Load‐based node ranked low‐energy adaptive clustering hierarchy: An enhanced energy‐efficient algorithm for cluster head selection in wireless sensor networks

2021 ◽  
Author(s):  
Deepak Mehta ◽  
Sharad Saxena
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Efficient Algorithm ◽  
Energy Efficient ◽  
Cluster Head ◽  
Low Energy ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Adaptive Clustering

scholarly journals Energy-Efficient Cluster-Head Selection for Wireless Sensor Networks Using Sampling-Based Spider Monkey Optimization

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5281 ◽  
Author(s):  
Jin-Gu Lee ◽  
Seyha Chim ◽  
Ho-Hyun Park
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Cluster Head ◽  
Spider Monkey ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Adaptive Clustering ◽  
Cluster Heads ◽  
Spider Monkey Optimization

Extending the lifetime and stability of wireless sensor networks (WSNs) through efficient energy consumption remains challenging. Though clustering has improved energy efficiency through cluster-head selection, its application is still complicated. In existing cluster-head selection methods, the locations where cluster-heads are desirable are first searched. Next, the nodes closest to these locations are selected as the cluster-heads. This location-based approach causes problems such as increased computation, poor selection accuracy, and the selection of duplicate nodes. To solve these problems, we propose the sampling-based spider monkey optimization (SMO) method. If the sampling population consists of nodes to select cluster-heads, the cluster-heads are selected among the nodes. Thus, the problems caused by different locations of nodes and cluster-heads are resolved. Consequently, we improve lifetime and stability of WSNs through sampling-based spider monkey optimization and energy-efficient cluster head selection (SSMOECHS). This study describes how the sampling method is used in basic SMO and how to select cluster-heads using sampling-based SMO. The experimental results are compared to similar protocols, namely low-energy adaptive clustering hierarchy centralized (LEACH-C), particle swarm optimization clustering protocol (PSO-C), and SMO based threshold-sensitive energy-efficient delay-aware routing protocol (SMOTECP), and the results are shown in both homogeneous and heterogeneous setups. In these setups, SSMOECHS improves network lifetime and stability periods by averages of 13.4%, 7.1%, 34.6%, and 1.8%, respectively.

scholarly journals Proficient Energy Efficient A2S Leach Technique in Wireless Sensor Networks

2020 ◽  
Vol 9 (5) ◽  
pp. 27-33
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Information Transmission ◽  
Energy Efficient ◽  
Low Energy ◽  
Wireless Sensor ◽  
Critical Test ◽  
Adaptive Clustering ◽  
Remote Sensor ◽  
The Ideal

In Wireless Sensor Networks (WSNs) lifetime of the system relies upon the vitality of the hubs, where vitality utilization is for the most part utilized for information transmission as opposed to detecting and preparing. A critical test in remote sensor systems is the ideal utilization of hub assets. Bunching of sensor hubs helps to utilize the hub vitality ideally and delay the lifetime of vitality compelled remote sensor arrange. Also, in WSN, the decision of steering convention assumes a significant job in using the vitality of hubs effectively. In this paper, another A2S LEACH (Active ↔Sleep - Low Energy Adaptive Clustering Hierarchy) directing strategy is proposed, which joins the two significant classes of various leveled conventions in particular bunch based methodology and chain based methodology. The proposed system is reproduced utilizing NS2 and the outcomes are examined. Reenactment results shows that the proposed A2S LEACH steering convention altogether diminishes vitality utilization and expands the all-out lifetime of the remote sensor organize when contrasted with the LEACH convention.

Sign in / Sign up

Export Citation Format

Share Document