The Internet of Things (IoT) interconnects vast numbers of sensors and devices that operate under different cryptographic infrastructures, making secure cross-domain communication essential. Most existing signcryption schemes are designed for a single infrastructure or, at best, two fixed ones, which limits applicability in heterogeneous and evolving IoT deployments. To address this need, we introduce LH3SC, a seamless elliptic-curve heterogeneous signcryption scheme that supports three infrastructures: certificateless cryptography, public key infrastructure, and identity-based cryptography, with a CLC sender. LH3SC enables devices across these domains to communicate securely without major architectural changes. The security analysis establishes IND-CCA2 confidentiality and EUF-CMA unforgeability, and the performance evaluation demonstrates lower computation and communication costs than representative schemes. These properties make LH3SC suitable for resource-constrained IoT settings, including healthcare automation, smart grids, and other distributed systems that require seamless cross-domain security.

Radio frequency identification (RFID) systems are an indispensable part of many critical Internet of Things (IoT) applications, including supply chain management and access control. Ensuring strong security in these systems is critical to safeguarding sensitive information and protecting user privacy. In recent years, in order to meet the diversified security needs of RFID systems, authentication and key protocols based on physical unclonable functions (PUFs) have received wide attention. Nevertheless, existing protocols typically require RFID tags to pre-store an excessive number of secret credentials and impose considerable computational and communication overheads, which prove challenging for resource-constrained RFID tag. Additionally, certain lightweight protocols fall short of achieving their intended security and functional objectives, exhibiting insufficient anonymity and untraceability, and vulnerability to desynchronization attacks. To address these critical challenges, this paper first proposes a lightweight anonymous authentication and key agreement protocol designed for an ideal PUF environment. The proposed protocol integrates the arbiter PUF with cryptographic hash functions, providing robust resistance to potential attacks while minimizing system overhead. Subsequently, an enhanced protocol specifically tailored for noisy PUF scenarios is presented. This protocol employs a fuzzy extractor to reliably derive stable keys from noisy PUF responses, thereby mitigating the instability caused by inherent noise. Through comprehensive security analysis and formal verification, as well as performance evaluations compared with existing state-of-the-art protocols, both protocols are demonstrated to overcome the limitations of prior protocols and provide efficient and practically feasible solutions well suited for resource-constrained RFID environments.

Emotion recognition from electroencephalography (EEG) signals is an important component of emotionally intelligent human–computer interaction systems. However, existing approaches often rely on handcrafted features or conventional deep learning architectures that struggle to generalize across subjects due to the high variability of EEG signals. Most prior studies focus primarily on binary emotion classification and provide limited investigation of more complex multi-class scenarios. This work presents EmoCaps, an end-to end deep learning framework based on a capsule network with a self-attention–guided routing mechanism to learn discriminative representations directly from raw EEG signals. The proposed model is evaluated on the DEAP dataset under both subject dependent and subject-independent settings and across binary and multi-class emotion recognition tasks involving valence, arousal, and dominance dimensions. Experimental results demonstrate that EmoCaps consistently outperforms several representative deep learning models. In subject-dependent binary classification, the proposed approach achieves accuracies of up to 97%, while in the more challenging subject-independent setting it exceeds 80% accuracy across emotional dimensions. The framework also achieves strong performance in four-class and eight-class emotion recognition tasks and provides, to the best of our knowledge, the first reported results for subject independent multi-class emotion recognition on this dataset. Although the capsule-based architecture introduces higher computational cost, the proposed model significantly improves robustness and generalization across subjects. These results highlight the potential of EmoCaps for real-world emotion-aware applications in healthcare, adaptive learning, and affective computing systems.

Cloud computing supports the Internet of Things (IoT) in handling diverse and large-scale data. However, outsourcing data control to the cloud raises security concerns, particularly in key management. Although Ciphertext-Policy Attribute-Based Encryption (CP-ABE) preserves data confidentiality, it entrusts key management to a centralized attribute authority, resulting in the key escrow problem. Furthermore, existing CP-ABE schemes lack mechanisms for key verification and identity authentication, leaving IoT systems susceptible to key errors and impersonation attacks. To overcome these limitations, we propose Secure and User-autonomous Attribute-based Data Sharing (SUAD) for cloud-assisted IoT. The SUAD scheme transfers key management from the authority to data users themselves, thereby eliminating key escrow. Built on a data user-centric architecture, the SUAD scheme removes the decryption privilege of the attribute authority. To prevent key forgeries and operational errors, we design a correctness verification mechanism covering five critical keys and the decryption result, along with a two-way interactive authentication protocol based on the Schnorr scheme for reliable identity verification. The SUAD scheme further supports dynamic user management, enabling user logout, replacement, and joining while optimizing maintenance overhead through periodic updates. We formally prove that SUAD achieves selective IND-CCA security in the random oracle model. Both theoretical analysis and experimental evaluations demonstrate that SUAD enhances user autonomy and strengthens security without incurring additional encryption or decryption costs, confirming its practicality for IoT deployments.

In cloud-Internet of Things (IoT) healthcare systems, private medical data leakage is a serious concern as the cloud server is not fully trusted. Dynamic searchable symmetric encryption (DSSE), with necessary forward and backward privacy security properties, enables doctors to retrieve ciphertexts while guaranteeing data privacy. However, existing forward and backward private DSSE schemes are not well-suited for cloud-IoT healthcare systems with attribute-value type databases. To this end, we propose an efficient privacy-preserving conjunctive searchable encryption scheme for cloud-IoT healthcare systems, called PC-SE. It is the first conjunctive DSSE scheme designed for attribute-value type databases. Specifically, we design flexible search capabilities for PC-SE to address users’ various search requirements. It can not only achieve precise conjunctive search based on keywords but also realize broad attribute search. Moreover, our scheme achieves fine-grained search for attribute values while maintaining forward and Type-I - backward privacy. This approach reduces the communication burden and minimizes the risk of privacy exposure. To ensure that users with different authorities can only access the corresponding attribute values, we introduce an attribute access control mechanism in PC-SE. Finally, security analysis and experimental results demonstrate that PC-SE is secure and effective.

Analyzing privacy breaches in Internet-based messaging applications is challenging due to overlapping and sometimes conflicting requirements such as confidentiality, anonymity, unlinkability, and user consent. Existing static analysis techniques typically target isolated aspects of privacy, limiting their scope. In this work, we introduce a static analysis framework based on a composite privacy model that captures the interdependencies among these requirements. This unified model enables the systematic identification of technical privacy violations and their associated legal implications, such as infringements of data protection laws and digital rights. We apply our framework to Ejabberd, a real-time communication server used in messaging platforms like WhatsApp. Our analysis focuses on confidentiality and consent-driven privacy concerns, including the right to be informed and the right to erasure. The results highlight the effectiveness of our approach in bridging technical analysis with legal accountability.

The ever-evolving Internet of Things (IoT) has ushered in a new era of intelligent manufacturing across multiple industries. However, the security and privacy of real-time data transmitted over the public channel of the Industrial IoT (IIoT) remain formidable challenges. Existing lightweight protocols often omit one or more critical security features, such as anonymity and untraceability, and are susceptible to threats like desynchronization attacks. Additionally, they struggle to achieve an optimal balance between robust security and performance efficiency. To bridge these gaps, we introduce a new lightweight key agreement security scheme that guarantees secure access to the IIoT-enabled flexible manufacturing system (FMS). The strength of our scheme lies in its utilization of the authenticated encryption with associative data (AEAD) primitive, AEGIS, along with hash functions and physical unclonable functions, which secure the IIoT ecosystem. Additionally, our scheme offers flexibility in the form of the addition of new machines, password updates, and revocation in cases of theft or loss. A comprehensive security analysis demonstrates the efficacy of the proposed scheme in thwarting various attacks. The formal analysis, based on the Real-or-Random (RoR) model, ensures session key indistinguishability, while the informal analysis highlights its resilience against known attacks. The comparative assessment demonstrates that the proposed scheme consistently outperforms the benchmark schemes across multiple dimensions, including security and functionality features, computational and communication overheads, and runtime efficiency. Specifically, the proposed scheme achieves peak performance enhancements of 77.55%, 44.73%, and 69.6% in computational overhead, runtime overhead, and communication overhead, respectively, underscoring its substantial performance advantages.

IoT sensors are made of physical materials, and due to natural decay in materials, sensor data drifts over time. Even though sensors are calibrated after deploying at the site, the accumulation of errors in sensor measurements due to sensor drifts renders the data progressively irrelevant, creating significant issues for end applications. In this article, we propose a software-driven drift detection and calibration framework based on probabilistic observation in latent space using variational autoencoders (VAEs). The proposed method utilizes the latent distribution of the generative model from sampled observational data, which are collected during the calibration phase of the deployed sensors. Variational inference in VAEs is employed to approximate the true posterior distribution for detecting sensor drifts, incorporating metrics such as Kullback-Leibler (KL) divergence. Additionally, reconstruction loss is utilized for calibrating the sensors. Both simulated and real-world sensor data are used to evaluate the proposed method. Experimental results demonstrate significant improvement over existing drift detection and calibration techniques.

By utilizing the sensing and perception capabilities of various devices, the Internet of Things (IoT) enables more precise awareness of the real world, thereby enhancing management and resource utilization efficiency. However, due to their open deployment environments and frequent message exchanges, IoT endpoints are highly vulnerable to a wide range of security threats and privacy breaches, including forgery, data theft, and information leakage. Therefore, to address these challenges and ensure device legitimacy verification and secure data exchange among IoT devices, we propose a privacy-preserving and traceable certificateless anonymous mutual authentication scheme (PPT-CLAMA). PPT-CLAMA not only eliminates the need for a secure channel during key generation but also prevents attackers from tracing the real identity of devices through their own identity or public keys while providing pseudonym and anonymous authentication to devices, demonstrating greater practicality. Furthermore, through security proofs and analysis, PPT-CLAMA satisfies various high-level security properties, including mutual authentication, key agreement, nonrepudiation, unlinkability, perfect forward secrecy, known session-specific temporary information security, traceability, anonymity, and privacy preservation. The simulation results indicate that, compared to authentication and key agreement schemes, PPT-CLAMA reduces the average computational overhead and average communication overhead during the authentication process by 6.73% and 3.31%, respectively, demonstrating higher computational and communication efficiency.

For wearable devices with constrained computational resources, it is typically required to offload processing tasks to more capable servers. However, this practice introduces vulnerabilities to data confidentiality and integrity due to potential malicious network attacks, unreliable servers, and insecure communication channels. A robust mechanism that ensures anonymous authentication and key agreement is therefore imperative for safeguarding the authenticity of computing entities and securing data during transmission. Recently, Guoet al.proposed an anonymous authentication key agreement and group proof protocol specifically designed for wearable devices. This protocol, benefiting from the strengths of previous research, is designed to thwart a variety of cyber threats. However, inaccuracies in their protocol lead to issues with authenticity verification, ultimately preventing the establishment of secure session keys between communication entities. To address these design flaws, an improved ultra-lightweight protocol was proposed, employing cryptographic hash functions to ensure authentication and privacy during data transmission in wearable devices. Supported by rigorous security validations and analyses, the proposed protocol significantly boosts both security and efficiency, marking a substantial advancement over prior methodologies.

The surge in smartphone and wearable device usage has propelled the advancement of the Internet of Things (IoT) applications. Among these, e-healthcare stands out as a fundamental service, enabling the remote access and storage of patient-related data on a centralized medical server (MS), and facilitating connections between authorized individuals such as doctors, patients, and nurses over the public Internet. However, the inherent vulnerability of the public Internet to diverse security threats underscores the critical need for a robust and secure user authentication protocol to safeguard these essential services. This research presents a novel, resource-efficient user authentication protocol specifically designed for healthcare systems. Our proposed protocol leverages the lightweight authenticated encryption with associated data (AEAD) primitive Ascon combined with hash functions and XoR, specifically tailored for encrypted communication in resource-constrained IoT devices, emphasizing resource efficiency. Additionally, the proposed protocol establishes secure session keys between users and MS, facilitating future encrypted communications and preventing unauthorized attackers from illegally obtaining users' private data. Furthermore, comprehensive security validation, including informal security analyses, demonstrates the protocol's resilience against a spectrum of security threats. Extensive analysis reveals that our proposed protocol significantly reduces computational and communication resource requirements during the authentication phase in comparison to similar authentication protocols, underscoring its efficiency and suitability for deployment in healthcare systems.

Encrypted sharing of Internet of Medical Things (IoMT) data is essential for facilitating collaboration, safeguarding patient privacy, and advancing clinical research. However, existing encryption schemes face numerous challenges in multi-user environments. Traditional proxy re-encryption requires exclusive ciphertext for each user, which is evidently unsuitable for IoMT's multi-user scenarios. Meanwhile, attribute-based encryption provides flexible data access control, but its complex computations and high resource demands limit its use in large-scale IoMT environments. Additionally, challenges like single-point failure and redundant backups emerge in ciphertext storage. To address these challenges, we propose a dual-cryptography mechanism integrating enhanced proxy re-encryption and attribute-based encryption. Our scheme enables unified ciphertext access for authorized users while applying attribute encryption exclusively to small data keys. To mitigate potential data loss from storage server failures, we propose a decentralized ciphertext storage and recovery mechanism with verifiable secret sharing. Furthermore, we implement decentralized ciphertext storage using verifiable secret sharing, ensuring recoverability from server failures. Formal analysis proves confidentiality under the random oracle model. Experimental results demonstrate high security strength, computational efficiency, and robustness. The solution prevents single-point failures, resists collusion attacks, and maintains traceability through blockchain-integrated audit trails.

Blockchain technology is used often as a merger with other technologies to achieve a high level of security, privacy, and robustness and to handle issues such as maliciousness of nodes, privacy leakage, the selfishness of nodes, communication delays, and high execution and transaction costs. There is currently a lack of a comprehensive system for automating and cost-effectively managing vehicle repairs, maintenance, and other associated services. To solve such issues we proposed a novel futuristic comprehensive model that integrates a blockchain-based framework to safely record vehicle maintenance, validate repair services, and oversee parts inventory. It employs smart contracts and consensus protocols to secure communications and data storage, thus reducing data breaches and vulnerabilities from single-point failures. A reward system is embedded within the network to encourage positive behavior and deter detrimental actions. We also incorporated advanced privacy-ensuring methods, like zero-knowledge proofs and secure multi-party computation, to safeguard sensitive data while preserving its utility. Our model features automatic detection and response mechanisms for node failure, improving network resilience by 25% thus also providing a 20% reduction in execution, operational costs, and scalability with an enhancement of 15%, underscoring the model’s efficiency in vehicular repair and maintenance activities. Results and simulations clearly depict the overall performance and efficiency in terms of security, privacy, node failure, and the management of vehicle repairs with respect to other closely related models.

Intelligent Transportation Systems (ITS) supported by smart vehicles have revolutionized modern transportation, offering a wide range of applications and services, such as electronic toll collection, collision avoidance alarms, real-time parking management, and traffic planning. However, the open communication channels among various entities, including smart vehicles, roadside infrastructure, and fleet management systems, introduce security and privacy vulnerabilities. To address these concerns, we propose a novel security framework, named blockchain-assisted lightweight authenticated key agreement security framework for smart vehicles-enabled ITS (BASF-ITS), which ensures data protection both during transit and while stored on cloud servers. BASF-ITS employs a combination of efficient cryptographic primitives, including hash functions, XOR operator, ASCON, elliptic curve cryptography, and physical unclonable functions (PUF), to design authenticated key agreement schemes. The inclusion of PUF significantly enhances the system’s resistance to physical attacks, preventing tampering attempts. To ensure data integrity when stored on the cloud, our framework incorporates blockchain technology. By leveraging the immutability and decentralization of the blockchain, BASF-ITS effectively safeguards data at rest, providing an additional layer of security. We rigorously analyze the security of BASF-ITS and demonstrate its strong resistance against potential security ass aults, making it a robust and reliable solution for smart vehicle-enabled ITS. In a comparative analysis with contemporary competing schemes, BASF-ITS emerges as a promising approach, offering superior functionality traits, enhanced security features, and reduced computation, communication, and storage costs. Furthermore, we present a practical implementation of BASF-ITS using blockchain technology, showcasing the computational time versus the “transactions per block” and the “number of mined blocks”, confirming its efficiency and viability in real-world scenarios.Note to Practitioners—This article is motivated by designing an efficient, lightweight, and anonymous blockchain-enabled authenticated security framework that can fix the security and privacy concerns in insecure environments for ITS applications, such as automated road speed enforcement, collision avoidance alarm systems, and traffic planning and management, etc. Authenticated key agreement schemes are extensively used to secure communications in the ITS environment. However, the existing state-of-the-art schemes are not efficient in terms of performance, are not resilient against potential security attacks, and do not support anonymity, untraceability, and unlinkability. Therefore, we propose the authenticated security framework to secure communication among the participating entities in the ITS environment. It utilizes efficient cryptographic primitives, such as hash function, XOR-operator, ASCON, elliptic curve cryptography, and PUF. It is shown that the proposed framework can be deployed as a robust tool to address the ITS security problems efficiently. Moreover, the proposed framework is lightweight and efficient and can be easily deployed in various ITS applications and other resource-constrained environments. However, the participating entities, such as vehicles and roadside units, must be PUF-enabled to deploy the proposed framework.

Collaborative resource scheduling between edge terminals and cloud centers is regarded as a promising means of effectively completing computing tasks and enhancing quality of service. In this paper, to further improve the achievable performance, the edge cloud resource scheduling (ECRS) problem is transformed into a multi-objective Markov decision process based on task dependency and features extraction. A multi-objective ECRS model is proposed by considering the task completion time, cost, energy consumption and system reliability as the four objectives. Furthermore, a hybrid approach based on deep reinforcement learning (DRL) and multi-objective optimization are employed in our work. Specifically, DRL preprocesses the workflow, and a multi-objective optimization method strives to find the Pareto-optimal workflow scheduling decision. Various experiments are performed on three real data sets with different numbers of tasks. The results obtained demonstrate that the proposed hybrid DRL and multi-objective optimization design outperforms existing design approaches.

Although attribute-based signcryption (ABSC) offers a promising technology to ensure the security of IoT data sharing, it faces a two-fold challenge in practical implementation, namely, the linearly increasing computation and communication costs and the heavy load of single authority based key management. To this end, we propose a Secure and Lightweight Multi-authority ABSC scheme called SLIM in this paper. The signcryption and de-signcryption costs of devices are reduced to a small constant by offloading most of the computation to the edge server. To minimize communication and storage costs, a short and constant-size ciphertext is designed. Moreover, we adopt a hierarchical multi-authority architecture, setting up multiple attribute authorities that manage keys independently to prevent the bottleneck. Rigorous security analysis proves that the SLIM scheme can resist adaptive chosen ciphertext attacks and adaptive chosen message attacks under the standard model. Simulation experiments demonstrate the correctness of our theoretical derivations and the cost reduction of the SLIM scheme in computation, communication and storage.

With ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large datasets often exhibit sparsity and pose cold start problems, necessitating the development of responsible recommender systems. Knowledge graphs have utility in responsibly representing information related to recommendation scenarios. However, many studies overlook explicitly encoding contextual information, which is crucial for reducing the bias of multi-layer propagation. Additionally, existing methods stack multiple layers to encode high-order neighbor information while disregarding the relational information between items and entities. This oversight hampers their ability to capture the collaborative signal latent in user-item interactions. This is particularly important in health informatics, where knowledge graphs consist of various entities connected to items through different relations. Ignoring the relational information renders them insufficient for modeling user preferences. This work presents an end-to-end recommendation framework named KGCAN (Knowledge Graph Enhanced Contextualized Attention-Based Network), which explicitly encodes both relational and contextual information of entities to preserve the original entity information. Furthermore, a user-specific attention mechanism is employed to capture personalized recommendations. The proposed model is validated on three benchmark datasets through extensive experiments. The experimental results demonstrate that KGCAN outperforms existing knowledge graph based recommendation models. Additionally, a case study from the healthcare domain is discussed, highlighting the importance of attention mechanisms and high-order connectivity in the responsible recommendation system for health informatics.

Wearable computing has shown tremendous potential to revolutionize and uplift the standard of our lives. However, researchers and field experts have often noted several privacy and security vulnerabilities in the field of wearable computing. In order to tackle these problems, various schemes have been proposed in the literature to improve the efficiency of authentication and key establishment procedure. However, the existing schemes have relatively high computation and communication overheads and are not resilient to various potential security attacks, which reduces their significance for applicability in constrained wearable devices. In this work, we propose an efficient and anonymous authenticated key exchange scheme for wearable computing (EAKE-WC), which performs mutual authentication between the user and the wearable device, and between the cloud server and the user. It also establishes secret session keys for each session to secure communication among the communicating entities. Additionally, the proposed EAKE-WC scheme is designed using authenticated encryption with associated data (AEAD) primitives like ASCON, bitwise XOR, and hash functions. Our results from the security analysis depict compliance of the proposed EAKE-WC with wearable computing's security criteria. In addition, we also demonstrate through a comprehensive comparative analysis that the proposed scheme, EAKE-WC, outperforms the existing benchmark schemes in various key performance areas, including lower communication and computational overheads, enhanced security, and added functionality.

An edge cloud architecture plays a key role in improving the user task computing service system by combining the powerful data processing capability of cloud centres with the low latency of edge computing. Existing methods for maximizing the efficiency of an edge cloud architecture take into account time and task parameters but ignore other factors such as load balancing, cost, and user satisfaction when scheduling resources. In this work, we propose a many-objective resource scheduling model for optimizing the performance of an edge cloud architecture, which takes into account the time spent on task, cost, load balance, user satisfaction, and trust measurement. The resource scheduling model converges to the optimal solution using a novel many-objective ensemble optimization algorithm based on a dynamic selection mechanism. The study also explores the support set convergence of eight evolutionary operators using the ensemble algorithm. The model solutions are dynamically updated with the help of the dynamic integration probability, and then a selection criteria is used to pick the best solutions from the pool of generated solutions. Two simulations on a benchmark dataset are used to verify the usefulness and performance of the designed algorithm. Our approach was able to locate more than half of the best solutions on the benchmark functions, and it also showed to be a better model solution than the some of the popular many-objective algorithms for dealing with the edge cloud resource scheduling problem, according to the results obtained from the simulations.

Although 5G and beyond communication technology empower a large number of edge heterogeneous devices and applications, the stringent security remains a major concern when dealing with the millions of edge computing tasks in the highly dynamic heterogeneous networks (HDHNs). Blockchains contribute significantly to addressing security challenges by guaranteeing the reliability of data and information. Since the node’s mobility, there are risks of exiting the network and leaving the remaining tasks noncomputed. Therefore, we model the cost function of offloaded computing tasks as a dynamic stochastic game. To reduce the computational complexity, the Time-Variant Mean-Field term (TVMF) is adopted to solve the cost-optimized problem. What’s more, we design an Adaptivity-Aware Practical byzantine fault tolerance consensus Protocol (AAPP) to dynamically formulate domains, execute leader node selection with regard to task completion and quickly verify computational results. In addition, a Dynamic Multi-domain Fractional Repetition uncoded repair storage (DMFR) scheme with variant redundancy is proposed to reduce the storage pressure and repair overhead. The simulation is implemented to demonstrate our scheme outperforms the benchmarks in terms of cost and time overhead.

Delayed delivery of packets hinders the performance of time-sensitive Internet of Things (IoT) applications and incurs increased power consumption. Stochastic routing schemes solve the problem of saving all participating nodes from getting their power drained out quickly. However, stochastic routing incurs the problem of delivery delays and reliable end-to-end delivery. This paper proposes a novel routing scheme, called $Q_{i j}$ routing, to solve these problems. The proposed $Q_{i j}$ routing scheme is a combination of a classic routing scheme, called Distance Vector Algorithm, with a novel re-definition of the cost of a link to find the best path from source to destination. $Q_{i j}$ takes into account the wireless link reliability of any connection between two nodes, and the transmission delay of IoT devices working together in a distributed network. With the presented mathematical model, a routing table is maintained that let an individual node in a network find the distinctly prominent reliable path among many routes from source to destination. The superior efficiency of $Q_{i j}$ routing scheme over eminent stochastic routing schemes is proven through simulation results in terms of reduced end-to-end expected delivery delay and increased expected delivery ratio.

This research aims to design a smart parking system using cloud and Internet of Things (IoT) technologies to improve the process of finding parking spaces in urban areas. The proposed system utilizes sensors and microcontrollers in each parking space to provide real-time data to users through a mobile application. The primary objective of our research is to address issues, such as time-consuming manual searches for parking spaces and reduce traffic congestion. This research suggests a system of reliable smart ultra-sonic sensors connected to a microcontroller for easy and remote parking automation. Users can select a parking space by clicking on the available slot in the mobile application. Additionally, the mobile application informs users about empty spots. This smart parking system can be implemented on both small and large-scale models, and the results suggest that it effectively replicates traditional automobile parking, with the added convenience of a mobile application, in urban areas.

It has been observed that the use of radio-frequency fingerprinting (RF-FP) for location estimation (LE) of vehicles can significantly improve secrecy capacity (SC) for urban scenarios in $6^{th}$ generation cellular vehicle-to-everything (6G C-V2X) communication. However, in most of the literature, interference is considered constant for simplicity. Thus, there is a need to investigate the impact of varying levels of interference and incomplete channel state information (CSI) on the performance of SC. This study examines the impact of shadowing and interference in the presence of heavy traffic and obstructions. We have proposed a technique that analyzes the effects of varying levels of interference for LE via RF-FP to improve SC with incomplete CSI. Simulation results demonstrate that interference has a significant impact on SC which depends on the distance and location of the eavesdropping vehicle. However, a decrease in SC is not solely caused by the increase in interference, since other factors, such as the speed and relative position of the illegitimate vehicles as well as incomplete CSI, can also significantly affect the SC performance, as demonstrated through simulations.

Abstract It is anticipated that sixth‐generation (6G) systems would present new security challenges while offering improved features and new directions for security in vehicular communication, which may result in the emergence of a new breed of adaptive and context‐aware security protocol. Physical layer security solutions can compete for low‐complexity, low‐delay, low‐footprint, adaptable, extensible, and context‐aware security schemes by leveraging the physical layer and introducing security controls. A novel physical layer security scheme that employs the concept of radio frequency fingerprinting (RF‐FP) for location estimation is proposed, wherein the RF‐FP values are collected at different points with in the cell. Then, based on the estimated location, the nearest possible road‐side unit for sending the information signal is located. After this, the effects on secrecy capacity (SC) and secrecy outage probability (SOP) in the presence of multiple eavesdropper per unit time are analysed. It has been shown via simulations that the proposed RF‐FP scheme increases SC by up to 25% for the same signal‐to‐noise ratio (SNR) values as those of the benchmarks, while the SOP tends to decrease by up to 30% as compared to the benchmark scheme for the same SNR value. Thus, the proposed RF‐FP‐based location estimation provides much better results as compared to the existing physical layer security schemes.

With the widespread popularity of mobile terminals in the Internet of Things (IoT), the demand for cross-domain access of mobile terminals between different regions has also increased significantly. The nature of wireless communication media makes mobile terminals vulnerable to security threats in cross-domain access. Identity authentication is a prerequisite for secure data transmission in the cross-domain, and it is also the first step to guarantee the credibility of data sources. Most existing authentication schemes are based on bilinear pairing or public-key encryption and decryption with high computation overhead, which are not suitable for the resource-limited mobile IoT terminals. Moreover, these schemes have some security drawbacks and cannot meet the security requirements of cross-domain access. In this article, we propose a lightweight cross-domain mutual identity authentication (LCDMA) for the mobile IoT environment. LCDMA uses a symmetric polynomial instead of high-complexity bilinear pairing in the traditional schemes. We theoretically analyze the security performance under the random oracle model. Our results show that LCDMA not only resists common attacks but also preserves secure traceability while guaranteeing anonymity. Performance evaluation further demonstrates that our scheme has better performance in terms of computation and communication overhead, compared with other existing representative schemes.

The next-generation Internet of vehicles (IoVs) seamlessly connects humans, vehicles, roadside units (RSUs), and service platforms, to improve road safety, enhance transit efficiency, and deliver comfort while conserving the environment. Currently, numerous entities communicate in the IoVs environment via insecure public channels that are susceptible to a variety of security assaults and threats. To address these security challenges, we design an anonymous authenticated key exchange mechanism for the IoVs in smart transportation supported by blockchain, referred to as AAKE-BIVT. AAKE-BIVT securely transmits traffic information to a cluster head, before heading to a nearby RSU utilizing the established secret session keys via mutual authentication and key agreement. A cloud server (CS) then securely aggregates data from related RSUs and generates transactions. The CS combines the transactions into blocks in a peer-to-peer network of CSs, and the blocks are confirmed and added to the blockchain via a voting-based consensus method. By means of rigorous informal security studies and formal security analysis through the random oracle model, we reveal that the proposed AAKE-BIVT is resistant to a broad range of potential security assaults in the IoVs environment. Furthermore, a comparative study reveals that AAKE-BIVT outperforms existing state-of-the-art techniques, in terms of security and functionality while being more efficient in terms of communication and computation. Additionally, the blockchain simulation validates the implementation viability of our proposed AAKE-BIVT.

In-vehicle network security plays a vital role in ensuring the secure information transfer between vehicle and Internet. The existing research is still facing great difficulties in balancing the conflicting factors for the in-vehicle network security and hence to improve intrusion detection performance. To challenge this issue, we construct a many-objective intrusion detection model by including information entropy, accuracy, false positive rate and response time of anomaly detection as the four objectives, which represent the key factors influencing intrusion detection performance. We then design an improved intrusion detection algorithm based on many-objective optimization to optimize the detection model parameters. The designed algorithm has double evolutionary selections. Specifically, an improved differential evolutionary operator produces new offspring of the internal population, and a spherical pruning mechanism selects the excellent internal solutions to form the selected pool of the external archive. The second evolutionary selection then produces new offspring of the archive, and an archive selection mechanism of the external archive selects and stores the optimal solutions in the whole detection process. An experiment is performed using a real-world in-vehicle network data set to verify the performance of our proposed model and algorithm. Experimental results obtained demonstrate that our algorithm can respond quickly to attacks and achieve high entropy and detection accuracy as well as very low false positive rate with a good trade-off in the conflicting objective landscape.

The proliferation of Internet of Things (IoT) devices in the 5G era has resulted in increased security vulnerabilities and zero-day attacks, underscoring the importance of network intrusion detection systems (NIDS). However, existing NIDS have limitations in terms of accuracy, recall rates, false alarm rates, and generalization capabilities, and they cannot meet the IoT's requirements for low latency and limited computing resources. To overcome these challenges, we propose a NIDS based on a pseudo-siamese stacked autoencoder (PSSAE), deployed in the fog computing layer. Our system uses unsupervised training of stacked autoencoders (SAEs) to extract deep semantic features of normal and abnormal traffic, followed by supervised learning with labels to improve characterization and classification capabilities. The results show that our proposed method's accuracy and detection rate (DR) is 2% to 15% and 1%–14% higher than the existing techniques using the KDDTest+ dataset, respectively. Our proposed method outperformed the existing methods by 1% to 4% using the KDDTest+ dataset. The F1-Score is higher by 3%–11.55% using the KDDTest+ dataset. On the other hand, using the KDDTest-21 dataset, the accuracy of our proposed method also outperformed the existing technique by 6.09%–13.81%. The DR and F1-Score are higher by 7.02% and 5.57%, respectively, using the KDDTest+ dataset. This is due to the fact that each layer of the network trained by SAEs is more capable of extracting the semantic features of the data than the DNN-trained network directly.

Mobile edge computing (MEC) is an effective computing tool to cope with the explosive growth of data traffic. It plays a vital role in improving the quality of service for user task computing. However, the existing solutions rarely address all the significant factors that impact the quality of service. To challenge this problem, a trusted many-objective model is built by comprehensively considering the task time delay, server energy consumption, trust metrics between task and server, and user experience utility factors in multi-server MEC networks. We decompose the original problem into task offloading (TO) and resource allocation (RA) to address the model. Then a novel hybrid many-objective optimization algorithm based on cascading clustering and incremental learning is designed to optimize the TO decision solutions. A low-complexity heuristic method is adopted based on the optimal TO decision solutions to optimize the RA problem continuously. To verify the model's validity and the optimisation algorithm's superiority, five other advanced many-objective algorithms are used for comparison. The results show that our algorithm has more than half the number of the superior values for the benchmark problem. And the obtained model solution shows good performance on different indicators metrics for the decomposition problem.

Internet of Things (IoT) is a promising technology that attains significant consideration in diverse industrial areas, i.e., agriculture, engineering, logistics, trading, ecological examining, security surveillance, energy, and healthcare. IoT gains much more attention with the rapid advancement of wireless communication and sensor networks as millions of intelligent devices get involved in IoT. These intelligent devices' raw data must be captured and processed to support decision-making. However, IoT applications trust the central server for information storage, processing, and mediators for wireless transmission. Consequently, it can leak the information and lead to high costs and delays. Hence, data security is the leading interest for the IoT. Blockchain technology can be deployed to overcome the security and effectiveness of the gigantic data in IoT. Blockchain is studied as a key to permitting storing, processing and sharing of data in an efficient, secure manner. In addition, reinforcement learning can convene the high data rate requirements. It will help us to optimize the performance of the blockchain-enabled IoT framework.

Mobile edge computing (MEC) is used for compu-tationally complex applications by offloading it to the nearby edge server either partially or entirely. The problem arises of selecting whether the component is to be offloaded to the mobile edge server (MES) for execution, or it needs to be executed locally. Therefore, we propose a time-efficient decision offloading scheme (TEDOS) to derive a data set and train an artificial neural network (ANN) on the derived data set. TEDOS provide the smart decision on the optimal permutation of the divided components based on delay. We developed a mathematical model for delays in communication, execution and component queuing. We obtained a final delay for all possible permutations of component offloading policies. Our model obtained 91 % accurate results as compared to the existing schemes. The simulation result shows that our proposed model outperforms the state-of-the-art.

Fog computing is an emerging technology that aims at reducing the load on cloud data centers by migrating some computation and storage toward end-users. It leverages the intermediate servers for local processing and storage while making it possible to offload part of the computation and storage to the cloud. Inspired by the benefits of fog computing, we present a novel paradigm that considers the context of social phenomena. Online and off-line human interactions and the mobile social network’s relentless growth allowed real-world data and created users’ traces. We categorize social phenomena into two main groups to integrate with fog computing from social interactions’ continuous development. In this regard, the first contribution addresses the social relationship between the end-users and fog nodes based on personal benefits. The social relationship considers trust, reciprocity, incentives, and selfishness mechanisms. The second contribution describes the group-based social behavior, i.e., centrality, community, and colocation in fog computing networks (FCNs). We also discuss the impact of social phenomena on FCNs in network performance, resource allocations, security, and privacy. We present open challenges and highlight future directions on social perception to encourage follow-up work.

The rapid growth of various vehicular apps such as automotive navigation and in-car entertainment has brought the explosion of vehicular data. Such a growth has given rise to a huge challenge of maintaining the quality of cloud storage services for the whole period of storage in vehicular networks. As a result, poor quality of services easily causes data corruption problems and thereby threats vehicular data integrity. Blockchain, a tamper-proofing technique, is considered a promising approach for mitigating data integrity risks in cloud storage. However, existing blockchain-based schemes for auditing long-term cloud data integrity suffer from poor communication performance in a vehicular network. In this study, a blockchain-based offline auditing scheme for cloud storage in the vehicular network is proposed to improve auditing performance. Inspired by the data structure of blockchain, we design an evidence chain to achieve offline auditing, which allows the cloud to spontaneously generate data integrity evidence without communicating with auditors during the evidence generation phase. Furthermore, we extend our scheme to support public and automatic validation based on the smart contract. We prove the security of the proposed scheme under the random oracle model and further provide the performance evaluation by comparing with the state-of-the-art approaches.

The modern computing applications are presently adapting to the convenient availability of huge and diverse data for making their pattern recognition methods smarter. Identification of dominant emotion solely based on the text data generated by humans is essential for the modern human–computer interaction. This work presents a multimodal text-keystrokes dataset and associated learning methods for the identification of human emotions hidden in small text. For this, a text-keystrokes data of 69 participants is collected in multiple scenarios. Stimuli are induced through videos in a controlled environment. After the stimuli induction, participants write their reviews about the given scenario in an unguided manner. Afterward, keystroke and in-text features are extracted from the dataset. These are used with an assortment of learning methods to identify emotion hidden in the short text. An accuracy of 86.95% is achieved by fusing text and keystroke features. Whereas, 100% accuracy is obtained for pleasure-displeasure classes of emotions using the fusion of keystroke/text features, tree-based feature selection method, and support vector machine classifier. The present work is also compared with four state-of-the-art techniques for the same task, where the results suggest that the present proposal performs better in terms of accuracy.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) offers a promising technique to fulfil the high data demand and connectivity of the Internet-of-Things (IoT) and 5G communications because it owns valuable and unknown spectrum resources. Using massive antennas with recently introduced drone-enabled aerial computing platforms, named unmanned aerial vehicles (UAVs), can cast high energy consumption if fully-digital precoding is employed at the UAVs. Using hybrid precoding at a UAV can reduce hardware complexity and energy consumption but is challenging with a need for joint optimization of three precoding matrices at the UAV (sixth-order polynomial objective function). In this paper, we propose to decompose the original UAV hybrid precoding challenge into three subproblems and develop a coordinated descent optimization (CDO) algorithm to solve the three problems recursively. In addition, the convergence and complexity of this new technique are analyzed. Numerical studies indicate the improved effectiveness of the proposed solution over existing solutions.

In this letter, we construct MaRine ShiP (MRSP-13), a novel dataset containing 37,161 ship target images belonging to 13 classes with bounding box annotation, and among them there are 3051 images labeled with pixel-level annotation. This dataset equips us with the capability to conduct baseline experiments on maritime target classification, detection and segmentation. We propose a cross-layer multi-task CNN model for maritime target detection, which can simultaneously solve ship target detection, classification, and segmentation. Experimental results have demonstrated the efficiency of the MRSP-13 dataset to be used for maritime target analysis. In addition, the results validate the fact that by adopting the strategies of feature sharing, joint learning, and cross-layer connections, the proposed model achieves superior performance with less annotations. We believe that our MRSP-13 dataset and corresponding baseline experiments will lay down the foundation for further research in maritime target processing.

Cognitive radio networks (CRNs) promise to accommodate billions of Internet of Things (IoT) devices within scarce spectrum by allowing secondary users (SUs) to use licensed spectrum. However, the devices need uninterruptible communication, which the conventional CRNs cannot fulfill. This necessitates successful service completion probability (SSCP) enhancement in CRNs. Further, maintaining fairness among SUs, in terms of availing network services, is a matter of consideration for ensuring the network services to be fairly available to all SUs. In this paper, we propose a hybrid CRN (HCRN) scheme to analyze two problems. Firstly, we investigate SSCP enhancement by utilizing hybrid underlay-interweave mode of CRNs and propose a dynamic channel reservation algorithm to support interrupted users. Secondly, we propose a multi-attributes based fairness-driven channel determination (MFD) algorithm for channel interruption, which ensures fairness among SUs in availing network services. Furthermore, continuous-time Markov chain is used for modelling, and mathematical formulations are derived for SSCP. The proposed scheme is evaluated under various network traffic loads and channel failure rates. Numerical results show significant improvement in SSCP and reduction in forced termination rate as compared to the benchmark. Similarly, the MFD algorithm brings a prominent improvement in fairness.

The intrinsic broadcast nature of wireless communication let the attackers to initiate several passive attacks such as eavesdropping. In this attack, the attackers do not disturb/stop or interrupt the communication channel, but it will silently steal the information between authentic users. For this purpose, physical layer security (PLS) is one of the promising methodologies to secure wireless transmissions from eavesdroppers. However, PLS is further divided into keyless security and secret key‐based security. The keyless security is not practically implemented because it requires full/part of instantaneous/statistical channel state information (CSI) of the eavesdroppers. Alternatively, key‐based security is exploiting the randomness and reciprocity of wireless channels that do not require any CSI from an eavesdropper. The secret key‐based security is due to the unpredictability of wireless channels between two users. However, the secret key‐based security mainly on two basic parameters, i.e. coherence time and transmission power. Nevertheless, the wireless channel between users has a short coherence time, and it will provide shorter keys' length due to which eavesdropper can easily extract keys between communicating parties. To overcome this limitation, we proposed the power allocation scheme to improve the secret key generation rate (SKGR) to strengthen the security between authentic users.

Training of the convolution neural network (CNN) is a problem of global optimisation. This study proposed a hybrid modified particle swarm optimisation (MPSO) and conjugate gradient (CG) algorithm for efficient training of CNN. The training involves MPSO–CG to avoid trapping in local minima. Particularly, improvements in the MPSO by introducing a novel approach for control parameters, improved parameters updating criteria, a novel parameter in the velocity update equation, and fusion of the CG allows handling the issues in training CNN. In this study, the authors validate the proposed MPSO algorithm on three benchmark mathematical test functions and also compared with three different variants of the baseline particle swarm optimisation algorithm. Furthermore, the performance of the proposed MPSO–CG is also compared with other training algorithms focusing on the analysis of computational cost, convergence, and accuracy based on a standard problem specific to classification applications on CIFAR‐10 dataset and face and skin detection dataset.

In future small cell networks, tracking areas (TAs) that are defined for location management will be updated frequently to cope with the massive signalling overhead. In this study, a TA planning method based on spectral clustering is proposed to minimise the network signalling overhead. Firstly, handover and paging statistics are simulated to construct a series of graphs showing user mobility and traffic. Then, the TA planning problem is formulated as a classical graph partitioning problem. Finally, a new TA planning method based on spectral clustering is used to build the new TA plan. Simulation results show that the proposed method can effectively reduce the system location update rate and signalling overhead, and improve the system performance.

This paper inquires the achievement of secret key generation (SKG) in device-to-device (D2D) communications with the aid of relay. The confidential information between D2D users is taken under the consideration of physical layer secret key generation scheme with the help of colluding or non-colluding relay node. The selected relay conforms to help in the generation of secret keys to keep the information confidential from eavesdropping. In order to ensure the information confidential between D2D users, we explicate a mechanism for selecting relay node based on two basic social phenomena for the selection of relay node. The non-colluding relay selection is considered under the scenario of social trust, while colluding relay selection is based on social reciprocity. Furthermore, we utilize coalition game theory for the selection of optimal relay node in order to improve secret key generation rate (SKGR). Particularly, to attain more eminent SKGR within channel coherence time, the coalition game approach is determined to select an optimal node for relaying by D2D users. On the basis of relay selection, social phenomena, and coalition game theory, we propose an algorithm for achieving higher SKGR. The generated keys are not only protected from eavesdropper but also from the selected (colluding or non-colluding) relay. The performance of our proposed scheme validates and guarantees information confidentiality in D2D communications.

Fog computing is a promising solution for new emerging applications requiring intensive computation resources and low latency. Devices at the edge of network can share idle resources and collaboratively accomplish the computing tasks in fog computing. Thus, task publishers have heterogeneous options when offloading computing tasks considering the quality of transmission links, energy consumption and other hardware constraints of fog nodes. To incentivize these devices to participate in computation offloading, effective incentive mechanisms are needed. In this paper, utilizing the framework of contract theory, we formulate the negotiation between task publisher and fog nodes as an optimization problem. The optimal contract is the Nash equilibrium solution achieved by task publisher and fog nodes. Simulation results show that an optimal contract can maximize the utility of task publisher meanwhile guarantee the individual rationality and incentive compatibility of fog nodes. Therefore, edge devices can be incentivized effectively to involve in the computation offloading.

With the popularity of proximity-based services, device-to-device (D2D) communication underlaying cellular networks is a promising technology to cope with the growing demands by improving network resource utilization. However, the wireless communication's broadcast nature is vulnerable to eavesdropping, and thus, ensuring a secrecy communication for both cellular user equipments (CUEs) and D2D pairs in an underlay network is a challenging issue. We investigate the problem of physical-layer secure transmission jointly with resource allocation in D2D communications. Different from existing works, we framed overlapping (partial) coalitional game where each D2D pair can access multiple CUEs' spectral resources. Moreover, the multiple D2D pairs can share single CUE subchannel in multiple eavesdroppers scenario to ensure information security for both CUEs and D2D pairs and to maximize system sum rate in a socially aware D2D network. We incorporate the mutual interference and propose different transmission modes for a secrecy-ensured resource allocation-based overlapping coalition formation scheme with transferable utility to obtain a final stable partition. We further prove the proposed algorithm stability, convergence, and computational complexity. Both analytical and numerical results demonstrate the effectiveness of our proposed scheme, which ensures a system-wide security and at the same time improves the performance by maximizing the system sum rate.

Physical layer security (PLS) is a promising technology in device-to-device (D2D) communications by exploiting reciprocity and randomness of wireless channels, which attracts considerable research attention in the D2D communications community. In this paper, we investigated PLS for secure key generation rate (SKGR) in D2D communications based on cooperative trusted and non-trusted relays. By leveraging social ties, we exploit three social phenomena for secure communications, i.e., trusted scenario (social trust), non-trusted scenario (social reciprocity), and partially trusted scenario (mixed social trust and social reciprocity). The coalition game theory is further utilized to select the optimal relay pairs for improving SKGR. On the basis of social ties, we develop an algorithm for SKGR that protects the keys secret from both eavesdropper and non-trusted selected relays. We incorporate secure relays selection and system wide security for D2D communications. The stability and convergence of the proposed algorithm are also proved in this paper. Both numerical and analytical results verify effectiveness and consistency of our proposed scheme, which ensures better SKGR performance in D2D communications.

Device-to-device communications are promising technology to enhance 5G cellular network. However, mobility greatly affects the transmission capacity of proximal devices. In this paper, we investigate the problem of mobility-assisted content transmission and resource allocation by leveraging the contact patterns determined by proximal users. We formulate the problem with the help of statistical properties of contact rate, and utilize convex optimization to solve the problem of content transmission and resource allocation for mobile users. We propose the optimal Resource Allocated Content Transmission (RACT) algorithm based on pseudo-polynomial time algorithm using dynamic programming. Extensive simulations are evaluated under realistic mobility factors, which indicates the efficiency of our proposed RACT algorithm.