RESUMEN
For some critical applications, successfully accomplishing the mission or surviving the system through aborting the mission and performing a rescue procedure in the event of certain deterioration condition being satisfied are both pivotal. This has motivated considerable studies on mission abort policies (MAPs) to mitigate the risk of system loss in the past several years, especially for standby systems that use one or multiple standby sparing components to continue the mission when the online component fails, improving the mission success probability. The existing MAPs are mainly based on the number of failed online components ignoring the status of the standby components. This article makes contributions by modeling standby systems subject to MAPs that depend not only on the number of failed online components but also on the number of available standby components remaining. Further, dynamic MAPs considering another additional factor, the time elapsed from the mission beginning in the event of the mission abort decision making, are investigated. The solution methodology encompasses an event-transition based numerical algorithm for evaluating the mission success probability and system survival probability of standby systems subject to the considered MAPs. Examples are provided to demonstrate the benefit of considering the state of standby components and elapsed operation time in obtaining more flexible MAPs.
RESUMEN
With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to their development. Therefore, outsourcing the encrypted health data to the cloud has been an appealing strategy. However, date aggregation will become difficult. Some recently-proposed schemes try to address this problem. However, there are still some functions and privacy issues that are not discussed. In this paper, we propose a privacy-enhanced and multifunctional health data aggregation scheme (PMHA-DP) under differential privacy. Specifically, we achieve a new aggregation function, weighted average (WAAS), and design a privacy-enhanced aggregation scheme (PAAS) to protect the aggregated data from cloud servers. Besides, a histogram aggregation scheme with high accuracy is proposed. PMHA-DP supports fault tolerance while preserving data privacy. The performance evaluation shows that the proposal leads to less communication overhead than the existing one.
Asunto(s)
Recolección de Datos , Informática Médica , Privacidad , Algoritmos , Redes de Comunicación de Computadores , Seguridad ComputacionalRESUMEN
The complexity of most biomedical/bioinformatics problems requires efficient solutions using collaborative/parallel computing. One promising solution is to implement Grid computing, as an emerging new field called BioGrid. However, one of the most stringent requirements in such a Grid-based solution is data privacy. This paper presents a novel solution to provide the Confidentiality when using the Grid to efficiently solve MISD biomedical problems. It is called the Grid-Based Pseudo-Cache (GBPC) solution. It is proved to have equal or better performance than traditional MIMD solution. Via case studies our theories are validated in practice, and the data dependence is also addressed.