The Internet of Things (IoT) is a series of recording devices, mechanical and automated machines, documents, creatures, or individuals that have one of the user identifiers and the ability to substitute information over a network without requiring human-to-human connections. The IoT technology is used in different applications to transfer information between two or more stations or devices using actuators/sensors. The IoT has many fields of application, including smart home, healthcare, transport and logistics, and industry. Each application gives highlights and important and explicit administrations. This paper provides an overview of IoT applications, discussing their current problems and potential challenges. In addition, it focuses on issues and challenges that arise for future IoT systems including enormous scaling, planning and needs, generating information and big data, strength, frankness, resource management, privacy, and security. The secure data transformation is more problematic in ongoing research. So, this survey presents security attacks and different security mechanisms involved in IoT applications including authentication, encryption, blockchain, and trust management. Blockchain with IoT applications provides good secure data transfer compared to other security mechanisms. So, this survey mainly concentrates on the blockchain with IoT applications and its latest issues. The objective of this paper is to analyze the issues and challenges of IoT applications, eight research topics in existing systems, security attacks and their countermeasures, and different security mechanisms confronting the IoT.

Internet of Things (IoT) has now become an embryonic technology to elevate the whole sphere into canny cities. Hasty enlargement 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. These 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. The 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. These current techniques predominantly focus on cost-effective monitoring of waste management, 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. The wastage capacity, gas level, and metal level can be monitored continuously using IoT based dustbins, which can be placed everywhere in city. Then, 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. The 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.

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