Cluster-based Epidemic Control Through Smartphone-based Body Area Networks

Increasing population density, closer social contact, and interactions make epidemic control difficult. Traditional offline epidemic control methods (e.g., using medical survey or medical records) or model-based approach are not effective due to its inability to gather health data and social contact information simultaneously or impractical statistical assumption about the dynamics of social contact networks, respectively. In addition, it is challenging to find optimal sets of people to be isolated to contain the spread of epidemics for large populations due to high computational complexity. Unlike these approaches, in this paper, a novel cluster-based epidemic control scheme is proposed based on Smartphonebased body area networks. The proposed scheme divides the populations into multiple clusters based on their physical location and social contact information. The proposed control schemes are applied within the cluster or between clusters. Further, we develop a computational efficient approach called UGP to enable an effective cluster-based quarantine strategy using graph theory for large scale networks (i.e., populations). The effectiveness of the proposed methods is demonstrated through both simulations and experiments on real social contact networks

Interference Mitigation for Cyber-Physical Wireless Body Area Network System Using Social Networks

Wireless body area networks (WBANs) are cyber-physical systems that emerged as a key technology to provide real-time health monitoring and ubiquitous healthcare services. WBANs could operate in dense environments such as in a hospital and lead to a high mutual communication interference in many application scenarios. The excessive interferences will significantly degrade the network performance, including depleting the energy of WBAN nodes more quickly and even eventually jeopardize people\u27s lives because of unreliable (caused by the interference) healthcare data collections. Therefore, it is critical to mitigate the interference among WBANs to increase the reliability of the WBAN system while minimizing the system power consumption. Many existing approaches can deal with communication interference mitigation in general wireless networks but are not suitable for WBANs because of ignoring the social nature of WBANs by them. Unlike the previous research, we for the first time propose a power game based approach to mitigate the communication interferences for WBANs based on the people\u27s social interaction information. Our major contributions include: 1) modeling the inter-WBANs interference and determine the distance distribution of the interference through both theoretical analysis and Monte Carlo simulations; 2) developing social interaction detection and prediction algorithms for people carrying WBANs; and 3) developing a power control game based on the social interaction information to maximize the system\u27s utility while minimize the energy consumption of WBANs system. The extensive simulation results show the effectiveness of the power control game for inter-WBAN interference mitigation using social interaction information. Our research opens a new research vista of WBANs using social networks

Modeling Epidemics Spreading on Social Contact Networks

Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion

Editorial for IEEE access special section on theoretical foundations for big data applications : challenges and opportunities

Big data is one of the hottest research topics in science and technology communities, and it possesses a great application potential in every sector for our society, such as climate, economy, health, social science, and so on. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, and manage. We can conclude that big data is still in its infancy stage, and we will face many unprecedented problems and challenges along the way of this unfolding chapter of human history

A survey of big data research

Big data create values for business and research, but pose significant challenges in terms of networking, storage, management, analytics, and ethics. Multidisciplinary collaborations from engineers, computer scientists, statisticians, and social scientists are needed to tackle, discover, and understand big data. This survey presents an overview of big data initiatives, technologies, and research in industries and academia, and discusses challenges and potential solutions