Improving the detection of intrusion in vehicular ad-hoc networks with modified identity-based cryptosystem

TELKOMNIKA Telecommunication Computing Electronics and Control
Vol. 21, No. 5, October 2023, pp. 1005~1012
ISSN: 1693-6930, DOI: 10.12928/TELKOMNIKA.v21i5.22207  1005
Journal homepage: http://telkomnika.uad.ac.id
Improving the detection of intrusion in vehicular ad-hoc
networks with modified identity-based cryptosystem
Zachaeus K. Adeyemo1
, Emmanuel B. Ajulo2
, Damilare O. Akande1
, Hammed O. Lasisi3
, Samson I. Ojo1
1
Department of Electronic and Electrical Engineering, Faculty of Engineering and Technology, Ladoke Akintola University of
Technology, Ogbomoso, Nigeria
2
Department of Computer Science and Mathematics, College of Basic and Applied Sciences, Mountain Top University, Ibafo, Ogun
State, Nigeria
3
Department of Electrical and Electronics Engineering, College of Science, Engineering and Technology, Osun State University,
Osogbo, Osun State, Nigeria
Article Info ABSTRACT
Article history:
Received Nov 10, 2021
Revised Mar 21, 2023
Accepted Apr 30, 2023
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that
form networks along the road. The security of this network has been a major
challenge. The identity-based cryptosystem (IBC) previously used to secure
the networks suffers from membership authentication security features. This
paper focuses on improving the detection of intruders in VANETs with a
modified identity-based cryptosystem (MIBC). The MIBC is developed
using a non-singular elliptic curve with Lagrange interpolation. The public
key of vehicles and roadside units on the network are derived from number
plates and location identification numbers, respectively. Pseudo-identities
are used to mask the real identity of users to preserve their privacy.
The membership authentication mechanism ensures that only valid and
authenticated members of the network are allowed to join the network.
The performance of the MIBC is evaluated using intrusion detection ratio
(IDR) and computation time (CT) and then validated with the existing IBC.
The result obtained shows that the MIBC recorded an IDR of 99.3% against
94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140
unregistered vehicles attempting to intrude on the network. The MIBC
shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can
be used to improve the security of VANETs.
Keywords:
Identity-based cryptosystem
Intrusion
On-board unit
Road side unit
VANET
This is an open access article under the CC BY-SA license.
Corresponding Author:
Damilare O. Akande
Department of Electronic and Electrical Engineering, Faculty of Engineering and Technology
Ladoke Akintola University of Technology, PMB 4000, Ogbomoso, Oyo State, Nigeria
Email: doakande@lautech.edu.ng
1. INTRODUCTION
The vehicular ad-hoc network (VANET) is an infrastructure-less network created as a result of
developments in wireless communications and networking technologies [1]. This new technology provides
ubiquitous connectivity to vehicles while in motion thereby creating efficient vehicular communications that
enable intelligent transportation systems (ITS) [2]. In VANET, there are two forms of communication,
namely: vehicle-to-vehicle (V2V) communication, in which vehicles fitted with an on-board unit (OBU)
wireless communication system communicate with other road vehicles, and vehicle-to-infrastructure (V2I)
communication, in which vehicles use their OBU to communicate with roadside units (RSUs) [3]. Improving
road safety and quality is the primary essence of this technology [2]. In VANET, the OBU of the vehicle acts
as an independent router, generates independent data, and periodically transmits information to other nearby
vehicles and RSUs on their current states, such as location, direction, speed, current time, and traffic events [4].

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TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 5, October 2023: 1005-1012
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In communicating with vehicles beyond the wireless range; intermediate vehicles or RSUs forward messages to
the intended destination over multiple hops. Thus, many initiatives in academic and standardization societies
have been inspired by this new model of communication [5]. Seven 10 MHz channels in the 5.9 GHz dedicated
short-range communication (DSRC) band have recently been allocated by the Federal Communications
Commission (FCC) of the United States of America (USA) to increase the protection and efficiency of the
transport system [5], [6]. Six 10 MHz channels are dedicated to comfort applications (internet facility,
information on the nearest restaurant, car park, filling station) in the new DSRC standard, and one channel is to
safety applications.
Given the tremendous benefits expected from vehicular communications, just like any other wireless
communication network, security has been a major challenge. Intruders can easily intercept, modify, and
replay messages transmitted on the network or even impersonate other vehicles to broadcast incorrect
information [6], [7]. Besides, the privacy of users on the network is also a challenge. The ideal is that the
travelling route of drivers should not be traceable by intruders through the message(s) sent or received by the
vehicles on the network [8], [9]. Consequent to these security challenges in VANETs, it has received due
attention from researchers, and the use of identity-based cryptosystem (IBC) to provide this security [10]-[12].
The uniqueness of IBC compare to other forms of security protocol is that the identity and pre-configuring
key of users is derived from an arbitrary string that uniquely identifies them [13]-[15]. Nonetheless,
the existing identity-based cryptosystem (EIBC) used to solve this problem lack membership authentication
security feature thereby allowing intruders to invade the network [10]. This informs the purpose of this work
to develop a modified identity-based cryptosystem (MIBC) to prevent and detect intrusion in VANETs. In this
paper, the MIBC was simulated using MATLAB R2018a. The performance of the MIBC was evaluated using
intrusion detection ratio (IDR) and computation time (CT) as performance metrics, and compared with EIBC.
2. OVERVIEW OF VEHICULAR AD-HOC NETWORKS (VANETS) AND IDENTITY-BASED
CRYPTOSYSTEM
Improving the detection of intruders in VANETs with a MIBC forms the basis for this study.
Therefore, the need to discuss the technology of VANETs and the fundamental of an identity-based
cryptosystem cannot be sidelined. The overview of VANETs technology and the IBC used for this work is
thus explained.
2.1. Vehicular ad-hoc networks
A sub-class of the wireless ad-hoc network (WANET) and the mobile ad-hoc network (MANET)
extension is the VANET. In VANET, nodes consist of self-organized vehicles and road-side infrastructure that
communicate with each other through wireless devices and function independently of any infrastructure in a
peer-to-peer mode. Each node operates and produces independent data as an independent router [16]-[19].
Network management and fault detection become distributed and hence more difficult to handle [20].
Nodes in VANETs are mobile which causes frequent changes to the network topology and makes it
unpredictable. It cannot be assumed that VANETs would always be under the protection of their owners
because of the ubiquitous existence of mobile nodes; nodes may be stolen or tampered with. For both
legitimate and unauthorized network users, the shared wireless medium is available [21]. While driving along
the road, these wireless-equipped vehicles spontaneously form networks. The key idea is to provide vehicle
nodes with ubiquitous connectivity while on the move and to establish effective vehicle-to-vehicle
communications that allow ITS. The ultimate objective of VANETs is to enhance the driving experience,
improve traffic safety and improve the performance of drivers [22]-[24].
2.2. Identity-based cryptosystem
Pre-configured vehicle keying materials and RSUs are created from their identities in identity-based
cryptosystems in such a way that the public and private keys of the vehicles and RSUs are bound to their
respective identities, thus avoiding certificate verification [25]. The cost of computing in this cryptosystem is
therefore small. Also, because in identity-based cryptosystems, the vehicle and the RSU do not need to
exchange certificates, the message length is reduced, and IBC is much more effective. IBC consists of two
mechanisms in basic form, namely identity-based encryption (IBE) and identity-based signature (IBS), with
four steps each. Setup/initialization, private key extraction, encryption, and decryption are the IBE process
steps while the IBS mechanism includes four steps include setup/initialization, private key extraction,
signature, and verification [25]-[27]. Both mechanisms follow the same process for setup/initialization and
private key extraction. These mechanisms are combined to achieve desired identity-based cryptosystems.

TELKOMNIKA Telecommun Comput El Control 
Improving the detection of intrusion in vehicular ad-hoc networks with … (Zachaeus K. Adeyemo)
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3. RESEARCH METHOD
The MIBC consists of four entities, namely: the trusted authority (TA), the network control unit
(NCU), the RSUs, and the vehicles. The TA is a trusted third party responsible for the system setup, registration,
configuration, and issuing of private keys to the NCU, RSUs, and vehicles on the network. The NCU is
responsible for the authentication of users on the network. The RSUs are static smart devices on the road such as
traffic sensors or pedestrian sensors mounted on the road, while the vehicles are mobile entities with a transceiver
known as OBU installed in them to enable communication. The cryptographic process involves four phases.
The first is the key and pseudo-identity generating phase which is solely carried out by the TA. The second is the
vehicle authentication phase where valid users of the network are allowed to join the network, the third is the data
transmission phase comprising message signing, and the fourth is the message verification phase.
3.1. Development of a secure communication system
The development of secure communication begins at this stage. The system was developed on a
non-singular elliptic curve as expressed in the (1) and Lagrange interpolation was used to define and
interpolates points on the curve. The use of Lagrange interpolation eliminates the need for point pairing
which may increase the cryptographic headcount.
𝑦2
= 𝑥3
+ 𝑎𝑥 + 𝑏 𝑚𝑜𝑑 𝑝 (1)
The registration and setup of vehicles, RSUs, and NCU which are entities on the network were
carried out by the TA. The TA creates a group 𝐺 defined by a non-singular elliptic curve as expressed in (1)
and selects a generator 𝑃 ∈ 𝐺. The TA then randomly generates a master private 𝑠 ∈ 𝑍𝑞
∗
for the system and
computes the system public key 𝑃𝑝𝑢𝑏 as in (2).
𝑃𝑝𝑢𝑏 = 𝑠. 𝑃 (2)
Where: ‘𝑠’ is the master private key of the system, and it is an integer within the order 𝑞, 𝑃 is the generator of
the element in the group, and 𝑍∗
is a set of integers other than zero. The pseudo-identity of entities
(participants) on the network was generated using (3).
𝑃𝐼𝐷 = 𝛾𝑖. 𝑃||𝑅𝐼𝐷 ⊕ 𝐻(𝛾𝑖. 𝑃𝑝𝑢𝑏). (3)
Where: 𝑃𝐼𝐷 is the pseudo-identity of entities (participants) on the network, 𝛾𝑖 is a set of integers randomly
generated in 𝛾𝑖 ∈ 𝑍𝑞
∗
, 𝑅𝐼𝐷 is the real identity of the entity on the network, 𝑃𝑝𝑢𝑏 is the master public key of the
system as derived in (2), and 𝑃 is the generator of the group as already defined. In order to generate the
private key of any entity on the network.
𝑠𝑘𝑖 = 𝛾𝑖 + 𝜃𝑖. 𝑠 𝑚𝑜𝑑 𝑞. (4)
Where: 𝛾𝑖 is a set of integers randomly generated in 𝛾𝑖 ∈ 𝑍𝑞
∗
, ‘𝜃𝑖’ is a hashed value of pseudo-identity
concatenated with a timestamp 𝑇𝑖, ‘𝑠’ is the master private key of the system, and 𝑚𝑜𝑑 𝑞 is a modular
arithmetic operation in prime 𝑞.
3.2. Development of vehicle authentication system
The TA selects randomly a live session code 𝑆𝐿𝑐 ∈ 𝑍𝑞
∗
and then generates vehicle membership
authentication code to the vehicle using (5), and the control authentication key for the NCU using (6)
respectively. However, the live session code 𝑆𝐿𝑐 is not given to the vehicle(s) directly, but to the NCU for
vehicle(s) validation and authentication whenever it has to join the network for communication.
𝑀𝑘 = 𝐻(𝐶𝑘||𝐼𝐷𝑁𝐶𝑈) ⊕ 𝑅𝐼𝐷𝑣 (5)
𝐶𝑘 = 𝑠. 𝐻(𝐼𝐷𝑇𝐴||𝑅𝐼𝐷𝑣||𝑆𝐿𝑐) (6)
Where: 𝑀𝑘 is the membership authentication code for participants on the network, 𝐶𝑘 is the control
authentication key for the NCU, 𝐼𝐷𝑇𝐴 is the identity of the TA, 𝑅𝐼𝐷𝑣 is the real identity of the vehicle, 𝐼𝐷𝑁𝐶𝑈
is the identity of NCU, ‘𝑠’ is the master secret key, 𝛾𝑖 is a set of integer randomly generated in 𝛾𝑖 ∈ 𝑍𝑞
∗
, 𝐻 is
the hash function, || is the concatenation operation and ⊕ is the exclusive-OR operation. The NCU checks
for the validity of the request using (7), and further checks the revocation list which contains the real
identities of revoked vehicles received securely from the TA to ascertain the authenticity of the vehicles’
membership.

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𝑅𝐼𝐷𝑣 ≟ 𝑀𝑘 ⊕ 𝐻(𝐶𝑘||𝐼𝐷𝑁𝐶𝑈) (7)
The correctness of this authentication is proven as recall from (5) that:
𝑀𝑘 = 𝐻(𝐶𝑘||𝐼𝐷𝑁𝐶𝑈) ⊕ 𝑅𝐼𝐷𝑣
Then,
𝑅𝐼𝐷𝑣 = 𝐻(𝐶𝑘||𝐼𝐷𝑁𝐶𝑈) ⊕ 𝑅𝐼𝐷𝑣 ⊕ 𝐻(𝐶𝑘||𝐼𝐷𝑁𝐶𝑈)
𝑅𝐼𝐷𝑣 = 𝑅𝐼𝐷𝑣
3.3. Message signing
The vehicle signs messages to be transmitted on the network via the OBU device by generating a
random number 𝜇𝑖 ∈ 𝑍𝑞
∗
and computes (8), (9), and (10). This is to ensure non-repudiation and to ensure
high-security protection because any computation towards breaking the scheme by any intruder requires
simultaneous extraction of two unknown secrets.
𝑅𝑖 = 𝜇𝑖. 𝑃 (8)
𝛽𝑖 = 𝐻(𝑃𝐼𝐷||𝑇𝑖||𝑅𝑖||𝑀𝑖) (9)
𝜎𝑖 = 𝑠𝑘𝑣 + 𝛽𝑖. 𝜇𝑖 𝑚𝑜𝑑 𝑞 (10)
The signed message 𝑀 = {𝑀𝑖, 𝑃𝐼𝐷𝑖
, 𝑇𝑖, 𝑅𝑖, 𝜎𝑖}. Then, the vehicle broadcasts the message 𝑀 to the nearby
RSUs and vehicles, which are the receivers.
3.4. Message verification
The verifier which may be an RSU or a vehicle checks the validity of the message received using (11).
The verifier checks the freshness of timestamp 𝑇𝑖; if ∆𝑇 ≥ 𝑇𝑁𝑜𝑤 − 𝑇𝑖, where: ∆𝑇 is the predefined endurable
transmission delay, 𝑇𝑁𝑜𝑤 is the recent message received, and 𝑖 = 1, 2, 3, … , 𝑛; otherwise, the verifier rejects
the message.
𝜎𝑖. 𝑃 = 𝑃𝐼𝐷𝑣 + 𝜃𝑖. 𝑃𝑝𝑢𝑏 + 𝛽𝑖. 𝑅𝑖. (11)
Where: 𝜃𝑖 is the hashed value of pseudo-identity concatenated with a timestamp 𝑇𝑖. If it does, the message is
approved by the verifier; otherwise, the message is rejected by the verifier.
3.5. System simulation and procedures
The MIBC was simulated using MATLAB R2018a. The simulation was conducted on a Windows 8
computer system with Intel® Core™ i3 processor, 4 GB RAM. The simulation was considered for the V2V
and vehicle-to-roadside unit (V2R) communications, and the frequency of operation is 5.9 GHz.
The driving scenario designer (DSD) Application on the MATLAB R2018a Environment was used as a
mobility generation tool; and the transmission rate used is 0.3 seconds. The graphical users interface (GUI) as
shown in Figure 1 was created to aid simulation and analysis of the system. The membership authentication
panel on the GUI checks the validity of the vehicle when a request to join the network is sent; such a request
is either valid or not valid. The key generating panel generates on the GUI generates the entire usable key for
the vehicles on the network.
3.6. System evaluation performance metrics
The performance evaluation of the system was done using IDR and cryptographic computation time.
The IDR which is the ratio of the detected number of vehicles trying to break into the network to vehicles not
detected was analysed. The simulation analyses the efficiency of the NCU in preventing invalid and
unregistered vehicles into the network. For an efficient system, the detection ratio is expected to be high, and
the percentage detection rate is expressed as in (12).
𝐼𝐷𝑅 =
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑡𝑠 𝑠𝑢𝑐𝑐𝑒𝑠𝑠𝑓𝑢𝑙𝑙𝑦 𝑑𝑒𝑡𝑒𝑐𝑡𝑒𝑑
𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑡𝑠
× 100% (12)

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In the same vein, the computation time which is the time required for computing the cryptographic
operations was also analyzed. In this paper, the computation time was carried out using the ‘tit-tot’ function
of the MATLAB R2018a for both the developed and existing systems. The percentage of performance
improvement analysis of the developed system was achieved using the (13).
𝐼𝑝𝑒𝑟 =
(𝑇𝑒𝑥𝑖𝑠𝑡−𝑇𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑒𝑑)
𝑇𝑒𝑥𝑖𝑠𝑡
% (13)
Where 𝐼𝑝𝑒𝑟 refers to the improvement percentage, 𝑇𝑒𝑥𝑖𝑠𝑡 refers to the time costs of the existing scheme, and
𝑇𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑒𝑑 refers to the time costs of the developed scheme. Consequently, the developed system (MIBC) was
compared with the existing system, (EIBC), and the percentage performance improvement analysis was
carried out using the (14).
𝐼𝑝𝑒𝑟 =
(𝑇𝑒𝑥𝑖𝑠𝑡−𝑇𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑒𝑑)
𝑇𝑒𝑥𝑖𝑠𝑡
100% (14)
Where 𝐼𝑝𝑒𝑟 refers to the improved percentage, 𝑇𝑒𝑥𝑖𝑠𝑡 refers to the value of the existing scheme, and 𝑇𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑒𝑑
refers to the value of the developed scheme.
Figure 1. Simulation interface for the modified identity-based cryptosystem
4. RESULTS AND DISCUSSION
The IDR which is the ratio of the detected number of vehicles trying to break into the network to
vehicles not detected was analysed. The simulated vehicles were divided into two parts, 70% of the vehicles
were unregistered and invalid vehicles and made to intrude into the network, while the remaining 30% were
registered and valid members of the network. From the simulation, it was observed that all attempts made by
the intruding vehicles were rejected by the NCU. The result is shown in Table 1, and the percentage intrusion
detection rate for the number of incidents (vehicles) is shown in Figure 2.
In the same vein, the CT of the cryptographic process of the MIBC obtained is 1.17 ms. This is an
average result of running the cryptographic algorithm for an average of 1000 times. Also, the MIBC was
compared with the EIBC. In terms of CT, the CT for MIBC is 1.17 ms while that of EIBC is 1.70 ms. The result
shows that the MIBC has a lower computation time compared to the EIBC. Consequently, the percentage
improvement of the computation time of MIBC over the EIBC, shows that the MIBC has 31.18%
improvements over the EIBC. The IDR result for the MIBC and EIBC for different vehicles were compared.
The values are as shown in Table 2, and illustrated in Figure 3.
The overall average percentage of performance improvement analysis of intrusion detection rate in
the MIBC and EIBC is presented in Figure 4. The result shows that the MIBC has a total average of 98.5%
intrusion detection rate compare to the EIBC which has a 93.2% intrusion detection rate. This is a 5.4%
performance improvement over EIBC. The results presented show that the performance of MIBC gets better
even with an increase in the number of vehicles released into the network which is a great improvement
compared to EIBC.

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Table 1. Intrusion detection ratios
No. of vehicle Total no. of incidents No. of incidents successfully detected % IDR
50 35 33 94.3
100 70 69 98.6
150 105 103 99.1
200 140 139 99.3
Figure 2. Intrusion detection rate for the number of
incidents (vehicles)
Figure 3. Comparison of intrusion detection rate
in the MIBC and EIBC
Table 2. Intrusion detection ratio for the MIBC and EIBC
Vehicle density Intrusion detection ratio for MIBC system (%) Intrusion detection ratio for EIBC system (%)
50 97.1 91.4
100 98.6 92.9
150 99.1 94.3
200 99.3 94.3
Figure 4. Overall average intrusion detection analysis
5. CONCLUSION
In this paper, a variant IBC that ensures secure communication and prevention of intruders in
VANET was developed. An authentication system for users’ validation on the network was incorporated into
the system. The validation is done via the NCU in a handshake with the TA of the system to securely prevent
intrusion and ensures that only valid members of the network are allowed to join the network. The NCU of
the system securely prevents intrusion and ensures that only valid members of the network are allowed to
join the network. The result shows that the proposed MIBC outperforms the EIBC in IDR and CT
performance and shows the superiority of the security features in terms of users’ membership authentication.
ACKNOWLEDGEMENTS
Our sincere appreciation goes to the Department of Electronic and Electrical Engineering, Ladoke
Akintola University of Technology, Ogbomoso, Nigeria, for the provision of the conducive environment
upon whch this research was conducted.

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 ISSN: 1693-6930
1012
BIOGRAPHIES OF AUTHORS
Zachaeus K. Adeyemo received his B.Eng., M.Eng. degrees in Electrical
Engineering from University of Ilorin, Ilorin, Nigeria in 1994 and 2002, respectively, and
Ph.D. in Electronic and Electrical Engineering (Communication Engineering) from Ladoke
Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria in 2009. He joined the
services of Ladoke Akintola University of Technology (LAUTECH) Ogbomoso in 1995 as a
Graduate Assistant in the department of Electronic and Electrical Engineering and rose from
this lowest level of the academic cadre through all the ranks until he was promoted to a full-
fledged Professor. His current research interests are wireless communications, signal
processing in communication systems, development of a path-loss prediction model using
adaptive neuro-fuzzy inference system, spectrum detection in a cognitive radio network. Prof.
Adeyemo is a corporate member, Nigerian Society of Engineers (MNSE), Registered member
of Council for the Regulation of Engineering in Nigeria (R. COREN) and Member, Africa
Engineering Education Association (AEEA). He can be contacted at email:
zkadeyemo@lautech.edu.ng.
Emmanuel B. Ajulo received his B.Tech and M.Tech degrees in Electronic and
Electrical Engineering from Ladoke Akintola University of Technology (LAUTECH),
Ogbomoso, Nigeria; and M.Tech and Ph.D degrees in Computer Science from the Federal
University of Technology, Akure, Nigeria in 2015 and 2020 respectively. He’s reputable for
developing I.T architectures and security frameworks to drive communication systems. He is a
member of Nigeria Society of Engineers, member of Nigeria Computing Society, and a
registered engineer with the Council for the Regulation of Engineering in Nigeria (COREN).
His research interest is on computational cryptography, signal processing, machine learning,
and artificial intelligence. He can be contacted at email: emmanuelajulo@gmail.com.
Damilare O. Akande obtained his B. Tech and M. Tech degrees in Electronic and
Electrical Engineering from Ladoke Akintola University of Technology (LAUTECH),
Ogbomoso and the Federal University of Technology, Akure, Nigeria in 2008 and 2012
respectively. He obtained his Ph. D degree from University Sans Malaysia, Malaysia in 2019.
He is a member of Nigeria Society of Engineers and a registered member of COREN. His
research interest is on signal processing, cooperative communications and MAC layer design.
He can be contacted at email: doakande@lautech.edu.ng.
Hammed O. Lasisi is a senior lecturer and researcher in the Department of
Electrical and Electronics Engineering, Osun State University, Osogbo, Nigeria. He holds
doctorate (Ph.D) and master’s degrees in Elecctrical and Electronics Engineering from
University of Ilorin, Nigeria. His area of specialization is telecommunication and telegraphic
engineering. He is a registered member of the Council for the Regulation of Engineering
(COREN) in Nigeria. He can be contacted at email: hammed.lasis@uniosun.edu.ng.
Samson I. Ojo received his B. Tech and M. Tech degrees in Electronic and
Electrical Engineering in 2011 and 2018, respectively, from Ladoke Akintola University of
Technology (LAUTECH), Ogbomoso, Nigeria. He is a registered member of Council for the
Regulation of Engineering in Nigeria (COREN). He obtained his Ph.D. in mobile
communication in the year 2021. His research interest is on signal processing, diversity and
cognitive radio. He can be contacted at email: siojo83@lautech.edu.ng.

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