Abstract

Privacy of data in Internet of Things (IoT) over fog networks is the biggest challenge in security of Wireless communication networks. In Wireless Sensor Network (WSN), current research on fog computing with IoT is gaining popularity among IoT devices over network. Moreover, the data aggregation will reduce the energy consumption in WSN. Due to the open and hostile nature of WSN, secure data aggregation is the major issue. The existing data aggregation methods in IoT and its associated approaches are lack of limited aggregation functions, heavyweight, issues related to the performance overhead. Besides, the overload on fog node will result in high latency, scalability, storage, degraded reliability and energy overhead. In order to overcome these issues, this proposed work has used two schemes for secure transmission of data over the network and reduce the energy consumption of the transmission. The secret data transferred between the IoT devices and the Fog server are transmitted through the aggregator node. If the aggregator node is placed far away from the Fog node, it may send the data to its neighbor aggregator. And it will append it with the current data and send it to the fog server through aggregator message receiving method. In addition to that, the fog server can extract the data through the fog message extractor method. In order to reduce the transmission cost and energy, Clustered Particle Swarm Optimization (CPSO) method is used to form the clusters. This proposed work can avoid the unnecessary energy consumption during the transmission and ensures secured aggregation so that the base station can know the origin of the sender and the validity of the received message. Therefore, the computation cost of the proposed work in authorization requires1MC+1H and the aggregation requires (n+2) MC+1H which is lesser than the existing methods.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.