Background: Pregnancy, though joyful, may be a time of fear and anxiety. Twenty percent of pregnant women in developed nations report a fear of childbirth, and 6%–10% describe a severe fear that is crippling. This could lead to adverse maternal and fetal outcomes. Data on fear of childbirth among pregnant women are lacking in India and would help in incorporating measures to enhance routine antenatal care. Methodology: With the objective of documenting fear of childbirth and associated factors, a cross-sectional study was conducted in rural Karnataka among women availing antenatal care services, using a face-validated 30 item questionnaire developed by the authors which was then scored to determine fear of childbirth. Results: Of 388 women studied, 45.4% (176) had a fear of childbirth. The commonest fears documented were: not feeling confident about childbirth, being afraid or tense about the process of childbirth, fear of labor pains, and fear of cesarean section. Teenage pregnancy, nulliparity, primigravida status, and having no living child were significantly associated with fear of childbirth. Conclusion: Overall, 45.4% (176) of women had a fear of childbirth. It is important to identify and address the various fears of childbirth that women may have, as revealed by this study, with a view to providing information and reassurance to the mother, with the aim of improved maternal and fetal outcomes.

WSN is a large network that consists of a group of spatially distributed sensors nodes. Sensor nodes are partial in power, computational capacities and memory. Sensor nodes compactly installed to monitor physical or environmental conditions, such as temperature, pressure, pollutants. This study examines a Cluster Head Selection (CHs) and takes the CH with the maximum outstanding energy node and less broadcast distance between the CH and BS. It discovered ideal stability between data quality, energy expenditure, and community management ease. The key decision is that the proposal of WSN algorithms must be processing-oriented. i.e., the process of energy on both the Clustering and in-network processing, which ensures both energy efficiency and data quality. Hence, it is more operational to achieve the wireless sensor networks' major loads, which persist the network lifetime.