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.

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.

The energy harvesting of unmanned aerial vehicle (UAV) has been researched extensively in recent years. However, the existing energy harvesting between the base station and UAVs does not consider the information asymmetry factor, which means the base station provides the radio frequency (RF) energy for UAVs in the context of UAVs’ partial private information. In order to maximize the base station’s utility or payoff, it is crucial for the base station to motivate more UAVs to harvest RF energy. In the paper, we propose an effective incentive energy harvesting mechanism in UAV communication network, which is a challenging problem since there exist interest conflicts that the base station and UAVs are rational individuals who maximize their utilities. Our objective is to make the base station’s utility maximum via balancing the tradeoff between transmit power cost and charged price benefit, while incentivizing UAVs to purchase transmit power. We design a series of optimal energy harvesting contract with different price discounts targeting different types of UAVs by contract theory. Owing to information asymmetry, we analyze two different information scenarios: complete and incomplete information. We suppose the base station knows each UAV’s type in complete information, then we analyze the practical case that the base station is aware of incomplete information of UAV’s private information. The base station aims to maximize its utility by providing contract. The UAVs choose the contract meeting the individual rationality (IR) and incentive compatibility (IC) rules while maximizing their utilities. Our simulation shows that the energy harvesting mechanism maximizes the base station’s utility and stimulates UAVs to purchase RF energy transmit power in different scenarios. Compared with other methods, our proposed optimal contract can improve the utility of the base station while maximizing the utility of UAVs.

For the cloud market, we proposed a Dynamic Combinatorial Double Auction (DCDA) model to improve the social welfare and resource utilization. In the model, cloud-agents represent cloud service providers, and user-agents represent cloud users. They bid for various combinations of resources in a dynamic environment. To overcome the computational complexity of combinatorial auctions, we employed a greedy approximation method to solve the winner determination problem together with a truthful payment scheme. The proposed model is proven to be approximately efficient, incentive compatible, individually rational, and budget-balanced. Considering both parties' interests and the relative scarcity of cloud resources, this model also ensures fairness and balances resource allocation.

The fifth-generation (5G) and beyond networks are designed to efficiently utilize the spectrum resources to meet various quality of service (QoS) requirements. The unlicensed frequency bands used by WiFi are mainly deployed for indoor applications and are not always fully occupied. The cellular industry has been working to enable cellular and WiFi coexistence. In particular, 5G New Radio in unlicensed channel spectrum (NR-U) supports the uplink and downlink transmission on the maximum channel occupation time (MCOT) duration. In this paper, we consider maximizing the total throughput of both downlink and uplink in NR-U by jointly optimizing the time and power allocation during MCOT while ensuring fair coexistence with WiFi. Fairness is guaranteed in two steps: 1) tuning the access related parameters of NR-U to achieve proportional fairness, and 2) including 3GPP fairness from the throughput perspective as a constraint in NR-U throughput maximization. Numerical analysis and simulation have demonstrated the superior performance of the proposed resource allocation algorithm compared to conventional deployment strategies.

Cellular vehicle‐to‐everything‐ (C‐V2X‐) based communications can support various content‐oriented applications and have gained significant progress in recent years. However, the limited backhaul bandwidth and dynamic topology make it difficult to obtain the multimedia service with high‐reliability and low‐latency communication in C‐V2X networks, which may degrade the quality of experience (QoE). In this paper, we propose a novel cluster‐based cooperative cache deployment and coded delivery strategy for C‐V2X networks to improve the cache hit ratio and response time, reduce the request‐response delay, and improve the bandwidth efficiency. To begin with, we design an effective vehicle cluster method. Based on the constructed cluster, we propose a two‐level cooperative cache deployment approach to cache the frequently requested files on the edge nodes, LTE evolved NodeB (eNodeB) and cluster head (CH), to maximize the overall cache hit ratio. Furthermore, we propose an effective coded delivery strategy to minimize the network load and the ratio of redundant files. Simulation results demonstrate that our proposed method can effectively reduce the average response delay and network load and improve both the hit ratio and the ratio of redundant files.

Cloud computing integrates a large number of resources through virtualization technology, and then provides users with personalized services on an on-demand basis. In response to this service model, this paper draws on the economic theories and proposed a game-based combinatorial double auction model for cloud resource allocation. Firstly, through Harsanyi transformation, the incomplete information game for cloud resource allocation is converted into a complete but imperfect information game, and the Bayesian Nash equilibrium solution is obtained. Then, we designed the resource allocation model supporting multiple infrastructure providers and service providers bidding on various combinations of resources. Considering both parties' interests, this model ensures fairness and high resource utilization. Simulation results show that this method not only forms a fair incentive mechanism for all parties in the transaction, but also optimizes the social welfare.

In this paper, we study the channel assignment problem in cellular-VANET heterogeneous wireless networks. The D2D communication technology can be applied to VANET. Vehicular device-to-device (D2D) network as an underlying network to the cellular network can share the uplink channel resources of the cellular network. Interference as a critical element has an impact on the utilization in channel assignment. To minimize the interference when allocating channels, we present a novel channel assignment algorithm based on reuse distance. Essentially we have limited spectrum resources that can be shared by vehicular transmitters and cellular users in an area, to assign the minimal number of channels to vehicles in a prescribed area is our first concern. Since the interference between co-channel devices is related to their distance, we divide the area to small hexagon regions then use Region-based Channel Assignment Algorithm to assign different channel sets to each region. In this case, three sets of resources can fulfill the channel assignment requirements to all vehicles. We also prove the theoretical guarantee as approximation factor of 3 for the minimal channel assignment problem. To improve the system throughput with limited channel resources in the HetNets, we propose a Local Search Throughput Maximization algorithm to find the vehicular transmitters and cellular users combinations. We prove the optimal approximation factor is (1-ε) and the complexity of our algorithm in each small region. We show the effectiveness and efficiency of proposed algorithm in experiments.

With the emerging demand for road safety and entertainment applications, efficient information exchanges among vehicles in Vehicular Ad-hoc Networks (VANETs) have attracted considerable attention. Since VANETs usually suffer from intermittent connectivity, long delay, and packet loss, Road-Side Unit (RSU) placement has been introduced to improve communication performance recently. Although some researchers have focused on scheduling and routing of buses, efficient communication in public transportation systems with infrastructure has not been well studied. In this paper, we use a space-time graph to model topology changes in Bus Ad-hoc Networks (BANETs), and then propose an effective greedy algorithm to minimize the number of RSUs, such that the communication delay between any two buses is within a given delay bound in BANETs. To evaluate the proposed scheme, we conduct simulations and analyze the performance. The simulation results show that our algorithm can significantly reduce the number of installed RSUs and the average end-to-end delay in the entire network.