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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Main Authors: Carolina Del-Valle-Soto, Ramiro Velázquez, Leonardo J. Valdivia, Nicola Ivan Giannoccaro, Paolo Visconti
Format: Article
Language:English
Published: MDPI AG 2020-06-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/13/11/3024
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spelling 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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