Abstract:
Internet of -ings (IoT) has now become an embryonic technology to elevate the whole sphere into canny cities. Hasty en largement of smart cities and industries leads to the proliferation of waste generation. Waste can be pigeon-holed as materials based waste, hazard potential based waste, and origin-based waste. -ese waste categories must be coped thoroughly to make
certain of the ecological finest run-throughs irrespective of the origin or hazard potential or content. Waste management should
be incorporated into ecological preparation since it is a grave piece of natural cleanliness. -e most important goalmouth of waste
management is to maintain the pecuniary growth and snootier excellence of life by plummeting and exterminating adversative
repercussions of waste materials on environment and human health. Disposing of unused things is a significant issue, and this
ought to be done in the best manner by deflecting waste development and keeping hold of cost, and it involves countless human
resources to deal with the waste. -ese current techniques predominantly focus on cost-effective monitoring of waste man agement, and results are not imprecise, so that it could not be developed in real time or practically applications such as in
educational organizations, hospitals, and smart cities. Internet of things-based waste management system provides a real-time
monitoring system for collecting the garbage waste, and it does not control the dispersion of overspill and blowout gases with poor
odor. Consequently, it leads to the emission of radiation and toxic gases and affects the environment and social well-being and
induces global warming. Motivated by these points, in this research work, we proposed an automatic method to achieve an
effective and intelligent waste management system using Internet of things by predicting the possibility of waste things. -e
wastage capacity, gas level, and metal level can be monitored continuously using IoT based dustbins, which can be placed
everywhere in city. -en, our proposed method can be tested by machine learning classification techniques such as linear
regression, logistic regression, support vector machine, decision tree, and random forest algorithm. -e proposed method is
investigated with machine learning classification techniques in terms of accuracy and time analysis. Random forest algorithm
gives the accuracy of 92.15% and time consumption of 0.2 milli seconds. From this analysis, our proposed method with random
forest algorithm is significantly better compared to other classification techniques
