Urban Smart Sensing Devices (SSDs) require timely and secure code updates to maintain reliable operation. However, trust verification in vehicle-assisted code dissemination remains insufficiently explored, making systems vulnerable to malicious vehicles that distribute corrupted or outdated code. This paper proposes a Trust-driven Reliable Code Dissemination (TRCD) scheme, where mobile vehicles opportunistically act as “code mules” for efficient and secure dissemination. TRCD employs a two-layer trust evaluation mechanism: (i) selected anchor SSDs evaluate local vehicle trust in real time, and (ii) the code center constructs global trust by aggregating UAV-verified and Local High-Trust Vehicle (LHTV) reported reputation information to identify trustworthy vehicles and exclude malicious ones. To improve dissemination efficiency, we further design (i) an anchor SSD selection algorithm for cost-efficient trust collection, (ii) a token-based mechanism for prioritizing high-value tokens under storage constraints, and (iii) a genetic algorithm-based UAV trajectory optimization method for trust-aware dissemination. We also provide theoretical analysis on convergence behavior, computational complexity, and performance bounds. Extensive simulations using real-world taxi trajectory datasets show that TRCD achieves 98.2% trusted-vehicle identification accuracy, 99.4% malicious-vehicle detection accuracy, and reduces UAV flight distance by 64.5% compared with baseline methods, while significantly improving dissemination reliability. These results demonstrate that TRCD provides an efficient, scalable, and secure solution for urban SSD code dissemination.

Recruiting trust workers to achieve high data quality at low cost has become a promising approach in Mobile Crowdsensing (MCS). However, most existing trust evaluation methods only adopt a single-dimensional trust model, neglecting the fact that a worker's trustworthiness can vary across different task types, which leads to suboptimal task–worker matching, poor data quality, and inefficient cost utilization. To this end, we propose a Multidimensional Trust Evaluation and Task Matching (MTE-TM) based workers recruitment scheme to improve data quality while reducing costs for MCS. First, we represent worker trustworthiness by a composite of expected trust values and variance, enabling task-specific trust assessment that extends traditional single-dimensional trust to a multidimensional domain. A novel Expectation-Maximization (EM)-based trust evaluation mechanism is also introduced to improve accuracy. Second, we design a new worker selection method that combines a worker's trust level and the width of the confidence interval to compute their Upper Confidence Bound (UCB) index, which effectively guides worker selection toward optimal outcomes. Third, we propose an Optimized Data Quality Matching (ODQM) algorithm that assigns tasks to workers with high priority and low bid prices under budget constraints, thereby further improving data quality. The experimental results demonstrate the significant performance improvements of our scheme, achieving 45.54$\sim$95.55% optimization in trust evaluation, 65.01$\sim$72.95% improvement in data quality, and a notable reduction in regret.

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.

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.

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.

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.

Assigning tasks to reliable workers to obtain reliable data is a critical issue in Mobile CrowdSensing (MCS). The challenge is compounded by the problem of Information Elicitation Without Verification (IEWV), which renders traditional data quality evaluation methods ineffective. While some studies attempt to address this, they often struggle to assess workers’ dynamic trustworthiness, resulting in unreliable data. To overcome these challenges, we propose the Trust and Time-sharing Task Allocation based Truth Discovery (TTTA-TD) scheme, designed to ensure reliable data collection in MCS. This scheme includes three components: (a) Classification-based Trust Evaluation (CTE) that classifies workers based on behavior and applies tailored penalties—lenient for honest workers and stricter for malicious ones, (b) Trust-based Truth Data Discovery (TTDD), which improves truth data accuracy by integrating trust scores, and (c) Trust and Time-sharing Task Allocation (TTTA) which allocates tasks to ensure data reliability and minimize time-sharing disparities. Experimental results show that the TTTA algorithm reduces average time-sharing by 93.95%. The TTDD algorithm improves truth estimates across all dataset qualities, and the TTTA-TD scheme enhances data reliability by 0.35%, 2.06%, and 7.41% in high, medium, and low-quality datasets respectively.

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.

To identify and explore the similarities between diseases is of great significance for u nderstanding t he pathogenic mechanisms of emerging complex diseases. Some methods try to measure the similarity of diseases through deep learning models. However, the insufficient number of labelled similar disease pairs cannot support the optimal training of the models. In this paper, we propose a Multi-Task Graph Neural Network (MTGNN) framework to retrieve similar diseases by few-shot learning. To deal with the problem of insufficient number o f labelled similar disease pairs, we design double tasks to optimize the graph neural network for disease similarity task (lack of labelled training data) by introducing link prediction task (sufficient labelled training data). The similarity between diseases can then be obtained by measuring the distance between disease embeddings in high-dimensional space learning from the double tasks. The experiment results illustrate the overall effectiveness by comparing with prior methods on few labeled training dataset.

The oceans cover more than 71% of the Earth's surface and have a surging amount of data. It is of great significance to seek energy-effective and ultrareliable communication and transmission mechanism for effectively gathering abundant maritime data. In this article, we propose an autonomous underwater vehicle (AUV)-assisted data gathering scheme based on clustering and matrix completion (ACMC) to improve the data gathering efficiency in the underwater wireless sensor network (UWSN). Specifically, we first improve the K-means algorithm by adopting the Elbow method to determine the optimal K and setting a distance threshold to select the separate initial cluster centers. Then, we introduce a two-phase AUV trajectory optimization mechanism to effectively reduce the trajectory length of the AUV. In the first phase, the optimized trajectory of the AUV is planned by adopting the greedy algorithm. In the second phase, the ordinary nodes close to the AUV trajectory are selected as secondary cluster heads to share the workload of cluster heads. Finally, we present an in-cluster data collection mechanism based on matrix completion. An extensive experiment validates the effectiveness of our proposed scheme in terms of energy and data collection delay.

For the cloud computing based on software-defined networks (SDNs), a larger amount of data is collected to cloud for analysis, which will cause the larger amount of redundancy data and longer service response time due to the capacity-limited Internet. To solve this problem, a novel service orchestration and data aggregation framework (SODA) is proposed, which can orchestrate data as services and aggregate data packets to reduce data redundancy and service response delay. In SODA, the network is divided into three layers. 1) Data centers layer (DCL). Data centers (DCs) release software with a specific function to all devices in the network, devices orchestrate data as services and aggregate data packets using software to reduce service response delay. 2) Middle routing layer (MRL). The routing path of data packets in this layer is adjusted according to the correlation of data packets and routing distance. The correlation of data packets is higher and routing distance is short, the probability that data packets are transmitted along the same routing path is higher to reduce redundancy data. 3) Vehicle network layer (VNL). Mobile vehicles are used to transmit data packets and services among devices. A series of experiments and simulation is conducted. The results illustrate that the proposed scheme has better performance compared with the traditional scheme.

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.

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.

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%.

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.

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.

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.