Comfort has become a requirement in our modern world. This level of comfort, particularly in automobiles, can only be accomplished by outfitting the vehicle with additional electrical equipment. Some electronic equipment must communicate with one another, which necessitates the use of a data transmission. Nowadays, it is challenging to sell a new automobile that includes traditional keys. Now, a sophisticated remote locking system is used to lock and unlock autonomous vehicles (AV). A remote unlocking system consists of a transponder that wirelessly connects with the automobile transmitter to lock/unlock the automobile. Unfortunately, wireless communication methods of the internet of vehicle (IoV) face a number of threats. This study provides a safe solution for the automobile unlocking system that uses dynamically generated temporary keys to meet the future demands of smart cars driven by the Internet of Things. To maintain security, machine learning (ML) and physical unclonable function (PUF) are used to permit auto locking and unlocking capability in this work. The technique has exhibited 99.91\% accuracy, which proves the reliability of securing the automobile  unlocking system. Moreover, the computation cost is 3.8 ms and communication overhead is 176 bytes for unlocking by the owner and 144 bytes when the car will be unlocked by other than the owner. Furthermore, both formal (Burrows–Abadi–Needham (BAN) logic) and informal security analysis of the proposed method are provided.

Waste collection and management is an integrated part of both city and village life. Lack of optimized and efficient waste collection system vastly affect public health and costs more. The prevailing traditional waste collection system is neither optimized nor efficient. Internet of Things (IoT) has been playing a great role in making human life easier by making systems smart, adequate and self sufficient. Thus, this paper proposes an IoT based efficient waste collection system with smart bins. It does real-time monitoring of the waste bins and determines which bins are to emptied in every cycle of waste collection. The system also presents an enhanced navigation system that shows the best route to collect wastes from the selected bins. Four waste bins are assumed in the city of Mount Pleasant, Michigan at random location. The proposed system decreases the travel distance by 30.76% on an average in the assumed scenario, compared to the traditional waste collection system. Thus it reduces the fuel cost and human labor making the system optimized and efficient by enabling real-time monitoring and enhanced navigation.