Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks.Video surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this domain.This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis.Managing theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic.The Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in Kaaba.The crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major flow.We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow.Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.

Adaptive Expanding Ring Search Based Per Hop Behavior Rendition of Routing in MANETs

Routing protocols in Mobile Ad Hoc Networks(MANETs)operate with Expanding Ring Search(ERS)mechanism to avoid ooding in the network while tracing step.ERS mechanism searches the network with discerning Time to Live(TTL)values described by respective routing protocol that save both energy and time.This work exploits the relation between the TTL value of a packet,trafc on a node and ERS mechanism for routing in MANETs and achieves an Adaptive ERS based Per Hop Behavior(AERSPHB)rendition of requests handling.Each search request is classied based on ERS attributes and then processed for routing while monitoring the node trafc.Two algorithms are designed and examined for performance under exhaustive parametric setup and employed on adaptive premises to enhance the performance of the network.The network is tested under congestion scenario that is based on buffer utilization at node level and link utilization via back-off stage of Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA).Both the link and node level congestion is handled through retransmission and rerouting the packets based on ERS parameters.The aim is to drop the packets that are exhausting the network energy whereas forward the packets nearer to the destination with priority.Extensive simulations are carried out for network scalability,node speed and network terrain size.Our results show that the proposed models attain evident performance enhancement.