November 2024, Volume 16, Number 6: Top10 Cited Articles in Computer Networks & Communications

November 2024, Volume 16, Number 6:
Top10 Cited Articles in Computer Networks
& Communications
International Journal of Computer Networks&
Communications (IJCNC)
http://airccse.org/journal/ijcnc.html
(Scopus, ERA Listed, WJCI Indexed)
Scopus Cite Score 2023—1.6
ISSN 0974 - 9322 (Online); 0975 - 2293 (Print)
Citations, h-index, i10-index

MODELING LOW-COST INERTIAL NAVIGATION SYSTEMS AND THEIR
ERRORS
Mohammed AFTATAH, Khalid ZEBBARA and Soufiane EL ASRI
Laboratory of Research in Innovation in Mathematics and Intelligent Systems (IMIS), Faculty
of Applied Sciences, Ibn Zohr University, BP: 523 Agadir, Morocco
ABSTRACT
Inertial Navigation Systems (INS) are critical for a wide range of applications due to their ability to provide
reliable navigation information, even without external references. This paper presents a comprehensive
studyofthe modeling oflow-cost INS sensors andtheir inherent errors, focusing onhowthese errors impact
the accuracy of the system's localization outputs. A MATLAB-based simulation platform was developed
to analyze the effects of common sensor errors on position, velocity, and attitude over time. The
experimental results show that these errors accumulate, leading to significant deviations from the true
trajectory. Notably, the maximum positional error in the upward direction reached 65 meters by the end of
the simulated trajectory, while the velocity error in the same direction deviated by 0.8 m/s. Initially, the
estimated trajectory closely followed the reference path, but as the simulation progressed, a substantial
divergence occurred, highlighting the cumulative impact of sensor errors. These findings underscore the
necessity of advanced error mitigation techniques to enhance the long-term accuracy and reliability of INS
in practical applications.
KEYWORDS
Low-Cost Inertial Navigation System, Sensor errors, Navigation, Modeling, Mechanization
For More Details: https://aircconline.com/ijcnc/V16N6/16624cnc08.pdf
Volume Link: https://airccse.org/journal/ijc2024.html

REFERENCES
[1] Hecker, P., Angermann, M., Bestmann, U. et al, (2019) “Optical Aircraft Positioning for Monitoring
of the Integrated Navigation System during Landing Approach”, Gyroscopy Navig. 10, 216–230.
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Seamless Integrated Navigation System Based on the Integration of INS/GNSS/LIDAR System”,
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[3] Abdelaziz N, El-Rabbany A, (2022) “An Integrated INS/LiDAR SLAM Navigation System for
GNSS-Challenging Environments”, Sensors. 22(12):4327.
[4] Zhanke He, Erhu Wei, Qiyuan Zhang, Lingxuan Wang, Yanlin Li, Jingnan Liu, (2023) “Earth
rotation parameters from BDS, GPS, and Galileo data: An accuracy analysis”, Advances in Space
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Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications”, Applied
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Navigation System to Overcome GPS Interruption of Unmanned Ground Vehicles”, IEEE Access, vol.
11, pp. 61070-61081.
[7] Mohamad Hashim IS, Al-Hourani A, (2023) “Satellite-Based Localization of IoT Devices Using
Joint Doppler and Angle-of-Arrival Estimation”, Remote Sensing. 15(23):5603.
[8] Shengyu Li, Xingxing Li, Huidan Wang, Yuxuan Zhou, Zhiheng Shen, (2023) “Multi-GNSS
PPP/INS/Vision/LiDAR tightly integrated system for precise navigation in urban environments”,
Information Fusion, Volume 90, Pages 218-232.
[9] Zhang Y, Sun H, Zhang F, Zhang B, Tao S, Li H, Qi K, Zhang S, Ninomiya S, Mu Y, (2023)
“RealTime Localization and Colorful Three-Dimensional Mapping of Orchards Based on Multi-Sensor
Fusion Using Extended Kalman Filter”, Agronomy. 13(8):2158.
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algorithm for tightly GNSS/INS integrated navigation system”, MBE, 21 (1): 963–983.

AN INNOVATIVE HYBRID MODEL FOR EFFECTIVE DDOS ATTACK
DETECTION IN SOFTWARE DEFINED NETWORKS
Quang Truong Can, Tien Dat Nguyen, Minh Bao Pham, Thanh Tung Nguyen, Thi
Hanh AnTran, Thi Thai Mai Dinh
Faculty of Telecommunication System, UET, VNU, 203-G2, 144 Xuan Thuy
Street, Hanoi, Viet Nam
ABSTRACT
Software-Defined Networking (SDN) is a sophisticated network architecture that offers enhanced
flexibility and streamlined management through a centralized controller. While these advantages
allow SDNs to adapt to growing network demands, they also introduce potential security risks.
Specifically, the centralized nature of SDN makes it vulnerable to network attacks, such as
Distributed Denial of Service (DDoS) attacks, which can overwhelm network resources and cause
widespread congestion. In this study, we propose a DDoS detection model that combines entropy-
based features with Support Vector Machine (SVM) machine learning to create a hybrid approach.
This model capitalizes on the strengths of both methods to improve detection accuracy. Our results,
based on simulations and practical SDN implementation, show that our approach effectively and
rapidly detects DDoS attacks with high precision. This paper addresses the challenge of enhancing
the efficiency and accuracy of DDoS attack detection by providing a comprehensive dataset collected
from both simulated and practical environments, thereby improving the detection system's
performance in real-time situations.
KEYWORDS
SDN, DDoS attacks, network security, machine learning, statistical analysis method, entropy,
dynamic entropy.

REFERENCES
[1] Cisco, “Cisco Annual Internet Report (2018–2023) White Paper,” 10-Mar-2020. [Online].
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[3] Ali, T. E., Chong, Y., & Manickam, S. (2022). Machine Learning Techniques to Detect a DDoS
Attack in SDN: A Systematic Review. Applied Sciences, 13(5), 3183.
https://doi.org/10.3390/app13053183
[4] Y. Cui, L. Yan, S. Li, H. Xing, W. Pan, J. Zhu, and X. Zheng, “SDAnti-DDoS: Fast and Efficient
DDoS Defence in Software-Defined Networks,” Journal of Network and Computer Applications, vol.
65, pp. 65-79, 2016.
[5] S. Salaria, S. Arora, N. Goyal, P. Goyal, and S. Sharma, “Implementation and Analysis of an
Improved PCA Technique for DDoS Detection,” in 2020 IEEE 5th International Conference on
Computing Communication and Automation (ICCCA), pp. 280-285, 2020.
[6] Abdul Adhim, Satoshi Okada, and Takuho Mitsunaga, “SDN-Based Detection Method Against
DoS/DDoS Attacks in an IoT Environment,” in Symposium on Cryptography and Information
Security, Osaka, Japan & Online, Jan. 18–21, 2022.
[7] Haymarn Oo, Nan, Risdianto, Aris Cahyadi, Teck, Chaw Ling, and Maw, Aung Htein. "Flooding
Attack Detection and Mitigation in SDN with Modified Adaptive Threshold Algorithm." International
Journal of Computer Networks & Communications, vol. 12, no. 3, May 2020, pp. 75– 95. Academy
and Industry Research Collaboration Center (AIRCC). DOI: 10.5121/ijcnc.2020.12305.
[8] Ranyelson N. Carvalho, Jacir L. Bordim, and Eduardo A. P. Alchier, "Entropy-Based DoS Attack
Identification in SDN," in 2019 IEEE International Parallel and Distributed Processing Symposium
Workshops (IPDPSW).
[9] Ming Xuanyuan, Visham Ramsurrun, and Amar Seeam, "Detection and Mitigation of DDoS
Attacks Using Conditional Entropy in Software-Defined Networking," in 2019 11th International
Conference on Advanced Computing (ICoAC).
[10] i. Özçelik and R.R. Brooks, “Deceiving Entropy-Based DoS Detection,” Computers & Security,
vol. 48, pp. 234-245, 2014. doi:10.1016/j.cose.2014.10.013.

ROUTING PERFORMANCES IN WIRELESS SENSOR
NETWORKS: DETERMINING SHORTEST PATH ALGORITHMS
EFFECTIVENESS
Khine Thazin Min 1
and N. Jeyanthi 2
1
Faculty of Computer System & Technology, Myanmar Institute of Information Technology
(MIIT), Mandalay, Myanmar
2
School of Computer Science Engineering and Information Systems, Vellore Institute of
Technology, Vellore, India
ABSTRACT
Wireless Sensor Networks (WSNs) play a pivotal role in a myriad of diverse fields, ranging from
crucial healthcare applications to the creation of smart cities and effective environmental monitoring
systems. Within the realm of WSNs, the feature of efficient routing assumes paramount importance
as it exerts a profound influence on resource utilization and the overall performance of the network.
In light of their remarkable efficiency and straightforwardness, shortest-path algorithms have
emerged as a prevailing choice for routing mechanisms in WSNs. Hence, the primary objective of
this specialized research is to understand a comprehensive investigation into the calculating
performance of sensor network routing, with a particular circle on leveraging the potential of
shortest-path algorithms. This ambitious effort will involve the establishment of a rigorous evaluation
framework encompassing various performance metrics, enabling a meticulous assessment of the
routing performance and the efficacy of the carefully selected algorithms. The culmination of this
research holds tremendous significance, as it is poised to bestow invaluable insights into the practical
implementation of WSNs. To attire with a comprehensive understanding of their performance across
various scenarios and metrics, the research community can make informed decisions when choosing
the most appropriate routing protocols suited to particular Wireless Sensor Network (WSN)
deployments. The potential results of this research promise to not only improve the network's
efficiency, reliability, and lifespan safe but also to pave the way for innovative applications of
Wireless Sensor Networks in critical areas of societal significance.
KEYWORDS
Wireless Sensor Networks (WSNs), Routing Protocols, Shortest -Path algorithms

REFERENCES
[1]. Sharma, Shweta, and Tarun Kumar. "Energy Efficient Modified Cluster Routing Algorithm for
Maximizing Stability Period of WSN." International Journal of Innovative Research in Computer and
Communication Engineering 3, no. 9 (2015).
[2]. Jayadharshini, P., T. Abirami, S. Santhiya, N. Bhavatharini, and G. Rithanya. "Energy efficient
wireless sensors architecture with LSTM based on Machine Learning Technique." In Intelligent
Wireless Sensor Networks and the Internet of Things, pp. 107-125. CRC Press, 2024.
[3]. Hussein, Wael Ali, Borhanuddin M. Ali, M. F. A. Rasid, and Fazirulhisyam Hashim. "Smart
geographical routing protocol achieving high QoS and energy efficiency based for wireless multimedia
sensor networks." Egyptian Informatics Journal 23, no. 2 (2022): 225-238.
[4]. Sinduja, M. S., K. R. Rekha, and Raghavendra Manjegowda. "Wireless Sensor Networks: A
Methodical Analysis." In ICDSMLA 2020: Proceedings of the 2nd International Conference on Data
Science, Machine Learning and Applications, pp. 1355-1367. Springer Singapore, 2022.
[5]. Sinduja, M. S., K. R. Rekha, and Raghavendra Manjegowda. "Wireless Sensor Networks: A
Methodical Analysis." In ICDSMLA 2020: Proceedings of the 2nd International Conference on
Data Science, Machine Learning and Applications, pp. 1355-1367. Springer Singapore, 2022.
[6]. Jayadharshini, P., T. Abirami, S. Santhiya, N. Bhavatharini, and G. Rithanya. "Energy efficient
wireless sensors architecture with LSTM based on Machine Learning Technique." In Intelligent
Wireless Sensor Networks and the Internet of Things, pp. 107-125. CRC Press, 2024.
[7]. Yang, Xiaoping, Mengjie Li, Zhihong Qian, and Te Di. "Improvement of GPSR protocol in
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[8]. Oreku, George S., and Tamara Pazynyuk. Security in wireless sensor networks. Cham, Switzerland:
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[9]. Patel, Nileshkumar R., and Shishir Kumar. "Wireless sensor networks’ challenges and future
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[10]. Gong, Yadong. "Network-structure-based energy aware routing in wireless sensor networks." In
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(IAEAC), pp. 1602-1605. IEEE, 2019.

INTRUSION DETECTION MODEL USING MACHINE LEARNING
ALGORITHMS ON NSL-KDD DATASET
Reem Alshamy and Muhammet Ali Akcayol
Department of Computer Science, Gazi University, Ankara, Turkey
ABSTRACT
Big data, generated by various sources such as mobile devices, sensors, and the Internet of Things
(IoT),has many characteristics such as volume, velocity, variety, variability, veracity, validity,
vulnerability, volatility, visualization, and value. An Intrusion Detection System (IDS) is essential
for cybersecurity to detect intrusions before or after attacks. Traditional software methods struggle
to store, manage, and analyze big data, developing new techniques for effective and rapid intrusion
detection in organizations and enterprises. This study introduces the IDS Random Forest (RF) model
in binary and multiclass classification for intrusion detection. In this model, we used the Synthetic
Minority Oversampling TEchnique (SMOTE) to address class imbalances, and the RF classifier to
classify attacks using the Network Security Laboratory (NSL)-KDD dataset. In the experiment, we
compared the IDS-RF model with the Support Vector Machine (SVM), k-Nearest Neighbor (k-NN),
and Logistic Regression (LR) classifiers in terms of accuracy, precision, recall, f1-score, and times
for training and testing. The experimental results showed that the IDS-RF model achieved high
performance in binary and multiclass classification compared to others. In addition, the proposed
model also achieved high accuracies for each class (Normal, DoS, Probe, U2R, or R2L) and obtained
98.69%, 99.72%, 98.93%, 95.13%, and 89%, respectively.
KEYWORDS
Intrusion Detection, Network Security Laboratory (NSL)-KDD dataset, SMOTE, Machine Learning

REFERENCES
[1] F. Bannat Wala and M. J. a. e.-p. Kiran, "5G Network Security Practices: An Overview and Survey,"
p. arXiv: 2401.14350, 2024.
[2] S. M. Othman, N. T. Alsohybe, F. M. Ba-Alwi, A. T. J. I. J. o. C.-S. Zahary, and D. Forensics,
"Survey on intrusion detection system types," vol. 7, no. 4, pp. 444-463, 2018.
[3] A. Touzene, A. Al Farsi, N. J. I. J. o. C. N. Al Zeidi, and Communications, "HIGH
PERFORMANCE NMF BASED INTRUSION DETECTION SYSTEM FOR BIG DATA IOT
TRAFFIC," vol. 16, no. 2, pp. 43-58, 2024.
[4] X. Sun, L. Zhao, J. Chen, Y. Cai, D. Wu, and J. Z. J. E. A. o. A. I. Huang, "Non-MapReduce
computing for intelligent big data analysis," vol. 129, p. 107648, 2024.
[5] F. Louati, F. B. Ktata, and I. J. C. C. Amous, "Big-IDS: a decentralized multi agent reinforcement
learning approach for distributed intrusion detection in big data networks," pp. 1-19, 2024.
[6] K. Pramilarani and P. V. J. A. S. C. Kumari, "Cost based Random Forest Classifier for Intrusion
Detection System in Internet of Things," vol. 151, p. 111125, 2024.
[7] M. Ghurab, G. Gaphari, F. Alshami, R. Alshamy, and S. J. A. J. o. R. i. C. S. Othman, "A detailed
analysis of benchmark datasets for network intrusion detection system," vol. 7, no. 4, pp. 14-33, 2021.
[8] M. Ghurab, R. Alshamy, S. J. I. J. o. S. R. Othman, and E. Development, "Performance Evaluation
for Attack Detection in Intrusion Detection System," vol. 4, no. 5, 2021.
[9] Trrad, "Applying Deep Learning Techniques for Network Traffic Classification: A Comparison
Study on the NSL-KDD Dataset," 2024.
[10] H. Lin, Q. Xue, J. Feng, and D. Bai, "Internet of things intrusion detection model and algorithm
based on cloud computing and multi-feature extraction extreme learning machine," Digital
Communications and Networks, vol. 9, no. 1, pp. 111-124, 2023.

FUZZY-BASED CLUSTERING OF WIRELESS SENSOR NETWORKS FOR
MULTIPLE MOBILE AGENT ITINERARY PLANNING
Nidhi Kashyap 1
, Shuchita Upadhyaya 1,
Monika
Poriye 1
, Sachin Lalar 2
1
Department of Computer Science and Applications, Kurukshetra University, Kurukshetra, India
4
Department of Engineering and Technology, Gurugram University Gurugram
ABSTRACT
Mobile agent (MA) technology exhibits remarkable efficiency when integrated into Wireless Sensor
Networks (WSNs) for information processing tasks. MAs reduce network overhead by executing
processing code locally on nodes and selectively transmitting significant data to designated remote
sensor nodes, thereby enhancing data fusion and acquisition while minimizing energy depletion.
However, in large-scale networks, relying on a single MA leads to significant delays, necessitating
the use of multiple MAs to operate asynchronously and minimize latency. The challenge lies in
effectively grouping nodes to ensure MAs reach their intended destinations. To address this
challenge, this paper introduces a novel approach, the Adaptive FCM Clustering Algorithm (AFCM),
a fuzzy-based clustering algorithm designed for addressing network partitioning challenges in
Multiple Mobile Agent Itinerary Planning (MIP). A systematic analysis of the existing literature
examines various MIP algorithms, emphasizing their strengths and uncovering potential research
gaps. AFCM is specifically developed to create disjoint and load-balanced partitions tailored for
multi-mobile agent itinerary planning. A Methodical analysis with three traditional clustering
algorithms is conducted. The correctness of the Adaptive Fuzzy C-Means (AFCM) algorithm is
demonstrated through a detailed manual application on a wireless network comprising 15 nodes.
KEYWORDS
Clustering, Itinerary planning, Mobile agent, Routing, Wireless sensor networks.

REFERENCES
[1] E. Fissaoui, M. Beni-hssane and S. A. Ouhmad, "A survey on mobile agent itinerary planning for
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[2] S. Khan, A. Tariq, H. Richard and A. A. Hussain, "Multi-Mobile Agent Security by Design Itinerary
Planning Approach in Wireless Sensor Network," in International Conference on Ubiquitous Security
(pp. 534-544).:Springer Nature S, Singapore, December, 2022.
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[4] Chou, Y. C., & Nakajima, M., "A clonal selection algorithm for energy-efficient mobile agent
itinerary planning in wireless sensor networks," Mobile Networks and Applications, vol. 23, no. 5,
pp. 1233-1246, 2018.
[5] Hong, W., Liu, Z., Chen, Y., & Guo, W., "Energy-efficient mobile agent communications for
maximizing lifetime of wireless sensor networks," in Wireless Communications, Networking and
Applications, New Delhi, 2016.
[6] S. P. Ardakani, "Data aggregation routing protocols in wireless sensor networks: A taxonomy,"
International Journal of Computer Networks & Communications, vol. 9, no. 2, 2017.
[7] S. P and S. R., "Invesigation of Ant Colony Optimization Algorithm for Efficient Energy Utilization
in Wireless Sensor Network," International Journal of Computer Networks & Communications, vol.
15, no. 4, 2023.
[8] Chen, Min, et al., "Mobile agent-based directed diffusion in wireless sensor networks," EURASIP
Journal on Advances in Signal Processing, pp. 1-13, 2006.
[9] Lingaraj, K., Biradar, R. V., & Patil, V. C., "Eagilla: An enhanced mobile agent middleware for
wireless sensor networks," Alexandria engineering journal, vol. 57, no. 3, pp. 1197-1204, 2018.
[10] M. M. Islam and J. Fariha Tasmin, "MAXIMIZING NETWORK INTERRUPTION IN
WIRELESS SENSOR NETWORK: AN INTRUDER'S PERSPECTIVE," International Journal of
Computer Networks & Communications, vol. 7, no. 2, p. 103, 2015.

PRIORITIZED SCHEDULING ROUTING PROTOCOL FOR MINIMIZING
PACKET DROP IN WIRELESS BODY AREA NETWORK
Md. Taslim Arefin 1,2
and Md Abul Kalam Azad 1
1
Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh
2
Department of ICE, Daffodil International University, Dhaka, Bangladesh.
ABSTRACT
The development of wireless body area networks, or WBANs, has altered people's lives through their
utilization in the fields of athletics, cultural activities, fitness, including healthcare, among others.
Energy conservation and ensuring the quality of offerings, however, are two of the main design
difficulties for WBAN. In a WBAN, the load balancing of various packet buffers is crucial to the
construction of a dependable and environmentally friendly technology. This paper proposes a
prioritized scheduling-based protocol for minimizing packet drops in wireless body area networks
on IEEE 802.15.6. This paper's primary goal is to reduce packet drops in the queues to increase
WBAN throughput. In this instance, we take into account the data packet's importance as well as its
source location to ensure that no packet is held in the designated buffer for an extended period before
being sent to the connection point. PyCrypto is used to replicate the suggested approach in order to
research and contrast its results with those of its competitors. According to the findings from the
simulation, the suggested protocol performs more efficiently in delay, throughput, and energy
consumption than the current approaches.
KEYWORDS
Routing Protocol, Wireless Body Area Network, Data Packet, Energy Consumption, Priority
Scheduling

REFERENCES
[1] Samanta, A., Bera, S., Misra, S.: Link-Quality-Aware Resource Allocation With Load
Balance in Wireless Body Area Networks. In: IEEE Systems Journal. 12. (2015).
10.1109/JSYST.2015.2458586.
[2] Mohanty, Rasmita Kumari, Satya Prakash Sahoo, and Manas Ranjan Kabat. "Sustainable
remote patient monitoring in wireless body area networks with Multi-hop routing and
scheduling: a four-fold objective based optimization approach." Wireless Networks 29.5 (2023):
2337-2351.
[3] Samal, Tusharkanta, and Manas Ranjan Kabat. "A prioritized traffic scheduling with load
balancing in wireless body area networks." Journal of King Saud university-computer and
information sciences 34.8 (2022): 5448-5455.
[4] Zuhra, F. T., Bakar, K. B. A., Arain, A. A., Almustafa, K. M., Saba, T., Haseeb, K., Islam,
N. (2019). LLTP-QoS: low latency traffic prioritization and QoS-aware routing in wireless body
sensor networks. IEEE Access, 7, 152777-152787.
[5] Forestieri, Sara D., Taylor M. Helgestad, Andrew T. Lambe, Lindsay Renbaum-Wolff,
Daniel A. Lack, Paola Massoli, Eben S. Cross et al. "Measurement and modeling of the
multiwavelength optical properties of uncoated flame-generated soot." Atmospheric Chemistry
and Physics 18, no. 16 (2018): 12141-12159.
[6] Mohammed, K. I., A. A. Zaidan, B. B. Zaidan, Osamah Shihab Albahri, M. A. Alsalem,
Ahmed Shihab Albahri, Ali Hadi, and M. Hashim. "Real-time remote-health monitoring
systems: a review on patients prioritisation for multiple-chronic diseases, taxonomy analysis,
concerns and solution procedure." Journal of medical systems 43 (2019): 1-21.
[7] Saba, Tanzila, Khalid Haseeb, Imran Ahmed, and Amjad Rehman. "Secure and energy-
efficient framework using Internet of Medical Things for e-healthcare." Journal of Infection and
Public Health 13, no. 10 (2020): 1567-1575.
[8] Pramanik, Pijush Kanti Dutta, Anand Nayyar, and Gaurav Pareek. "WBAN: Driving e-
healthcare beyond telemedicine to remote health monitoring: Architecture and protocols." In
Telemedicine technologies, pp. 89-119. Academic Press, 2019.
[9] Ahmad, Naveed, Basit Shahzad, Muhammad Arif, Diana Izdrui, Ioan Ungurean, and Oana
Geman. "An Energy‐Efficient Framework for WBAN in Health Care Domain." Journal of
Sensors 2022, no. 1 (2022): 5823461.
[10] Habib, Carol, Abdallah Makhoul, Rony Darazi, and Raphaël Couturier. "Health risk
assessment and decision-making for patient monitoring and decision-support using wireless
body sensor networks." Information fusion 47 (2019): 10-22..

OUTSTANDING FRAMEWORK FOR SIMULATING AND
GENERATING ANCHOR TRAJECTORY IN WIRELESS SENSOR
NETWORKS
Abdelhady Naguib 1, 2
1
Department of Computer Science, College of Computer and Information Sciences, Jouf
University, Sakaka, Saudi Arabia
2
Department of Systems and Computers Engineering, Faculty of Engineering, Al-Azhar University,
Cairo, Egypt
ABSTRACT
This paper proposes a framework that has the ability to animate and generate different scenarios
for the mobility of a movable anchor which can follow various paths in wireless sensor networks
(WSNs). When the researchers use NS-2 to simulate a single anchor-assisted localization model,
they face the problem of creating the movement file of the movable anchor. The proposed
framework solved this problem by allowing them to create the movement scenario regarding
different trajectories. The proposed framework lets the researcher set the needed parameters for
simulating various static path models, which can be displayed through the graphical user interface.
The researcher can also view the mobility of the movable anchor with control of its speed and
communication range. The proposed framework has been validated by comparing its results to NS-
2 outputs plus comparing it against existing tools. Finally, this framework has been published on
the Code Project website and downloaded by many users.
KEYWORDS
Localization, Movable anchor, Simulation, Path planning, Wireless sensor networks

REFERENCES
[1] Singh, Saurabh, et al., (2024) "Advanced lightweight encryption algorithms for IoT devices: survey,
challenges and solutions", Journal of Ambient Intelligence and Humanized Computing, 1- 18.
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Network for Intelligent Transportation System”, International journal of Computer Networks &
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for Discrete Event Simulation and Cost-Effectiveness Analysis", Mathematics, 11.7, 1602.
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of simulation models", Mathematics, 11.4, 845.
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construction sites using discrete event simulation (DES) and path Planning in unity", Applied Sciences,
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systems: Deterministic, probabilistic, and l

A COMPARATIVE STUDY OF COOPERATIVE AND NON-COOPERATIVE
WIDEBAND SPECTRUM SENSING IN COGNITIVE RADIO NETWORKS
FOR 5G APPLICATIONS
Blessing C. Dike, Cajetan M. Akujuobi, and
Shumon Alam
Center of Excellence for Communication Systems Technology Research, ECE Dept,
rairie View A&M University Prairie View, Texas, USA
ABSTRACT
The rapid advancements in 5G technologies have created an unprecedented need for efficient
spectrum utilization to support increasing data traffic and diverse communication services. In this
context, accurate and reliable spectrum sensing is essential. This study explores wideband spectrum
sensing strategies, comparing non-cooperative cognitive radio (CR) techniques with cooperative
methods across multiple subbands. A novel cooperative wideband spectrum sensing framework was
developed, incorporating a K-outof-N fusion rule at the fusion center to make optimal decisions by
selecting an appropriate K for a given number of cooperating CRs. This approach addresses noise
uncertainty, a common challenge in traditional non-cooperative energy detection methods,
particularly in 5G environments under Additive White Gaussian Noise (AWGN) conditions,
assumed to be identically and independently distributed (i.i.d.). However, while cooperative sensing
significantly improves detection in low signal-to-noise ratio (SNR) scenarios with higher false alarm
rates (between 0.5 and 1), our findings reveal that it does not consistently outperform non-
cooperative methods at very low false alarm rates (0.01 and 0.1) under poor SNR conditions. These
findings highlight the need for further research to enhance cooperative sensing strategies for various
operational environments.
KEYWORDS
Cooperative wideband spectrum sensing, non-cooperative wideband spectrum sensing, energy
detection, additive white gaussian noise, hard fusion rule, Cooperative Radio.

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November 2024, Volume 16, Number 6: Top10 Cited Articles in Computer Networks & Communications