Browsing by keyword "Body, Simon C."
Now showing items 1-2 of 2
-
Bridging Vital Signs and Social Interactions for Resource Constrained Epidemic ControlThis paper proposes a new approach that uses people's social interaction behavior collected by mobile phones and vital signs collected by wireless body area networks (WBAN) for epidemic control. By this approach, infectious people who are socially active can be quickly identified to be quarantined. To realize this approach, we introduce a notion of critical network and critical node identification algorithm. Observing some resource constraints such as quarantine cost and hardware limitation, we focus on optimizing the proposed approach such that high epidemic control effectiveness is achieved while the corresponding overhead is minimized. Our simulation results demonstrate that our approach can effectively control the spread of epidemic diseases in various situations.
-
Effective Epidemic Control and Source Tracing Through Mobile Social Sensing over WBANsAccurate and real-time tracing of epidemic sources is critical for epidemic origin analyses and control when outbreaks of epidemic diseases occur. Such tracing requires the simultaneous availability of information about social interactions among people as well as their body vital signs. Existing epidemic control methods are limited due to their inability to collect the above two types of information at the same time. In this paper, for the first time, we propose integrating wireless body area networks (WBANs) for body vital signs collection with mobile phones for social interaction sensing to achieve the desired epidemic source tracing. In particular, we design a mobile phone capability driven hierarchical social interaction detection framework integrated with WBANs. With this framework, we further propose a set of epidemic source tracing and control algorithms including genetic algorithm based search and dominating set identification algorithms to effectively identify epidemic sources and inhibit epidemic spread. We have also conducted extensive simulations, analyses, and case studies based on real data sets, which demonstrate the accuracy and effectiveness of our proposed solutions.