Reliability analysis of body sensor networks with correlated isolation groups.

Body sensor networks (BSNs) are playing a crucial role in tackling arising challenges during the COVID-19 pandemic. This work contributes by modeling and analyzing the BSN reliability considering the effects of correlated functional dependence (FDEP) and random isolation time behavior. Particularly, the FDEP exists in BSNs where a relay is utilized to assist the communication between some biosensors and the sink device. When the relay malfunctions, the dependent biosensors may communicate directly with the sink for a limited, uncertain time. These biosensors then become isolated from the rest of the BSN when their remaining power depletes to the level insufficient to support the direct communication. Moreover, multiple biosensors sharing the same relay and a biosensor communicating with the sink via several alternative relays create correlations among different FDEP groups. In addition, the competition in the time domain exists between the local failure of the relay and the propagated failures of dependent biosensors. Both the correlation and competition complicate the reliability modeling and analysis of BSNs. This work proposes a combinatorial and analytical methodology to address both effects in the BSN reliability analysis. The proposed method is demonstrated using a detailed case study and verified using a continuous-time Markov chain method.

Guidewire simulation of endovascular intervention: A systematic review.

BACKGROUND: Endovascular intervention is an important minimally invasive surgery that requires professional skills to operate surgical instruments. Such skills are mainly gained through the traditional training paradigm of "see one, do one, teach one", rather than the guidewire simulation system. METHODS: To identify limitations of existing guidewire simulation research and suggest further research orientations, a comprehensive search on literature published from 2007 to 2021 is performed in 11 selected electronic databases. Through our scrutiny and filtration, 34 articles are selected as major studies for careful examinations. RESULTS: We identify challenges faced in the field of endovascular intervention guidewire simulation. We examine and classify guidewire simulation techniques (including guidewire models, collision detection methods and collision response methods), accuracy evaluation methods, error sources, and performance optimization methods. CONCLUSIONS: Guidewire simulation can satisfy the urgent need to train surgeons, thus more efforts should be dedicated enabling its wide application in clinical environment.

Mission Abort Policy for Systems with Observable States of Standby Components.

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.

An improved matrix-based endovascular guidewire position simulation using fusiform ternary tree.

In this paper, a new matrix-based method is proposed to real-time determine, the guidewire position inside an arterial system. The guidewire path is obtained by the optimal path method, particularly, the fusiform ternary tree method according to the principle of minimum output value of root node. An adaptive sampling strategy, and an optimization strategy based on the proximal end and distal end of the guidewire are proposed to change the guidewire position for obtaining an ideal guidewire path. Compared to the existing methods, the proposed method can achieve 74%, 64%, and 70% improvements in accuracy for phantoms 1, 2, and 3, respectively, investigated in this work.

Security of Separated Data in Cloud Systems with Competing Attack Detection and Data Theft Processes.

Empowered by virtualization technology, service requests from cloud users can be honored through creating and running virtual machines. Virtual machines established for different users may be allocated to the same physical server, making the cloud vulnerable to co-residence attacks where a malicious attacker can steal a user's data through co-residing their virtual machines on the same server. For protecting data against the theft, the data partition technique is applied to divide the user's data into multiple blocks with each being handled by a separate virtual machine. Moreover, early warning agents (EWAs) are deployed to possibly detect and prevent co-residence attacks at a nascent stage. This article models and analyzes the attack success probability (complement of data security) in cloud systems subject to competing attack detection process (by EWAs) and data theft process (by co-residence attackers). Based on the suggested probabilistic model, the optimal data partition and protection policy is determined with the objective of minimizing the user's cost subject to providing a desired level of data security. Examples are presented to illustrate effects of different model parameters (attack rate, number of cloud servers, number of data blocks, attack detection time, and data theft time distribution parameters) on the attack success probability and optimization solutions.