Dwarf Mongoose Optimization-Based Secure Clustering with Routing Technique in Internet of Drones

Abstract

Over the last few years, unmanned aerial vehicles (UAV), also called drones, have attracted considerable interest in the academic field and exploration in the research field of wireless sensor networks (WSN). Furthermore, the application of drones aided operations related to the agriculture industry, smart Internet of things (IoT), and military support. Now, the usage of drone-based IoT, also called Internet of drones (IoD), and their techniques and design challenges are being investigated by researchers globally. Clustering and routing aid to maximize the throughput, reducing routing, and overhead, and making the network more scalable. Since the cluster network used in a UAV adopts an open transmission method, it exposes a large surface to adversaries that pose considerable network security problems to drone technology. This study develops a new dwarf mongoose optimization-based secure clustering with a multi-hop routing scheme (DMOSC-MHRS) in the IoD environment. The goal of the DMOSC-MHRS technique involves the selection of cluster heads (CH) and optimal routes to a destination. In the presented DMOSC-MHRS technique, a new DMOSC technique is utilized to choose CHs and create clusters. A fitness function involving trust as a major factor is included to accomplish security. Besides, the DMOSC-MHRS technique designs a wild horse optimization-based multi-hop routing (WHOMHR) scheme for the optimal route selection process. To demonstrate the enhanced performance of the DMOSC-MHRS model, a comprehensive experimental assessment is made. An extensive comparison study demonstrates the better performance of the DMOSC-MHRS model over other approaches.

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

“…The proposed improvements of the IDMO were tested to establish performance using 31 benchmark functions (classical and CEC2020 benchmark functions [33,34], twelve (12) engineering benchmark problems, and realworld feature selection problems. The results of IDMO for benchmark functions were compared with that of DMO and eight existing population-based metaheuristic algorithms, namely differential evolution (DE), arithmetic optimization algorithm (AOA), particle swarm optimization (PSO), constriction-coefficient-based (PSO) and GSA (CPSOGSA), salp swarm algorithm (SSA), grey wolf optimizer (GWO), biogeography-based optimization (BBO), sine cosine algorithm (SCA).…”

“…The DMO was also applied to the clustering problem in [ 12 ], where a novel DMO-secure-based clustering combined with a multi-hop scheme of routing (DMOSC-MHRS) was developed in the internet of drones arena. Moreover, in [ 14 ] the study proposed a binary dwarf mongoose optimizer (BDMO) and was applied to solve the multiclass high-dimensional feature selection problem.…”

“…The bibliographic diversity of hybridized approaches is enormous; therefore, a few examples of this approach involving the DMO are mentioned in this study. The DMO was hybridized with a Multi-Hop Routing Scheme (DMOSC-MHRS) and applied to solve the clustering problem [12]. The binary variant of DMO, the BDMO, was hybridized with a local search algorithm simulated annealing to tackle feature selection challenges of varying dimensional Predator-Prey Optimization [69] problems (BDMSAO) [13].…”

“…Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed improved algorithm is used to solve benchmark test functions and twelve (12) different optimization problems in the engineering domain. The major contributions of this study can be summarized as follows:…”

Optimal Cluster Head Selection in Wireless Sensor Network via Combined Osprey-Chimp Optimization Algorithm: CIOO

“…Additionally, the performance of both the CIOO and conventional approaches was evaluated across various metrics, including Distance, Total Packets Transmitted to the Base Station (BS), Residual Energy, Delay, Alive Nodes, and Risk. Furthermore, the CIOO method was compared with state-ofthe-art approaches such as DMOSC-MHRS [32] and PSO [33]. Additionally, a comparative analysis was conducted between the CIOO method and traditional algorithms, including GOA [34], SMO [35], BOA [36], COA [37], and OOA [38].…”