Improving the performance of IoT devices that use Wi-Fi

International Journal of Reconfigurable and Embedded Systems (IJRES)
Vol. 13, No. 3, November 2024, pp. 748∼757
ISSN: 2089-4864, DOI: 10.11591/ijres.v13.i3.pp748-757 ❒ 748
Improving the performance of IoT devices that use Wi-Fi
Ali Ahmed Razzaq, Kunjam Nageswara Rao
Department of Computer Science and Systems Engineering, College of Engineering, Andhra University, Visakhapatnam, India
Article Info
Article history:
Received Nov 10, 2023
Revised May 26, 2024
Accepted Jul 3, 2024
Keywords:
Identity management system
Internet of things
Machine learning
Power consumption
Quality of service
ABSTRACT
Providing quality service to users of the internet of things (IoT) entails address-
ing two crucial aspects: one related to security and the other concerning the
limited resources of IoT devices. We will face a challenge while using time-
sensitive applications within a network that utilizes a high-performance Wi-Fi
technology with exceeding energy consumption. Due to this research challenge,
we propose a new algorithm, IoT-quality of service (QoS), designed to achieve
a true balance between enhancing the security aspects of IoT devices and im-
proving network-hardware performance. Thus, the algorithm efficiently man-
ages the limited energy resources by monitoring energy levels, communication
quality, and queuing delay at access points. This is accomplished by utilizing
a streamlined identity management system capable of achieving authentication
and access authorization with reduced loading for IoT devices. The research hy-
pothesis underwent validation through a comparative analysis of its performance
against the conventional model of a Wi-Fi-based IoT device. This evaluation was
conducted utilizing the NS3 simulator and was based on a predetermined set
of parameters influencing the examined performance metrics, including power
consumption, throughput, delay, and response time. The findings exposed the
superiority of the proposed algorithm.
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ali Ahmed Razzaq
Department of Computer Science and Systems Engineering, College of Engineering, Andhra University
Visakhapatnam 530003, Andhra Pradesh, India
Email: taifali607@gmail.com
1. INTRODUCTION
The quality of service (QoS) is a parameter that assesses the overall performance of a service, par-
ticularly the performance observed (experienced) by service users. In light of the extensive utilization and
implementation of internet of things (IoT) services in our day-to-day activities, it becomes essential to lower
the expenses associated with IoT devices, all while ensuring that the level of provided QoS remains uncom-
promised. Also, Under the concept of the IoT, there are countless devices with different characteristics and
capabilities. So to improve the QoS associated with IoT, two key elements should be ensured, namely: network
security to achieve privacy and the security of network resources, and the efficient administration and allocation
of network resources. Prominennt research papers in IoT devices and QoS can be found in [1]-[6].
The concept of device isolation is crucial, as it prohibits direct access from the internet, ensuring
prevention of unauthorized access and privacy violations. Both authentication and access authorization pose
challenges for the IoT, given its divergence from traditional internet components, wherein IoT devices are
predominantly purpose-specific and typically have limited resources. The identity management (IdM) system
provides both authentication and access authorization for internet users (user identity information management)
This system consists of four components, as illustrated in Figure 1, namely entities (users or devices), identifiers
(entity Identities), identifier provider (IdP), and service provider (SP).
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Int J Reconfigurable & Embedded Syst ISSN: 2089-4864 ❒ 749
Figure 1. Components of the IdM system
Efforts and research have been directed towards proposing several approaches that could leverage
IdM with IoT, specifically in the realms of authentication and access authorization schemes. A proposed
authentication and access control framework for IoT devices considered devices as final entities in the internet
architecture, communicating through unique IPv6 addresses. It utilized the OpenID protocol for authentication
and the role-based access control (RBAC) protocol for access authorization. However, the proposal did not
address single sign-on (SSO) issues and did not highlight any results that could validate the suggestion carried
out by Liu et al. [7]. In Chibelushi et al. [8] studied an IdM system for IoT in the healthcare context, but it
failed to provide secure communication, leaving IoT devices accessible directly from the internet. Later, Leo et
al. [9] utilized web services between the internet and IoT to ensure confidentiality and security of transmitted
information. This study, however, is not considered to be capable of securing end-to-end security interaction
between the internet and IoT, secure communication channels, or even a SSO service. However, Witkovski et al.
[10] suggested integrating IdM with IoT to maintain SSO and data encryption between communicating parties.
However, the study did not provide any results related to power consumption, especially considering that the
provision of SSO is based on encryption keys. Recenctly, Santos et al. [11] introduced the unified federated
lightweight authentication of things (FLAT) authentication protocol, combining symmetric encryption systems
and embedded certificates, bypassing the principles of asymmetric/symmetric encryption used in traditional
federated IdM systems. Yet, it did not take into account access authorization processes and service discovery.
Other studies about using IoT with IdM are found in [12]-[17].
This research aims at leveraging artificial intelligence (AI) techniques to accomplish this task, given
their notable presence in addressing the challenges posed by the upcoming generations in the field of wireless
communications. In this research, we use IoT devices employing Wi-Fi technology for network connectivity.
This choice is based on the widespread use of wireless local area networks (WLAN) in the unlicensed spectrum,
highlighting the increasing complexity in wireless networks. Quality of communication standards in the IoT
must ensure stability and accuracy for the utilized learning technology. Sindjoung and Minet [18] distinguished
between two types of communication quality standards, one is hardware-dependent and the other is software-
dependent. Hardware-dependent standards directly collect data from the devices without preprocessing and
include indicators such as received signal strength indicator (RSSI), link quality indicator (LQI), and signal-
to-noise ratio (SNR). The precision provided by hardware-dependent standards is insufficient for two main
reasons. Firstly, only successfully transmitted packets are considered, and secondly, the evaluation does not take
into account the entire packet but only its initial symbols. However, acquiring its values requires undertaking
computational operations, namely: packet delivery ratio, the required number of packet transmissions, and the
degree.
As attaining performance quality for the IoT network is the primary objective, it is necessary to con-
sider the allocated resources and attempt to utilize them optimally based on the outcomes of machine learning
algorithms (i.e., channel state). Channel access is often congested simultaneously, particularly when a large
Improving the performance of IoT devices that use Wi-Fi (Ali Ahmed Razzaq)

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number of devices connect to the same wireless channel at the same time. Consequently, the channel becomes
overloaded, So Ma et al. [19] proposed a deep learning-based channel allocation algorithm, applying time of
wait (ToW) for selecting communication channels in massive cognitive IoT networks. Their results demon-
strated significant improvement in interference detection compared to traditional methods not relying on deep
learning. Energy allocation and interference management are crucial aspects affecting IoT networks. For this
reason Lynggaard [20] proposed a dynamic system for interference detection and energy allocation, based on
the interference level in radio channels. They applied the channel state information (CSI) algorithm to predict
transmission energy levels (based on CSI). Considering that many IoT devices are small-sized with limited bat-
tery capacity, intelligent management and allocation of this scarce resource are essential. Hence, Zekić-Sušac
et al. [21] suggested an AI based energy management system for smart cities relying on IoT. Neural networks,
decision trees, and random learning methods were employed to predict energy consumption in those cities,
demonstrating improved energy consumption predictions compared to non-AI-based approaches.
In Becvar et al. paper’s [22], it was found that predicting channel quality using machine learning,
leveraging network correlations, proved efficient in reducing overall expenses compared to the traditional pilot-
based approach, exceeding 90%. It’s noteworthy that the study network includes a large number of nodes that
communicate with each other. Yet, another study Torres-Alvarado et al. [23] emphasized the importance
of adopting machine learning algorithms to predict channel quality (low or high) for IoT networks, where
authentication processes are affected by noise and radiation (associated with channel quality), especially when
implemented in hardware (such as cognitive radio devices). According to their experiments, the random forest
algorithm achieved the highest classification accuracy of 95.54%.
In our presented research, we seek to preserve service quality with both its key elements via a two
fold strategy. Firstly, we utilize a modified IdM to enhance cybersecurity. Secondly, we adopt AI techniques
to predict communication quality. This is coupled with monitoring energy levels and queuing delays at access
points to efficiently manage the energy resource in IoT devices. This will be achieved without adversely
impacting latency, recognizing it as a critical criterion for time-sensitive applications. This paper is structured
as follows: the proposed algorithm presented in section 2, In section 3, discussion and results. Finally, section
4 concludes our paper.
2. THE PROPOSED ALGORITHM
In this section, it is essential to review the key points upon which our research proposal is based,
aiming to achieve the research goal, before delving into the detailed operational mechanisms (as outlined in the
flowchart review). The foundational aspects of the work are divided into two parts according to its objective.
2.1. Fundamentals of resource management mechanism
In this research, we rely on several key points to accomplish our work. Due to the limited resources of
IoT devices, effective resource management translates to enhancing the quality of service provided to network
users. In our proposed algorithm, the focus is directed towards the limited energy resource and how to effi-
ciently utilize it while employing Wi-Fi as a means of data exchange. This includes considering the potential
delays introduced by energy-saving measures and mitigating their impact on time-sensitive applications. In
the following argument, we will review the mathematical models employed to check both parameters. More-
over, we will identify the machine learning algorithm that will contribute to enhancing energy efficiency by
encouraging the wireless card to enter a sleep mode when the channel quality is poor.
2.1.1. Power consumption model
The proposed approach depends on predicting connection quality while concurrently monitoring net-
work load directed towards the activated IoT device in power-saving mode (i.e., when it is in sleep mode)
facilitated by Wi-Fi. However, such prediction and monitoring are contingent on the device remaining power
capacity. It is crucial to emphasize that the algorithm necessitates dependence on a mathematical model to
compute the wireless card’s power consumption, as defined by (1) [24].
Pavg =
PT x ∗ TT x + PRx ∗ TRx + PI ∗ TI + PS ∗ TS
T
(1)
As it is revealed by the former equation, the lifetime of the wireless card stays in each of its operating
modes (transmitting TT x), receiving TRx, idle TI, and sleeping TS) is multiplied by the basic power consump-
tion value of the mode (transmitting mode PT x, receiving mode PRx, idle mode PI, and Sleeping mode PS),
Int J Reconfigurable & Embedded Syst, Vol. 13, No. 3, November 2024: 748–757

gives a simplified model for calculating consumption, noting that the symbol T indicates the simulation time
(the sum of the presence times in the operating modes).
2.1.2. Average delay
The proposal did not overlook the nature of the transmitted data, taking into consideration the exis-
tence of two types or classifications of data, one of which requires calculating the delay standard, given that it is
time-sensitive (as in critical industrial applications). Therefore, the average delay experienced by a data packet
destined for an IoT device depends on the power-saving mode provided by Wi-Fi communication technology,
with consideration of two factors, one of which is the probability of the packet arriving while the wireless card
of the IoT device is in sleep mode Prsleep, and this leads to a delay in the queue of the access point, considering
that notification of its existence (in order to be recovered by the device) will be made only at the beginning of
the next beacon period. Not only that, but there is another waiting(delay) that the stored packet suffers from,
with the beginning of the beacon period, namely the serving time of the packets that precede it in the queue of
the access point ¯
d|(sleepn). Another factor contributing to the calculation of the average delay criterion is the
average recovery time of packets stored at the access point by the IoT device, after waking up davg. Based on
the above, the average is given according to (2) (see [25], [26]):
DSIn
= Prsleep ∗ ¯
d|sleepn
+ davg (2)
2.1.3. Communication quality prediction
Machine learning enables systems to offer dynamically learning and enhance performance without
being explicitly programmed. There exist both linear and non-linear models for machine learning techniques
(see [12]). The random forest classifier was used as part of the proposed algorithm for assessing the quality of
network communication, based on two standards. These are: the RSSI by the IoT device, which is a simple
hardware standard that can provide an accurate and fast estimate of the quality of communication (see [22]).
The RSSI average of an IoT device retrieving data packets from the access point (AP) (which is numbered n
packets during the beacon period) is given according to (4) (see [18]):
RSSIavg =
Pn
i=1 RSSIi
n
(3)
Furthermore, there is the standard called packet delivery ratio (PDR), which is a software standard
equal to the ratio of the number of packets successfully received by an IoT device (successful receipt necessarily
means the recipient sending an acki notification to the sender (which is the access point here)) to the number
of packets. Packet j sent by the access point at the beginning of each beacon period and is given according to
the relationship as in (4) (see [18]):
PDR =
Pn
i=1 acki
Pm
j=1 packetj
(4)
2.2. Fundamentals of security-related operational mechanism
The research objective is to achieve service quality in IoT networks, and true service quality cannot be
attained without considering the security aspect of the network. The proposed algorithm relies on the concept of
IdM system to execute authentication and access authorization operations, yet the adopted system is a modified
one.
2.2.1. The used identity management system
The modified IdM system adopted in the proposed algorithm depends basically on two fundamental
points. Firstly: using contextual parameters that distinguish the user (such as its identity, role, activities,
location, whether physical (global positioning system (GPS)) or virtual (internet protocol (IP) address) and the
social networks it utilizes, in addition to the type of data that determines the sensitivity of the data), within the
user identifiers, in which they will participate in the access control mechanism. Secondly: encryption of the
transmitted data at two levels using two encryption keys key encryption key (KEK) (encrypts the content of
messages exchanged during the session), and MKK (key encryption KEK), where the ANSI X.9.17 standard is
used to manage the distribution of keys (see [10]). In accordance with what have been discussed, an IoT device
should implement two programmed modules (see [8]): i) the context unit, composed of two sub-modules. One
sub-module deals with identifiers and utilizes them within an algorithm that filters response content to serve the

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request. The other sub-module focuses on constructing identifiers based on requests from users or IoT device
users; and ii) the privacy unit, responsible for sending requests and receiving responses subject to authentication
and authorization processes through dedicated servers external to the IoT network (the crucial point here is the
offloading of privacy policy burdens from the IoT device, contrary to the study), in addition to the required
encryption and decryption operations.
2.3. The proposed algorithm internet of things-quality of service
The energy of the IoT device is considered a vital and crucial resource in the network. It should not
be compromised, as preserving it without neglecting service quality is essential to meet the users’ expectations.
Therefore, we proposed the IoT-QoS algorithm, which operates as follows:
- The algorithm is invoked at the beginning of each Beacon Frame guidance period when the wireless card of
the IoT device wakes up and receives the guidance frame. The device utilizes a power-saving mode supported
by Wi-Fi technology.
- The algorithm first looks at the device’s battery power as the threshold for decision-making in maintaining
service quality. Predicting poor connection quality is done using the random forest classifier, relying on the
RSSI and PDR metrics or a drop in the IoT device’s energy level (PIoT ) below the threshold (PT hre). In such
cases, the Wi-Fi radio is turned off (transitioning the wireless card to sleep mode), and entering sleep mode
for the longest possible period (Sleepmax) helps extend the device’s operational lifespan due to low power
consumption in this state. Furthermore, there is a need for packet aggregation for uplink data. The wireless
card transitions from sleep mode to an active state when data packets are available in the transmission queue.
Failure to aggregate data would result in transmission operations at a low transfer rate (due to poor channel
conditions), increasing power consumption.
- If the prediction indicates good connection quality and the device’s energy level is higher or equal to the
threshold level (PT hre), and there are no stored packets awaiting transmission, the card is put into sleep
mode for a duration equal to twice the previous sleep period. This is because data movement on the internet
occurs in the form of bursts (see [27]).
- If the IoT device possesses stored data packets at the access point, it will perform the required network
transmission and reception operations. Here, a distinction is made based on whether the stored data is a
data request (external query) accepted in terms of authentication and authorization (subject to the modified
IdM system). In this case, an algorithm is invoked to provide packet fragmentation to reduce the amount
of data to be sent according to the request context, relying on contextual information used to isolate users.
Yet, if the transferred data is time-sensitive, the average delay of the retrieved packets must be calculated,
and the duration of the wireless card’s stay in sleep mode is reduced to the minimum value assigned if the
imposed time constraint is not met. This condition serves as a real constraint for any delay caused by both
data aggregation and entering sleep mode operation.
It is worth noting that the network transmission and reception operations for the mentioned data types
are conducted according to a higher access priority to the wireless medium, compared to traditional IoT user
data. This is achieved by granting IoT devices applying the proposed algorithm a shorter back-off time than
those not using it (traditional devices). The latter applies the binary exponential increase for back-off time
(distributed coordination function (DCF) access pattern), while our algorithm relies on linear increase. The
proposed algorithm IoT-QoS diagram shown in Figure 2.
3. DISCUSSION AND RESULTS
In this section, we explained the results by using the NS3 simulator [28] to complete the evaluation
process according to the parameters shown in Table 1, noting that the training data set is collected from the trace
file, which is used to train the random forest classifier, which in turn generates a training model that is used
to complete the classification process. According to Figure 3, devices using the proposed IoT-QoS algorithm
were able to achieve lower energy consumption than their counterparts relying solely on the standard power-
saving mode (IoT), with an increase in both the primary sleep interval and the number of network users. This
is a direct result of the algorithm monitoring three parameters: energy levels, the presence of stored packets at
the access point, and connection quality. The algorithm utilizes these parameters to achieve energy savings in
devices. Additionally, it prioritizes access to the medium, reducing the wireless card’s idle time during network
contention. This positively affects the device’s battery energy.

Figure 2. The proposed algorithm IoT-QoS diagram
Table 1. The parameters used in the simulator (NS3)
Parameter Value
PT hre 0.5 mJ
Transmitting power 1400 mW
Receiving Power 900 mW
Idle power 700 mW
Sleeping power 60 mW
CWmin 32
CWmax 1024
PSM timeout 25 msec
Max sleep interval 1,000 msec
Simulation time 200 Sec

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Figure 3. Energy consumption as a function of increasing IoT devices and primary sleep duration (insert link
image)
Also, the proposed algorithm distinguishes between two types of data, one of these was subjected to
modified authentication processes. The critical network standard for this data type is response time, considering
that the additional security system load will affect data transfer time. According to our study, devices imple-
menting the proposed algorithm achieved lower response times than those not using it, even with increased
network data traffic, as depicted in Figure 4. This can be justified as the priority access scheme, which plays a
crucial role in faster access to the wireless medium. Moreover, the algorithm for providing packet width had a
significant impact on adjusting the transmitted information in response to the request context.
Figure 4. Response time as a function of increasing network load
Hence, the proposed algorithm managed to mitigate the impact of increasing the number of devices in
the wireless network on latency, a crucial performance metric for time-sensitive applications. This data type,
distinguished as the second type by the IoT-QoS algorithm, is illustrated in Figure 5, where the superiority of
the proposal becomes evident, especially under network congestion. This action can be considered a natural
outcome of the IoT-QoS algorithm’s ability to indirectly reduce congestion levels compared to IoT. This reduc-
tion is achieved by predicting the wireless link state through random forest, which forces the wireless card into
a sleep state when devices are not in proximity to the access point. In such cases, the card can enter transmis-
sion and reception operations at a higher rate with the beginning of each beacon frame period. This effectively
reduces both contention time for the wireless medium and the time required for reception operations, resulting
in faster data retrieval.

Figure 5. Delay as a function of increasing the number of IoT devices
The results depicted in Figure 6 indicate that the behavior of the proposed IoT-QoS algorithm has
an impact on the round trip time (RTT) between an IoT device and the server, especially in the presence of a
small number of devices in the network. This led to a decrease in productivity compared to traditional devices.
However, the performance of the IoT-QoS algorithm surpasses that of traditional devices under network con-
gestion. This improvement can be attributed to the algorithm’s ability to reduce contention time for the wireless
medium through the linear access scheme. It allows for transmission operations with improved channel con-
ditions, avoiding the need for retransmission and enabling data transfer at a higher rate. Naturally, putting the
wireless card to sleep under poor channel conditions reduces congestion levels, and impacting congestion in
one way or another.
Figure 6. Throughput as a function of increasing the number of IoT devices
4. CONCLUSION
In light of the findings, it is evident that the IoT-QoS algorithm has demonstrated a capacity to enhance
the performance of IoT devices utilizing Wi-Fi as their communication medium, all while ensuring the pivotal
aspect of security is not compromised. Consequently, a strong recommendation emerges for the adoption of the
proposed IoT-QoS algorithm, particularly for IoT devices characterized by constrained resources, notably in
power. The algorithm showcases its efficacy by successfully upholding QoS standards while notably diminish-
ing power consumption within the wireless cards of IoT devices operating in congested network environments,
thereby outperforming traditional devices in similar conditions. In the future, we seek to enhance the security

756 ❒ ISSN: 2089-4864
aspect of the IoT network. Several research directions may be explored in this regard, including the adoption
of blockchain technology for authentication processes. Additionally, there is a growing interest in leveraging
AI and software defined networks to detect attacks and make informed decisions to mitigate their impact.
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BIOGRAPHIES OF AUTHORS
Ali Ahmed Razzaq had a master’s degree in computer network engineering from Andhra
University. Now I am a research scholar at Andhra University in the IoT specialty for the purpose of
obtaining a Ph.D. He have several skills in the field of artificial intelligence and its programming in
the field of networks and design websites by framework django in python. He currently work in the
field of air navigation and its systems as a data entry for aviation transit (FDO) in the Iraqi Air Traffic
Management Center in the Area Control Center (ACC) and now an air traffic controller at Baghdad
International Airport (Iraq). He can be contacted at email:taifali607@gmail.com.
Prof. Kunjam Nageswara Rao is a Professor in Department of Computer Science and
Systems Engineering at Andhra University College of Engineering. He has more than 24 years of
teaching experience. He has published 3 patents and more than 50 research papers so far in vari-
ous highly reputed international journals. His research interest includes cloud computing, wireless
networks, sensor networks, IoT, bioinformatics, medical image processing, network security, data
mining and data analyticss. He can be contacted at email: kunjamnag@gmail.com.

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