Genetic Algorithm-Based Multi-Hop Routing to Improve the Lifetime of Wireless Sensor Networks

An Unmanned Aerial Vehicle (UAV) network specifies a novel type of Mobile Ad hoc Network (MANET) in which drones serve as nodes and facilitate the retransmission of messages to their final destinations. Aside from its military application, it has recently begun to seep into the civilian sector. Similar to MANET and vehicular ad hoc networks, Flying Ad hoc Networks (FANET) are a subset of ad hoc networks. An FANET is different because it is founded on UAVs. Due to the characteristics of this sort of network, which is defined by a highly changing topology in a 3D environment, we must employ an adjusted configuration method to ensure good routing performance. Therefore, to deal with this problem, a technique that responds to any change in topology by always finding the best route is required. In this work, we propose a new protocol based on the hybrid optimization of the 2-opt heuristic and Honey Badger Algorithm (HBA), called HB-AODV. In order to locate its prey, a badger must move slowly and continuously while using scent markers and mouse-digging skills to catch it. In other words, the most efficient routes in terms of the number of hops are identified. Several simulations were conducted via the 3D version of Network Simulator (NS-2) on different deployment strategies. In comparison to AODV, DSDV, and AntHocNet, the obtained results demonstrated the proposed scheme’s good performance in terms of quality of service metrics.

Section: Related Workmentioning

confidence: 99%

A Hybrid Optimization Solution for UAV Network Routing

Kout

Bouaita

Beghriche

et al. 2023

“…The choice fell on the GA because it is the most used with CSM as shown above. Also, it has been widely used in the field of electrical engineering [40][41][42][43]. Figure 8 illustrates the graphs of the electric fields calculated by this method, by CSM-GA, and the measured values (the values are listed in Table II).…”

Section: Calculate the Mean And Standard Deviation Of C R And C Amentioning

confidence: 99%

Improved Optimization of the Charge Simulation Method for the Calculation of the Electric Field Around Overhead Transmission Lines Using Statistical Methods

Allal

Boubakeur

Mujezinović

2022

In order to decide the appropriate arrangements of fictitious charges in the charge simulation method, the use of the Monte Carlo method is proposed for the estimation of the probability density function of two variables, the radius ratio, and the angle ratio. Τhe scale and shape parameters of the Weibull's distribution are determined by the maximum likelihood estimator. The obtained results are used to calculate the electric field at arbitrary points in the neighborhood of high voltage transmission lines. The comparisons between the results computed by this method, the results calculated by the genetic algorithm, and those measured, confirm the effectiveness and accuracy of the proposed method.

“…Cooperative Spectrum Sensing (CSS) has been introduced to provide solutions in challenging scenarios by increasing the reliability of spectrum sensing [7]. CRSN performance has been improved by grouping adjacent nodes into clusters using cluster routing protocols, inter-cluster relay, and intra-cluster aggregation using multi-hop data delivery [8][9]. Thus, the clustering routing protocol introduced for CRSNs has become a trending research topic.…”

Section: Introductionmentioning

confidence: 99%

“…CRSN assumes perfect spectrum sensing while designing cluster routing protocols. However, this assumption is suitable only for designing simple routing protocols that do not consider the original perceptual performance of CRSN nodes [9].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Cognitive Radio WSN Communication through Cluster Head Selection Technique

Panbude,

Deshpande,

Iyer

et al. 2024

The demand for frequency spectrum is increasing rapidly with the wide growth of wireless communications. Spectrum sensing issues present in Cognitive Radio Sensor Networks (CRSN) are detected dynamically using spectral sensing techniques, which also help to utilize frequency bands more effectively. The study proposes a novel Cosine Sand Cat Optimization (CSCO) protocol to address spectral sensing problems by selecting the optimal Cluster Head (CH) in a CRSN. The CRSN is simulated, and spectral allocation is performed using LeNet to extract signal components. Then, Primary User (PU) aware optimal CH selection is performed using the proposed CSCO by taking account of multi-objective fitness parameters. Finally, data communication is performed between nodes after CH selection using the CSCO protocol. The simulation results of CSCO were validated to determine its superiority concerning Secondary User (SU) density, and it attained residual energy, network lifetime, Packet Delivery Ratio (PDR), normalized throughput, and delay of 69.457 J, 77, 75.89%, 74.473, and 4.782ms, respectively.