Energy aware Clustered blockchain data for IoT: An end-to-end lightweight secure & Enroute filtering approach

Abstract

Internet of things (IoT) include smart homes, smart buildings and smart factories, in which all the devices are connected over the internet through wireless network for data transmission or sharing. IoT faces many security challenges due to sharing the data in an open source. Security and energy are the most focusing challenges in IoT due to its resource constrained nature. IoT devices have low energy that is wasted by the attackers, and the data also gets hacked by the attacker, which leads to poor security. This paper proposed CLIENT (Clustered blockchain IoT with En-route) to provide energy efficient and secure communication. For that, this research proposed five processes. Such as Chaotic map based Elgamal authentication, credit score based clustering, capuchin search optimization based packets routing, signature based Enroute filtering and deep leaning based anomaly packet detection. First process is Chaotic Map based Elgamal Authentication, in which chaotic map is used in Elgamal key generation. Second process is Credit score based clustering which minimizes the complexity and overhead during packet transmission. Third process is Capuchin search optimization based packets routing which is used to select optimal route from cluster member to cluster head (CH). Fourth process is signature based Enroute filtering which used to detect and drop false report from the network. Edwards signature based digital signature algorithm (EdDSA) is used for verification purpose. And final process is deep learning based anomaly packets detection, this process used Katz centrality based feed forward small world neural network (FFSWNN) for packet classification (normal or malicious). All the transactions are stored in blockchain to enhance security. The proposed system implemented by using NS3. The result shows that proposed system achieves superior performance compared to existing works in terms of energy consumption, packet loss rate, end to end delay, routing overhead and network life time, precision, recall, accuracy, false alarm rate, encryption time, decryption time and security strength.