An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation
The continuous evolution of the Internet of Things (IoT) makes it possible to connect everyday objects to networks in order to monitor physical and environmental conditions, which is made possible due to wireless sensor networks (WSN) that enable the transfer of data. However, it has also brought ab...
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doaj-eb0a972e64534e1590561006467148ca2020-11-25T03:03:32ZengMDPI AGEnergies1996-10732020-06-01133024302410.3390/en13113024An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance EvaluationCarolina Del-Valle-Soto0Ramiro Velázquez1Leonardo J. Valdivia2Nicola Ivan Giannoccaro3Paolo Visconti4Universidad Panamericana, Facultad de Ingeniería, Álvaro del Portillo 49, Zapopan, Jalisco 45010, MexicoUniversidad Panamericana, Facultad de Ingeniería, Josemaría Escrivá de Balaguer 101, Aguascalientes, 20290, MexicoUniversidad Panamericana, Facultad de Ingeniería, Álvaro del Portillo 49, Zapopan, Jalisco 45010, MexicoDepartment of Innovation Engineering, University of Salento, 73100 Lecce, ItalyDepartment of Innovation Engineering, University of Salento, 73100 Lecce, ItalyThe continuous evolution of the Internet of Things (IoT) makes it possible to connect everyday objects to networks in order to monitor physical and environmental conditions, which is made possible due to wireless sensor networks (WSN) that enable the transfer of data. However, it has also brought about many challenges that need to be addressed, such as excess energy consumption. Accordingly, this paper presents and analyzes wireless network energy models using five different communication protocols: Ad Hoc On-Demand Distance Vector (AODV), Multi-Parent Hierarchical (MPH), Dynamic Source Routing (DSR), Low Energy Adaptive Clustering Hierarchy (LEACH) and Zigbee Tree Routing (ZTR). First, a series of metrics are defined to establish a comparison and determine which protocol exhibits the best energy consumption performance. Then, simulations are performed and the results are compared with real scenarios. The energy analysis is conducted with three proposed sleeping algorithms: Modified Sleeping Crown (MSC), Timer Sleeping Algorithm (TSA), and Local Energy Information (LEI). Thereafter, the proposed algorithms are compared by virtue of two widely used wireless technologies, namely Zigbee and WiFi. Indeed, the results suggest that Zigbee has a better energy performance than WiFi, but less redundancy in the topology links, and this study favors the analysis with the simulation of protocols with different nature. The tested scenario is implemented into a university campus to show a real network running.https://www.mdpi.com/1996-1073/13/11/3024wireless sensor networksenergy consumptionsleeping algorithmsperformance metricsRouting ProtocolInternet of Things |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Carolina Del-Valle-Soto Ramiro Velázquez Leonardo J. Valdivia Nicola Ivan Giannoccaro Paolo Visconti |
spellingShingle |
Carolina Del-Valle-Soto Ramiro Velázquez Leonardo J. Valdivia Nicola Ivan Giannoccaro Paolo Visconti An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation Energies wireless sensor networks energy consumption sleeping algorithms performance metrics Routing Protocol Internet of Things |
author_facet |
Carolina Del-Valle-Soto Ramiro Velázquez Leonardo J. Valdivia Nicola Ivan Giannoccaro Paolo Visconti |
author_sort |
Carolina Del-Valle-Soto |
title |
An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation |
title_short |
An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation |
title_full |
An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation |
title_fullStr |
An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation |
title_full_unstemmed |
An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation |
title_sort |
energy model using sleeping algorithms for wireless sensor networks under proactive and reactive protocols: a performance evaluation |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2020-06-01 |
description |
The continuous evolution of the Internet of Things (IoT) makes it possible to connect everyday objects to networks in order to monitor physical and environmental conditions, which is made possible due to wireless sensor networks (WSN) that enable the transfer of data. However, it has also brought about many challenges that need to be addressed, such as excess energy consumption. Accordingly, this paper presents and analyzes wireless network energy models using five different communication protocols: Ad Hoc On-Demand Distance Vector (AODV), Multi-Parent Hierarchical (MPH), Dynamic Source Routing (DSR), Low Energy Adaptive Clustering Hierarchy (LEACH) and Zigbee Tree Routing (ZTR). First, a series of metrics are defined to establish a comparison and determine which protocol exhibits the best energy consumption performance. Then, simulations are performed and the results are compared with real scenarios. The energy analysis is conducted with three proposed sleeping algorithms: Modified Sleeping Crown (MSC), Timer Sleeping Algorithm (TSA), and Local Energy Information (LEI). Thereafter, the proposed algorithms are compared by virtue of two widely used wireless technologies, namely Zigbee and WiFi. Indeed, the results suggest that Zigbee has a better energy performance than WiFi, but less redundancy in the topology links, and this study favors the analysis with the simulation of protocols with different nature. The tested scenario is implemented into a university campus to show a real network running. |
topic |
wireless sensor networks energy consumption sleeping algorithms performance metrics Routing Protocol Internet of Things |
url |
https://www.mdpi.com/1996-1073/13/11/3024 |
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