Flow classification is a critical component of the Network Intrusion Detection System (NIDS) for dealing with various network attacks. However, existing flow classification schemes based on Software-Defined Networking (SDN) heavily rely on control-plane machine learning techniques, which require mirroring the entire data packet to the control plane. These schemes suffer from high packet processing latency, or limited capability to perform comprehensive flow collection and classification. In this paper, we propose P4NFC, a P4-based comprehensive Network Flow Classification scheme in SDN. P4NFC utilizes the programmable data plane to extract the necessary features of packets, and selectively process and store features by combining a cuckoo filter and dual circular queues. Meanwhile, P4NFC employs an Energy-based Flow Classifier (EFC) in the control plane for classification. This design ensures comprehensive flow collection and accurate flow classification, while maintaining a low packet processing latency. Experimental results show that P4NFC achieves a 98.9 % flow collection rate, improves flow classification accuracy to 99.1%, and maintains a packet processing latency of 1.6 ms under a network bandwidth of 100 Mbps.

To address the limitations of existing traffic signal control methods, including the failure to differentiate vehicle types, inadequate perception of critical zone information, and inefficient utilization of experience replay samples, this paper proposes a multi-agent deep reinforcement learning signal control method for multi-priority vehicles. The approach integrates spatial and channel attention mechanisms to achieve efficient feature extraction of priority information and waiting times in lane regions. Targeting the constraints of conventional experience replay mechanisms in sample utilization, we design a priority vehicle traffic impact assessment-based prioritized experience replay strategy, incorporating a candidate elimination mechanism and a weighted loss function. This significantly enhances the agent's training efficiency for priority-sensitive samples. Simulation results demonstrate that the proposed method not only maintains overall traffic efficiency but also substantially improves the traffic performance of high-priority vehicles.

Mobile crowdsensing (MCS) faces significant challenges in selecting tasks with expected high revenue and recruiting workers with expected high qualities to maximize overall utility. Existing approaches often assume the revenue of posting tasks or the quality of workers is determined, which limits their practical applicability. This article tackles these challenges by modeling the long-term multitask, multiworker selection problem as a two-stage multiarmed bandit (TS-MAB) problem under uncertainty. In each round, the MCS platform selects a long-term task from a pool and recruits the necessary workers, determining payments accordingly. We model the task selection phase as a MAB problem, and the worker recruitment phase, influenced by the task selection, as a combinatorial MAB (CMAB) problem. We propose an extended upper confidence bound (UCB)-based strategy and develop an incentive mechanism based on Auction theory, combined with TS-MAB (i.e., ATS-MAB), for unknown task selection and worker recruitment. Our mechanism theoretically guarantees truthfulness and individual rationality, with a theoretical analysis of its regret bounds. Furthermore, we introduce an adaptive incentive mechanism called AATS-MAB, which improves worker recruitment and quality updates, achieving higher total sensing quality and lower regret. Extensive simulations demonstrate the effectiveness and scalability of the proposed methods.

Light Detection and Ranging (LiDAR) odometry is a critical technology widely applied in pose estimation for autonomous driving and in Simultaneous Localization and Mapping (SLAM). By using a laser scanner, LiDAR captures environmental information to enable precise spatial localization and mapping. However, traditional LiDAR odometry methods mainly depend on static environmental features for positioning and mapping, limiting adaptability in dynamic settings and reducing pose estimation accuracy. To overcome this limitation, we propose ALO, a novel adaptive LiDAR odometry approach designed for dynamic environments. First, an adaptive constant velocity model predicts the expected motion trajectory, supplying prior pose information, while a first-in-first-out voxel grid manages the local map in dynamic conditions. Next, a linear system with dynamic weights based on point-surface residuals is established, minimizing the influence of dynamic features on pose estimation. Finally, the predicted prior pose serves as the initial value for adaptive Iterative Closest Point (ICP) registration, enhancing pose estimation accuracy and enabling real-time local map updates. Extensive experiments on the public KITTI dataset demonstrate that the proposed method achieves at least a 23.69% improvement in pose estimation accuracy over existing solutions.

Mobile crowdsensing (MCS) leverages large-scale mobile users to execute tasks and contribute sensing data. Developing an effective incentive mechanism is critical to ensure both the quality and quantity of sensing data. However, existing incentive mechanisms often overlook key factors, such as the social networks of users, the presence of malicious participants, and the dynamic interplay among multiple stakeholders. In this article, we propose a Trilateral Social-aware Incentive Mechanism (TSIM) to address these limitations. TSIM is built upon a three-stage Stackelberg game framework that incorporates social relationships to enhance recruitment and improve data quality. First, we analyze the data quality and the historical reputation of the users, and based on this, we construct utility functions for the requester, service provider, and mobile users, with the latter integrating data quality, personal, social, and historical reputation utilities. Second, we formulate the payment problem as a three-stage game among the three parties, employing backward induction to derive optimal strategies that maximize their respective utilities. Next, we theoretically prove the unique existence of the Stackelberg equilibrium, ensuring a multiwin outcome. Numerical experiments demonstrate that incorporating social networks significantly boosts task participation and rewards for users, increases profit for the requester and revenue for the service provider, and effectively mitigates malicious data uploads.

Task assignment is a challenging problem in mobile crowdsensing (MCS), especially since workers and tasks are online. Existing work does not consider the portrait of the workers when assigning tasks, which may result in workers being assigned to fields they are not familiar with, thus affecting the quality of task completion. In this paper, we focus on online scenarios and identify a more practical task assignment problem, a two-sided (workers and tasks) online task assignment problem based on worker portrait in MCS. We decompose this problem into two subproblems: the worker portrait analysis problem (WPA) and the two-sided online personalized assignment problem (TOPA). To solve the WPA problem, we propose a worker portrait analysis algorithm that uses the semi-supervised model to describe the worker portrait at a fine-grained level. Then, based on the worker portrait, we propose a two-sided online personalized assignment algorithm to solve the TOPA problem. The proposed algorithm guarantees a lower bound on the assignment results by analyzing the worker portrait data. Moreover, we prove the TOPA problem is NP-hard and demonstrate the competitive ratio can achieve ln(max(ui,j)+1). Finally, we conduct extensive experiments on two datasets, and the experimental results show that our method outperforms baseline algorithms.

The fifth generation (5G) network is a crucial foundation for the industrial Internet. Key performance indicator (KPI) anomaly detection in the 5G core network (5GC) plays a pivotal role in 5G applications. Some researchers have introduced Generative Adversarial Networks (GAN)-based techniques to detect anomalies. However, these methods remain limited, such as pattern collapse. In this paper, we propose MTMG, a Masked Transformer-based Multi-GAN model, to achieve highly accurate and robust anomaly detection. We use Transformer to learn the associations between data better. Specifically, MTMG employs multiple generators and a discriminator to deflect the pattern collapse dilemma. In addition, we introduce the mask mechanism to learn the normal distribution of data better and prevent the model degradation caused by anomalies in the training set. We also adopt a root cause strategy to locate the anomalies. Experimental results demonstrate that our model outperforms the baselines significantly in terms of detection performance.

In mobile crowdsensing, the quality and quantity of data play an important role in the design of incentive mechanisms. However, existing work seldom considers the impact of social relationships among users on the quality and quantity of data. In this paper, to effectively recruit mobile users and improve data quality, we design a social-aware incentive mechanism (SIM) based on a two-stage Stackelberg game that considers social relationships. First, we consider the utility of both the users and the service provider, designing distinct utility functions for each. The utility function for the user considers personal utility, social utility, and historical reputation. Second, we model the payment problem as a two-stage game between the two parties, analyze the optimal incentives for both the service provider and the users using backward induction, and then derive the optimal strategy groups to maximize the utility of the two parties. Through theoretical analysis, we prove the unique existence of Stackelberg equilibrium, resulting in a multi-win situation. Numerical results confirm that the introduction of social networks significantly increases task participation and rewards for users, while also helping service providers gain greater revenue.

Mobile crowdsensing (MCS) is a potential technology for large-scale data collection. This technology requires the platform to recruit users to complete tasks in specific areas. A vital issue in MCS is task assignment, and most existing task assignment efforts consider only a single user type, which is not reasonable in real scenarios. Task assignment becomes more complicated when the platform tries to assign tasks to heterogeneous users with a limited budget in the platform. In this paper, we consider a quality-oriented task assignment for heterogeneous users problem. Professional users have a high sensing quality with a high cost, and normal users have a low sensing quality with a low cost First, we model the sensing capabilities and the costs of different users and formulate the quality-oriented task assignment for heterogeneous users problem. Then, we design the integer linear programming form and prove that the problem is NP-hard. By verifying the submodularity of the objective function, we present a greedy algorithm. Considering the inefficiency of the algorithm, we design two genetic algorithms to improve the total sensing quality. Finally, we evaluate the proposed algorithms under different cost cases based on a real dataset The results show that our proposed algorithm performs well under different cost distribution scenarios.

Task assignment is a key challenge in mobile crowd-sensing because of the varying capabilities of crowd users. Location-based task assignment schemes require users to upload their location to an untrusted platform, which raises many privacy concerns. However, stronger privacy preservation may lead to lower system utility. It is challenging to maximize system utility under privacy preservation for users. In this paper, we propose a privacy-preserving task assignment for heterogeneous users (PTAH) problem in mobile crowdsensing. Specifically, we divide users into two groups: private users with location privacy requirements and public users without location privacy requirements. We first design a privacy-preserving mechanism to obfuscate the actual location of private users. Then we construct a relationship graph based on the locations between users and tasks. We prove that the PTAH problem is an NP-hard problem, so to maximize the system utility, we propose an approximation algorithm based on the greedy algorithm. Then we propose a multi-thread cooperative simulated annealing algorithm to search for a better approximate solution. Finally, we conducted simulations based on the widely-used real-world Roma dataset. The results show that our proposed algorithm consistently outperforms other baseline algorithms.

5G Core network (5GC) employs a Service Based Architecture (SBA). This architecture decomposes the control plane into multiple independent Network Functions (NFs). NFs open interfaces to provide services to other NFs, which makes the control plane more susceptible to external malicious attacks. However, existing anomaly detection methods focus more on traffic statistics features and are difficult to apply to the 5GC control plane. In this paper, we proposed GSAD, a Graph-Sequence analysis-based Anomaly Detection method for 5GC control plane traffic. We model control plane traffic as a directed graph to depict topological and NF interaction information. Further, we use the normalizing flows with temporal dependencies to mine the sequential information in the traffic. GSAD combines the topological and sequential information to provide fine-grained detection. We evaluate our proposed framework on the 5GC testbed using Free5GC and UERANSIM in various scenarios. Experimental results demonstrate that our framework outperforms the baselines significantly in terms of detection performance.

This paper investigates the duration-aware task assignment problem for heterogeneous mobility users in MCS (DTAH problem). There are two types of users in MCS, vehicle users and pedestrian users. The vehicle user gets to the task fast, but the task duration is short. Pedestrian user meets the task duration limit, but it takes a long time to reach the task. Given a set of tasks and two types of users, each user, and each task has a duration limit. The DTAH problem is how to assign tasks to maximize the utility of the system. We prove that the DTAH problem is NP-hard by reducing the weighted maximum set coverage problem to the DTAH problem. Then, we solve the problem from a pedestrian perspective and a vehicle perspective. From the pedestrian user perspective, we propose a pedestrian user task assignment (PTA) algorithm based on the Kuhn-Munkres algorithm. From the vehicle user perspective, we propose a greedy vehicle user task assignment (VTA) algorithm. We prove that the VTA algorithm can obtain the approximate ratio of 1-1/e to the optimal value. Finally, we design the heterogeneous user task assignment (HTA) algorithm based on these PTA and VTA algorithms. Extensive experiments have proved that our proposed HTA algorithm achieves more efficient performance than other comparison algorithms.

In mobile crowdsensing, there are generally two types of tasks, popular tasks, and unpopular tasks. For popular tasks, many people can perform that task, and the budget is overallocated. For unpopular tasks, fewer or no one is willing to complete them. How to motivate users to complete different popularity tasks during their work time is a challenging problem. In this paper, we design a task priority-aware incentive mechanism to solve this problem. First, we use hierarchical clustering to classify tasks into different priorities by considering their budgets, deadlines, and density distribution. The higher the task priority, the higher the extra rewards and credits the participating users get. To motivate more users to perform unpopular tasks, we give high priority to unpopular tasks. Then, we propose a greedy algorithm that allows more users to do high-priority tasks. However, too many budget adjustments can cause most users to do unpopular tasks as users are obsessed with their income. To prevent users from all selecting high-priority tasks, we further propose a differential privacy-based algorithm to protect task priority and reduce users’ attention to their income. This algorithm protects users’ income and allows users to focus more on task characteristics, such as task distribution and task deadline. Through many experiments in the reality Roma dataset, we evaluate two proposed algorithms compared with other solutions.