Energy consumption evaluation of low energy adaptive clustering hierarchy routing protocol for wireless sensor network

2013 ◽  
Author(s):  
V. Windha Mahyastuti ◽  
A Adya Pramudita
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Protocol ◽  
Low Energy ◽  
Wireless Sensor ◽  
Adaptive Clustering

scholarly journals Consumption of Energy and Routing Protocols in Wireless Sensor Network

2016 ◽  
Vol 8 (3) ◽  
pp. 76 ◽  
Author(s):  
Ridha Azizi
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Protocols ◽  
Cluster Head ◽  
Low Energy ◽  
Wireless Sensor ◽  
Simulation Tool ◽  
New Approach ◽  
Adaptive Clustering

Extend the life of a wireless sensor network (WSN) is a fundamental challenge, as they have a limited supply. Multiple protocols and approaches have been proposed to minimize power consumption. Routing protocols and especially the hierarchical approach is one of the techniques used to minimize energy consumption and to improve the duration of network life. In this paper we propose a new approach to transfer and select the CH (Cluster Head). ART-LEACH (Advanced Routing Transfer- Low-Energy Adaptive Clustering Hierarchy) is a self-organizing protocol based on clustering. Our approach is to use energy more evenly the selected nodes as CH. We evaluated the performance of LEACH (Low-Energy Adaptive Clustering Hierarchy) and IB-LEACH (Improved and Balanced Low Energy Adaptive Clustering Hierarchy) protocol with the proposed new approach using MATLAB as a simulation tool. The simulation results showed that our proposal provides a reduction in energy consumption and increase the duration of network life.

Relay Wireless Network Communication Protocol Technology Based on Balanced Energy and Distance

2014 ◽  
Vol 513-517 ◽  
pp. 2403-2407
Author(s):  
Mao Tong Xu
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Protocol ◽  
Data Transmission ◽  
Network Routing ◽  
Wireless Sensor ◽  
Practical Applications ◽  
Adaptive Clustering ◽  
Key Steps

Optimizing the energy consumption of sensor node and prolonging the network lifetime are the key steps to enable the wireless sensor network to enter into practical applications. This paper focuses on studying the energy efficient wireless sensor network routing protocol from the perspective of node energy consumption efficiency and balance, designing the Energy Balanced Adaptive Clustering Hierarchical Protocol (EBACH) and two-layer transceiver model, improving data transmission process to conform to the energy-saving principle of wireless multi-hop data transmission, and analyzing the feasibility and accuracy of the algorithm through simulation.

scholarly journals Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 728
Author(s):  
Carolina Del-Valle-Soto ◽  
Carlos Mex-Perera ◽  
Juan Arturo Nolazco-Flores ◽  
Ramiro Velázquez ◽  
Alberto Rossa-Sierra
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Protocol ◽  
Routing Protocols ◽  
Energy Model ◽  
Low Energy ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Actual Performance

In this study, a Wireless Sensor Network (WSN) energy model is proposed by defining the energy consumption at each node. Such a model calculates the energy at each node by estimating the energy of the main functions developed at sensing and transmitting data when running the routing protocol. These functions are related to wireless communications and measured and compared to the most relevant impact on an energy standpoint and performance metrics. The energy model is validated using a Texas Instruments CC2530 system-on-chip (SoC), as a proof-of-concept. The proposed energy model is then used to calculate the energy consumption of a Multi-Parent Hierarchical (MPH) routing protocol and five widely known network sensors routing protocols: Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), ZigBee Tree Routing (ZTR), Low Energy Adaptive Clustering Hierarchy (LEACH), and Power Efficient Gathering in Sensor Information Systems (PEGASIS). Experimental test-bed simulations were performed on a random layout topology with two collector nodes. Each node was running under different wireless technologies: Zigbee, Bluetooth Low Energy, and LoRa by WiFi. The objective of this work is to analyze the performance of the proposed energy model in routing protocols of diverse nature: reactive, proactive, hybrid and energy-aware. Experimental results show that the MPH routing protocol consumes 16%, 13%, and 5% less energy when compared to AODV, DSR, and ZTR, respectively; and it presents only 2% and 3% of greater energy consumption with respect to the energy-aware PEGASIS and LEACH protocols, respectively. The proposed model achieves a 97% accuracy compared to the actual performance of a network. Tests are performed to analyze the consumption of the main tasks of a node in a network.

scholarly journals An Energy-Balanced Routing Protocol for a Wireless Sensor Network

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Lin Li ◽  
Donghui Li
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Protocol ◽  
Cluster Head ◽  
Low Energy ◽  
Fuzzy Rules ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Adaptive Clustering ◽  
Energy Balanced Routing

The wireless sensor network is an intelligent self-organizing network which consists of many sensor nodes deployed in the monitoring area. The greatest challenge of designing a wireless sensor network is to balance the energy consumption and prolong the lifetime of the network, seeing that the nodes can be powered only by batteries in most conditions. An energy-balanced routing protocol (EBRP) for wireless sensor networks is proposed in this paper. In EBRP, we divide the network into several clusters by using K-means++ algorithm and select the cluster head by using the fuzzy logical system (FLS). Since the previous researches did not demonstrate how to get the fuzzy rules for different networks, we propose a genetic algorithm (GA) to obtain the fuzzy rules. We code the rules as a chromosome, and the lifetime of the network is treated as a fit function. Then, through the selection, crossover, and mutation of each generation, the best offspring can be decoded as the best rule for each network model. Through the simulation, comparing with the existing routing protocols such as low-energy adaptive clustering hierarchy (LEACH), low-energy adaptive clustering hierarchy-centralized (LEACH-C), and stable election protocol (SEP), the EBRP prolongs the network lifetime (first node dies) by 57%, 63%, and 63%, respectively.

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