Optimizing Drone-Based IoT Networks: The ISCT Approach

Abstract

The use of a cognitive technique to handle network management is an interesting area of solution in a drone-based Internet of Things environment. Through cognitive capabilities, we can alleviate multiple networking issues associated with the IoT environment. In current research, focuses to address networking drawbacks in a drone-based IoT scenario through a self-organized cluster-based networking solution termed Innovative Self-organized Clustering Technique (ISCT), aiming to increase network efficiency and effective network performance.  The ISCT is an integrated solution which incorporates the concept of Enhanced Coyote Optimization Algorithm (ECOA).  The ISCT we proposed includes an ECOA-based cluster formation and cluster head selection process.  Moreover, the dead cluster member identification mechanism is processed within the maintenance procedure to stabilize the network. Additionally, one of the crucial mechanisms we introduce in our proposed ISCT is the routing mechanism that facilitates data transmission to the next hop neighbors through the route selection function that increases the communication process efficiency. To analyse the presented ISCT performance, as the proposed fusion bio-inspired clustering algorithm, we consider the duration of the cluster build, energy consumption, cluster lifetime, and the delivery ratio as the ISCT performance metrics. These metrics are compared with the existing fusion bio-inspired clustering schemes to analyse the comparative performance of the ISCT.