Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

Air pollution is one of the major concerns considering detriments to human health.This type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and throat and eye infections.Air pollution also poses serious issues to the planet.Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions.Thus,real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions.The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks.Localization is the main issue in WSNs;if the sensor node location is unknown,then coverage and power and routing are not optimal.This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities.These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants,such as PM2.5 particulate matter,PM10,nitrogen dioxide(NO2),carbon monoxide(CO),ozone(O3),and sulfur dioxide(SO2).The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization.The dataset is divided into training and testing parts based on 10 cross-validations.The evaluation on predicting the air pollutant for localization is performed with the training dataset.Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.

QoS Constrained Network Coding Technique to Data Transmission Using IoT

The research work presents,constrained network coding technique to ensure the successful data transmission based composite channel cmos technology using dielectric properties.The charge fragmentation and charge splitting are two components of the filtered switch domino(FSD)technique.Further behavior of selected switching is achieved using generator called conditional pulse generator which is employed in Multi Dynamic Node Domino(MDND)technique.Both FSD and MDND technique need wide area compared to existing single nodekeeper domino technique.The aim of this research is to minimize dissipation of power and to achieve less consumption of power.The proposed research,works by introducing the method namely Interference and throughput aware Optimized Multicast Routing Protocol(IT-OMRP).The main goal of this proposed research method is to introduce the system which can forward the data packets towards the destination securely and successfully.To achieve the bandwidth and throughput in optimized data transmission,proposed multicast tree is selected by Particle Swarm Optimization which will select the most optimal host node as the branches of multi cast tree.Here node selection is done by considering the objectives residual energy,residual bandwidth and throughput.After node selection multi cast routing is done with the concern of interference to ensure the reliable and successful data transmission.In case of transmission range size is higher than the coverage sense range,successful routing is ensured by selecting secondary host forwarders as a backup which will act as intermediate relay forwarders.The NS2 simulator is used to evaluate research outcome from which it is proved that the proposed technique tends to have increased packet delivery ratio than the existing work.