FOG computing based energy efficient and secured iot data sharing using SGSOA and GMCC

Abstract

A Free and open-source Ghost (FOG) computing is a decentralized computing infrastructure, that helps in processing the data efficiently to end-user. None of the existing works concentrated on authorization between source, destination, and intermediate server during the Internet of Things (IoT) sensor data transmission. Therefore, the paper presents the authentication of the servers using Cholesky-HAVAL for secure IoT sensor data transmission. Initially, the IoT sensor devices are registered and logged into the FOG server. Next, the sensor nodes are clustered using Bray Pearson K-Means (BP-KMeans) clustering method. Through the cluster head, the IoT data is sensed, and the attributes are extracted. The sensed data is then secured using Gauss Montgomery Curve Cryptography (GMCC). The secured data is stored in the Hadoop Distributed File System (HDFS) FOG server. Here, the data is mapped using BP-KMeans and then reduced using the Schwefel Group Search Optimization Algorithm (SGSOA). Meanwhile, Merkle Tree (MT) is created using Cholesky-HAVAL regarding the sensor data attributes, IoT sensor ID (Identification), and FOG server ID. Next, to retrieve the sensor data, the user registers and logs into the server. Then, the user gives a query request for accessing the data present in the cloud. The attributes are extracted from the query, and using SGSOA, the query is optimized. Finally, the hashcode verification is done regarding the attributes from sensed data and the query. The IoT data is thus retrieved for the verified hashcodes. Thus, the proposed work clustered the sensor nodes in 4578 ms and generated the hashcode in 1476 ms.