Scalability Aware Energy Consumption and Dissipation Models for Wireless Sensor Networks

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 1, February 2017, pp. 424~431
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i1.pp424-431  424
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
Scalability Aware Energy Consumption and Dissipation Models
for Wireless Sensor Networks
Mohamed El Fissaoui1
, Said Benkirane2
, Abderrahim Beni-Hssane3
, Mostafa Saadi4
1,3
LAROSERI Laboratory, Computer Science Department, Sciences Faculty, Chouaïb Doukkali University El Jadida,
Morocco
2
High School of Technology, Essaouira, Cadi Ayyad University, Morocco
4
Département Informatique & Télécoms Ecole Nationale des Sciences Appliquées(ENSA),
Khouribga Université Hassan 1er - Settat, Morocco
Article Info ABSTRACT
Article history:
Received Jul 29, 2016
Revised Nov 20, 2016
Accepted Dec 5, 2016
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.
Keyword:
Energy consumption
Energy dissipation models
Wireless sensor networks
Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Mohamed El Fissaoui,
LAROSERI laboratory,
Computer Science Department,
Faculty of Sciences,
Chouaïb Doukkali University,
Route Ben Maachou, 24000, El Jadida, Morocco.
Email: Elfissaoui.m@ucd.ac.ma
1. INTRODUCTION
Wireless sensor networks are formed by a large number of sensor nodes, which are commonly
known as motes forming wireless ad-hoc networks. These motes are small in size and have limited
processing power, memory and battery life. Motes typically have sensors such as thermometers attached to
them in order to gather data about the physical environment [1].
A critical constraint in sensor networks is that sensor nodes employ batteries. The second constraint
is that sensors will be deployed unattended and in large numbers, so it’s difficult to change or recharge
sensors’ batteries. Therefore, all systems, processes and communication protocols for sensor networks must
minimize the power consumption. Some of the most challenging issues that have been studied in different
areas of WSNs are the medium access [2], routing strategies, clustering schemes [3], and application layer
dynamics, etc. All these approaches contribute to reduce the energy consumption and increase the network
lifetime [4].
Simulation plays a very important role, it permits to test and to evaluate the proposed solutions by
researchers in different domains, thus it should provides correct measurements as those that will be gotten in
real-world environment. Therefore, the energy model used must be more realistic and more suitable to give
accurate results.

 ISSN: 2088-8708
IJECE Vol. 7, No. 1, February 2017 : 424 – 431
425
The rest of this paper is organized as follows. Section II presents different sources of energy
consumption in WSNs. Section III exhibits some energy consumption models used. Section IV evaluates the
performance of two energy models by simulations. Finally, Section V gives concluding remarks.
2. SOURCES OF ENERGY CONSUMPTION
2.1. Normal Sources
In WSNs energy consumption occurs in three domains: sensing, data processing, and
communications [5]. Sensor node expends maximum energy in data communication (Emission and
Reception) than data processing or sensing (Figure 1). Therefore, to save energy and prolong network
lifetime, we should consider how to minimize communication costs. In addition, any method which considers
these problems needs to maintain network stability [6], [7].
Figure 1. Energy Consumption in WSNs
2.2. Additional Sources
In addition to sources mentioned above, there are some other unexpected sources that are considered
as sources of energy wasting. The major sources of energy waste are:
a) Collisions: Occurs when two or more nodes attempt to transmit a packet across the network at the same
time. The transmitted packets must be discarded and then retransmitted, thus the retransmission of those
packets increases the energy consumption and the latency.
b) Idle listening: Listening to receive possible traffic that is not sent (major source of energy consumption).
Usually a node in a WSN doesn’t know when to wake up to receive a packet, thus it must keep its radio
ON which consumes most of the energy.
c) Overhearing: When nodes receive a packet for another node. Overhearing can be a major reason of
energy waste mainly with a high node density causing a heavy traffic load.
d) Protocol overhead: Control frames do not carry useful information although their transmission consumes
energy.
e) Adaptation – Reconfiguring: When nodes join and leave the network it needs to be known in the network
(Neighboring, Routing, localization…)
f) Overmitting: means that a transmission takes place while the intended receiver is not ready to receive.
Overmitting is common in asynchronous protocols using preamble sampling.
g) Computational for location: If nodes are equipped with localization device (GPS) it consumes more
energy, if not, it uses some techniques to be localized and thus it consumes also the energy.
h) Control Packet Overhead: Sending and receiving control packets consumes energy too, and less useful
data packets can be transmitted.
3. ENERGY CONSUMPTION MODELS
3.1. Network Model
We consider a wireless sensor network with N nodes randomly distributed in a field (Figure 2). We
assume that nodes always have data to transmit to the base station, which is often far from the sensing area.
The network is organized into a clustering hierarchy, and the cluster-heads execute fusion function to reduce
correlated data produced by the sensor nodes within the clusters. The cluster-heads (CH) transmit the
aggregated data to the base station directly. We assume that all nodes are homogeneous, stationary and
equipped with initial energy of E0.
Commun
ication

IJECE ISSN: 2088-8708 
Scalability Aware Energy Consumption and Dissipation Models for WSNs (Mohamed El Fissaoui)
426
Figure 2. The network with 100 nodes randomly deployed (o Normal node, + Cluster head)
3.2. Energy Models
a) Model proposed by Heinzelman et al.
According to the radio energy dissipation model proposed by Heinzelman et al. [8] the energy
expended by the radio is given by:
{ (1)
Where L is the number of bits in every packet, Eelec is the energy dissipated per bit to run the
transmitter ETx or the receiver ERx circuit, ϵfs is the free space fading energy, ϵmp is the multi-path fading
energy and d is the distance between the sender and the receiver and d0 distance fixed by the model
(Figure 3).
To receive this message the radio expends energy:
ERx(L) = LEelec (2)
Figure 3. Radio Energy Dissipation Model
Total energy during each round is given by:
[ ] (3)

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Where k is the number of clusters, EDA is the data aggregation cost expended in CH, dtoBS is the
average distance between the cluster-head and the base station and dtoCH is the average distance between
cluster members and the cluster-head.
The optimal number of clusters is:
√
√
√
√
(4)
The model proposed considers microcontroller processing and radio transmission and receiving
only. This model does not consider other important sources of energy consumption, such as transient energy,
sensor sensing, sensor logging and actuation.
b) Model proposed by Shih et al.
Shih et al. [9] presented a model developed for μAMPS a specific wireless sensor node. The authors
present a formulation for the radio power consumption (Pc) as:
Pc = NT[PT (Ton + Tst) + Pout Ton] + NR [PR(Ron + Rst)] (5)
Where PT is the power consumed by the transmitter/receiver, Pout is the output power of the
transmitter, Ron is the transmitter/receiver on time, Rst is the transmitter/receiver startup time and NT is the
number of times transmitter/receiver is switched on per unit time, which depends on the task and Medium
Access Control (MAC) scheme used. Ton can further be rewritten as L/R, where L is the packet size and R is
the data rate.
The μAMPS model doesn't specify the power consumed in transmitting or receiving one bit.
Hill et al. propose in [10] how calculate the energy required for transmitting one bit.
Time to send or receive one bit = 1 / 1 Mbps = 1 μsec:
Energy = Power × Time (6)
Where Power is in Watts and Time is in seconds
c) Mica2 Specific Model
Polastre et al. [11] proposed a model that presents the total energy consumption for Mica2 as the
summation of energy transmitting, receiving, listening, sampling data and sleeping.
As the authors present current consumption and time, and assuming that Mica2 is powered by a 3V
source [4], one can calculate energy in transmitting and receiving one bit, as:
Energy = Current × Voltage × Time (7)
where current is in Amperes, Voltage is in Volts and Time is in seconds.
Energy Tx = 20 × 10-3 A × 3 Volts × 416 ×10-6 sec / 8 bits
Thus: Energy Tx = 3.12 μJ/bit
Energy Rx = 15 × 10-3 A × 3 Volts × 416 ×10-6 sec / 8 bits
Thus: Energy Rx = 2.34 μJ/bit (8)
This model does not consider other important sources of energy consumption, such as transient
energy, sensor logging and actuation.
d) Mica2 Specific Model with actual measurements
Shnayder et al. [12] presented a current consumption model based on measurements on the Mica2
platform.
Energy cost per bit transmitted is:
Energy Tx = (8+21.5) × 10-3 A × 3 Volts × 416 ×10-6 sec / 8 bits
Thus: Energy Tx = 4.602 μJ/bit
Energy Rx = (8+7) × 10-3 A × 3 Volts × 416 ×10-6 sec / 8 bits
Thus: Energy Rx = 2.34 μJ/bit (9)

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e) Model proposed by Miller et al.
Miller et al [13] propose a model based on the Mica2 Motes, this energy model take in consideration
the time and power required for the radio to transition from the sleep state to idle and from the idle state to
sleep. In practice, these values are non negligible, but not accounted for in most previous work.
Considered values are:
Ttrans-on = 2450 μs
Ttrans-off = 250 μs
Ptrans-on = 30 mW
Ptrans-off = 30 mW (10)
The model proposed does not consider energy consumption of sensor sensing, sensor logging and
actuation.
f) Halgamuge et al.
Halgamuge et al [14] propose a model that takes in consideration other sources of energy
consumption such as transient energy (Etrans), sensor sensing (Esens ), sensor logging (Elogg) and actuation.
Total energy consumed by a sensor node during each round is:
ENode=[LEsensN +LEloggN+LEelec +Lϵfsd2
toCH +EtransN ] (11)
Total energy consumed by a CH during each round is:
ECH = [LEsensCH + LEloggCH + L1 EDA( ) + h2 Eelec [L1 ( - 1)+ (γL2) + L2 (1+γ)] +
L2 (1+γ)ϵmpd4
toBS +EtransCH ] (12)
Where:
a. L1 = L× number of sensors from its own cluster
b. γ ∈0,1,2…k-1
c. L2 Number of bits transmitted by CH to base station (BS).
d. h2 Is a weighting factor that applies to a CH to indicate by how much it consumes more energy than a
regular sensor node for energy transmission.
We assume that the energy dissipation for actuation is not performed.
The energy dissipation in a single cluster during each round is given by:
ECluster = ECH + ( -1 )ENode (13)
The total energy for k clusters, during each round:
ETotal=kECluster (14)
The optimal number of clusters in this model is given by:
√
√
√
√
(15)
Where:
Eα = (ϵmp + EsensCH + EloggCH + EtransCH) (16)
The model proposed here is more realistic and comprehensive energy model for wireless sensor networks.
g) Other models
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.

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4. SIMULATION RESULTS
We evaluate the performance of two energy models. The first proposed by Heinzelman et al. and the
second proposed by Halgamuge et al. using MATLAB software. We consider a wireless sensor network with
N=100 nodes randomly distributed in a 100m×100m field. We assume the base station is far from the sensing
region. The radio parameters used in our simulations are shown in Table 1.
Table 1. Radio characteristics used in our simulations
Parameter Value
Eelec 50 nJ/bit
ϵfs 10 pJ/bit/m2
ϵmp 0.0013 pJ/bit/m4
d0 70 m
Message size (L) 2000 bits
k 10 Clusters
In our simulation we use a hierarchical routing protocol which used cluster based approach in
wireless sensor network called LEACH [8].
Using Mica2 Motes hardware values [16] and formulas cited in [14] and Parameter values used
in [17] we obtain the following results:
Figure 4. Average Energy Dissipation for every Node
Figure 4 shows the average energy dissipation for every node for all components considered in the
model proposed by Halgamuge et al. These energy components are considerable. Therefore they can change
results of simulations if they are taken in consideration.
Figure 5. Average Energy Dissipation in two Models

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According to figure 5 above, the average energy dissipation in the model proposed by Halgamuge
et al. is heigher as compared with the one proposed by Heinzelman et al. The main reason is that Halgamuge
et al. model takes in consideration other sources of energy consumption that are not included in Heinzelman
et al. model, i.e., transmit energy, sensor sensing, sensor logging and actuation.
The optimal number of clusters has significant effect on sensor network lifetime. Figure 6 shows
that the optimal number of clusters increases with the number of sensors used for two compared energy
models. The change is modest in the model proposed by Halgamuge et al. it is nearly stable.
Figure 6. Optimal Number of Clusters Versus Number of Nodes
According to formulas (4) and (15) it is clear that the optimal number of clusters vary with several
factors such as distance from CH to sink node, free space fading energy ϵfs, square of the sensing field, etc.
With each of this factors the optimal number of clusters it is more sensible in Heinzelman et al. model than
Halgamuge et al model. That changes significantly simulation results.
Finally Table 2 shows energy sources used in two models compared in this study.
Table 2. Energy Source Considered by two Models
Energy Sources Heinzelman et al. Halgamuge et al.
Processing Х Х
Communication Х Х
Sensing Х
Transeint Х
Logging Х
Actuation Х
Clusters formation Х Х
5. CONCLUSION
In the first part in this paper, we have presented different sources of the energy consumption in
wireless sensor networks and major sources of energy waste. In the second part, we have provided several
energy models used in WSN, finally, a comparative study is conducted for two energy models proposed by
Heinzelman et al. and proposed by Halgamuge et al. It permits to help researchers to choice a suitable and
appropriate model to use it in their simulations in order to have good results for their propositions.
In the future work we will provide a large comparative study while take in account other models
such as the model proposed by Zhu et al. [18].
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