Social bots are virtual accounts controlled by automated programs that can disseminate harmful content on social media and even manipulate public opinion. Social bot detection aims to identify bot accounts, which is crucial for maintaining a healthy online ecosystem. However, advances in multimedia technology and smart device prevalence have diversified user-generated social media content, which now encompasses text, images, videos, and more. Effectively leverage multimodal content for robust social bot detection presents a significant research challenge. Furthermore, existing detection methods often overlook the latent social relationships, which we believe can significantly enhance bot detection. To address the issues, in this paper we propose a novel approach for social bot detection that comprehensively leverages users’ multimodal information. Specifically, we first develop an adaptive multimodal fusion mechanism capable of effectively integrating heterogeneous modal information under imbalanced data distributions to obtain more discriminative user representations. Second, we design a latent social relationship mining algorithm that reconstructs more complete social graphs to enhance the objectivity and completeness of user multimodal representations. Finally, on the basis of our proposed multimodal information fusion mechanism and latent social relationship mining algorithm, we design a new social bot detection model. We conduct extensive experiments on the TwiBot-20 dataset, demonstrating superior performance over baseline methods with significant improvements in both detection accuracy and F1-score. Comprehensive ablation studies and dimensionality-reduced visualizations of user representations further validate the critical role of multimodal information and the effectiveness of our proposed model.

Recently, the edge-aided vehicular crowdsensing (EAVC) system has become a promising data collection mode, which utilizes vehicles to collect sensing data under the guidance of edge servers. Participant selection is a fundamental problem in vehicular crowdsensing. The available relevant schemes are unsuitable for newly arrived participants, ignore the differentiated sensing requirements of different areas, and underestimate heterogeneity among participants, which seriously damages the service quality. To handle these problems, this paper proposes an improved reinforcement learning-based online participant selection scheme incorporating coverage quality and participant ability (PSCQA) in EAVC. Coverage quality considering different spatiotemporal partitions is formulated based on the entropy theory to measure coverage uniformity of all areas and the coverage degree of the hotspot areas . Participant ability is designed by combining data quality , movement predictability, and priorities of passing areas to comprehensively measure performance differences among participants. In PSCQA, the coverage quality and ability of the selected participants are optimized through two separate value functions . In particular, participants are dynamically grouped into vehicle clusters based on the similarity of their trajectories to solve the state explosion problem that plagues traditional Q-learning. Simulation results on a real-world dataset demonstrate that the proposed PSCQA outperforms other reinforcement learning-based online participant selection schemes and traditional offline participant selection schemes.

With the global outbreak of COVID-19, an increasing number of countries have made imported epidemic control a priority, imposing restriction measures to prevent the spread of the virus caused by imported cases. To control the imported epidemic, it is necessary to accurately predict the number of imported cases from different source countries. This paper proposes a novel time series prediction approach called PNICA ( P rediction on N umber of I mported CA ses) that uses deep learning to predict the number of COVID-19 imported cases. On the one hand, the proposed PNICA approach adopts a multi-modal learning strategy to fuse three sources of data: flight data, the epidemic data, and the data of historical imported cases. On the other hand, the proposed PNICA approach extends the traditional transformer model with cross-modal attention to learn the interactions between different data modalities to improve prediction accuracy. We use China as the target country and collect the number of imported cases from four source countries—Japan, USA, Russia, and the UK—as well as the epidemic data and flight data from May to November 2020. Experiments on the collected data demonstrate that the proposed PNICA approach outperforms the baseline methods in predicting the number of imported cases. The ablation study shows that both the multi-modal learning strategy and cross-modal attention can significantly improve prediction performance.

Traditional communication focuses on bit accuracy, while semantic communication improves efficiency by considering the meaning of the data. This paper introduces semantic communication to intelligent transportation systems (ITS), specifically image tasks in vehicle-to-everything (V2X) networks. We propose an adaptive signal-to-noise ratio (SNR) image semantic communication model (ASISC) to address the dynamic nature of V2X environments. An evaluation of the real utility function (ERUF) based on task performance is proposed, which takes into account the semantic transmission rate and the semantic energy consumption. We define the semantic importance scores (SIS) to quantify the complexity of image content. To maximize the ERUF and SIS for image transmission, an optimization problem is formulated to promote high SIS image transmission in V2X networks. In particular, to address the interpretability challenge of neural networks, we propose an independent univariate approach consisting of a content equalization sampling step and an approximate modeling step, to transform the original optimization problem into decoupled power allocation and transmission order subproblems. A ternary search algorithm is used for power allocation, and a distance-based transmission order scheme (DistO) is proposed to give preferential treatment to high SIS image tasks. Simulation results on the CIFAR10 dataset demonstrate that our method outperforms the benchmark schemes, especially in high SIS scenarios, and the transmission efficiency is greatly improved. The proposed scheme is easy to implement and exhibits excellent performance in the V2X networks.

Low Earth Orbit (LEO) satellite network can provide the seamlessly global coverage and relatively low propagation delay, which has been envisioned as a key component of future 6G communication. However, the highly dynamic network topology and quite limited communication and computation resources of LEO satellites, alongside with uneven traffic distribution and demand of ground users impose a severe challenge for the routing algorithm design. To tackle this issue, we propose a ground block division and topology switching routing algorithm with software-hardware separation to handle the frequent uploading network topology and routing tables issue and reduce the computational and communication overheads. Furthermore, we estimate the long-term traffic demand in different ground blocks, and calculate the potential traffic demand for each block, and devise the Traffic Distribution Oriented Load Balancing Routing Algorithm (TDOLBRA) to avoid bottleneck links and reduce unnecessary overheads. Moreover, considering different Quality of Service (QoS) requirements of ground users’ traffic, we classify the traffic into high priority traffic and regular traffic, and adopt different routing strategies to further promote the network performance and resources utilization. Simulations results on STK and OPNET demonstrate that the proposed TDOLBRA scheme can have superior performance over three existing methods. To be specific, our proposed TDOLBRA scheme can achieve the best performance with respect to the fairness index, average traffic per link and packet drop rate Ploss. Especially, compared with three benchmark schemes, Ploss under our scheme is reduced by 3.4x, 7.6x, and 13.2x respectively when the total traffic is 18000 packets/min.

The spread of rumors on social networks can diminish public interests, and even pose a threat to social security. The first prerequisite for effectively suppressing rumors in social networks is the precise detection of rumors. However, factors such as hidden social relationships between users, various social contents, and diverse application scenarios in the vast social network bring severe challenges to the timely and accurate detection of rumors. Therefore, for the sake of exploring the infulence of the above factors, we propose an adaptive rumor detection model combining propagation link prediction (PLP) and semantic-structure adaptive fusion, PLP and semantic-structure adaptive fusion (PLP-SSAF). First, we investigate in depth the impact of hidden social relationships on the propagation structure of posts. We leverage the hidden social relationships between users, and utilize the graph attention neural network to extract propagation structure features that simultaneously include both current and future propagation structure. Second, in order to overcome regional and cultural differences among users in multidialect environment, we enhance the text in the original dataset and merge it with the source text. We use a language representation model to extract semantic features from the joint text, getting joint semantic features that can more comprehensively represent the text. Finally, we propose a parallel features adaptive fusion mechanism that can dynamically update the weights between propagating structure features and semantic features. This enables the obtained post features to better adapt to rumor detection scenarios in social networks under massive data environments. Extensive experiments on three real-world datasets show that our PLP-SSAF significantly improving rumor detection performance in accuracy and generalization over existing methods, and demonstrates superior rumor detection capabilities at early stages.

Intelligent transportation systems (ITS) are transforming modern mobility, with vehicle-to-everything (V2X) communication emerging as a critical technology for enhancing transportation safety and efficiency. However, the dynamic nature of vehicular networks presents significant challenges, including unreliable channel state information and limited spectrum resources. These limitations can compromise the reliable and low-latency transmission of safety-critical data. To address these challenges, this paper proposes a robust approach for device-to-device (D2D)-based V2X communication networks, focusing on jointly optimizing channel reuse and power allocation to maximize information transmission success rate (SR) and average energy efficiency (AE). A two-step strategy is developed: Firstly, a long-timescale Kuhn-Munkres (LTKM) algorithm is devised to improve channel efficiency through intelligent channel reuse decisions. Secondly, the power allocation problem is modeled as a Markov decision process (MDP) and resolved using a proximal policy optimization (PPO)-based algorithm, enhancing the network’s robustness to time-varying vehicular network scenario. Simulation results demonstrate the effectiveness of our proposed method. Compared to the original Kuhn-Munkres algorithm, the signaling overhead of our approach is reduced by approximately 82%. Furthermore, compared to three benchmark schemes, our approach improves overall performance by approximately 11%, 20%, 33%, and 61%, respectively. Moreover, our approach exhibits more stable performance under different vehicle speeds, which further highlights the robustness of the proposed method.

With the explosive popularity of mobile devices, mobile crowdsensing (MCS) has emerged as a promising large-scale data collection paradigm. Suitable incentive mechanisms are essential for encouraging user participation. Current MCS work more or less ignores three factors. First, mobile users are assumed to be independent of each other, ignoring social effects. Second, due to the heterogeneity of users, if you just blindly attract users without distinguishing them, the data quality can decrease. Finally, the limited communication resource allocation problem during uploading sensing results is ignored. Therefore, we model the quality-improved and delay-aware incentive mechanism with social concerns as a two-stage Stackelberg game, in which the rational use of social effects not only motivates user participation but also avoids a serious decline in information value due to repetition, and reasonable allocation of communication resources ensures the timeliness of delay-sensitive tasks. Furthermore, data screening, similarity analysis, voting, and reputation are used simultaneously to improve data quality. The Hessian matrix in a multiuser, multitask hyperspace setting is utilized to verify the existence and uniqueness of the game equilibrium. The closed-form expressions of the optimal requester pricing and the optimal user data load strategies are derived, respectively. The proposed mechanism is compared with gather–scatter, incentive-G, Blockchain-based secure, interactive, and fair MCS (BSIF), and Socially-aware incentive mechanism (SAIM) algorithms. Extensive simulation results on a real trajectory dataset show that compared with these state-of-the-art algorithms, the proposed incentive mechanism can motivate users to provide more data loads with few rewards, greatly improve the requester utility, and suppress the data upload of malicious users.

Blended learning networks (BLNs) based on the integration of online learning networks and offline learning environments provide new opportunities and platforms for people to acquire and update useful knowledge and carry out all kinds of learning activities anytime and anywhere. Effective modeling and regulation of the knowledge dissemination process can accurately grasp its dissemination process, promote knowledge innovation and collaborative sharing among learners, and accelerate the maximization of knowledge dissemination. However, it is a challenge to establish a comprehensive dynamics model and adopt the optimal regulation for the knowledge dissemination process under the constraints of a limited budget in large-scale BLNs with diverse learners. To this end, we first explore the evolution process of knowledge dissemination in BLNs and the blended learning interaction process of learners. Based on the system dynamics modeling theory, a dynamics model of knowledge dissemination is established. Second, two kinds of effective regulation strategies are proposed. We establish an optimal regulation system intending to maximize the dissemination of knowledge and use the optimal control theory to tackle the optimal solution distribution of regulation strategies. Then, we propose a knowledge dissemination regulation task allocation method based on the collaborative participation of users, and the reverse auction theory is used to quickly solve the task allocation scheme while ensuring performance. Finally, we demonstrate the effectiveness of proposed models and methods through extensive simulation experiments based on real datasets.

With the rapid development of smart cities, vehicles equipped with various sensors can effectively sense traffic, thus forming a crowdsensing paradigm for the intelligent transportation system (ITS). Although mobile crowdsensing in ITS has broad application advantages, it still faces many challenges, such as single point of failure, inefficient independent task allocation, and the inability to deal with safety emergency tasks in time. To handle the abovementioned issues, we establish a decentralized ITS architecture based on blockchain and propose the concurrent tasks assignment problem proved to be NP-hard and safety emergency tasks assignment problem. Then, we propose reinforcement learning-based concurrent tasks and the safety emergency tasks assignment method, which can maximize the utility of concurrent tasks based on satisfying the requirements of safety emergency tasks. Simulation results demonstrate the effectiveness of the proposed methods.

Thanks to the booming development of artificial intelligence, 5G technology, and intelligent manufacturing technology, numerous intelligent edge devices contained in the industrial Internet of Things (IIoT) are endowed with the ability to mine knowledge from perceived massive data. Knowledge-driven IIoT plays an unprecedented role in application fields such as cyber-physical systems and Industry 4.0. However, knowledge is generally scattered across the distributed edge devices of IIoT. Therefore, in order to further achieve the edge intelligence in IIoT, it is very important to explore an efficient edge knowledge sharing method. In this article, we establish a decentralized knowledge sharing platform in IIoT. First, for public knowledge, a dynamics model that can quantitatively describe its sharing process is established by using the system dynamics theory. Furthermore, a control method for maximizing public knowledge sharing under constraints based on the optimal control theory is presented. Second, for private knowledge, a trusted transaction control method based on blockchain technology is proposed. By developing both smart contract and lightweight consensus mechanism, the efficient peer-to-peer sharing of private knowledge is realized, and the integrity of knowledge and the privacy of participants are protected. The results of extensive experiments show that the proposed method can eliminate the obstacles of knowledge sharing among edge devices in IIoT, and further promote the development of edge intelligence empowered 5G-enabled IIoT applications.

With the advent of 5G, Mobile Edge Computing (MEC), a promising computing paradigm sits closer to users than cloud computing, is being broadly used in various Internet of Things (IoT) applications, and achieve high-quality user experience. Task offloading, as a critical research issue in MEC, is playing an important role in optimizing computational resources and management. However, many tasks are executed dependent on the computational results of other tasks. Moreover, in the case of offloading tasks with other devices, it is often required to consider the success rate of offloading, since not all users are willing to lend their mobile devices to others for task execution. To address this challenge, by taking social relationships between users into account, this paper intends to combine computational resources of local devices and edge clouds and provide more flexible offloading and execution solutions, for achieving the efficient offloading of dependent tasks with the joint consideration of network latency and energy consumption. This paper develops a dependent task offloading strategy based on Bipartite Graph Matching. Extensive simulations are conducted for validating the effectiveness of our proposed strategy. Experimental results demonstrate that our proposed strategy can significantly minimize the overhead compared with other baseline strategies. In particular, the overhead is reduced 8.2%, compared with the strategy which consider the Device-to-Device (D2D) offloading only.

The vigorously rising of social media brings a new opportunity for information diffusion in online social networks. However, the existing models of information diffusion only consider the single information, such as rumor. What's more, most of intervention frameworks are modeled under the ideal circumstances without reality constraints. In this article, we propose a novel model of competitive information coupling diffusion to describe the complex process of information diffusion in online social networks. Especially, in order to intervene the process of competitive information coupling diffusion, we introduce three intervention strategies and propose an intervention framework. More importantly, we take the dynamic constraints into consideration such as the budget of intervention and current state of the system, and further propose the constrained intervention model. To reduce the system loss, we establish an optimal control problem with constraints to achieve the optimal allocation of intervention strategies over time and minimize the total loss. We theoretically prove the existence and uniqueness of the optimal solution of the problem, and derive the optimal control solution. Through the experiments, we verify the effectiveness of the model and analyze the efficiency of different intervention strategies about competitive information coupling diffusion with or without constraints, respectively. The results show that the collaborative intervention strategies can effectively impact the process of diffusion and get the minimum system loss. This article provides high realistic significance to the commercial marketing in online social networks.

In this article, we investigate spectrum allocation in vehicle-to-everything (V2X) network. We first express the V2X network into a graph, where each vehicle-to-vehicle (V2V) link is a node in the graph. We apply a graph neural network (GNN) to learn the low-dimensional feature of each node based on the graph information. According to the learned feature, multi-agent reinforcement learning (RL) is used to make spectrum allocation. Deep Q-network is utilized to learn to optimize the sum capacity of the V2X network. Simulation results show that the proposed allocation scheme can achieve near-optimal performance.

Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. In this paper, we develop a hybrid architecture consisting of centralized decision making and distributed resource sharing (the C-Decision scheme) to maximize the long-term sum rate of all vehicles. To reduce the network signaling overhead, each vehicle uses a deep neural network to compress its observed information that is thereafter fed back to the centralized decision making unit. The centralized decision unit employs a deep Q-network to allocate resources and then sends the decision results to all vehicles. We further adopt a quantization layer for each vehicle that learns to quantize the continuous feedback. In addition, we devise a mechanism to balance the transmission of vehicle-to-vehicle (V2V) links and vehicle-to-infrastructure (V2I) links. To further facilitate distributed spectrum sharing, we also propose a distributed decision making and spectrum sharing architecture (the D-Decision scheme) for each V2V link. Through extensive simulation results, we demonstrate that the proposed C-Decision and D-Decision schemes can both achieve near-optimal performance and are robust to feedback interval variations, input noise, and feedback noise.

Routing is one of the most challenging problems in opportunistic networks (OppNets) because of the intermittence of the network connection. To address the issue, many routing schemes have been proposed, however, most of them assume that nodes are willing to forward messages for others. In fact, due to limited resources and poor social relations, nodes in OppNets may be selfish and not reluctant to participate in message forwarding. To tackle this issue, in this paper, we propose a Probabilistic Routing scheme based on Game Theory (PRGT) to stimulate cooperation among selfish nodes. Firstly, we introduce virtual money to buy the message for gaining more profits. Then, according to the historical meeting records among different nodes, we establish a Markov-based probability prediction model, in which the message carrier selects a node with higher probability of encountering the destination node as the relay node. Finally, a game theory approach is employed to simulate trading price for message forwarding. The simulation results demonstrate that our proposed routing scheme can effectively improve the delivery rate of messages and reduce network latency.

The increasing popularization of large-scale online social networks (OSNs) facilitates information sharing. Users are able to diffuse positive and negative information independently owing to the high openness of the OSNs. Due to the intervention of user’s emotions and social relationships, the positive and negative information diffusion exhibits a complicated dynamic coupling diffusion process, in which the negative information diffusion can cause social panic and confusion. However, prior works mainly focus on the diffusion of single type of information. To fill this gap, this paper aims to investigate the dynamic diffusion process of the positive and negative information and control the negative information diffusion timely. Specifically, we first establish a coupling diffusion model to characterize the dynamic diffusion process under the coexistence of positive and negative information, then derive the critical condition for the negative information diffusion and certify the stability of the diffusion model. Furthermore, we propose two collaborative control strategies to persuade and guide users to diffuse the positive information simultaneously. Then, the issue of minimizing the total system costs is transformed to an optimal control problem. Finally, we prove the existence and uniqueness of optimal control solutions and obtain the dynamic distribution of optimal control strategies over time to minimize system costs. The experimental results obtained from two real-world datasets verify the effectiveness of our model and the high efficiency of the collaborative control strategies.

As the mobile communication technologies evolving, spectrum resource has become extremely scarce. Accordingly, spectrum aggregation (SA) is proposed as an emerging solution to efficiently utilize the dispersive resource. To address such a challenging issue, this paper introduces power domain into the conventional SA which only works in the time and frequency domains, and extends the resource block (RB), which is a time-frequency spectrum management unit in LTE standard, to a time-frequency-power spectrum unit termed resource cube. Based on this, a novel radio resource management (RRM) scheme is proposed to manage the spectrum with multiplexing division in time, frequency, and power domains (3D) simultaneously. Through theoretical derivations, we show that under certain simplifications, the proposed 3D RRM could be formulated as a convex objective function with linear constrains, and can be solved with low computational complexity. When compared with the conventional RB-based RRM, the proposed 3D RRM matches the real-time requirement in SA. Furthermore, it is proved that the proposed scheme could achieve better energy efficiency than the RB-based ones.

With the proliferation of smartphones and the usage of the smartphone apps, privacy preservation has become an important issue. The existing privacy preservation approaches for smartphones usually have less efficiency due to the absent consideration of the active defense policies and temporal correlations between contexts related to users. In this paper, through modeling the temporal correlations among contexts, we formalize the privacy preservation problem to an optimization problem and prove its correctness and the optimality through theoretical analysis. To further speed up the running time, we transform the original optimization problem to an approximate optimal problem, a linear programming problem. By resolving the linear programming problem, an efficient context-aware privacy preserving algorithm (CAPP) is designed, which adopts active defense policy and decides how to release the current context of a user to maximize the level of quality of service (QoS) of context-aware apps with privacy preservation. The conducted extensive simulations on real dataset demonstrate the improved performance of CAPP over other traditional approaches.

The advent of simultaneous wireless information and power transfer (SWIPT) offers a promising approach to providing cost-effective and perpetual power supplies for energy-constrained mobile devices in heterogeneous cellular networks (HCNs). As energy efficiency (EE) has been envisioned as a key performance metric in 5G wireless networks, we consider a multiple-input single-output (MISO) femtocell cochannel overlaid with a Macrocell to exploit the advantages of SWIPT while promoting the EE. The femto base station sends information to information decoding (ID) femto users (FUs) and transfers energy to energy harvesting (EH) FUs simultaneously, and also suppresses its interference to Macro users. We maximize the information transmission efficiency (ITE) of ID FUs and energy harvesting efficiency (EHE) of EH FUs, respectively, with the QoS of all users, and investigate their relationship. We formulate these problems as fractional programming, which are nontrivial to solve due to the nonconvexity of ITE and EHE. To tackle these problems, we devise two beamformers namely zero-forcing (ZF) and mixed beamforming (MBF), and then propose an efficient algorithm to obtain the optimal power under both beamformers. Simulation results demonstrate that MBF provides better ITE and EHE than ZF, and there exists a tradeoff between ITE and EHE in general.

The incorporation of dynamic voltage scaling technology into computation offloading offers more flexibilities for mobile edge computing. In this paper, we investigate partial computation offloading by jointly optimizing the computational speed of smart mobile device (SMD), transmit power of SMD, and offloading ratio with two system design objectives: energy consumption of SMD minimization (ECM) and latency of application execution minimization (LM). Considering the case that the SMD is served by a single cloud server, we formulate both the ECM problem and the LM problem as nonconvex problems. To tackle the ECM problem, we recast it as a convex one with the variable substitution technique and obtain its optimal solution. To address the nonconvex and nonsmooth LM problem, we propose a locally optimal algorithm with the univariate search technique. Furthermore, we extend the scenario to a multiple cloud servers system, where the SMD could offload its computation to a set of cloud servers. In this scenario, we obtain the optimal computation distribution among cloud servers in closed form for the ECM and LM problems. Finally, extensive simulations demonstrate that our proposed algorithms can significantly reduce the energy consumption and shorten the latency with respect to the existing offloading schemes.

Energy efficiency (EE) is very crucial for future wireless communication systems, especially for cognitive radio networks (CRNs). The EE performance relies on channel state information (CSI) of channels. Besides, the interference from secondary users (SUs) to primary users (PUs) also closely depends on CSI in underlay CRNs. However, available works on EE usually assume that CSI is perfect, which is often inaccurate in practical systems. Thus, in this paper we investigate the robust EE maximization problem in underlay CRNs with multiple SUs and PUs. Assuming CSI error to be bounded, we consider that all channels lie in some bounded uncertainty regions. From the perspective of worst-case optimization, we formulate it as the max-min problem with infinite constraint, which is nontrivial even without this constraint. This is because that the outer-maximization problem is non-convex and the inner-minimization problem is a concave minimization problem known as NP-hard in general. We propose a scheme to handle this problem via the fractional programming and global optimization techniques. Particularly, we efficiently solve this problem in two special cases. Simulation results validate that our proposed scheme can improve the worst-case EE of SUs distinctly and strictly guarantee the quality-of-service (QoS) of PUs under all parameters' uncertainties.

Interference management is a hot issue in Heterogeneous Networks (HetNets), which is very crucial for the performance promotion in heterogeneous cellular networks with full frequency reuse. Focusing on mitigating the interference between Macrocell and Femtocell, we propose the IM-Torch (Interference Mitigation via Traffic Offloading in Macro/Femtocell + WiFi Heterogeneous Networks) scheme to handle this problem via traffic offloading. We formulate it as a Mixed Integer Nonlinear Program which is hard to solve, and design a two-step heuristic algorithm to solve this problem. In the first self-scheduling step, Femtocell tries to reallocate the power and PRBs (Physical Resource Blocks) for HUEs (Home User Equipment) to mitigate the interference. After the failure of the first step, Femtocell offloads some necessary HUEs with data services to WiFi and re-adjusts the resources for HUEs to alleviate the interference. The analysis and simulations validate that IM-Torch scheme can greatly alleviate the interference in HetNets and thus improve the system total throughput of Femtocell while guarantee the QoS of Macrocell and HUEs with real time applications.