Modeling of Energy Consumption for Wired Access Control Systems
M. Oussayran
, J.-C. Pr
, J.-Y. Baudais
and A. Maiga
FDI MATELEC, Cholet, France
Univ. Rennes, INSA Rennes, CNRS, IETR-UMR 6164, F-35000 Rennes, France
Energy Consumption, Energy Model, Polling Protocol, Access Control System, Simulation Model, Wired
Network, OMNeT++.
Access control systems consist in managing access to buildings or any secure area where access is restricted.
This paper presents a model that helps build access control systems along with its internal architecture. This
system is modeled according to the behavior of the access control system. The OMNeT++ network simulator,
in addition to the INET framework, is used to model the behavior of a studied system as well as its energy
consumption. The paper aims to compare the energy consumption of the studied system and its simulated
model with the same working scenario. The challenge is to create a simulation model with a set of configurable
parameters, where users will be able to modify the value of the latter, based on the intended application. By this
way, the simulated model calculates promptly the energy consumption.
Based on the published results on the energy consump-
tion in France (Minist
ere de la transition
2018), the two major consumers sectors are transport
and buildings, followed by the industry sector. Re-
garding the building sector, for new buildings and
major refurbishments, improvements in energy effi-
ciency stemming from increasingly strict greenhouse
gas emissions targets is leading to a focus on techno-
logical improvements (Escriv
a et al., 2010;
Moriarty and Honnery, 2019). The buildings sector is
divided into two sub-sectors: Residential and tertiary
sectors. In 2018, the residential sector in France hits
36 % of the total energy consumption, where 28 %
refers to the energy consumption of the home automa-
tion. A home automation system monitors and controls
home attributes such as lighting, climate, entertain-
ment systems, and appliances. It may also include
home security such as access control and alarm sys-
tems. Since 2019, the home automation market and
especially the smart security was worth US$ 21.8 bil-
lion, and expected to reach US$ 64.4 billion by the
year 2030 (Lee, 2021). Obviously, in case of increas-
ing the demand on the home security systems, the total
energy consumption of smart security systems will
also increase dramatically over the years.
Simply defined, the term Access Control System
(ACS) describes any technique used to control pas-
sage into or out of any area. The standard lock that
uses a brass key may be thought of as a simple form
of an ACS. Over the years, ACSs have become pro-
gressively sophisticated, where different technologies
have widely emerged to improve usability and secu-
rity. Today, this term most often refers to a complex
computer-based or card-based access control system.
The electronic card access control system uses a spe-
cial access card or tag, rather than a brass key, to permit
access into the secured area (Domb, 2019). This sys-
tem is one of the home automation applications, where
its energy consumption might also be considered.
All these systems are ubiquitous in buildings and
becoming more and more complex. They often require
an associated management system that can also be very
sophisticated and power consuming. In this context, it
becomes very important to be able to optimize the per-
formance of such systems while reducing their power
This paper highlights a simulation model of the
ACS is implemented in the OMNeT++ network simu-
lator with the addition of the INET 4.1.2 framework.
Using this framework, we are able to integrate several
functionalities such as the evaluation of the energy
consumption of each electronic component embedded
in the nodes. Also, it provides many MAC protocols
such as CSMA (Sanabria-Russo et al., 2013), SCM-
MAC (Ullah et al., 2013) etc., and makes it possible to
evaluate the performance of a given network and the
energy consumption in particular.
Over the decades, the energy consumption in net-
works has been studied widely (Bouguera et al., 2018).
Oussayran, M., Prévotet, J., Baudais, J. and Maiga, A.
Modeling of Energy Consumption for Wired Access Control Systems.
DOI: 10.5220/0010841300003118
In Proceedings of the 11th International Conference on Sensor Networks (SENSORNETS 2022), pages 144-151
ISBN: 978-989-758-551-7; ISSN: 2184-4380
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reser ved
Recently, many simulations models take into account
the sending, and the receiving activities to estimate
the energy consumption of the system. As well as,
the computation of the hardware components is ex-
tensively considered. Depending on the message rate
and the used duty-cycle, idle listening and message
reception can be even more costly than sending mes-
sages (Lebreton and Murad, 2015) (Le et al., 2013).
That why, we need to consider the communication as
well the computation energy consumption, in order to
estimate the global energy consumption of a system.
These models are developed based on the be-
haviour of real ACS modules and the energy models
are obtained using reference measures of real ACS. Af-
ter simulating the real scenario, we validate the ACS
model of the system by comparing results to those
obtained on a real ACS.
This paper is organized as follows. In Section 2,
we give a brief description of the ACS that is devel-
oped in OMNeT++. Section 3 presents the developed
ACS with its internal architecture including the mod-
eling of energy consumption. Simulations are listed
and discussed in Section 4. Finally, conclusions and
perspectives are given in Section 5.
The architecture of the studied ACS as well as the
communication between nodes are described in the
following parts.
2.1 Architecture
Figure 1 illustrates the architecture of the studied ACS.
This system consists of two interconnected nodes, the
controller node (CN) and the reader node (RN). The
RFID (Radio Frequency Identification) tag is an end
user device used in the ACS. The communication be-
tween the RN and the CN is a wired connection, while
the communication between the end user RFID tag and
the RN is based on RFID technology. Once the RN
detects a tag in the proximity of its antenna, it reads
the tag’s data which they are related to the identity
of the end-user, then it sends a response to the CN.
Afterwards, the latter node will be charged to accept
or refuse the access request.
Nowadays, the ACS has become more and more so-
phisticated by integrating audio and video applications
as well as the radio communication (Barsocchi et al.,
2018) . Such system has emerged several technology
like RFID, Bluetooth, and so on, in order to provide
the multi-solutions provided in one system. Figure 2
shows the internal architecture of both nodes, where
Figure 1: Access control system architecture.
Figure 2: Model of ACS in OMNeT++.
only the main modules are illustrated. Other modules
like RS485, memory, BLE are not considered in the
scope of this paper, where those modules provide op-
tional solutions. The only module considered in this
paper are described as follow.
The Microcontroller.
The Microcontroller (MCU)
manages all the resources utilized for the system oper-
ation. This block is responsible for acquiring output
data, processing data after acquisition, generating new
data and communicate the new generated data to the
B2F circuit.
The B2F Circuit.
The B2F circuit has been invented
by the same company who developed the studied ACS.
This circuit couples power and data on a single wire,
which is also considered as a communication bus.
Through this bus, the reader and the controller nodes
are connected where they communicate according to
the specified communication protocol. Therefore, the
circuit takes in charge the transmission and the recep-
tion of data packets. We should note that the electronic
circuits, related to the B2F implemented in the CN and
the RN, are completely different.
The RFID Module.
The RFID system consists of
a tiny radio transponder, a radio receiver and a trans-
mitter. Through these units, an RFID module is able
to detect a proximity tag near its antenna. The RFID
Modeling of Energy Consumption for Wired Access Control Systems
Figure 3: Polling access control method: (a) Without tag; (b)
With tag.
module is implemented in the RN whereas the tag also
implements a passive RFID system.
2.2 Communication between Nodes
The communication between nodes is based on the
polling protocol, in which master and slave architec-
tures must be selected. In the studied ACS, the CN
is the master node, whereas the RN is the slave node.
Using polling as a controlled access protocol for net-
works, the communication is managed by the master.
The RN then communicates only when it receives a
request from the CN.
The ACS presents a limitation with its connection
capabilities where only one RN is able to be connected
to the CN. In case, we connect more than one RN to
the CN, the communication will not establish due the
the frequent collision occurred on the bus. Figure 3 (a)
and 3 (b) depicts, respectively, the communication
sequences in absence and presence of the end user
RFID tag close to the RN’s antenna.
The polling protocol is as follow. Initially, the CN
requests, by sending a poll request, the RN to verify
if there is any RFID tag close to the RN’s antenna.
After that, the RN verifies through its RFID module
and then responds to the CN. In case where the RN
has not detected an RFID tag, the RN sends a packet
accordingly. Hence, a new polling request will be sent
after a predefined delay. This delay is one on the main
parameters to be integrated in the ACS model. In the
other case, where the reader detect a RFID tag, the
RN responds to the CN, then the CN requests more
information about the detected tag. At the end of
the second case, the CN acknowledges the RN. This
communication sequence refers to the actual scenario
implemented in the studied ACS. The time spent to
read an RFID is difficult to be estimated accurately,
because this time depends on many factors, mainly the
types of the tag, the antenna, etc. .
In the following, we introduce the system model-
ing, where the studied ACS has been modeled includ-
ing the described protocols.
Based on several surveys and research (Patel et al.,
2018), (Kabir et al., 2014), OMNeT++ has been cho-
sen to model the global system. This simulator is a
widely used network simulator by both academic and
research communities. The last ten years have shown
that the OMNeT++ approach is viable, and several
OMNeT++ based open-source simulation models and
model frameworks have been published by various re-
search groups and individuals (Birajdar and Solapure,
2017). One of the main motivations of using OM-
NeT++ is to model the communication channel and
the associated polling protocol, by taking advantage
of the features provided by the INET framework.
In the previous section, we mentioned the main
components used in the ACS. To enable prediction of
the energy consumption, we must take into account a
detailed model of the components embedded in con-
troller and reader nodes. The first challenge for the
prediction of the energy consumption is to build a de-
tailed energy model of the ACS. Our approach for
such a model consists of three steps: (1) We measured
the current consumption of each state of all the ACS
internal modules while running a specific application;
(2) The model derived from these measurements, for
example, the power consumption of the modules states,
is implemented in the ACS model; (3) The model must
be calibrated and simulated according to the measured
3.1 Experimental Setup
An application has been developed and deployed on
the ACS. During the program execution, we are able
to measure the current draw of various combinations
of modules states using a digital multimeter. Figure 4
depicts the methods used to measure the current of
each internal module. The MCU module operates as a
Finite State Machine (FSM), where each state executes
a specific task. Also, the B2F circuit operates based an
its own developed FSM. Those FSM will be discussed
in details later. The multi-meter measures the current
consumption at the input of each module. Furthermore,
the time spent in each operating state is also estimated.
Figure 5 illustrates the measured current of the RN’s
B2F circuit, using the DMM7510 multi-meter. Based
on the developed application, we determined the be-
SENSORNETS 2022 - 11th International Conference on Sensor Networks
Figure 4: Current measurements of the ACS.
Figure 5: The measured current consumption of the RN’s
B2F circuit.
havior of the circuit. Four successive sequences are
mentioned in the latter figure. Each sequence is de-
fined by an operating state. We extracted the draw of
current of each module’s state and calculated its power
Finally, the estimated power consumption of each
module forms our intended energy model for the ACS.
As the ACS is mainly based on electronic components,
potential results might deviate from the power con-
sumption measurements. Table 1 compiles the values
employed to characterize the nodes used in the ACS.
Table 1: Power consumption of the nodes’ modules.
Devices States Power
Idle 26 mW
Transmit 32 mW
and Process
28 mW
Idle 28 mW
Transmit 31 mW
and Process
31 mW
Idle 44 mW
Transmit 10 mW
Receive 49 mW
Idle 20 mW
Transmit 5 mW
Receive 22 mW
Table 2: Main characteristics of the communication cycle
without tag.
Devices States Time
Idle 48 ms
and Transmit
0.9 ms
and Process
1.1 ms
Idle 48 ms
Transmit 0.9 ms
Receive 0.9 ms
Table 2 shows the time spent during the commu-
nication sequence without a tag. The MCUs embed-
ded in both nodes are running with the same clock
frequency. Moreover, the polling request and the re-
sponse have both the same packet length (4 bytes).
Therefore, the generation time and the transmission
time required by the RN’s MCU is identical to the
CN’s MCU. Finally, the idle time spent in the CN’s
MCU is also the same and similar to the idle time of
the B2F circuits.
3.2 Nodes Modeling
Both CN and the RN contain many internals elec-
tronic components such as RFID, BLE, accelerometer,
RS485, Wiegand, etc. . Considering the modeling of
these components, it potentially increases the complex-
ity of the implementation and the simulation time can
be slower than expected. That is why, we decided to
abstract their behavior while guaranteeing a high level
of accuracy when dealing with power consumption.
The implemented design is outlined in Figure 2. In
this work, we look toward modeling the system as a
network, where nodes communicate with respect to
the polling protocol.
Both nodes of the ACS are modeled with their in-
ternal modules, where these modules are implemented
according to their layers. For example, the MCUs
are modeled according to two layers: Application and
MAC, whereas the B2F circuit is modeled according
to its PHY layer. The RFID device, embedded in the
RN, is also modeled as an application layer. The B2F
circuits are modeled as a transmitter and a receiver
modules in both nodes. Figure 6 depicts the mod-
ules’ layers implemented in the reader and the con-
troller nodes. Each modules takes into account only
the power consumption of its internal layer. Therefore,
the power consumption of the RN’s MCU does not
include the consumed power of the RN’s RFID appli-
cation layer. In this paper, we mainly focused on the
energy consumption of the MCUs and B2F circuits.
Modeling of Energy Consumption for Wired Access Control Systems
Figure 6: Layers modeling for ACS’ nodes.
Table 3: Model parameters.
Parameters Explanations
iaTime Inter arrival time between
two detected tags (s)
PacketLength Different packet length
to be assigned (Byte)
Bitrate Communication speed
(actual speed: 38400 kbps)
NbRN Number of connected
reader nodes
MCU Freq Operating frequency of
the MCU (MHz)
MCU States.
To identify the operating state of the
MCU and precisely its application layer during the
operation, we modeled the application layer as a FSM.
This makes it possible to integrate the intended commu-
nication by adding several adjustable parameters. For
example, a given parameter may be related to the time
duration required to process a data packet. Other pa-
rameters are integrated, such as data rate, packet length
etc., where most of them are adjustable to simulate dif-
ferent nodes’ configurations. Table 3 lists some of the
integrated parameters. Leveraging of these parameters,
the energy consumption of the intended application
could be promptly estimated.
Figure 7 illustrates the operating states of the MCU
and its transition sequences for both nodes. Initially,
the MCU is in the idle state. After a predefined delay,
the controller’s MCU switches from its idle state to
the generate state in order to generate the appropri-
ate polling packet. After the generation time which
depends on the running frequency of the MCU, this
latter transmits the generated packet then returns back
to the idle state. The controller MCU receives and
then processes the data packets sent by the reader. The
described states’ transition are related to the controller
MCU, where this sequence is periodically performed.
The following sequence refers to the operating states
Figure 7: The operating states of the MCUs.
Table 4: States of B2F circuit.
Working state Transition state
Idle Idle to transmit
Transmit Idle to receive
occurred by the RN’s MCU: 1, 4, 5, 1, 2, 3 (see Fig-
ure 7) and then, the MCU returns back to the idle state,
waiting for a new request from the CN.
B2F States.
Regarding the B2F circuit, the operating
states are listed in Table 4, as well as the correspond-
ing states’ transition. The B2F circuit is responsible
for transmitting and receiving data accordingly. All
these states are implemented according to the applica-
tion requirements. Based on these states, the energy
consumption has been evaluated.
Note that the B2F circuit is made by off-the-shelf
electronic components (e.g. resistances, transistors).
This circuit has been developed to perform communi-
cation through two wires, where power and data are
coupled on the same wire. This circuit has some lim-
itation and drawback, where the maximum bitrate is
38400 bps and the power consumption during the idle
state is higher than the transmitting state. This power
consumption literally depends on the behavior of the
3.3 Energy Modeling
After modeling the nodes on OMMeT++, we take
advantage of the INET framework to integrate the
power consumption for each element embedded in the
nodes. In this framework, several existing features
were commonly used to model the energy of the ACS.
The energy model computes the energy consumption
of the modules based on the power that is specified
during operating states.
SENSORNETS 2022 - 11th International Conference on Sensor Networks
Figure 8: State-based energy consumption modeling.
Since most implemented layers are modeled as
FSM, in which each operating state of a module is
described, we are able to assign a power consumption
value to each of these states. Each module has its own
energy model, where the energy models of the MCUs
are implemented in the MCU’s application layer, and
the energy models of the B2F circuits is modeled in
the B2F circuit’s PHY layer.
Typically, using simulators, the energy consump-
tion is modeled by rather simple state-based ap-
proaches, where the time
of the module, i.e., the
MCU or the B2F circuit, being in a state
is recorded
and multiplied with the maximum current
in state
as well as the constant supply voltage
to calculate
the energy consumption. We further have to sum up
the energy consumed
by the duration
in each state.
Equation 1 determines
, the overall consumed energy
by a module.
E =
· I
· U (1)
where N expresses the number of operating states for
a module.
Figure 8 illustrates the working sequence of the
MCU module used in both nodes. This module is
in on-state all along the working sequence because it
manages the different operating modes.
During the measurement phase of the ACS, the
power consumption of the ACS tends to fluctuate in
each operating state. As the power consumption in
the operating states is not stable, the maximum mea-
sured power consumption is considered. In that way,
the integrated power in the energy model refers to the
maximum power measured with the ACS. The energy
consumed to generate a packet, transmit it through its
pins, receive a packet, etc. is tracked by the energy
model. Models are able to calculate energy consump-
tion during a predefined simulation time.
We faced some difficulties while measuring the
power consumption during the communication cycle
when RN ’s has a tag. In this case, The estimated time
and power were complicated to be estimated correctly
due to fast fluctuations. Thereby, the energy model
during this communication cycle is not integrated ad-
equately. Thus, the energy model of the CN’s B2F
circuit may deviate.
Table 5: Model parameters of the ACS application scenario.
Parameters Values
iaTime 40 ms
PacketLength polling request
(4 Byte)
Bitrate 38400 kbps
NbRN 1
NbCN 1
Freq. MCU 64 MHz
Table 6: Simulation time of the modeled ACS.
Case Time
Communication without tag 50 ms
Communication with tag 400 ms
In this section, we study the modeled system by simu-
lating the same scenario as the actual ACS. The imple-
mented scenario has been described in Section 2.2.
4.1 Network Simulation
To illustrate the simulation model of the ACS, the con-
sidered application has been developed to compare the
energy consumption of the measured ACS against the
simulated model. Table 5 lists some of the parame-
ters that are calibrated in order to run the simulation
similarly to the measured ACS.
Two communication sequences were integrated in
the simulation model. We ran two different simula-
tions, one is related to the communication sequence
without a tag, the other refers to the communication
sequence along with a tag detected. For each simula-
tion, we were able to measure the energy consumption
during one operating cycle, except for the CN’s B2F
circuit, which was really complex because of the cur-
rent fluctuations. The operating cycle specify the time
between requesting two successive polling request.
Table 6 lists the operating cycle time. While the com-
munication without tag, we estimated that the period
between to successive request is approximately 50 ms.
For the communication sequence with tag detected,
the time depends on many parameters related to the
end user tag and the component’s tolerance embedded
in the RN. Nonetheless, we estimated its period by
attempting several tests using the ACS.
Modeling of Energy Consumption for Wired Access Control Systems
4.2 Simulation Results
In this section, we present the energy consumption
measured as well as simulation using the developed
energy model. Validating is important for reliable and
accurate results. We discussed about the energy mea-
sured using the digital current measurements equip-
ment and the estimated current integrated into the en-
ergy models.
Energy Consumption of the Simulated ACS.
following table 7 lists the energy consumption of the
modules while the RN has not detected tags. The mea-
sured energy consumption is calculated by the equa-
tion 2, where the average current has been calculated
with the ACS.
E = I
· U · T (2)
is the average current measured, and
the time of one operating cycle.
With both equations (1, 2), the voltage
is as-
signed with the adequate value. For example, the MCU
of the CN is powered with 4.8 V, meanwhile the MCU
module embedded in the RN is powered by 3.2 V. The
difference between the measured and the simulated
energy consumption depends mainly on the current
consumption. The error between the simulated and
the measured energy consumption is determined by
the equation 3. This error presents the deviation be-
tween the average current measured and the maximum
current integrated in the energy models.
AbsoluteError(%) =
× 100 (3)
The measured value of the CN’s MCU is approxi-
mately 1.3 mJ, whereas the simulated value is 1.4 mJ.
The main difference in those value is due to current
consumption that fluctuates during operating states,
except in the idle state. An example of the current fluc-
tuation is depicted in Figure9. A packet of response
polling is transmitted to the CN B2F circuit.
The difference between the measured energy con-
sumption and the simulated is less than 5
. We mod-
eled this system with the aim to validate the simulated
ACS along with its energy model. It must be noted
that it is not possible to simulate precisely the behavior
of MCU and B2F circuit compared to their real-time
operations, especially during detecting a tag. This ab-
solute error value is regarded as acceptable given the
lack of power measurement precision. We note that
the power consumption integrated in the energy model
is stable during the operating states. Meanwhile, in
measured ACS, the power consumption oscillates in
uncontrollable behavior.
Figure 9: Current consumption of the transmitting state.
Table 7: Energy consumption without detected tag.
No tags
Measured (mJ) 1.3 7.5 1.4 1.02
Simulated (mJ) 1.4 7.8 1.36 1.03
Error (%) 1.3 3.8 2.8 0.9
Table 8: Energy consumption with detected tag.
Tag detected
Measured (mJ) 115 671k 90.9 64.3
Simulated (mJ) 107 587k 93.02 63.3
Error (%) 6.9 12.5 2.2 1.4
Table 8 lists the energy consumption of the mod-
ules while the RN detects a tag. When RN’s detect
a tag, the measurement was not sufficiently accurate.
Therefore, a deviation of 12.5 % for the CN’s B2F
circuit is observed due to these limitations.
During the first communication sequence,
when the RN has not detected a tag, the communica-
tion was implemented precisely due to its simplicity.
The energy model calculates the energy consumption
based on the current draw for each operating state.
Some parameters related to the operating states has
been reconfigured in order to analyze the feasibility of
the energy model with different application scenarios.
We find that the energy model behaves as expected.
For example, when the parameter iaTime is assigned a
value of 80 ms, we noted that the energy consumption
Regarding the second communication sequence
related to a tag detected by the RN, the difference be-
tween the measured and the simulated energy is due to
the complexity of the communication sequence, espe-
cially for the CN’s B2F circuit. We have modeled the
B2F circuit as a PHY layer working with three operat-
ing states. As the measured and the simulated energy
SENSORNETS 2022 - 11th International Conference on Sensor Networks
consumption are very close, we validated the energy
model implemented in OMNeT++. However, we can
use this modeling for future studies with the aim to
reduce the global energy consumption effectively.
In this paper, we presented the access control system
used to limit the physical access to any secured and
restricted area. The architecture and the internal mod-
ules of the ACS are presented in details. This work
focuses on the energy consumption of the communi-
cation and the computation of hardware devices used
in the studied systems. A simulation environment for
ACS based on OMNeT++ and the INET framework is
described as well. The purpose of this paper is to com-
pare the energy consumption of the studied system and
its simulated model with the same working scenario.
After the implementation and the calibration phases,
both energies were calculated to evaluate the system’s
performance, where we have validated the modeled
system. For future works, we will study and simulate
several configurations in order to achieve better energy
efciency. We will also take into account their impacts
on the ACS quality of service (QoS). In addition, we
argue that evaluating the energy performance with a
single RN is not enough to assess the network. There-
fore, we will evaluate different scenarios dealing with
multiple interconnected systems.
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Modeling of Energy Consumption for Wired Access Control Systems