Majority Voting based Hybrid Ensemble Classification Approach for Predicting Parking Availability in Smart City based on IoT
A. Sampathkumar, R. Maheswar, Pon Harshavardhanan, S. Sakthivel Murugan, P. Jayarajan, V. Sivasankaran
Abstract
Internet of Things (IoT) deployed enormous amount of data. The most challenging research is scrutinizing the parking availability among the distributed traffic in smart city. Usage of IoT in smart city organization sector endureslarge amounts of data. Smart city has sectorized its infrastructure operation via IoT communications. Smart city is dumped with huge amount of traffic due to existence of large number of vehicles. So the issue prevail most commonly in smart city is traffic congestion, and this setback can be overcome by a novelty based proposed techniques. In this research, to overcome above setback, we project a voting based hybrid ensemble classification, new hybrid methodology. This hybrid framework is utilized to predict the accessibility of parking place. The experimental results of proposed method achieve the 96% of accuracy and the availability rate achieves 89% when compared to existing method hence achieving better results.