Hybrid data compression using fuzzy logic and Huffman coding in secure IOT

Abstract

Research in the area of Internet of things (IoT) and cloud computing has gained a considerable attention in today's world of information technology (IT). Data compression and security are increasingly appreciated due to their imperative roles in online data sharing and transfer in multimedia networks. Hence, image compression is focused on reducing data redundancy and saving memory and transmission bandwidth. Because of the increased entropy of coded images, it is difficult to reduce the amount of lossless information after encryption using the existing methods. Accordingly, using the hybrid Huffman coding algorithm, it is possible to guarantee the security and verification of information. Therefore, an efficient compression method is proposed that combines fuzzy logic with Huffman coding. In this method, coding is based on Hoffman code, which is statistically independent and defines fuzzy logic-based weighting functions for the frequency of the existing symbols in data to generate efficient code for compression. Fuzzy logic is then coded to provide a secure information mapping on the information codes. In this proposed system, the key information is coded using the special key of that information and the data is embedded using the data encryption key and data in the receiver are decoded using the same key. Here, different compression techniques are compared with Huffman coding. The simulation is performed under matlab2017b software. The results show that a good compression rate is achieved, there is no significant difference between the decoded and original images and compression ratio in the image is increased to over 40%.