6 research outputs found
Energy Efficient Clustering and Routing in Mobile Wireless Sensor Network
A critical need in Mobile Wireless Sensor Network (MWSN) is to achieve energy
efficiency during routing as the sensor nodes have scarce energy resource. The
nodes' mobility in MWSN poses a challenge to design an energy efficient routing
protocol. Clustering helps to achieve energy efficiency by reducing the
organization complexity overhead of the network which is proportional to the
number of nodes in the network. This paper proposes a novel hybrid multipath
routing algorithm with an efficient clustering technique. A node is selected as
cluster head if it has high surplus energy, better transmission range and least
mobility. The Energy Aware (EA) selection mechanism and the Maximal Nodal
Surplus Energy estimation technique incorporated in this algorithm improves the
energy performance during routing. Simulation results can show that the
proposed clustering and routing algorithm can scale well in dynamic and energy
deficient mobile sensor network.Comment: 9 pages, 4 figure
Monitoring Social Distancing by Smart Phone App in the effect of COVID-19
Social distancing measures are necessary for many infectious diseases that spreads through droplets and microdroplets. According to WHO, the preventive measure for COVID-19 is to follow strict social distancing. It is not easy to enforce social distance easily in a crowded region and people often not maintain sufficient distance with neighbours. Driven by the need for energy-efficient and cost-effective social distancing monitoring, this paper proposes Smart Social Distancing (SSD) mobile application based monitoring, which can predict the social distancing between two people assisted by mobile bluetooth and mobile camera. SSD involves two major steps to predict the social distance: first the pedestrian in the video frames is identified with the aid of Deep Learning (DL) and in the second step, distance between the two pedestrian is estimated through image processing techniques. The application can also be configured to calculate the distance using Bluetooth Low Energy (BLE) by calculating its received signal strength. The application demonstrates 85% accuracy on predicting the social distancing and alert the user using beep sound or alert messag