Heterogeneous wireless sensor networks (WSNs) are widely deployed, owing to their good capabilities in terms of network stability, dependability, and survivability. However, they are prone to the spread of malware because of the limited computational capabilities of sensor nodes. To suppress the spread of malware, a malware spread model is urgently required to discover the rules of malware spread. In this paper, a heterogeneous susceptible-iNsidious-infectious-recovered-dysfunctional (SNIRD) model was proposed, which not only considers the communication connectivity of heterogeneous sensor nodes but also reflects the characteristics of malware hiding and dysfunctional sensor nodes. Then, the fraction evolution equations of heterogeneous sensor nodes in different states in discrete time were obtained. Furthermore, the existence of equilibria for the heterogeneous SNIRD model was proved, and the malware spread threshold was obtained, which indicates whether malware will spread or fade out. Finally, the heterogeneous SNIRD model was simulated and it was contrasted with the conventional SIS and SIR models to validate its effectiveness. The results construct a theoretical guideline for administrators to suppress the spread of malware in heterogeneous WSNs.
The dynamic localization is a kind of technology by which the mobile robot tries to localize the position by itself. According to the dynamic localization failure of mobile robots in indoor network blind areas, an autonomous-dynamic localization system which dynamically chooses beacon node and establishes grids is proposed in this paper. This method applies received signal strength indication (RSSI) for distance measurement. Furthermore, the proposed grid-based improved maximum likelihood estimation (GIMLE) fulfills the localization. Finally, the localization error correction is implemented by Kalman filter. The approach combines the classical Kalman filter with the other localization algorithms. The purpose is to smooth and optimize the results of the algorithms, in order to improve the localization accuracy. In particular, in network blind spots, the Kalman filter provides better performance than the other algorithms listed in the paper. Experimental results show the accuracy, adaptivity, and robustness of the dynamic self-localization of mobile robots.
Heterogeneous and mobile wireless sensor networks (HMWSNs) are generally practical in constructing smart Internet of Things. However, malware can easily propagate itself over HMWSNs and make harm such as data interception and unauthorized activities. To defend such malware, developing a model to disclose dynamics of malware propagation becomes urgently required. In this context, a heterogeneous and mobile vulnerable-compromised-quarantined-patched-scrapped (VCQPS) model is proposed by considering both the heterogeneity and mobility of HMSNs (heterogeneous and mobile sensor nodes). Then, differential equations of transition proportions among all states are achieved by analyzing the changeable quantities of HMSNs belonging to different states. Further, the existence of the stationary points of the VCQPS model is proved, upon which the malware propagation threshold is derived by calculating the reproduction number. The stability of the malware-free stationary-point is also proved. Experiments are performed to validate the stability of the malware-free stationary-point and show the effectiveness of the VCQPS model by comparing our model with traditional SIS and SIR models.INDEX TERMS Heterogeneous and mobile wireless sensor networks, malware, epidemic theory, heterogeneity, mobility.
Linguistic neutrosophic numbers (LNNs) include single-value neutrosophic numbers and linguistic variable numbers, which have been proposed by Fang and Ye. In this paper, we define the linguistic neutrosophic number Einstein sum, linguistic neutrosophic number Einstein product, and linguistic neutrosophic number Einstein exponentiation operations based on the Einstein operation. Then, we analyze some of the relationships between these operations. For LNN aggregation problems, we put forward two kinds of LNN aggregation operators, one is the LNN Einstein weighted average operator and the other is the LNN Einstein geometry (LNNEWG) operator. Then we present a method for solving decision-making problems based on LNNEWA and LNNEWG operators in the linguistic neutrosophic environment. Finally, we apply an example to verify the feasibility of these two methods.
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