# Nature Inspired Algorithms for Internet of Things: A Comprehensive Survey

## Keywords:

Nature-inspired Algorithms, Internet of Things, optimal coverage, data aggregation, energy-efficiency, localization, load balancing, fault tolerance, security## Abstract

The Internet of Things (IoT) is permeating many aspects of our daily lives (AI) with the growth of intelligent services and applications powered by AI. Traditional AI algorithms require centralized data gathering and processing due to the enormous scalability of modern IoT networks and growing data privacy concerns, which may not be feasible in real-world application settings. IoT functioning depends on the Wireless Sensor Networks (WSNs) architecture. Nature-inspired algorithms are emerging as a viable solution to the pressing problems in Wireless Sensor Networks (WSNs), with worry about the limited sensor lifetime. Before any network configuration, it is important to carefully consider how to have the best possible network coverage. Optimal network coverage reduces the amount of redundant data that is sensed and also lowers the restricted energy consumption of battery-powered sensors. This article focuses on nature-inspired optimization algorithms for data aggregation, optimal coverage, sensor localization, energy-efficient clustering and routing, load balancing, fault tolerance, and security in wireless sensor networks (WSNs). We have briefly discussed the classification of optimization techniques as well as the WSN issue domains. The genetic algorithm (GA), differential evolution (DE), ant colony optimization (ACO), grey wolf optimization (GWO), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), cuckoo search (CS), lion optimization (LO) and crow search algorithm (CSA) are a few of the algorithms that take inspiration from nature.

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