Code models are increasingly adopted in software development but remain vulnerable to backdoor attacks via poisoned training data. Existing backdoor attacks on code models face a fundamental trade-off between transferability and stealthiness. Static trigger-based attacks insert fixed dead code patterns that transfer well across models and datasets but are easily detected by code-specific defenses. In contrast, dynamic trigger-based attacks adaptively generate context-aware triggers to evade detection but suffer from poor cross-dataset transferability. Moreover, they rely on unrealistic assumptions of identical data distributions between poisoned and victim training data, limiting their practicality. To overcome these limitations, we propose Sharpness-aware Transferable Adversarial Backdoor (STAB), a novel attack that achieves both transferability and stealthiness without requiring complete victim data. STAB is motivated by the observation that adversarial perturbations in flat regions of the loss landscape transfer more effectively across datasets than those in sharp minima. To this end, we train a surrogate model using Sharpness-Aware Minimization to guide model parameters toward flat loss regions, and employ Gumbel-Softmax optimization to enable differentiable search over discrete trigger tokens for generating context-aware adversarial triggers. Experiments across three datasets and two code models show that STAB outperforms prior attacks in terms of transferability and stealthiness. It achieves a 73.2% average attack success rate after defense, outperforming static trigger–based attacks that fail under defense. STAB also surpasses the best dynamic trigger–based attack by 12.4% in cross-dataset attack success rate and maintains performance on clean inputs.

Existing multi-label topic models face several challenges when interpreting texts annotated with multiple labels: (1) they often associate irrelevant text segments with incorrect labels, which negatively impacts both segment and label interpretation; (2) they fail to effectively capture the semantic relationships between tokens and labels within the segment; (3) they do not integrate contextualized knowledge that could improve interpretability. To overcome these issues, we introduce the Contextualized Prompting Topic Model (CPTM). CPTM utilizes Chain-of-Thought (CoT) prompting to better align text segments with their semantically relevant labels. Furthermore, it integrates label-specific token visualization and topic mining procedure to facilitate the interpretation of tokens and labels. Experimental evaluations conducted on three multi-label text datasets show that CPTM significantly outperforms existing models in both segment and label interpretation. Human assessments also verify CPTM's effectiveness in accurately identifying label-relevant tokens within segments and providing insightful token-level interpretation.

In resource-constrained environments such as IoT sensors and mobile devices, there is a strong demand for efficient conjunctive keyword search over privacy-sensitive data. However, existing schemes struggle to simultaneously suppress sterm equality leakage, the cross-query intersection pattern (IP), and the volume pattern without incurring prohibitive overhead. In this paper, we present XORCMM, a practical volume-hiding encrypted conjunctive multi-map (EMM) designed for robust leakage suppression. First, we shift the index construction from single keywords to global-ordering co-occurrence pairs, which ensures that search tokens are no longer tied to static keyword identities, thereby suppressing sterm equality leakage. Second, we integrate an incremental multiset hash aggregation mechanism directly into a fully padded Xor filter. This allows the server to aggregate multiple conjunctive results into a single, fixed length response, concealing both IP and volume patterns while eliminating the data redundancy of prior schemes. Third, we employ a prefix-constrained PRF to compactly encode keyword pairs, generating succinct query tokens whose size is independent of keyword volumes. Formal security analysis proves that XORCMM is adaptively secure with sterm equality, IP, and volume leakages hidden. Experimental results demonstrate that XORCMM achieves up to a 2.99× speedup in client setup, a 3.3× speedup in server query time, and reductions of 47% in response size and 84.61% in search token size, providing a stronger security guarantee with significantly higher efficiency.

With the rapid growth of connected vehicles and the increasing demand for low-latency and reliable communication, traditional vehicular networks face severe security challenges. Existing cross-domain authentication schemes often suffer from complex certificate management, key leakage, and inefficient trust coordination. In this paper, we propose a Secure and Efficient Blockchain-based Cross-Domain Authentication (SEBA) scheme for vehicular networks that integrates blockchain with physical unclonable functions (PUFs) to establish a robust trust management framework. The proposed scheme adopts a hybrid on-chain/off-chain architecture, where blockchain-based smart contracts provide tamper-resistant credential storage, while time-critical authentication operations are executed off-chain to improve system throughput and response latency. By incorporating PUF technology, SEBA enables reliable binding between physical devices and cryptographic identities, effectively resisting impersonation and cloning attacks. In addition, lightweight cryptographic primitives are employed to further reduce computation and communication overhead. The security of the proposed scheme is formally verified using ProVerif and further analyzed through theoretical security analysis. Performance evaluation is conducted using a simulation framework that models vehicular communication latency, cryptographic operation costs, and blockchain transaction confirmation delays. Experimental results under varying vehicle loads demonstrate that SEBA significantly outperforms existing schemes, achieving up to 83% higher throughput and 38% lower authentication latency. Overall, SEBA provides a privacy-preserving and low-latency authentication framework suitable for large-scale and highly dynamic vehicular networks.

In the era of quantum computing, data sharing in the Internet of Vehicles (IoV) confronts the challenges of auditability, efficiency, and quantum security. However, existing research remains insufficient to meet the requirements of high mobility, resource constraints, and resilience against quantum attacks. In this paper, we propose a new quantum-secure auditable data sharing framework, in which we first present a quantum-resistant puncturable signature algorithm (QRPPRFS). Combining the low-noise LPN-based pseudorandom function with an optimized trapdoor generation mechanism, it achieves compact key sizes and millisecond-level signing; second, the blockchain and dual-commitment proof mechanism are integrated to ensure anonymity, transparent auditability and robustness. Finally, we rigorously demonstrate the correctness of our scheme, the EUF-CMA with puncturing of QRPPRFS, and the knowledge soundness and witness zero-knowledge of the dual-commitment proof system. Experimental evaluations show that, under the practical setting$n=256$and$q \approx 2^{23}$, the proposed scheme keeps both signing and verification latencies below 10 ms, and reduces the initial secret-key storage to only 0.22 MB. These results demonstrate that the proposed scheme achieves both enhanced security and high efficiency, outperforming existing schemes.

Redactable blockchains are challenging the core principle of traditional blockchains: immutability. One such example is the chameleon hash-based blockchain. Despite rapid academic advances, most solutions have not yet simultaneously considered four key aspects: the degree of modification privileges, the transparency of the modification process, the consistency in the post-redaction global state, and system security after redaction. In this paper, we present a controllable, publicly auditable, and redactable blockchain with a main-auxiliary architecture. Specifically, we integrate weighted secret sharing, digital signature, and non-interactive zero-knowledge proof technologies to propose a verifiable and controllable chameleon hash primitive. To encourage logical nodes, it includes a reputation evaluation mechanism and a DAO-based governance model. Additionally, we construct a redactable bi-directionally anchored main-auxiliary blockchain structure, where the auxiliary chain exclusively maintains the modification proofs associated with each block of main chain. Any node can audit the modification history or, in the event of an accusation, self-prove. This structure also simplifies global state updates for newly joined or restarted nodes. Finally, we provide comprehensive security proofs for our construction, conduct extensive experiments to evaluate its functionality and performance, and compare it with analogous solutions to demonstrate its superiority.

With the increasing sophistication of malware, enhanced Attributed Control Flow Graphs (ACFGs) have become a fundamental representation and are widely applied in malware detection. However, existing CFG-based detection techniques primarily extract shallow features of malware, neglecting deeper structural and semantic characteristics. Additionally, retaining all basic blocks in CFGs significantly increases the memory overhead of detection models. To address these issues, we propose MCLPF, collaborative malware detection with interpretable pruning, to improve the overall performance of existing malware detection systems that rely on fine-grained control flow features. MCLPF first introduces a novel Attributed Interpretable Flow Graph (AIFG) to extract functional attributes, integrating node-level features, edge-level features, and assembly language embedding features derived from Large Language Models (LLMs). Subsequently, it proposes an efficient and reliable detection scheme by alternately updating the graph structure and language learning modules through L-Step and G-Step, rather than synchronously training Language Models (LMs) with Graph Neural Networks (GNNs) on large-scale graphs. We conduct experiments using public datasets involving four different architectures (i.e., PE-32, PE-64, ELF-32, and ELF-64) and demonstrate that our model achieves an exceptionally high detection accuracy (i.e., 99.30%). After pruning 100% of noncritical nodes and edges, the sample size is reduced to approximately 8% of the original, with an average time cost reduction of 74.7%, while the detection performance fluctuation averages only about 1%. Extensive cross-dataset evaluations validate the effectiveness and efficiency of the proposed method.

Choosing an appropriate division method is crucial for partitioning two-dimensional spatial data under the constraints of differential privacy. The current mainstream partitioning methods include grid-based partitioning and hierarchical partitioning. In order to optimize query accuracy while satisfying differential privacy conditions, it remains challenge to achieve the sum minimization of noise error and uniformity assumption error. To address this issue, we propose the HOLG (Hierarchical Optimization of Logical Grids) algorithm, employing a ”divide-merge-divide” approach. It begins with fine-grained grid partitioning of the data domain, followed by heuristic merging of grids with similar data distributions. After determining the scale of the query domain, the merged regions are further subdivided into smaller regions with similar query probabilities, constructing a hierarchical structure to reduce uniformity assumption errors. Additionally, we design a novel noise injection method and introduce consistency constraints to further minimize noise errors. To reduce the time complexity of the HOLG partitioning method, Huffman trees is employed to optimize the processing of the hierarchical tree set generated by HOLG, ensuring query utility while effectively reducing the query response time for the partitioning algorithm. Experimental results on large-scale spatial datasets demonstrate that HOLG outperforms similar algorithms in query accuracy. Furthermore, when combined with the Huffman tree optimization, it effectively reduces query response time.

This paper investigates the integration of Unmanned Aerial Vehicles (UAV) with Internet of Things (IoT) infrastructure to enhance Mobile Edge Computing capabilities in urban environments. While UAVs offer promising solutions for mobile edge computing, their deployment in high-rise urban areas presents significant challenges, particularly in computational resource balancing, energy-efficient trajectory planning, and dynamic IoT service provisioning. We propose a comprehensive low-altitude UAV-assisted mobile edge computing framework that jointly optimizes UAV trajectory planning, the assignment of offloaded tasks to specific UAVs, and the strategic deployment and energy management of the UAV fleet to maximize system utility. We first formulate this as a multi-objective optimization problem and prove its NP-hardness due to its non-convex and integer linear programming nature. To tackle this challenge, we develop a decomposition-based approach that systematically addresses the coupled variables. We then propose a novel Variable Strategy Reinforcement Learning-based Lin-Kernighan-Helsgaun algorithm that synergistically combines Q-learning, Sarsa, and Monte Carlo methods with the LKH algorithm. The proposed solution is further enhanced by incorporating two refined trajectory optimization mechanisms, the Trajectory Refining Algorithm and the Service-Oriented Segmented Trajectory Refining Algorithm, specifically designed to improve the robustness and reliability in solving the Computation Offloading Trajectory Optimization Problem. Extensive simulation results demonstrate that our proposed algorithms consistently outperform state-of-the-art approaches, achieving faster convergence, higher energy efficiency for UAVs, and lower computational latency for IoT devices.

The adoption of blockchain technology in healthcare has significantly enhanced data integrity, transparency, and user privacy. However, high storage overhead and resource-intensive operations remain major challenges to its widespread deployment, particularly in large-scale or resource-constrained healthcare environments. To address these challenges, we propose STORChain, a storage-optimized blockchain framework designed for data services in healthcare. The framework introduces the Clustered Merkle Patricia Tree (C-MPT), a novel logical structure that aggregates similar transaction types to maximize storage efficiency while ensuring Proof of Inclusion (PoI). A Selective Transaction Pruning Strategy (STPS) is employed to prioritize and prune essential historical data, improving data access efficiency. Additionally, an incentive-based Delegated Proof-of-Stake (DPoS) consensus algorithm is utilized, integrating a probabilistic election mechanism to promote fairness and node inclusivity. Comprehensive theoretical analysis and practical experiment results indicate that STORChain significantly reduces storage overhead, optimizes data access, and outperforms existing schemes.

Adversarial Example (AE) generation is a key instrument for stress-testing and hardening malware detectors, yet most existing techniques target Portable Executable (PE) files and do not transfer cleanly to Executable and Linkable Format (ELF) binaries prevalent in Internet of Things (IoT) environments. We proposeMalElves, a reinforcement learning-driven AE generation framework for cross-platform ELF malware.MalElvesmakes three core technical contributions. First, a code-data-aware manipulation framework unifies obfuscation and rewriting across ARM, ×86, and ×64 architectures while preserving functionality. Second, a sample-efficient state design reduces 2,350 raw ELF features to a compact 21-dimensional input. Third, a shaped multi-detector reward uses fully disclosed PPO settings for full reproducibility. We evaluateMalElveson 161,414 malware samples and 74,260 benign samples. We test against four static detectors and a behavioral-ensemble stress test. The method achieves average ASRs of 89.9%, 85.3%, 63.8%, 60.6%, and 24.7% across detectors. The overall average ASR reaches 64.8%. Each successful evasion requires 2.24 interaction rounds on average.

Efficient and privacy-preserving medical data sharing remains a key challenge in the era of digital healthcare. Existing schemes often suffer from limited access control, substantial computational overhead, and reliance on trusted third parties. This article proposes a revocable Attribute-Based Searchable Encryption (ABSE) scheme built upon a hierarchical blockchain architecture, which enables a secure keyword search over encrypted data via expressive attribute-based access policies, and a revocation mechanism is integrated to support dynamic user management. We design a lightweight and privacy-preserving computation framework that offloads expensive cryptographic operations to the cloud, thereby reducing the burden on user-side devices. Furthermore, we propose a lightweight consensus protocol, termed lottery consensus, which replaces traditional proof-of-work hash computations with meaningful operations that are tied to the ABSE-based data-sharing process. Extensive theoretical analysis and simulation experiments demonstrate the superior efficiency of the proposed scheme, and formal security proofs demonstrate its robustness against threats.

The increasing frequency of forest fires has become a serious threat to both ecological environments and public safety. Recent advances in unmanned aerial vehicle (UAV) technology have provided new opportunities for monitoring field environments, owing to UAVs’ high maneuverability and real-time data collection capabilities. To address the challenges of real-time performance and energy efficiency in forest fire data collection networks, our work integrates UAVs with Internet of Things (IoT) technologies to efficiently collect and process environmental parameters and human activity information. The aim of our study is to develop an instantaneous and energy-efficient forest fire data collection system that rapidly predicts and responds to fire risks by optimizing UAV data acquisition and trajectory planning. First, we propose a model based reinforcement learning framework and a dataset difficulty definition method to refine the training sample distribution. Subsequently, an adaptive learning mechanism is introduced to gradually increase dataset difficulty, thereby accelerating model convergence to better adapt to environmental changes. Furthermore, considering the dual challenges of information freshness and UAV energy constraints, a novel node partitioning strategy is designed to decompose the overall problem into multiple subproblems for cooperative solution. Extensive simulation results demonstrate that the proposed method outperforms existing approaches in terms of real-time capabilities and energy efficiency, validating its effectiveness for forest fire detection and warning systems.

Mining topics relevant to the advanced AI dialogue system, such as ChatGPT, from short-length posts on social media poses several challenges for existing topic-mining approaches. Firstly, Bag-Of-Words approaches, including probabilistic topic models and their embedding-based variants, may struggle to extract interpretable topics due to insufficient word co-occurrence. Secondly, contextualized based approaches, built on the autoencoding framework, often yield entangled topic spaces, resulting in the mixing of irrelevant words into topics. To address these limitations, we propose a novel Dis entangled Contextualized-neural Topic Model (DisCTM) based on textual representation learning. DisCTM leverages a pre-trained transformer language model to incorporate word sequence information and deal with the sparsity in short text. Additionally, it employs a topic disentangling mechanism to decorrelate dimensions of the latent topic space, effectively separating semantically irrelevant words into different topics. Extensive experiments have been conducted on three publicly available text corpora, and the results demonstrate the effectiveness of DisCTM in extracting high-quality topics, as measured by topic coherence and diversity metrics.

Customer-generated reviews on e-commerce websites often contain valuable insights into users' interests in product genres and provide a rich source for mining user preferences. However, most existing neural topic models tend to generate meaningless topics that share low correlations with product genres. Furthermore, they often fail to mine user preferences and discover personalized topic profiles due to the absence of explicit user modeling. To address these limitations, we propose a novel Genre-aware Personalized neural Topic Model (GPTM), which incorporates product genre information into the topic modeling process to ensure the relevance between mined topics and product genres. Moreover, it could produce a personalized topic profile for each user by performing user preference modeling. Extensive experimental results on three publicly available Amazon review corpora validate the effectiveness of the proposed GPTM in genre-aware topic modeling. Furthermore, GPTM surpasses state-of-the-art baselines in user preference mining and generates high-quality personalized topic profiles.

The development of vehicular networks has significantly improved driving safety and enabled a wide range of intelligent transportation applications. However, in cross-domain scenarios, vehicular networks still face challenges, such as security risks, privacy breaches, and heavy computing burden. In this article, we propose a novel cross-domain authentication scheme for vehicular networks based on Mobile Edge Computing (MEC), in which the computing tasks on the vehicle side are offloaded to the edge server, which effectively alleviates the computation overhead on the resource-constrained vehicle side. Unlike existing schemes, we adopt a server function division and a distributed registration center approach to alleviate the burden and risk associated with the trusted authority (TA) during the authentication process. In addition, we use anonymity mechanism and batch verification to achieve privacy protection and improve authentication efficiency, respectively. Through rigorous security proofs and detailed security analyses, it is demonstrated that the proposed scheme meets the security requirements of vehicular networks and can withstand a broader range of security attacks. Performance comparison results indicate that the proposed scheme outperforms existing related schemes in terms of both communication and computation overheads.

With the integrating development of Internet of Things (IoT) and edge computing, data sharing among various IoT devices has become the trend for extensive applications. However, data sharing in IoT environments is challenged by limited terminal resources and distributed data storage, which places higher demands on security and effectiveness. Even though existing searchable encryption technologies provide feasible solutions, there remain challenges in terms of trustworthy retrieval and execution efficiency. To address these issues, this paper proposes an efficient supply-demand-aligned and trustworthy multi-keyword (ESTM) search scheme in edge-assisted IoT environments, where encrypted documents are stored in edge servers. Furthermore, blockchain-based smart contracts are employed so that search results are consensus on the Fabric ledger and data users can verify whether the returned encrypted documents are reliable using encrypted hashes. To achieve the supply-demand-aligned requirement, the RoBERTa (Robustly optimized BERT approach) model is introduced for text classification and data users can judge which edge server stores data best suits their demands. Meanwhile, coordinate (COO) format is adopted into index vectors and search vectors, which can decrease the time required for constructing an index tree to about 2.7% and the time required for generating trapdoors to about 4.6%. Finally, we conducted an in-depth security analysis and performance comparison with existing works, results show that the proposed scheme is effective and feasible.

As the airport terminal area becomes progressively crowded, costly delays and adverse environmental impact due to excessive fuel burn require effective aircraft routing and scheduling in the airport terminal area. Aircraft routing and scheduling in the airport terminal area refers to the safe and efficient movement of aircraft among airport facilities such as runways, aircraft stands and taxiways. It is a hybrid optimization problem that involves both the airport ground movement problem and the aircraft sequencing problem. The paper investigates the hybrid problem with constraints such as the safe distance between aircraft, queuing limit at the runways, and release time difference for sake of safety. The objective is to minimize the average taxiing time of the aircraft. A particle swarm optimization and Q-learning based aircraft routing and scheduling algorithm (PSO-QL-ARS) is proposed for the problem considered. The proposal adopts the main framework of PSO. It consists of three major components: the ideal shortest path algorithm, the semi-no-wait schedule generation method and the Q-learning based particle evolving method. The shortest path algorithm takes into consideration turning time according to the turning angles on the path and different taxiing speeds depending on the taxiway types. The semi-no-wait schedule generation method is presented to compute the feasible trajectory plan for an aircraft, including the waiting time at each vertex and the taxiing time on each taxiway. It attempts to place the waiting time of an aircraft on the aircraft stand. The Q-learning-based particle evolving method employs a Q-table to select the finest evolving action which is used to update the position of the given particle. The proposed algorithm is compared with four baseline algorithms. The experimental results show that the proposal outperforms the compared baseline algorithms in effectiveness and robustness.

With the development of blockchain technology, Blockchain-based Crowdsensed Data Trading (BCDT) has emerged as an attractive data exchange paradigm. Although it addresses security issues in data transactions, most recent research primarily focuses on offline scenarios, overlooking the critical importance of enabling real-time online data trading, where it suffers from dynamic worker participation and potential malicious attacks. In this paper, we propose a Blockchain-based Secure and Fair Online Incentive Mechanism (BSFOIM), which primarily incorporates a smart contract called BSFOIMToken, designed to function in online scenarios. In particular, we first introduce a multi-stage auction combined with a time discount factor in BSFOIM to quantify the contribution of workers in completing sensing tasks. Meanwhile, to ensure sensing data quality and worker selection fairness, we propose a Fairness-based Truth Discovery Mechanism (FTDM) with two core modules: a fine-grained reputation system to identify reliable workers and filter out malicious ones, and an upper confidence bound algorithm to optimize worker selection and avoid local optima. Finally, we implement these functions in BSFOIMToken and deploy a prototype on the Ethereum blockchain, demonstrating its practicality and robust performance. Rigorous theoretical and comprehensive experimental tests have proven their adherence to truthfulness, budget feasibility and individual rationality.

Blockchain transaction privacy is a highly researched topic across various application scenarios. Current privacy-preserving schemes in blockchain employ advanced cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, to balance transaction privacy with regulatory requirements. However, these schemes encounter challenges, including computational inefficiency, data expansion, and overlooked metadata privacy, such as timestamp protection. In this paper, we first propose a privacy-enhanced traceable anonymous transaction scheme based on data transaction scenarios. This scheme integrates ring signature and Merkle hash tree techniques, effectively shortening the signature size and optimizing the verification process compared to existing combinations of ring signatures and zero-knowledge proofs. A novel verifiable timestamp privacy protection method is introduced, which obfuscates timestamps to prevent tampering without compromising integrity. To enhance scalability, this method extends to multiple transaction processing scenarios and implements a timestamp-sharing strategy to reduce the computational burden. It also allows tracking authorities to monitor the long-term addresses of both transaction parties if necessary. Rigorous security analysis and extensive experimental evaluations demonstrate that this scheme achieves superior privacy, traceability, and scalability compared to existing approaches.

To automatically mine structured semantic topics from text, neural topic modeling has arisen and made some progress. However, most existing work focuses on designing a mechanism to enhance topic coherence but sacrificing the diversity of the extracted topics. To address this limitation, we propose the first neural-based topic modeling approach purely based on mutual information maximization, called the mutual information topic (MIT) model, in this article. The proposed MIT significantly improves topic diversity by maximizing the mutual information between word distribution and topic distribution. Meanwhile, MIT also utilizes Dirichlet prior in latent topic space to ensure the quality of mined topics. The experimental results on three publicly benchmark text corpora show that MIT could extract topics with higher coherence values (considering four topic coherence metrics) than competitive approaches and has a significant improvement on topic diversity metric. Besides, our experiments prove that the proposed MIT converges faster and more stable than adversarial-neural topic models.

Unmanned aerial vehicles (UAVs) are increasingly utilized in smart city applications, particularly for urban surveillance. UAVs can be provisioned as mobile surveillance to avoid various difficulties in ground operations and reduce extensive labor cost. However, their limited energy capacity restricts flight time and coverage, making it difficult to build a large-scale, long-term city-wide monitoring network. To address this problem, an ubiquitous public transportation network is introduced for UAVs to periodically recharge by landing on public transportation buses. We propose a novel public transportation-assisted UAV scheduling framework that leverages the existing bus network to enable recharging of UAVs. Two intertwined sub-problems are addressed: the UAV trajectory planning and the surveillance task offloading problems. The trajectory planning is modeled as a Traveling Salesman Problem (TSP) and it is solved via the Lin-Kernighan heuristic (LKH), decomposing the bus station network into sub-graphs for efficient routing. For task offloading, a time-slot-based scheduling method is proposed that dynamically assigns UAVs to monitor points of interest (PoIs) while ensuring energy constraints and full coverage. Experimental results demonstrate that the proposal outperforms baseline algorithms, achieving a 1.72%-3.46% average extension in system lifetime compared to state-of-the-art baselines, while maintaining computational efficiency (average runtime: 277.57 ms). The robustness of the proposal is further validated across diverse testing instances with various parameter settings.

With the advancement of cloud computing technology, cloud-based personal health record (CB-PHR) has become an increasingly popular way for modern patients to flexibly manage and share their health records with doctors. However, the confidentiality of CB-PHR privacy is vulnerable to threats due to unauthorized users and untrusted cloud service provider (CSP). Additionally, patients and doctors may be constrained by changes in access permissions and limited device resources. To address these challenges, we propose an efficient decentralized privacy-preserving attribute-based access control scheme with verifiable policy update (DPVPU) for CB-PHR systems. DPVPU supports large attribute universe and safeguards the privacy of both the access policy and the doctor’s identity through partially hiding the access policy and employing a one-way anonymous key agreement technique. Unlike re-encrypting ciphertext, it can dynamically update policy by fully utilizing the previous policy and outsourcing the computation of ciphertext update to the CSP. Also, we design an efficient verification algorithm enabling patients to check the correctness of updated ciphertext. For devices with limited resources, we use online/offline and outsourced decryption techniques to reduce system costs. Finally, we provide formal security proofs and performance analysis to demonstrate the security and practicality of DPVPU.

The widespread use of smartphones has yielded a wealth of behavioral sequence data from user interactions. These interactions offer insights into user preferences and patterns for personalized behavior prediction. However, there are some challenges in current privacy-preserving works for analyzing these data. These approaches have suboptimal service quality with smaller but longer datasets and insufficient emphasis on secure pattern storage and retrieval in real-world applications. To handle these challenges on smartphones, we propose a novel Privacy-enhanced Frequent Sequence Mining and Retrieval (PrivFSMR) framework for this scenario. Specifically, we first introduce a dynamic sequence truncation to anonymize the maximum sequence length of datasets. Following this, we design a privacy-enhanced FSM algorithm to uncover patterns, effectively reducing the privacy budget by integrating differential privacy and the Markov assumption. During the secure pattern storage, PrivFSMR employs symmetric encryption for protection and constructs an encrypted index forest for retrieval. Lastly, future behavior retrieval leverages current device information and the index forest to search similar patterns, thereby predicting potential user behaviors in the future. A comprehensive security analysis proves the PrivFSMR framework guarantees differential privacy and maintains storage and retrieval confidentiality in the lifecycle. In addition to using two publicly available datasets, we also collected a real behavior dataset within 2-4 weeks from 30 users for evaluation. Experimental results on three datasets demonstrate that PrivFSMR excels in mining frequent patterns and predicting future behaviors compared to existing approaches.

In the paper, the Unmanned Aerial Vehicle (UAV) path planning and task offloading problem in UAV-assisted mobile edge computing (MEC) systems is investigated. A bi-criterion ant colony optimization (bi-ACO) framework is proposed for the considered problem with the objectives of minimizing the total cost and the completion time, meanwhile satisfying the energy, deadline, location, and priority constraints. In the bi-ACO framework, multiple heterogeneous colonies are introduced with different preferences of objectives. Each colony maintains five pairs of pheromone matrices for constructing feasible solutions. Besides the colony settings, three key components of bi-ACO are delicately designed: feasible solution generation method (FSGM) to construct a feasible solution, solution division method (SDM) to improve obtained solutions of good quality, and pheromone update method (PUM) to updates pheromone matrices by pheromone evaporation operation and pheromone enhancement operation based on the preferences of colonies. Four Pareto-based metrics are introduced to evaluate the performance of the compared algorithms. Experimental results show that the proposal outperforms the compared baseline algorithms in effectiveness and robustness.

The resource-constrained task scheduling problem has been one of the popular research topics in cloud computing systems. By employing the dynamic voltage and frequency scaling (DVFS) techniques, the task scheduling can be further constrained by energy consumption. The paper investigates the DAG task scheduling considering both the resource and energy constraints in heterogeneous distributed systems. The objective is to minimize the scheduling length. An energy-constrained task scheduling framework is employed, where tasks are initially scheduled according to their upward rank values. Then two heuristics are proposed to improve the initial solution, namely, the simulated annealing local search method and the frequency adjustment method. Experiments are conducted by testing a large number of instances with multiple parameter settings, and the results show that the proposed algorithms are effective and efficient.

When it comes to the fifth generation, collaborative edge computing is preferred for offloading computation-intensive tasks of low-latency applications in Internet of Things. In this paper, we consider the partial task offloading problem where tasks can be divided into subtasks and offloaded to nearby devices. The flow scheduling problem is integrated in the offloading process, i.e., multiple and conflicting route paths are considered. We propose the latency-aware partial task offloading framework (LaPTOF) for the considered problem. LaPTOF integrates a weighted priority ranking strategy (WPRS) which generates multiple solutions with different weights on task arrival time and the task processing time. A feasible solution generation method (FSGM) is designed where the best offloaded proportion of tasks are computed, and the appropriate offloaded devices and offload paths are determined. The proposed LaPTOF has an advantage in providing a scheduling plan that minimizes total completion time of task offloading in a shorter duration. The experimental results show that the proposal is suitable for the considered problem compared with JPOFH and its variants on both effectiveness and efficiency.

The integration of Internet of Things (IoT) with cloud–edge computing in cyber–physical systems has revolutionized the way healthcare enterprises manage electronic health records (EHRs). With more healthcare enterprises outsourcing encrypted EHRs to the cloud, searchable encryption (SE) is utilized to retrieve encrypted data, especially attribute-based SE (ABSE) can achieve fine-grained access control. However, ABSE usually requires a lot of computation, which imposes a serious burden on resource-limited devices. Moreover, ensuring fairness in data access is crucial in the healthcare domain, where both data users and owners may have conflicting interests. In order to overcome these problems, this article proposes an SE scheme with fine-grained access control for cloud-based EHRs sharing assisted by blockchain. It transfers computing tasks to edge servers and enables users to control who has access to their EHRs. The adoption of blockchain and smart contracts guarantees data integrity and transaction fairness. Moreover, a consensus algorithm is designed for the higher efficiency of the proposed scheme. Finally, security analysis proves that the proposed scheme resists adaptive chosen keyword attacks (CKAs). Performance analysis further confirms that it has more functionalities and is efficient for smart healthcare.

The Augmented Intelligence of Things enables many edge or end devices in the Internet of Things (IoT) to perform machine reasoning to make decisions, thus become more intelligent. For healthcare enterprises, huge physical data generated by smart devices facilitate to iterate their products. However, traditional data sharing models based on cloud outsourcing meet many security challenges, such as data confidentiality, reliability, and privacy protection, and most existing schemes have high computational complexity for the utilization of time-consuming cryptographic operations, such as bilinear pairing, which is not suitable for those resource-constrained IoT devices. To tackle the abovementioned issues, we present Double Rainbows, a promising data sharing scheme based on pairing-free searchable encryption. It is constructed on the cloud-edge-end architecture, supporting computing task transfer and reliable data storage and retrieval. The experimental results show that it outperforms in efficiency and exhibit more security functionalities.

The UAV-enable Mobile Edge Computing (MEC) systems and Vehicular Ad-hoc Network (VANET)-supported applications are very popular topics these days. The paper considers the vehicular task offloading problems for the Software-Defined Vehicular Network (SDVN)-supported services in the UAV-enabled MEC system. In the considered problem, one UAV and one edge server (ES) are provisioned for the workload from the moving vehicles in a certain region. For each vehicle in the region, it would periodically submit requests to the UAV-enable MEC system until it leaves the region. Each request will be taken as a computation task and could be offloaded locally on the vehicle, the UAV, or the ES. Multiple communication and energy consumption models are employed to formulate the problem model. The objectives are to minimize the total time delays and the energy consumption. A greedy heuristic based dynamic scheduling framework is proposed for the problem under study. Simulated experiments are delicately designed with dynamic traffics, various road and building distributions. Experimental results show that the proposal is more effective than the compared algorithm.

In the cloud-assisted Industrial Internet of Things (IIoT), ciphertext-policy attribute-based encryption (CP-ABE) could help the data owner (DO) share his sensitive data via the cloud under self-defined access structures. Among general CP-ABE schemes, the decryption overhead, the key generation cost, and the ciphertext length increase with the number of involved attributes. Additionally, only regular attributes are taking into consideration rather than weighted attributes. In this article, we proposed a secure, efficient, and weighted access control scheme (SEWAC) for cloud-assisted IIoT applications. SEWAC enables the DO to formulate any fine-grained access structure over weighted attributes without making it more complicated. Furthermore, such weighted attributes would not add the length of ciphertext. SEWAC also supports online/offline key generation to alleviate the computational cost of the authority from answering mass key requests in the online phase, while most computational tasks are executed in the offline phase. The heavy decryption overhead is offloaded to the cloud. To ensure the cloud to honestly execute the process of outsourced decryption, we design an efficient batch verification method, which allows the user to spend only three bilinear pairing operations in checking the correctness of batch results. We also give the formal security proof of the proposed scheme. Comprehensive comparisons and implementation results indicate that SEWAC can better achieve weighted access control, compressed ciphertext length, efficient key generation, and the assurance of the outsourced decryption result.

Mobile healthcare (mHealth) enables people to collect and share their personal health records (PHRs) and gain rapid medical treatment via mobile 5G-enabled Industrial Internet of Things (IIoT) devices, which also brings the challenge of keeping the PHRs confidentiality and preventing unauthorized access. By the emerging ciphertext-policy attribute-based encryption (CP-ABE), the PHR owner can encrypt his/her PHR data under self-defined access policies. However, existing CP-ABE schemes are suffering from either heavy computation cost and storage overhead or traitor tracing and direct revocation. In this article, we propose an efficient, traceable, and revocable access control scheme named TRAC for mHealth in 5G-enabled IIoT. In TRAC, the ciphertext is composed of the attribute-relevant ciphertext encrypted under anand-gate access structure and the identity-relevant ciphertext associated with some potential receivers. The malicious user who leaks his/her privilege to unauthorized entities will be precisely tracked and added in the revocation list, by which the cloud server can update the identity-relevant ciphertext by itself. The length of final ciphertext and the time of bilinear pairing operations used in decryption are constant. The security analysis and performance evaluation indicate the security, efficiency, and practicality of TRAC.

In this paper, we investigate the task scheduling problem in the UAV-enable Mobile Edge-Computing (MEC) system with the objectives of minimizing the cost and the completion time. A NSGA-II algorithm is proposed for the problem under study. The solution is represented as a two-dimension location sequence. Major components of NSGA-II are delicately designed including the feasible solution generation method (FSGM) and genetic operations of crossover, mutation and selection. Three strategies are introduced in FSGM. A simulated annealing local search is integrated into the crossover operation, and meanwhile two novel mutation methods are proposed. The Pareto-based metrics are introduced to evaluate the performance of the compared algorithms. Experimental results show that the proposal is more effective and robust than the three existing algorithms.

In this paper, we investigate the dynamic task scheduling problem with stochastic task arrival times and due dates in the UAV-based intelligent on-demand meal delivery system (UIOMDS) to improve the efficiency. The objective is to minimize the total tardiness. The new constraints and characteristics introduced by UAVs in the problem model are fully studied. An iterated heuristic framework SES (Stochastic Event Scheduling) is proposed to periodically schedule tasks, which consists of a task collection and a dynamic task scheduling phases. Two task collection strategies are introduced and three Roulette-based flight dispatching approaches are employed. A simulated annealing based local search method is integrated to optimize the solutions. The experimental results show that the proposed algorithm is robust and more effective compared with other two existing algorithms.

Regional medical consortium systems facilitates medical information sharing. However, many security issues exposed by the dominant centralized architectures, such as single points of failure, unauthorized operations and illegal access, are increasingly apparent constraints on the security and efficiency of data sharing across domains. Even more, any malicious operation detected, effective measures should be executed promptly for identity tracing. In this paper, we propose a secure and efficient cross-domain authentication scheme based on two cooperative blockchains (BCs) for medical consortium systems. Specifically, an intra-domain BC records any legal users’ registration and authentication information while an inter-domain BC is responsible for writing users’ cross-domain authentication information. In each domain, the general hospital acts as a trusted third service provider to achieve cross-chain interactions. For the entire cross-domain authentication procedure, anonymity mechanism is utilized to enhance security, and to trace malicious users, the improved chameleon hash is used in the intra-domain BC to redact the state of the user, and blacklist merkle tree is extended in the inter-domain BC to protect different domains’ services from illegal accessing. In addition, security analysis and performance evaluation are completely given to prove the superior security features and performance compared with other schemes.

The implementation of immunity passport has been hampered by the controversies over vaccines in various countries, the privacy of vaccinators, and the forgery of passports. While some existing schemes have been devoted to accelerating this effort, the problems above are not well solved in existing schemes. In this paper, we present an immunity passport scheme based on the dual‐blockchain architecture, which frees people from the cumbersome epidemic prevention process while traveling abroad. Specially, the dual‐blockchain architecture is established to fit with the scenarios of immunity passport. Searchable encryption and anonymous authentication are utilized to ensure users’ privacy. In addition, the performance and security evaluations show that our scheme achieves the proposed security goals and surpasses other authentication schemes in communicational and computational overheads.

Mobile edge computing (MEC), which merits in reducing response time by executing services in close proximity to end devices, has recently emerged as one of promising solutions for mobile services and hence attracted increasing research. Edge nodes usually pre-download parts of private data which are stored in the cloud to enable end devices' fast access, which mitigates overburden of the centralized cloud. However, malicious attackers or unreliable services providers may corrupt with those private data on edge servers. To this end, how to validate data integrity in MEC environment safely and efficiently has become a crucial problem. In this paper, we propose a ZSS Signature based Data Integrity Verification solution for MEC (ZSDIV-MEC), which supports privacy protection and public auditing by introducing a third party auditor (TPA) and the employment of ZSS signature scheme. In the proposed ZSDIV-MEC, system architecture is described followed with a detailed verification protocol, which takes full consideration of verification for data on three cases including single edge, multiple edges, and a joint of multiple edges and the center cloud. Performance analysis of the protocol including feasibility, security, privacy, and dynamicity is discussed respectively. Experimental results in comparison with baseline schemes demonstrate that our solution has better performance in data verification and computation overhead.

The application of unmanned aerial vehicle (UAV) to achieve precision irrigation in agriculture is a hot research topic in the industry. However, much spay and much leakage of pesticide are tricky for the current UAV-based irrigation methods to deal with. In this paper, we propose a new UAV-based irrigation system for precision agriculture. First, considering that different areas in the same farmland may have different pesticide shortage, a map preprocess strategy is introduced to divide the entire farmland into pieces. Second, we establish a UAV precision irrigation model and put forward an adaptive and fast dynamic ant colony optimization (AFD-ACO) algorithm to minimize the longest flight path with the lowest energy consumption and pesticide residues. In order to promote the efficiency and the optimization effect, we utilize the scent pervasion rule to make the global map preprocessed and the neighborhood adaptive search policy to accomplish planning work. Finally, comparing with other two ACO-based algorithms, the proposed algorithm is proved to be effective for the research problem, especially when the more pieces the farmland is divided, the better our solution performs.

To improve the flexibility and efficiency of the logistics distribution process, a new logistics distribution model, namely Community Logistics System(CLS) model, is established. In this paper, we consider that it is impossible to transport every package to the express cabinet closest to the customer. Hence, the optimizing objective of this problem is to minimize the total fetching distance. In addition, considering the capacity constraints of express cabinets, a reasonable allocation strategy is designed. We propose a Simulated Annealing Genetic (SAG) algorithm to solve the problem. In order to promote the efficiency and the optimization effect, we generate the first generation population by using Simulated Annealing algorithm instead of using random selection as in most case. By comparing with other two heuristic algorithm (SA and GA), the proposed algorithm is proved to be robust and effective for the research problem.

A fuzzy workflow scheduling problem is investigated with fuzzy temporal parameters, such as the fuzzy task processing times, fuzzy data transmission times and the fuzzy due dates. In the considered problem, the resources are elastic cloud resources with multiple price structures. Due to the fuzzy temporal parameters, the deadline constraint cannot be ensured. Therefore, we formulate a triangle fuzzy number-based workflow scheduling problem model with the soft deadline constraint. A greedy local search method is proposed. The objectives are minimizing the total rental cost and the average dissatisfaction degree. The Pareto-based metrics are introduced to evaluate the performance of the compared algorithms. Experimental results show that the proposal is more effective and robust than the two existing algorithms.

Abstract This paper examines a scheduling problem with heterogeneous logistics unmanned aerial vehicles (UAVs) in urban environment. Different from traditional vehicle routing problem (VRP), it introduces some new characteristics such as the loading capacity, the maximum flight time and the flight speed. As a variant of VRP, the considered scheduling problem is known to be an non‐deterministic Polynomial (NP)‐hard problem. The UAV scheduling problem model with the heterogeneous UAV settings is formulated first. Secondly, a genetic‐based algorithm framework is presented for solving the scheduling problem, in which the encoding/decoding method, the initial population generation method and genetic operations are delicately designed. In order to reduce the search space and faster the execution of this algorithm, a weight‐based loading method is adopted. For the purpose of performance evaluation and statistical analysis, the proposed algorithm is compared with the other two existing algorithms. The experimental results show that the presented algorithm can solve this problem efficiently.

The Internet of Things (IoT) has built an information bridge between people and the objective world, wherein wireless sensor networks (WSNs) are an important driving force. For applications based on WSN, such as environment monitoring, smart healthcare, user legitimacy authentication, and data security, are always worth exploring. In recent years, many multifactor user authentication schemes for WSNs have been proposed using smart cards, passwords, as well as biometric features. Unfortunately, these schemes are revealed to various vulnerabilities (e.g., password guessing attack, impersonation attack, and replay attack) due to nonuniform security evaluation criteria. Wang et al. put forward 12 pieces of widely accepted evaluation criteria by investigating quantities of relevant literature. In this paper, we first propose a lightweight multifactor authentication protocol for multigateway WSNs using hash functions and XOR operations. Further, BAN logic and BPR model are employed to formally prove the correctness and security of the proposed scheme, and the informal analysis with Wang et al.’s criteria also indicates that it can resist well-known attacks. Finally, performance analysis of the compared schemes is given, and the evaluation results show that only the proposed scheme can satisfy all 12 evaluation criteria and keep efficient among these schemes.

Due to the lack of trusted third parties as guarantees in peer-to-peer (P2P) networks, how to ensure trusted transactions between peers has become a research hotspot. However, the open and distributed characteristics of P2P networks have brought challenges to network security, and there are problems such as node fraud and unavailability of services in the network. To solve the problem of how to select trusted transaction peers in P2P groups, a new trust model, GT-Bidding, is proposed in this paper. This model follows the bidding process of human society. First, each service peer applies for a group of guarantee peers and carries out credit mortgages for this service. Second, based on the entropy and TOPSIS method (Technology for Order Preference by Similarity to an Ideal Solution) approaching the ideal solution, a set of ideal trading sequences is selected. Then, the transaction impact function is used to assign weights to the selected guarantee peers and service nodes, respectively; thus, the comprehensive trust of each service node can be calculated. Finally, the service peer is verified using feedback based on the specific confidence level, which encourages the reputation of the service and its guarantee peers to update. Experiments show that GT-Bidding improves the successful transaction rate and resists complex attacks.

Named data networking (NDN) is an emerging information-centric networking paradigm, in which the Internet of Things (IoT) achieves excellent scalability. Recent literature proposes the concept of NDN-IoT, which maximizes the expansion of IoT applications by deploying NDN in the IoT. In the NDN, the security is built into the network by embedding a public signature in each data package to verify the authenticity and integrity of the content. However, signature schemes in the NDN-IoT environment are facing several challenges, such as signing security challenge for resource-constrained IoT end devices (EDs) and verification efficiency challenge for NDN routers. This article mainly studies the data package authentication scheme in the package-level security mechanism. Based on mobile edge computing (MEC), an efficient certificateless group signature scheme featured with anonymity, unforgeability, traceability, and key escrow resilience is proposed. The regional and edge architecture is utilized to solve the device management problem of IoT, reducing the risks of content pollution attacks from the data source. By offloading signature pressure to MEC servers, the contradiction between heavy overhead and shortage of ED resources is avoided. Moreover, the verification efficiency in NDN router is much improved via batch verification in the proposed scheme. Both security analysis and experimental simulations show that the proposed MEC-based certificateless group signature scheme is provably secure and practical.

In most RFID localization systems, acquiring a reader antenna's position at each sampling time is challenging, especially for those antenna-carrying robot or drone systems with unpredictable trajectories. In this article, we present RF-MVO that fuses RFID and computer vision for stationary RFID localization in 3D space by attaching a light-weight 2D monocular camera to two reader antennas in parallel. First, the existing monocular visual odometry only recovers a camera/antenna trajectory in the camera view from 2D images. By combining it with RF phase, we design a model to estimate a scale factor for real-world trajectory transformation, along with spatial directions of an RFID tag relative to a virtual antenna array due to the mobility of each antenna. Then we propose a novel RFID localization algorithm that does not require exhaustively searching all possible positions within the pre-specified region. Second, to speed up the searching process and improve localization accuracy, we propose a coarse-to-fine optimization algorithm. Third, we introduce the concept of horizontal dilution of precision (HDOP) to measure the confidence level of localization results. Our experiments demonstrate the effectiveness of proposed algorithms and show RF-MVO can achieve 6.23 cm localization error.

Recent RFID systems start attaching a tag array consisting of two or more tags on an object to deal with polarization mismatch and RF phase periodicity for battery-free sensing and localization. The multi-tag solution can also provide target orientation estimation. However, when these tags are closely spaced apart, mutual coupling will be induced, producing the unexpected changes in reported RSSI and RF phase. In this paper, we present RF-Mirror that enables compensating the distortion in a two-tag array labeled RFID system. The system would output the accurate difference in tag-to-antenna distances between two tags, which is a fundamental parameter in previous works for use. Firstly, we model the backscatter signal of a responding tag in a two-tag scenario, and then formulate novel RSSI- and RF phase-distance models with coupling terms. Secondly, we design an algorithm to characterize the coupling effect on tag gain by fusing RSSI and RF phase. Thirdly, we design a decoupling algorithm based on an observation that tag mutual coupling is independent of the position of a tag array relative to a reader antenna. Our experiments show the effectiveness of our models and RF-Mirror achieves the decoupling error of 0.197 cm in calculating the tag-to-antenna distance difference.

As an complement of cloud computing, fog computing provides computing services with closer geographic distance and focuses on distributed computing. In this paper, we consider the bi-objective task scheduling problem with heterogeneous resources in a fog-cloud computing system. There are two minimization objectives: energy consumption and delay. We formulate a workload allocation problem model involving fog devices (FDs) and the cloud servers (CSs). The computing resources are heterogeneous on the energy consumption, processing capability and delay. An energy-aware greedy heuristic algorithm (EG) is developed to search for Pareto Front solutions. For the problem under discussed, Experimental results indicate that the proposal algorithm is effective and robust compared with the comparison algorithm.

As the infrastructure of the intelligent transportation system, vehicular ad hoc networks (VANETs) have greatly improved traffic efficiency. However, due to the openness characteristics of VANETs, trust and privacy are still two challenging issues in building a more secure network environment: it is difficult to protect the privacy of vehicles and meanwhile to determine whether the message sent by the vehicle is credible. In this article, a blockchain-based trust management model, combined with conditional privacy-preserving announcement scheme (BTCPS), is proposed for VANETs. First, an anonymous aggregate vehicular announcement protocol is designed to allow vehicles to send messages anonymously in the nonfully trusted environment to guarantee the privacy of the vehicle. Second, a blockchain-based trust management model is present to realize the message synchronization and credibility. Roadside units (RSUs) are able to calculate message reliability based on vehicles' reputation values which are safely stored in the blockchain. In addition, BTCPS also achieves conditional privacy since trusted authority can trace malicious vehicles' identities in anonymous announcements with the related public addresses. Finally, a mixed consensus algorithm based on proof-of-work and practical Byzantine fault tolerates algorithm is suggested for better efficiency. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and effective in VANETs.

This article studies multiple unmanned aerial vehicles (multi-UAVs)-enabled wireless-powered Internet of Things (IoT), where a group of UAVs is dispatched as mobile power sources to charge a set of ground IoT devices. Different from the conventional radio-frequency (RF) wireless power transfer (WPT) systems, magnetic resonance-coupled (MRC) WPT systems can guarantee high power transfer efficiency without the complete alignment, which is remarkable. In this article, we extend the charging range by the wired connection between the energy receiving systems and IoT devices. Due to the restriction of carriable energy on the UAVs, designing the shortest possible trajectory for each UAV is necessary. We formulate it as a multidepots multi-UAVs trajectory optimization problem, jointly with constraints of the UAV's energy capacity and the area of the target region, to maximize the resource utilization of UAVs. To tackle this nonconvex problem, we decompose it into two subproblems, i.e., hovering locations selection and multi-UAVs trajectory optimization. For the first subproblem, we propose two approximation algorithms to obtain the near-optimal solution in the sparse networks. Then, we adopt a heuristic algorithm, a memetic algorithm-based variable neighborhood search (MAVNS), to achieve the quasioptimal trajectory rapidly. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithms. New insights are investigated on the estimation of feasibility that whether the given UAVs with energy capacity constraint can fully charge ground IoT devices within open areas.

Broadcast authentication is a fundamental security primitive in wireless sensor networks (WSNs), which is a critical sensing component of IoT. Although symmetric-key-based μ TESLA protocol has been proposed, some concerns about the difficulty of predicting the network lifecycle in advance and the security problems caused by an overlong long hash chain still remain. This paper presents a scalable broadcast authentication scheme named DH- μ TESLA, which is an extension and improvement of μ TESLA and Multilevel μ TESLA, to achieve several vital properties, such as infinite lifecycle of hash chains, security authentication, scalability, and strong tolerance of message loss. The proposal consists of the t,n -threshold-based self-reinitializable hash chain scheme (SRHC-TD) and the d -left-counting-Bloom-filter-based authentication scheme (AdlCBF). In comparison to other broadcast authentication protocols, our proposal achieves more security properties such as fresh node’s participation and DoS resistance. Furthermore, the reinitializable hash chain constructed in SRHC-TD is proved to be secure and has less computation and communication overhead compared with typical solutions, and efficient storage is realized based on AdlCBF, which can also defend against DoS attacks.

Currently, lots of real social relations in social networks force users to face the potential risk of privacy leakage. Consequently, data holders would like to disturbor anonymize their individual data before publishing them, for the purpose of privacy protection. Due to the characteristics of high sensitivity and large volume data of social network graph structure, it is difficult for privacy protection schemes to enable a reasonable allocation of noises while keeping desirable data availability and execution efficiency. On the basis of differential privacy model, combining with clustering and randomization algorithms, a privacy protection approach PBCN (Privacy Preserving Approach Based on Clustering and Noise) is proposed. This proposal is composed of five algorithms including random disturbance based on clustering, graph reconstruction after disturbing degree sequence and noise nodes generation, etc. Furthermore, a privacy measure algorithm based on adjacency degree is put forward in order to objectively evaluate the privacy-preserving strength of various schemes against graph structure and degree attacks. Simulation experiments are conducted to achieve performance comparisons between PBCN, Spctr Add/Del, Spctr Switch, DER and HPDP. The experimental results show that PBCN realizes more satisfactory data availability and execution efficiency. Finally, parameters utility analysis demonstrates PBCN can achieve a “trade-off” between data availability and privacy protection level.

We investigate a workflow scheduling problem with stochastic task arrival times and fuzzy task processing times and due dates. The problem is common in many real-time and workflow-based applications, where tasks with fixed stage number and linearly dependency are executed on scalable cloud resources with multiple price options. The challenges lie in proposing effective, stable, and robust algorithms under stochastic and fuzzy tasks. A triangle fuzzy number-based model is formulated. Two metrics are explored: the cost and the degree of satisfaction. An iterated heuristic framework is proposed to periodically schedule tasks, which consists of a task collection and a fuzzy task scheduling phases. Two task collection strategies are presented and two task prioritization strategies are employed. In order to achieve a high satisfaction degree, deadline constraints are defined at both job and task levels. By designing delicate experiments and applying sophisticated statistical techniques, experimental results show that the proposed algorithm is more effective and robust than the two existing methods.

With the rapid development of Internet services, mobile communications, and IoT applications, Location-Based Service (LBS) has become an indispensable part in our daily life in recent years. However, when users benefit from LBSs, the collection and analysis of users’ location data and trajectory information may jeopardize their privacy. To address this problem, a new privacy-preserving method based on historical proximity locations is proposed. The main idea of this approach is to substitute one existing historical adjacent location around the user for his/her current location and then submit the selected location to the LBS server. This method ensures that the user can obtain location-based services without submitting the real location information to the untrusted LBS server, which can improve the privacy-preserving level while reducing the calculation and communication overhead on the server side. Furthermore, our scheme can not only provide privacy preservation in snapshot queries but also protect trajectory privacy in continuous LBSs. Compared with other location privacy-preserving methods such as k -anonymity and dummy location, our scheme improves the quality of LBS and query efficiency while keeping a satisfactory privacy level.

With the increasing number of smart mobile devices, applications based on mobile network take an indispensable role in the Internet of Things. Due to the limited computing power and restricted storage capacity of mobile devices, it is very necessary to design a secure and lightweight authentication scheme for mobile devices. As a lightweight cryptographic primitive, the hash chain is widely used in various cryptographic protocols and one-time password systems. However, most of the existing research work focuses on solving its inherent limitations and deficiencies, while ignoring its security issues. We propose a novel construction of hash chain that consists of multiple different hash functions of different output lengths and employ it in a time-based one-time password (TOTP) system for mobile device authentication. The security foundation of our construction is that the order of the hash functions is confidential and the security analysis demonstrates that it is more secure than other constructions. Moreover, we discuss the degeneration of our construction and implement the scheme in a mobile device. The simulation experiments show that the attacker cannot increase the probability of guessing the order by eavesdropping on the invalid passwords.

To improve the flexibility and efficiency of the logistics distribution system, a new type of logistics distribution mode, namely the Unmanned Aerial Vehicle and Shared Reception Box (UAV-SRB) logistics distribution mode, is studied. In this paper, we consider logistics scheduling problem of using multiple homogeneous UAVs to transport a batch of packages from one logistics center to multiple SRBs. The optimizing objective of this problem is to minimize the average mission execution time of UAVs. In addition, considering the capacity constraints of UAVs, a corresponding single-objective mathematical model is established. Combined with the actual logistics situation, we propose an Adaptive Tabu Search Algorithm (ATSA) to solve this problem. In order to improve the optimization performance, the algorithm designs adaptive tabu length and divides neighborhood space into several subsets according to different destinations. By comparing with other two heuristic algorithms (LSA and SA), the proposed algorithm is proved to be robust and effective for the research problem.

With the rapid development of social networks, privacy has also attracted attention. Based on this problem, a privacy protection scheme for social networks based on classified attribute encryption (PPSSN) is proposed for the data owner and attribute management server to manage user permissions; the approach reduces data owner overhead and also avoids use of a property management server to limit access user collusion attacks. To balance the privacy and security of data publication, this scheme classifies users and designs access control for different users and different privileges. In addition, this paper also introduces a good friend data cache mechanism to improve and optimize the original scheme to reduce the cost of decryption. The efficiency and system overhead of the proposed scheme are compared and analyzed based on experiments. The experiments show that the proposed scheme improves query efficiency, reduces system cost, and enhances privacy security.

In recent years, rapid development of sensing and computing has led to very large datasets. There is an urgent demand for innovative data analysis and processing techniques that are secure, privacy-protected and sustainable. In this article, taking human activities and interactions with Cyber-Physical Systems (CPS) into consideration, we propose a human behavior learning system based on Channel State Information (CSI) utilizing a series of algorithms for data analysis and processing. Aiming to recognize a set of gestures, our system is designed based on the observation that different gestures have different effects on signals and specific gesture signals have a unique energy spectrum. Specifically, an improved Linear Discriminant Analysis Algorithm (I-LDA) is devised to reduce the dimension of human behavior signals. Additionally, behaviors are learned by Logistic Regression Algorithm (LRA). Bandwidth ratios in an energy spectrum are selected as features to eliminate the impact of speed differences on results. The system is based on commercial off-the-shelf WiFi devices and we conduct a large number of experiments in a typical indoor environment to evaluate its performance. Experimental results show that our system is robust with average recognition accuracy of up to 96%.

As the Industry 4.0 or Internet of Things (IoT) era begins, security plays a key role in the Industry Internet of Things (IIoT) due to various threats, which include escape or Distributed Denial of Service (DDoS) attackers in the virtualization layer and vulnerability exploiters in the device layer. A successful cross-VM escape attack in the virtualization layer combined with cross-layer penetration in the device layer, which we define as an Advanced Persistent Escaper (APE), poses a great threat. Therefore, the development of detection and rejection methods for APEs across multiple layers in IIoT is an open issue. To the best of our knowledge, less effective methods are established, especially for vulnerability exploitation in the virtualization layer and backdoor leverage in the device layer. On the basis of this, we propose Escaper Cops (EscaperCOP), a detection method for cross-VM escapers in the virtualization layer and cross-layer penetrators in the device layer. In particular, a new detection method for guest-to-host escapers is proposed for the virtualization layer. Finally, a novel encryption method based on Identity-based Broadcast Encryption (IBBE) is proposed to protect the critical components in EscaperCOP, detection library, and control command library. To verify our method, experimental tests are performed for a large number of APEs in an IIoT framework. The test results have demonstrated the proposed method is effective with an acceptable level of detection ratio.

Most of the existing RFID-based localization systems cannot well locate RFID-tagged objects in a 3D space. Limited robot-based RFID solutions require reader antennas to be carried by a robot moving along an already-known trajectory at a constant speed. As the first attempt, this paper presents RF-MVO, which fuses battery-free RFID and monocular visual odometry to locate stationary RFID tags in a 3D space and recover an unknown trajectory of reader antennas binding with a 2D monocular camera. The proposed hybrid system exhibits three unique features. Firstly, since the trajectory of a 2D monocular camera can only be recovered up to an unknown scale factor, RF-MVO combines the relative-scale camera trajectory with depth-enabled RF phase to estimate an absolute scale factor and spatially incident angles of an RFID tag. Secondly, we propose a joint optimization algorithm consisting of coarse-to-fine angular refinement, 3D tag localization and parameter nonlinear optimization, to improve real-time performance. Thirdly, RF-MVO can determine the effect of relative tag-antenna geometry on the estimation precision, providing optimal tag positions and absolute scale factors. Our experiments show that RF-MVO can achieve 6.23cm tag localization accuracy in a 3D space and 0.0158 absolute scale factor estimation accuracy for camera trajectory recovery.

To reduce the operating cost, leasing appropriate amount of public resources becomes a popular practice among small and medium sized enterprises. Many hybrid cloud workflow management systems (HCWMSs) have been developed to provision applications on both local and rented resources. One of the critical issues in the HCWMS is the dynamic resource allocation for stochastically arriving requests. Therefore, we propose a dynamic interval scheduling based heuristic for the resource allocation problem, in which stochastic requests are taken as a set of linearly dependent tasks and distributed to idle and feasible time slots on multiple virtual machines (VMs), either local or rented VMs. The objective is to minimize the idle time slots on the rented VMs, which is relative to the renting cost of VMs, especially for the on-demand pricing structure. Requests arrive at the same time are taken as a batch of tasks to schedule. Tasks are scheduled batch by batch, obeying the precedence constraint and the deadline constraint. We develop a fast heuristic integrated with an interval scheduling to obtain feasible and effective solutions. Three interval scheduling method are proposed and compared: Max Interval Number Scheduling (MINS), Max Working Time Scheduling (MWTS) and Select-the-better Method (STBM). The experimental results show that the interval scheduling based heuristic can reduces the cost of renting VMs.

Currently, brain-computer interface technology (BCI) has been widely used in brain disease diagnosis and motor disabilities recovery. In this paper, it proposes a novel scheme that using deep learning model based on Long Short-Term Memory (LSTM) to extract and classify ECoG signals. First, it preprocesses the ECoG and voltage signal when the subject's finger is bent. Second, according to the time characteristics of the ECoG, it designs a 6-layer deep learning model to classify ECoG signals directly, while avoiding the time-consuming of feature extraction. This method is applied to an open ECoG dataset and experimental results achieve 83.3% accuracy over 5 categorical ECoG data. The accuracy is significantly higher than traditional linear analysis and ordinary machine learning methods. To demonstrated the feasibility of this proposal, it is applied to real-time control of the mechanical arm.

The convergence of Internet of Things, cloud computing, and wireless body-area networks (WBANs) has greatly promoted the industrialization of electronic-/mobile-healthcare (e-/m-healthcare). However, the further flourishing of e-/m-healthcare still faces many challenges including information security and privacy preservation. To address these problems, a healthcare system (HES) framework is designed that collects medical data from WBANs, transmits them through an extensive wireless sensor network infrastructure, and finally, publishes them into wireless personal-area networks via a gateway. Furthermore, HES involves the groups of send-receive model scheme to realize key distribution and secure data transmission, the homomorphic encryption based on matrix scheme to ensure privacy, and an expert system able to analyze the scrambled medical data and feedback the results automatically. Theoretical and experimental evaluations are conducted to demonstrate the security, privacy, and improved performance of HES compared with current systems or schemes. Finally, the prototype implementation of HES is explored to verify its feasibility.

Along with the improvement of cloud technologies, we envision that thousands of web applications whether owned by individuals or enterprises will be migrated into cloud. These web applications share the cloud resources while require different quality of service guarantees and management policies. It is challenging while with abundant innovation opportunities given the easy and dynamic provisioning of cloud resources. In order to facilitate practitioners to evaluate, experiment, or enhance existing functionalities of distributed web application management in cloud environment, a componentized distributed service foundation named TANSO is proposed. TANSO targets serving as platform to manage thousands of web application server instances and provide plenty of interfaces for user to extend and experiment new ideas. TANSO has implemented framework components to enable fundamental management. It also provides interfaces to admit and foster user's innovative algorithms and designs for managing large-scale web applications.