Mobile Crowdsensing (MCS) has emerged as an effective paradigm for large-scale data collection, yet it faces two inherently conflicting challenges: minimizing platform cost and reducing the Age of Information (AoI). Existing approaches primarily focus on task assignment optimization or assume fully rational worker behavior, which often limits their effectiveness in achieving timely data updates without incurring substantial costs. To address these challenges, a Herd Effect-based AoI-aware (HEA) data collection scheme is proposed to minimize AoI under budget constraints. Specifically, the HEA scheme incorporates insights from behavioral economics to influence workers’ mobility decisions by exploiting the herd effect. By incentivizing a subset of pioneer workers to adopt a high-effort mobility mode (i.e., the Boosted Mode), the observable behaviors of these pioneers induce subsequent workers to adjust their decisions accordingly, thereby forming a self-reinforcing herd behavior. This mechanism enables timely data collection while effectively controlling the overall incentive expenditure. The effectiveness of the proposed scheme is validated through rigorous theoretical analysis and extensive experiments conducted on multiple real-world datasets. Experimental results demonstrate that HEA consistently outperforms representative baseline schemes, achieving significant AoI reductions with marginal additional cost and substantially improving the trade-off between data timeliness and budget efficiency.

As an important part of mechanical equipment, rolling bearing holds significant importance in the normal operation of machinery. However, the parameter and computation of fault diagnosis approaches based on deep learning technique are huge, and most methods are diagnosed in the cloud, which can lead to time delays and non-real-time. To overcome these issues, in edge computing scenarios, a real-time bearing fault diagnosis network MobiKanViT is proposed in this paper. The network is a lightweight and low-latency vision fault diagnosis network with enhanced discriminative feature learning capability. The Efficient Multi-scale Attention (EMA) module is introduced to enhance the ability of feature recognition. The ordinary convolution module is substituted with the Kolmogorov-Arnold Networks (KAN) convolution module to solve the problem of large parameter and computation. The MobiKanViT is compressed and quantized with depth parameter γ to make the model more lightweight and easy to be deployed on edge devices. Verification experiments were conducted on two sets of experimental equipment, and three mobile phones were selected as mobile edge computing platforms. The experimental results show that a depth parameter of γ = 0.5 and INT8 quantization yield the most effective results for MobiKanViT. When juxtaposed with current large model techniques, the suggested approach decreases memory consumption by an average of 94.6%, while simultaneously boosting inference speed by about 15.87 times. In comparison to existing lightweight models, this new method also improves diagnostic accuracy by an average of 2.6%.

Mobile crowd sensing (MCS) has gained much attention as an important component of next-generation Internet in recruiting workers to collect data to build and deliver services to users for profit. However, recruiting reliable workers with low cost remains a critical challenge under the Information Elicitation Without Verification (IEWV) problem, where worker data quality cannot be directly observed. A large number of studies have been conducted assuming known data quality of workers and using incentives to motivate them to actively participate in data collection. Such assumptions limit their applicability in real-world MCS systems, as platforms cannot directly observe data quality after collection. Therefore, recruiting credible workers to maximize platform profit remains a challenging issue that has not been adequately addressed. In this paper, an LSTM-assisted Trust Worker Recruitment (LTWR) scheme is proposed to maximize platform profit in MCS. First, we propose a trust evaluation mechanism that utilizes the credible data to estimate the quality of workers, effectively addressing the IEWV problem. Second, an LSTM-based data completion method is employed to reduce sensing cost while preserving data quality. Third, we model platform revenue as a function of data quality and design a worker recruitment strategy to maximize profit. Finally, extensive experiments validate the effectiveness of the proposed LTWR scheme in terms of data quality, cost, and profit.

The rapid growth of sensing-enabled mobile devices has accelerated mobile crowdsensing (MCS), enabling large-scale data collection. However, because tasks are location-dependent, protecting geographic privacy and ensuring data reliability remain critical challenges. In practice, honest-but-curious platforms and malicious workers submitting falsified reports may lead to incorrect recruitment and degraded system performance. Therefore, achieving bilateral location privacy and mitigating falsified data are essential problems. A Trust-aware and bilateral Location-Private Worker Recruitment scheme, named TLPWR, is proposed to address these challenges. TLPWR safeguards both worker and task locations during the recruitment process and enables continuous updates of worker trustworthiness, which have not been simultaneously achieved in prior studies. The scheme employs additive secret sharing combined with a distributed comparison method to securely compute spatial proximity without revealing precise locations. A Multi-Armed Bandit strategy based on the upper-confidence-bound algorithm is further incorporated to recruit high-quality workers, while a trust-oriented evaluation mechanism dynamically assesses and updates worker reliability. Extensive experiments on real-world datasets demonstrate that TLPWR effectively preserves bilateral location privacy and significantly reduces data errors compared with baseline methods, thereby improving data quality while maintaining strong privacy guarantees in MCS systems.

Integrated Sensing and Communications (ISAC) systems face challenges in balancing sparse human resource allocation with high-quality data acquisition, especially in scenarios involving highly mobile workers and uneven task coverage. While Sparse Mobile CrowdSensing (SMCS) offers a resource-efficient solution by inferring unobserved data from partial sensing tasks, two critical issues remain unaddressed: (1) existing data inference methods often assume collected data is trustworthy, neglecting the detrimental impact of malicious workers on data quality, and (2) the lack of privacy-preserving mechanisms in sparse ISAC may deter worker participation, compromising service quality. To address these, we propose a Quality-driven Privacy-preserving Truth Discovery scheme (QPTD), which optimizes sparse sensing and data quality with privacy constraints in ISAC systems. QPTD integrates a trust-aware Deep Reinforcement Learning (DRL) algorithm for dynamic cell selection, prioritizing cells with high-trust workers and minimizing recruitment costs, while a matrix encryption protocol ensures end-to-end privacy protection for both workers and tasks. By leveraging Matrix Factorization (MF) on encrypted sparse data aggregated from high-trust workers, QPTD infers unobserved tasks and safeguards sensitive information. Theoretical analysis proves the robustness of our privacy guarantees against adversarial attacks. Evaluations on real-world datasets demonstrate that QPTD improves ISAC system performance by increasing total data quality by 1.73%–142.07%, reducing estimation errors by 24.82%–75.32%, and cutting recruitment costs by up to 62.03% while ensuring service performance.

Mobile crowdsensing (MCS) has emerged as a promising paradigm for collecting high-quality data to support applications, such as large artificial intelligence models and autonomous driving. However, MCS faces two critical challenges: privacy leakage and inefficient task allocation, which hinder its deployment in industrial environments. In this article, we propose a distributed Privacy-preserving and Truthful intelligent Task Allocation (PTTA) system that bridges the gap between theory and practice by addressing data privacy concerns and task allocation inefficiencies. The system is validated through industrial case studies, ensuring its real-world applicability. The main contributions are: 1) a distributed blockchain-based privacy-preserving approach ensuring both data and location privacy, suitable for industrial-scale MCS; 2) a reputation-driven deep-reinforcement-learning-based task allocation algorithm that assigns tasks to trustworthy workers with shorter travel distances, reducing costs and improving data quality; and 3) an effective method for truth discovery and reputation evaluation under privacy constraints, improving the estimation of estimated truth value and workers' reputations. Experimental results show that PTTA improves task allocation efficiency by up to 23.7%–56.5% compared to traditional greedy and ant colony algorithms and the highest accuracy with 4.9%–5.9% increase. This work offers a scalable solution for privacy-preserving MCS in industrial environments.

With the popularity of electric vehicles (EVs), wireless power transmission (WPT) technology has become a hot research topic for next-generation battery charging technology. However, the vulnerability of wireless networks to malicious interference attacks is inherited by WPT. To alleviate the privacy and security issues of WPT, we propose a novel FedDQ, a federated deep reinforcement learning with Q-ensemble, to cope with interference attacks in EV wireless charging network environments. Federated learning protects the security privacy of EVs by training a global model that exploits the property that data and models will not be transmitted. In order to trade-off the training cost and efficiency, we introduce offline-to-online training models by pre-training the offline Q-network with pre-collected data, and the trained model serves as an initialization of the online model. Then, the online Q-network is obtained by weakening or removing the original pessimistic constraints to enhance the training speed. Secondly, we introduce the intelligent reflective surface (IRS) to enhance the security performance of WPT by modifying the IRS phase shift and amplitude to cancel the malicious interference signal. Experimental results show that our proposed FedDQ algorithm has superior performance and outperforms existing baseline methods in terms of anti-jamming metrics.

Mobile Crowdsensing (MCS) has emerged as a promising paradigm for large-scale, real-time data collection by leveraging the sensing capabilities of widely distributed mobile workers. However, its practical adoption is challenged by privacy risks and unsustainable incentive structures that inadequately compensate for workers' inherent participation costs, leading to diminished motivation, sparse task coverage, and reduced data availability. Existing approaches either provide limited and utility-degrading privacy protection or design incentive mechanisms that incur substantial costs, even under the Nash equilibrium. To bridge this gap, we propose C-PRISM (Compre hensive Privacy-preserving and Behavioral-Incentive Sustainable crowdsensing Mechanism), an integrated framework that seamlessly combines privacy-preserving techniques with behavioral economic incentive design. Specifically, C-PRISM employs ran domized matrix perturbation for fine-grained location protection and a two-phase proxy re-encryption protocol to secure task details and sensing data across evaluation, recruitment, and transmission. Building upon this secure foundation, behavioral economic incentives grounded in prospect theory, the Aronson effect, and a dual reference-point model are introduced to promote sustained worker participation at sub-Nash-equilibrium costs. Rigorous theoretical analysis validates C-PRISM's security and individual rationality. Extensive experiments on real-world datasets demonstrate that C-PRISM increases data collection efficiency by 7.42%-193.75%, improves worker retention by 2.53%-69.78%, and effectively maintains overall system utility.

Spatial Crowdsourcing (SC) has become a popular paradigm for executing spatiotemporal tasks, such as urban sensing, ride-hailing, and last-mile delivery. In many of these service applications, it is common for workers to undertake multiple tasks simultaneously, as this allows them to efficiently complete tasks with a convenient route. In multi-task SC, location privacy, movement cost, and service quality constitute the three most critical factors. However, the inherent contradictions among these factors pose significant challenges in simultaneously ensuring them all, and to date, no existing work has addressed all three factors concurrently. To fill the research gap, in this paper, we propose a Privacy-Preserving, Cost-Aware, and Quality-Enabled Service Planning (PCQ-SP) scheme for multi-task SC. First, we introduce a secure distance acquisition method with additive homomorphic encryption and garbled circuits, which enables the calculation of distances between workers and tasks while preserving both their location privacy. Second, we design a Thompson Sampling-based quality evaluation method to assess each worker's service quality by balancing exploration and exploitation. Third, we propose a minimum-cost flow-driven service planning method to derive the optimized service plans, which aims for high quality and low cost while preserving location privacy. To our knowledge, our PCQ-SP scheme is the first to simultaneously consider location privacy, movement cost, and service quality in SC systems. Extensive evaluations demonstrate the effectiveness of our PCQ-SP scheme in enhancing service quality, reducing movement costs, and ensuring location privacy.

In Sparse Mobile Crowdsensing, many data inference schemes have been proposed without considering the sensing dataset with different trust levels which contains unrecognizable false data, resulting in high inference error and poor practical utility. To address this challenge, we propose a Transformer based Truthful Data Inference (TTDI) scheme that can tolerate false data interference to enhance data inference quality. Firstly, the trust-weighted loss function (TLF) is proposed to reduce the false interference for data inference model training. The TLF implements a differential model parameter updating strategy for data with different trust levels, which can enhance and weaken the effect of high-trust and low-trust data on the trained data inference model, respectively. Secondly, the transformer based truthful spatiotemporal feature extraction (T-TSFE) algorithm is proposed to mitigate the mutual interference between biased spatiotemporal features. The T-TSFE extracts data features through temporal and spatial multi-head attention respectively, which reduces the propagation of false information across the spatiotemporal dimensions. Finally, we conduct extensive experiments with four real-world air quality datasets to verify the effectiveness of the TTDI in data inference quality.

Cloud-fog Federated Learning (FL) is promising for collaborative model training in large-scale edge networks. In cloud-fog FL with constrained communication resources, selecting high-quality client models for aggregation is critical to boost the global model. However, the clients in real world hold multimodal and heterogeneous data, while existing selection strategies rarely consider the imbalance of communication cost and model convergence rates across modalities, thus seriously degrading the efficiency of model aggregation. Moreover, most previous multimodal fusion methods require aligned multimodal samples. However, the data of modality-heterogeneous clients may be isolated and unaligned in FL, so these previous methods cannot be applied to such scenarios, thereby hindering the knowledge fusion across various modalities. To address the above issues, we propose a cloud-fog FL system named COOL, which achievesunimodal aggregationat the fog layer andmultimodal fusionat the cloud layer. First, we propose a Modality-aware Online Client Selection (MOCS) strategy to assist unimodal aggregation. Unlike previous selection strategies, MOCS realizes the dynamic selection budget allocation for various modalities by monitoring the convergence gap of modalities, thus striking the performance balance among modality-heterogeneous clients. Second, to overcome the limitation of previous methods that require aligned multimodal data, we propose a Multimodal Fusion strategy with Feature Synthesis (MFFS). MFFS realizes multimodal fusion with isolated samples via adaptive feature synthesis and cross-modal attention training, thus building a more powerful multimodal predictor while preserving the client data privacy. Finally, experimental results demonstrate that COOL has superior performance compared to the existing algorithms.

Many studies on trust relationship establishment in Social Networks (SNs) have assumed that the trustworthiness of partners can be identified by participants through interaction. However, in practice, participants not only struggle to discern the trustworthiness of their counterparts but also find it difficult to effectively determine whether the messages they spread are Useful Messages (UMs) or Malicious Messages (MMs). Therefore, designing an efficient information propagation scheme that promotes UMs dissemination while blocking MMs remains a challenging issue in real-world SNs. In this paper, we propose an efficient Trust-based Distributed Incentive (TDI) scheme that aligns with actual SN practices. First, an effective Bidirectional Trust Identification (BTI) approach is proposed to verify the trustworthiness of messages and participants without assuming that interacting participants can evaluate each other's trustworthiness. In BTI, the trustworthiness of participants is evaluated based on their evaluations of trusted participants and reliable messages, while the trust of messages is verified through feedback from trusted participants, laying a foundation for trust information propagation. Then, a Trust-based Message Forwarding (TMF) mechanism is proposed to facilitate the dissemination of trusted messages while blocking the forwarding of low-trust messages. Finally, a Proactive Trust Evaluation (PTE) mechanism is introduced to accelerate and effectively obtain participants' reliable evaluations. Specifically, some UMs are disseminated as Probing Messages (PMs) to accurately evaluate the trustworthiness of participants based on whether they evaluate them truthfully. Extensive simulations demonstrate that the TDI scheme outperforms the existing main schemes in terms of accurately identifying message trust, increasing UMs dissemination, blocking the spread of MMs, and purifying SNs.

In Mobile Crowd Sensing (MCS), privacy and quality are two pivotal issues. Specifically, Bilateral Location Privacy Preservation (BLPP) and Bilateral Data Privacy Preservation (BDPP) represent the most critical dual-bilateral privacy in MCS. However, recruiting high-quality workers typically necessitates calculating the spatial proximity of each worker-task pair and the trustworthiness of the reported data, which tends to introduce significant dual-bilateral privacy risks. Additionally, due to the lack of prior knowledge about the workers' trustworthiness at the initial stage, the platform also faces the exploration-exploitation dilemma in worker recruitment. Existing work either overlooks the service quality of worker recruitment or fails to preserve the dual-bilateral privacy, making these two issues have not yet been adequately addressed. To bridge the gaps by addressing the associated challenges, this paper proposes aQuality-awareDual-BilateralPrivacyPreserving (Q-DBPP) scheme, for upholding service quality under both BLPP and BDPP. Specifically, we present two perturbation-based privacy preservation stages to calculate Degree of Proximity (DoP) and Degree of Trust (DoT) while safeguarding BLPP and BDPP, respectively. Meanwhile, to address the exploration-exploitation dilemma, we employ an upper confidence bound-based high-quality worker recruitment stage, which integrates the estimated DoT and DoP into the reverse auction to comprehensively optimize service quality. To the best of our knowledge, our Q-DBPP scheme is the first to simultaneously achieve high service quality and dual-bilateral privacy in MCS. Theoretical analyses and extensive experiments validate the superior performance of our Q-DBPP scheme in terms of privacy, incentive, efficiency, and quality.

Intelligent adaptive bitrate (ABR) schemes have been widely recognized for their excellent learning strategies. However, existing intelligent ABR methods have limitations, i.e., the lack of logical reasoning capability for video-aware symbolic representations leads to low sampling efficiency and fails to achieve the optimal performance of Bitrate Adaptation. We introduce NeuroBA, a learning-based approach to realize ABR using neuro-symbolic deep reinforcement learning. NeuroBA trains a neuro-symbolic deep network model without making any assumptions about the edge video scene and without relying on a predefined model. Instead, it enables bitrate decision-making under uncertainty and partial observability by knowledge-driven video quality perception in symbolic first-order logic. To enhance wireless signals, we have introduced Intelligent Reflecting Surface (IRS) technology to address this issue. By dynamically adjusting the phase shift of IRS, the throughput performance of wireless networks is significantly improved. Based on trace-driven and real-world experiments covering a variety of edge video scenarios, and network performance metrics, NeuroBA is compared with state-of-the-art ABR schemes, and NeuroBA exhibits superior performance, with an average QoE improvement of 16.58% (BOLA)-25.34% (Fugu). In particular, it outperforms existing baseline approaches even without pre-programmed models and network scenarios assumed for the edge network.

Numerous mobile devices have enabled Mobile Crowdsensing (MCS) to recruit a massive number of workers to sense and collect data for data requestors, thereby supporting a wide range of data-driven services. However, due to its crowdsourcing nature, MCS services face two critical challenges: ensuring privacy preservation and maintaining high-quality data collection. To address these issues, this paper proposes a privacy preserving and data quality optimization framework, named DQO-P5PI, which integrates the Preservation of Five Privacy Information (P5PI) framework with a Data Quality Optimization (DQO) scheme based on Fuzzy sets. Specifically, the P5PI frame work focuses on protecting five types of sensitive information for workers, namely the sensing data, the Reputation, the Identity, the Bid, and the Location (collectively referred to as DRIBL). In the P5PI framework, homomorphic encryption is employed to ensure DRIBL information privacy; the pseudonym mechanism is adopted to protect worker identities during interactions; Credit Verification Methods (CVM) verify the validity of workers' reputation scores, thereby protecting reputation privacy; and a Homomorphic Encryption Comparison Method (HECM) enables the platform to assess worker eligibility and select the top-k workers with the highest comprehensive scores. The DQO scheme further incorporates a Worker Reputation Update Method (WRUM) to dynamically update workers' reputation parameters, and a Credit Rating Method (CRM) that leverages Fuzzy sets to integrate bid values with historical reputation information for more reliable task allocation. We theoretically prove the correctness of the P5PI framework and DQO scheme, and extensive experimental results demonstrate that the proposed framework effectively improves sensing quality while keeping costs low.

In sparse mobile crowdsensing, the platform con-structs services based on low-cost data collection through data inference schemes, where the quality of the inferred data directly affects the service quality. Existing data inference schemes assume that workers report trustworthy data, which is not practical in SMCS. It is urgent to establish a high-quality data collection scheme for data inference that can tolerate false data to enhance inferred data quality. To address this challenge, we propose a Truthful Multi-Task Allocation (TMTA) scheme. First, we estimate the spatiotemporal correlation between areas for iden-tifying areas with high importance to the data inference process. Second, a trust-based multi-task allocation algorithm is proposed to ensure that the sensing data from high-importance areas have high trust levels. Third, a multi-armed bandit based trustworthy worker identification strategy is proposed to prioritize Multi-Task allocation for workers who can be effectively identified as trustworthy. Finally, a truthful discrete heuristic algorithm is proposed to optimize the multi-task allocation using the proposed hybrid neighbor-mode strategy, which reduces the difficulty of searching for high-utility multi-task allocations. Extensive exper-iments on two real-world air-quality datasets demonstrate that TMTA consistently outperforms six baseline methods, achieving average reductions of 24.70% in RMSE and 17.79% in sensing cost across five experimental scenarios.

Orchestration of Data as Services (ODS) at the Edge Layer (EL) in a Cloud-Edge-Seamless System (CESS) facilitates user access and avoids the long-distance transmission of massive raw data to Cloud Servers (CSs), thereby reducing network load. Building on this foundation, we argue that deploying both services and Data/Services Orchestration Programs (DSOPs), together with performing resource allocation at Edge Servers (ESs), further minimizing data processing time, service response time, and placement costs. To this end, we propose a novel service network architecture that integrates Service/DSOP placement and edge resource allocation to enhance overall system performance. First, a new service network architecture is proposed to jointly optimize Service/DSOP placement and resource allocation. Then, we design a Data-Driven Service/DSOP Placement (DDSDP) scheme that employs a Parameterized Deep$Q$-Network (P-DQN) to effectively tackle the hybrid action space optimization of such joint problem. Moreover, we develop a Demand-Calibrated Service/DSOP Placement (DCSDP) approach, which first leverages Long Short-Term Memory (LSTM) networks at the CS to capture spatio-temporal patterns of data and service demands, enabling ESs to dynamically train a demand-aware service/DSOP deployment model. Extensive simulations demonstrate that DDSDP and DCSDP significantly reduce service response time and adapt more effectively to network dynamics, achieving reductions of 47.89% and 53.88% compared to the baseline IFSP and P-DQN methods, respectively.

Mobile Crowd Sensing (MCS) has emerged a promising paradigm for large-scale data collection, which recruits truthful workers to enhance data quality, thereby maximizing the platform's profit. However, most existing work has the assumption that workers' trust remains stable once verified and truthful workers can consistently provide high-quality data. However, in real-world scenarios, there exist workers whose trust may change dynamically over time, which makes it difficult to ensure high-quality data collection due to the current lack of sustainable trust verification research, thereby damaging the platform's profit. To tackle the above challenge, we propose a novel Truthful Workers Recruitment with Sustainable Trust Verification (TWR-STV) scheme. The proposed TWR-STV scheme identifies the dynamic trust of workers and assesses the data value based on the quality of collected data, thereby selecting high-value workers to maximize the profit. First, we establish a dynamic trust model to capture the change in workers' trust. Second, we propose a stable and trustworthy worker identification method based on a two-layer sustainable data verification to identify workers' trust and the stability of workers' trust. Third, a multi-armed bandit-based stable and trustworthy worker recruitment scheme is proposed, in which high-value workers are recruited to increase platform profit and trustworthy workers who can be effectively verified are prioritized for recruitment to maintain sustainable trust verification. Furthermore, the proposed TWR-STV scheme takes into account the decay effect of time and trust on data value, which is more practical. Finally, we theoretically analyze the regret bound of TWR-STV and conduct experiments on a real dataset. Experimental results show that our proposed solution is superior to existing solutions in terms of total profit.

Satellite edge computing (SEC) extends the capabilities of task offloading from the ground to near-Earth space by leveraging the wide coverage of satellites. However, the high-speed movement and long-range communication of satellites lead to Doppler shifts and signal loss, which can result in a high bit error rate (BER) and degrade communication link quality. As a result of the high BER, error correction is required, inevitably incurring additional delay during task offloading. Therefore, in this paper, we focus on the problem ofErrorCorrection awareDependencyTaskOffloading (EC-DTO) in satellite environments. By jointly considering task dependencies and BER-induced error correction delays, we formulate the EC-DTO problem as a constrained optimization problem with the objective of minimizing the makespan of users' tasks. To solve this problem, we propose APOS, which is an event-driven offloading framework based on task-AdaptivePrioritization andOptimistic-finish-time-based nodeSelection. Specifically, APOS adopts an online offloading framework that triggers scheduling upon task arrivals and completions with task dependency awareness. It iteratively updates task readiness and performs offloading decisions to reduce the overall makespan in the SEC environment. Simulation results show that APOS outperforms the representative methods, lowering the average makespan by 20.7% and the deadline violation ratio by 84.5% on average across all cases.

We introduce MM-Mixing, a multi-modal mixing alignment framework for 3D understanding. MM-Mixing applies mixing-based methods to multi-modal data, preserving and optimizing cross-modal connections while enhancing diversity and improving alignment across modalities. Our proposed two-stage training pipeline combines feature-level and input-level mixing to optimize the 3D encoder. The first stage employs feature-level mixing with contrastive learning to align 3D features with their corresponding modalities. The second stage incorporates both feature-level and input-level mixing, introducing mixed point cloud inputs to further refine 3D feature representations. MM-Mixing enhances intermodality relationships, promotes generalization, and ensures feature consistency while providing diverse and realistic training samples. We demonstrate that MM-Mixing significantly improves baseline performance across various learning scenarios, including zero-shot 3D classification, linear probing 3D classification, and cross-modal 3D shape retrieval. Notably, we improved the zero-shot classification accuracy on ScanObjectNN from 51.3% to 61.9%, and on Objaverse-LVIS from 46.8% to 51.4%. Our findings highlight the potential of multi-modal mixing-based alignment to significantly advance 3D object recognition and understanding while remaining straightforward to implement and integrate into existing frameworks.

Point Cloud Transformers have become a cornerstone in 3D representation for their ability to model long-range dependencies via self-attention. However, these models tend to overemphasize salient regions while neglecting other informative regions, which limits feature diversity and compromises robustness. To address this challenge, we introduce BlindFormer, a novel contrastive attention learning framework that redefines saliency by explicitly incorporating features typically neglected by the model. The proposed Attentional Blindspot Mining (ABM) suppresses highly attended regions during training, thereby guiding the model to explore its own blind spots. This redirection of attention expands the model’s perceptual field and uncovers richer geometric cues. To consolidate these overlooked features, BlindFormer employs Blindspot-Aware Joint Optimization (BJO), a joint learning objective that integrates blindspot feature alignment with the original pretext task. BJO enhances feature discrimination while preserving performance on the primary task, leading to more robust and generalizable representations. We validate BlindFormer on several challenging benchmarks and demonstrate consistent performance gains across multiple Transformer backbones. Notably, it improves Point-MAE by +13.4\% and PointGPT-S by +6.3\% on OBJ-BG under Gaussian noise. These results highlight the importance of mitigating attentional biases in 3D representation learning, revealing BlindFormer’s superior ability to handle perturbations and improve feature discrimination.

Mobile crowdsensing (MCS) has emerged as a novel method for decentralized Internet of Things (IoT) environments where collaborative intelligence is fostered through meta-compu-ting. It is urgent to establish an effective incentive mechanism that can enhance task coverage, maximize the data quality, and mini-mize costs for MCS. To address this issue, this paper proposes a Trust, Probability Deviation, and Path Dependence-based Incen-tive (TPD2I) mechanism to improve task quality and coverage at a low cost for MCS. The main contributions are as follows: (1) We introduce a Lightweight Probability Deviation and Path Depend-ence-based Incentive (LPD2I) mechanism, leveraging behavioral economics path dependence theory to promote task coverage while maintaining low costs. LPD2I consists of two stages: the first stage focuses on user training, where users actual earnings exceed the platforms published rewards, encouraging them to take on un-popular tasks, thus enhancing task coverage. The second stage is to maintain high task coverage by utilizing users path dependency and probability estimation deviation, while keeping costs low. (2) A trust-based user identification method is proposed to address the IEWV problem. This method allows the LPD2I mechanism to select trustworthy users as much as possible, which improves data quality. Extensive experimental results demonstrate that TPD2I outperforms existing methods in terms of data quality, task cover-age, and cost efficiency.

Mobile crowdsensing is an emerging sensing paradigm that utilizes widely distributed mobile smart devices to rapidly collect data to support multiple applications in the Internet of Things. In mobile crowdsensing, collecting high-quality data at a low cost while protecting participants’ privacy is essential for developing high-quality applications and providing superior services. However, the paradigm faces many challenges, including protecting participants’ private information, improving data quality, and efficiently assigning tasks. To address these challenges, a Reliable Task Allocation Framework (RTAF) is designed to protect participant privacy, enhance data quality, and reduce costs. Specifically, the RTAF consists of three key algorithms: (1) A Bilateral Zero-bias Location Privacy-preserving (BZLP) algorithm is designed to protect the location privacy of tasks, ensuring the confidentiality and security of participants’ privacy. (2) An Unmanned Aerial Vehicle (UAV) Truth-driven Workers’ Trust degree Recognition (UAV-TWTR) algorithm is proposed to evaluate the data truth of tasks and identify trusted workers, reducing the risk of malicious workers attacking the system. (3) A Bi-objective Optimization Allocation of Tasks (BOAT) algorithm is developed to construct an optimal allocation scheme to ensure that the tasks can be assigned to appropriate workers, improving data collection accuracy and reducing the cost. Experimental results demonstrate that the RTAF surpasses the state-of-the-art method, enhancing the F1-score by 24.5%, increasing accuracy by 24.3%, and reducing the movement costs of workers by 19.1%.

Wireless Sensor Networks (WSNs) face critical security challenges due to their distributed architecture, dynamic topologies, and the complexity introduced by multisensor nodes. These factors result in highly variable traffic patterns, making stealthy and long-duration attacks difficult to detect. Recent Mamba-based sequence models have shown promise in capturing long-range dependencies with high efficiency, making them attractive for such scenarios. However, Mamba’s inherent causal modeling limits its ability to fully exploit hierarchical patterns that are crucial for detecting subtle or dispersed attacks. In this work, we propose MRMamba, a multi-resolution state-space architecture that equips Mamba with non-causal and cross-scale modeling capabilities. Specifically, MRMamba introduces a Multi-Resolution State Space Module that performs cross-scale attention fusion. This mechanism dynamically integrates context at multiple temporal resolutions and helps alleviate long-range dependency decay. Furthermore, a dual-branch architecture is constructed around MRMamba to model attacks from complementary perspectives, enabling more accurate and robust detection. Extensive experiments on five real-world public datasets show that MRMamba consistently outperforms existing attack detection methods. It improves the F1 score by up to 4.53% on CIC2023 and 6.6% on IoM2024 over the best of the compared methods.

Vehicle Edge Computing (VEC) has become a new computing paradigm in the field of intelligent transportation systems. In the VEC environment, system performance is seriously challenged by delay-sensitive and dependent tasks. To address this issue, this paper investigates the joint problem of task offloading and resource allocation in VEC, taking into account and highlighting the delay sensitivity and dependencies of tasks. The corresponding mixed-integer nonlinear programming problem is formulated. Then this joint optimization problem is modeled as a Markov decision process. In order to solve the problem, an algorithm for joint optimization Delay-Sensitive and Dependent Tasks offloading decision and resource allocation in VEC Based on Double Deep Q-Network (DDT-DDQN) is proposed. Given the resource allocation and bandwidth allocation, the task queues are arranged by a prioritization policy, which takes into account the dependencies of tasks and delay-sensitive of tasks, and the Double Deep Q-Network (DDQN) is used to solve the offloading decision and channel allocation.Then, given the offloading decision and channel allocation schemes, the DDQN is utilized to solve the problem the computational resources and bandwidth allocation. Simulation results show that the DDT-DDQN algorithm proposed in this paper outperforms other comparative algorithms in terms of task completion rate and significantly improves the network performance, given the same task arrival rate, computational resources and taking into account the inter-task dependencies.

Multi-Unmanned Aerial Vehicle (UAV)-supported Mobile Edge Computing (MEC) can meet the computational requirements of tasks with high complexity and latency sensitivity to compensate for the lack of computational resources and coverage. In this paper, a multi-user and multi-UAV MEC networks is built as a two-tier UAV system in a task-intensive region where base stations are insufficient, with a centralized top-center UAV and a set of distributed bottom-UAVs providing computing services. The total energy consumption of the system is minimized by jointly optimizing the task offloading decision, 3D deployment of two-tier UAVs, the elevation angle of the bottom UAV, the number of UAVs, and computational resource allocation. To this end, an algorithm based on Differential Evolution and greedy algorithm with the objective of minimizing Energy Consumption (DEEC) is proposed in this paper. The algorithm uses a two-tier optimization framework where the upper tier uses a population optimization algorithm to solve for the location and elevation angle of the bottom UAV and the number of UAVs based on the actual ground equipment and the lower tier uses clustering and greedy algorithms to solve for the position of the top UAV, the offloading decision of the task, and the allocation of computational resources based on the results of the upper layer. The simulation results show that the algorithm effectively reduces the total energy consumption of the system while satisfying the task computation success rate and time delay.

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), Information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches.

Nowadays, Mobile Crowd Sensing (MCS) has become a popular paradigm for large-scale data collection using ubiquitous mobile sensing devices. However, most existing works do not consider that requester's payments are unknown prior, and assume that workers are honest, which may not be true in practice. To address these problems, we propose a novel Truthful and Dual-direction Combinatorial Multi-Armed Bandit (TD-CMAB) scheme, which maximizes the total profit of the dual-direction platform for both the worker side and the requester side. Specifically, for the worker side, to overcome the problem that the platform is not clear whether sensed data are true, we propose a worker recruitment strategy that identifies and recruits honest workers at low cost through the Upper Confidence Bound (UCB) algorithm based on truth data discovery. For the requester side, where requesters’ payments are unknown prior, we model requester selection as a CMAB problem and solve it by the proposed adaptive UCB algorithm. Furthermore, we theoretically prove the worst regret bound of the TD-CMAB. Finally, we evaluate the effectiveness of the TD-CMAB scheme through extensive experiments using the Beijing taxi dataset.

As a collaborative and open network, billions of devices can be free to join the IoT-based data collection network for data perception and transmission. Along with this trend, more and more malicious attackers enter the network, they steal or tamper with data, and hinder data exchange and communication. To address these issues, we propose a Proactive Trust Evaluation System (PTES) for secure data collection by evaluating the trust of mobile data collectors. Specifically, PTES guarantees evaluation accuracy from trust evidence acquisition, trust evidence storage, and trust value calculation. First, PTES obtains trust evidence based on active detection of drones, feedbacks from interacted objects, and recommendations from trusted third parties. Then, these trust evidences are stored according to interaction time by adopting a sliding window mechanism. After that, credible, untrustworthy, and uncertain evidence sequences are extracted from the storage space, and assigned with positive, negative, and tendentious trust values, respectively. Consequently, the final normalized trust is obtained by combining the three trust values. Finally, extensive experiments conducted on a real-world dataset demonstrate PTES is superior to benchmark methods in terms of detection accuracy and profit.

Unmanned Aerial Vehicle (UAV) is a promising technology that can serve as aerial base stations to assist the Internet of Things (IoT) network and solve various problems, such as expanding network coverage, improving network performance, transmitting energy to IoT devices, and performing IoT compute-intensive tasks. However, due to the communication between UAVs and the migration of computing tasks, privacy and security during the computing offloading process are challenging issues. To this end, we design an air-to-air multi-UAVs MEC network system based on multi-coalition game, and introduce blockchain technology to ensure privacy and security between UAVs, effectively ensuring the security and confidentiality of computing offloading between UAVs. In this paper, the joint optimization problem of UAV channel selection, UAV location deployment, block processor decision, block processor transmission power, and block processor generation frequency is studied. The goal is to minimize the weighted average sum of energy consumption and delay for MEC task computing and blockchain task processing. To handle this intractable issue, the original problem is decomposed into two subproblems and solved alternately with each other. In addition, the Joint Convex Optimization and Stackelberg Game Hierarchical (JCSH) algorithm is proposed, which solves the problem of blockchain-enabled computing offloading and resource allocation. The simulation results show that the JCSH algorithm has better performance and stronger robustness compared to other algorithms under different parameter settings.

Parkinson's Disease (PD) is one of the most critical neurodegenerative diseases, yet there is no cure for it, and the state-of-the-art treatment is to slow its progression. Thus, the earlier a patient with PD is recognized, the better he can be treated. Our project joins the research effort that aims to support early PD diagnosis by designing a Virtual Reality (VR)-based system to monitor pupil diameter patterns as new biomarkers (e.g., Pupil Light Reflex and Task-evoked Pupil Response) and provide early warning of potential PD onset. A follow-up experiment with 55 participants shows that the accuracy of recognizing early PD from healthy controls could reach 0.8942. Our study shows early results of a promising research direction that leverages VR-based technology to non-intrusively recognize patterns and provide alerts to early PD patients who would otherwise not know their symptoms until much later.

Mobile Crowdsensing (MCS) has emerged as a promising sensing paradigm for accomplishing large-scale tasks by leveraging ubiquitously distributed mobile workers. Due to the variability in sensory data provided by different workers, identifying truth values from them has garnered wide attention. However, existing truth discovery schemes either offer limited privacy protection or incur high participation costs and lower data aggregation quality due to malicious workers. In this paper, we propose an Efficient and Trusted Bilateral Privacy-preserving Truth Discovery scheme (ETBP-TD) to obtain high-quality truth values while preventing privacy leakage from both workers and the data requester. Specifically, a matrix encryption-based protocol is introduced to the whole truth discovery process, which keeps locations and data related to tasks and workers secret from other entries. Additionally, trust-based worker recruitment and trust update mechanisms are first integrated within a privacy-preserving truth discovery scheme to enhance truth value accuracy and reduce unnecessary participation costs. Our theoretical analyses on the security and regret of ETBP-TD, along with extensive simulations on real-world datasets, demonstrate that ETBP-TD effectively preserves workers’ and tasks’ privacy while reducing the estimated error by up to 84.40% and participation cost by 54.72%.

Enhancing the communication rate and quality has become the primary goal for the development of next-generation mobile communication networks, and traditional techniques such as MIMO and increasing the transmit power of the base station (BS) have not achieved a leapfrog effect. The emergence of Intelligent Reflective Surfaces (IRS) provides more reliable technical support for providing high-energy and high-rate communications. However, IRS-aided joint optimization with communication rate and quality of service constraints is a non-convex non-smooth optimization problem, and the optimal global solution cannot be obtained due to its computational complexity. In this paper, we propose an intelligent Advanced Non-convex Non-smooth Scheme (ANNS) in IRS-aided Next Generation Mobile Communication Networks for making the transmission rate of mission communication and communication quality effective. To ensure that the inequality constraints in the joint optimization problem are not violated and the equation constraints are satisfied, a hybrid deep reinforcement learning and data-experience-driven constraint security layer network is proposed, which maps the constraint violations into the safe feasible domain by mapping the constraint variables into the constraint variables mapping method, and the convergence of the algorithm is theoretically demonstrated. Experimental results show that the proposed ANNS performs superior optimization compared to SAC, DDPG, and A2C for solving non-convex non-smooth problems. The proposed ANNS has the potential to be generalized to other mobile computing applications with non-convex non-smooth characteristics.

Mobile Crowdsensing (MCS) has emerged as a compelling paradigm for data sensing and collection, leveraging the widespread adoption of mobile devices and the active participation of numerous users. Despite its potential, MCS faces critical challenges, particularly in recruiting reliable workers and acquiring high-quality sensing data. Most existing approaches assume prior information on worker quality and are vulnerable to collusion attacks, especially having not comprehensively considered workers’ reliability and stability. To address these problems, we propose a Multi-Armed Bandit (MAB) based Worker Recruitment Scheme (MWRS) integrated with the Tripartite Stackelberg Game (TSG) for MCS. Specifically, a trust evaluation and truth inference mechanism is introduced to assess the trustworthiness of workers through active truth detection. To enhance recruitment quality, we employ a trust-aware worker selection mechanism that utilizes a modified Upper Confidence Bound (UCB) algorithm, achieving an optimal balance between exploration and exploitation. Furthermore, the interactions among participants are modeled using a TSG framework, which formulates their respective payoffs to determine optimal decision-making strategies, thus achieving mutually beneficial outcomes. Extensive evaluations on real-world datasets demonstrate that our proposed scheme improves total quality by up to 30.8% and reduces regret by up to 80.3% compared to existing methods.

In pursuit of an immersive virtual experience within the Cyber-Physical Metaverse Systems (CPMS), the construction of Avatars often requires a significant amount of real-world data. Mobile Crowd Sensing (MCS) has emerged as an efficient method for collecting data for CPMS. While progress has been made in protecting the privacy of workers, little attention has been given to safeguarding task privacy, potentially exposing the intentions of applications and posing risks to the development of the Metaverse. Additionally, existing privacy protection schemes hinder the exchange of information among entities, inadvertently compromising the quality of the collected data. To this end, we propose a Quality-aware and Obfuscation-based Task Privacy-Preserving (QOTPP) scheme, which protects task privacy and enhances data quality without third-party involvement. The QOTPP scheme initially employs the insight of “showing the fake, and hiding the real” by employing differential privacy techniques to create fake tasks and conceal genuine ones. Additionally, we introduce a two-tier truth discovery mechanism using Deep Matrix Factorization (DMF) to efficiently identify high-quality workers. Furthermore, we propose a Combinatorial Multi-Armed Bandit (CMAB)-based worker incentive and selection mechanism to improve the quality of data collection. Theoretical analysis confirms that our QOTPP scheme satisfies essential properties such as truthfulness, individual rationality, and ε-differential privacy. Extensive simulation experiments validate the state-of-the-art performance achieved by QOTPP.

The proliferation of mobile smart devices with ever-improving sensing capacities means that Mobile Crowd Sensing (MCS) can economically provide a large-scale and flexible solution. However, existing MCSs face threats to privacy and fairness when recruiting workers due to information sensitivity, uncertainty about worker behavior, and budget constraints. To address the above issues, we propose an Anonymity, Trust, and Fairness in Privacy Protection (ATFPP) service construction framework to cost-effectively improve the quality of data at MCS. The main innovations are as follows: Firstly, on anonymity, in order to protect the privacy of workers, we propose a Privacy-Preserving (PP) framework based on an anonymous three-party platform, which realizes a full-process privacy-preserving scheme for workers. Second, on trust, we design more efficient Truth Discovery (TD) algorithm and adopt multifactor trust assessment method to identify more trustworthy workers. In addition, in terms of fairness, the fair distribution of compensation is realized through reasonable budget and approximate Shapley method. Finally, the proposed ATFPP scheme is theoretically proven to be correct and effective. Simulations based on real-world datasets illustrate that our ATFPP service construction scheme outperforms the state-of-the-art method in terms of both privacy protection and data quality.

Mobile Crowd Sensing is a prevalent and efficient paradigm for multi-source data collection, where Multi-source Data Fusion (MDF) plays a crucial role in constructing quality data collection services. Current MDF methods often require the majority of participating sensing sources to be credible, or assume that the workers’ credibility is either prior known or easily calculable. However, due to the presence of uncredible environments and the problem of Information Elicitation Without Verification (IEWV), these methods are impractical. It may lead to a vicious cycle where the recruitment of uncredible workers affects the quality of the estimated truth, which can further lead to misjudgments of worker credibility, thereby exacerbating the quality of subsequent recruitment. In this article, a Reliable Multi-source Data Fusion scheme with Cross Validation (RMDF-CV) is proposed to obtain reliable truth for service construction. Specifically, we first introduce the Combinatorial Multi-Armed Bandit (CMAB) model to recruit high-credibility workers by balancing exploration and exploitation. Then, we establish three-stage truth data through three different data sources: Unmanned Aerial Vehicles, credible workers, and Deep Matrix Factorization. Theoretical analyses and extensive simulations confirm the excellent performance of our RMDF-CV scheme.

In the service of Mobile Crowd Sensing (MCS), High-quality Data Collection (HDC), Bilateral Location Privacy Preservation (BLPP), and sensing cost are three pivotal issues. It is widely believed that HDC necessitates the recruitment of workers with high Quality of Service (QoS), which is related to the sensing data capabilities of the recruited workers and the worker-task distances. However, submitting high-quality data demands more resources from the workers, incurring higher costs. Meanwhile, BLPP techniques, aiming to conceal the locations of the workers and tasks, may impede the evaluation of the workers' QoS. Therefore, there is still a lack of a low-cost and BLPP high QoS data collection research. Motivated by this, we propose a Quality-Aware, Low-Cost, and Privacy-Preserving Data Collection Service (QLP-DCS) for MCS. First, we propose a matrix perturbation-based approach to achieve BLPP while preserving the partial order relationship of distances. Subsequently, we employ the Upper Confidence Bound indexes-based reverse auction recruiting workers to balance exploration and exploitation with the low sensing cost. Then, we propose a multi-level truth discovery approach and establish an effective trust verification mechanism. Theoretical analysis and extensive experiments validate the superior performance of our QLP-DCS.

UAVs-enabled Mobile Crowdsensing (UMCS) has gained considerable attention recently, but it is challenging to meet the data collection needs of the entire city using only the UAV with limited energy. Furthermore, how to effectively minimize Age-of-Information (AoI) and ensure data quality has not been well solved in previous studies. Therefore, this paper proposes a hybrid optimization framework for AoI minimization, which recruits massive distributed workers as the main force for data collection, while the UAV acts as a data collection collaborator and is more inclined to fly to the SNs that cannot establish connections with workers, To mitigate the potential security threats incurred by dishonest workers of the MCS system, we first provide a Greedy-based Multi-worker Task Assignment (GMTA) strategy, aiming to assign more urgent data collection tasks to reliable workers under workload constraints. Then, we propose a Deep-Reinforcement-Learning-based Global AoI Minimization (DRL-GAM) strategy for the UAV path planning to find a set of optimal actions to minimize the global AoI. Based on the real dataset, our simulation experiments show that compared with traditional strategies, our DRL-GAM strategy can reduce the global AoI by an average of 6.49%$\sim$68.21% in various network sizes, and is more stable for the average standard deviation is only 51.75% of other strategies.

As a promising distributed computing paradigm, federated learning (FL) has been widely applied to industrial manufacturing. To enhance the safety of FL system against growing privacy attacks, secret sharing techniques are introduced to realize secure model aggregation. However, the huge extra transmission in vanilla secret sharing aggravates the inherent communication bottleneck of FL system in industrial cellular networks. To reduce transmission overhead, this article proposes a lightweight FL system based on rotated partial secret sharing (FedRPS), which divides industrial terminals into several subgroups and allows them to alternately share partial local secrets. Our FedRPS ensures the global model to converge at a high rate with incomplete local parameters. Moreover, we theoretically analyze the convergence error of FedRPS, and discuss the impact of unbalanced data in subgroups on model convergence. To further improve global model performance under non-IID settings, we design a multiparty greedy grouping (MPGG) strategy to balance the training data in each subgroup. Finally, we conduct experiments on several datasets, which demonstrate our FedRPS trades for significant improvement on communication with only small loss of model accuracy. In addition, we also simulate the privacy attack scenarios to verify the better defense of FedRPS compared with previous complete secret sharing.

Growth of computing capacity at edge drives edge computing as an efficient computing model, and cloud computing is considered as an indispensable computing model as various cloud services emerge. However, edge computing performs gracelessly in persistent tasks and the tasks requiring dispersed data, whereas cloud computing is often accompanied with high latency. To deal with more complex network scenarios, we fuse the two computing models and propose cloud-edge-seamless system (CESS) which enables all infrastructures to orchestrate data as services in a 5G/6G network, realizing infrastructure as a service (IaaS). In CESS, raw data is processed on intermediate processing devices (IPDs) before reaches the cloud. Thereafter, we proposed OBRI scheme to minimize processing and routing delay in which IPD can orchestrate data as a service if it is busy and handle data roughly if it is idle, thus reducing data volumes and total delay according to IPD congestion. Furthermore, there is still a large number of flows queuing on downstream IPDs, then NOB scheme is proposed to reduce delay further, in which IPD sends notice to upstream IPDs when congestion occurs to restrict them to orchestrate data only. Thus, the flow velocity slows down and more flows are orchestrated as services upstream, resulting greater delay reduction. Extensive experimental results show that the proposed CESS is immensely efficient. OBRI and NOB can effectively reduce delay, balance load, and improve quality of experience (QoE). In addition, NOB provides great flexibility and is extremely suitable to process delay-sensitive data.

Data augmentation is an effective regularization strategy for mitigating overfitting in deep neural networks, and it plays a crucial role in 3D vision tasks, where the point cloud data is relatively limited. While mixing-based augmentation has shown promise for point clouds, previous methods mix point clouds either on block level or point level, which has constrained their ability to strike a balance between generating diverse training samples and preserving the local characteristics of point clouds. The significance of each part component of the point clouds has not been fully considered, as not all parts contribute equally to the classification task, and some parts may contain unimportant or redundant information. To overcome these challenges, we propose PointPatchMix, a novel approach that mixes point clouds at the patch level and integrates a patch scoring module to generate content-based targets for mixed point clouds. Our approach preserves local features at the patch level, while the patch scoring module assigns targets based on the content-based significance score from a pre-trained teacher model. We evaluate PointPatchMix on two benchmark datasets including ModelNet40 and ScanObjectNN, and demonstrate significant improvements over various baselines in both synthetic and real-world datasets, as well as few-shot settings. With Point-MAE as our baseline, our model surpasses previous methods by a significant margin. Furthermore, our approach shows strong generalization across various point cloud methods and enhances the robustness of the baseline model. Code is available at https://jiazewang.com/projects/pointpatchmix.html.

Quality control of sensing data poses a fundamental challenge in Spatial Crowdsourcing (SC) applications, and various incentive mechanisms have been proposed to effectively motivate workers. However, real-world SC systems involve multiple stakeholders, namely workers, platforms, and requesters, who exhibit bounded rationality. Moreover, prior research has often neglected the practical aspect of platforms needing to verify sensing data against ground truth information, which incurs a cost. Therefore, platforms face the constraint of verification cost and must judiciously select the verification rate. In this paper, a S ustainable Q uality C ontrol S ystem (SQCS) is proposed, which employs a three-party evolutionary game to model the dynamic interaction among workers, platforms, and requesters in practical SC scenarios. Based on this dynamic model: (1) In theory, we derive the minimum verification rate required for effective worker monitoring and the maximum verification cost that platforms can sustainably bear via the analysis of evolutionary stable strategies. These findings serve as guidelines for achieving sustainable quality control within the SQCS framework; (2) In practice, we apply the SQCS framework to the SC scenario of urban air pollution monitoring. The optimal quality control strategy that maximizes overall social benefits is analyzed via numerical methods . Extensive experimental validation is conducted to demonstrate the effectiveness of the proposed theory and practical approach. These findings provide valuable insights for the sustainable development of SC systems.

The Energy Internet (EI) aims to build a sustainable energy ecosystem by connecting diverse energy sources and prosumers. Mobile Crowd Sensing (MCS) enables efficient data collection for monitoring and aggregation from distributed devices. Given the complex behavior of workers driven by self-interest, recruiting trustworthy, high-quality, and inexpensive workers remains a significant challenge in research and practice. Previous studies often assume that worker characteristics are known or can be obtained after data collection. However, evaluating worker qualities is quite challenging in the face of multi-source data and complex workers. To address this, we propose a Completion Rate Learning based Multi-Armed Bandit reverse Auction (CRL-MABA) scheme for identifying and selecting high-quality workers in MCS. Our CRL-MABA scheme first proposes a Spatial-Temporal Upper Confidence Bound (STUCB) method to recruit workers, considering both the quality of workers for exploitation and the spatiotemporal features for exploration. In addition, the Dual-Stage Data Estimation Mechanism (DSDEM) and Long-Term and Short-Term Memory Learning (LTSTML) are designed to identify workers accurately and efficiently. Importantly, our proposed scheme avoids the impractical assumptions in previous works while satisfying important criteria such as truthfulness, individual rationality, and computational efficiency. The effectiveness of our scheme is demonstrated through extensive experimental results, which show its superiority over existing strategies.

Sparse mobile crowdsensing (SMCS) is a prospective solution for large-scale data sensing through mobile devices of Internet of Things (IoT) systems where IoT systems cannot obtain the sensing data of the area under extreme environments. The unsensed area data can be obtained by the data inference algorithm trained by the sensed data of recruited workers. However, recruited workers may upload false data in exchange for payment, and the platform is unable to distinguish between true and false data. In this article, our goal is to maximize the SMCS platform’s total profit, where the platform cannot verify the authenticity of the sensed data, and the requester’s payment is based on the sensing task’s data quality. To meet the objective, we propose the area-constrained truthful worker recruitment-based sensing map recovery (ATWR-SMR) scheme, which includes the area constraint, the area-constrained truthful worker recruitment, and the sensing map recovery. 1) The area constraint establishes the importance of areas by history data differences in the sliding window. 2) The truthful worker recruitment identifies trustworthy workers by the truthful upper confidence bound algorithm and recruits low-cost trustworthy workers to sense high-importance areas. 3) The sensing map recovery infers the unsensed data by the deep matrix factorization algorithm trained by the history truthful data set. Finally, we verify the effectiveness of the ATWR-SMR scheme in improving the total profit of the platform through extensive comparison experiments based on the China air quality data set.

Intelligent Reflecting Surface (IRS) enabled Unmanned Aerial Vehicle (UAV) edge computing, a new communication technology, can provide sufficient capacity for edge computing system. However, due to the Line-of-Sight (LoS) or the Non Line of Sight (NLoS) of communicating environments will impact transmitting rate or delay, the Intelligent Reflective Surface (IRS) can be utilized to compensate the channel fading in the IRS-enabled UAV edge computing. In this paper, the joint problem of IRS phase shift, UAV trajectory and power allocation in the system is investigated, aiming to maximize the energy efficient. The corresponding optimization problem, which consists of mixed integer nonlinear programming problem, is formulated. To solve the problem, the original problem is decomposed into two subproblems, and an iterative method framework based on ConVex optimization and Deep Reinforcement Learning (CV-DRL) is proposed. Given the UAV trajectory and IRS phase shift, the Convex optimization algorithm is used to solve the power allocation schemes. Then, given the power allocation schemes, the Double Deep Q Network (Double DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms are utilized to solve the problem of optimal UAV trajectory and IRS phase shift. The simulation results demonstrate that our proposed method outperforms other schemes in terms of energy efficiency, providing significant enhancements

Mobile crowdsensing (MCS) is a crucial component in the Industrial Internet of Things (IIoT), mainly due to its role in collecting data and enhancing applications. Nonetheless, it faces challenges in maintaining data quality and cost efficiency. Low-quality workers and their deceptive data bids undermine the trustworthiness of MCS data collection. Despite this, prior studies have not sufficiently scrutinized the validity of data and bids. These issues could render MCS services ineffective and unaddressed, hindering IIoT development. In response, we propose an Intelligent Data and Bid dual truth discovery (DTD) scheme. Initially, the scheme applies a detection algorithm to identify the features of ground truth data sensed by unmanned aerial vehicles. The approach uses features to evaluate the data trust from unknown workers and filter out low-quality workers. Subsequently, the scheme evaluates the bid trust from reliable workers by calculating their bid confidence intervals. Upon completing this assessment, the scheme identifies high-quality workers. This process hinges on a contribution value incorporating both data and bid trust. Ultimately, the scheme assigns these high-quality workers to sense the subsequent tasks. This approach significantly improves the data quality and reduces costs for MCS in IIoT. The experimental results demonstrated that the DTD scheme outperforms the existing main schemes in terms of sensitivity (improvement 44%), specificity (improvement 5%), accuracy (improvement 18%), and F1-score (improvement 47%). It also reduced data bias by 24 percentage points and reduced costs by 38 percentage points.

The high mobility of Internet of Unmanned Aerial Vehicles (IUAVs) has attracted attention in the field of data collection. With the rapid development of the Internet of Things (IoT), more and more data are generated by IoT networks. IUAV-aided IoT networks can efficiently collect data in specific areas, which is of great significance in disaster relief. In the data collection task, it is necessary to plan the flight trajectory for the data collector—IUAV, so that the IUAV can collect data efficiently. However, existing research basically only considers the efficiency of data collection by IUAVs, but rarely considers the safety of IUAVs during flight. Therefore, this paper proposes an IUAV trajectory planning algorithm that integrates energy efficiency and safety using local search to address the issues mentioned above. At the same time, a Tiny Machine Learning (TinyML) algorithm is designed to assist the IUAV in making real-time decisions during flight. First, we build a general mathematical model that describes the risk in a particular region. Then consider guiding the IUAV to a safer trajectory by introducing virtual nodes in the flight trajectory. Furthermore, we designed a local search algorithm for the three tasks of IUAV access sequence, IoT Networks cluster heads selection and virtual nodes selection, and solved them through iterative optimization. We also consider the unreachable situation of the virtual nodes and use TinyML technology to help the IUAV adjust the position of the virtual nodes in real time in case of an emergency.In the end, an IUAV trajectory is obtained that can efficiently collect IoT networks’ data and fly safely. We have conducted a large number of simulation experiments to demonstrate the efficiency of the proposed algorithm compared to the baseline algorithm.

Mobile crowd sensing (MCS) is a cloud–edge–terminal collaboration model that relies on edge terminal devices, or “workers,” to sense data and build applications for cloud-hosted platforms. However, to ensure high-quality application development, recruiting truthful workers in the edge network is crucial. With the emergence of artificial intelligence (AI), the Internet of Things (IoT) is entering a new era, known as AI of Things (AIoT). This article proposes an AI-enabled MCS system, which includes MLM-WR, a cloud–edge–terminal collaboration data collection scheme for AIoT. MLM-WR leverages swarm intelligence to match truthful workers with sensing tasks, enabling efficient and effective data collection for AIoT applications. The matching theory is applied from two perspectives: 1) truthful workers discovery and 2) sensing difference discovery. To identify truthful workers, we adjust their credibility based on the deviation of their sensing data with ground truth data (GTD) obtained through collaboration with the unmanned aerial vehicle (UAV). In the sensing difference discovery, we obtain workers’ sensing attribute reliability by calculating attribute data errors and incorporate absolute and relative sensing location preferences to determine workers’ sensing quality at different locations. Additionally, MLM-WR employs the particle swarm optimization (PSO) algorithm to assign workers while considering sensing attribute and location reliability and recruitment cost, thus addressing the tradeoff between recruitment cost and data quality. The effectiveness of our approach is demonstrated through extensive evaluations, where MLM-WR outperformed the state-of-the-art approaches.

Mobile crowdsourcing (MCS) is an emerging paradigm that harnesses the collective power of the crowd to tackle large-scale tasks. To ensure the high-quality worker selection, various combinatorial multiarmed bandit (CMAB)-based schemes have been proposed. However, previous schemes often overlook critical issues. First, the post-unknown worker recruitment (PUWR) problem emerges when the quality of a worker remains unknown despite reported worker data. Second, the presence of Sybil Requesters is often neglected, who manipulate ratings to deceive workers for malicious purposes. To tackle these challenges, we present an innovative scheme called bi-directional trust verification-based CMAB (BTV-CMAB). First, we propose a truth quality discovery approach that effectively addresses the PUWR problem by estimating worker quality. Additionally, we employ a BTV mechanism to assess the Degree of Trust (DoT) of requesters and the reputation of workers. To select top-notch workers for MCS, we combine the worker quality and reputation into an upper confidence bound (UCB) index. The effectiveness of the BTV-CMAB scheme is supported by theoretical proof, which demonstrates its ability to ensure truthfulness and individual rationality. Furthermore, experimental results reveal promising improvements achieved by our scheme, including a 17.44%, increase in the platform’s revenue and a significant decrease in regret of up to 88.26%. To the best of our knowledge, this study is the first to propose utilizing a BTV mechanism to effectively address the PUWR problem and counter the threat of Sybil attacks in the CMAB-based worker recruitment process.

Emerging crowdsensing paradigm enables a large number of sensing applications, where much attention is drawn to the fundamental problems for maximizing the system utility and improving the data quality. However, the existing works only consider one of the above issues. In fact, the truthful value of data lies in its veracity. The false data submitted by malicious workers brings no benefits and even causes losses to the system. Therefore, the utility calculations without considering the veracity of data in the previous incentive mechanisms are inaccurate. While current truth discovery (TD) methods cost a lot and cannot improve the quality of data at source. To overcome these problems, we design a truthful value discovery and reverse auction (TVD-RA) framework. First, a TD and trust inspection approach are proposed to find the truth and get the trust degree of workers. This combination can capture the truthful value of data and identify malicious workers. In addition, an incentive mechanism based on reverse auction is proposed. Discovered malicious workers are excluded when selecting task performers, thereby improving the quality of data. In addition, tasks are redundantly assigned in the initial period, creating opportunities for trust inspections. Although this entails losses, the influence of malicious workers can be ruled out. Therefore, we gain long-term utility at the expense of current benefits. Theoretical analysis demonstrates that the proposed incentive mechanism is incentive compatible and individual rational. We finally carry out extensive evaluations, where results demonstrate the superiority of our mechanism over the state-of-the-art approaches.

As Industrial Internet of Things (IIoT) systems have superior distributed characteristics, federated learning (FL) has gradually become a mainstream distributed computing paradigm in recent years, promoting its intelligent development. A key factor of FL is the model accuracy, which relies on the truth of the participating industrial edge device (IED)’s parameter. However, some untrusted IEDs can contaminate models by fabricating false FL parameters, thereby deceiving rewards, known as model poison attack (MPA). In this article, a truth-discovery-based multidimensional bidding (TD-MDB) strategy is proposed to obtain truthful parameters at a low cost for FL. In the TD-MDB strategy, parameter server (PS) will randomly assign the lightweight trust evaluation model to test the trust of each IED according to the accuracy of IED’s calculation. Then, in order to resist MPA and inference attack, we use an improved clustering algorithm to divide the trusted IEDs into different clusters based on their different trust and stability. Finally, a device selection strategy is proposed to select cost-effective IEDs for model training based on Asynchronous Advantage Actor–Critic (A3C) algorithm. In the TD-MDB strategy, the IEDs that provide more resources, high trust, and low bid will be selected to train the model and maximize the real revenue of the system with high accuracy. We finally carry out extensive evaluations, where results demonstrate that the TD-MDB strategy can effectively provide an efficient offloading strategy, resist MPA and ensure that truth computing for FL, which is better than the state-of-the-art strategies in model accuracy, system revenue, and other performance indicators.

Mobile Crowd Sensing (MCS), as a promising sensing paradigm, significantly relies on wireless communication networks and widely distributed mobile workers to capture data from the surroundings. However, the positioning-dependent nature of most MCS tasks often requires workers to embed their positionings in reports, which may result in privacy leakage and a decline in their participation enthusiasm. Considering workers’ diverse perceptions of positioning privacy, in this paper we propose the Lightweight Positioning-Privacy Protection Scheme with Double-Layer Incentives (L3P-DLI) to meet their personalized privacy requirements in an efficient and low-cost way while stimulating their participation. To the best of our knowledge, this scheme is the first attempt to employ proxy forwarding to protect workers’ sensitive positionings while ensuring high-quality sensing results. Moreover, our double-layer incentivizing mechanism is elaborately designed to motivate workers to actively participate or serve as proxies. Specifically, the bidirectional auction between data collectors and proxies can safeguard the security of data collectors, and compensate for the potential privacy leakage cost of proxies helping to forward data. Additionally, the reverse auction mechanism enables the platform to reward recruited workers to compensate for their various costs. Extensive experiments conducted on real-world datasets validate that L3P-DLI effectively preserves workers’ positioning privacy while maximizing their income to encourage participation.

Human-centric communication metaverse relies on the convergent integration of multiple existing technologies such as 5G and beyond networks, virtual reality, augmented reality, and digital twins, and thus their security vulnerabilities and vulnerability to interference attacks may also be inherited by the metaverse. In particular, existing security policies may be inefficient for communication interference problems encountered in multi-device collaborative computing in a metaverse 5G environment and lack adaptability to metaverse applications. In this paper, we propose a novel intelligent game anti-interference collaborative computing model that accurately describes the interference relationships among source devices, cooperating devices, and interferers in metaverse collaborative computing. We model the offensive and defensive confrontation between multiple metaverse devices as a Stackelberg game, where the source device is the leader, the collaborative computing device acts as the sub-leader, adjusts its antijamming strategy according to the source device’s strategy to improve the source device’s communication anti-jamming performance, and the jammer acts as the follower. We design an intelligent Stackelberg Game-theoretic Policy-based Learning (SGPL) algorithm for jamming resistance in metaverses over 5G and Beyond Networks, where the leaders (co-computing devices) update their training parameters using the total derivatives of the objective function, while the followers (i.e., jammers) update their training parameters using an independent gradient dynamics strategy. Loops are eased and convergence is accelerated by introducing differential dynamics into the leader training network to reflect the interaction structure of the critic and actor network layers. Finally, numerical results demonstrate the effectiveness of the proposed SGPL algorithm in metaverse anti-jamming countermeasures. The proposed SGPL algorithm has the potential to be generalized to other metaverse applications with multi-user attack and defense characteristics.

Edge-cloud networks face security threats during data collection, data routing, and service construction, resulting in data tampering, stealing, and communication interruption. Trust mechanism can predict data quality and cooperation probability of nodes before purchasing data or establishing cooperation, so as to select trusted participants for data perception and interaction. However, there are some problems with existing trust methods, such as limited evaluation scope, incomplete trust evidence, and inaccurate evaluation results. To address these issues, a Trust mechanism-based Multi-Tier Computing system (TMTC) is proposed in this paper. Specifically, we propose a two-tier trust evaluation model. At the data collection layer, it conducts trust evaluation on data reporters based on data submission and communication interactions. At the network layer, it evaluates trust of routers through path backtracking verification, multi-service analysis and coincident path analysis. Then, based on evaluation results, a differentiated trust detection is initiated for normal and abnormal nodes. And high-frequency detection tasks are initiated for malicious nodes to improve accuracy, sparse detection tasks are initiated for normal nodes to reduce costs. Finally, extensive experiments conducted on the synthetic and real-world datasets demonstrate that, TMTC can resist data tampering and good-bad mouth attacks effectively. And whether in a dense or uniform scene, it outperforms two benchmark methods by increasing malicious node detection rate by 13.37%-21.87% and reducing cost by 18.8%-50.32%.

Recently, hyperspectral image (HSI) classification methods based on Transformers have developed rapidly. However, these methods still face challenges in handling the widely varying scales and diverse spatial distribution patterns commonly found in HSIs. To address these issues, this article proposes a simple, yet novel HSI classification framework named the spectral view attention Transformer (SVAFormer). Built on the Transformer mechanism, this framework enhances the integration of spectral and spatial features by allowing the spectral token, corresponding to the pixel to be classified, to access spatial neighborhood information from multiple perspectives and levels. Specifically, the framework employs random masking techniques to provide spectral tokens with spatial neighborhood information from different viewpoints, enabling the model to handle diverse land-cover distribution patterns. Additionally, the framework introduces a spectral token-aware pooling layer between adjacent Transformer blocks, which preserves the central role of spectral tokens while progressively expanding the spatial scale represented by each token. This reduces the Transformer’s focus on spatially fragmented information and enables spectral tokens to concentrate on spatial neighborhood information at various levels and scales. The key characteristic of this framework is its ability to effectively handle land-cover features of different scales and shapes by strengthening the fusion of spectral and spatial characteristics. Experimental results on multiple public datasets demonstrate that our framework outperforms previous state-of-the-art methods. For the sake of reproducibility, the source code of SVAFormer will be publicly available athttps://github.com/chenning0115/SVAFormer.

The emergency of intelligent transportation system provides new methods to monitor and alleviate traffic problems by employing many Internet of Things (IoT) devices, namely workers, to collect data. However, there are many malicious workers (or spammers) negatively impacting the system, so it is urgent to propose an effective true data evaluation method. In this paper, a UAV-assisted Intelligent True Data Evaluation (UITDE) method has been proposed to solve this problem effectively. UITDE has two main contributions. First, we abandon traditional algorithms that only use workers’ data to obtain estimated truths and compute their trusts directly. In contrast, we employ Unmanned Aerial Vehicles (UAVs) to collect data as ground truths to test and calculate workers’ trusts. Second, there are some unmalicious workers who collect inaccurate data due to their aged sensing equipment, extreme weather, or other objective reasons, so we propose a data rectification algorithm to rectify their data. The results show that our proposed method outperforms the compared methods, in terms of workers’ trust evaluation, utility, and accuracy.

As a promising approach, Clustered Federated Learning (CFL) enables personalized model aggregation for heterogeneous clients. However, facing dynamic and open edge networks, previous CFL rarely considers the impact of dynamic client data on clustering validity, or sensitively identifies low-quality parameters from highly heterogeneous client models. Moreover, the device heterogeneity in each cluster leads to unbalanced model transmission delay, thus reducing the system efficiency. To tackle the above issues, this paper proposes a Robust and Efficient Clustered Federated System (REC-Fed). First, a Hierarchical Attention based Robust Aggregation (HARA) method is designed to realize layer-wise model customization for clients, meanwhile keeping the clustering validity under dynamic client data distribution. In addition, the fine-grained parameter detection in HARA provides a natural advantage to detect low-quality parameters, which improves the robustness of CFL systems. Second, to realize efficient synchronous model transmission, an Adaptive Model Transmission Optimization (AMTO) is proposed to jointly optimize the model compression and bandwidth allocation for heterogenous clients. Finally, we theoretically analyze the convergence of REC-Fed and conduct experiments on several personalization tasks, which demonstrate that our REC-Fed has significant improvement on flexibility, robustness and efficiency.

In edge computing, Internet of Things devices with weak computing power offload tasks to nearby edge servers for execution, so the task completion time can be reduced and delay-sensitive tasks can be facilitated. However, if the task is offloaded to malicious edge servers, then the system will suffer losses. Therefore, it is significant to identify the trusted edge servers and offload tasks to trusted edge servers, which can improve the performance of edge computing. However, it is still challenging. In this article, a trust Active Detecting-based Task Offloading (ADTO) scheme is proposed to maximize revenue in edge computing. The main innovation points of our work are as follows: (a) The ADTO scheme innovatively proposes a method to actively get trust by trust detection. This method offloads microtasks to edge servers whose trust needs to be identified, and then quickly identifies the trust of edge servers according to the completion of tasks by edge servers. Based on the identification of the trust, tasks can be offloaded to trusted edge servers, to improve the success rate of tasks. (b) Although the trust of edge servers can be identified by our detection, it needs to pay a price. Therefore, to maximize system revenue, searching the most suitable number of trusted edge servers for various conditions is transformed into an optimization problem. Finally, theoretical and experimental analysis shows the effectiveness of the proposed strategy, which can effectively identify the trusted and untrusted edge servers. The task offloading strategy based on trust detection proposed in this article greatly improves the success rate of tasks, compared with the strategy without trust detection, the task success rate is increased by 40.27%, and there is a significant increase in revenue, which fully demonstrates the effectiveness of the strategy.

Mobile Crowd Sensing (MCS) has emerged as a novel paradigm in massive data collection, which leverages many individual mobile devices (called workers) to collect data. MCS platform utilizes the collected data to construct various services for service requesters, thus obtaining profit based on the data values contributed by workers. However, untrustworthy data would greatly reduce the data value, leading to a decline in platform profit, so it is crucial for the platform to recruit high-trust workers and collect truthful data, thereby providing high-quality service and obtaining high profit. To address this problem, we propose a Maximize Profit Scheme, called MPS, for MCS platforms, which consider that the data value declines as data trust decreases and discounts over time. MPS scheme is the first work that systematically addresses the impact of untruthful data on the platform profit, which is not well addressed in previous research. First, we utilize historical data of trusted workers as truthful data to identify the truth of data, which is a low-cost method. Then, a trust-discounting and time-discounting value model is proposed, which is more practical than previous methods. Based on the proposed value model, we propose a novel worker recruitment strategy combined with a trust-related and time-dependent reward threshold, which prioritizes workers with high trust and low latency, thereby promoting the data value of workers and maximizing the platform's profit. By comparing the MPS with existing schemes, the experimental results show that our MPS can achieve better performance in terms of total profit.

Mobile Crowd Sensing (MCS) is an emerging paradigm that constructs various services by recruiting massive workers in edge networks to sense data. However, ensuring Quality-of-Service (QoS) while preserving bilateral location privacy remains a critical challenge for effective service provisioning in the context of spatiotemporal crowdsourcing. Previous studies have achieved privacy preservation through location obfuscation, which has the problem of low task-worker matching rate, and data loss adversely affected QoS. To tackle this issue, we propose a Deep Neuro-Fuzzy based Bilateral Location Privacy-Preserving (DNF-BLPP) scheme to construct services in spatiotemporal crowdsourcing. In this article, we first present a novel obfuscation strategy that obfuscates the location of each task and worker respectively to${\rm{\lambda }}$locations with the highest correlation, ensuring privacy preservation while providing assurance for accurate data recovery. Then, we further introduce a deep neural-fuzzy approach to solve the worker selection problem under obfuscated locations. Based on that, a Non-negative Constraint Matrix Factorization algorithm is employed to accurately impute missing data based on time-space correlation. Theoretical analysis and extensive simulations show that the proposed scheme has a strong ability to protect location privacy, and is better than the state-of-the-art schemes in performance indicators such as task-worker matching rate, QoS and computational efficiency.

The widespread adoption of mobile smart devices has ushered in the era of Mobile Crowd Sensing (MCS), serving as an efficient method for large-scale data collection. Inherently location-sensitive, the service construction of MCS faces a crucial challenge of Location Privacy Preservation (LPP). Prior studies for LPP often necessitate a Trusted Third Party (TTP), which is not always feasible. Moreover, these privacy-preserving techniques may inadvertently obscure dishonest or malicious behaviors, leading to compromised Quality of Service (QoS). Motivated by this, we propose a Quality-enabled Bilateral Location Privacy-Preserving (Q-BLPP) service construction scheme, ensuring Bilateral LPP without TTP, while maintaining QoS. To achieve bilateral LPP, we introduce a novel BI-LBE algorithm using Bloom Indexing (BI) and Location-Based Encryption (LBE). Additionally, for high-quality recruitment, we present a Combinatorial Multi-Armed Bandit (CMAB) approach to balance exploration and exploitation. Furthermore, to ensure privacy during recruitment, worker profiles are anonymized using differential privacy. To our knowledge, our approach is the first to integrate QoS and LPP in MCS, with theoretical proofs of truthfulness and individual rationality. Simulations demonstrate that our Q-BLPP scheme strikes a favorable balance between computational efficiency, privacy security, and service quality, outperforming existing schemes.

Truth and Privacy-preserving service are two key issues for data collection in Mobile Crowd Sensing (MCS). However, most of the existing data collection studies use CRH to calculate the truth value, which is difficult to guarantee the data accuracy. The proposed privacy-preserving service methods either neglect the truth-value computation or still adopt the CRH method. To solve the challenge, we propose a Reputation and Privacy-Preserving Service based Truth Data Collection (RPPS-TDC) scheme to achieve the privacy-preserving accurate data collection for MCS. RPPS-TDC scheme consists of two steps: worker trust calculation and reputation-based truth value calculation. Firstly, data requester performs the initial weights calculation on the received data using DBCRH method. Secondly, reputation center uses the initial weights to update the reputation, which is based on the trustworthy worker inference. Finally, the workers are reweighted according to their reputation. We select workers based on trust_MaxHeap, thus obtaining more accurate data. At the same time, we give workers payments based on their trust weights. All the above operations are completed under data encryption, ensuring that only the data requesters have access to the workers’ submitted data. The platform consists of mining nodes, and reputation center resides on the blockchain, thus achieving distributed computing which overcomes the shortcomings of the traditional MCS system. Extensive experiments demonstrate that RPPS-TDC is effective and outperforms previous strategies in two key performance metrics: data accuracy and compensation rationality allocation.

With the prosperous development of the Internet of Things (IoT), large numbers of IoT devices have been deployed in many scenarios to perceive the data of surroundings. To provide real-time service, devices prefer to transfer the data to servers for faster processing. However, in some special areas with limited communication infrastructure, devices cannot be directly connected to the Internet, so the requirements of data collection and processing are agnostic, which brings a huge challenge for real-time and dynamic data processing. We make the first attempt to tackle this challenge by proposing an Intelligent Active Data Processing Scheme (IADP) in unmanned aerial vehicle (UAV)-enabled edge computing. The main innovations of IADP are as follows: 1) when IoT devices generate or sense data, they can intelligently and proactively spread data or data information to other areas to inform UAV to collect; 2) our scheme combines active data processing with data deadline priority and artificial intelligence (AI)-based UAV trajectory optimization to achieve maximum data processing rate and minimum UAV flight cost. Specifically, genetic algorithm-deadline constraint traveling Salesman problem (GA-DCTSP) algorithm is proposed based on improved GA to achieve it; and 3) an intelligent data processing decision mechanism of optimally determining transmitting to UAV or edge server is proposed to further optimize network performance. Finally, the experimental results show that our proposed IADP increases the data processing rate by 48.77%–232.54% and reduces the UAV flight distance by 63.45%–88.36% compared with the existing schemes.

Large number of Internet of Things (IoT) devices deployed on demand to monitor surroundings and offload computation-intensive tasks to edge servers, which favors low-latency IoT applications. However, some of them scatter in areas with limited or no available communication infrastructure (no-ACI), which rises a huge challenge for task offloading, due to when and which devices have tasks cannot be known in advance. In this article, a trust-based active notice task offloading (TANTO) scheme in the aerial computing system (ACS) assisted by the unmanned aerial vehicle (UAV) is proposed to provide trust and low-delay task offloading for resource-limited IoT devices in areas with no-ACI. The main innovations of TANTO in ACS are as follow: 1) a novel task offloading mechanism for IoT devices in networks with no-ACI is proposed, where devices broadcast task offloading notice, making it easy for UAV to know where tasks need to offload without aimless search, which can effectively reduce UAV’s flight distance and task completion delay at the same time; 2) a trust calculation and reasoning method is proposed to calculate network trust, and divide the network into grids with different trust values, which guides UAV through grids with higher trust to increase the task completion rate and reduce delay; and 3) an online UAV trajectory optimization algorithm is proposed to dynamically prioritize tasks with urgent deadline. A large number of experimental results show that TANTO has better performance than previous studies in terms of task completion rate, tasks’ average completion time, and UAV’s flight cost.

6G networks provide faster communication for connected vehicles. These vehicles are connected to the Internet, forming the Internet of Vehicles (IoV). Due to the development of Intelligent Transportation Systems (ITS), more and more vehicles are deployed with data-intensive applications. These applications interact heavily with IoT devices at the edge of the network, which causes IoT devices to consume a lot of limited and valuable power. Task offloading can help overcome resource constraints of IoT devices by offloading task to edge server which has sufficient computational power in ITS. Unmanned Aerial Vehicles (UAV) is a promising solution by serving as Computing-Communications Edge Server (CCES) for resource-constrained IoT devices that there is no edge server nearby that can offload task. Due to the IoT devices' limited battery capacity and UAV energy budget, it is a challenging issue to reduce the energy for task offloading in UAV-enable edge network. In this paper, an UAV-enabled Computing-Communications Intelligent Offloading (UAV-CCIO) scheme is proposed to offload task energy-efficiently. First, some nodes with a large amount of data are selected as Task Gathering Nodes (TGNs), and TGNs collect all the tasks of the left nodes. In this way, the UAV can only fly the TGNs and so all the IoT devices' tasks can be offloaded. The distance needed for the UAV can be greatly reduced and energy is saved. On the other hand, tasks that route to TGNs have a relatively small amount of data, while nodes with a large amount of data have already been selected as TGNs without routing, thus saving energy. Second, an optimization strategy for collection tasks is proposed to reduce UAV's energy. The extensive experimental simulations indicate that the performance of UAV-CCIO scheme is better than the existing scheme.

In this paper, a novel task offloading architecture called Flex-MEC is proposed, which achieves efficient task allocation and scheduling (TAS) between MEC servers. By adding metadata before task data, we redesign the offloading process in Flex-MEC, the TAS planning can be conducted without finishing the task data receiving. Once planning is done the task data can be directly forwarded to the allocated server and executed. This reduces latency compared to the traditional way of transmitting, planning, forwarding and executing sequentially. For TAS planning, a multi-server multi-task allocation and scheduling (MMAS) problem is formulated to maximize the MEC system profit. The MMAS problem is proven as an NP-complete problem, thus is challenging to solve. Then, a distributed scheme and a centralized scheme are proposed to solve the MMAS problem with low complexity. In the distributed scheme, the MMAS problem is converted into a non-cooperative game and the existence of Nash Equilibrium (NE) is proven and a low complexity response update algorithm is proposed to converge to NE. And the centralized scheme is based on a greedy idea and runs on a MEC controller in a centralized way. Verified by experiments, these two schemes can achieve better performance than compared schemes.

The emerging edge computing technology is crucial for Cooperative Intelligent Transport System (C-ITS), enabling rapid service response and efficient data processing in vehicular networks. A reservation service strategy that utilizes vehicle-to-vehicle and vehicle-to-infrastructure communication is introduced to improve the service experience. However, ensuring the reliability of service relays necessitates the evaluation of vehicle trustworthiness, particularly considering the potential presence of malicious behaviors. To address this problem, a trusted service prerequest (TSPR) system is proposed. First, we design a trust evaluation mechanism based on backtracking to incentivize vehicles to participate in the relay process and ensure reliable delivery of service requests. Additionally, we consider the dynamic traffic condition and present a trust-aware relay vehicle selection mechanism that guarantees the effectiveness of service relays. Specifically, the selection process is optimized by jointly considering cost and trust. Furthermore, we establish an evolutionary game model involving the system supervision center and vehicles, analyzing the factors that influence strategy selection. Extensive simulations demonstrate that the proposed system can effectively enhance edge services, achieving a higher request arrival rate and lower relay cost.

The multiuser cooperative offloading mobile edge computing (COMEC) system has attracted much attention because it can realize delay-sensitive tasks. However, in the coupling optimization of offloading decision and resource allocation, the existing numerical optimization algorithms are difficult to obtain high-quality optimization solutions. In this paper, we propose a differential dynamic gradient descent (DDGD) optimization algorithm to solve the above optimization problems. DDGD algorithm decomposes the constrained NP-hard optimization problem into two network layers and integrates the constraint function into a larger end-to-end training network. These two-layer networks encode the dependencies and optimization constraints between parameter hidden states, which cannot be captured by a numerical optimization model or a full connection layer neural network. Because the ring learning and self-repeating learning architecture are adopted and the information is stored in the differential dynamics network, the proposed algorithm can achieve better and more intelligent decision-making in searching the solution trajectory without setting accurate parameters in advance and reduce the complexity of the network. We show that compared with the baseline method, the DDGD method has superior optimization performance in the energy consumption optimization of COMEC.

Recent developments in collection, computation and communication have expanded the way of data reporting in intelligent Internet of Things (IoT). However, diversity and complexity of data sources also impose new trust challenge in data collection process since untrust reporters tend to report false or even malicious data, which highlights the need to develop a novel methodology to solve such challenge. Thus, based on this domain, inspired by deterrence theory, this article proposes a blockchain-based trust system with assistant of drones to deter malicious data reporting in intelligent IoT. Specifically, to deter malicious data reporting, based on the blockchain technology, the data sensed by fully trusted drones is public published on blockchain showing participants the data standards, named as malicious deterrence scheme. This scheme provides a barrier for malicious reporters to arbitrarily publish false data to blockchain, since the false data can be easily detected while they cannot deny. Second, to further reduce malicious data reporting, a strict penalty mechanism is proposed to punish malicious reporters who have reported false data to blockchain to reduce the malicious data reporting in the following task through punishment. Third, note that the sensing of data standard generates additional costs, therefore, a drone flight route scheme based on a simper deep reinforcement learning with multihead attention mechanism (MA-DRL) is designed to reduce the flight distance for drones. Finally, extensive experiments demonstrate efficiency of our proposed system in terms of reducing malicious data reporting in advance as well as reducing drone flight distance.

Nowadays, with the explosive growth of sensor-based devices connected to Internet of Things (IoT), massive amount of data are generated every day with potential tremendous value. We argue that the value of those data can be extracted through monetize data platform in IoT-Edge-Cloud ecosystems for many parts of the business. In such monetize data platform, the data can be computed and transformed into services in IoT-Edge-Cloud ecosystems and provide data-as-a-service (DAAS) for applications. The key to implement such a monetize data platform is to evenly distribute DAAS computing tasks to network devices to maximize the benefits of the system. So, in this article, we study the task type-based computation offloading algorithm (TTCO) to implement such platform. We use the “IoT-Edge-Cloud” three-layer multihop model, which is closer to the complex scene in monetize data platform. We divide tasks into data-intensive tasks and CPU-intensive tasks, and then combine the cost model of computation offloading with task type to make data-intensive tasks prefer local computing and CPU-intensive tasks prefer offload computing, thereby reducing the monetize data platform transmission volume and improving the overall quality of computation offloading. We then use a hierarchical game model combined with fictitious play to solve the Nash equilibrium (NE) of the system and obtain the mixed strategies of the devices. Finally, we propose a TTL-constrained flood strategy transmission mechanism to make the algorithm apply to practice. The experimental results prove that our algorithm has a large performance gain in various scenarios, which can be severed as a monetize data platform for IoT-Edge-Cloud ecosystems.

In wireless sensor networks (WSNs), the imbalanced energy consumption in data transmission may cause energy holes around the sink, which dramatically shortens the network lifespan. Current solutions have two limitations: one is the inevitable traffic gathered around the sink, and the other is the overly ideal network model, e.g., the square region or circular area. In this article, we focus on strip-shaped networks and propose a novel data transmission scheme, which employs a few super nodes near the sink to take traffic load. Due to the high energy capacities and communication abilities, super nodes transfer a part of the data of the network and send it to the sink directly. The entire network is partitioned into multiple clusters, and super nodes are placed in a specific cluster. We discover that the greatest effect on the network lifetime is the energy consumption of two clusters: 1) the cluster nearest to the sink and 2) the upstream cluster nearest to the super nodes. To improve the system longevity, we make the two clusters have equal energy dissipation, and thus obtain the optimal location of super nodes. Some simulations are carried out to verify the reasonability of the results and exhibit the advantage of our scheme in terms of network lifetime.

Future heterogeneous services and applications require the provisioning of unprecedented massive user access, heterogeneous data traffic, high bandwidth efficiency, and low latency services in next generation multiple access. In response to the requests from these services and applications, a large number of workers with scattered computing power need to be managed uniformly and scheduled in an efficient manner to perform various tasks. Therefore, task allocation has become a crucial issue to determining whether next generation multiple access can support future heterogeneous services and applications. In this paper, we propose a novel Two-stage Multi-task Allocation method based on Discrete Particle Swarm Optimization (TMA-DPSO). TMA-DPSO is easy to implement and has good search efficiency, which is suitable for large-scale task allocation in next generation networks. Under TMA-DPSO, we redefine the particles in discrete coding form, iteratively update the position and velocity based on the individual optimal particles and the global optimal particle, and finally obtain a corrected optimal solution. Unlike previous methods that only focused on the first-stage task allocation, we make full use of workers’ remaining time to perform second-stage redundant task allocation, which can not only increase workers’ income, but also potentially improve fault tolerance and security. As far as we know, this is the first attempt to utilize the remaining time after first-stage allocation. Finally, we evaluate TMA-DPSO extensively using the synthetic and real-life datasets. The results demonstrate that whether in a compactly or uniformly distributed scene, TMA-DPSO outperforms three benchmark methods by increasing 2.15%-42.24% platform revenue and 6.1%-46.63% workers income.

As an emerging technology, industrial Internet of Things (IIoT) connects massive sensors and actuators to empower industrial sectors being smart, autonomous, efficient, and safety. However, due the large number of build-in sensors of IIoT smart devices, the IIoT systems are vulnerable to side-channel attack. In this article, a novel side-channel-based passwords cracking system, namely MAGLeak, is proposed to recognize the victim's passwords by leveraging accelerometer, gyroscope, and magnetometer of IIoT touch-screen smart device. Specifically, an event-driven data collection method is proposed to ensure that the user's keystroke behavior can be reflected accurately by the obtained measurements of three sensors. Moreover, random forest algorithm is leveraged for the recognition module, followed by a data preprocessing process. Extensive experimental results demonstrate that MAGLeak achieves a high recognition accuracy under small training dataset, e.g., achieving recognition accuracy 98% of each single key for 2000 training samples.

A large number of devices with communication and sensing capabilities are connected to the 5G network and facilitate distributed industrial Internet of Things (IIoT) applications. Those devices can update their program code by using software-defined technologies and upgrade functions without hardware replacement, thus, greatly facilitating the development of IIoT applications. Disseminating code safely to these devices is a pivotal issue. However, the existing methods rarely consider whether the code received by devices is integrated. Therefore, an unmanned aerial vehicle (UAV) assisted trustworthy code dissemination (UTCD) framework in 5G-enabled intelligent IIoT systems is proposed to select credible mobile vehicles (MVs) to disseminate code with opportunistic routing style. In the UTCD framework, a verifiable trust evaluation scheme is proposed to identify the trust of the MVs by sending the UAV to collect the code wait to be verified (CWV) directly from the selected devices (bedrock devices) as evidence. This scheme is a fundamental change from the previous passive, indirect and unverifiable trust evaluation schemes. In UTCD, a bedrock devices selection scheme is presented by selecting as few bedrock devices as possible to collect as much the CWV as possible with the minimum cost. After that, based on the CWV, which is collected from bedrock devices, a complete trust evaluation scheme is proposed to obtain the trust of MVs. Furthermore, a UAV trajectory optimization algorithm is proposed to obtain as much CWV as possible within the limited conditions of UAV. Finally, comprehensive experiments conduct on a real-life dataset demonstrate that the proposed scheme outperforms the existing schemes in terms of the efficiency and security of code dissemination.

Large number of Internet of Things (IoT) applications with intelligent sensing devices (ISDs) are combined with cloud computing to collect and process data more efficiently. However, ISDs are vulnerable to various attacks. Compromised devices may maliciously provide unreliable data to the cloud, causing damage to IoT applications. Therefore, it is a critical issue to design an effective mechanism to ensure the security of data collection in cloud computing. In this article, a cloud-assisted reliable trust computing (CRTC) scheme is proposed to identify the trust of ISDs at low cost, providing high-quality data for IoT applications. The CRTC scheme mainly includes the following parts: First, a reliable approach of obtaining the real data of ISDs at a low cost is proposed to identify the trust of ISDs. In the proposed method, ISDs fits the forwarding packets information to form inspection information (II) with a small amount of data and then sends II to inspection nodes, effectively reducing the cost of routing II. Second, an effective method of trust computing is given to evaluate the trustworthiness of ISDs based on the data and II collected by unmanned aerial vehicles (UAV). Then, the aggregators are selected from high-trust ISDs to ensure secure data collection. Third, to obtain more reliable trust at lower cost, a trajectory optimization algorithm for UAV is proposed to collect as much II as possible and reduce the moving distance. Theoretical analysis and experimental results show that the proposed CRTC scheme is superior to previous strategies in terms of the success rate of data collection, the speed of identifying the trust of ISDs, and the UAV's trajectory distance.

Vehicular Ad hoc Networks (VANETs), as the integration of mobile vehicle and intelligent technology, has raised plenty of attentions. In VANETs, mobile smart vehicles can timely get information from surrounding environment, which brings great convenience for society. However, this highlights the need to improve data quality while protecting privacy for vehicular data provider. Thus, in this article, we propose an AI-based Trust-aware and Privacy-preserving System (ATPS) to preserve privacy for vehicular data providers while improving quality of data collections in VANETs. Our proposed ATPS system mainly consists of two schemes: 1) a Partial Ordering based Trust Management (POTM) scheme and 2) a Trajectory Privacy Preserving (TPP) scheme jointly designed by Wasserstein Generative Adversarial Networks (WGAN) and differential privacy. The POTM scheme uses partial ordering relationship to accurately evaluate and manage trusts for vehicular data providers with the assistance of fully trusted drones, and selects the vehicles with top ranks to collect data. Then, TPP scheme skillfully incorporates WGAN and differential privacy to preserve trajectory privacy for vehicular data providers in VANETs, which also guarantees data availability by adding carefully designed noise to the original trajectory. Compared to existing scheme, extensive experiments conducted on the real-world datasets demonstrates efficiency of our ATPS in terms of improving the data quality by 45.76% to 52.57%, reducing the malicious vehicle participants by 15.48% to 16.95%, preserving privacy of vehicles, and guaranteeing data availability.

Vehicular Edge Computing (VEC) is a promising paradigm that leverages the vehicles to offload computation tasks to the nearby VEC server with the aim of supporting the low latency vehicular application scenarios. Incentivizing VEC servers to participate in computation offloading activities and make full use of computation resources is of great importance to the success of intelligent transportation services. In this paper, we formulate the competitive interactions between the VEC servers and vehicles as a two-stage Stackelberg game with the VEC servers as the leader players and the vehicles as the followers. After obtaining the full information of vehicles, the VEC server calculates the unit price of computation resource. Given the unit prices announced by VEC server, the vehicles determine the amount of computation resource to purchase from VEC server. In the scenario that vehicles do not want to share their computation demands, a deep reinforcement learning based resource management scheme is proposed to maximize the profits of vehicles and VEC server. The extensive experimental results have demonstrated the effectiveness of our proposed resource management scheme based on Stackelberg game and deep reinforcement learning.

Data collection with mobile elements can improve energy efficiency and balance load distribution in wireless sensor networks (WSNs). However, complex network environments bring about inconvenience of path design. This work addresses the network environment issue, by presenting an objective-variable tour planning (OVTP) strategy for mobile data gathering in partitioned WSNs. Unlike existing studies of connected networks, our work focuses on disjoint networks with connectivity requirement and serves delay-hash applications as well as energy-efficient scenarios respectively. We first design a converging-aware location selection mechanism, which macroscopically converges rendezvous points (RPs) to lay a foundation of a short tour. We then develop a delay-aware path formation mechanism, which constructs a short tour connecting all segments by a new convex hull algorithm and a new genetic operation. In addition, we devise an energy-aware path extension mechanism, which selects appropriate extra RPs according to specific metrics in order to reduce the energy depletion of data transmission. Extensive simulations demonstrate the effectiveness and advantages of the new strategy in terms of path length, energy depletion, and data collection ratio.

With the integration of Artificial Intelligence (AI) and Internet of Things (IoT), the Federated Edge Learning (FEL), a promising computing framework is developing. However, there are still unsolved issues on communication efficiency and data security due to the huge models and unreliable transmission links. To address these issues, this paper proposes a novel federated edge learning system, called LightFed, where the edge nodes upload only vital partial local models, and successfully achieve lightweight communication and model aggregation. First, a novel model aggregation method Model Splitting and Splicing (MSS) and a Selective Parameter Transmission (SPT) scheme are proposed. By detecting the updating gradients of local parameters and filtering significant parameters, selective rotated transmission and efficient aggregation of local models are achieved. Second, a Training Filling Model (TFM) is proposed to infer the total data distribution of edge nodes, and train a filling model to mitigate the unbalanced training data without violating the data privacy of individual users. Moreover, a blockchain-powered confusion transmission mechanism is proposed for defending the attacks from external adversaries and protecting the model information. Finally, extensive experimental results demonstrate that our LightFed significantly outperforms the existing FEL systems in terms of communication efficiency and privacy security.

In inhospitable environments, connectivity recovery plays a significant role in enabling normal data transmission in wireless sensor networks (WSNs). However, existing approaches lack thorough load-balancing and delay-control functions. In this article, we present a load-balanced connectivity recovery (LBCR) strategy to address these problems. This strategy consists of two relay-segment selection approaches. The first one is the delay-controlled connectivity mechanism, in which mobile relays and static relays are employed to connect isolated segments, and the path length of the two kinds of relays is adjusted based on the requirement of data delivery delay. The second one is the load-balanced connectivity mechanism, in which both the intra-segment and the inter-segment traffic distribution are evaluated to balance the traffic load. For intra-segment load equilibrium, we use the A-Star algorithm to calculate the number of disjoint paths of a relay segment, which helps to evaluate the load sharing ability from a micro perspective. For inter-segment load equilibrium, we compare the traffic load of different paths, which helps to evaluate the load sharing ability from a macroscopic angle. Extensive simulations demonstrate the effectiveness and advantage of our strategy in terms of connectivity cost, data collection delay, and system lifespan.

Mobile edge computing (MEC) is an emergent architecture, which brings computation and storage resources to the edge of mobile network and provides rich services and applications near the end users. The joint problem of task offloading and resource allocation in the multi-user collaborative mobile edge computing network (C-MEC) based on Orthogonal Frequency-Division Multiple Access (OFDMA) is a challenging issue. In this paper, we investigate the offloading decision, collaboration decision, computing resource allocation and communication resource allocation problem in C-MEC. The delay-sensitive tasks of users can be computed locally, offloaded to collaborative devices or MEC servers. The goal is to minimize the total energy consumption of all mobile users under the delay constraint. The problem is formulated as a mixed-integer nonlinear programming (MINLP), which involves the joint optimization of task offloading decision, collaboration decision, subcarrier and power allocation, and computing resource allocation. A two-level alternation method framework is proposed to solve the formulated MINLP problem. In the upper level, a heuristic algorithm is used to handle the collaboration decision and offloading decisions under the initial setting; and in the lower level, the allocation of power, subcarrier, and computing resources is updated through deep reinforcement learning based on the current offloading decision. Simulation results show that the proposed algorithm achieves excellent performance in energy efficient and task completion rate (CR) for different network parameter settings.

Summary With the popularity of Sensor‐Cloud, its security issues get more attention from industry and academia. Especially, Sensor‐Cloud underlying network is very vulnerable to internal attacks due to its limitations in computing, storage, and analysis. Most existing trust evaluation mechanisms are proposed to detect internal attack issues from the behavior level. However, there are some special internal attacks in the data level such as hidden data attacks, which are normal in the behavior level but generate malicious data to lead user to make wrong decisions. To detect this type of attacks, we design a fog‐based detection system (FDS), which is based on the trust evaluation mechanism in the behavior level. In this paper, three types of scenes (the redundant data, the parameter curve characteristic, and the data validation) are defined, and three detection schemes are given. Some experiments are conducted, which manifest that FDS has certain advantages in detecting hidden data attacks.

With billions of sensing devices are deployed in smart cities to monitor regions of interests and collect large sensing data, the Internet-of-Things (IoT) applications are being widely used in various fields and empower the intelligent smart cities. Due to the smart decision made by IoT applications depends on the reliability of data collection, it is pivotal to collect data from the trust sensing devices. However, how to identify the credibility of sensor nodes to ensure the credibility of data collection is a challenge issue. In this article, an active and traceable trust-based data collection (ATTDC) scheme is proposed to collect trust data in Internet of Thing. The main contribution of this article are as follows: 1) an active trust framework is proposed to quickly obtain the trustworthiness of sensor nodes by using unmanned aerial vehicles (UAVs) with a piggybacking method; 2) in order to accurately obtain the trust degree of the sensor nodes, a traceable trust method of obtaining is proposed in which nodes in the network send data packets by digital signature, Tracing suspicious nodes according to data routing paths to obtain active trust, therefore, the acquisition cost of the network can be effectively reduced; and 3) In order to reduce the acquisition cost of UAV, an ant colony algorithm-based flight path algorithm is designed to reduce the flight path of UAV, and obtain the credibility evaluation of as many nodes as possible. The experimental results show that the ATTDC scheme proposed in this article can identify the trust of the sensing nodes faster and more accurately, ensuring the credibility of data collection.

The development of the Internet of Things (IoT) and intelligent vehicles brings a comfortable environment for users. Various emerging vehicular applications using artificial intelligence (AI) technologies are expected to enrich users' daily life. However, how to execute computation-intensive applications on resource-constrained vehicles based on AI still faces great challenges. In this article, we consider the vehicular computation offloading problem in mobile-edge computing (MEC), in which multiple mobile vehicles select nearby MEC servers to offload their computing tasks. We propose a multiagent deep reinforcement learning (DRL)-based computation offloading scheme, in which the uncertainty of a multivehicle environment is considered so that the vehicles can make offloading decisions to achieve an optimal long-term reward. First, we formalize a formula for the computation offloading problem. The goal of this article is to determine the optimal offloading decision to the MEC server under each observed system state, so as to minimize the total task processing delay in a long-term period. Then, we use a multiagent DRL algorithm to learn an effective solution to the vehicular task offloading problem. To evaluate the performance of the proposed offloading scheme, a large number of simulations are carried out. The simulation results verify the effectiveness and superiority of the proposed scheme.

UAV-assisted wireless communications facilitate the applications of Internet of Things (IoT), which employ billions of devices to sense and collect data with an on-demand style. However, there are numerous malicious Mobile Data Collectors (MDCs) mixing into the network, stealing or tampering with data, which greatly damages IoT applications. So, it is urgent to build a ubiquitous trust communication system. In this paper, a UAV-assisted Ubiquitous Trust Evaluation (UUTE) framework is proposed, which combines the UAV-assisted global trust evaluation and the historical interaction based local trust evaluation. We first propose a global trust evaluation model for data collection platforms. It can accurately eliminate malicious MDCs and create a clean data collection environment, by dispatching UAVs to collect baseline data to validate the data submitted by MDCs. After that, a local trust evaluation model is proposed to help select credible MDCs for collaborative data collection. By letting UAVs distribute the data verification hash codes to MDCs, the MDCs can verify whether the exchanged data from the interacted MDCs is reliable. Extensive experiments conduct on a real-life dataset demonstrate that our UUTE system outperforms the existing trust evaluation systems in terms of accuracy and cost.

Smart cities can manage assets and resources efficiently by using different types of electronic data collection sensors, devices and vehicles. However, growing complexity of systems and heterogeneous networking also enlarge the destructive effect of compromised or malicious sensor nodes. In this paper, we introduce electric vehicles to conduct trust evaluation for heterogeneous vehicle network in smart cities. Compared with traditional trust evaluation mechanism, mobility-based trust evaluation owns the advantages of low energy consumption and high evaluation accuracy. Meanwhile, we investigate the problem of minimizing transmission hops of trust evaluation and refers to this as the mobile trust evaluation problem (MTEP). We first formalize the MTEP into an optimization problem and present a heuristic moving strategy of single electric vehicle. Then, we consider the MTEP with multiple electric vehicles. By scheduling the electric vehicles to access the nodes on spanning tree with maximum neighbor distance ratio, the algorithm can improve the efficiency of trust evaluation. In experiments, we compare moving strategy of single electric vehicle and multiple electric vehicles with existing methods respectively. The results demonstrate that the proposed algorithms are able to effectively reduce the entire transmission hops of trust evaluation and thus prolong the life of the network.

The rapid and convenient travel of people and the timely transportation of goods depend on the correct decision of the Intelligent Transportation Systems (ITS). Due to the decision-making of ITS requires a large amount of data to support, UAV-enabled periodic data collection is an effective method. However, due to the limited resources of UAV, UAV cannot directly collect data from all storage devices, resulting in unfair data collection. Therefore, we propose a UAV Speed Control based Fairness Data Collection (USCFDC) scheme. First, since the fairness of data collection will affect the decision-making of ITS, a framework for controlling the flight speed of the UAV is proposed to improve the fairness of data collection. The flight speed of UAV will slow down in areas with a large number of nodes, thereby improving the fairness of data collection. Second, a novel method is proposed to maximize the amount of data collected by UAV from each node. With this method, the value of the amount of data will be used as the dichotomous value in the dichotomy algorithm, and the UAV must collect a certain amount of data from each node. The upper and lower limits of the dichotomy algorithm are adjusted according to the time duration for UAV to collect data. Compared with previous schemes, the fairness of data collection can be improved by a maximum of 15.89% under the same flight time of UAV. Besides, the energy consumption is reduced by 49.31%-52.55% and the flight time of the UAV is reduced by 48%-62.38% when the amount of collected data is the same.

Mobile crowdsensing is an emerging paradigm that selects users to complete sensing tasks. Recently, mobile vehicles are adopted to perform sensing data collection tasks in the urban city due to their ubiquity and mobility. In this article, we study how mobile vehicles can be optimally selected in order to collect maximum data from the urban environment in a future period of tens of minutes. We formulate the recruitment of vehicles as a maximum data limited budget problem. The application scenario is generalized to a realistic online setting where vehicles are continuously moving in real-time and the data center decides to recruit a set of vehicles immediately. A deep learning-based scheme through mobile vehicles (DLMV) is proposed to collect sensing data in the urban environment. We first propose a deep learning-based offline algorithm to predict vehicle mobility in a future time period. Furthermore, we propose a greedy online algorithm to recruit a subset of vehicles with a limited budget for the NP-Complete problem. Extensive experimental evaluations are conducted on the real mobility dataset in Rome. The results have not only verified the efficiency of our proposed solution but also validated that DLMV can improve the quantity of collected sensing data compared with other algorithms.

Sink mobility is a significant technique to improve the performance of wireless sensor networks (WSNs). Generally a mobile sink visits several rendezvous points (RPs), forming a trip tour for data collection. However, the low movement speeds of mobile sinks tend to incur serious data delivery delays. In this article, we propose a quick convex hull-based rendezvous planning (QCHBRP) scheme, which aims to not only achieve full connectivity for disjoint WSNs but also construct a shorter trip tour and minimize the data delivery latency accordingly. The trajectory formation of the mobile sink is based on a path skeleton, i.e., a near-convex hull, which is created by the quick determination of several special locations as RPs. The benefits of QCHBRP are threefold. First, it is especially designed for disjoint WSNs where sensor nodes are deployed in multiple isolated segments and the network connectivity is lost in advance. Second, it is suitable for delay-harsh applications which require short paths of the mobile sink. Third, it is of much lower computational complexity compared with existing methods. The extensive analysis and experiments validate the effectiveness and advantages of this new scheme in terms of connectivity cost and data delivery delay.

Modern societies are becoming increasingly reliance on inter-connected digital systems. Despite numerous benefits, it is important to overcome existing security problems in a highly inter-connected system, like Sensor-Cloud systems. Sensor-Cloud is the product of the integration of wireless sensor networks and cloud computing. However, when a physical sensor node receives multiple service commands simultaneously, there will be some service collisions, namely, coupling security problem. This coupling security problem may lead to the failure of sustainable services and the system security threat. In order to solve the problem, sustainable resource management and maximum resource utilization are important. In this paper, we extend the Kuhn-Munkres algorithm based on fog computing to achieve sustainability. To begin with, we design a buffer queue in fog computing layer which will return the result to the cloud layer directly to increase the resource utilization. Then, we extend the Kuhn-Munkres algorithm to get the initial assignments of resources. The last step is to determine whether the initial assigned resources can be further scheduled, which means that we further improve the resource utilization to realize sustainable resource management. The results demonstrate that our method outperforms the traditional scheduling methods, which decreases both of the rounds and computational costs of scheduling by 24.04-57.78 percent and 9.88-31.51 percent, respectively. The experimental evaluations proved that the performance of the proposed fog-based scheme can effectively solve coupling security problem for sustainable Sensor-Cloud systems.

Media access control (MAC) plays a pivotal role in ensuring proper operation in wireless body area networks (WBANs). However, current solutions still cannot satisfy the stringent requirements of low power and low delay for emergency data reporting. In this paper, we propose an energy-efficient and emergency-aware MAC (EEEA-MAC) protocol for meeting such a rigorous demand. First, we design a node-different channel access scheme, in which source nodes use the CSMA/CA pattern while relay nodes adopt the hybrid CSMA/CA-TDMA pattern. Second, we devise an emergency-first time-slot allocation scheme, in which channel sensing is performed and the emergency data is handled by relay nodes according to different cases. EEEA-MAC has two striking features. One is that, source nodes adopt the CSMA/CA scheme instead of the conventional CSMA/CA-TDMA scheme, ensuring the requirement because there are almost no collisions and no confirmation messages in this scheme. The other is that, relay nodes use a sensing-based emergency data handling mechanism instead of the traditional empty-slot occupying mechanism, further guaranteeing the requirement owing to the immediate handling of emergency data and the short time of channel sensing. Extensive simulations demonstrate the advantages of EEEA-MAC in terms of energy dissipation and latency.

Mobile-edge nodes, as an efficient approach to the performance improvement of wireless sensor networks (WSNs), play an important role in edge computing. However, existing works only focus on connected networks and suffer from high calculational costs. In this article, we propose a rendezvous selection strategy for data collection of disjoint WSNs with mobile-edge nodes. The goal is to achieve full network connectivity and minimize path length. From the perspective of the application scenario, this article is distinctive in two aspects. On the one hand, it is specially designed for partitioned networks which are much more complex than conventional connected scenarios. On the other hand, this article is specially designed for delay-harsh applications rather than usual energy-oriented scenarios. From the viewpoint of the implementation method, a simplified ant colony optimization (ACO) algorithm is performed and displays two characteristics. The first one is the path segmenting mechanism, simplifying the path construction of each part and consequently reducing the computational cost. The second one is the candidate grouping mechanism, reducing the search space and accordingly speeding up the convergence speed. Simulation results demonstrate the feasibility and advantages of this approach.

Billions of devices are connected to the Internet of Things (IoT). These devices generate a large volume of data, which poses an enormous burden on conventional networking infrastructures. As an effective computing model, edge computing is collaborative with cloud computing by moving part intensive computation and storage resources to edge devices, thus optimizing the network latency and energy consumption. Meanwhile, the software-defined networks (SDNs) technology is promising in improving the quality of service (QoS) for complex IoT-driven applications. However, building SDN-based computing platform faces great challenges, making it difficult for the current computing models to meet the low-latency, high-complexity, and high-reliability requirements of emerging applications. Therefore, a cloud-mobile edge computing (MEC) collaborative task offloading scheme with service orchestration (CTOSO) is proposed in this article. First, the CTOSO scheme models the computational consumption, communication consumption, and latency of task offloading and implements differentiated offloading decisions for tasks with different resource demand and delay sensitivity. What is more, the CTOSO scheme introduces orchestrating data as services (ODaS) mechanism based on the SDN technology. The collected metadata are orchestrated as high-quality services by MEC servers, which greatly reduces the network load caused by uploading resources to the cloud on the one hand, and on the other hand, the data processing is completed at the edge layer as much as possible, which achieves the load balancing and also reduces the risk of data leakage. The experimental results demonstrate that compared to the random decision-based task offloading scheme and the maximum cache-based task offloading scheme, the CTOSO scheme reduces delay by approximately 73.82%-74.34% and energy consumption by 10.71%-13.73%.

Connectivity restoration plays an important role in maintaining the long-term operation in wireless sensor networks (WSNs), especially, in environment-harsh cases. However, current solutions lack the ability to handle the second damages and the capacity of designing requirement-different connectivity approaches according to different needs. In this article, we propose a durability-based connectivity establishment (DBCE) scheme for disjoint segments of WSNs. This scheme includes three approaches regarding segment evaluation or segment selection: 1) a segment shape evaluation approach; 2) a region different connectivity approach; and 3) a data traffic transfer approach, for their respective objectives. The unique characteristics of this article are twofold. On the one hand, this is the first attempt to investigate segment shapes, which we demonstrate have great impact on the robustness of the network. On the other hand, distinguished from the existing networks with uniform connectivity rule, the network is divided into two parts and different connectivity sequences and connectivity approaches are designed according to disparate features and requirements of the network. The performance of DBCE is validated through extensive simulation experiments.

It is generally accepted that the edge computing paradigm is regarded as capable of satisfying the resource requirements for the emerging mobile applications such as the Internet of Things (IoT) ones. Undoubtedly, the data collected by underlying sensor networks are the foundation of both the IoT systems and IoT applications. However, due to the weakness and vulnerability to attacks of underlying sensor networks, the data collected are usually untrustworthy, which may cause disastrous consequences. In this article, a new model is proposed to collect trustworthy data on the basis of edge computing in the IoT. In this model, the sensor nodes are evaluated from multiple dimensions to obtain accurately quantified trust values. Besides, by mapping the trust value of a node onto a force for the mobile data collector, the best mobility path is generated with high trust. Moreover, a mobile edge data collector is used to visit both the sensors with quantified trust values and collect trustworthy data. The extensive experiment validates that the IoT systems based on trustworthy data collection model gain a significant improvement in their performance, in terms of both system security and energy conservation.

With the widespread application of infotainment services in intelligent connected vehicles (ICVs), network traffic has grown exponentially, bringing huge burden and energy consumption to the ICV network. Edge caching, which enables edges [e.g., vehicles or roadside units (RSUs)] with cache storages, is a promising technology to alleviate this problem. In this article, in terms of the hybrid communication mode of vehicle to vehicle (V2V) and vehicle to RSU (V2R), an energy-aware caching scheme for infotainment services is proposed. Considering the geographical distribution of vehicles and RSUs as well as the size of transmission content, the energy consumption model in the ICV network is formulated to implement the optimal selection of cache nodes. Then, the selection of the cache node in the ICV network is transformed into the optimal stopping problem and solved by the optimal stopping theory. Finally, we propose a new algorithm for optimal energy-efficiency cache node selection (OEECS). The simulation results show that the proposed OEECS can obtain higher energy saving and lower average access latency than other baseline schemes.

The development of smart cities and deep learning technology is changing our physical world to a cyber world. As one of the main applications, the Internet of Vehicles has been developing rapidly. However, privacy leakage and delay problem for data collection remain as the key concerns behind the fast development of the cyber intelligence technologies. If the original data collected are directly uploaded to the cloud for processing, it will bring huge load pressure and delay to the network communication. Moreover, during this process, it will lead to the leakage of data privacy. To this end, in this article we design a data collection and preprocessing scheme based on deep learning, which adopts the semisupervised learning algorithm of data augmentation and label guessing. Data filtering is performed at the edge layer, and a large amount of similar data and irrelevant data are cleared. If the edge device cannot process some complex data independently, it will send the processed and reliable data to the cloud for further processing, which maximizes the protection of user privacy. Our method significantly reduces the amount of data uploaded to the cloud, and meanwhile protects the user's data privacy effectively.

With the advent of 5G, the industrial Internet of Things has developed rapidly. The industrial sensor-cloud system (SCS) has also received widespread attention. In the future, a large number of integrated sensors that simultaneously collect multifeature data will be added to industrial SCS. However, the collected big data are not trustworthy due to the harsh environment of the sensor. If the data collected at the bottom networks are directly uploaded to the cloud for processing, the query and data mining results will be inaccurate, which will seriously affect the judgment and feedback of the cloud. The traditional method of relying on sensor nodes for data cleaning is insufficient to deal with big data, whereas edge computing provides a good solution. In this article, a new data cleaning method is proposed based on the mobile edge node during data collection. An angle-based outlier detection method is applied at the edge node to obtain the training data of the cleaning model, which is then established through support vector machine. Besides, online learning is adopted for model optimization. Experimental results show that multidimensional data cleaning based on mobile edge nodes improves the efficiency of data cleaning while maintaining data reliability and integrity, and greatly reduces the bandwidth and energy consumption of the industrial SCS.

As an enabler for smart industrial Internet of Things (IoT), sensor cloud facilitates data collection, processing, analysis, storage, and sharing on demand. However, compromised or malicious sensor nodes may cause the collected data to be invalid or even endanger the normal operation of an entire IoT system. Therefore, designing an effective mechanism to ensure the trustworthiness of sensor nodes is a critical issue. However, existing cloud computing models cannot provide direct and effective management for the sensor nodes. Meanwhile, the insufficient computation and storage ability of sensor nodes makes them incapable of performing complex intelligent algorithms. To this end, mobile edge nodes with relatively strong computation and storage ability are exploited to provide intelligent trust evaluation and management for sensor nodes. In this article, a mobile edge computing-based intelligent trust evaluation scheme is proposed to comprehensively evaluate the trustworthiness of sensor nodes using probabilistic graphical model. The proposed mechanism evaluates the trustworthiness of sensor nodes from data collection and communication behavior. Moreover, the moving path for the edge nodes is scheduled to improve the probability of direct trust evaluation and decrease the moving distance. An approximation algorithm with provable performance is designed. Extensive experiments validate that our method can effectively ensure the trustworthiness of sensor nodes and decrease the energy consumption.

The tremendous growth of interconnected things/devices in the whole world advances to the new paradigm, i.e., Internet of Things (IoT). The IoT use sensor-based embedded systems to interact with others, providing a wide range of applications and services to upper-level users. Undoubtedly, the data collected by the underlying IoTs are the basis of the upper-layer decision and the foundation for all the applications, which requires efficient energy protocols. Moreover, if the collected data are erroneous and untrustworthy, the data protection and application becomes an unrealistic goal, which further leads to unnecessary energy cost. However, the traditional methods cannot solve this problem efficiently and trustworthily. To achieve this goal, in this paper we design a novel energy-efficient and trustworthy protocol based on mobile fog computing. By establishing a trust model on fog elements to evaluate the sensor nodes, the mobile data collection path with the largest utility value is generated, which can avoid visiting unnecessary sensors and collecting untrustworthy data. Theoretical analysis and experimental results validate that our proposed architecture and method outperform traditional data collection methods in both energy and delay.

Under the combination of Internet-of-Things (IoT) technology and traditional industry, the Industrial IoT (IIoT) came into being and received wide attention from all walks of life. With the increase of the number of IIOT devices in industrial environments, security threats, and quality of service (QoS) issues increase drastically. Internal attack is one type of important security threat that makes service environment worse and less reliable. However, there is no unified and fine-grained trust evaluation mechanism to deal with the threats of internal attack and improve QoS of IIoT. To this end, a unified trustworthy environment based on edge computing is established and maintained, which can timely detect malicious service providers and service consumers, filter unreal information, and recommend credible service providers. Edge computing is introduced as an effective service access point, since it supports collecting service records to perform trust evaluations. Moreover, a service selection method is designed to choose the corresponding trustworthy and reliable service providers based on the trust evaluation and the recording criterion, which has distinctive advantages in the succinct trust management, convenient searching service, and accurate service matching. Experiments validated the feasibility of the proposed trustworthy environment.

The proliferation of advanced underwater technology and the emergence of various cloud services promote the horizon of cloud-based underwater acoustic sensor network (UASN). Sending end data to cloud for analysis is becoming a prominent trend, driving cloud computing as an indispensable computing paradigm. However, UASN bears tremendous burdens with respect to data collection from end to cloud, such as large transmission power consumption and high delay, which makes it difficult to meet the delay-sensitive and context-aware service requirements by using cloud computing alone. To this end, a two-level bidirectional data prediction model for end-edge-cloud orchestration is proposed in this article. The mobility and computing ability of edge elements are exploited to analyze and collect data. Edge elements predict the future data based on historical information and trend to decrease acoustic communication. Moreover, a data collection protocol with mobile edge elements is designed. With this protocol, computing paradigms are shifted from centralized cloud to distributed edge, and the differentiated capability of heterogeneous devices is exploited. After extensive experiments, the results show that the data collection cost is dramatically decreased while the bandwidth utilization is increased, which is critical for underwater acoustic communication. The proposed method and protocol strike a good balance between data accuracy and energy consumption for the new end-edge-cloud orchestrated system.

IT companies need to monitor various Key Performance Indicators (KPIs) and detect anomalies in real time to ensure the quality and reliability of Internet-based services. However, due to the diversity of KPIs, the ambiguity and scarcity of anomalies and the lack of labels, anomaly detection for various KPIs has been a great challenge. Existing KPI anomaly detection methods have not explored the properties of anomalies in KPIs in detail to our best knowledge. Therefore, we explore anomalies in KPIs and recognize a common and important form of anomalies namedabrupt changes, which often indicate potential failures in the relevant services. Forabrupt changesin various KPIs, we proposeDDCOL, an unsupervised online anomaly detection algorithm with parameter adaptation from the perspective of anomalies for the first time. We propose three techniques: high order${D}$ifference extraction and combination,${D}$ensity-based${C}$lustering with parameter adaptation and${O}\text{n}{L}$ine detection with subsampling (DDCOL). Compared with traditional statistical methods and unsupervised learning methods, extensive experimental results and analysis on a large number of public KPIs show the competitive performance ofDDCOLand the significance ofabrupt changes. Furthermore, we provide an interpretation for the promising results, which shows thatDDCOLcan be robust to KPI expected concept drifts, and obtain a good feature distribution of normal data in KPIs.

There is an inherent reliance on collaboration among the participants of mobile ad hoc networks in order to achieve the fixed functionalities. However, they are susceptible to the destruction of the malicious attacks or denial of cooperation. Therefore, it becomes obvious that the security issue is urgently needed to be addressed. Over the last few years, many trust-considered countermeasures have been proposed. The design of trust quantification methods is the key of these countermeasures. In this study, we abstract a novel light-weight subjective trust inference framework, which is divided into trust assessment and trust prediction. The process of node trust assessment is based on node's historical behaviours. Then utilizing the obtained trust data sequence, we introduce the SCGM(1,1)-weighted Markov stochastic chain measure to predict node's trust for future decision making. Experimental results have been conducted to evaluate the effectiveness of the proposed trust model. As an important security application, based on the standard On-Demand Multicast Routing Protocol (ODMRP), we make four major improvements which take the issue of trust into consideration, and propose a novel trust-based routing protocol called the On-Demand Trust-Based Multicast Routing protocol (ODTMRP). And finally, convincing experimental results are presented using three routing evaluation metrics.

Mobile edge computing (MEC) is a promising technique to enhance the computation capacity at the edge of mobile networks. The joint problem of partial offloading decision, offloading scheduling and resource allocation for MEC systems is a challenge issue. In this paper, we investigate the problem of partial offloading scheduling and resource allocation for mobile edge computing systems with multiple independent tasks. Our goal is to minimize the weighted sum of the execution delay and energy consumption while guaranteeing the transmission power constraints of the tasks. The execution delay of tasks running in MEC and mobile edge devices are both considered. The energy consumption of both the tasks computing and task data transmission are considered as well. In order to tackle these issues, we formulate an energy-efficient and low-delay partial offloading scheduling and power allocation problem in single-user MEC systems, which is a non-convex mixed-integer optimization problem. A two-level alternation method framework based on decomposition optimization strategy is proposed. Furthermore, we propose Joint Partial Offloading scheduling and power Allocation (JPOA) iterative algorithm based on Lagrangian constrained optimization and Johnson method. Numerical results demonstrate JPOA algorithm achieves the most noticeable delay performance with a large energy consumption reduction.

Mobile edge computing (MEC) is a promising technique to enhance computation capacity at the edge of mobile networks. The joint problem of partial offloading decision, offloading scheduling, and resource allocation for MEC systems is a challenging issue. In this paper, we investigate the joint problem of partial offloading scheduling and resource allocation for MEC systems with multiple independent tasks. A partial offloading scheduling and power allocation (POSP) problem in single-user MEC systems is formulated. The goal is to minimize the weighted sum of the execution delay and energy consumption while guaranteeing the transmission power constraint of the tasks. The execution delay of tasks running at both MEC and mobile device is considered. The energy consumption of both the task computing and task data transmission is considered as well. The formulated problem is a nonconvex mixed-integer optimization problem. In order to solve the formulated problem, we propose a two-level alternation method framework based on Lagrangian dual decomposition. The task offloading decision and offloading scheduling problem, given the allocated transmission power, is solved in the upper level using flow shop scheduling theory or greedy strategy, and the suboptimal power allocation with the partial offloading decision is obtained in the lower level using convex optimization techniques. We propose iterative algorithms for the joint problem of POSP. Numerical results demonstrate that the proposed algorithms achieve near-optimal delay performance with a large energy consumption reduction.

In this paper, unmanned aerial vehicles (UAVs) are used to efficiently collect information in an areas of interest. Based on the matrix completion, an optimal UAV data collection trajectory (OUDCT) scheme is proposed for improving energy efficiency and reducing redundant data by optimizing the trajectory of the UAV. With the proposed scheme, the backbone sampling points can be selected as follows. First, sampling points with higher degrees are selected as dominator sampling points. Second, sampling points with lower degrees are selected as virtual dominator sampling points to ensure that the information in all rows and columns is collected. Third, sampling points with lower degrees are selected as follower sampling points until the total number of selected sampling points satisfies the minimum requirement of the matrix completion. Thus, all the information in the monitoring area can be recovered by using the matrix completion. Finally, the optimal simulated annealing algorithm is used to plan the path of UAV based on the selected sampling points. The experimental results indicate that the performance of the OUDCT scheme is better than those in previous studies. Extensive simulation results are provided, which demonstrate that the OUDCT scheme can reduce data redundancy by 50%-52% and increase the lifetime by 17% compared with the random selection sampling points scheme.

The Internet of Things (IoT)-Cloud combines the IoT and cloud computing, which not only enhances the IoT's capability but also expands the scope of its applications. However, it exhibits significant security and efficiency problems that must be solved. Internal attacks account for a large fraction of the associated security problems, however, traditional security strategies are not capable of addressing these attacks effectively. Moreover, as repeated/similar service requirements become greater in number, the efficiency of IoT-Cloud services is seriously affected. In this paper, a novel architecture that integrates a trust evaluation mechanism and service template with a balance dynamics based on cloud and edge computing is proposed to overcome these problems. In this architecture, the edge network and the edge platform are designed in such a way as to reduce resource consumption and ensure the extensibility of trust evaluation mechanism, respectively. To improve the efficiency of IoT-Cloud services, the service parameter template is established in the cloud and the service parsing template is established in the edge platform. Moreover, the edge network can assist the edge platform in establishing service parsing templates based on the trust evaluation mechanism and meet special service requirements. The experimental results illustrate that this edge-based architecture can improve both the security and efficiency of IoT-Cloud systems.

With the development of Internet of Things (IoT) technologies, increasingly many devices are connected, and large amounts of data are produced. By offloading the computing-intensive tasks to the edge devices, cloud-based storage technology has become the mainstream. However, if the end IoT devices send all of their data to the cloud, then data privacy becomes a great issue. In this paper, we propose a new architecture for data synchronization based on fog computing. By offloading part of computing and storage work to the fog servers, the data privacy can be guaranteed. Moreover, to decrease the communication cost and reduce the latency, we design a differential synchronization algorithm. Furthermore, we extend the method by introducing Reed-Solomon code for security consideration. We prove that our architecture and algorithm really have better performance than traditional cloud-based solutions in terms of both efficiency and security through a series of experiments.

Localization for mobile group users is one of the important applications in cyber-physical systems. However, due to the sparse deployment of anchors and the instability of signals in the wireless environment, users cannot receive information from adequate anchors, which leads to the localization quality being undependable and unacceptable. To solve this problem, we propose exploiting the localized users as the mobile anchors for localizing the nonlocalized users. These mobile users cooperate as a whole group to improve the localization accuracy. Moreover, to decrease the communication cost among users, an algorithm for electing mobile anchors is designed, with several provable properties. This electing algorithm is a distributed method, without advanced negotiations among mobile users. In addition, for the scenarios with a crowd of users, we divide the users into different groups according to their distance information, which can ensure that only the dependable anchors are used for the localization. Extensive experimental results demonstrate that the localization dependability can be improved obviously. In terms of localization probability, our method outperforms the traditional fixed anchors-based method by approximately 70% with a small increment of communication cost (about 30%), and outperforms the method in which all the localized users are exploited as mobile anchors by about 50% with about a 70% decrement of communication cost.

For the cyber-physical systems (CPS) in the smart industrial environments, a larger amount of smart sensor nodes, processors and actuators are deployed to sense information from physical world, which is vulnerable to be attacked because the nodes are deployed in unattended areas. Security is a pivotal issue for CPS, therefore, a trust-based active detection (TBAD) scheme is proposed for improving the reliability of collecting data packets and reducing the data redundancy. In TBAD scheme, the trust of the nodes is evaluated by the neighboring nodes and the evaluation results are added into the header of data packets. Thus, according to the reliability of data packets collected by the unmanned aerial vehicle (UAV), the trust of sensor nodes is evaluated by the UAV. In addition, the evaluation trust of nodes stored in the header of data packets will be detected when the UAV suspects the stored trust of sensor nodes. Then, the trust of corresponding sensor nodes is adjusted according to the detection results. The sensor nodes with higher trust are selected to form movement trajectory. A series of simulation experiments are conducted to evaluate the performance of the scheme. The results illustrate that the proposed scheme can greatly improve the efficiency and security of data routing in CPS.

Security is a pivotal issue for the development of Cyber Physical Systems (CPS). The trusted computing of CPS includes the complete protection mechanisms, such as hardware, firmware, and software, the combination of which is responsible for enforcing a system security policy. A Trust Detection-based Secured Routing (TDSR) scheme is proposed to establish security routes from source nodes to the data center under malicious environment to ensure network security. In the TDSR scheme, sensor nodes in the routing path send detection routing to identify relay nodes’ trust. And then, data packets are routed through trustworthy nodes to sink securely. In the TDSR scheme, the detection routing is executed in those nodes that have abundant energy; thus, the network lifetime cannot be affected. Performance evaluation through simulation is carried out for success of routing ratio, compromised node detection ratio, and detection routing overhead. The experiment results show that the performance can be improved in the TDSR scheme compared to previous schemes.

Millions of dedicated sensors are deployed in smart cities to enhance quality of urban living. Communication technologies are critical for connecting these sensors and transmitting events to sink. In control systems of mobile wireless sensor networks (MWSNs), mobile nodes are constantly moving to detect events, while static nodes constitute the communication infrastructure for information transmission. Therefore, how to communicate with sink quickly and effectively is an important research issue for control systems of MWSNs. In this paper, a communication scheme named first relay node selection based on fast response and multihop relay transmission with variable duty cycle (FRAVD) is proposed. The scheme can effectively reduce the network delay by combining first relay node selection with node duty cycles setting. In FRAVD scheme, first, for the first relay node selection, we propose a strategy based on fast response, that is, select the first relay node from adjacent nodes in the communication range within the shortest response time, and guarantee that the remaining energy and the distance from sink of the node are better than the average. Then for multihop data transmission of static nodes, variable duty cycle is introduced novelty, which utilizes the residual energy to improve the duty cycle of nodes in far-sink area, because nodes adopt a sleep-wake asynchronous mode, increasing the duty cycle can significantly improve network performance in terms of delays and transmission reliability. Our comprehensive performance analysis has demonstrated that compared with the communication scheme with fixed duty cycle, the FRAVD scheme reduces the network delay by 24.17%, improves the probability of finding first relay node by 17.68%, while also ensuring the network lifetime is not less than the previous researches, and is a relatively efficient low-latency communication scheme.

Pervasive sensing devices make an unprecedented increase in data sensing, collection, and processing in edge network and they form the edge content server system. The edge content server system combines the content-centric network (CCN) to form a huge content propagation system which makes it challenging to achieve efficient content propagation. Different from IP-based, host-oriented Internet architecture, the CCN systems focus on the information that is contained in network and directly accessible, providing more secure and flexible Internet services. This emerging network architecture supports a number of novel applications, such as common interests sharing, mobile data offloading, and information dissemination without Internet access. In this paper, an analytical framework is proposed to address the problem of content propagation among users with the same interests in leveraging location-based social networks, where the check-in patterns of users are recorded. Particularly, we propose a content propagation effectiveness quantitative model that considers the distance between users, users' interests, and contact rates to formulate the propagation effect and latency. We also apply our framework to two real-world datasets for the evaluation of its effectiveness. Compared with previous studies, our simulated annealing-based algorithm can greatly improve the effects by as much as 25.4%-65.6%, and the contents can be disseminated faster by about 24.6%-57.8%.

The large number of mobile-sensing vehicles traveling in cities offer a novel solution to the collection of vast amounts of multimedia data packets. When a vehicle passes through the data center (DC), the collected multimedia data packets will be transmitted to the DC. Due to the mobile characteristic of vehicular sensor networks, the main challenge lies in how to improve the multimedia data delivery ratio and balance the data packet collections. In this paper, in consideration of delay and delivery factors, a novel routing method is proposed to optimize multimedia data collections in mobile sensing vehicles (DDSVs). This method targets at balancing multimedia data collections, improving the delivery ratio of the multimedia data, and reducing the delay ratio in Internet of Things (IoT) networks. In the DDSV scheme, two rules are designed for improving the collection of multimedia data in the IoT. These rules pertain to: 1) data and 2) vehicular priorities. First, different regions hold different priorities of data packet transmission, which can improve the delivery ratio in the suburban areas and reduce the delay ratio. Meanwhile, this scheme is capable of guaranteeing the balance of multimedia data collection. Second, the vehicular priority is proportional to the probability of a vehicle reaching a DC. Therefore, the data should be forwarded to vehicles with higher priorities, that is, the vehicles which are more likely to pass by the DC. By using these two rules, the DDSV scheme can improve the performances of the multimedia data delivery ratio, compared with the conventional optimal vehicular data forwarding scheme. In the simulation experiments, the DDSV scheme utilizes multidatasets of Beijing city, where the average delay for data collection can be decreased by 17.3% in general, and by 41.8% in the suburban areas; the average data delivery ratio can be improved by 16.9% in comparison to the previous studies.

The current boom in the Internet of Things (IoT) is changing daily life in many ways, from wearable devices to connected vehicles and smart cities. We used to regard fog computing as an extension of cloud computing, but it is now becoming an ideal solution to transmit and process large-scale geo-distributed big data. We propose a Byzantine fault-tolerant networking method and two resource allocation strategies for IoT fog computing. We aim to build a secure fog network, called “SIoTFog,” to tolerate the Byzantine faults and improve the efficiency of transmitting and processing IoT big data. We consider two cases, with a single Byzantine fault and with multiple faults, to compare the performances when facing different degrees of risk. We choose latency, number of forwarding hops in the transmission, and device use rates as the metrics. The simulation results show that our methods help achieve an efficient and reliable fog network.

An Aggregate Signature based Trust Routing (ASTR) scheme is proposed to guarantee safe data collection in WSNs. In ASTR scheme, firstly, the aggregate signature approach is used to aggregate data and keep data integrity. What is more important, a light aggregate signature based detour routing scheme is proposed to send abstract information which includes the data sending time and ID of data, nodes’ ID to the sink over different paths which can verify whether the data reaches the sink safely. In addition, the trust of a path is evaluated according to the success rate of the path. The trust of paths susceptible to frequent attack will be lowered and the path with high trust will be selected for data routing to avoid data gathering through low trust path and thereby increase the success rate of data gathering. Our comprehensive performance analysis has shown that, the ASTR scheme is able to effectively ensure an increase in success rate of data transmission by 23.23%, reduce the data amount loaded by the node by 53.59%, reduce the redundant data by 41.70%.

For wireless networks in the Internet of Things (IoT), cognitive radio (CR) is a promising way to obtain the available spectrum for objects. Wide-band spectrum sensing plays an important role in building such CR networks of IoT. In this paper, we propose a novel dynamic compressive wide-band spectrum sensing method based on channel energy reconstruction. After a bank of wide-band random filters is employed to measure the channel energy, rather than to recover all the channel energy in the whole spectrum, only the channel energy with a changing occupancy status in consecutive time slots is recovered. Furthermore, it is unnecessary to use reconstruction algorithm unless there are two or more channels changing their occupancy status. Compared to the existing methods, our proposed schemes bear significant improvements in the probability of detection and reduction of probability of false alarms. Simulation results also show its fast speed and robustness to noise.

Industrial communication systems (ICSs) have become an important part of many automated applications. Smart sensors for ICSs have attracted much attention. Using smart sensors for ICSs can allow the surrounding world to connect with the production equipment to reduce human resource requirements. However, due to their operating nature, smart sensors are often unattended and prone to different kinds of attacks. An on-off attack is one behavior that can cause serious damage to wireless sensor networks in ICSs. To ensure network security, a trust joint light probe based defense (TLPD) mechanism is proposed in which each smart sensor node selects a smart sensor node with high trust as the next hop to improve network security. The TLPD scheme is constituted by three major components, including light probe routing, trust estimation, and trust-aware routing. Light probe routing is adopted for increasing the operation times of malicious nodes, and a trust evaluation scheme evaluates nodes trust according to operation behavior of nodes. Thus, the discrimination degree of node trust between temporary errors and disguised malicious behaviors can be increased compared to existing trust evaluation schemes. Thus, a malicious smart sensor node can be quickly and easily identified. The analytical results of the TLPD mechanism assessed are with a simulation experiment.

Energy‐efficient data gathering techniques play a crucial role in promoting the development of smart portable devices as well as smart sensor devices based Internet of Things (IoT). For data gathering, different applications require different delay constraints; therefore, a delay Differentiated Services based Data Routing (DSDR) scheme is creatively proposed to improve the delay differentiated services constraint that is missed from previous data gathering studies. The DSDR scheme has three advantages: first, DSDR greatly reduces transmission delay by establishing energy‐efficient routing paths (E2RPs). Multiple E2RPs are established in different locations of the network to forward data, and the duty cycles of nodes on E2RPs are increased to 1, so the data is forwarded by E2RPs without the existence of sleeping delay, which greatly reduces transmission latency. Secondly, DSDR intelligently chooses transmission method according to data urgency: the direct‐forwarding strategy is adopted for delay‐sensitive data to ensure minimum end‐to‐end delay, while wait‐forwarding method is adopted for delay‐tolerant data to perform data fusion for reducing energy consumption. Finally, DSDR make full use of the residual energy and improve the effective energy utilization. The E2RPs are built in the region with adequate residual energy and they are periodically rotated to equalize the energy consumption of the network. A comprehensive performance analysis demonstrates that the DSDR scheme has obvious advantages in improving network performance compared to previous studies: it reduces transmission latency of delay‐sensitive data by 44.31%, reduces transmission latency of delay‐tolerant data by 25.65%, and improves network energy utilization by 30.61%, while also guaranteeing the network lifetime is not lower than previous studies.

To tackle the issue in deep crowd sensing, a Time and Location Correlation Incentive (TLCI) scheme is proposed for deep data gathering in crowdsourcing networks. In TLCI scheme, a metric named “Quality of Information Satisfaction Degree” (QoISD) is to quantify how much collected sensing data can satisfy the application’s QoI requirements mainly in terms of data quantity and data coverage. Two incentive algorithms are proposed to satisfy QoISD with different view. The first algorithm is to ensure that the application gets the specified sensing data to maximize the QoISD. Thus, in the first incentive algorithm, the reward for data sensing is to maximize the QoISD. The second algorithm is to minimize the cost of the system while meeting the sensing data requirement and maximizing the QoISD. Thus, in the second incentive algorithm, the reward for data sensing is to maximize the QoISD per unit of reward. Finally, we compare our proposed scheme with existing schemes via extensive simulations. Extensive simulation results well justify the effectiveness of our scheme. The QoISD can be optimized by 81.92%, and the total cost can be reduced by 31.38%.

The vehicular communication networks, which can employ mobile, intelligent sensing devices with participatory sensing to gather data, could be an efficient and economical way to build various applications based on big data. However, high quality data gathering for vehicular communication networks which is urgently needed faces a lot of challenges. So, in this paper, a fine‐grained data collection framework is proposed to cope with these new challenges. Different from classical data gathering which concentrates on how to collect enough data to satisfy the requirements of applications, a Quality Utilization Aware Data Gathering (QUADG) scheme is proposed for vehicular communication networks to collect the most appropriate data and to best satisfy the multidimensional requirements (mainly including data gathering quantity, quality, and cost) of application. In QUADG scheme, the data sensing is fine‐grained in which the data gathering time and data gathering area are divided into very fine granularity. A metric named “Quality Utilization” (QU) is to quantify the ratio of quality of the collected sensing data to the cost of the system. Three data collection algorithms are proposed. The first algorithm is to ensure that the application which has obtained the specified quantity of sensing data can minimize the cost and maximize data quality by maximizing QU. The second algorithm is to ensure that the application which has obtained two requests of application (the quantity and quality of data collection, or the quantity and cost of data collection) could maximize the QU. The third algorithm is to ensure that the application which aims to satisfy the requirements of quantity, quality, and cost of collected data simultaneously could maximize the QU. Finally, we compare our proposed scheme with the existing schemes via extensive simulations which well justify the effectiveness of our scheme.

In wireless sensor networks (WSNs), many applications require a high reliability for the sensing data forwarding to sink. Due to the lossy nature of wireless channels, achieving reliable communication through multihop forwarding can be very challenging. Broadcast technology is an effective way to improve the communication reliability so that the data can be received by multiple receiver nodes. As long as the data of any one of the receiver nodes is transmitted to the sink, the data can be transmitted successfully. In this paper, a cross‐layer optimization protocol named Adaptive transmission Power control based Reliable data Forwarding (APRF) scheme by using broadcast technology is proposed to improve the reliability of network and reduce communication delay. The main contributions of this paper are as follows: (1) for general data aggregation sensor networks, through the theoretical analysis, the energy consumption characteristics of the network are obtained. (2) According to the case that the energy consumption of near‐sink area is high and that in far‐sink area is low, a cross‐layer optimization method is adopted, which can effectively improve the data communication by increasing the transmission power of the remaining energy nodes. (3) Since the reliability of communication is improved by increasing the transmission power of the node, the number of retransmissions of the data packet is reduced, so that the delay of the packet reaching the sink node is reduced. The theoretical and experimental results show that, applying APRF scheme under initial transmission power of 0 dBm, although the lifetime dropped by 13.77%, delay could be reduced by 40.37%, network reliability could be reduced by 10.08%, and volume of data arriving at sink increased by 10.08% compared with retransmission‐only mechanism.

An Adaptive Transmission Range Based Topology Control (ATRTC) scheme is proposed to reduce delay and improve reliability for data collection in delay and loss sensitive wireless sensor network. The core idea of the ATRTC scheme is to extend the transmission range to speed up data collection and improve the reliability of data collection. The main innovations of our work are as follows: (1) an adaptive transmission range adjustment method is proposed to improve data collection reliability and reduce data collection delay. The expansion of the transmission range will allow the data packet to be received by more receivers, thus improving the reliability of data transmission. On the other hand, by extending the transmission range, data packets can be transmitted to the sink with fewer hops. Thereby the delay of data collection is reduced and the reliability of data transmission is improved. Extending the transmission range will consume more energy. Fortunately, we found the imbalanced energy consumption of the network. There is a large amount of energy remains when the network died. ATRTC scheme proposed in this paper can make full use of the residual energy to extend the transmission range of nodes. Because of the expansion of transmission range, nodes in the network form multiple paths for data collection to the sink node. Therefore, the volume of data received and sent by the near‐sink nodes is reduced, the energy consumption of the near‐sink nodes is reduced, and the network lifetime is increased as well. (2) According to the analysis in this paper, compared with the CTPR scheme, the ATRTC scheme reduces the maximum energy consumption by 9%, increases the network lifetime by 10%, increases the data collection reliability by 7.3%, and reduces the network data collection time by 23%.

Rapid, reliable and energy-efficient programing code dissemination is a challenging issue and offers a programmable and flexible network architecture for software-defined wireless networks (SDWNs). However, many schemes for programing codes in large-scale network incur a longer dissemination convergence time (DCT) and lower energy efficient in loss nature of wireless channels. In this paper, an adaptive broadcast dissemination (ABD) scheme is proposed to achieve low DCT and high network lifetime for SDWNs. An ABD scheme makes full use of energy left of nodes in areas far from the sink. In an ABD scheme, codes are broadcast constantly few times by nodes near the sink to save energy, and many times by nodes in areas far from the sink to rapidly spread software codes. Thus, an ABD scheme can reduce transmission delay for spreading software code while retaining network lifetime. Theoretical analysis and experimental results show that DCT in an ABD scheme is reduced by 28.12–29.86% compared with the hybrid scheme, while retaining network lifetime.

Summary In service‐oriented cloud computing systems (CCSs), the aim of cloud service organizers (CSOs) is to achieve maximum profit by collecting metadata with low cost from big data reporters (BDRs) and to provide advanced services to customers at a high price. In these systems, BDRs receive payoffs by reporting metadata to CSOs and exchanging metadata with other BDRs, and customers expect to get high‐quality services at a low price. However, because the missing of a critical parameters decision model in such service‐oriented CCSs, it is difficult to measure key parameters in CCSs such as price and quality of services in the competitive market. In this paper, we propose a multiple game (MG) model to formulate the critical parameters decision process. In the MG model, there are multiple games: games among BDRs and games among CSOs under the rule of “survival of the fittest,” games between BDRs and CSOs under the rule of “the highest payoff first,” and games between customers and CSOs under the rule of “the lowest price and the highest quality of service (QoS) first.” With the proposed multiple game (MG) model, the optimal key parameters can be obtained and the Pareto‐optimal equilibrium point can be achieved. Extensive simulation results demonstrate the effectiveness and efficiency of the proposed MG model in dynamically deciding key parameters in CCSs.

A Semi Random Circle routing for mobile Sink joint Ray Routing for data (SRCRR) scheme is proposed for preserving sink-location privacy with a delay guaranteed. In the SRCRR scheme, the data are directionally routed along ray paths and stored at intermediate nodes probabilistically. The Sink moves in a semirandom circular pattern to collect data from the local nodes occasionally, which guarantees that the data will be collected with an acceptable delay and prevents attackers from predicting their locations and movements. The experimental results indicate that the performance of the SRCRR scheme is better than that of the previous schemes.

A multi-representative re-fusion (MRRF) approximate data collection approach is proposed in which multiple nodes with similar readings form a data coverage set (DCS). The reading value of the DCS is represented by an R-node. The set near the Sink is smaller, while the set far from the Sink is larger, which can reduce the energy consumption in hotspot areas. Then, a distributed data-aggregation strategy is proposed that can re-fuse the value of R-nodes that are far from each other but have similar readings. Both comprehensive theoretical and experimental results indicate that the MRRF approach increases lifetime and energy efficiency.

Rapid, reliable and energy efficient programing code dissemination is a challenging issue and offer a programmable and flexible network architecture for software defined wireless networks (SDWNs). Many schemes for programing codes in large-scale network incur longer dissemination convergence time (DCT) in loss nature of wireless channels. In this paper, an integrated adjusted broadcast (TAB) scheme is proposed to achieve lower dissemination convergence time and longer network lifetime for SDWNs.

We propose a novel event data collection approach named reliability and multipath encounter routing (RMER) for meeting reliability and energy efficiency requirements. The contributions of the RMER approach are as follows. 1) Fewer monitor nodes are selected in hotspot areas that are close to the Sink, and more monitor nodes are selected in nonhotspot areas, which can lead to increased network lifetime and event detection reliability. 2) The RMER approach sends data to the Sink by converging multipath routes of event monitoring nodes into a one-path route to aggregate data. Thus, energy consumption can be greatly reduced, thereby enabling further increased network lifetime. Both theoretical and experimental simulation results show that RMER applied to event detection outperforms other solutions. Our results clearly indicate that RMER increases energy efficiency by 51% and network lifetime by 23% over other solutions while guaranteeing event detection reliability.

Abstract Currently, the analysis result of fair probability marking approach can only be got in the single‐source linear and single‐source tree network, which cannot meet the requirement of applications. This paper fills in this gap by providing an accurate marking probability analysis model for linear network, tree network, and planar network. Compare with the marking packets mechanism with equal marking probability, the analysis result of this paper shows that FPM mechanisms cannot only reduce convergence time up to 20%–60% for medium‐sized plane sensor networks, but also reduces the amount of data, so the network lifetime is 1.2 times to 3.37 times than previous methods. The mark probability τ of each node is given and its performance is comprehensively analyzed for FPM approach. The results have important guiding significance for designing traceback scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Cyber-physical systems (CPSs) have recently become an important research field not only because of their important and varied application scenarios, including transportation systems, smart homes, surveillance systems, and wearable devices but also because the fundamental infrastructure has yet to be well addressed. Wireless sensor networks (WSNs), as a type of supporting infrastructure, play an irreplaceable role in CPS design. Specifically, secure communication in WSNs is vital because information transferred in the networks can be easily stolen or replaced. Therefore, this paper presents a novel distributed low-storage clone detection protocol (LSCD) for WSNs. We first design a detection route along the perpendicular direction of a witness path with witness nodes deployed in a ring path. This ensures that the detection route must encounter the witness path because the distance between any two detection routes must be smaller than the witness path length. In the LSCD protocol, clone detection is processed in a nonhotspot region where a large amount of energy remains, which can improve energy efficiency as well as network lifetime. Extensive simulations demonstrate that the lifetime, storage requirements, and detection probability of our protocol are substantially improved over competing solutions from the literature.

Wireless sensor networks (WSNs) are increasingly being deployed in security-critical applications. Because of their inherent resource-constrained characteristics, they are prone to various security attacks, and a black hole attack is a type of attack that seriously affects data collection. To conquer that challenge, an active detection-based security and trust routing scheme named ActiveTrust is proposed for WSNs. The most important innovation of ActiveTrust is that it avoids black holes through the active creation of a number of detection routes to quickly detect and obtain nodal trust and thus improve the data route security. More importantly, the generation and the distribution of detection routes are given in the ActiveTrust scheme, which can fully use the energy in non-hotspots to create as many detection routes as needed to achieve the desired security and energy efficiency. Both comprehensive theoretical analysis and experimental results indicate that the performance of the ActiveTrust scheme is better than that of the previous studies. ActiveTrust can significantly improve the data route success probability and ability against black hole attacks and can optimize network lifetime.

Network lifetime is a crucial performance metric to evaluate data-gathering wireless sensor networks (WSNs) where battery-powered sensor nodes periodically sense the environment and forward collected samples to a sink node. In this paper, we propose an analytic model to estimate the entire network lifetime from network initialization until it is completely disabled, and determine the boundary of energy hole in a data-gathering WSN. Specifically, we theoretically estimate the traffic load, energy consumption, and lifetime of sensor nodes during the entire network lifetime. Furthermore, we investigate the temporal and spatial evolution of energy hole and apply our analytical results to WSN routing in order to balance the energy consumption and improve the network lifetime. Extensive simulation results are provided to demonstrate the validity of the proposed analytic model in estimating the network lifetime and energy hole evolution process.

In this paper, we propose an energy-efficient location-aware clone detection protocol in densely deployed WSNs, which can guarantee successful clone attack detection and maintain satisfactory network lifetime. Specifically, we exploit the location information of sensors and randomly select witnesses located in a ring area to verify the legitimacy of sensors and to report detected clone attacks. The ring structure facilitates energy-efficient data forwarding along the path towards the witnesses and the sink. We theoretically prove that the proposed protocol can achieve$100$percent clone detection probability with trustful witnesses. We further extend the work by studying the clone detection performance with untrustful witnesses and show that the clone detection probability still approaches$98$percent when$10$percent of witnesses are compromised. Moreover, in most existing clone detection protocols with random witness selection scheme, the required buffer storage of sensors is usually dependent on the node density, i.e.,$O(\sqrt{n})$, while in our proposed protocol, the required buffer storage of sensors is independent of$n$but a function of the hop length of the network radius$h$, i.e.,$O(h)$. Extensive simulations demonstrate that our proposed protocol can achieve long network lifetime by effectively distributing the traffic load across the network.

This paper first presents an analysis strategy to meet requirements of a sensing application through trade-offs between the energy consumption (lifetime) and source-to-sink transport delay under reliability constraint wireless sensor networks. A novel data gathering protocol named Broadcasting Combined with Multi-NACK/ACK (BCMN/A) protocol is proposed based on the analysis strategy. The BCMN/A protocol achieves energy and delay efficiency during the data gathering process both in intra-cluster and inter-cluster. In intra-cluster, after each round of TDMA collection, a cluster head broadcasts NACK to indicate nodes which fail to send data in order to prevent nodes that successfully send data from retransmission. The energy for data gathering in intra-cluster is conserved and transport delay is decreased with multi-NACK mechanism. Meanwhile in inter-clusters, multi-ACK is returned whenever a sensor node sends any data packet. Although the number of ACKs to be sent is increased, the number of data packets to be retransmitted is significantly decreased so that consequently it reduces the node energy consumption. The BCMN/A protocol is evaluated by theoretical analysis as well as extensive simulations and these results demonstrate that our proposed protocol jointly optimizes the network lifetime and transport delay under network reliability constraint.

In cyber-physical systems (CPS), service organizers (SOs) aim to collect service from service entities at lower price and provide better combined services to users. However, each entity receives payoffs when providing services, which leads to competition between SOs and service entities or within internal service entities. In this paper, we first formulate the price competition model of SOs where the SOs dynamically increase and decrease their service prices periodically according to the number of collected services from entities. A game based services price decision (GSPD) model which depicts the process of price decisions is proposed in this paper. In the GSPD model, entities game with other entities under the rule of “survival of the fittest” and calculate payoffs according to their own payoff-matrix, which leads to a Pareto-optimal equilibrium point. Numerous experiments demonstrate that the GSPD model can explain the price dynamics in the real world, and also can help decision makers a lot under various scenarios.

Cooperative sensing and monitoring event is one of the important applications of sensor networks. Node deployment and duty cycle configuration of event detection in a large wireless sensor networks is an enormous challenge. In this paper, through in-depth analysis we find that there is a tradeoff between node duty cycle and event quality monitoring. That is, adopting larger duty cycle enables network to deploy fewer nodes. Or deploying more nodes can reduce their duty cycle. Both of them can reach the event detecting quality requirement of application. Based on the above findings, a novel subtraction deployment strategy (SDS) combined with the unequal node duty cycle in the network is presented. This strategy improves the duty cycle of nodes in non-hotspots area when reduces the number, thereby minimizing the deployment cost on the premise of meeting the detecting quality of application. We introduce a formal model to indicate the tradeoff between deployment cost and the quality of event detection. Subsequently, we present a heuristic algorithm to find near-optimal deployment solutions for practical scenarios. Both theoretical and experimental simulation results show that the proposed scheme SDS for event detection can reduce the deployed number of nodes by 11.49% and do no harm to detecting quality. In the deployment of the same node number, the weighted delay of detection can be reduced as much as 47%, and the weighted detection percentage can be increased by 1.56 times without reducing the network lifetime compared with those in uniform deployment scheme.

Wireless sensor networks (WSNs) play an increasing role in a wide variety of applications ranging from hostile environment monitoring to telemedicine services. The hardware and cost constraints of sensor nodes, however, make sensors prone to clone attacks and pose great challenges in the design and deployment of an energy-efficient WSN. In this paper, we propose a location-aware clone detection protocol, which guarantees successful clone attack detection and has little negative impact on the network lifetime. Specifically, we utilize the location information of sensors and randomly select witness nodes located in a ring area to verify the privacy of sensors and to detect clone attacks. The ring structure facilitates energy efficient data forwarding along the path towards the witnesses and the sink, and the traffic load is distributed across the network, which improves the network lifetime significantly. Theoretical analysis and simulation results demonstrate that the proposed protocol can approach 100% clone detection probability with trustful witnesses. We further extend the work by studying the clone detection performance with untrustful witnesses and show that the clone detection probability still approaches 98% when 10% of witnesses are compromised. Moreover, our proposed protocol can significantly improve the network lifetime, compared with the existing approach.

Cost function based routing has been widely studied in wireless sensor networks for energy efficiency and network lifetime elongation. Existing algorithms however have limited effects because they adopt a single cost function that does not fully capture nodal energy consumption situation. In this paper, we propose a novel Double Cost Function based Routing (DCFR) algorithm, which takes into account end-to-end energy consumption, nodal remaining energy, and energy consumption rate altogether. An extensive simulation indicates that DCFR can lead to more balanced and efficient energy usage among nodes than existing algorithms.

This paper studies the design of optimal location of buffer area in a movement-assisted data gathering scheme. In [7], the author only considered a network scenario where rLtR and the transmission power of sensor was fixed. This paper extends the network to a general network where the relationship between r and R is arbitrary and the transmission power of sensor is variable. In the network, the maximum transmission radius is k times of the minimum transmission radius. In order to minimize the total energy consumption of network and maximize the network lifetime, this paper employs a more accurate analytical method to select parameters of the transmission power level and location of buffer area. Finally, a conclusion is obtained that the scheme using optimized location of buffer area and transmission power excels greatly the one that employs fixed transmission power in the scenario of improving the network lifetime. Therefore, our study on the parameter selection of network can present a better guidance on the optimization of wireless sensor networks.