Improved Hybrid Swarm Intelligence for Optimizing the Energy in WSN

In this current century, most industries are moving towards automation, where human intervention is dramatically reduced. This revolution leads
to industrial revolution 4.0, which uses the Internet of Things (IoT) and wireless sensor networks (WSN). With its associated applications, this IoT device is
used to compute the received WSN data from devices and transfer it to remote
locations for assistance. In general, WSNs, the gateways are a long distance
from the base station (BS) and are communicated through the gateways nearer
to the BS. At the gateway, which is closer to the BS, energy drains faster
because of the heavy load, which leads to energy issues around the BS. Since
the sensors are battery-operated, either replacement or recharging of those
sensor node batteries is not possible after it is deployed to their corresponding
areas. In that situation, energy plays a vital role in sensor survival. Concerning
reducing the network energy consumption and increasing the network lifetime,
this paper proposed an efficient cluster head selection using Improved Social
spider Optimization with a Rough Set (ISSRS) and routing path selection to
reduce the network load using the Improved Grey wolf optimization (IGWO)
approach. (i) Using ISSRS, the initial clusters are formed with the local
nodes, and the cluster head is chosen. (ii) Load balancing through routing
path selection using IGWO. The simulation results prove that the proposed
optimization-based approaches efficiently reduce the energy through load
balancing compared to existing systems in terms of energy efficie