A novel hybrid meta-heuristic concept for green communication in IoT networks An intelligent clustering model

Nowadays, there is an emerging need for applications based on the Internet
of Things (IoT). The sensor nodes present in the IoT network produce data
constantly, which directly influences the durability of the network. There-
fore, two major challenges while designing IoT systems are network lifetime
and energy consumption. Although the ability of IoT applications is huge,
there are several limitations such as energy optimization, heterogeneity of
devices storage, load balancing, privacy, and security that have to be
addressed. These constraints have to be optimized for improving the effi-
ciency of the networks. Hence, the main intention of this paper is to develop
the intelligent-based cluster head selection model for accomplishing green
communication in IoT. The two famous algorithms like spotted hyena opti-
mization (SHO) and sun flower optimization (SFO) are integrated to form
sun flower-spotted hyena optimization (SF-SHO) by utilizing the hybrid
meta-heuristic concept for the optimal cluster head selection. The most sig-
nificant parameters in IoT networks like delay, distance, energy, tempera-
ture, and load are considered for deriving a multi-objective function to offer
optimal clustering. The cluster head of the model is optimally tuned based
on the hybrid SF-SHO, to solve the multi-objective problem, thus showing
the enhanced green communication performance. The proposed model is
analyzed and evaluated over different approaches in terms of energy-specific
factors, and the attained results confirm the efficiency of the developed
method.