In this paper, an efficient and fault-proof active node selection approach for localization tasks in Internet of Things (IoT) and Mobile Crowd Sensing (MCS) systems is proposed. The proposed approach is resilient to the presence of anomalous nodes. Localization is the process of fusing data readings from multiple sensing nodes with the aim of finding the location of a specific target, such as radiation source, forest fires and noisy areas. Current active node selection systems for localization tasks perform algorithms like greedy and genetic methods over the whole Area of Interest (AoI). As such, a system that considers anomalous data is required to detect anomalies and perform localization over a large number of active nodes, which usually takes multiple iterations and is computationally costly. To overcome this, we propose a resilient localization approach which a) uses the median filter based image filtering technique to level out anomalous readings, b) uses the filtered readings to reduce the AoI to be around the target location without being influenced by anomalous nodes, c) detects and eliminates anomalies in the new AoI based on the deviation between filtered readings and original readings, and d) selects remaining nodes in new AoI for localization. As a result, there is a huge reduction in the complexity of active node selection and thus reduction in time taken by the system to perform the task of source localization. The efficacy of the proposed system is evaluated for radiation source localization tasks using simulated radiation dataset, by performing experiments for several test scenarios. The results demonstrate that the system is able to perform localization tasks in significantly reduced time and therefore generate near real-time results while also maintaining low localization error.

Utilizing a Community-Based Participatory Research model, faculty members of a local university school of social work completed a qualitative study of an emerging Bhutanese minority group’s subjective view of their living experiences related to Covid-19 while living in Northeast, Pennsylvania, U.S.A. Utilizing purposive sampling methodology, fifty samples, such as bilingual (English & Nepali) community leaders and Bhutanese residents participated in individual telephone interviews due to the high surge of Covid-19, from October 2020 to January 2021. The purpose of the study is to understand the subjective views of Bhutanese residents’ lived experience during the peak of the global pandemic, COVID-19. The interview incorporated two components: 1. Demographic information and 2) Questionnaires developed by the researchers which were reviewed by two independent researchers in the university before their use. The study found that the Bhutanese community residents identified challenging needs in the areas of language barriers, unemployment, multigenerational living, and strategies to overcome hardship of Covid-19. The study findings point to the benefits of an interprofessional collaborative action with community organizations (faith-based organizations, social institutions, and cultural centers) to close the gap of social and health care disparities among minority populations. Community health care and social service institutions and organizations need to build relationships with leaders of local minority organizations in order to provide culturally and linguistically appropriate information about treatment, care and prevention of Covid-19 in the future.

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