Reward and Punishment Strategy Based Security Enhancement in IoT Using Mobile Cloud Computing

The rapid development in Internet of Things (IoT) helps to interconnect the IoT based devices with the day-to-day life of human beings. However, it is still facing challenges due to insecure data storage and transmission. Moreover, to ensure the integrity and authenticity, IoT network consume more resource from IoT device and leads to wastage of resources caused by duplicated data. To overcome these issues, this research proposed a new methodology known as Reward and Punishment Strategy based Security Enhancement (RPS-SE) to improve the security in IoT using block chain and edge computing on the basis of reward and punishment method. The secured data deduplication method utilized three layered architectures which consist of Member Equipment (ME), Local Manager (LM) and Cloud Service Provider (CSP). Moreover, there are five stages involved in obtaining a secured data blocks such as initialization, assigning labels, uploading the files, sharing and downloading the files. After, downloading files, the security of the block is analysed using consensus model which operates using reward and punishment strategy. The results show that the proposed RPS-SE attained minimum time of 49.38 ms during computation while the existing methods such as consumed more time BLS-Homomorphic Linear Authenticator (BLS-HLA) and Edge Computing Block Chain Security Consensus Model (ECBCM) achieved computational time of 58.12 ms and 53.82 ms respectively.