Mobile crowd sensing (MCS) is an emerging sensing paradigm that leverages mobile devices for large-scale data collection, where an MCS platform recruits workers to perform time-sensitive sensing tasks. In practice, worker travel times are inherently uncertain due to dynamic urban environments—traffic congestion, road closures, and unplanned detours cause actual delays to deviate from historical patterns. However, existing worker recruitment methods typically optimize expected-case performance based on static historical distributions, providing no robustness guarantee under distribution shift. To address these challenges, this paper proposes DRO-IGR, a worker recruitment framework based on Distributionally Robust Optimization (DRO) and an Iterative Greedy Recruitment algorithm. The framework introduces a DRO Probability Estimation Engine that treats each worker’s true delay distribution as lying within a Wasserstein ambiguity set centered on its empirical history, with a radius that adapts to data sparsity, behavioral variability, and task importance. Strong duality reduces the resulting worst-case optimization to an efficient one-dimensional convex search. Driven by these robust probability estimates, a marginal-gain scoring rule iteratively selects worker–task pairs that maximize importance-weighted robust utility per unit cost. Extensive experiments on three synthetic spatial distributions and the real-world T-Drive Beijing taxi dataset show that, compared to existing approaches, DRO-IGR completes more tasks, achieves a higher total importance sum, and attains greater utility within the same budget constraint.

Crowdsourcing has been widely used for labeling data in artificial intelligence and machine learning due to its cost-saving benefits. However, inconsistencies in worker-provided labels degrade the quality of the integrated labels. Most noise correction methods proposed by researchers in recent years focus on training a corrector using subsets of data that are filtered to be clean. These methods depend on limited data to train the corrector model, which inevitably compromises the corrector’s reliability. To address this problem, this paper aims to enhance the reliability of integrated labels by applying a novel noise correction method that uses correctors trained on both clean and noisy subsets. This method comprises two stages: confidence filtering and an improved co-teaching+ algorithm. In the first stage, this method calculates the confidence levels of the integrated labels for each instance in the crowdsourced dataset, and then divides the dataset into clean and noisy subsets according to the confidence level. In the second stage, our method initially trains a corrector, denoted as $C_{\text {clean}}$ , on the clean dataset. Subsequently, $C_{\text {clean}}$ guides the co-teaching+ algorithm to adaptively train two correctors on the complete crowdsourced dataset, which includes both clean and noisy subsets. Finally, the instances in the crowdsourced dataset are corrected by these three correctors. Within this study, comparative experiments have been carried out on fifteen simulated datasets and four real-world datasets. The proposed noise correction method attains the best outcomes on the majority of the datasets evaluated.

The growth of IoT and mobile devices has led to Mobile Crowdsensing (MCS), a costeffective data collection method crucial for smart cities. While MCS outperforms wireless sensor networks, it may expose workers' sensitive data, such as location and identity, in air quality monitoring. Traditional privacy-preserving techniques, such as location obfuscation and data perturbation, have inherent limitations in ensuring strong privacy protection. Moreover, the frequent uploading of numerical data during task execution requires a larger privacy budget, thereby increasing the risk of privacy leakage. To solve these problems, this paper proposes a key-value data collection scheme based on local differential privacy for air quality monitoring in smart cities. The proposed scheme aims to protect user privacy while ensuring data utility. It consists of two main phases: data collection and data prediction. During the data collection phase, workers locally perturb both the task location (key) and the sensed data (value), utilizing the correlation between keys and values to enhance data utility. The system subsequently aggregates the perturbed data and applies bias correction to ensure unbiased estimation. In the prediction phase, an exponential smoothing technique is introduced to mitigate the impact of privacy-preserving mechanisms on prediction accuracy. This method effectively reduces random fluctuations in the data, thereby enhancing the overall prediction performance. Experiments on real-world datasets show that the proposed scheme outperforms other privacy-preserving algorithms in efficiency while maintaining nearly the same prediction accuracy as non-privacy-preserving methods, effectively balancing privacy and data utility.

In this paper, we propose an improved quadrotor controller by integrating the fixed-time disturbance observer (FTDO), the singularity-free recursive fast nonsingular terminal sliding mode control (RFNTSMC), and a deep reinforcement learning (DRL)-based hyperparameter optimizer. First, we decompose the overall quadrotor system into two loops: the attitude loop and the position loop. For each loop, we use an FTDO to provide a precise estimation of the disturbances within a fixed time. Then, we design for each loop an RFNTSMC to achieve finite-time tracking of a class of reference trajectories. Moreover, by adopting a DRL-based parameter optimization framework, the design parameter of our controller can be optimized while ensuring the trajectory of the closed-loop system is uniformly ultimately bounded during the learning process. We use both numerical simulations and physical experiments to illustrate the effectiveness and superiority of our results.

For space missions such as extraterrestrial sample collection, robotic rover exploration, and astronaut landings, the complex terrain and diverse gravitational environments make ground-based micro-low-gravity experimental systems essential for testing and validating spacecraft performance as well as supporting astronaut training. The suspended gravity unloading (SGO) system is a key device commonly used to simulate micro-low-gravity environments. However, the SGO system faces challenges due to model uncertainty and external disturbances, which limit improvements in control accuracy. To effectively address these issues, this paper proposes an adaptive extended state observer (ESO)-based dynamic event-triggered robust model predictive control (MPC) strategy for SGO system control. Firstly, the adaptive ESO effectively estimates the system's unmodeled dynamics and external disturbances. By integrating ESO-derived observations with the nominal model, an improved predictive model is developed. Secondly, the dynamic event-triggered (ET) mechanism significantly alleviates the computational burden of MPC. Finally, comparative numerical simulations and experimental validations are conducted to verify the effectiveness and superiority of the proposed control framework.

Suspended constant force (SCF) control is a critical technology in suspended gravity offloading systems. However, inherent underactuation, unmodelled dynamics, and external disturbances can significantly degrade control performance and even compromise system stability. In this article, pneumatic artificial muscle (PAM) actuators are used as a replacement for traditional passive dampers to address the underactuation problem. Additionally, we propose a novel systematic robust output feedback model predictive control (ROFMPC) framework, which incorporates a radial basis function neural network (RBFNN)-based model compensator, a Luenberger state estimator, and a tube model predictive controller. The RBFNN-based model compensator compensates for unmodelled dynamics, while the Luenberger state estimator observes external disturbances. The model predictive controller then generates the optimal control sequence. Analytical results indicate that our designed SCF system encounters similar control challenges as those in antagonistic PAM (APAM). Therefore, sufficiently comprehensive numerical simulations and physical experiments are conducted on the APAM platform to verify the effectiveness of the proposed control framework. These results demonstrate that the proposed ROFMPC framework significantly improves force trajectory tracking performance for constant force control.

In Mobile Crowdsensing, platforms typically require all users to upload their location information during the user recruitment phase, then select a subset of users to perform tasks based on location and reputation. However, this approach results in users who upload their location information but do not participate in tasks essentially providing their location data without compensation, posing a risk of location data leakage. If users repeatedly upload location information without receiving compensation for tasks, they may lose confidence in the platform and consequently leave it. Therefore, this paper proposes a multi-objective task allocation scheme based on differential privacy and regional heat, named MTADPRH. During the user recruitment phase, the MTADPRH scheme uses the Optimized Unary Encoding (OUE) mechanism to statistically analyze the distribution of all users, providing privacy protection that meets local differential privacy. In the location upload phase, the scheme adds planar Laplace noise to the location coordinates of participating users to achieve geo-indistinguishability. During the task allocation phase, MTADPRH employs the multi-objective evolutionary algorithm C3M to find Pareto optimal solutions, aiming to maximize the reward per unit distance for users and the revenue for the platform. The experimental results show that, with privacy protect, the MTADPRH scheme achieves the best results in terms of platform revenue, task completion rate, and per-unit distance compensation for users, and it provides a superior Pareto solution.

As a data-centric network, the Mobile Crowd Sensing (MCS) collects and uploads sensing data through intelligent terminal devices carried by workers. However, due to resource limitations, the confidentiality, integrity and communication cost issues of sensing data have not been well coordinated and resolved in the actual MCS data collection process. In this regard, this paper proposes an edge computing-assisted MCS Chaotic Compressed Sensing Secure Data Collection scheme (CCS-SDC), which supports the secure collection of sensing data and saves communication cost. In CCS-SDC, workers first use the encryption algorithm based on chaos theory to encrypt the collected sensing data, and then adopt the hash location algorithm based on chaos theory to calculate the corresponding hash verification code of the sensing data. After receiving the encrypted sensing data transmitted by the worker, the edge server recomputes the hash verification code of the encrypted sensing data and verifies the integrity of the data, which can locate the changed sensing task data to a certain extent. Then the sensing data is compressed and sampled based on the generated chaos measurement matrix to reduce the amount of data transmission and further enhance the confidentiality of the sensing data. In addition, the same hash positioning algorithm is used between the edge server and the sensing platform to protect data integrity. For the changed data located by integrity verification, in addition to choosing to let workers re-sense and submit, the sensing platform can also choose to discard the changed sensing data under appropriate circumstances, and still reconstruct and decrypt the remaining data through the proposed algorithm to obtain effective original sensing data. The experimental evaluation results on real data sets show that CCS-SDC achieves the best effects, not only achieving lower sensing data communication cost than other related schemes, but also better protecting the confidentiality and integrity of sensing data, which is very useful for resource-constrained MCS data collection scenarios.

Crowdsourcing platforms provide an efficient and cost-effective means to acquire the extensive labeled data necessary for supervised learning. However, the labels provided by untrained crowdsourcing workers often contain a considerable amount of noise. Although the application of ground truth inference algorithms to deduce integrated labels effectively enhances label quality, a certain level of noise persists. To further diminish the noise within crowdsourced labeling, this paper introduces a novel Small Loss-based Noise Correction algorithm (SLNC). SLNC first filters the crowdsourced data, leveraging the characteristic of neural networks to preferentially fits clean samples, thereby obtaining relatively clean and noisy sets. It then employs data augmentation techniques to enhance the clean set and subsequently trains the corrector on this augmented set to rectify the noisy set. SLNC has been evaluated using 16 simulated and two real-world datasets. The results indicate that SLNC surpasses comparative algorithms in the quality of the final labels.

With the increasing popularity of mobile smart devices, Mobile Crowd Sensing (MCS) has gained significant attention from the research community. Worker recruitment is a key research problem in MCS systems, where platforms recruit suitable workers for tasks in specified locations. A recently proposed approach to worker recruitment is the socially-aware MCS model, which utilizes workers’ social connections to expand the platform’s worker pool. This approach effectively improves the quality of task sensing. In the past, most worker recruitment ignored the combined utility of all parties and the privacy of the worker location, instead considering the interests of only one of the task requesters, the platform, or the worker. Therefore, we propose a Socially-Aware and Privacy-Preserving Multi-Objective Worker Recruitment (SPMWR) model. The objective is to use social network-assisted recruitment to weigh the interests of workers and platforms while protecting worker location information. To address the model, we first introduce a differential privacy mechanism to protect worker location information. Then the Weighted Combinatorial Multi-Objective Genetic Algorithm (WCMOGA) is proposed, aiming to discover potentially better worker selection options as much as possible. The effectiveness of SPMWR is verified through comparative experiments on different scenarios with real data sets.

As the number and type of mobile crowd sensing (MCS) data collection increases, more and more computation and processing are required, resulting in higher service cost and service delay, posing a huge challenge to traditional MCS. Currently, edge computing is being introduced to MCS to collect data to reduce service cost and service latency. In the offline mode, there are two issues that need to be addressed with the edge computing-based MCS data collection. First, for large-scale, multi-player, and multiple types of task data, edge servers are limited in computational resources and need to address issues such as task offloading and service cache scheduling. Second, in traditional MCS data collection, workers usually carry in a single type of task data, but it has now been proposed that workers need to carry multiple types of task data. To address the above problems, this paper proposes a joint optimization strategy for edge computing based on multi-player cooperative game and greedy differential evolution algorithm (MCG-GDE) to improve the service rate of edge servers and minimize the service cost and service latency in the data collection. We build a mathematical optimization problem for edge computing based on MCS data collection. The formulation of the optimization problem proves to be a NP-hard problem, so this optimization strategy constructs a task propagation scheme for multi-player cooperative games (MCG), where tasks carried by workers are reassigned to effectively reduce problem complexity and produce sub-optimal solutions to the mathematical model. Then, on the basis of the suboptimal solution, the optimal solution of the problem is obtained by the greedy differential evolution algorithm (GDE). Simulation results demonstrate that MCG-GDE outperforms other baseline strategies.

Task assignment is a key issue in mobile crowdsensing (MCS). Previous task assignment methods were mainly static offline assignment. However, the MCS platform needs to process dynamically changing workers and tasks online in the actual assignment process. Hence, a reliable dynamic assignment strategy is crucial to improving the platform’s efficiency. This paper proposes an MCS dynamic task assignment framework to solve the task maximization assignment problem with spatiotemporal properties. First, a single worker is modeled for the Markov decision process, and a deep reinforcement learning algorithm (DDQN) is used to perform offline learning on historical task data. Then, in the dynamic assignment process, we consider the impact of current decisions on future decisions. Use the maximum flow model to maximize the number of tasks completed in each period while maximizing the expected Q value of all workers to achieve the optimal global assignment. Experiments show that the strategy proposed in this paper has good performance compared with the baseline strategy under different conditions.

物联网促进了无线传感器网络的发展,同时对无线传感器网络在能量损耗和通信质量保证等方面的性能也提出更高的要求.本文提出一个基于多目标协同进化的多播路由算法(CCMRA)来解决同时优化功耗、时延和丢包率3个目标的无线传感器网络多播路由问题.CCMRA算法的种群个体是目标传感器网络其中一颗多播树,将种群分成两个子种群LP和GP,它们针对不同的最优多播树结构分别采用Local操作和Global操作策略生成下一代多播树种群个体,利用竞争性协同进化机制在两个子种群之间通过选择和融合交换种群之间的信息,经过逐代进化最终得到最优的多播树.上述种群内部和种群之间的协同进化机制,增加了种群多样性和随机性,从而提高算法的搜索能力,使算法能够跳出局部最优,获得良好的收敛精度和速度.本文采用8种不同复杂程度的无线传感器网络场景,引入3种对比算法.实验表明CCMRA在功耗、时延和丢包率3个指标上表现出优良的性能.同时在超体积、反向世代距离和世代距离等多目标指标上的表现也优于对比算法,进一步验证CCMRA算法在收敛性和多样性保持上具有竞争力.

In this paper, a fully-actuated system prescribed performance controller is proposed to achieve attitude tracking control of the combined spacecraft. In order to make the combined spacecraft satisfy the required steady-state and transient performance in the attitude tracking control process, a fully actuated system controller with prescribed performance is proposed. The attitude dynamics model of the combined spacecraft is transformed into a tracking error function. Furthermore, the controller of the fully actuated system is designed according to the error function to realize the attitude tracking control of the combined spacecraft. Lyapunov function is introduced to prove the stability of the system. With the presented way of designing fully actuated system prescribed performance controller, the numerical simulation results verify the effectiveness of the proposed method.

With the coverage of sensor-rich smart devices (smartphones, iPads, etc.), combined with the need to collect large amounts of data, mobile crowd sensing (MCS) has gradually attracted the attention of academics in recent years. MCS is a new and promising model for mass perception and computational data collection. The main function is to recruit a large group of participants with mobile devices to perform sensing tasks in a given area. Task assignment is an important research topic in MCS systems, which aims to efficiently assign sensing tasks to recruited workers. Previous studies have focused on greedy or heuristic approaches, whereas the MCS task allocation problem is usually an NP-hard optimisation problem due to various resource and quality constraints, and traditional greedy or heuristic approaches usually suffer from performance loss to some extent. In addition, the platform-centric task allocation model usually considers the interests of the platform and ignores the feelings of other participants, to the detriment of the platform's development. Therefore, in this paper, deep reinforcement learning methods are used to find more efficient task assignment solutions, and a weighted approach is adopted to optimise multiple objectives. Specifically, we use a double deep Q network (D3QN) based on the dueling architecture to solve the task allocation problem. Since the maximum travel distance of the workers, the reward value, and the random arrival and time sensitivity of the sensing tasks are considered, this is a dynamic task allocation problem under multiple constraints. For dynamic problems, traditional heuristics (eg, pso, genetics) are often difficult to solve from a modeling and practical perspective. Reinforcement learning can obtain sub-optimal or optimal solutions in a limited time by means of sequential decision-making. Finally, we compare the proposed D3QN-based solution with the standard baseline solution, and experiments show that it outperforms the baseline solution in terms of platform profit, task completion rate, etc., the utility and attractiveness of the platform are enhanced.

Mobile Crowd Sensing (MCS) has emerged as an effective method for sensing and collecting large-scale computational data in real time, which is favored by researchers from a wide range of industries. Task assignment is an important research direction in mobile crowd sensing. In the scenario of multiple workers and multiple tasks, it is a key challenge to develop an excellent task assignment scheme to ensure that both the platform and workers can also get high utility while workers’ location privacy is not leaked. In this regard, this paper proposes a task assignment scheme (PCTA-SG) with worker location privacy protection and worker merit-based selection mechanism, which improves platform utility and equilibrates worker utility through worker path planning and Stackelberg game under the constraint of a limited perceived budget. First, the platform publishes the task set and its related information. Next, each candidate worker perturbs its own location locally based on geographical indistinguishability, and uploads the required information to the platform after selecting the tasks. Then a worker priority factor model is established by comprehensively considering the factors such as worker credibility, perceived device power and so on. According to the priority factors of workers, the platform uses a greedy strategy to select high-quality and suitable workers and assign tasks to them, thereby forming an initial solution for task assignment. To enhance the utility of the platform, the initial solution for task assignment is continued as the initial population of the genetic algorithm to optimize the moving distance of workers and generate the final solution for task assignment. Finally, on the basis of the final solution for task assignment, a two-stage Stackelberg game is carried out between the platform and workers to further optimize the utility of the platform and take into account the utility of workers, thus perfecting the task assignment scheme. The experimental evaluation results with other baseline schemes under real-world dataset show that the scheme in this paper achieves the best overall effect. It not only protects the location privacy of workers, but also achieves good worker utility while obtaining the optimal platform utility and platform input–output ratio.

With the rapid development of sensor technology and mobile services, the service model of mobile crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried mobile terminal devices, thereby completing large-scale and distributed tasks. Task allocation is an important link in MCS, but the interests of task publishers, users, and platforms often conflict. Therefore, to improve the performance of MCS task allocation, this study proposes a repeated overlapping coalition formation game MCS task allocation scheme based on multiple-objective particle swarm optimization (ROCG-MOPSO). The overlapping coalition formation (OCF) game model is used to describe the resource allocation relationship between users and tasks, and design two game strategies, allowing users to form overlapping coalitions for different sensing tasks. Multi-objective optimization, on the other hand, is a strategy that considers multiple interests simultaneously in optimization problems. Therefore, we use the multi-objective particle swarm optimization algorithm to adjust the parameters of the OCF to better balance the interests of task publishers, users, and platforms and thus obtain a more optimal task allocation scheme. To verify the effectiveness of ROCG-MOPSO, we conduct experiments on a dataset and compare the results with the schemes in the related literature. The experimental results show that our ROCG-MOPSO performs superiorly on key performance indicators such as average user revenue, platform revenue, task completion rate, and user average surplus resources.

Vehicular Crowdsensing (VCS) aims to collect sensing data over a range of areas using a large number of on-board sensors and resources in intelligent vehicles. The mobility of vehicles allows for large-scale mobile sensing data, but it remains a challenging problem to recruit the right participating vehicles and to actively maximize the sensing benefits. In this paper, we formulate the vehicle recruitment problem as the maximizing completion rate with limited budget problem (MCRLB) and prove that it is NP-complete. A hybrid recruitment scheme based on deep learning in vehicular crowdsensing (HR-DLVCS) is proposed in this paper, which consists of two phases: an opportunistic vehicle recruitment phase and a participatory vehicle recruitment phase. In the first phase, a deep learning-based opportunistic vehicle recruitment algorithm (DL-OVR) is proposed to maximize the sensing task completion rate within a limited budget. It aims to recruit the most suitable vehicles to collect sensing data according to their daily movement patterns. In the second phase, a sensing task density-based participatory vehicle recruitment algorithm (STD-PVR) is proposed to reduce the computational complexity of matching vehicles with uncompleted sensing tasks. It is designed to recruit vehicles to arrive at designated locations to complete the sensing tasks within a given budget. Extensive evaluations based on a real-world dataset show that HR-DLVCS achieves higher sensing task completion rate than other baseline approaches in a variety of settings.

Black widow optimization algorithm (BWOA) is a swarm intelligence optimization algorithm, which has the advantages of fast convergence and high precision. However, the update strategy adopted by BWOA is too simple, and it is easy to fall into the local optimal solution. Moreover, the search ability in multi-dimensional space is lacking, the population structure is single, and the convergence and diversity of the algorithm need to be improved. In order to improve the comprehensive performance of BWOA and make it applicable to multi-objective optimization problems, this paper proposes a multi-objective black widow optimization algorithm (MBWOA) guided by a competition mechanism and an improved pheromone mechanism. MBWOA adopts the method of dynamic allocation of populations, which divides the populations into two in the iterative process and uses different competition mechanisms to enhance the diversity of the populations in the iterative process and improve the convergence of the algorithm. At the same time, it uses the improved pheromone mechanism to guide offspring individuals that have gone through the competition mechanism to optimize in the direction of population gap, improve the distribution of population, and enhance the convergence ability of the algorithm. Using MBWOA and four comparison algorithms to conduct comparative experiments on three indicators of IGD, HV and Spread respectively, the results show that MBWOA has better convergence accuracy, convergence speed and diversity. Finally, the effectiveness of the used mechanism is confirmed by the experiments of MBWOA and the comparison algorithms on three indicators.

The RapidIO standard is a packet-switching interconnection technology similar to the Internet Protocol (IP) conceptually. It realizes the high-speed transmission of RapidIO packets at the transport layer, but this greatly increases the probability of network blocking. Therefore, it is of great significance to optimize the RapidIO routing strategy. For this problem, this paper proposes a Double-Antibody Group Multi-Objective Artificial Immune Algorithm (DAG-MOAIA), which improves the local search and global search ability of the population by adaptive crossover and adaptive mutation of the double-antibody groups, and uses co-competition of multi-antibody groups to increase the diversity of population. Through DAG-MOAIA, an optimal transmission path from the source node to multiple destination nodes can be selected to solve the Quality Of Service (QoS) problem during data transmission and ensure the QoS of the RapidIO network. Simulation results show that DAG-MOAIA could obtain high-quality solutions to select better routing transmission paths, and exhibit better comprehensive performance in all simulated test networks, which plays a certain role in solving the problem of the RapidIO routing strategy.