Showing papers by "Anupam Shukla published in 2021"
TL;DR: In this article, the authors proposed a virtual-infrastructure-based delay-aware green routing protocol (DGRP) that creates multiple rings in the sensor field and limits the updation of mobile sink location information to the nodes belonging to the rings.
Abstract: Mobile sinks were introduced in wireless sensor networks (WSNs) to mitigate the infamous hotspot problem. However, routing in mobile-sink-based WSNs requires frequent updation of sink location information to all the sensor nodes; which is an energy-expensive process for resource-constrained WSNs. Therefore, it is required to develop a green routing protocol that can minimize the energy overhead in sink location updation as well as reduce the data delivery delay. This article proposes a virtual-infrastructure-based delay-aware green routing protocol (DGRP) that creates multiple rings in the sensor field and limits the updation of mobile sink location information to the nodes belonging to the rings only. Simulation results show that DGRP outperforms existing routing protocols in terms of energy consumption and throughput. In addition to this, DGRP results in $\approx 26$ %, $\approx 39$ %, and $\approx 35$ % improvement in data delivery delay for a varying number of sensor nodes, sink speeds, and network sizes, respectively, when compared with the state of the art.
27 citations
TL;DR: An event-driven virtual wheel-based data dissemination scheme that constructs multiple wheels in the sensor field with the aim of reducing the cost of updating the mobile sink location and provides easy-accessibility of wheel to each sensor node in order to reduce the delay while transmitting the data to mobile sink.
Abstract: Despite the several advantages of mobile sink-enabled wireless sensor networks, the data dissemination to mobile sink is challenging for resource-constrained sensor nodes due to sink mobility. Sensor nodes need to be aware of current location of the mobile sink to successfully transmit their data. However, network-wide updation of mobile sink’s location would cause high communication overhead and undermine the energy conservation goal. In this paper, we propose an event-driven virtual wheel-based data dissemination (EDVWDD) scheme that constructs multiple wheels in the sensor field with the aim of reducing the cost of updating the mobile sink location. Furthermore, EDVWDD provides easy-accessibility of wheel to each sensor node in order to reduce the delay while transmitting the data to mobile sink. Simulation results reveal improved performance of EDVWDD in terms of energy consumption, and data delivery delay as compared to state-of-the-art mechanisms at varying node density and sink speed.
7 citations
01 Jan 2021
TL;DR: Some of the key machine learning techniques to predict the pregnancy outcome as a stillbirth or not are discussed and some of the factors that majorly cause stillbirth are analyzed.
Abstract: One of the main issues in developing countries is the lack of policies for ensuring good public health conditions in rural areas. Maternal and child health care is one such area that has not improved in developing countries. Although child health has improved noticeably over the years, infant or under-5-mortality has not become any better. There remain major knowledge gaps in our understanding of how factors such as prenatal care, antenatal care, social and economic backgrounds, living conditions and lifestyle of pregnant women and their family members affect the pregnancy outcomes. Understanding such factors that affect the poor pregnancy outcome helps in formulating plans to prevent such issues and to treat them effectively. Determining health policies will be easier from a deeper analysis of such factors involved. This paper discusses some of the key machine learning techniques to predict the pregnancy outcome as a stillbirth or not and analyze some of the factors that majorly cause stillbirth.