A smart optimizer approach for clustering protocol in UAV-assisted IoT wireless networks

Abstract

The Internet of Things (IoT) is a network in the socio-physical space that provides a platform for large-scale data collection. In this context, Wireless Sensor Nodes (WSN) and Unmanned Aerial Vehicles (UAVs) can play an essential role in reducing costs and ease of usage. One of the critical challenges of this network is energy consumption, the main part is related to the transmission unit. In this way, the Smart Optimizer Approach (SOA) to solving global optimization, engineering problems, and clustering in UAV-Assisted IoT Wireless Networks is presented. The SOA uses a Hybrid Vector-Based Operator (HVO), a Smart Selection Search Mode (S3M), and a Smart Re-Randomize Schema (SRS). Numerical comparison and discussions are done on CEC 2017 functions and benchmark functions and engineering problems. Comparison of performance with other algorithms shows the high generality of the SOA. Finally, this paper presented a new SOA-based Clustering protocol (SOAC) for large-scale UAV-Assisted IoT Wireless Networks. In this network, UAVs are used as Air Base Station (ABS). According to this large-scale network and the UAVs' movement, traditional protocols with high control message overhead aren't suitable. Clustering is presented with the aim of reducing overhead and increasing throughput by UAVs simultaneously. SOAC uses two new mechanisms, Assistant to the Cluster Head (ACH) and discretion license (DL), which have been introduced recently. The results of the simulations show the proposed clustering has a good improvement in stability, energy/load balancing, network lifetime, and throughput.