An Optimal Routing Protocol Using Multi-Objective Cultural Algorithm for Wireless Sensor Networks (ORPMCA)

2021 ◽  
Vol 10 (2) ◽  
pp. 79-86
Author(s):  
Seyed Reza Nabavi ◽  
Mehdi Najafi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Wireless Sensor ◽  
Optimal Routing ◽  
Cultural Algorithm ◽  
Multi Objective

An Evolutionary Mobility Aware Multi-Objective Hybrid Routing Algorithm for Heterogeneous WSNs

2020 ◽  
pp. 221-237
Author(s):  
Nandkumar Prabhakar Kulkarni ◽  
Neeli Rashmi Prasad ◽  
Ramjee Prasad
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Routing Algorithm ◽  
Wireless Sensor ◽  
Hybrid Routing ◽  
Multi Objective ◽  
Mobile Wireless Sensor ◽  
Average Energy Consumption ◽  
Hybrid Routing Protocol

Researchers have faced numerous challenges while designing WSNs and protocols in numerous applications. Amongst all sustaining connectivity and capitalizing on the network lifetime is a serious deliberation. To tackle these two problems, the authors have considered Mobile Wireless Sensor Networks (MWSNs). In this paper, the authors put forward an Evolutionary Mobility aware multi-objective hybrid Routing Protocol for heterogeneous wireless sensor networks (EMRP). EMRP selects the optimal path from source node to sink by means of various metrics such as Average Energy consumption, Control Overhead, Reaction Time, LQI, and HOP Count. The Performance of EMRP when equated with Simple Hybrid Routing Protocol (SHRP) and Dynamic Multi-Objective Routing Algorithm (DyMORA) using parameters such as Average Residual Energy (ARE), Delay and Normalized Routing Load. EMRP improves AES by a factor of 4.93% as related to SHRP and 5.15% as related to DyMORA. EMRP has a 6% lesser delay as compared with DyMORA.

A Hybrid Swarm Intelligence Algorithm for Clustering-Based Routing in Wireless Sensor Networks

2019 ◽  
Vol 29 (10) ◽  
pp. 2050163 ◽  
Author(s):  
Amirhossein Barzin ◽  
Ahmad Sadegheih ◽  
Hassan Khademi Zare ◽  
Mahbooeh Honarvar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Objective Function ◽  
Routing Protocol ◽  
Wireless Sensor ◽  
Average Lifetime ◽  
Hybrid Swarm ◽  
Energy Efficient Routing ◽  
Multi Objective ◽  
Nature Inspired Algorithm

Wireless sensor networks (WSNs) comprise a large number of tiny sensing nodes, which are battery-powered with limited energy. An energy-efficient routing protocol is of utmost importance to prolong the network lifetime. Clustering is the most common technique to balance energy consumption among all nodes, while minimizing traffic and overhead during the data transmission phases. In this paper, a Multi-Objective nature-inspired algorithm based on Shuffled frog-leaping algorithm and Firefly Algorithm (named MOSFA) as an adaptive application-specific clustering-based multi-hop routing protocol for WSNs is proposed. MOSFA’s multi-objective function regards different criteria (e.g., inter- and intra-cluster distances, the residual energy of nodes, distances from the sink, overlap, and load of clusters) to select appropriate cluster heads at each round. Moreover, another multi-objective function is proposed to select the forwarder nodes in the routing phase. The controllable parameters of MOSFA in both clustering and multi-hop phases can be adaptively tuned to achieve the best performance based on the network requirements according to the specific application. Simulation results demonstrate average lifetime improvements of 182%, 68%, 30%, and 28% when compared with LEACH, ERA, SIF, and FSFLA, respectively, in different network scenarios.

An Evolutionary Mobility Aware Multi-Objective Hybrid Routing Algorithm for Heterogeneous WSNs

2017 ◽  
Vol 4 (3) ◽  
pp. 17-32 ◽  
Author(s):  
Nandkumar Prabhakar Kulkarni ◽  
Neeli Rashmi Prasad ◽  
Ramjee Prasad
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Wireless Sensor ◽  
Hybrid Routing ◽  
Multi Objective ◽  
Heterogeneous Wireless Sensor Networks ◽  
Hybrid Routing Protocol

Researchers have faced numerous challenges while designing WSNs and protocols in numerous applications. Amongst all sustaining connectivity and capitalizing on the network lifetime is a serious deliberation. To tackle these two problems, the authors have considered Mobile Wireless Sensor Networks (MWSNs). In this paper, the authors put forward an Evolutionary Mobility aware multi-objective hybrid Routing Protocol for heterogeneous wireless sensor networks (EMRP). EMRP selects the optimal path from source node to sink by means of various metrics such as Average Energy consumption, Control Overhead, Reaction Time, LQI, and HOP Count. The Performance of EMRP when equated with Simple Hybrid Routing Protocol (SHRP) and Dynamic Multi-Objective Routing Algorithm (DyMORA) using parameters such as Average Residual Energy (ARE), Delay and Normalized Routing Load. EMRP improves AES by a factor of 4.93% as related to SHRP and 5.15% as related to DyMORA. EMRP has a 6% lesser delay as compared with DyMORA.

An Improved Genetic Algorithm Based Annulus-sector Clustering Routing Protocol for Wireless Sensor Networks

2021 ◽  
Author(s):  
Wang Chu-hang ◽  
Liu Xiao-li ◽  
Youjia Han ◽  
Hu Huang-shui ◽  
Wu Sha-sha
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Fitness Function ◽  
Minimum Energy ◽  
Wireless Sensor ◽  
Optimal Routing ◽  
Improved Genetic Algorithm

Abstract In wireless sensor networks, uniform cluster formation and optimal routing paths finding are always the two most important factors for clustering routing protocols to minimize the network energy consumption and balance the network load. In this paper, an improved genetic algorithm based annulus-sector clustering routing protocol called GACRP is proposed. In GACRP, the circular network is divided into sectors with the same size for each annulus, whose number is determined by calculating the minimum energy consumption of each annulus. Each annulus-sector forms a cluster and the best node in this annulus-sector is selected as cluster head. Moreover, an improved genetic algorithm with a novel fitness function considering energy and load balance is presented to find the optimal routing path for each CH, and an adaptive round time is calculated for maintaining the clusters. Simulation results show that GACRP can significantly improve the network energy efficiency and prolong the network lifetime as well as mitigate the hot spot problem.

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