Trust Dynamics in WSNs: An Evolutionary Game-Theoretic Approach

A sensor node (SN) in Wireless Sensor Networks (WSNs) can decide whether to collaborate with others based on a trust management system (TMS) by making a trust decision. In this paper, we study the trust decision and its dynamics that play a key role to stabilize the whole network using evolutionary game theory. When SNs are making their decisions to select action Trust or Mistrust, a WSNs trust game is created to reflect their utilities. An incentive mechanism bound with one SN’s trust degree is incorporated into this trust game and effectively promotes SNs to select action Trust. The replicator dynamics of SNs’ trust evolution, illustrating the evolutionary process of SNs selecting their actions, are given. We then propose and prove the theorems indicating that evolutionarily stable strategies can be attained under different parameter values, which supply theoretical foundations to devise a TMS for WSNs. Moreover, we can find out the conditions that will lead SNs to choose action Trust as their final behavior. In this manner, we can assure WSNs’ security and stability by introducing a trust mechanism to satisfy these conditions. Experimental results have confirmed the proposed theorems and the effects of the incentive mechanism

Identification of Pns6, a putative movement protein of RRSV, as a silencing suppressor

RNA silencing is a potent antiviral response in plants. As a counterdefense, most plant and some animal viruses encode RNA silencing suppressors. In this study, we showed that Pns6, a putative movement protein of Rice ragged stunt virus (RRSV), exhibited silencing suppressor activity in coinfiltration assays with the reporter green fluorescent protein (GFP) in transgenic Nicotiana benthamiana line 16c. Pns6 of RRSV suppressed local silencing induced by sense RNA but had no effect on that induced by dsRNA. Deletion of a region involved in RNA binding abolished the silencing suppressor activity of Pns6. Further, expression of Pns6 enhanced Potato virus × pathogenicity in N. benthamiana. Collectively, these results suggested that RRSV Pns6 functions as a virus suppressor of RNA silencing that targets an upstream step of the dsRNA formation in the RNA silencing pathway. This is the first silencing suppressor to be identified from the genus Oryzavirus

Synchronization of Chaotic Systems with Decomposition Method

This paper designs four simple adaptive feedback controllers for chaos synchronization with linear–nonlinear decomposition method. Synchronization of two identical chaotic systems is realized by designed controller; Synchronization of two different chaotic systems is also realized, and the proposed method is simple and robust. Numerical simulations are given to show the effectiveness of this method

Analysis for Ad Hoc Network Attack-Defense Based on Stochastic Game Model

The attack actions analysis for Ad Hoc networks can provide a reference for the design security mechanisms. This paper presents an analysis method of security of Ad Hoc networks based on Stochastic Game Nets (SGN). This method can establish a SGN model of Ad Hoc networks and calculate to get the Nash equilibrium strategy. After transforming the SGN model into a continuous-time Markov Chain (CTMC), the security of Ad Hoc networks can be evaluated and analyzed quantitatively by calculating the stationary probability of CTMC. Finally, the Matlab simulation results show that the probability of successful attack is related to the attack intensity and expected payoffs, but not attack rate

Nodes Availability Analysis of NB-IoT Based Heterogeneous Wireless Sensor Networks under Malware Infection

The Narrowband Internet of Things (NB-IoT) is a main stream technology based on mobile communication system. The combination of NB-IoT and WSNs can active the application of WSNs. In order to evaluate the influence of node heterogeneity on malware propagation in NB-IoT based Heterogeneous Wireless Sensor Networks, we propose a node heterogeneity model based on node distribution and vulnerability differences, which can be used to analyze the availability of nodes. We then establish the node state transition model by epidemic theory and Markov chain. Further, we obtain the dynamic equations of the transition between nodes and the calculation formula of node availability. The simulation result is that when the degree of node is small and the node vulnerability function is a power function, the node availability is the highest; when the degree of node is large and the node vulnerability function satisfies the exponential function and the power function, the node availability is high. Therefore, when constructing a NBIOT-HWSNs network, node protection is implemented according to the degree of node, so that when the node vulnerability function satisfies the power function, all nodes can maintain high availability, thus making the entire network more stable

Evolutionary Game-Based Trust Strategy Adjustment among Nodes in Wireless Sensor Networks

Wireless sensor networks (WSNs) provide network services through the cooperation of sensor nodes, while the basis of cooperation depends on the trust relationships among the nodes. In this paper, we construct an evolutionary game-based trust strategy model among the nodes in WSNs, and we subsequently introduce a strategy adjustment mechanism into the process of game evolution to make up for the deficiency that the replicator dynamic model cannot reflect the requirement of individual strategy adjustments. Afterward, we derive theorems and inferences in terms of the evolutionary stable state through dynamic analyses, providing a theoretical basis for WSN trust management. Furthermore, we verify the theorems and inferences with different parameter values, especially the trust incentive and the upper limit of data retransmission after packets are lost, and both of them are closely related to the evolutionary stable state. The experiments demonstrated that, under certain conditions, the involved nodes can finally reach a stable state of the system by constantly adjusting their trust strategy. At the same time, the speed of evolution of our strategy adjustment mechanism in achieving the stable state is much faster than that of the usual replicator dynamic evolution method

Reliability Evaluation for Clustered WSNs under Malware Propagation

We consider a clustered wireless sensor network (WSN) under epidemic-malware propagation conditions and solve the problem of how to evaluate its reliability so as to ensure efficient, continuous, and dependable transmission of sensed data from sensor nodes to the sink. Facing the contradiction between malware intention and continuous-time Markov chain (CTMC) randomness, we introduce a strategic game that can predict malware infection in order to model a successful infection as a CTMC state transition. Next, we devise a novel measure to compute the Mean Time to Failure (MTTF) of a sensor node, which represents the reliability of a sensor node continuously performing tasks such as sensing, transmitting, and fusing data. Since clustered WSNs can be regarded as parallel-serial-parallel systems, the reliability of a clustered WSN can be evaluated via classical reliability theory. Numerical results show the influence of parameters such as the true positive rate and the false positive rate on a sensor node’s MTTF. Furthermore, we validate the method of reliability evaluation for a clustered WSN according to the number of sensor nodes in a cluster, the number of clusters in a route, and the number of routes in the WSN

Quantal Response Equilibrium-Based Strategies for Intrusion Detection in WSNs

This paper is to solve the problem stating that applying Intrusion Detection System (IDS) to guarantee security of Wireless Sensor Networks (WSNs) is computationally costly for sensor nodes due to their limited resources. For this aim, we obtain optimal strategies to save IDS agents’ power, through Quantal Response Equilibrium (QRE) that is more realistic than Nash Equilibrium. A stage Intrusion Detection Game (IDG) is formulated to describe interactions between the Attacker and IDS agents. The preference structures of different strategy profiles are analyzed. Upon these structures, the payoff matrix is obtained. As the Attacker and IDS agents interact continually, the stage IDG is extended to a repeated IDG and its payoffs are correspondingly defined. The optimal strategies based on QRE are then obtained. These optimal strategies considering bounded rationality make IDS agents not always be in Defend. Sensor nodes’ power consumed in performing intrusion analyses can thus be saved. Experiment results show that the probabilities of the actions adopted by the Attacker can be predicted and thus the IDS can respond correspondingly to protect WSNs

Optimal Report Strategies for WBANs Using a Cloud-Assisted IDS

Applying an Intrusion Detection System (IDS) to Wireless Body Area Networks (WBANs) becomes a costly task for body sensors due to their limited resources. To solve this problem, a cloud-assisted IDS framework is proposed. We adopt a new distributed-centralized mode, where IDS agents residing in body sensors will be triggered to launch. All IDS agents are only responsible for reporting the monitored events, not intrusion decision that is processed in the cloud platform. We then employ the signaling game to construct an IDS Report Game (IDSRG) depicting interactions between a body sensor and its opponent. The pure- and mixed-strategy Bayesian Nash Equilibriums (BNEs) of the stage IDSRG are achieved, respectively. As two players interact continually, we develop the stage IDSRG into a dynamic multistage game in which the belief can be updated dynamically. Upon the current belief, the Perfect Bayesian Equilibrium (PBE) of the dynamic multistage IDSRG is attained, which helps the IDS-sensor select the optimal report strategy. We afterward design a PBE-based algorithm to make the IDS-sensor decide when to report the monitored events. Experiments show the effectiveness of the dynamic multistage IDSRG in predicting the type and optimal strategy of a malicious body sensor