Network Resource Allocation Security Techniques and Challenges for Vehicular Communication Network Management

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Network resource Allocation security techniques and challenges for Vehicular
Communication Network Management
Vartika Agarwal1*
, Sachin Sharma2
, Gagan Bansal3
Graphic Era Deemed to be University, Dehradun, India
*vartikaagarwal_20942026.cse@geu.ac.in
Abstract- Internet of things describes the network of physical objects such as sensors,
receivers, transmitters and other technologies which are used in VCN. In Vehicular
communication network two or more vehicles are communicate with each other. VCN use
advanced technologies to solve transportation related problems like long traffic delays, road
accidents and air pollution. IOT based technologies make vehicular network smart. In this
paper we reviewed about network resource allocation security techniques, challenges and
also discuss how we can make vehicular communication network smarter. We reviewed
about different models and schemes for V2V communication. These schemes were developed
to ensure a fair, efficient and transparent allocation of resource in an intelligent transportation
system.
Keywords – Vehicular Communication Network (VCN), Network Resource Allocation
(NRA).
1 Introduction
Allocation means assignment of available resources for the best use. In vehicular communication
network there are various resources such as sensor, transmitter, receiver which plays a major role in
vehicular communication network. Such resources are helpful for avoiding any accident etc. There
are various resource allocation models which is responsible for vehicular communication
• Peer-To-Peer System Model – In vehicular communication Network, there are a number of
resources. Such resources help to transfer message from one vehicle to another. Peer to peer system
model consist of task allocated to the resources and time taken by the resources to accomplish their
task successfully.

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Fig. 1. Communication between Vehicles
In Fig. 1, there are 6 vehicles. Each vehicle contains sensors and receiver. Sensor and receiver are
basically used for message passing among vehicles. They share message among traffic jams,
accident etc. Peer to peer system model consist of n number of vehicles and n number of resources
which plays an important role in communication process.
• Rule Based Resource Allocation Model - This method focusses on time allocation and effective
use of resources. In this model, tasks are allocated to the resources on the basis of priority. This
model uses queuing system. For example – In vehicular communication network, firstly task is
allocated to the sensor, sensor have to provide response immediately if vehicle comes in dangerous
situation.
• Resource allocation for controlling of Congestion – When multiple tasks are allocated to the same
resources, chances of congestion will take place. So we have to use congestion control method for
efficient use of resources.
These resource allocation methods are used for proper communication between vehicles. These
methods make resource allocation effective and efficient.
Structure of paper is as follows: This paper has following sections
Section 2 examines about the connected work which involved in resource allocation process. Section
3 explains the overview of vehicular communication network. Section 4 represent the vehicular
communication network properties and Section 5 represent the requirements of Network Resource
Allocation for Vehicular Communication Network Management. Section 6 explain importance of
VCN and Section 7 highlight internet of things in VCN. Section 8 describe challenges of network
resource allocation techniques. Section 9 represent the techniques used for network resource
allocation. Section 10 shows the comparative study of different resource allocation scheme. Section
11 conclude the importance of resource allocation in vehicular communication networks.

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2 Literature Review
In 2010, Alia Asheralieva investigate about predictive resource allocation technique to predict
network loading. Such algorithm can find out changes in feature of traffic. Main objective of this
algorithm is to increase power consumption [1]. In 2012, Andres Farragut study about network
resource allocation schemes that manage multiple connections through different routes. This
schemes are implemented in NS2 and able to solve stability and fairness related issues [2]. In 2014,
Farshad Shams reviewed about radio resource allocation techniques such as OFDM and OFDMA
systems with its advantage and disadvantage. These techniques focus on energy efficient
approaches. They highlight difference between different radio resource allocation schemes for better
throughput [3]. In 2014, Georgios highlight cognitive radio networks. They proposed on dynamic
resource allocation technique and discuss about game theory, linear programming as well as fuzzy
logic [4]. In 2015, Hoon Lee focus on multi-user wireless powered communication networks. They
proposed an algorithm for energy and time allocations. There algorithm provide better performance
as compared to other conventional techniques [5]. In 2015, Wei Wu examined about the problem of
secrecy outage probability minimization. They design the energy efficient and secure NOMA based
MEC network. This network provides better throughput in comparison of other networking models
[6]. In 2016, Sanjeevi pandiyan present a review on energy consumption. they highlight difficulties
occur in existing energy reduction method. They compare different energy consumption techniques
with their advantage and disadvantage [7]. In 2017, Belen Bermejo highlight important concept
regarding energy consumption optimization. These concepts enable effective and efficient resource
management [8]. In 2017, Quingyang propose the concept of proportional share resource allocation
algorithm. They increased the network throughput and share network resources with other users [9].
In 2017, Le Liang proposed spectrum sharing and power allocation technique for reliable
communication between vehicles and maximize the throughput of V2V communication. [10]. In
2019, Wei Wu investigate about NOMA based MEC networks. Goal of this network is to reduce
consumption of energy. Experimental results are used to validate accuracy and correctness of system
[11]. In 2020, Steffi Jaya kumar proposed various resource allocation schemes for improving the
performance of device to device communication. This schemes enable high-speed data transmission,
information transmission as well as improve the system performance [12]. In 2020, Sahrish khan
Tayyaba proposed flow based policy framework on the source of software defined networking.
Some deep learning algorithm such as LSTM, DNN and CNN are used in their research [13]. In
2020, Xuemei Li review about resources used in IOT. It highlights network resource allocation
techniques such as RFID, Cache, Wireless etc. which are used in internet of things [14]. In 2020,
Steffi Jayakumar analyze and evaluate different methodologies used for resource allocation they
find out the research difficulties and drawn strong conclusion about device to device communication
[15]. In 2020, Bodhaswar TJ Maharaj analyse highly resource allocation models for cognitive radio
networks. These models are built for the hybrid architecture which is the most common technique
in CR networks [16]. In 2020, J Praveenchandar proposed an efficient dynamic resource allocation
algorithm to improve the efficiency of resource allocation. This algorithm reduce power
consumption & provide better result as compared to other techniques [17]. In 2021, Kurdistan Wns
introduced process of allocating workload among multiple resources. They use different resource
allocation algorithm for 5G networks. Various resource allocation techniques are used in distributed
system [18]. In 2021, Arwa Mohamed present a review paper on allocation of resources. They
highlight operations of virtual machine, traffic Performance as well as conservation of energy. [19]
In 2021, Vartika Agarwal highlight the different scheduling techniques in vehicular communication
networks. These techniques help to plan the whole process in advance. [20]. In 2021, Miao Zhang
investigate about power allocation schemes. Neural network is used to solve power allocation
problem. This approach provide 95% accurate results as compared to other algorithms [21]. In 2021,
Vartika Agarwal highlight the different technologies such as Lifi, LORAWAN, VANET and RFID.
These technologies help in vehicle to vehicle communication. This paper reviewed about
comparison in these different technologies [22]. In 2021, Vartika Agarwal reviewed about internet
of things in transport management system. Such technology reduces road traffic and accidents [23].
In 2021, Vartika et al. investigate the deep learning techniques to solve radio resource issue in VCN.

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[24]. In 2021, Fan Liang introduce a Deep learning based technique for utilization of bandwidth in
IOT based system. They perform various experimental results for efficiency of system. This scheme
can improve efficiency of energy as compared to other schemes [25]. In 2021, Mariem Allouch
reviewed about emerging technologies in radio resource management scheme. These technologies
are cellular vehicle to everything, device to device communication etc. They highlight many
algorithms to address resource allocation issue. [26]. In 2021, Heena wadhwa designed a novel
approach for allocation of resources. This approach is used to ensure resource optimization at bottom
layer. IFogSim simulator is used for simulation of this scheme. This scheme minimize time of
execution as well as consumption of energy and network [27]. In 2021, Yanmei Cao proposed a
deep Q-Learning based scheme for resource allocation. This scheme maximize the throughput and
achieve high system spectrum efficiency [28]. In 2021, Muhammad Ali Jamshed propose a novel
electromagnetic field scheme and K means approach to control the resource allocated to the number
of users. This schemes makes resource allocation process more effective and efficient [29]. In 2021,
Sumarga kumar shah tyagi propose a distributed artificial intelligence approach for resource
allocation. This scheme is based on Bayesian neural network and back-propagation neural network
[30].
3 Overview of Vehicular Communication Network
Vehicular Communication Network is a network which shared information between different
vehicles. Goal of VCN is to allow different vehicles to communicate with their drivers, roadside
infrastructures, pedestrian and fleet management systems. In this concept, every vehicle is turned a
smart node on the highway, with its own compute, storage and networking capability. Vehicular
communication network is important because it reduce accidents and traffic jams. Some statistical
data represents that the main reason of road accidents are human mistake. These accidents can be
reduced if driver had been warned half second beforehand. This could be done with the help of
vehicular communication networks. It provides communication among vehicles with an onboard unit
and nearby roadside unit (Fig.2).

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Fig. 2. Vehicular Communication Network
Vehicular Communication network offer following benefits
i. Cost Reduction – Many IOT devices are used in vehicular communication network. These
devices are responsible for communication between vehicles. Sensors, transmitter, receiver are
used for message passing between vehicles.
ii. Efficiency – IOT devices increase efficiency of whole processes in vehicular communication
network. These devices warn driver about any mishappenings or suggest shortest route for
reaching the destination quickly.
iii. Mobility and agility - IOT devices helps to track vehicle from any location. This technology
helps to reduce crime and help to improve traffic congestion.
iv. Security – IOT devices are highly secure and safe. These devices validate that message between
vehicles has not been modified during transmission process.
v. Save Time – IOT devices save time of passenger by informing advance about the current
situation of road.
vi. Boost Productivity - Resource allocation gives a clear picture about work done by resources
for vehicular communication. It increases productivity.
vii. Changes in order of resources - If any resource does not work properly in run time situation.
we can change the order of resources.
viii. Effectiveness - Resource allocation makes vehicular communication process effective and
significant. IOT devices are responsible for proper execution of whole communication process.
4 Properties of Vehicular Communication Network
Following properties of vehicular communication network makes communication process more
effective and efficient (Fig.3).

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Fig. 3. Properties of Vehicular Communication Network
 Scalability: It is needed for proper communication between vehicles. Sometimes large no
of vehicles passing through the network may be higher. In case of an emergency situations,
driver have to take decision very fast. He has to receive information from trustworthy
sources and network should be scalable.
 Privacy: In vehicular communication network, there is a need to be care of owner’s
information such as their address, vehicle registration no etc. driver have to use public key
encryption technique for sharing information with other vehicles.
 Robustness: In vehicular communication network, there are some malicious nodes which
modify the information and misguide the network. network have to identify such nodes
and remove it from the network.
 Information Sparsity: It means weightage of information. Information which has highest
priority should be delivered first.
 Mobility: The mobility of vehicular communication networks is too high. mobility is an
important feature that plays a major role in this network. It reduces communication time
in the network.
 Safety: VCN enhance the safety of the driver, passenger comfort and improve the flow of
traffic. Main benefit of VCN is that vehicles can communicate directly with another
vehicle through sensors, transmitter, receiver etc.
 Frequent network connection: Important property of vehicular communication network
is frequent network connection. This network avoid any disconnection between
communicating vehicles due to weather conditions or other issues.
 No power limitation : In vehicular communication network, vehicles can communicate
through transmitter, receiver etc. So there is no need of power or long lasting battery just
like vehicular adhoc network.
 Strength of Network: In vehicular communication network, strength of network is
depend upon the flow of traffic. If there is a traffic Jam, it can be high otherwise it can be
low.
 Large Computational processing: For VCN, vehicles should be embedded with sensors,
transmitter, receiver, Global positioning system, antenna etc. These resources provide

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reliable communication to obtain the exact information about speed, position and location
of another vehicle.
 Decrease Travel Time : Through VCN, we can find the shortest route for reaching the
destination quickly. It reduce travel time and increase passenger comfort.
 Process Real Time Data: Main motive of VCN is to avoid traffic and process real time
data. It can be save time and fuel. VCN update their data from time to time so that user
can get current information.
5 Requirements of Network Resource Allocation for Vehicular Communication
Network Management
Vehicular Communication Network exhibits how resources have been allocated over the network.
Vehicular network consists of transforming vehicles into an intelligent mobile entity that are able to
interact with roadside units, Today, due to high Urbanizations, there are problems associated with
road networks. New technologies and systems have been developed that can change way of life and
example is VCN.VCN establish communication between vehicles and other road side entities. It
saves money, ensure safety and reduce environmental impact as well as traffic congestion. Trend of
vehicular communication network has increased with the enhancement in technologies and
development of smart capitals across the country. VCN has a major impact in traffic services
enhancement as well as reducing road accidents. Information shared in VCN are robust and secured
but sometimes its security is being interrupted by different types of attackers.
6 Importance of Vehicular Communication Network
Vehicular Communication Network allow a wide range of applications such as
 Collisions prevention
 It gives post-crash notification.
 Traffic optimization
 Inter-vehicle communication,
 Intelligent traffic system
 Vehicular communication network can help you to find an alternative route to your destination if
there is traffic on one route.
 Use of Vehicular communication network was to provide safety and comfort to drivers in vehicular
environments.
 E-ZPass is a very good example of a payment service application of vehicular communication
network
 Vehicular communication network could help in preventing crime by reporting on-demand.
 Traffic signal violation warning.
7 IOT in Vehicular Communication Network
Internet of things plays major role in vehicular communication network. IOT devices makes
communication process more effective. In Fig. 4, we can see that there is an application server which
is used to maintain the record of whole communication process. There is a network which is used to
connect vehicles from other road side units. Sensors are basically used to find the speed and location
of vehicles. Transmitters are very effective for vehicular communication network because it
transmits information from one vehicle to another vehicle.

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Fig. 4. IOT Devices in Vehicular Communication Network
 IOT enables vehicular communication network where large number of sensors are connected with
each other for sharing status about vehicle.
 IOT devices detect collision in advance and avoid any mishappenings.
 IOT devices help drivers to know in advance about the road condition, traffic jams etc.
 IOT devices enhance safety and security of passengers by sharing information at right time.
 IOT devices use advanced technologies to solve problem related to transportation like long traffic
delays, road accidents etc.
 IOT devices can monitor traffic jams, animals on road, car accidents. These devices improve the
quality of life, safety, security. Vehicular network offers a range for monitoring and data sharing on
various aspects of traffic so, vehicles can share different kind of data.
 Vehicles are now moving towards becoming computer on wheels. Through IOT devices there will
be vehicle-to-vehicle and vehicle-to-roadside communication has been proposed for enhancing
safety. It is the foundation of Intelligent traffic system.
8 Challenges of Network Resource Allocation security techniques
There are different challenges which occurred in vehicular communication process. We have to work
on them for providing secure and reliable network (Fig.5).

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Fig. 5. Challenges of Network Resource Allocation Security Techniques
• Security Attacks - Since vehicles are completely dependent on their resources for all
aspects of their operation, they are vulnerable to a broad spectrum of cyber security attacks.
For solving security issue, communication efficiency, we have to design a smart vehicular
communication network because present system lacks security and reliability.
• Risk for lives of Passengers or Drivers - Sometimes sensors are not working properly, In
this case, missing information place the lives of drivers or passengers at risk.
• Computational Performance is limited – Computational performance of vehicle is limited
as compared to the computer. As a result, vehicles are more likely to be hacked than
computers.
• Unpredictable Attack Scenarios – A vehicular communication network has different entry
points. attacks are continually being developed, automakers will find it difficult to predict
the information about hackers.
• Insufficient Testing – In Vehicular communication network, concept of cryptography can
be used for sharing information between vehicles. But sometimes this technique destroy the
authenticity of an information. Means hackers may be able to find this information and
misuse it.
• Phishing Attacks – Phishing is a tricking attempt to obtain credentials of users. In VCN,
hackers send wallet key and steal information of an author using false hypelinks.
• Sybil Attack - In a Sybil attack, hackers crash the system using false network identities.
• Routing Attack- In routing attack, attackers tries to interrupt the information and extracted
confidential data .

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9 Network Resource Allocation Security Techniques
There are different network based resource allocation security techniques which is responsible for
Faster communication between vehicles (Fig.6).
Fig. 6. Network Resource Allocation Security Techniques
• Optimization – Optimization of resource allocation means efficient resource utilization for
an environmental safety. This scheme improved the throughput by increasing the use of
virtual and physical resources. In vehicular communication network, sometimes we have to
use additional resources due to problem occur in run time situation. Such resources are
responsible for proper flow of whole communication process.
• Resource Utilization – Main objective of resource utilization means proper allocation of
resources among the tasks for efficient resource utilization and at the same time minimize
the operational consumption of data centers. It is the measurement of effectiveness of
resources.
• Quality of Service – Main objective of this technique is to accomplish resource requirement
of vehicular communication networks such as speed, stability etc. This technique minimizes
response time.

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• Predictive resource allocation technique - This technique is based on prediction of
network loading. It analyses the uncertainty which occur during communication process
and improving the network stability.
• Energy and time resource allocation technique - This technique is based on optimizing
energy and time resource allocation . In Vehicular communication network, multiple drivers
can communicate with multiple vehicles. This scheme maximize the speed of
communication among vehicles.
• Distributed Resource Allocation Algorithm – In this algorithm various types of base
stations such as heterogeneous cellular networks, macrocell base stations are used for
communication between vehicles. This scheme enhances throughput and focus on security
aspect of communication process.
• Direction method of multiplier algorithm – This scheme is able to resolve the problem
of resource allocation in software defined network architecture. This scheme focus on
privacy aspect of communication process.
• Sub-gradient based resource allocation algorithm – This scheme is able to achieve
resource allocation in heterogenous network.
10 Comparative Study and Discussion
Table 1. Comparative study
Author Proposed Scheme Advantage Future Scope
Alia Asheralieva et
al [1]
Predictive Resource
Allocation
Technique
This technique can
improve policies of
network resource
allocation in radio
network.
We can enhance this
technique by using
different kinds of
traffic generators.
Georgios I.
Tsiropoulos et al [4]
Cognitive radio
network in resource
allocation technique
This technique uses
the unused portion
of spectrum and
allocate to the user.
We can add more
parameters including
adaptability,
reconfigurability,
energy efficiency in
cognitive radio
network
Hoon Lee et al [5] Energy and Time
resource allocation
Scheme
This scheme offer
30% average sum
rate performance
over other schemes
Improve its
performance by adding
concept of wireless
powered
communication
network
QINGYANG
SONG et al [9]
Distributed
Resource
Allocation
algorithm
Enhance network
throughput with the
help of
heterogenous
cellular network.
We can improve this
scheme by increasing
resource utilization.

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Hama Ali et al [17] Direction method
for multiplier
algorithm
Solving resource
allocation problem
in software defined
networking
environment.
We can improve the
cost of implementation
and application.
Zhang et al [31] Proposed resource
allocation
optimization
Scheme for
distributed joint
computation.
Reduce power
consumption
Energy efficiency and
data rates are
significantly
improved.
Deng et al [32] Proposed
Distributed Sub-
gradient Scheme
Solving resource
allocation problem
in heterogenous
networks.
We can explore this
scheme for better
throughput.
Kim et al [33] Design a resource
allocation controller
Decrease upto 45%
latency and
maintaining the
QOS of application.
We can improve this
scheme by maintaining
operational request
rate.
Toporkov et al [34] General Window
allocation algorithm
Maintaining non-
dedicated and
heterogenous
resources.
We can refine this
algorithm to decrease
the computational
complexity
Wang et al [35] Distributed
continuous time
algorithm
Minimize cost
function
We can achieve better
result if we can use
energy storage system
in grid connected
battery.

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11 Conclusion and Future Scope
In our research, we discuss about vehicular communication network. Its importance,
properties, advantage, limitations, challenges etc. After conducting comparative study we
can understand that Vehicular communication network is distributed network. It enhances
the safety features while driving. It enable different vehicles to communicate with each
other. It reduces accidents. It help us to know about current situation of road so that we can
search next route for destination. It warns the driver about any mishappenings. In future,
we need to make vehicular communication network smarter because present system lacks
data reliability, security and easy deployment.
References
[1] Asheralieva, A., Khan, J. Y., Mahata, K., & Ong, E. H. (2010, December). A predictive network resource
allocation technique for cognitive wireless networks. In 2010 4th International Conference on Signal
Processing and Communication Systems (pp. 1-9). IEEE.
[2] Ferragut, A., & Paganini, F. (2013). Network resource allocation for users with multiple connections:
fairness and stability. IEEE/ACM Transactions on networking, 22(2), 349-362.
[3] Shams, F., Bacci, G., & Luise, M. (2014). A survey on resource allocation techniques in OFDM (A)
networks. Computer Networks, 65, 129-150.
[4] Tsiropoulos, G. I., Dobre, O. A., Ahmed, M. H., & Baddour, K. E. (2014). Radio resource allocation
techniques for efficient spectrum access in cognitive radio networks. IEEE Communications Surveys &
Tutorials, 18(1), 824-847.
[5] Lee, H., Lee, K. J., Kim, H., Clerckx, B., & Lee, I. (2015). Resource allocation techniques for wireless
powered communication networks with energy storage constraint. IEEE Transactions on Wireless
Communications, 15(4), 2619-2628.
[6] W. Wu, F. Zhou, R. Q. Hu and B. Wang, "Energy-Efficient Resource Allocation for Secure NOMA-Enabled
Mobile Edge Computing Networks," in IEEE Transactions on Communications, vol. 68, no. 1, pp. 493-505,
Jan. 2020, doi: 10.1109/TCOMM.2019.2949994.
[7] Pandiyan, S., & Perumal, V. (2017). A survey on various problems and techniques for optimizing energy
efficiency in cloud architecture. Walailak Journal of Science and Technology (WJST), 14(10), 749-758.
[8] Bermejo, B., Filiposka, S., Juiz, C., Gómez, B., & Guerrero, C. (2017). Improving the energy efficiency in
cloud computing data centres through resource allocation techniques. In Research Advances in Cloud
Computing (pp. 211-236). Springer, Singapore.
[9] Song, Q., Wang, X., Qiu, T., & Ning, Z. (2017). An interference coordination-based distributed resource
allocation scheme in heterogeneous cellular networks. IEEE Access, 5, 2152-2162.
[10] Liang, L., Li, G. Y., & Xu, W. (2017). Resource allocation for D2D-enabled vehicular
communications. IEEE Transactions on Communications, 65(7), 3186-3197.
[11] Wu, W., Zhou, F., Hu, R. Q., & Wang, B. (2019). Energy-efficient resource allocation for secure NOMA-
enabled mobile edge computing networks. IEEE Transactions on Communications, 68(1), 493-505.
[12] Jayakumar, S., & Nandakumar, S. (2021). A review on resource allocation techniques in D2D
communication for 5G and B5G technology. Peer-to-Peer Networking and Applications, 14(1), 243-269.

14
[13] Tayyaba, S. K., Khattak, H. A., Almogren, A., Shah, M. A., Din, I. U., Alkhalifa, I., & Guizani, M.
(2020). 5G vehicular network resource management for improving radio access through machine
learning. IEEE Access, 8, 6792-6800.
[14] X. Li and L. D. Xu, "A Review of Internet of Things—Resource Allocation," in IEEE Internet of Things
Journal, vol. 8, no. 11, pp. 8657-8666, 1 June1, 2021, doi: 10.1109/JIOT.2020.3035542.
[15] Jayakumar, S., & Nandakumar, S. (2021). A review on resource allocation techniques in D2D
communication for 5G and B5G technology. Peer-to-Peer Networking and Applications, 14(1), 243-269.
[16] Maharaj, B. T., & Awoyemi, B. S. (2022). Modelling and Analyses of Resource Allocation Optimisation
in Cognitive Radio Networks. In Developments in Cognitive Radio Networks (pp. 85-118). Springer,
Cham.
[17] Praveenchandar, J., & Tamilarasi, A. (2021). Dynamic resource allocation with optimized task scheduling
and improved power management in cloud computing. Journal of Ambient Intelligence and Humanized
Computing, 12(3), 4147-4159.
[18] HamaAli, K. W., & Zeebaree, S. R. (2021). Resources allocation for distributed systems: A
review. International Journal of Science and Business, 5(2), 76-88.
[19] Mohamed, A., Hamdan, M., Khan, S., Abdelaziz, A., Babiker, S. F., Imran, M., & Marsono, M. N. (2021).
Software-defined networks for resource allocation in cloud computing: A survey. Computer Networks, 195,
108151.
[20] Agarwal, V., Sharma, S., & Bansal, G. (2021, May). Secured Scheduling Techniques of Network Resource
Management in Vehicular Communication Networks. In 2021 5th International Conference on Intelligent
Computing and Control Systems (ICICCS) (pp. 198-202). IEEE.
[21] Zhang, M., Cumanan, K., Thiyagalingam, J., Tang, Y., Wang, W., Ding, Z., & Dobre, O. A. (2021).
Exploiting deep learning for secure transmission in an underlay cognitive radio network. IEEE Transactions
on Vehicular Technology, 70(1), 726-741.
[22] Agarwal, V., Sharma, S., & Agarwal, P. (2021). IoT Based Smart Transport Management and Vehicle-to-
Vehicle Communication System. In Computer Networks, Big Data and IoT (pp. 709-716). Springer,
Singapore.
[23] Agarwal, V., & Sharma, S. (2020, December). IoT based smart transport management system. In
International Conference on Advanced Informatics for Computing Research (pp. 207-216). Springer,
Singapore.
[24] Vartika Agarwal, Sachin Sharma (2021), Deep Learning Techniques to improve radio resource management
in vehicular communication network, International Conference on sustainable Advanced Computing
(ICSAC 2021).
[25] Liang, F., Yu, W., Liu, X., Griffith, D., & Golmie, N. (2021). Towards Deep Q-Network Based Resource
Allocation in Industrial Internet of Things. IEEE Internet of Things Journal.
[26] Allouch, M., Kallel, S., Soua, A., Shagdar, O., & Tohme, S. (2021). Survey on radio resource allocation in
long‐term evolution‐vehicle. Concurrency and Computation: Practice and Experience, e6228.
[27] Wadhwa, H., & Aron, R. (2021). TRAM: Technique for resource allocation and management in fog
computing environment. The Journal of Supercomputing, 1-24.
[28] Cao, Y., Zhang, G., Li, G., & Zhang, J. (2021). A Deep Q-Network Based-Resource Allocation Scheme for
Massive MIMO-NOMA. IEEE Communications Letters, 25(5), 1544-1548.
[29] Jamshed, M. A., Heliot, F., & Brown, T. (2021). Unsupervised Learning Based Emission-aware Uplink
Resource Allocation Scheme for Non-Orthogonal Multiple Access Systems. IEEE Transactions on
Vehicular Technology.
[30] Tyagi, S. K. S., Mukherjee, A., Pokhrel, S. R., & Hiran, K. K. (2020). An intelligent and optimal resource
allocation approach in sensor networks for smart agri-IoT. IEEE Sensors Journal.
[31] Zhang, J., Xia, W., Yan, F., & Shen, L. (2018). Joint computation offloading and resource allocation
optimization in heterogeneous networks with mobile edge computing. IEEE Access, 6, 19324-19337.
[32] Deng, Z., Nian, X., & Hu, C. (2019). Distributed algorithm design for nonsmooth resource allocation
problems. IEEE transactions on cybernetics, 50(7), 3208-3217.
[33] Kim, Y. K., Hoseiny, F. R., Lee, Y. C., & Zomaya, A. Y. (2017, October). A resource allocation controller
for key-value data stores. In 2017 IEEE 16th International Symposium on Network Computing and
Applications (NCA) (pp. 1-4). IEEE.
[34] Toporkov, V., & Yemelyanov, D. (2018, October). Resources Allocation Optimization in Distributed and
Heterogeneous Computing Environments. In 2018 IV International Conference on Information
Technologies in Engineering Education (Inforino) (pp. 1-6). IEEE.
[35] Wang, B., Fei, Q., & Wu, Q. (2020). Distributed Time-Varying Resource Allocation Optimization Based
on Finite-Time Consensus Approach. IEEE Control Systems Letters, 5(2), 599-604.

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