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.

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.

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.

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.

E-commerce systems have become tremendously popular and important for modern business processes in the world of the digital economy. E-commerce business processes rely on the distributed and concurrent interaction process among Web applications of participants, such as clients, merchants, third-party payment platforms (TPPs), and bank systems. Such complex business interactions bridge the gap of trustiness among participants and introduce new security challenges in the form of logical vulnerabilities, which are prevalent in the business process at the application level. The most pressing challenge is to guarantee security throughout the checkout process at the conceptual design phase such that the logic errors can be detected before the actual implementation. Maintenance and repair of implemented e-commerce systems can be extremely costly. To this end, this article proposes a novel modeling and analyzing methodology for multiparticipants and multisessions e-commerce interaction processes based on colored Petri nets (CPNs). First, we define a novel model that can efficiently depict the key properties of e-commerce business interaction processes. Second, several modeling principles are formulated based on the design specification of e-commerce systems. Finally, the concept of Transaction-Logical Consistency is defined to analyze and verify the logical vulnerabilities of e-commerce systems. Through a discussed case study, we demonstrate the feasibility and applicability of the proposed methodology and its efficiency in detecting problems those can potentially lead to logical vulnerabilities.

The wide availability of online social networks (OSNs) facilitates positive information spread and sharing. However, the high autonomy and openness of the OSNs also allow for the rapid spread of negative information, such as unsubstantiated rumors and other forms of misinformation that often elicit widespread public cognitive misleads and huge economic losses. Therefore, how to effectively control the negative information spread accompanied by positive information has emerged as a challenging issue. Unfortunately, this issue still remains largely unexplored to date. To fill this gap, we propose an efficient feedback control mechanism for the simultaneous spread of the positive and negative information in OSNs. Specifically, a novel computational model is first proposed to present the temporal dynamics of the positive and negative information spread. Furthermore, the proposed mechanism restrains the negative information spread with minimal system expenses by devising and performing three synergetic intervention strategies. Technically, this mechanism intensively evaluates the number of seed users performing three intervention strategies. Besides, each seed user performs the received control task independently, and then the control plan for the next time step is adjusted dynamically according to the previous feedback results. Finally, we evaluate the efficiency of the proposed mechanism based on the extensive experimental results obtained from two real-world networks.

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.

Mobile social networks (MSNs) provide real-time information services to individuals in social communities through mobile devices. However, due to their high openness and autonomy, MSNs have been suffering from rampant rumors, fraudulent activities, and other types of misuses. To mitigate such threats, it is urgent to control the spread of fraud information. The research challenge is: how to design control strategies to efficiently utilize limited resources and meanwhile minimize individuals' losses caused by fraud information? To this end, we model the fraud information control issue as an optimal control problem, in which the control resources consumption for implementing control strategies and the losses of individuals are jointly taken as a constraint called total cost, and the minimum total cost becomes the objective function. Based on the optimal control theory, we devise the optimal dynamic allocation of control strategies. Besides, a dynamics model for fraud information diffusion is established by considering the uncertain mental state of individuals, we investigate the trend of fraud information diffusion and the stability of the dynamics model. Our simulation study shows that the proposed optimal control strategies can effectively inhibit the diffusion of fraud information while incurring the smallest total cost. Compared with other control strategies, the control effect of the proposed optimal control strategies is about 10% higher.

In mobile crowdsensing, numerous smartphone users fulfill a complex environmental or social task in a cooperative way, in which participant recruitment or task allocation is a fundamental issue. Various mechanisms have been proposed to motivate normal users to participate in sensing tasks or provide high-quality sensing data. However, there exist some conflicts among tasks and participants, which make participant recruitment a challenging issue. For this issue, a conflict-aware participant recruitment (CAPR) mechanism is proposed for mobile crowdsensing, where there may exist task correlations and conflicts. First, two definitions of conflicts are introduced, and then, the participant recruitment problem is formalized. Next, an efficient heuristic algorithm is proposed followed by the payment determination and reputation update of participants. Simulation results indicate that the proposed mechanism can effectively improve the platform utility and the average task quality while guaranteeing no conflicts in fulfilling sensing tasks.

Mobile Edge Computing (MEC) is providing cloud computing capabilities within the radio access networks and offering a new paradigm to liberate the mobile devices from heavy computational workloads. Importantly, MEC can effectively reduce latency, avoid congestion, and prolong the battery lifetime of mobile devices by offloading the computation tasks from the mobile devices to a physically proximal MEC servers. Particularly, Virtual Machines (VMs) scheduling is a critical issue for tasks offloading and computation in MEC. Regarding to the VMs scheduling problem in MEC environmnet, this paper pioneers the use of Formal Concept Analysis (FCA) methodology for identifying the mapping from tasks to VMs. Specifically, the VMs profile and tasks descriptions are initially characterized as the formal contexts, respectively. With the constructed formal contexts, the corresponding formal concepts which refer to the rules set, are then generated. To better infuse the rules set of VMs and tasks, this paper defines a similarity measurement between formal concepts of VMs and tasks. Consequently, the matching problem from a given task to a virtual machine is to return the expected virtual machine according to the principle of maximum similarity degree between formal concepts of virtual machine and task. Extensive simulations are conducted with a real dataset for the validation of feasibility and effectiveness of the proposed approach. Specifically, the proposed approach can significantly reduce the energy consumption around 28 percent comparing to the approach without consideration of energy consumption. Overall, It is demonstrated that FCA-based VMs scheduling is a novel solution for a sustainable VMs scheduling in MEC environment.

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.

Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries’ arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via Jenson-Shannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose ϵ -correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the ϵ -CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee.

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 advances in sensing capabilities of smartphones give rise to a variety of mobile participatory sensing applications that collect users' personal data. Because of the existence of both sensitive, private personal data, and untrusted aggregator, serious privacy concerns on users arise. Currently, existing privacy preserving data collection methods either require bidirectional communications between an untrusted aggregator and mobile users in every aggregation period, or have high computation or communication overhead. To address these problems, we propose an efficient data aggregation approach by which an untrusted aggregator in mobile sensing can collect the statistics over the data contributed by multiple mobile users, while supporting privacy preservation of each user and data integrity verification. In this approach, information hiding and homomorphic encryption are applied to guarantee the data privacy of mobile users. In detail, a breadth-first search tree is first constructed at the initial phase among the mobile users, and then the original datum of each user is perturbed among its neighbors in ciphertext space by using information hiding and homomorphic encryption. The evaluations of our approach show that our protocol requires lower communication and computation overhead and thus more feasible for the computation constrained mobile devices. Copyright © 2016 John Wiley & Sons, Ltd.

This paper explores the rationale behind the Complete Voronoi Diagram (CVD) Localization, which is a computational geometry approach to the wireless network localization. Our work consists mainly of three parts. The first part focuses on the analysis of CVD's mathematical properties. We characterize CVD's central tendency as the mirror-image distribution and provide mathematical formula for its probability density function. We also provide a closed formula for the relationship between CVD's vertices, chords, and faces, the average chord length, and the average edge number of a CVD polygon. And, the expressions for the average overall and local positioning error are also provided. Based upon these findings, we show that the convergence speed for a CVD based localization scheme is quadratic, and the optimal time and space complexities are Θ(n2) and Θ(n), respectively. The second part proposes a novel approach, called Polling, which utilizes the concept of the Error Region, to further improve the accuracy. Polling, in theory, enables us to make use of the topology information with the quantity up to O(n4) provided by CVD for localization, while a conventional CVD scheme can use only O(1) such information. The third part, through simulations, shows how to use the quasi Analog-to-Digital Conversion (qADC) strategy to handle signal errors. Combined with Polling and qADC, a CVD scheme can provide a simple, robust, and powerful solution to the wireless network localization. Some of our findings and methods may also contribute to the field of computational geometry its own.

Users can enjoy personalized services provided by various context-aware applications that collect users' contexts through sensor-equipped smartphones. Meanwhile, serious privacy concerns arise due to the lack of privacy preservation mechanisms. Currently, most mechanisms apply passive defense policies in which the released contexts from a privacy preservation system are always real, leading to a great probability with which an adversary infers the hidden sensitive contexts about the users. In this paper, we apply a deception policy for privacy preservation and present a novel technique, FakeMask, in which fake contexts may be released to provably preserve users' privacy. The output sequence of contexts by FakeMask can be accessed by the untrusted context-aware applications or be used to answer queries from those applications. Since the output contexts may be different from the original contexts, an adversary has greater difficulty in inferring the real contexts. Therefore, FakeMask limits what adversaries can learn from the output sequence of contexts about the user being in sensitive contexts, even if the adversaries are powerful enough to have the knowledge about the system and the temporal correlations among the contexts. The essence of FakeMask is a privacy checking algorithm which decides whether to release a fake context for the current context of the user. We present a novel privacy checking algorithm and an efficient one to accelerate the privacy checking process. Extensive evaluation experiments on real smartphone context traces of users demonstrate the improved performance of FakeMask over other approaches.

The spread of rumors in Online Social Networks (OSNs) poses great challenges to the social peace and public order. It is imperative to model propagation dynamics of rumors and develop corresponding countermeasures. Most of the existing works either overlook the heterogeneity of social networks or do not consider the cost of countermeasures. Motivated by these issues, this paper proposes a heterogeneous network based epidemic model that incorporates both the network heterogeneity and various countermeasures. Through analyzing the existence and stability of equilibrium solutions of the proposed ODE (Ordinary Differential Equation) system, the critical conditions that determine whether a rumor continuously propagates or becomes extinct are derived. Moreover, we concern about the cost of the main two types of countermeasures, i.e., Blocking rumors at influential users and spreading truth to clarify rumors. Employing the Pontryagin's maximum principle, we obtain the optimized countermeasures that ensures a rumor can become extinct at the end of an expected time period with lowest cost. Both the critical conditions and the optimized countermeasures provide a real-time decision reference to restrain the rumor spreading. Experiments based on Digg2009 dataset are conducted to evaluate the effectiveness of the proposed dynamic model and the efficiency of the optimized countermeasures.

Data mining in wireless sensor networks (WSNs) is a new emerging research area. This paper investigates the problem of real time clustering of sensory data in WSNs. The objective is to cluster the data collected by sensor nodes in real time according to data similarity in a d-dimensional sensory data space. To perform in-network data clustering efficiently, a Hilbert Curves based mapping algorithm, HilbertMap, is proposed to convert a d-dimensional sensory data space into a two-dimensional area covered by a sensor network. Based on this mapping, a distributed algorithm for clustering sensory data, H-Cluster, is proposed. It guarantees that the communications for sensory data clustering mostly occur among geographically nearby sensor nodes and sensory data clustering is accomplished in in-network manner. Extensive simulation experiments were conducted using both real-world datasets and synthetic datasets to evaluate the algorithms. H-Cluster consistently achieves the lowest data loss rate, the highest energy efficiency, and the best clustering quality.

We investigate the p-percent coverage problem in this paper and propose two algorithms to prolong network lifetime based on the fact that for some applications full coverage is not necessary and different subareas of the monitored area may have different coverage requirements. The first algorithm, CPCA, is a centralized algorithm which selects the least number of nodes to monitor p-percent of the monitored area. The second algorithm, DPCP, is a distributed algorithm which can determine a set of nodes in a distributed manner to cover p-percent of the monitored area. Both of the algorithms guarantee network connectivity. The simulation results show that our algorithms can remarkably prolong network lifetime, have less than 5% unrequired coverage for large networks and employ nodes fairly for most cases.