Energy and Throughput Management in Delay-Constrained Small-World UAV-IoT Network

Multihop data routing over a large-scale Internet of Things (IoT) network results in energy imbalance and poor data throughput performance. In addition, data transmission using a large number of hops causes more delay. In light of this, in this work, a novel method of energy and throughput management in a delay-constrained small-world unmanned aerial vehicle (UAV)-IoT network is proposed. The proposed small-world framework optimizes the number of hops required for the data transmission leading to improved energy efficiency and quality of service. The method introduces optimal long-range links between device pairs resulting in low average path length and high clustering coefficient which are called as small-world characteristics. Therefore, in this work, UAVs are deployed to collect the data from IoT devices and forward it to the ground station (GS) utilizing the small world framework. It is shown through results that the network delays corresponding to the proposed method, conventional routing method, low-energy adaptive clustering hierarchy (LEACH) protocol, modified LEACH protocol, and canonical particle multiswarm (CPMS) method are 789.39, 1602.53, 1000.92, 873.63, and 999.79 s, respectively. It is also observed that the number of dead UAVs in case of the proposed method is reduced when compared to other existing methods. It is also noticed that the proposed method results in 100% packet delivery ratio (PDR) dominating LEACH and modified LEACH protocols. Thus, it is shown that the proposed method outperforms the other shortest path methods in terms of network latency, lifetime, and PDR. Further, the effect of location of GS, velocities of UAVs, and hovering heights of UAVs is considered for the performance evaluation of the proposed method. The obtained results validate the significance of utilization of the proposed method over various network scenarios.