Energy and Security Aware Clustering in Internet of Things with Improved SandPiper Optimization Model

Gorikapudi, Sateesh; Kondaveeti, Hari Kishan

doi:10.21203/rs.3.rs-2857819/v1

Cited by 1 publication

(1 citation statement)

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“…The proposed Energy Aware Cluster-based Routing in IoT was implemented using MATLAB. The proposed Custom Honey Badger and Coot Optimization (CHBCO) model is validated against existing classifiers such as Elephant Herding Optimization (EHO), Harris Hawks Optimization (HHO), COOT, Honey Badger Algorithm (HBA), Moth Flame Optimization (MFO) [29], Delay Constrained Particle Swarm Optimization (DCPSO) [30], Hunger Game Search (HGS), Cyclic Rider Optimization Algorithm (C-ROA) [26], Over taker Assisted Wolf Update (OA-WU) [27] and Sandpiper Optimization with Cycle Crossover Process (SOCCP) [28], respectively. Additionally, the examination was done with respect to Alive Nodes, Distance, Link Quality and Normalization Energy.…”

Section: A Simulation Proceduresmentioning

confidence: 99%

Energy aware Cluster based Routing Algorithm for Optimal Routing and Fault Tolerance in Wireless Sensor Networks

Gorikapudi¹,

Kondaveeti²

2023

Preprint

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In order to prevent the overloading, the routing algorithm aids in building productive paths both within and between clusters. When sending information from the source IoT device to a base station, not all IoT devices are utilized in the path. We introduced an energy aware cluster-based routing in this paper, in which Improved Fuzzy C-means model plays a major role in clustering initially. Meanwhile, the clustering procedure considers the factors like energy and distance. Subsequent to the clustering process, optimal routing will be takes place by a new hybrid optimization algorithm named Custom Honey Badger and Coot Optimization (CHBCO) that combines the models like Honey badger optimization and Coot optimization model, respectively. For routing, the model considers the constraints like Energy as well as link quality. Also, this model establishes the fault tolerance method, which ensures that the network will continue to operate normally even in the situation of a CH failure. During this, the cluster members switch to another CH. The performance of proposed CHBCO based routing model is compared over existing models with respect to convergence rate, distance evaluation, energy, alive nodes, distance, normalized energy and link quality under five scenarios such as 100, 500, 1000, 1500, and 2000. Moreover, the proposed model obtained 56 alive nodes in the round 1500 for scenario 1(i.e node 100). Also in the round 1500, the CHBCO model obtain a minimum distance as 25 however the existing models achieve higher distance like EHO as 50, HHO as 70, COOT as 40, HBA as 60, MFO as 75, DCPSO as 150, and HGS as 85 for the node setup 1000. The proposed method achieve a better normalized energy as 0.29 in the 2000 round along with node setup 100, while the conventional models reach the lowest normalized energy. Moreover, the CHBCO method fine link quality values such as 0.96, 0.96, 0.95, 0.97, and 0.97 at the node setups of 100, 500, 1000, 1500, 2000 respectively.

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Section: A Simulation Proceduresmentioning

confidence: 99%