Lichen Zhang

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.

Spectrum-Availability Based Routing for Cognitive Sensor Networks

With the occurrence of Internet of Things (IoT) era, the proliferation of sensors coupled with the increasing usage of wireless spectrums especially the ISM band makes it difficult to deploy real-life IoT. Currently, the cognitive radio technology enables sensors transmit data packets over the licensed spectrum bands as well as the free ISM bands. The dynamic spectrum access technology enables secondary users (SUs) access wireless channel bands that are originally licensed to primary users. Due to the high dynamic of spectrum availability, it is challenging to design an efficient routing approach for SUs in cognitive sensor networks. We estimate the spectrum availability and spectrum quality from the view of both the global statistical spectrum usage and the local instant spectrum status, and then introduce novel routing metrics to consider the estimation. In our novel routing metrics, one retransmission is allowed to restrict the number of rerouting and then increase the routing performance. Then, the related two routing algorithms according to the proposed routing metrics are designed. Finally, our routing algorithms in extensive simulations are implemented to evaluate the routing performance, and we find that the proposed algorithms achieve a significant performance improvement compared with the reference algorithm.

Fuzzy colored time Petri net and termination analysis for fuzzy Event-Condition-Action rules

Knowledge systems are characterized by being active, uncertain and adaptive in pervasive computing environments, and Fuzzy Event-Condition-Action (ECA) rules can effectively describe these features of knowledge systems. To analyze the static structure and dynamic behavior of fuzzy ECA rules, a fuzzy colored time Petri net model (FCTPN) is proposed. The FCTPN is able to model the fuzzy ECA rules with characteristics such as fuzzy composite events, fuzzy composite conditions, event consumption modes and rule coupling modes. Based on the FCTPN, termination analysis of fuzzy ECA rules is examined, and an efficient termination analysis algorithm is proposed with consideration of all the characteristics of fuzzy ECA rules. Compared with existing termination analysis algorithms of ECA rules, the proposed algorithm has higher accuracy and lower time complexity, and thus it is more suitable for pervasive computing environments.

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.

Exploiting Spectrum Availability and Quality in Routing for Multi-hop Cognitive Radio Networks

Cognitive radio networks (CRNs) are considered as a promising solution to the problem of spectrum under utilization and artificial radio spectrum scarcity. The paradigm of dynamic spectrum access allows secondary users (SUs) to utilize wireless spectrum resources which belong to primary users (PUs) with minimal interference to PUs. Due to the dynamic spectrum availability and quality, routing for SUs in multi-hop CRNs is a challenge. In this paper, we introduce novel routing metrics that estimate both the future spectrum availability and the average transmission time. Then, we propose two routing algorithms for multi-hop CRNs that attempt to reduce the probability of spectrum handoff and rerouting upon PU’s arrival. Finally, we conduct simulations, whose results show that our proposed algorithms lead to a significant performance improvement over the reference algorithm.

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.

User social activity-based routing for cognitive radio networks

The social activities of Primary Users (PUs) and Secondary Users (SUs) affect actual accessible whitespace in Cognitive Radio Networks (CRNs). However, the impacts of primary activities on available whitespace have been extensively investigated due to the dominating priority of PUs, while the impacts of secondary activities on actual accessible whitespace have been ignored. Therefore, we propose to incorporate the primary and secondary activities in the analysis and decision of the accessible whitespace, namely, both the dominance of PUs over SUs and the competitions among SUs are simultaneously taken into account. Specifically, we first approximate primary activity probability based on the real datasets of mobile phone usage records, then the spectrum opportunity between a pair of communication SUs is deduced based on primary activities. Next, we infer the access probability limit of SUs successfully accessing the whitespace according to the primary activity probability, and depict the secondary activity probability from the views of social activity patterns and social networks respectively. Furthermore, the actual accessible probability of whitespace is given by introducing the competitions among SUs. Finally, a greedy routing algorithm, considering the accessible whitespace and the distance to the destination, is proposed to verify our idea. The experiment results based on the real datasets demonstrate the correctness of our analysis and the advantages of the proposed algorithm.

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.