to detect similar software applications written in different programming codes. First, the Abstract Syntax Tree (AST) features are collected from different programming codes. These are high-quality features that can show the abstract view of each program. Then, Methods Description (MDrep) in combination with AST is used to examine the relationship among different method calls. Second, the Term Frequency Inverse Document Frequency approach is used to retrieve the local and global weights from AST-MDrep features. Third, the Latent Semantic Analysis-based features extraction and selection method is proposed to extract the semantic anchors in reduced dimensional space. Fourth, the Convolution Neural Network (CNN)-based features extraction method is proposed to mine the deep features. Finally, a hybrid deep learning model of CNN-Long-Short-Term Memory is designed to detect semantically similar software applications from these latent variables. The data set contains approximately 9.5K Java, 8.8K C#, and 7.4K C++ software applications obtained from GitHub. The proposed approach outperforms as compared with the state-of-the-art methods.

It is a new trend in healthcare informatization construction to build the smart healthcare system by using the Internet of Things (IoT) and cloud. This IoT-oriented and cloud-assisted healthcare system enables the doctor to monitor the patient’s health state to respond to the paroxysmal diseases in real time. Considering the sensitivity of the patient’s privacy, it is necessary to encrypt the cloud-stored health data to prevent the semi-trusted cloud and unauthorized users from accessing them. However, the encrypted health data stored in the cloud brings inconvenience to the retrieval for the data user. In addition, the expensive computational consumption also raises the challenge to the resource-constrained devices in the patient and doctor sides. To support efficient ciphertext retrieval and cope with the performance challenge, in this article we propose a lightweight attribute-based searchable encryption (LABSE) scheme, which realizes fine-grained access control and keyword search, while reducing the computational overhead for the resource-constrained devices. We rigorously prove the semantic security of the proposed LABSE scheme, and analyze other security properties to response the security requirements under the healthcare scenario. Subsequently, we construct a concrete deployment model for LABSE under the healthcare system. We also compare LABSE with the state-of-the-art related schemes in terms of functionality and complexity. Finally, we demonstrate the practicality and performance advantages by the experiment.

The expeditious development in cloud-enabled industrial Internet of Things (IIoT) healthcare has significantly reduced the costs to monitor and protect people at home while notably improving the quality of human healthcare. Despite its considerable convenience and benefits, it confronts some security and privacy challenges in the aspects of bilateral fine-grained access control, the authenticity and tamper resistance of shared health data. To tackle these constraints, a secure privacy-preserving bilateral access control scheme with fine granularity (PBAC-FG) is proposed in this article. Our PBAC-FG exploits fine-grained access control and matchmaking encryption technologies to ensure both participants (e.g., patients and healthcare providers) can specify their respective fine-grained access control over the encrypted health data, such that only authorized counterparts can efficiently access the health data. Besides, the correct rigorous security proofs are indicated to verify that our PBAC-FG is indeed secure. We carry out comprehensive performance evaluations and comparisons to demonstrate the efficiency and practicality of the PBAC-FG for IIoT healthcare applications.

The cloud-assisted medical Internet of Things (MIoT) has played a revolutionary role in promoting the quality of public medical services. However, the practical deployment of cloud-assisted MIoT in an open healthcare scenario raises the concern on data security and user's privacy. Despite endeavors by academic and industrial community to eliminate this concern by cryptographic methods, resource-constrained devices in MIoT may be subject to the heavy computational overheads of cryptographic computations. To address this issue, this paper proposes an efficient, revocable, privacy-preserving fine-grained data sharing with keyword search (ERPF-DS-KS) scheme, which realizes the efficient and fine-grained access control and ciphertext keyword search, and enables the flexible indirect revocation to malicious data users. A pseudo identity-based signature mechanism is designed to provide the data authenticity. We analyze the security properties of our proposed scheme, and via the theoretical comparison and experimental results we demonstrate that for the resource-constrained devices in the patient and doctor side of MIoT, in comparison with other related schemes, ERPF-DS-KS just consumes the lightweight and constant size communication/storage as well as computational time cost. For the keyword search, compared with related schemes, the cloud can quickly check whether a ciphertext contains the specified keyword with slight computations in the online phase. This further demonstrates that ERPF-DS-KS is efficient and practical in the cloud-assisted MIoT scenario.

The distribution of large-size data block in the Internet of Vehicles (IoV), especially in the urban IoV with dense vehicles, is still a challenge issue. Though the methods based on 5G-cellular network commonly can efficiently distribute the large-size file in IoVs, they also have obvious defects, such as occupying scarce 5G resources, generating communication fees and limited service coverage. This paper focuses on distributing large-size data block in IoVs based on dedicated vehicular ad hoc network to reduce the relying on cellular (5G/6G) resources. A heterogeneous vehicular network (HetVNET), in which a short-range OFDM wideband communication (SOWC) with ultra-high data rate and the original IEEE 802.11p protocol are included, is first proposed. To match with the proposed HetVNET, a content-centric data distribution scheme based on edge caching is designed. The content-centric data distribution architecture for distributing large-size file, the cache node selection and the process of data distribution and control based on the HetVNET are studied. The 5G/6G communication can be enabled in the scheme to further enhance the engineering stability of the massive infrastructure-to-vehicle (I2V) broadcast in IoVs. The evaluation results show that the proposed approach has low delivery delay and high penetration ratio.

Autonomous Vehicle Platoon (AVP) is conceived as a promising solution to enhance the traffic capacity and reduce the energy consumption in the intelligent transportation system. Nevertheless, AVP without security guarantees are prone to various attacks, which probably lead to life-threatening accidents. Motivated by solving this issue, an outsourced attribute-based access control mechanism with direct revocation for AVP (RFAP) is introduced in this paper. Among them, attribute-based encryption is utilized to implement fine-grained access control during the encryption process. Furthermore, RFAP can not only realize the immediate revocation of platoon member who is about to leave the platoon without affecting others, but also achieve secure outsourced decryption with the help of edge computing units for minimizing the computational overhead of decryption on the vehicle side. Security analysis and simulation results indicate that our RFAP mechanism is practical in aspects of security and efficiency.

Fashion recommendation is an essential component of user shopping that it is capable of selecting and presenting fascinating items to customers. The fact that humans exhibit inconsistencies for fashion items in their choice is known to all due to the visual aesthetic features and fine-grained differences of fashion items. Previous research on fashion recommendations mainly focuses on sequential models, most of them only consider complex similarity relationships in fashion compatibility while neglecting the real-world compatible information often desired in practical applications. To learn the fashion compatibility and generate for the outfit, we propose an approach that jointly learns latent fashion concepts in visual-semantic space to measure compatibility between items. The fashion concepts are shaped by design elements such as color, material, and silhouette. Accordingly, we model a unified representation to learn different notions of similarity by mapping text descriptors and images into latent space to learn high-level representations. Experimental results reveal that our method effectively reaches the aimed results on the fill-in-the-blank and outfit compatibility tasks.

Location estimation is one of the critical requirement for developing smart environment products. Due to huge utilization and accessibility of WiFi infrastructure facility in indoor environments, researchers widely studied this technology to locate users accurately to provide several services instantly. In this research work, a hybrid algorithm namely fuzzy decision tree (FDT) with evolutionary fuzzy clustering methods is adopted for optimal user localization in a closed environment. Here we consider the wireless signal strengths received from the smart phones as predictors and the location of the user as the classification label. The required data for the current research is collected from the physical facility available at an office location in USA. The classification results obtained are promising enough to show that the evolutionary clustering approaches provide good fuzzy clusters for FDT induction with better accuracy.

Most existing research about task allocation in mobile crowdsensing mainly focus on requester-centric mobile crowdsensing (RCMCS), where the requester assigns tasks to workers to maximize his/her benefits. A worker in RCMCS might suffer benefit damage because the tasks assigned to him/her may not maximize his/her benefit. Contrarily, worker-centric mobile crowdsensing (WCMCS), where workers autonomously select tasks to accomplish to maximize their benefits, does not receive enough attention. The workers in WCMCS can maximize their benefits, but the requester in WCMCS will suffer benefit damage (cannot maximize the number of expected completed tasks). It is hard to maximize the number of expected completed tasks in WCMCS, because some tasks may be selected by no workers, while others may be selected by many workers. In this paper, we apply task bundling to address this issue, and we formulate a novel task bundling problem in WCMCS with the objective of maximizing the number of expected completed tasks. To solve this problem, we design an algorithm named LocTrajBundling which bundles tasks based on the location of tasks and the trajectories of workers. Experimental results show that, compared with other algorithms, our algorithm can achieve a better performance in maximizing the number of expected completed tasks.

Attribute-based signature (ABS) can provide fine-grained and anonymous authentication for the Industrial Internet of Things (IIoT). However, key exposure and the computational performance bottleneck of resource-constrained devices raise a challenge to the IIoT-oriented ABS schemes. To confront the above challenge, this article proposes an intrusion-resilient server-aided ABS (IR-SA-ABS) scheme. This scheme designs to periodically update the signing key with the assistance of a helper device, and the signing key is refreshed for multiple rounds aperiodically within each time period under the supervision of the helper device. In this way, the system remains secure even if both the helper device and the signing device are compromised. During this process, we significantly reduce the computational overheads of resource-constrained devices by delegating all of the key update and refresh operations and most of the signature generation and verification operations to a powerful server. Subsequently, we present the rigorous security proof of IR-SA-ABS. The comparison and experiment demonstrate the efficiency and practicality of IR-SA-ABS in the IIoT scenario.

As mobile phone is widely used in social network communication, it attracts numerous malicious attacks, which seriously threaten users’ personal privacy and data security. To improve the resilience to attack technologies, structural information analysis has been widely applied in mobile malware detection. However, the rapid improvement of mobile applications has brought an impressive growth of their internal structure in scale and attack technologies. It makes the timely analysis of structural information and malicious feature generation a heavy burden. In this paper, we propose a new Android malware identification approach based on malicious subgraph mining to improve the detection performance of large-scale graph structure analysis. Firstly, function call graphs (FCGs), sensitive permissions, and application programming interfaces (APIs) are generated from the decompiled files of malware. Secondly, two kinds of malicious subgraphs are generated from malware’s decompiled files and put into the feature set. At last, test applications’ safety can be automatically identified and classified into malware families by matching their FCGs with malicious structural features. To evaluate our approach, a dataset of 11,520 malware and benign applications is established. Experimental results indicate that our approach has better performance than three previous works and Androguard.

In this article, a novel fuzzy-based dynamic multiobjective evolutionary algorithm is presented. In this article, giving a valid and true response to the change is an essential task to improve the diversity of solutions when an environmental change occurs. The basic idea is to randomly remove some solutions and replace by newly created solutions. However, the random selection detours the algorithm's trajectory and deteriorates the performance of the optimization algorithm. Recently, the Borda method has been deployed to find the best candidates to be removed from the solutions list. Although the Borda method outperforms the random strategy, it suffers from some drawbacks. In this article, we propose an improved Borda count method incorporated with fuzzy tuned parameters so that its parameters are adjusted by Mamdani fuzzy rules. Our new Borda method can distinguish the information before and after change with different fuzzy weights. In addition to the fuzzy-based Borda, we employ an improved evolutionary algorithm based on fuzzy logic. We propose a novel nondominated sorting genetic algorithm with its parameters tuned with fuzzy rules so that it is adapted to the new environment. Experiments are conducted on standard benchmarks and the results are compared with recent algorithms. Then, multimedia data analysis, such as segmentation of moving objects, is experimented as a dynamic multiobjective problem and solved by the proposed algorithm.

Mobile Crowdsensing (MCS) is a powerful sensing paradigm, which provides sufficient social data for cognitive analytics in industrial sensing, and industrial manufacturing. Considering the sensing costs, sparse MCS, as a variant, only senses the data in a few subareas, and then infers the data of unsensed subareas by the spatio-temporal relationship of the sensed data. Existing works usually assume that the sensed data are linearly spatiotemporal dependent, which cannot work well in real-world nonlinear systems, and thus, result in low data inference accuracy. Moreover, in many cases, users not only require inferring the current data, but also have an interest in predicting the near future, which can provide more information for users' decision making. Facing these problems, we propose a deep learning-enabled industrial sensing, and prediction scheme based on sparse MCS, which consists of two parts: matrix completion and future prediction. Our goal is to achieve high-precision prediction of future moments under the hypothesis of sparse historical data. To make full use of the sparse data for prediction, we first propose a deep matrix factorization method, which can retain the nonlinear temporal-spatial relationship, and perform high-precision matrix completion. In order to predict the subareas' data in several future sensing cycles, we further propose a nonlinear autoregressive neural network, and a stacked denoising autoencoder to obtain the temporal-spatial correlation between the data from different cycles or subareas. According to the results gained by experiments on four real-world industrial sensing datasets consisting of six typical tasks, it can be seen that the method in this article improves the accuracy of prediction using sparse data.

Graph convolutional neural networks have attracted increasing attention in recommendation system fields because of their ability to represent the interactive relations between users and items. At present, there are many session-based methods based on graph neural networks. For example, SR-GNN establishes a user’s session graph based on the user’s sequential behavior to predict the user’s next click. Although these session-based recommendation methods modeling the user’s interaction with items as a graph, these methods have achieved good performance in improving the accuracy of the recommendation. However, most existing models ignore the items’ relationship among sessions. To efficiently learn the deep connections between graph-structured items, we devised a dynamic attention-aware network (DYAGNN) to model the user’s potential behavior sequence for the recommendation. Extensive experiments have been conducted on two real-world datasets, the experimental results demonstrate that our method achieves good results in capturing user attention perception.

Network coding improves transmission efficiency by combining packets at relay nodes and thus reduces the number of packets sent to the network. It is a network layer solution to improve network throughput and transmission efficiency. However, a coded packet must be decodable by the destination, otherwise it is a waste of resource to combine them together and to deliver the coded packet. This paper addresses how to find the coding solution that guarantees decodability at the destination. We first quantify the coding gain as the number of transmissions reduced, and then provide a method for runtime check whether a coding pair can be separated at the destination. The optimal coding solution is selected as the one that provides the maximum coding gain among all the decodable pairs. The algorithms can be applied to both unicast and multicast traffic. Simulation results show the number of transmissions can be reduced significantly, especially for multicast traffic where there are rich opportunities to apply network coding.

Network coding is a network layer technique to improve transmission efficiency. Coding packets is especially beneficial in a wireless environment where the demand for radio spectrum is high. However, to fully realize the benefits of network coding two challenging issues that must be addressed are: (1) Guaranteeing separation of coded packets at the destination, and (2) Mitigating the extra coding/decoding delay. If the destination has all the needed packets to decode a coded packet, then separation failure can be averted. If the scheduling algorithm considers the arrival time of coding pairs, then the extra delay can be mitigated. In this paper, we develop a network coding method to address these two issues, i.e., decodability and delay, for multi-source multi-destination unicast and multicast sessions. We use linear programming to find the most efficient coding design solution with guaranteed decodability. To reduce network relay, we develop a scheduling algorithm to minimize the extra coding/decoding delay and store-and-forward delay. Our coding design method and scheduling algorithm are validated through experiments. Simulation results show improved transmission efficiency and reduced network delay.

This paper addresses how to leverage the spatial multiplexing function of MIMO links to improve wireless network throughput. Wireless interference modeling of a half-duplex MIMO node is presented, based on which, routing, spatial multiplexing and scheduling are jointly considered in one optimization model. A linear program-based algorithm is proposed for the joint optimization, and numerical simulation results show that the joint optimization of routing with spatial-temporal multiplexing is superior to the separate design approaches, including separating routing from the other two designs, and separating scheduling from the other two designs.

One of the attacks observed against HTTP protocol is HTTP-GET attack using sequences of requests to limit accessibility of web servers. This attack has been researched in this report, and a novel detection technique has been developed to tackle it. In general, the technique uses entropy-based clustering and application of Bayes factors to distinguish among legitimate and attacking sequences. It has been presented that the introduced method allows for formation of recent patterns of behaviours observed at a web server, that remain unknown to the attackers. Subsequently, Bayes factors are introduced to measure anomaly of web sessions. The method performs reasonably well, against strategy and scope varying attackers.

In this paper, we present a formal model and analysis for the security extensions of the Domain Name System (DNSsec) in the interactive theorem prover Isabelle/HOL. Based on the inductive approach of security protocol analysis by Paulson in Isabelle/HOL, we show how the protocol can be modelled and important properties are proved. We prove that origin authentication works securely. In order to illustrate that the model is adequate, we show that previous domain name requests can be replayed - as in the classical DNS -by an attacker. These replays luckily can be uniquely identified in DNSsec due to the origin authentication mechanism that we establish to enhance security.

The paper focuses on the field of research on next generational e-Learning facility, in which knowledge-enhanced systems are the most important candidates. In the paper, a reference architecture based on the technologies of knowledge engineering is proposed, which has following three intrinsic characteristics, first, education ontologies are used to facilitate the integration of static learning resource and dynamic learning resource, second, based on semantic-enriched relationships between Learning Objects (LOs), it provides more advanced features for sharing, reusing and repurposing of LOs, third, with the concept of knowledge object, which is extended from LO, an implementing mechanism for knowledge extraction and knowledge evolution in e-Learning facilities is provided. With this reference architecture, a prototype system called FekLoma (Flexible Extensive Knowledge Learning Object Management Architecture) has been realized, and testing on it is carrying out.

The emergence of ubiquitous learning arouses demand of a new style of generating and managing learning resources, which urgently demands a new type of learning content management system (LCMS). Based on the service-oriented architecture, this research will construct a new type of LCMS (bottom-up) to support registration, management and sharing of learning objects (LO). Besides, fitting the upper-oriented application needs, the new LCMS can also support the generation of multi-mode courseware, which is adapted to multiple terminals, and it can also support the semi-automatic generation of courseware which meets the needs of E-learning. In this paper, we will first present semantic-oriented ubiquitous learning object model (SULOM), which concerns the semantic relationship modeling of LO. Then we will introduce the service-oriented realization of the SULOM, which is semantic-oriented ubiquitous learning object management system (SULOMS). Based on the Fedora system, the SULOMS supports the Web service, and can meet the teachers' need of ubiquitous, semantic, reorganization and interoperability of resource.

Password Authentication Protocol (PAP) is widely used in the Wireless Fidelity Point-to-Point Protocol to authenticate an identity and password for a peer. This paper uses a new knowledge-based framework to verify the PAP protocol and a fixed version. Flaws are found in both the original and the fixed versions. A new enhanced protocol is provided and the security of it is proved. The whole process is implemented in a mechanical reasoning platform, Isabelle. It only takes a few seconds to find flaws in the original and the fixed protocol and to verify that the enhanced version of the PAP protocol is secure.

Cryptographic protocols have been widely used to protect communications over insecure network environments. Existing cryptographic protocols usually contain flaws. To analyze these protocols and find potential flaws in them, the secure properties of them need be studied in depth. This paper attempts to provide a new framework to analyze and prove the secure properties in these protocols. A number of predicates and action functions are used to model the network communication environment. Domain rules are given to describe the transitions of principals' knowledge and belief states. An example of public key authentication protocols has been studied and analysed.

Program partitioning is a task of splitting a large, complex software system into functionally independent program modules. It is a key step in program understanding, software maintenance and software reuse. Traditional program partitioning methods are nonlinear. In most cases, the computational efforts needed for partitioning a source program will increase exponentially with the size of the source program. The NP-hard complexity constitutes a computational barrier for partitioning legacy software systems properly and efficiently. In this paper, we propose a new method that can partition a source program into program modules within a timescale that is linear with the size of the program. Our method uses special heuristic knowledge, based on psychological analysis on human programming styles, to partition a source program into domain-oriented program modules. A case study on a legacy C program that consists of 92 functions is reported to demonstrate the efficiency and effectiveness of this method.

In ad hoc wireless networks, to compute the transmission power of each wireless node such that the resulting network is connected and the total energy consumption is minimized is defined as a Minimum Energy Network Connectivity (MENC) problem, which is an NP-complete problem. In this paper, we consider the approximated solutions for the MENC problem in ad hoc wireless networks. We present a theorem that reveals the relation between the energy consumption of an optimal solution and that of a spanning tree and propose an optimization algorithm that can improve the result of any spanning tree-based topology. Two polynomial time approximation heuristics are provided in the paper that can be used to compute the power assignment of wireless nodes in both static and low mobility ad hoc wireless networks. The two heuristics are implemented and the numerical results verify the theoretical analysis.