Yaguang Lin

A novel approach for inhibiting misinformation propagation in human mobile opportunistic networks

Mobile Opportunistic Networks (MONs) are effective solutions to uphold communications in the situations where traditional communication networks are unavailable. In MONs, messages can be disseminated among mobile nodes in an epidemic and delay-tolerant manner. However, MONs can be abused to disseminate misinformation causing undesirable effects in the general public, such as panic and misunderstanding. To deal with this issue, we first propose a formal model to formulate the process of misinformation propagation in MONs, considering human psychological behaviors. Secondly, we explore a general framework to describe the random node mobility, and derive a new contact rate between nodes, which is closely related to mobility properties of nodes. Thirdly, we propose a novel approach based on vaccination and treatment strategies for inhibiting misinformation propagation in human MONs. Moreover, a novel pulse control model of misinformation propagation is developed. Finally, through the derivation and stability analysis of a misinformation-free period solution of the proposed model, we obtain a threshold upon which misinformation dies out in a human MON. The extensive simulation results validate our theoretical analysis.

A social activity and physical contact-based routing algorithm in mobile opportunistic networks for emergency response to sudden disasters

ABSTRACT Sudden disasters such as earthquake, flood and hurricane necessitate the employment of communication networks to carry out emergency response activities. Routing has a significant impact on the functionality, performance and flexibility of communication networks. In this article, the routing problem is studied considering the delivery ratio of messages, the overhead ratio of messages and the average delay of messages in mobile opportunistic networks (MONs) for enterprise-level emergency response communications in sudden disaster scenarios. Unlike the traditional routing methods for MONS, this article presents a new two-stage spreading and forwarding dynamic routing algorithm based on the proposed social activity degree and physical contact factor for mobile customers. A new modelling method for describing a dynamic evolving process of the topology structure of a MON is first proposed. Then a multi-copy spreading strategy based on the social activity degree of nodes and a single-copy forwarding strategy based on the physical contact factor between nodes are designed. Compared with the most relevant routing algorithms such as Epidemic, Prophet, Labelled-sim, Dlife-comm and Distribute-sim, the proposed routing algorithm can significantly increase the delivery ratio of messages, and decrease the overhead ratio and average delay of messages.

An on-demand coverage based self-deployment algorithm for big data perception in mobile sensing networks

Abstract Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks.

A Double Pulse Control Strategy for Misinformation Propagation in Human Mobile Opportunistic Networks

Mobile Opportunistic Networks (MONs) are effective solutions to uphold communications in the situations where traditional communication networks are unavailable. However, MONs can be abused to disseminate misinformation causing undesirable effects in public. To prevent misinformation from propagating, we first propose a formal model to formulate the process of misinformation propagation based on the ordinary differential equation. Secondly, we explore a general framework to describe the random mobility of nodes, and derive a new contact rate between nodes. Thirdly, we propose a double pulse control strategy of vaccination and treatment for inhibiting misinformation propagation. Moreover, a novel pulse control model of misinformation propagation is developed based on the impulsive differential equation. Finally, through the derivation and stability analysis of a misinformation-free period solution of the proposed model, we obtain a threshold upon which misinformation dies out. The simulation results validate our theoretical analysis.

Computational models and optimal control strategies for emotion contagion in the human population in emergencies

Emotions play an important role in the decision-making of individuals. Emotional contagion has an influence on individual and group-level behaviors. Particularly, the contagion of negative emotions like panic emotions may result in devastating consequences in the human population in emergencies. This work develops novel computational models of emotion contagion and solves the optimal control problem of emotion contagion in the human population in emergencies. Firstly, by introducing a concept of latent state and considering complicated interactions among individuals, we develop a novel conceptual model of emotion contagion, and further establish a computational model for describing the dynamics of emotion contagion, called the susceptible-latent-infectious-recovered-susceptible (SLIRS) model. Secondly, by considering vaccination, quarantine and treatment as control measures, we expand the SLIRS model into a controlled SLIRS model, and formulate the control problem of emotion contagion as an optimal control problem, so that the total costs of inhibiting emotion contagion are minimized. Finally, we theoretically discuss the existence and uniqueness of the solution of the controlled SLIRS model, and further derive an optimal control solution of the controlled SLIRS model. The simulation results on the synthesis dataset and the real trace dataset show that the optimal control strategies have significant impact on emotion contagion. Especially, the optimal control strategy with a mixture of vaccination, quarantine and treatment can significantly decrease the scale of the outbreak of negative emotions, and incur the lowest total costs of inhibiting emotion contagion. This enables the optimal decision-making for inhibiting emotion contagion under the consideration of limited resources in the human population in emergencies. Hence, this work will make contributions to crisis management and crowd evacuation in emergencies.

The impact of node velocity diversity on mobile opportunistic network performance

Abstract Mobile opportunistic network is a special kind of mobile ad hoc networks, in which nodes can communicate and interact with each other without a fixed communication infrastructure. Data dissemination between nodes utilizes a store-carry-forward paradigm. In this paper, we explore the impact of node velocity diversity on the performance of mobile opportunistic networks while keeping the average velocity of nodes consistent with each other. The numerical results indicate that greater node velocity diversity always implies longer average communication and the smaller number of communications within the constant total communication time. Thus, it is important to improve the performance of mobile opportunistic networks by adjusting the velocity diversity in response to the requirement of the network and in order to make full use of communication resources. In particular, we construct mathematical models to analyze node contact times and link numbers. Lastly, we verify the correctness of models and theories we proposed by using the Opportunistic Network Environment simulator.

An Efficient Energy-Aware Probabilistic Routing Approach for Mobile Opportunistic Networks

Routing is a concerning and challenging research hotspot in Mobile Opportunistic Networks (MONs) due to nodes’ mobility, connection intermittency, limited nodes’ energy and the dynamic changing quality of the wireless channel. However, only one or several of the above factors are considered in most current routing approaches. In this paper, we propose an efficient energy-aware probabilistic routing approach for MONs. Firstly, we explore and exploit the regularity of nodes’ mobility and the encounter probability among nodes to decide the time when to forward messages to other nodes. Secondly, by controlling the energy fairness among nodes, we try to prolong the network lifetime. Thirdly, by fully taking the dynamic changing quality of the wireless channel into consideration, we effectively reduce the retransmission number of messages. Additionally, we adopt a forwarding authority transfer policy for each message, which can effectively control the number of replicas for each message. Simulation results show that the proposed approach outperforms the existing routing algorithms in terms of the delivery ratio and the overhead ratio.

A Routing Algorithm Based on the Prediction of Node Meeting Location in Opportunistic Networks

The opportunistic network is a kind of self-organizing network which makes use of the meeting opportunities created by moving nodes to realize short-distance wireless communication, in which the dynamic characteristics of the network topology often result in low communication efficiency. To improve the efficiency of messages transmission, firstly we combine the Markov model to establish a node location prediction model and a distance prediction and calculation model between nodes respectively in order to accurately predict the location of the nodes at the next moment and calculate the distance between nodes; secondly based on the significance and timelessness of messages, a priority based buffer management strategy is proposed to realize the efficient use of buffer. Finally in this paper we propose an efficient opportunistic network routing algorithm, named LIBR, based on the prediction of node meeting location to forward the messages. Compared with the well-known routing algorithms, the simulation results show that our proposed algorithm can significantly improve the delivery ratio, reduce the overhead ratio and average delay of messages.

An Efficient Routing Algorithm Based on Interest Similarity and Trust Relationship Between Users in Opportunistic Networks

In opportunistic networks, due to the randomness of node moving and the uncertainty of network topology, it’s a challenging issue to establish a complete communication link between the source and the destination node. Fortunately, the “store-carry-forward” strategy can be used to solve this problem. However, such forwarding strategy heavily relies on the cooperation among nodes. Thus, the selection of a proper relay node has a great impact on the performance of the whole network. In this paper, considering the differences between users’ interest and the variability of interest with the change of time, firstly, we propose a dynamic update and calculation method of the value of interests, and then establish a calculation model of interest. Secondly, according to the Ebbinghaus forgetting curve and the ability of users to forward messages, we propose a dynamic calculation method of the trust value of users and establish a model for computing trust relationships. Finally, we propose an efficient routing algorithm based on interest similarity and trust relationship (BIST) between users. The simulation results show that our proposed algorithm has better routing performance, and it validates the correctness and validity of our proposed models and algorithm.