An energy consumption model for wireless sensor networks

Abo‐Zahhad, Mohammed; Farrag, Mohammed; Ali, Abdelhay; Amin, Osama

doi:10.1109/iceac.2015.7352200

Cited by 55 publications

(27 citation statements)

References 9 publications

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“…WSN users often use node voltage, obtained directly or through energy models to measure node energy consumption [11] and to increase node lifetime, such as in TinyDB [5]. However, continuous polling for meta-data causes severe energy drains in the system, making its lifetime shorter.…”

Section: B Meta-datamentioning

confidence: 99%

Behavioral modeling of Wireless Sensor nodes using meta-data

Wanniarachchi

Perera

Goonatillake

2015

2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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Many Wireless Sensor Network systems are deployed under extreme conditions. Thus, it's important to maximize their lifespan by optimal use of network's resources. Battery lifespan of a node is a crucial resource that needs to be used carefully. Energy aware routing & scheduled sensing have been introduced for careful use of battery. It is necessary to optimize battery usage by predicting it's future behavior. This will lead the users to take early decisions, thus minimizing network downtime. Hence, we explore the possibility of using meta-data on each node, to represent and predict node behavior using machine learning models. In this research, we use node voltage level as an indicator of the energy used, as voltage is proportional to available energy. Node energy consumption is modeled and predicted by ARIMA models using these voltage readings. We also classify nodes as high, medium & low use, with respect to its current and future usage, thus allowing user to take early decisions maximizing network throughput (lifetime). After evaluating predicted node behavior against a created base set of behavioral classifications, we achieved a 80% accuracy rate. Using different and increasing window sizes, we evaluated the validity of our model. In these experiments, our prediction method produced highly accurate results for all considered prediction windows. By being able to predict a node's energy consumption behavior at a higher accuracy rate, WSN users can make optimization decisions beforehand to increase the network lifetime.

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Section: B Meta-datamentioning

confidence: 99%

Behavioral modeling of Wireless Sensor nodes using meta-data

Wanniarachchi

Perera

Goonatillake

2015

2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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“…In [15] authores an energy model for WSNs considering the physical layer and MAC layer parameters by determining the energy consumed per payload bit transferred without error over AWGN channel.…”

Section: G) Other Modelsmentioning

confidence: 99%

Scalability Aware Energy Consumption and Dissipation Models for Wireless Sensor Networks

Fissaoui¹,

Benkirane²,

Beni‐Hssane³

et al. 2017

IJECE

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Most of Wireless Sensor Networks researches focus on reducing the amount of energy consumed by nodes and network to increase the network lifetime. Thus, several papers have been presented and published to optimize energy consumption in each area of WSNs, such as routing, localization, coverage, security, etc. To test and evaluate their propositions, authors apply an energy dissipation model; this model must be more realistic and suitable to give good results. In this paper we present a general preview on different sources of energy consumption in wireless sensor networks, and provide a comparative study between two energy models used in WSNs that offer an effective and an adequate tool for researchers.

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“…The power unit is used to provide the system supply voltage and it can be supported by an energy harvesting unit such as small-scale wind mills or solar cells. Also, it may have optional unit such as mobilize subsystem and location finding system [13], [3], [14].Therefore, the average energy consumption per unit time of sensor node can be express as: P avg = P Transciver + P processing + P sensing + P Optional -PH arvest (4) where P Transciven P processing , P sensing and P Optional are the energy consumption per unit time in transceiver unit, processing unit, sensing unit, and optional units respectively, and PH arvest is the energy gained per unit time in harvest unit. The lifetime (L T) of sensor node is given by…”

Section: A Network Modelmentioning

confidence: 99%

“…Therefore, it is essential to develop energy models which capture all sources of energy consumption [4]. Much research has been done on the energy modeling of WSNs [8]- [12].…”

mentioning

confidence: 99%

C20. Energy consumption and lifetime analysis for Wireless Sensor Networks

Abo‐Zahhad

Farrag

Ali

et al. 2015

2015 32nd National Radio Science Conference (NRSC)

Self Cite

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Energy consumption and energy modeling are important issues in designing and implementing of Wireless Sensor Networks (WSNs), which help the designers to optimize the energy consumption in WSN nodes. Good knowledge of the sources of energy consumption in WSNs is the first step to reduce energy consumption. Therefore, an accurate energy model is required for the evaluation of communication protocols. In this paper, we present an energy analysis technique for WSNs considering the physical layer parameters by determining the energy consumed per payload bit transferred without error over A WGN channel. We show how the transmission power must be chosen in order to achieve energy-efficient communications over A WGN channel and provide a closed-form expression for optimum transmission power. We also find that, for each modulation scheme, there are optimal transmission power at which the energy consumption is minimized. The proposed model can be used to analyse the WSNs energy consumption, to evaluate communication protocols, and it can also use to estimate energy consumption and network lifetime which used for on-line energy accounting.

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An energy consumption model for wireless sensor networks

Cited by 55 publications

References 9 publications

Behavioral modeling of Wireless Sensor nodes using meta-data

Behavioral modeling of Wireless Sensor nodes using meta-data

Scalability Aware Energy Consumption and Dissipation Models for Wireless Sensor Networks

C20. Energy consumption and lifetime analysis for Wireless Sensor Networks

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