Abstract
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.
Highlights
The development of a new generation of mobile technologies has provided support conditions for the application of wireless sensor networks in more fields and has boosted the development of wireless sensor networks in smart traffic, smart wearable, remote medical monitoring, smart meter development, and other industries
The Narrowband Internet of Things (NB-IoT) standard based on mobile cellular network commonly participated and formulated by Huawei, ZTE, and other companies and many global enterprises shows itself in a variety of standards
In this paper, based on the previous research on the availability of heterogeneous wireless sensor network nodes, combined with the characteristics of NBIOT-HWSNS, by extending the classical epidemiological model and Markov chain, based on the node heterogeneity, the node state analysis method attacked by malicious programs was given and the effects of the degree of node on the availability of nodes are studied
Summary
The development of a new generation of mobile technologies has provided support conditions for the application of wireless sensor networks in more fields and has boosted the development of wireless sensor networks in smart traffic, smart wearable, remote medical monitoring, smart meter development, and other industries. The nodes in the hybrid network have heterogeneity They have different degrees of node and vulnerability and constitute a Heterogeneous Wireless Sensor Networks [2] (HWSNs); here I define it as NBIOT-HWSNs. The availability of WSNs node represents the probability with that the node can work normally in the network [3]. How to evaluate the availability of nodes is one of the key issues in measuring the performance of NBIOT-HWSNs. In this paper, based on the previous research on the availability of heterogeneous wireless sensor network nodes, combined with the characteristics of NBIOT-HWSNS, by extending the classical epidemiological model and Markov chain, based on the node heterogeneity, the node state analysis method attacked by malicious programs was given and the effects of the degree of node on the availability of nodes are studied. The formula for calculating the availability of heterogeneous nodes is given when the NBIOT-HWSN reach the dynamic equilibrium state
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
