Content uploaded by Wasswa Shafik
Author content
All content in this area was uploaded by Wasswa Shafik on May 17, 2024
Content may be subject to copyright.
International Journal of Technology 15(3) 654-664 (2024)
Received May 2022/ Revised August 2022 / Accepted June 2023
International Journal of Technology
http://ijtech.eng.ui.ac.id
Technical Loopholes and Trends in Unmanned Aerial Vehicle Communication
Systems
Wasswa Shafik1,3, Kassim Kalinaki2,3, S. Mojtaba Matinkhah4
*
1Dig Connectivity Research Laboratory (DCRLab), P. O. Box. 600040, Kampala, Uganda
2Department of Computer Science, Islamic University in Uganda. P. O. Box 2555, Mbale, Uganda
3Borderline Research Laboratory, P. O. Box. 7689, Kampala, Uganda
4Computer Engineering Department, Yazd University, University Boulevard, Safayieh-Yazd, Yazd 89195-741
Yazd Iran
Abstract. Advancements in technology have positioned Unmanned Aerial Vehicles (UAVs) as
current and future computational paradigms. This novel artificial intelligence tool facilitates various
applications, including communication, sports, real-time data collection in agriculture, and
commercial purposes. Even though UAVs were much known for military usage in navigating hard-
to-reach areas and hostile environments for physical human interaction. Considering UAV's
application in communications, the maximum adaption of diversity combination properties has
been used for some years ago, facing the Bit Error Rate (BER) of selective combing, maximal ratio
combining, equal gain combining, direct combining, selective combining, BER of Rayleigh, and
Ricean approach. This paper surveys models and current UAV systems to analyze the most common
technologies in UAV diversity, combining Rayleigh and Ricean based on the diversity combination
properties. Based on the survey, the existing proposed models have not depicted the most reliable
and effective model for adopting Rayleigh or Ricean in UAV communication systems and UAV-
associated concerns.
Keywords: Diversity combining; Rayleigh scheme; Ricean scheme; Unmanned aerial vehicle
1. Introduction
Sustainable development depicted smart cities and integration of the Internet of Things
(IoT); for instance, drones that operate autonomously or semi-autonomous are supported
by artificial intelligence like machine learning (ML) and Deep Learning (DL) has
demonstrated an increase in the market arena (Berawi, 2023; Shafik et al., 2020a). Proper
resource utilization and allocation using the existing technologies are prevailing, like using
electric motorcycles to reduce environmental pollution (Suwignjo et al., 2023) and social
benefit-cost assessments (Sarkar, Sheth, and Ranganath, 2023). The future unmanned
aerial vehicle (UAV) projection plans boost UAV-networked communication productivity.
The precise time logs in UAV maintenance contribute to accurate data collection, enabling
the forecasting of data transmissions for grounded nodes in new city development projects
(Berawi, 2022; Shafik et al., 2020b), despite the fact monitoring unmanned aerial vehicle
flight, for example, velocities that reduce the multiple data packet losses that are after
*
Corresponding author’s email: matinkhah@yazd.ac.ir, Tel.: +98 933 470 1475; Fax: +98 351 725 0119
doi: 10.14716/ijtech.v15i3.5644
Shafik, Kalinaki, and Matinkhah 655
the buffer overflows and the channel fading. The most current model was proposed in
search of better resource allocations (Khan et al., 2021; Zhou et al., 2020).
The collection of onsite and current information assortment in Unmanned Aerial
Vehicles-assisted Internet-of-Things networks is a network challenge. This increases safer
storage, UAV data backups, and quick access to collectible data files, this is often done when
the UAV collected data is transferred towards the network sensors in collecting points that
collect, manipulate, retrieve, and update packets inside a specified time frame in conscious
to the enduring positive energies (Shafik et al., 2020c). Figure 1 presents different parts of
drones from UAVs through SD, given that 𝜇UAV represents macro UAVs, MAV illustrates
micro air vehicles, NAV depicts nano air vehicles, PAV pico air vehicles, and SD smart dust.
Figure 1 Drone types (Hassanalian, 2016)
Energy Efficiency novelty, the machine learning model to challenge the energy
delinquent was to formulate a group of UAVs energy-resourcefully, also accommodatingly
accumulate statistics beginning from lower levels of the sensors, though incriminating the
batteries from multi haphazardly installed arraigning positions (Shafik et al., 2020d). The
proposed proximal policy optimization (PPO) and the convolution neural turing machine
(ConvNTM) termed as j-PPO+ConvNTM model contains a novel spatiotemporal module to
better model long-sequence spatiotemporal data and a deep reinforcement learning model
called "j-PPO", where it has the capability to make continuous (for instance, route planning)
and discrete (that is to say, either go for charging or to collect data) action decisions
simultaneously for all UAVs, moreover to accumulate data action decisions instantaneously
for all UAVs (Liu, Piao, and Tang, 2020).
In human target search and detection, several optimum procedures are used in
searching and formulating the target from the quantified exploration area terminuses.
Subsequently, in acknowledgment of the targets, the Unmanned Aerial Vehicle could have
the capacity either be cast-off to embrace its condition so that it may also further have the
audio-visual feeds of the targets or reappearance to its base station the minute the
synchronizes the predictable utilizing GPS components or convey the GPS position to the
base station (Shafik et al., 2020e).
656 Technical Loopholes and Trends in Unmanned Aerial Vehicle Communication Systems
The Markov decision process is articulated through the enterprise of state,
accomplishment interstellar, and reward functions. The deep learning approach in specific
reinforcement is inspected to accomplish the optimum multiparty path planning and power
allocation strategies (Glock and Meyer, 2022). An energy-constrained unmanned aerial
vehicle was installed to accumulate information on each sensor network when it hovered
through these sensor networks. The flight trajectory of the Unmanned Aerial Vehicle has
been enhanced to minimize the sensor network's information age while preserving its
packet drop rate. More still, the authors have proposed and developed a learning algorithm
to navigate an optimal policy. This paper primarily presents the following contributions:
• A comprehensive literature review of state-of-the-art models related to Unmanned
Aerial Vehicles (UAVs) and associated issues.
• Studies on the technical loopholes in UAV communication systems include autonomous
navigation, task offloading, energy efficiency, human target search, detection, power
allocation in UAV networks, resource allocation, and real-time trajectory planning.
• The survey also consolidates UAV challenges identified in existing models and
highlights loopholes. This will assist various stakeholders in selecting models that align
with public demand.
• The study further identifies technical trends as UAV standard technologies attain
complex tasking and maneuver decisions, minimum throughput maximization,
continuous communication service, fairness, a segmentation method, wireless
networking, and detection.
The rest of this paper is structured as follows. In section 2, technical loopholes in the
state-of-the-art UAVs are presented with simplified tables of illustrations. Section 3 holds
the technical trends of UAVs. In section 4, the conclusion and present future works are
presented.
2. Technical Loophole UAV Models
In this section, the study focuses on the most currently identified challenges that the
UAV faces; considering the challenges in response to the proposed model and technical
outcomes, the main interest is mainly put on the proposed model loopholes in search of
better technical recommendations that researchers who intend to develop models,
architectures, and frameworks to develop models are in-line with the most technical
network challenges factors in specific.
Multi-UAV Navigation, a calculated and ultimately disseminated controller, leverages
the chronological standard exposure path achieved by all UAVs in the mission. It also
utilizes the terrestrial fairness of all carefully considered points of interest and minimizes
energy consumption by keeping them within the designated area boundaries. This
approach ensures national reflection, exploitation space, and compensation are managed
in a prominent style. Lastly, it perfectly optimizes each UAV using deep neural networks.
Conducted extensive simulations and found the appropriate set of hyperparameters,
including experience replay buffer size, number of neural units for two fully connected
hidden layers of actor, critic, and their target networks, and the discount the factor for
remembering the future reward (Shafik and Tufail, 2023). Real-time trajectory Planning of
the UAV for aerial data assortment and the WPT to minimize bumper excess at the ground
sensors and unsuccessful transmission due to lossy airborne frequencies. Contemplate net
states of battery-operated levels and buffer lengths of the pulverized instruments, channel
conditions, and position of the UAV (Liu et al., 2020). Deep Q-learning-based resource
supervision is proposed to minimize the general statistics packet loss of the IoTs' nodes by
optimally determining the IoTs' node for data assortments and energy transfer and the
Shafik, Kalinaki, and Matinkhah 657
connected modulation arrangement of the IoTs' node; see (Li et al., 2020a; Shafik et al.,
2020c; Wu et al., 2020).
2.1. UAV autonomous navigation
The assumption was that a previous policy (nonexpert assistant) might be of meager
presentation available to the educational agent. The preceding policies theatres the person
of decision-making as the agent in peripatetic the state area by reshaping the behavior
policy used for environmental interaction. It conjointly assists the agent in achieving goals
by setting dynamic learning objectives with an increasing problem. To evaluate our
proposed technique, we constructed a machine for UAV navigation in large-scale complex
environments and compared our algorithm with several baselines. The proposed model
significantly outperforms the existing algorithms that handle distributed rewards and
produces impressive navigation policies similar to those learned in an environment with
dense rewards (Wang et al., 2020).
2.2. Energy efficiency
A new deep model referred to as j-PPO+ConvNTM encompasses a unique Convert-NTM
to higher archetypal long-sequence spatiotemporal knowledge, and a deep reinforcement
learning model referred to as "j-PPO," wherever it is potential to create continuously that
is to say path forecasting and distinct that is to say additionally to pucker information or
select charging action choices at the same time for all UAVs; see (Liu, Piao, and Tang, 2020).
2.3. Human Target Search and Detection
The application of developing associate in nursing autonomous closed-circuit
television victimization associate with nursing UAV to spot an assumed target and objects
of interest within the parcel over that it flies, such a system may be employed in rescue
operations, particularly in remote areas wherever physical access is challenging. The UAV
industrialized throughout this effort is accomplished by object recognition. A mounted
camera is active to offer visual feedback and is associated with the nursing and aboard
process; the unit runs an image recognition software package to spot the target in real time.
2.4. Power allocation in UAV networks
The machine learning-based approach is expected to get the utmost long-run network
utility whereas summit with user equipment's quality of service mandate. The
arithmetician call methods are developed with the look of slate, action area, and reward
performance. The deep reinforcement learning approach is inspected to realize the joint
optimal policy of power-driven phenomenon styles and power allocation. Because of the
continual action area of the MDP model, a deeply settled policy gradient approach is
bestowed. The predictable learning algorithmic program will significantly scale back the
age of knowledge and packet drop rates associated with the baseline greedy algorithms,
respectively.
2.5. Multi-UAV navigation
Maximizing the average temporal coverage, the score achieved by whole UAVs during
a task, maximizing the geographical fairness of all thought of point-of-interests, and
minimizing the overall energy consumptions, keeping them connected and not flying out of
the realm border. The state, observation, auction house, and reward were designed
meticulously, and each UAV was modeled using a deep neural network; for details, refer to
(Liu et al., 2020).
2.6. Real-time trajectory planning
Considering system states of battery levels and buffer lengths of the ground sensors,
channel conditions, and site of the UAV. An aeronautical trajectory planning optimization is
658 Technical Loopholes and Trends in Unmanned Aerial Vehicle Communication Systems
formulated as an obvious incomplete MDP, where the UAV has the fractional reflection of
the system conditions (Li et al., 2020b). In rehearsal, the UAV-enabled sensor system
encompasses many system states and travels in the model, while the up-to-date
information on the system situations is not available at the Unmanned Ariel.
2.7. Resource management
The resource management downside is insensible situations, wherever the UAV has no
a-prior statistics on battery-operated levels and evidence train lengths of the nodes.
Construction of the resource supervision of UAV-assisted WPT and information variety as
Markoff call process, somewhere the states carry with its battery levels and evidence queue
dimensions of the internet of things' nodes, frequency qualities, and locations of the UAV. A
Q-Learning mainly constructed resource administration is premeditated to decrease the
all-purpose data packet loss of the IoTs nodes, power transfer, and the associated
modulation theme of the IoTs node (Li et al., 2020c).
2.8. Partially explainable big data
Using telecommunications and social media data, the study aimed to analyze the
impact of UAV power and communication constraints on the practice and quality of service
in a specific area. The objective was to determine the level of excellence and competition
within the system of dimensions being examined. The Proposed greener (Guo, 2020) UAVs
on the close-to-middle-percent energy saved address each quantitative quality of service
and qualitative quality-of-experience difficulties (Li et al., 2020c).
2.9. UAV pursuer and evader problem
A Takagi-Sugeno-fuzzy model is precisely a constant UAV management technique for
perception. In dealing with the struggle conundrum amongst UAVs, thus verbalize this
downside into a pursuer-evader negative and influence the strengthening learning
principally founded machine knowledge practice to resolve this (Shafik, 2023). The
projected profound letter system relies on antique dispatch learning yet is ready to address
some deficiencies. A deep letter network has three critical improvements: victimization
neuronal networks to elucidate the letter operation, the enterprise of twofold networks,
and proficiency replay (Chen et al., 2020).
2.10. Automatic recognition & secure communications
To achieve high accuracy, we tend to appraise tetrad deep neural network
reproductions qualified with different restrictions for fine-tuning and allocation learning.
Information extension waster was used through the schmoosing coaching to avoid
overfitting. The proposed methodology contains the victimization of the SLIC model to
section the plant leaves inside the top-view pictures obtained throughout the flight (Tetila
et al., 2020). The authors tested our information set from accurate flight inspections in an
associated end-to-end laptop vision approach (Shafik, 2024).
2.11. Path planning and trajectory design
The UAV is employed to trace users that move on to some specific methods, and the
authors tend to propose a proximal policy optimization-based rule to maximize the fast
total rate. The UAV is sculptural as a deep reinforcement learning agent to be expressed the
technique to traffic by interrelating with the troposphere (Li et al., 2020d). On one occasion,
the UAV attends users on unidentified approaches for disasters, a random coaching
proximal strategy optimization rule which may obligate the pre-trained model to
innovative tasks to appreciate fast organizing.
Shafik, Kalinaki, and Matinkhah 659
2.12. Energy-Efficiency
The initial tendency is to see the energy efficiency problem for UAVs as a secondary
obstacle to optimizing performance. This is because there are different performance
measures for the internet, synchronous extra-terrestrial users, and UAVs as aerial users.
Investment tackles beginning deep learning; the authors have become a predisposition to
network this shortcoming into a profound column learning drawback and gift a learning-
powered answer that comes with the KPIs of interest inside the design of the reward
performed to resolve energy efficiency maximization for aerial users whereas minimizing
interference to terrestrial users (Ghavimi and Jantti, 2020) and (Shafik and Tufail 2023).
Any stream understanding on the reimbursements of finance intelligent energy-efficient
theme. A severe increase in energy effectiveness of aerial users relative to an intensification
in their spectral effectiveness, whereas an extensive reduction in incurred interferes with
the extra-terrestrial.
2.13. Multi-UAV target-finding
The enactment discontinues the matter into two separate stages of designing and
management, with each stage modeled as a part of a discernible Markov call method.
International redistributed designing employs a fashionable online part discernible Markov
call method convergent thinker, whereas a contemporary DRL formula is employed to
supply a policy for instinctive supervision. The outline is proficient in target-finding
privileged a replicated indoor checked location in the interior. The attendance of
mysterious obstacles, connected once protracted to real-world maneuvers, might adjust
UAVs to be applied in a cumulative variety of submissions (Walker et al., 2020).
2.14. Real-time data processing
A mobile machine-like Associate in a nursing autonomous guided vehicle will interfere
during this situation because the vehicle additionally desires service by the sting node; over
that, the quality of service performance will decrease; as detailed in (Shafik et al., 2020d).
Therefore, the study deploys an associate in the nursing pilotless aerial vehicle as a
positioning node to supply service to the sting network through optimizing the flight of UAV
wherever the sting network requests tasks employing deep Q-network Learning (Sakir et
al., 2020). Machine learning, notably the deep Q-network rule, will increase the number of
machines that may provide service. Afterward, the period information can do either the
interruption happens at the sting node (Shafik et al., 2020a).
2.15. UAV navigation
Within the confrontations and these uncomplicated limitations, the study has a
propensity to amalgamate the advanced DRL with the UAV navigations to complete an
enormous multiple-input-multiple-output performance. To be explicit, there is a propensity
to meticulously style a deep Q-network for augmenting the UAV navigation by picking the
optimal policy, and so the study inclines to recommend a learning machine for the
progression of the mysterious Q-networks. The unfathomable Q-network is trained so the
agent can construct choices supported by the received signal strengths for navigating the
UAVs with the benefit of confident Q-learning (Zhang et al., 2020).
2.16. Anti-intelligent UAV emptiness
A simple observant of the fragmentary channel government info of the latest users, the
study tends to prototypical such spearhead sub-game as a partly evident Markov call
method for the primary try. Obtain the optimum jam flight via the developed deep repeated
Q-networks within the three-dimensional area. The authors got the optimal announcement
flight via the industrialized deep Q-networks inside the two-dimensional extent. The
660 Technical Loopholes and Trends in Unmanned Aerial Vehicle Communication Systems
presence of the Stackelberg equilibriums and the closed-form countenance for the
Stackelberg equilibrium in a particular case obtained some sensitive remarks area unit (Gao
et al., 2019).
2.17. Trajectory design and power allocation
Additionally, because of that, the utility of every UAV is set supported by the network
setting and alternative UAVs' actions, the standard flight style, and power allocation
negative is sculpturesque as a random game. Due to the high machine quality instigated by
the recurring action area and massive state area, a multi-agent deep settled policy gradient,
the methodology is projected to get the best policy for the standard flight style and power
allocation issue. The proposed methodology will get the upper network utility and system
capability than alternate optimizations strategy in multi-UAV grids with lower mechanism
eminence (Wang et al., 2019).
3. Technical Trending of UAV
This section presents current UAV trends, including complex tasking and maneuver
decisions, minimum throughput maximization, continuous communication service,
fairness, segmentation method, wireless networking and detection, target tracking, and
node positioning.
3.1. Complex tasking and maneuvering decision
An imitation-increased deep reinforcement Learning learns the underlying
complementary behaviors of UGVs and UAVs from an indication dataset collected from
some straightforward situations with non-optimized methods (Dong et al., 2022). Coaching
exploitation ancient strategies, a phased coaching technique known as "Basic-
confrontation," that relies on the concept that the masses step by step learn from easy to
complicate is planned to assist in cutting back the coaching time whereas obtaining
suboptimal however economical results (Hu et al., 2022). The matched short-range air
combats are simulated under different target maneuver policies. The planned maneuver
decision model and training technique will help the UAV achieve autonomous decision-
making in air contests and establish an effective decision policy to outperform its rivals
(Ben-Aissa and Ben-Letaifa, 2022).
3.2. Minimum throughput maximization
The joint remote-controlled UAV aeronautical deceitful and time resource allocation
for tiniest output maximization during a multiple UAV-enabled wireless high-powered
communication network. Specifically, the UAVs perform as base stations to broadcast
energy signals within the downlink to charge IoT devices, whereas the Internet of Things
strategies send their freelance data within the transmission by utilizing the composed
energy. The established output optimizations downside that involves joint optimizations of
3-dimensional path styles and channel resource obligation with the constraint of flight
speed of UAVs and transmission transmit power of IoT devices is not bulging and resolving
directly (Wang et al., 2022).
3.3. Persistent communication service and fairness
A projected energy-economical and honest third-dimensional UAV programming with
energy filling, wherever UAVs move around to serve users and recharge timely to refill
energy (Nørskov et al., 2022). Impressed by the success of deep reinforcement learning, we
tend to propose a UAV management policy supported by deep settled policy gradient UC-
Deep settled Policy Gradient to deal with the mixed downside of third-dimensional quality
of multiple UAVs and energy-filling programming that ensures energy-efficient and honest
Shafik, Kalinaki, and Matinkhah 661
coverage of every user in an exceedingly massive region and maintains the continuous
service using deep learning YOLO-v2 for UAV Applications in real-time (Boudjit and
Ramzan, 2021).
3.4. Segmentation Method
The initial victimization of UAVs to get Multiview inaccessible sensing pictures of the
forest victimizes the structure from motion formula to hypothesize the timberland thin
resolution cloud and patch-based MVS formula to construct the dense purpose cloud
(Mostafavi and Shafik, 2019). A targeted purpose cloud deep learning technique is planned
to extract the purpose cloud of one tree (Song et al., 2022). The analysis results show that
the accuracy of single-tree purpose cloud division of deep learning ways is quite ninetieth,
and the accuracy is higher than ancient flat image separation and resolves cloud
segmentation (Ling et al., 2022).
3.5. Wireless networking and detection
The performance of the planned methodology was quantitatively compared to ancient
approaches, and it exhibited the most practical and sturdy result. An authentic crop row
detection rate of 94.61% was obtained with an assistant degree promissory note score per
crop row higher than 71% (Medaiyese et al., 2022).
4. Conclusions
In conclusion, the field of UAV communication systems is evolving at a rapid pace, with
advancements being made in both hardware and software. While these advancements have
improved the capabilities of UAVs, they have also created technical loopholes that malicious
actors could exploit. Developers need to be aware of these vulnerabilities and work towards
securing UAV communication systems. The trend seems to be toward developing more
autonomous and intelligent UAVs. These UAVs will be capable of performing complex tasks
without human intervention, relying on advanced communication systems to operate in
various environments. In addition, integrating UAVs with other technologies, such as
artificial intelligence and the IoT, will further enhance their capabilities. As the use of UAVs
becomes more widespread in various industries, the need for secure and reliable
communication systems will only become more critical. Developers must work towards
addressing current vulnerabilities and designing communication systems capable of
withstanding new threats. Ultimately, the future of UAV communication systems will be
shaped by the continued evolution of technology and the ability of developers to adapt to
changing demands and challenges.
References
Ben-Aissa, S., Ben-Letaifa, A., 2022. UAV Communications with Machine Learning:
Challenges, Applications and Open Issues. Arabian Journal for Science and Engineering.
Volume 47(2), pp. 1559–1579
Berawi, M.A., 2022. New City Development: Creating a Better Future and Added Value.
International Journal of Technology, Volume 13(2), pp. 225–228
Berawi, M.A., 2023. Smart Cities: Accelerating Sustainable Development Agenda.
International Journal of Technology, Volume 14(1), pp. 1–4
Boudjit, K., Ramzan, N., 2021. Human Detection Based on Deep Learning YOLO-v2 for Real-
Time UAV Applications. Journal of Experimental & Theoretical Artificial Intelligence,
Volume 34(3), pp. 527–544
662 Technical Loopholes and Trends in Unmanned Aerial Vehicle Communication Systems
Chen, D., Wei, Y., Wang, L., Hong, C.S., Wang, L., Han, Z., 2020. Deep Reinforcement Learning
Based Strategy for Quadrotor UAV Pursuer and Evader Problem. In: IEEE International
Conference on Communications Workshops, Volume 2020, pp. 1–6
Dong, X., He, M., Yang, X., Miao, X., 2022. Efficient Low-Complexity Turbo-Hadamard Code
in UAV Anti-Jamming Communications. Electronics, Volume 11(7), p. 1088
Gao, N., Qin, Z., Jing, X., Ni, Q., Jin, S., 2019. Anti-Intelligent UAV Jamming Strategy via Deep
Q-Networks. IEEE Transactions on Communications. Volume 68(1), pp. 569–581
Ghavimi, F., Jantti, R., 2020. Energy-Efficient UAV Communications with Interference
Management: Deep Learning Framework. In: 2020 IEEE Wireless Communications and
Networking Conference Workshops, pp. 1–6
Glock, K., Meyer, A., 2022. Spatial Coverage In Routing And Path Planning
Problems. European Journal of Operational Research, Volume 305(1), pp. 1–20
Guo, W., 2020. Partially Explainable Big Data Driven Deep Reinforcement Learning for
Green 5G UAV. In: IEEE International Conference on Communications, pp. 1–7
Hassanalian, M., 2016. Wing Shape Design and Kinematic Optimization of Bio-Inspired Nano
Air Vehicles for Hovering and Forward Flight Purposes. Doctoral Dissertation, New
Mexico State University, Mexico
Hu, J., Wang, L., Hu, T., Guo, C., Wang, Y., 2022. Autonomous Maneuver Decision Making of
Dual-UAV Cooperative Air Combat Based on Deep Reinforcement Learning. Electronics,
Volume 11(3), p. 467
Khan, R.U., Khan, K., Albattah, W., Qamar, A.M., 2021. Image-Based Detection Of Plant
Diseases: From Classical Machine Learning To Deep Learning Journey. Wireless
Communications and Mobile Computing, Volume 2021, pp. 1–13
Li, K., Emami, Y., Ni, W., Tovar, E., Han, Z., 2020a. Onboard Deep Deterministic Policy
Gradients for Online Flight Resource Allocation of UAVs. IEEE Networking Letters,
Volume 2(3), pp. 106–110
Li, K., Ni, W., Tovar, E., Guizani, M., 2020b. Deep Reinforcement Learning for Real-Time
Trajectory Planning in UAV Networks. In: 2020 International Wireless
Communications and Mobile Computing, pp. 958–963
Li, K., Ni, W., Tovar, E., Jamalipour, A., 2020c. Deep Q-Learning based Resource Management
in UAV-assisted Wireless Powered IoT Networks. In: IEEE International Conference on
Communications, pp. 1–6
Li, X., Wang, Q., Liu, J., Zhang, W., 2020d. Trajectory Design and Generalization for UAV
Enabled Networks: A Deep Reinforcement Learning Approach. In: IEEE Wireless
Communications and Networking Conference, pp. 1–6
Ling, M., Cheng, Q., Peng, J., Zhao, C., Jiang, L., 2022. Image Semantic Segmentation Method
Based on Deep Learning in UAV Aerial Remote Sensing Image. Mathematical Problems
in Engineering, Volume 2022, p. 5983045
Liu, C.H., Ma, X., Gao, X., Tang, J., 2020. Distributed Energy-Efficient Multi-UAV Navigation
for Long-Term Communication Coverage by Deep Reinforcement Learning. IEEE
Transactions on Mobile Computing, Volume 19(6), pp. 1274–1285
Liu, C.H., Piao, C., Tang, J., 2020. Energy-Efficient UAV Crowdsensing with Multiple Charging
Stations by Deep Learning. In: IEEE Conference on Computer Communications, Volume
2020, pp. 199–208
Medaiyese, O.O., Ezuma, M., Lauf, A.P., Guvenc, I., 2022. Wavelet Transform Analytics for RF-
Based UAV Detection and Identification System Using Machine Learning. Pervasive and
Mobile Computing. Volume 82, p. 101569
Shafik, Kalinaki, and Matinkhah 663
Mostafavi, S., Shafik, W., 2019. Fog Computing Architectures, Privacy and Security Solutions.
Journal of Communications Technology, Electronics and Computer Science. Volume 24,
pp. 1–14
Nørskov, S., Damholdt, M.F., Ulhøi, J.P., Jensen, M.B., Mathiasen, M.K., Ess, C.M., Seibt, J., 2022.
Employers' and Applicants' Fairness Perceptions in Job Interviews: Using A
Teleoperated Robot as Fair Proxy. Technological Forecasting and Social Change,
Volume 179, p. 121641
Sakir, R.K.A., Ramli, M.R., Lee, J., Kim, D. 2020. UAV-assisted Real-time Data Processing using
Deep Q-Network for Industrial Internet of Things. In: International Conference on
Artificial Intelligence in Information and Communication, pp. 208–211
Sarkar, D., Sheth, A., Ranganath, N., 2023. Social Benefit-Cost Analysis for Electric BRTS in
Ahmedabad. International Journal of Technology. Volume 14(1), pp. 54–64
Shafik, W, Tufail, A., 2023. Energy Optimization Analysis on Internet of Things. Advanced
Technology for Smart Environment and Energy, In: IOP Conference Series: Earth and
Environmental Science, pp. 1–16.
Shafik, W. (2024). Blockchain-Based Internet of Things (B-IoT): Challenges, Solutions,
Opportunities, Open Research Questions, and Future Trends. Blockchain-based Internet
of Things, pp. 35–58.
Shafik, W., 2023. Cyber Security Perspectives in Public Spaces: Drone Case Study. In:
Handbook of Research on Cybersecurity Risk in Contemporary Business Systems, pp. 79–
97
Shafik, W., Matinkhah, M., Etemadinejad, P., Sanda, M.N., 2020a. Reinforcement Learning
Rebirth, Techniques, Challenges, and Resolutions. JOIV: International Journal on
Informatics Visualization, Volume 4(3), pp. 127–135
Shafik, W., Matinkhah, M., Sanda, M.N., 2020b. Network Resource Management Drives
Machine Learning: A Survey And Future Research Direction. Journal of Communications
Technology, Electronics and Computer Science, Volume 2020, pp. 1–15
Shafik, W., Matinkhah, S.M, Ghasemazadeh, M., 2019. Fog-Mobile Edge Performance
Evaluation and Analysis on Internet of Things. Journal of Advance Research in Mobile
Computing, Volume 1(3), pp. 1–17
Shafik, W., Matinkhah, S.M., Afolabi, S.S., Sanda, M.N., 2020c. A 3-Dimensional Fast Machine
Learning Algorithm for Mobile Unmanned Aerial Vehicle Base Stations. International
Journal of Advances in Applied Sciences, Volume 10(1), pp. 28–38
Shafik, W., Matinkhah, S.M., Ghasemzadeh, M., 2020d. Theoretical Understanding of Deep
Learning In Uav Biomedical Engineering Technologies Analysis. SN Computer Science,
Volume 1(6), p. 307
Shafik., W., Matinkhah, S. M., Asadi, M., Ahmadi, Z., Hadiyan, Z. 2020e. A Study On Internet
Of Things Performance Evaluation. Journal of Communications Technology, Electronics
and Computer Science, Volume 28, pp. 1–19
Song, H., Kim, M., Park, D., Shin, Y., Lee, J.G., 2022. Learning From Noisy Labels With Deep
Neural Networks: A Survey. IEEE Transactions on Neural Networks and Learning
Systems, Volume 34(11), pp. 8135–8153
Suwignjo, P., Yuniarto, M.N., Nugraha, Y.U., Desanti, A.F., Sidharta, I., Wiratno, S.E., Yuwono,
T., 2023. Benefits of Electric Motorcycle in Improving Personal Sustainable Economy:
A View from Indonesia Online Ride-Hailing Rider. International Journal of Technology,
Volume 14(1), pp. 38–53
Tetila, E.C., Machado, B.B., Menezes, G.K., Oliveira, A.D.S., Alvarez, M., Amorim, W.P., Belete,
N.A.D.S., Silva, G.G.D., Pistori, H., 2020. Automatic Recognition of Soybean Leaf Diseases
664 Technical Loopholes and Trends in Unmanned Aerial Vehicle Communication Systems
Using UAV Images and Deep Convolutional Neural Networks. IEEE Geoscience and
Remote Sensing Letters. Volume 17(5), pp. 903–907
Walker, O., Vanegas, F., Gonzalez, F., Koenig, S., 2020. Multi-UAV Target-Finding in
Simulated Indoor Environments using Deep Reinforcement Learning. In: 2020 IEEE
Aerospace Conference, pp. 1–9
Wang, C., Wang, J., Wang, J., Zhang, X., 2020. Deep-Reinforcement-Learning-Based
Autonomous UAV Navigation with Sparse Rewards. IEEE Internet of Things Journal,
Volume 7(7), pp. 6180–6190
Wang, Q., Zhang, W., Liu, Y., Liu, Y., 2019. Multi-UAV Dynamic Wireless Networking with
Deep Reinforcement Learning. IEEE Communications Letters, Volume 23(12), pp.
2243–2246
Wang, Y., Wang, J., Wong, V.W., You, X., 2022. Effective Throughput Maximization of NOMA
with Practical Modulations. IEEE Journal on Selected Areas in Communications, Volume
40(4), pp. 1084–1100
Wu, F., Zhang, H., Wu, J., Song, L., 2020. Cellular UAV-To-Device Communications: Trajectory
Design and Mode Selection by Multi-Agent Deep Reinforcement Learning. IEEE
Transactions on Communications, Volume 68(7), pp. 4175–4189
Zhang, Y., Zhuang, Z., Gao, F., Wang, J., Han, Z., 2020. Multi-Agent Deep Reinforcement
Learning for Secure UAV Communications. In: IEEE Wireless Communications and
Networking Conference, Volume 2020, pp. 1–5
Zhou, S., Li, B., Ding, C., Lu, L., Ding, C., 2020. An Efficient Deep Reinforcement Learning
Framework for UAVs. In: 21st International Symposium on Quality Electronic Design,
pp. 323–328
