Journal of Networking and Communication Systems

Cluster Based Dense using Hybrid Genetic and Grasshopper Optimization algorithm in WSN

K.Srinivas — Mon, 11 Oct 2021 00:00:00 +0530

Nowadays, Wireless Sensor Networks (WSNs) play an important role in the communication due to their performance in diverse physical and environmental parameters by using minimum cost sensor devices. Owing to the scientific enhancement, the development of networks is practicable to model the cross-layer protocol on the basis of the energy effectual network. This clearly distresses the extending the lifespan of the network. Hence, this paper tries to develop a new Cross-Layer Design Routing technique in the clustering approach.The developed technique is based upon the cross-layer model through different layers such as physical and network layers. Here, a cluster-based routing model is developed, therefore, an optimal cluster head can be performed by exploiting a novel optimization algorithm named Hybrid Genetic Algorithm (GA) and Grasshopper Optimization Algorithm (GOA). Hence, the extension of the network lifetime can be attained by defining the shortest path. Additionally, based on numerous constraints such as energy utilization, distance, and delay, the optimal cluster head selection is performed. At last, the adopted model performance is confirmed with the existing techniques regarding the lifetime of the network and alive node.

Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm for Video Transmission in VANET

Amal Abdulrahman Juma Al Raisi — Tue, 13 Jul 2021 00:00:00 +0530

In Vehicular Ad Hoc Network (VANET), the communication amid the vehicle plays an important role in the enhancement of security in dangerous circumstances of road cases. The video transmission to other vehicles with the VANET implementation is performed in an enhanced manner. In this work, a novel hybrid method named the Butterfly Optimization (BO)- Particle Swarm Optimization (PSO) algorithm is adopted in order to decide the optimal multipath for the video transmission from one vehicle to another in the VANET network. At first, VANET has experimented as well as the optimal multipath is selected and it is performed by exploiting the adaptive geographic routing model on basis of fitness metrics. The adopted model performance is calculated using the measures namely, Packet end-to-end delay, Packet Delivery Ratio (PDR), as well as throughput. The proposed method yields the least end-to-end delay, as well as utmost PDR and throughput that exhibit the advantage of the proposed technique in efficient video transmission.

Route Maintenance and Multi-Hop Routing in Iot using Optimization Algorithm

Shyama Chandra Prasad — Mon, 11 Oct 2021 00:00:00 +0530

In various Internet of Things (IoT) applications, messages are distributed to a few nodes or objects, based on the multicast transmissions. Nevertheless, in IoT, preceding multicast routing models are mainly concentrated on adhoc sensor networks; however, they are not robust and receptive in IoT environment. Therefore, this work develops a multicast routing protocol on the basis of the adopted optimization technique named Enhanced Shark Smell Optimization (ESSO) Algorithm on the basis of World Cup Optimization (WCO) Algorithm in the IoT network. From the multicast source node, the multicast path is modeled to several destinations using the multicast routing protocol. To multiple destinations, the multicast source node forwards the packet concurrently. At first, by adopting the adopted optimization model, the nodes are experimented with together in the IoT network and carry out the multi-cast routing model in an efficient way. The multicast routing model is performed by the multicast routing protocol by exploiting the multi-objective parameters namely delay, distance, link quality factors, energy, and trust. Moreover, using the fitness measure, the multicast routing path is effectually selected on the basis of the proposed method. By exploiting various fitness parameters, the path with the least distance is chosen as the optimal path. The multicast routing model is performed efficiently by the developed optimization approach by combining the parametric features.

Hybrid Seagull Optimization Algorithm and Thermal Exchange Optimization for optimal routing in VANET

Fatima-ezzahra Lagrari — Mon, 05 Jul 2021 00:00:00 +0530

Vehicle Ad-hoc Network (VANET), can setup scalable solutions, cost-efficient and therefore, it is applied in many applications like transportation fields with help of multi-hop wireless communications from vehicle-to-vehicle. On the other hand, due to the rapid change in regular and topology network disconnection, the development of multi-hop communication is considered as challenging task in VANET conditions. In conventional mobile ad-hoc routing protocols, this directs to routing failure or ineffectual. To solve the routing problem and also to obtain the multi-constrained Quality of Service (QoS) measures, many research works are performed in VANET. The main intention of this work is to present a cost model as the solution for the vehicle routing issue and the network quality measures are considered such as travel, congestion, collision, and QoS awareness cost. In complete routing cost, the QoS fuzzification factor is also considered. Moreover, the main aim of this paper is to concentrate on the routing cost model minimization and identifies the optimal route. Here, a new optimization algorithm called Hybrid Seagull Optimization Algorithm (SOA) and Thermal Exchange Optimization (TEO) is presented to recognize the optimal route. Finally, developed technique performance is evaluated against the existing techniques regarding the cost analysis as well as shows efficiency of the adopted model with minimized routing cost.

Hybrid PSO-GSA Algorithm for Channel Estimation in Massive MIMO System

Manjunath Gangadhar Kallur — Tue, 27 Jul 2021 00:00:00 +0530

For advanced communication MILLIMETER wave (mmWave) is represented as a buoyant technology against wireless networks because of its prosperous frequency spectral resources in Multiple Input Multiple Output (MIMO). Nevertheless, the mmWave is diagnosed in MIMO remnants as a complex task that appears as the problems such as maximized propagation loss. Hence, this work introduces a novel optimization helped-out estimation technique to calculate the mm-wave channel parameters. On mm-wave massive MIMO system the performance of hybrid precoding, as well as channel estimation, is developed through using optimization procedure in codebook model principles. In reality, existing models are carried out the uniform distribution of azimuth angles in the codebook model, the wherein developed method estimates it as a single objective optimization issue without contravening characteristics of angle. To resolve the aforesaid optimization issue, the Hybrid Particle Swarm Optimization (PSO)- Gravitational Search Algorithm (GSA) technique is developed which hybridizes the idea of PSO and GSA method correspondingly. At last, the developed model performance is evaluated and examined with the conventional methods regarding the Channel State Information (CSI) and error metrics.