Wireless Sensor Network, 2011, 3, 18-23
doi:10.4236/wsn.2011.31003 Published Online January 2011 (http://www.SciRP.org/journal/wsn)
Modeling of Node Energy Consumption
for Wireless Sensor Networks
Hai-Ying Zhou, Dan-Yan Luo, Yan Gao, De-Cheng Zuo
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Received January 4, 2011; revised January 7, 2011; accepted January 9, 2011
Abstract
Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model
that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol
development, system design and performance evaluation in WSNs. In this paper, by studying component
energy consumption in different node states and within state transitions, the authors present the energy mod-
els of the node core components, including processors, RF modules and sensors. Furthermore, this paper re-
veals the energy correlations between node components, and then establishes the node energy model based
on the event-trigger mechanism. Finally, the authors simulate the energy models of node components and
then evaluate the energy consumption of network protocols based on this node energy model. The proposed
model can be used to analyze the WSNs energy consumption, to evaluate communication protocols, to de-
ploy nodes and then to construct WSN applications.
Keywords: Wireless Sensor Networks, Energy Model, Event-Trigger
1. Introduction
Currently, researches on the basic theories and system
models of WSN (Wireless Sensor Networks) are not
perfect, especially due to lack of a set of WSN models
that can accurately reveal WSN characteristics [1]. Tra-
ditional sensor networks address the system QoS, in
which the node energy supply is unlimited and the en-
ergy issue is not the main prob lem for its application [2].
In WSN applications, in views of limited power resource
(batteries) and long lifetime requirement, the energy
consumption becomes the core issue in WSN designs.
Most of studies focus on the energy efficiency and opti-
mization techniques for the design of WSN platforms,
system software and protocols. Some WSN system mod-
els and simulation technologies have been studied, but
these studies generally focused on the development of
simulation platforms and modeling of communication
protocols, being lack of the basic system model for plat-
forms and protocols. Some typical simulation tools and
platforms, such as OPENET, NS2, SHAWN, SensorSim,
EmStar, OMNet, GloMoSim, TOSSIM, PowerTOSSIM
etc., are implemented basing on the traditional mathe-
matical models of wireless network, which can not accu-
rately reflect the features of WSN applications, resulting
in low precision simulation. In addition, current studies
are lack of modeling of WSN energy consumption, and
thus cannot evaluate the key performance indicator of
WSN-system lifetime. Traditional energy modeling me-
thod is to deduce the node energy consumption by ana-
lyzing and modeling the energy consumption of each
node module based on the theoretical energy consump-
tion data or model of different modules. This method
cannot analyze and evaluate the node energy situations
from the views of the energy supply module (battery)
and the energy consumption modules, i.e., processor
module (PM), transceiver module (TM) and sensor mod-
ule (SM), in different operation mode [3].
WSN applications can provide the unattended and
long-time surveillance services, which implements the
basic function of signal collection, processing and
transmission. Due to low accuracy caused by error or
inaccurate system models, current WSN simulation
platforms (tools) have seriously affected the protocol
development, system construction and performance ana-
lysis of WSN system, and thus impeded the WSN in-
dustrialization. This paper addresses the research on the
WSN energy modeling, aiming to provide the accurate
energy model of WSN node to improve the simulation
accuracy.

H. Y. ZHOU ET AL.
19



n
2. Node Energy Modeling
WSN nodes consist of several functional modules: Micro-
processor, Transceiver, Sensor, and power supply modules.
By studying the energy consumption issues of node com-
ponents in different component states and during state
transitions, this section presents the energy models of the
processor module, communication module and sensing
module of WSN nodes, and then establishes the node en-
ergy model by adopting the event -driven m echani sm .
2.1. Processor Energy Model (PEM)
1) Processor Operation State: The processor module is
the node control and data processing center, being re-
sponsible for sensor controlling, protocol communicating
and data processing, and etc. The microprocessor nor-
mally supports three operation states (sleep, idle, run)
and has five state transitions [4], shown in Figure 1:
2) Processor Energy Function: Processor energy con-
sumption is the sum of the state energy con-

cpu
E

E
sumption and the state-transition energy
consumption , as in (1), while ,
-cpu state

-cpu c
Ehange 1, 2,,im
i is the processor operation state and m is the number of
the processor state ; j is the type
of state transition and n is the number of the
state-transition

3m1, 2,,,j


5
E
n.
11
()()( )( )
cpucpu statecpu change
mn
cpu statecpu statecpu changecpu change
ij
EE
P
iTi Njej

 




(1)
where is the power of state i that can be
found from the reference manual, and

cpu state
P
i


cpu state
T
i
j
is the
time interval in state i which is a statistical variable that
can be calculated in this processor energy model.
is the frequency of state transition j, and
cpu change is the energy consumption of one-time
state transition j. To simplify the processor energy mod-
eling, which can be expressed as in (2).

hange j

j

cpu change
e

cpu c
N

e
()()( ()())/2
cpu changeinit endinitend
ejTjPjPj

 (2)
Figure 1. State transition diagram of PM.
where


init
Pj and


end
Pj are the power of state init
and end in the state transition j, is the time
interval for the state transition j from the init state to the
end state. The power of state transition is considered as
the average power of state init and end.

init end
T
j
E
2.2. Transceiver Energy Model (TEM)
1) Transceiver Operation State: The communication
module includes baseband and RF, being responsible for
nodes data sending and receiving. The transceiver nor-
mally has six states (Tx, Rx, Off, Idle, Sleep, CCA/ED)
and nine state transition [5], shown in Figure 2.
2) Transceiver Energy Function: Being similar to the
processor energy function, Transceiver energy consum-
ption (Etrans) is the sum of state energy consumption
(Etrans-state) and state-transition energy consumption
(Etrans-change). Etrans-state can be expressed as in (3).
11
11
//
//
()
TX RX
TX RX
trans stateTXRXIdlesleepCCA
NN
TXiRXiIdle Idle
ii
sleepsleepCCA CCA
NN
trTXitrRX i
ii
trIdleIdlesleepsleepCCA CCA
EEEEE
PL RPL R PT
PT PT
VILR VILR
VITITI T




 






(3)
where Ex, Px, Ix, and Tx are the energy consumption, the
power, the electric current and the time interval of
transceiver in state x. Vtr is the working voltage, Li is
the size length of the ith packet of receiving or sending,
R is the data transferring rate, Ntx and Nrx are the total
numbers of sending and receiving packets. Etrans-change
can be expressed as in (4), where , j is the
type of state transition and n is the number of the
state-transition (n = 9). trans c is the fre-
quency of state transition j, and
1, 2,,j

hange
Nj
n


trans change
e
j
j
is the
energy consumption of one-time state transition j,
which can expressed as in (5).
1() ()
n
trans transitiontrans changetrans change
j
ENje


 (4)
()()( ()())/2
()( ()())/2
trans changeinit endinitend
trinit endinitend
ejTjPjPj
VTj IjIj





(5)
2.3. Sensor En ergy Mode l (SE M)
The sensing module consists of sensors and digital-ana-
log converters, be responsible for the information collec-
tion and digital conversion. The energy consumption of
the sensing module come from multiple operations, in-
cluding signal sampling, AD signal conversion, and sig-

20 H. Y. ZHOU ET AL.
nal modulation, etc. The sensing module can operate
either in burst or periodic mode.
In this paper, the sensing module is supposed to oper-
ate in the periodic mode, in which sensors are opened
and closed periodically and then the module enters the
‘on’ and ‘off’ states alternately. Supposing the energy
consumptions of the ‘open’ and ‘close’ operations are
constant, the sensor energy consumption (Esensor) can be
expressed as in (6).
()
s
ensoron offoffonsensorrun
on offoffonsss
EEEE
Nee VIT





(6)
where eon-off is the one time energy consumption of clos-
ing sensor operation, eoff-on is the one time energy con-
sumption of opening sensor operation, Esensor-run is the
energy consumption of sensing operation. Vs and Is are
the working voltage and current of sensors, Ts is the time
interval of sensing operation, N is the number of sensor
opening and closi ng operat i on .
2.4. Nole Energy Model (NEM)
In real systems, the processor, transceiver and sensor
components of WSN nodes must work cooperatively to
perform a task, and thus have mutual relationships espe-
cially concerning with the energy issue.
The node modules perform tasks and state transitions
triggered by different external or internal events. When
taking into account the origination of events, they can be
classified into two types: events coming from outside and
inside of the node energy model. The external events are
triggered by external requests or commands, for example,
external clock interrupts, sending packet requests, packet
arriving action, channel detection commands and etc; the
internal events are triggered by internal responses or ac-
tions come, such as sending packet action, receiving
packet action and periodical data collection. The node
overall energy consumption model is sho wn in
Figure 2, and the event-driven mechanism of different
node modules is as fol l ows:
 Event trigger in the sensor module: The sensor en-
ergy model (SEM) enters the ‘on’ state periodically
triggered by the external clock event. After sensing
and statistically calculating sensor energy con-
sumption, the senor module enters ‘off’ state auto-
matically.
 Event trigger in the processor module: The proces-
sor energy model (PEM) enters the ‘run’ state trig-
gered by the following three events: the periodical
data collection event generated by the sensor energy
model; the sending packet requests generated by
external applications or protocols; the packet arriv-
ing action generated by the transceiver energy
Figure 2. Event trigger mechanism of NEM.
model (TEM).
 Event trigger in the transceiver module: The trans-
ceiver energy module (TEM) enters the ‘Tx’ state
triggered by the sending packet event generated by
PEM, enters the ‘Rx’ state triggered by the external
packet arriving action, and enters the ‘CCA/ED’
state triggered by channel detection commands.
3. Simulation of Energy Models
In order to simulate and evaluate the node energy model
in OPENET simulation environment [6], we suppose a
WSN node that consists of a Intel Strong ARM SA-1100
Microprocessor [7], a Chipcon CC2420 transceiver [8]
and a Dallas digital temperature DS18B20 [9]. Table 1
presents the state power and state transition time of Intel
Strong ARM SA -1100 and Table 2 lists the state current
and state transition time of Chipcon CC2420. The work
voltage (Vs) and current (Is) of DS18B20 are 5V and1mA,
and the conversion ti me is 750 ms. Supposing the switch
energy consumption of DS18B20 are eoff-on = 0.0002 J
and eon-off = 0.0001 J.
In this simulation, the node model adopts the AODV
routing protocol [10]. The simulation time length is set to
200 s: in 0 – 50 s, data is sent with 1s period; in 50 – 130 s,
data is sent by Possion distribut ion wit h the expected val ue
of 10 s; after 130 s, data is sent with 1s period. The range
of wireless communication is 200 m. The analysis of en-
ergy consumption is based on a random selecti ng node.
Table 1. State power and transition time of Strong ARM
SA-1100.
State Power(mW)
Run 400
Idle 50
Sleep 0.16
State transition Class State transition time
Trun-idle 10 µs
Tidle-run 10 µs
Tidle-sleep 90 µs
Trun-sleep 90 µs
Tsleep-run 160 ms

H. Y. ZHOU ET AL.
21
Table 2. State current and transition time of CC2420.
State Current
Ioff 0.02 µA
Itx 17.4 mA
Irx 19.7 mA
Iidle 426 µA
Icca/ed 17.4 mA
Isleep 20 µA
State transition Class State transition time
Toff-idle 1 ms
Tcca/ed-idle 2 µs
Tidle-cca/ed 192 µs
Tsleep-idle 0.6 ms
Tidle-sleep 192 µs
Ttx-idle 2 µs
Tidle-tx 192 µs
Trx-idle 2 µs
Tidle-rx 192 µs
3.1. Simulation of PEM
This test is to evaluate the energy consumption tend ency
of PEM in different states. In the simulation results
shown in Figure 3, ARM SA-1100 consumes the energy
of 65J when running 200 seconds. The trend of PM en-
ergy consumption depends on the processes tasks: packet
transmission and route maintenance. PM has different
energy consumption in different states: the Run one has a
majority of energy consumption which determines the
energy change trends, the Idle one follows and the
SLEEP one is at least. Furthermore, the energy con-
sumption for state transitions is very small, which is al-
most negligible. Hence, increasing sleep time and low-
ering power consumption in the state is the main solu-
tions for reducing processor energy consumption.
3.2. Simulation of TEM
This experiment is to evaluate the energy consumption
tendency of TEM in different states. Figure 4 explores
that the system load conditions determines the trends of
energy consumption, while the data packet sending has a
great impact on the system loads. In this experimental
load conditions, TM has the largest energy consumption
in the RX state, followed by the TX state. The RX one
has more than half of the total energy consumption, hav-
ing the greatest impact on the energy trend curve. That
because, in this experiment, all packets within the sens-
ing area will be received by nodes and the node deter-
mines to accept or discard the packet, so that the number
of packet receiving is much greater than the number of
packets sent. The energy consumption of TM is similar
in the states of IDLE, CCA/ED and state transition, and
the Sleep one is at least. Therefore, to meet the needs of
WSN applications, TM should adopts multi-hop short-
range wireless communication, so that more nodes can
be in the sleep state to reduce power consumption and to
decrease the single-hop communication distance.
3.3. Simulation of SEM
This experiment compares the energy consumption of
SM in different sensing periods (Ts = 5 s and 10 s).
Figure 5 indicates that the SEM energy consumption
curve is a straight line, while the slope of the 10 s line is
twice than the 5 s one, that is, the energy consumption in
the 10 s period is twice than the 5 s one. Figure 5 further
reveals that the energy consumption of SEM is inversely
proportional to the sensing period, which has no related
to the protocol and load traffics.
3.4. Evaluation of NEM
The experiment evaluates the influence of PM, TM and
SM to the overall energy consumption of the node, and
analyze the node energy consumption in certain circum-
stances within different locations. This experiment adopts
MSP430f149 microprocessor [11], CC2420 communica-
tion module, a nd DS18B20 temperature sensor.
020406080100 120 140 160 180 200 220
0
10
20
30
40
50
60
70
Energy(J)
Time(S)
change_energy
cpu_energy
idle_energy
sleep_energy
run_energy
Figure 3. PEM state energy consumption.
020406080100 120 140 160 180 200 220
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
Energy(J)
Time(S)
CCA_energy
idle_energy
TX_energy
sleep_energy
RX_energy
radio_energy
change_energy
Figure 4. TEM state energy consumption.

22 H. Y. ZHOU ET AL.
020406080100 120 140 160 180 200 220
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.11
0.12
0.13
0.14
0.15
0.16
0.17
Energy(J)
Time(S)
sensor_10sec_energy
sensor_5sec_energy
Figure 5. SEM energy consumption.
050100 150 200 250 300
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Energy(J)
Time(S)
cpu_energy
node_energy
radio_energy
sensor_energy
Figure 6. Single node energy consumption in DSR protoc ol.
This experiment deploys a 1000 × 500 WSN sensing
area within a random distribution of 20 nodes. The WSN
nodes employ the routing protocols with DSR [12], the
system tasks including sending data packets and main-
taining routing protocol. The simulation time length is
set to 300 s: from 0 – 50 s, data is sent with 3 s period;
from 50 – 130 s, data is sent by a passion distribution
with the expected value of 10 s; from 130 s to 230 s,
data is sent with 1s period; from 230 – 300 s, data is sent
by a passion distribution with the expected value of 5 s.
The range of wireless communication is 200 m. The
analysis of energy consumption is based on a random
selecting node.
Figure 7 shows the energy consumption of node and
modules under the DSR protocol, in which TM has the
largest energy consumption, followed by PM, and SM has
the least consumption. Hence, TM is the main energy unit
in a WSN node. In DSR protocol, the energy consump-
tion situation from node 0 to node 19 is shown in Figure
7, in which we can find that the edge nodes consume less
energy than the center nodes because the center nodes
carry more routing tasks in WSN networ ks.
0510 15 20
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Energy(J)
Node ID
node_energy
Figure 7. Multi-node energy consumption distribution in
DSR protocol.
0510 15 20
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Energy(J)
Node ID
node_energy
Figure 8. Multi-node energy consumption distribution in
DSR protocol.
4. Conclusion
Current WSN researches focus more on communication
protocols than on energy consumption modeling. Tradi-
tional energy analysis method is to deduce the energy
consumption statuses of nodes and networks based on
the theoretical energy consumption data or theoretical
models of system components. Most of existed energy
models only analyze the energy status of communication
module, being lack of studying the overall energy con-
sumption from the view of nodes. By modeling the en-
ergy consumption of different node components in dif-
ferent operation modes and state transitions, this paper
proposes a new node energy model based on the event-
trigger mechanism. This model can be used to analyze
the energy status of WSN nodes and systems, to evaluate
the communication protocols and to help to deploy nodes
and construct WSN applications.

H. Y. ZHOU ET AL.
23
5. Acknowledgements
The authors would like to thank all the colleagues and
copartners who have contributed to the study. The work
was supported by the grants from the Doctoral Fund of
Ministry of Education of China (200802131024), the
Harbin Science and Technology Development Funds
(2009RFLXG009), the National Key Technologies R &
D Program of China (No. 2011BAH04B03), and the In-
ternational scientific cooperative research program of
China (2010DFA14400).
6. References
[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E.
Cayirci, “Wireless Sensor Networks: A Survey,” Com-
puter Networks, Vol. 38, No. 4, March 2002, pp. 393-422.
doi:10.1016/S1389-1286(01)00302-4
[2] I. Howitt and J. Wang, “Energy Efficient Power Control
Policies for the Low Rate WPAN,” Sensor and Ad Hoc
Communications and Networks, Vol. 10, 2004, pp. 49-52.
[3] B. Han, D. Z. Zhang and T. Yang, “Energy Consumption
Analysis and Energy Management Strategy for Sensor
Node,” International Conference on Information and
Automation, Proceedings of the 2008 IEEE, Vol. 6, 2008,
pp. 211-214.
[4] F. Lu, G. Z. Xu and R. D. Ying, “Power Consumption
Simulation Model Based on the Working Status of Intel
PXA250 Processor,” Control & Automation, Vol. 21, No.
1, 2005, pp. 131-132.
[5] X. F. Wang, J. Xiang and B. J. Hu, “Evaluation and Im-
provement of an Energy Model for Wireless Sensor Net-
works,” Chinese Journal of Sensors and Actustors, Vol.
22, No. 9, 2009, pp. 1319-1321.
[6] X. Li, “Network Modeling and Simulation with OPNET
Modeler,” Xidian University Press, Xi’an, 2006, pp. 1-15.
[7] Intel Corp., “Intel StrongARM SA-1100 Microproces-
sor,” Developer’s Manual, August 1999.
[8] V. Tsiatsis, S. Zimbeck and M. Srivastava, “Architecural
Strategies for Energy Efficient Packet Forwarding in
Wireless Sensor Networks,” Proceedings of the 2001 In-
ternational Symposium on Low Power Electronics and
Design, Vol. 3, 2001, pp. 25-31. doi:10.1145/383082.383
102
[9] T. H. Jiang and Z. W. Jiang, “Characteristics and Appli-
cation of the Digital Temperature Sensor DS18B20,”
Electronic Technology, Vol. 12, 2003, pp. 46-49.
[10] Y. Bin and S. Bin, “Modify AODV for MANET/INTER-
NET Connection through Multiple Mobile Gateways,”
Advanced Communication Technology, Vol. 11, No. 3,
2009, pp. 1519-1523.
[11] Q. Yang, X. H. Chen and J. H. Shi, “Low Power Design
of the Terminal Node for Wireless Sensor Network,”
Journal of Xiamen University (Natural Science), Vol. 47,
No. 3, 2008, pp. 357-358.
[12] W. Huang, N. Y. Li and Q. Sun, “Accomplishment and
Simulation for Dynamic Source Routing (DSR) Protocol
Based on OPNET in MANET,” Journal of Wuhan Uni-
versity of Technology, Vol. 29, No. 2, 2005, pp. 182-185.