With the hit of new pandemic threats, scientific frameworks are needed to understand the unfolding of the epidemic. At the mitigation stage of the epidemics in which several countries are now, the use of mobile apps that are able to trace contacts is of utmost importance in order to control new infected cases and contain further propagation. Here we present a theoretical approach using both percolation and message-passing techniques, to the role of contact tracing, in mitigating an epidemic wave. We show how the increase of the app adoption level raises the value of the epidemic threshold, which is eventually maximized when high-degree nodes are preferentially targeted. Analytical results are compared with extensive Monte Carlo simulations showing good agreement for both homogeneous and heterogeneous networks. These results are important to quantify the level of adoption needed for contact-tracing apps to be effective in mitigating an epidemic.
Abstract-Wireless sensor network (WSN) is widely applied in many fields since its emergence. However, the limited resources of a sensor, especially limited battery life, limited bandwidth and limited processing power, are the main challenges for deploying and operating WSNs. This paper proposes a novel architecture based on cloud computing for wireless sensor network, which can improve the performance of WSN. Based on this architecture, a cloud acts as a virtual sink with many sink points that collect sensing data from sensors. Each sink point is responsible for collecting data from the sensors within a zone. Sensing data are stored and processed in distributed manner in cloud. Our simulation results show that the proposed architecture improves the performance of WSN, e.g., reduced packet transmission error rate, decreased number of end-to-end hops, and improved efficiency of energy consumption.
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