Energy Efficient Enhanced Duty Cycle Data Appropriation-Based Load Balancing (Edcdablb) Strategies for Iot Applications

Resumen

Covid 19 outbreaks increase the impact on the usability of based Internet of Things (IoT)
services. When the number of service requests increases, the network's load and traffic
increase. The biggest challenge to regulating the network traffic is utilizing an efficient load
balancer. It distributes the client's request across multiple servers. The traditional loadbalancing
strategies are botched to handle the massive network traffic caused by today's web
services. So, an efficient load balancer is required to handle today's challenging factors. This
research designed an enhanced duty cycle data appropriation-based load balancing
(EDCDABLB) strategy. It combines the features of a duty cycle data appropriation (DCDA)
load balancer and sleeps scheduling strategies to reduce network traffic for IoT applications to
address the issues. It has three modules: dynamic load balancing, monitoring, and service
classification. All messages are handled in real time, and a controller node maintains host pools.
The proposed approach is implemented in the MATLAB simulator. The performance analysis
shows that the EDCDABLB strategies balance the energy among the nodes and extend the
network lifetime more efficiently than existing methods.