April 2024 - Top 10 Read Articles in Computer Networks & Communications

April 2024: Top10
Read 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 2022—1.8
ISSN 0974 - 9322 (Online); 0975 - 2293 (Print)
Citations, h-index, i10-index

DYNAMIC ROUTING OF IP TRAFFIC BASED ON QOS PARAMETERS
Martin Kriška1 , Jozef Janitor2 and Peter Fecilak3
1Computer Networks Laboratory, Technical University of Kosice, Slovakia 2 Institute of
Computer Technology, Technical University of Kosice, Slovakia 3Department of Computers and
Informatics, Technical University of Kosice, Slovakia
ABSTRACT
The article looks into the current state of the art of dynamic routing protocols with respect to
their possibilities to react to changes in the Quality of Service when selecting the best route towards
a destination network. New options that could leverage information about the ever changing QoS
parameters for data communication are analysed and a Cisco Performance Routing solution is
described more in detail. The practical part of this work focuses on a design and implementation
of a test bed that provides a scalable laboratory architecture to manipulate QoS parameters of
different data communications flowing through it. The test bed is used in various use cases that
were used to evaluate Cisco Performance Routing optimization capabilitiesin different scenarios.
KEYWORDS
Performance Routing, PfR, Quality of Service, QoS, Optimized Edge Routing
For More Details: https://airccse.org/journal/cnc/6414cnc02.pdf
Volume Link: https://airccse.org/journal/ijc2014.html

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te-ext02.
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R. Pandi Selvam, V.Palanisamy: An efficient cluster based approach for multi-sourcemulticast routing
protocol in mobile ad hoc networks, International Journal of Computer

GPS SYSTEMS LITERATURE: INACCURACY FACTORS AND
EFFECTIVE SOLUTIONS
Li Nyen Thin, Lau Ying Ting, Nor Adila Husna and Mohd Heikal Husin
School of Computer Sciences, Universiti Sains Malaysia, Malaysia
ABSTRACT
Today, Global Positioning System (GPS) is widely used in almost every aspect of our daily life.
Commonly, users utilize the technology to track the position of a vehicle or an object of interest.
They also use it to safely navigate to the destination of their choice. As a result, there are countless
number of GPS based tracking application that has been developed. But, a main recurring issue that
exists among these applications are the inaccuracy of the tracking faced by users and this issue has
become a rising concern. Most existing research have examined the effects that the inaccuracy of
GPS have on users while others identified suitable methods to improve the accuracy of GPS based
on one or two factors. The objective of this survey paper is to identify the common factors that affects
the accuracy of GPS and identify an effective method which could mitigate or overcome most of
those factors. As part of our research, we conducted a thorough examination of the existing factors
for GPS inaccuracies. According to an initial survey that we have collected, most of the respondents
has faced some form of GPS inaccuracy. Among the common issues faced are inaccurate object
tracking and disconnection of GPS signal while using an application. As such, most of the
respondents agree that it is necessary to improve the accuracy of GPS. This leads to another objective
of this paper, which is to examine and evaluate existing methods as well as to identify the most
effective method that could improve the accuracy of GPS.
KEYWORDS
GPS, accuracy factors, improve accuracy, global positioning system
For More Details: https://aircconline.com/ijcnc/V8N2/8216cnc11.pdf

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CONGESTION AND ENERGY AWARE MULTIPATH LOAD
BALANCING ROUTING FOR LLNS
Kala Venugopal and T G Basavaraju
Department of Computer Science and Engineering, Government Engineering College, Hassan,
Karnataka, India
ABSTRACT
The Internet of Things (IoT) is presently in its golden era with its current technological evolution
towards digital transformation. Low-power and Lossy Networks (LLNs) form the groundwork for
IoT, where the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is designated by
Internet Engineering Task Force as the benchmark protocol for routing. Although RPL, with its
unique capabilities, has addressed many IoT routing requirements, Load balancing and Congestion
control are the outliers. This paper builds on the RPL protocol and proposes a multipath Congestion
and Energy Aware RPL (CEARPL) that alleviates the load balancing and congestion concerns
associated with RPL and improves the network performance. For congestion avoidance, a
Congestion and Energy Aware Objective Function (CEA-OF) is suggested during parent selection
that considers multiple metrics like Child Count metric, Estimated Lifetime metric, and Queue
Occupancy metric, to equally distribute the traffic in LLNs. The Queue Occupancy metric is used
to detect congestion in the network, and a Multipath routing strategy is utilized to mitigate the
congestion in the network. A comparison of the performance of CEA-RPL was made against the
existing Objective Functions of RPL, OFO, and MRHOF, as well as COM-OF, utilizing Contiki
OS 3.0's Cooja emulator. CEA-RPL projected superior results with power consumption lowering
by 33%, endto-end delay decreasing by 30%, queue loss ratio reducing by 49%, and packet
receiving rate and network lifetime improving by 7% and 49%, on an average, respectively.
KEYWORDS
Congestion, Multipath routing, Internet of Things, Load balancing, Low-power Lossy Networks,
Objective function & RPL

REFERENCES
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CONTAINERIZED SERVICES ORCHESTRATION FOR EDGE
COMPUTING IN SOFTWARE-DEFINED WIDE AREA NETWORKS
Felipe Rodriguez Yaguache and Kimmo Ahola
5G Networks & Beyond, Technical Research Centre of Finland (VTT), Espoo, Finland
ABSTRACT
As SD-WAN disrupts legacy WAN technologies and becomes the preferred WAN technology
adopted by corporations, and Kubernetes becomes the de-facto container orchestration tool, the
opportunities for deploying edge-computing containerized applications running over SD-WAN are
vast. Service orchestration in SD-WAN has not been provided with enough attention, resulting in the
lack of research focused on service discovery in these scenarios. In this article, an in-house service
discovery solution that works alongside Kubernetes’ master node for allowing improved traffic
handling and better user experience when running micro-services is developed. The service
discovery solution was conceived following a design science research approach. Our research
includes the implementation of a proof-ofconcept SD-WAN topology alongside a Kubernetes cluster
that allows us to deploy custom services and delimit the necessary characteristics of our in-house
solution. Also, the implementation's performance is tested based on the required times for updating
the discovery solution according to service updates. Finally, some conclusions and modifications are
pointed out based on the results, while also discussing possible enhancements.
KEYWORDS
SD-WAN, Edge computing, Virtualization, Kubernetes, Containers, Services

REFERENCES
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HN OLOGIES_STATE-OF-THE-ART.
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Available at: https://atomix.io/docs/latest/user-manual/introduction/what-is-atomix/
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PERFORMANCE EVALUATION OF DIFFERENT RASPBERRY PI
MODELS AS MQTT SERVERS AND CLIENTS
Faiza Al-Salti1
, N. Alzeidi2
, Khaled Day2
, Abderezak Touzene2
, 1
Sultan Qaboos Comprehensive
Cancer Care and Research Centre, Oman, 2
Sultan Qaboos University, Oman
5G Networks & Beyond, Technical Research Centre of Finland (VTT), Espoo, Finland
ABSTRACT
This paper studies the impact of different localization schemes on the performance of location-based
routing for UWSNs. Particularly, LSWTS and 3DUL localization schemes available in the literature
are used to study their effects on the performance of the ERGR-EMHC routing protocol. First, we
assess the performance of two localization schemes by measuring their localization coverage,
accuracy, control packets overhead, and required localization time. We then study the performance
of the ERGR-EMHC protocol using location information provided by the selected localization
schemes. The results are compared with the performance of the routing protocol when using exact
nodes’ locations. The obtained results show that LSWTS outperforms 3DUL in terms of localization
accuracy by 83% and localization overhead by 70%. In addition, the results indicate that the
localization error has a significant impact on the performance of the routing protocol. For instance,
ERGR-EMHC with LSWTS is better in delivering data packets by an average of 175% compared to
3DUL.
KEYWORDS
Underwater wireless sensor networks (UWSNs), localization, ranging localization methods,
localization error, location-based routing

REFERENCES
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and Routing Design,” in The Art of Wireless Sensor Networks, H. M. Ammari, Ed. Berlin,
Heidelberg: Springer Berlin Heidelberg, 2014, pp. 367–409.
[2] H. P. Tan, R. Diamant, W. K. G. Seah, and M. Waldmeyer, “A survey of techniques and
challenges in underwater localization,” Ocean Engineering, vol. 38, no. 14–15, pp. 1663–1676,
October 2011, doi: 10.1016/J.OCEANENG.2011.07.017.
[3] F. Al-Salti, N. Alzeidi, and K. Day, “LOCALIZATION SCHEMES FOR UNDERWATER
WIRELESS SENSOR NETWORKS: SURVEY,” International journal of Computer Networks &
Communications, vol. 12, no. 3, pp. 113–130, May 2019.
[4] M. Erol, L. F. M. Vieira, and M. Gerla, “AUV-Aided Localization for Underwater Sensor
Networks,” in International Conference on Wireless Algorithms, Systems and Applications (WASA
2007), 1-3 August 2007, pp. 44–54, Chicago, IL, USA.
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Underwater Sensor Networks,” Sensors, vol. 17, no. 12, p. 726, March 2017.
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PROTOCOL FOR UNDERWATER WIRELESS SENSOR NETWORKS,” International Journal of
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An IDE for Android Mobile Phones with Extended Functionalities Using Best
Developing Methodologies
Sakila Banu1 and Kanakasabapathi Vijayakumar2
1
College of Computer Science and Information Technology, Taif University, Taif, Saudi Arabia
2
Department of Mathematics,Anna University,Chennai,India.
ABSTRACT
Google's Android platform is a widely anticipated open source operating system for mobile phones. The
mobile phone landscape changed with the introduction of smart phones running Android, a platform marketed
by Google. Android phones are the first credible threat to the iPhone market. Google not only target the
consumers of iPhone, it also aimed to win the hearts and minds of mobile application developers. As a Result,
application developers are developing new software’s everyday for Android Smart Phones and are competing
with the previous in Market. But so far there is no Specific IDE developed to create mobile application easily
by just Drag and Drop method to make even the non-programmers to develop application for the smart phones.
This paper presents an IDE with Extended Functionalities for Developing Mobile Applications for Android
Mobile Phones using the Best developing Methodologies. The New IDE comes with the Extended
Functionalities like Executing the created Application, Previewing the Application Created, Roll Back and
Cancel Functions with the newly added Icons like Execute, Preview, Roll Back and Cancel Respectively.
Another important feature of this paper is that the IDE is developed using the Best Developing Methodologies
by presenting the possible methods for developing the IDE using JAVA SWING GUI Builder in Android
ADT plug-in. The developed IDE is tested using the Android Runtime Emulator in Eclipse Framework.
KEYWORDS
IDE-Integrated Development Environment, GUI-Graphical User Interface, ADT-Android
Development Tool.
For More Details: https://airccse.org/journal/cnc/5413cnc11.pdf

REFERENCES
[1] Understanding Android Security by Enck, W.; Ongtang, M.; McDaniel, P.; Pennsylvania State Univ.,
University Park, PA
[2] Android: Changing the Mobile Landscap by Margaret Butler from
http://developerlife.com/tutorials/?p=289
[3] Android – How to build a service-enabled Android app – Part 1/3 UI Posted June 4th, 2008 by
Nazmul
[4] Android-An Open Handset Alliance Project. http://code.google.com/android/.
[5] Bloom S.Book, M.Gruhn, V.Hrushchak, R.Kohler, A.(2008). Write Once Run Anywhere. A survey of
Mobile Runtime Environments. Proceedings of the 3rd International Conference On Grid and Pervasive
Computing(GPC2008):132-137
[6] Holzer, A.Ondrus,J.(2009). Trends in Mobile Application Development. Proceedings of the 2nd
International Conference Mobile Wireless Middleware, Operating Systems and Applications(Mobile ware
2009):55-64
[7] http://www.vogella.com/articles/AndroidDragAndDrop/article.html
[8] http://javapapers.com/android/android-drag-and-drop/
[9] http://developer.android.com/guide/topics/ui/drag-drop.html
[10] http://en.wikipedia.org/wiki/App_Inventor_for_Android
[11] JForm Designer from http://www.formdev.com/jformdesigner/
[12] http://beta.appinventor.mit.edu/about/moreinfo/
[13] Jigloo GUI Builder ,http://www.ibm.com/developerworks/opensource/tutorials/

ITA: THE IMPROVED THROTTLED ALGORITHM OF LOAD
BALANCING ON CLOUD COMPUTING
Hieu N. Le1
and Hung C. Tran2
1
Department of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City,
Vietnam
2
Posts and Telecommunication Institute of Technology, Ho Chi Minh City, Vietnam
ABSTRACT
Cloud computing makes the information technology industry boom. It is a great solution for businesses who
want to save costs while ensuring the quality of service. One of the key issues that make cloud computing
successful is the load balancing technique used in the load balancer to minimize time costs and optimize costs
economically. This paper proposes an algorithm to enhance the processing time of tasks so that it can help
improve the load balancing capacity on cloud computing. This algorithm, named as Improved Throttled
Algorithm (ITA), is an improvement of Throttled Algorithm. The paper uses the Cloud Analyst tool to
simulate. The selected algorithms are used to compare: Equally Load, Round Robin, Throttled and TMA. The
simulation results show that the proposed algorithm ITA has improved the processing time of tasks, time spent
processing requests and reduced the cost of Datacenters compared to the selected popular algorithms as above.
The improvement of ITA is because of selecting virtual machines in an index table that is available but in
order of priority. It helps response times and processing times remain stable, limits the idling resources, and
cloud costs are minimized compared to selected algorithms
KEYWORDS
Cloud Computing, Load Balancing, Processing Time, Improved Throttle Algorithm.

REFERENCES
[1] S. Kemp, “Digital 2019: global internet use accelerates,” 30 January 2019. [Online]. Available:
https://wearesocial.com/blog/2019/01/digital-2019-global-internet-use-accelerates.
[2] Soni Gulshan and Mala Kalra, “A novel approach for load balancing in cloud datacenter,” Advance
Computing Conference (IACC), 2014 IEEE International, 2014.
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International Conference on Ubiquitous and Future Networks (ICUFN), pp. 199-204, 2014.
[4] K. Verma, "Cloud Computing and its Types," Cloudkul, [Online]. Available:
https://cloudkul.com/blog/what-is-cloud-computing/.
[5] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F. and Buyya, R., ""CloudSim: atoolkit for
modeling and simulation of cloud computing environments and evaluation of resource provisioning
algorithms," Journal of Software: Practice and Experience, vol. 41, pp. 23-50, 2010.
[6] D. C.Marinescu, Cloud Computing (Second edition), Elsevier, 2018.
[7] Bui Thanh Khiet, Nguyen Thi Nguyet Que, Ho Dac Hung, Pham Tran Vu, Tran Cong Hung, "A Fair
VM Allocation for Cloud Computing based on Game Theory," Proceedings of the 10th National
Conference on Fundamental and Applied Information Technology Research (FAIR'10), 2017.
[8] Bhaskar, R., Deepu, S.R., Shylaja, B.S., "Dynamic allocation method for efficient load balancing in
virtual machines for cloud computing," Advanced Computing An International Journal, vol. 3, 2012.
[9] Rajwinder Kaur, Pawan Luthra, "Load Balancing in Cloud Computing," Recent Trends in Information,
Telecommunication and Computing, Association of Computer Electronics and Electrical Engineers, pp.
374-381, 2014.
[10] Bhathiya Wickremasinghe and Assoc Prof and Rajkumar Buyya Contents, "Cloud Analyst: A
CloudSim- based Tool for Modelling and Analysis of Large Scale Cloud Computing Environments," 2010.
[11] D. Asir Antony Gnana Singh, R. Priyadharshini and E. Jebamalar Leavline, "Analysis of Cloud
Environment Using CloudSim," in Springer.
[12] Klaithem Al Nuaimi, Nader Mohamed, Mariam Al Nuaimi and Jameela Al-Jaroodi, "A Survey of
Load Balancing in Cloud Computing: Challenges and Algorithms," Second Symposium on Network Cloud
Computing and Applications, 2012.
[13] Abhay Kumar Agarwal and Atul Raj, "A New Static Load Balancing Algorithm in Cloud Computing,"
International Journal of Computer Applications, vol. 132, p. 0975 – 8887, 2015.
[14] N.Swarnkar, A. K. Singh and Shankar, "A Survey of Load Balancing Technique in Cloud Computing,"
International Journal of Engineering Research & Technology(IJERT), vol. 2, pp. 800- 804, 2013.
[15] S. S. Moharana, R. D. Ramesh and D. Powar, "Analysis of Load Balancers in Cloud Computing,"
International Journal of Computer Science and Engineering (IJCSE), vol. 2, pp. 101-108, 2013.
[16] Vikas Kumar, Shiva Prakash, "Modified Active Monitoring Load Balancing Algorithm in Cloud
Computing Environment," International Journal for Scientific Research and Development (IJSRD), vol. 2,
pp. 132-135, 2014.
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Conference on Applied Mathematics, Computational Science & Engineering (AMCSE 2014), Varna, 2014.

[18] Rakesh Kumar Mishra, Sreenu Naik Bhukya, "Service Broker Algorithm for CloudAnalyst,"
International Journal of Computer Science and Information Technology (IJCSIT), pp. 3957-3962, 2017.
[19] Er. Imtiyaz Ahmad , Er. Shakeel Ahmad, Er. Sourav Mirdha, "An Enhanced Throttled Load Balancing
Approach for Cloud Environment," International Research Journal of Engineering and Technology
(IRJET), 2017.
[20] Nguyen Xuan Phi, Tran Cong Hung, "LOAD BALANCING ARGORITHM TO IMPROVE
REPSPONSE TIME ON CLOUD COMPUTING," International Journal on Cloud Computing: Services
and Architecture (IJCCSA), 2017.
[21] Nguyen Xuan Phi, Cao Trung Tin, Luu Nguyen Ky Thu, Tran Cong Hung, "Proposed Load Balancing
Algorithm to Reduce Response time and Processing time on Cloud Computing," International Journal of
Computer Networks & Communications (IJCNC), 2018.
[22] Gupta, S., Dixit, N., & Yadav, P., "An Advanced Throttled (ATH) Algorithm and Its Performance
Analysis with Different Variants of Cloud Computing Load Balancing Algorithm," Communication,
Networks and Computing, pp. 385-399, 2018.
[23] Tran Cong Hung, Phan Thanh Hy, Le Ngoc Hieu, Nguyen Xuan Phi, "MMSIA: Improved Max-Min
Scheduling Algorithm for Load Balancing on Cloud Computing," Proceedings of The 3rd International
Conference on Machine Learning and Soft Computing (CMLSC 2019), pp. 60-64, 2019.
[24] Moses, A. K., Joseph, A., Oluwaseun, O. R., Misra, S., & Emmanuel, A., "APPLICABILITY OF
MMRR LOAD BALANCING ALGORITHM IN CLOUD COMPUTING," International Journal of
Computer Mathematics: Computer Systems Theory, 2020.
[25] B. P. Mulla, C. Rama Krishna, and R. Kumar Tickoo, “Load balancing algorithm for efficient VM
allocation in heterogeneous cloud,” International Journal of Computer Networks & Communications
(IJCNC), vol. 12, no. 1, pp. 83–96, 2020.
[26] A. S. a. P. A. T. J. B. Durga Devi, “Modified adaptive neuro fuzzy inference system based load
balancing for virtual machine with security in cloud computing environment,” Journal of Ambient
Intelligence and Humanized Computing, vol. 12, no. 3, pp. 3869-3876, 2021.
[27] N. Z. J. a. A. A. D. A. Shafiq, “Load balancing techniques in cloud computing environment: A review,”
Journal of King Saud University – Computer and Information Sciences, 2021.

A DEEP LEARNING TECHNIQUE FOR WEB PHISHING DETECTION
COMBINED URL FEATURES AND VISUAL SIMILARITY
Saad Al-Ahmadi1
and Yasser Alharbi 2
1
College of Computer and Information Science, Computer Science Department, King Saud
University, Riyadh, Saudi Arabia
2
College of Computer and Information Science, Computer Engineering Department, King Saud
University, Riyadh, Saudi Arabia
ABSTRACT
The most popular way to deceive online users nowadays is phishing. Consequently, to increase
cybersecurity, more efficient web page phishing detection mechanisms are needed. In this paper,
we propose an approach that rely on websites image and URL to deals with the issue of phishing
website recognition as a classification challenge. Our model uses webpage URLs and images to
detect a phishing attack using convolution neural networks (CNNs) to extract the most important
features of website images and URLs and then classifies them into benign and phishing pages. The
accuracy rate of the results of the experiment was 99.67%, proving the effectiveness of the
proposed model in detecting a web phishing attack.
KEYWORDS
Phishing detection, URL, visual similarity, deep learning, convolution neural network.

REFERENCES
[1] H. Thakur, “Available Online at www.ijarcs.info A Survey Paper On Phishing Detection,” vol.
7, no. 4, pp. 64–68, 2016.
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schemes,” Security and Communication Networks. 2016, doi: 10.1002/sec.1674.
[3] E. S. Aung, T. Zan, and H. Yamana, “A Survey of URL-based Phishing Detection,” pp. 1–8,
2019, [Online]. Available: https://db-event.jpn.org/deim2019/post/papers/201.pdf.
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detection,” in IIH-MSP 2009 - 2009 5th International Conference on Intelligent Information Hiding
and Multimedia Signal Processing, 2009, doi: 10.1109/IIH-MSP.2009.147.
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Security and Communication Networks. 2017, doi: 10.1155/2017/5421046.
[6] A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A Survey of the Recent Architectures of
Deep Convolutional Neural Networks,” pp. 1–68, 2019, doi: 10.1007/s10462-020-09825-6.
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J. Wirel. Commun. Netw., 2019, doi: 10.1186/s13638-019-1361-0.
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An image layout analysis approach,” in Lecture Notes in Computer Science (including subseries
Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, doi:
10.1007/978-3-642- 02617-1_28.
[9] T. C. Chen, T. Stepan, S. Dick, and J. Miller, “An anti-phishing system employing diffused
information,” ACM Trans. Inf. Syst. Secur., vol. 16, no. 4, 2014, doi: 10.1145/2584680.
[10] A. S. Bozkir and E. A. Sezer, “Use of HOG descriptors in phishing detection,” in 4th
International Symposium on Digital Forensics and Security, ISDFS 2016 - Proceeding, 2016, doi:
10.1109/ISDFS.2016.7473534.
[11] F. C. Dalgic, A. S. Bozkir, and M. Aydos, “Phish-IRIS: A New Approach for Vision Based
Brand Prediction of Phishing Web Pages via Compact Visual Descriptors,” ISMSIT 2018 - 2nd Int.
Symp. Multidiscip. Stud. Innov. Technol. Proc., 2018, doi: 10.1109/ISMSIT.2018.8567299.
[12] K. L. Chiew, E. H. Chang, S. N. Sze, and W. K. Tiong, “Utilisation of website logo for phishing
detection,” Comput. Secur., 2015, doi: 10.1016/j.cose.2015.07.006.
[13] K. L. Chiew, J. S. F. Choo, S. N. Sze, and K. S. C. Yong, “Leverage Website Favicon to Detect
Phishing Websites,” Secur. Commun. Networks, 2018, doi: 10.1155/2018/7251750.
[14] Y. Zhou, Y. Zhang, J. Xiao, Y. Wang, and W. Lin, “Visual similarity based anti-phishing with
the combination of local and global features,” in Proceedings - 2014 IEEE 13th International
Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014,
2015, doi: 10.1109/TrustCom.2014.28.

[15] A. P. E. Rosiello, E. Kirda, C. Kruegel, and F. Ferrandi, “A layout-similarity-based approach
for detecting phishing pages,” in Proceedings of the 3rd International Conference on Security and
Privacy in Communication Networks, SecureComm, 2007, doi: 10.1109/SECCOM.2007.4550367.
[16] J. Mao, P. Li, K. Li, T. Wei, and Z. Liang, “BaitAlarm: Detecting phishing sites using similarity
in fundamental visual features,” in Proceedings - 5th International Conference on Intelligent
Networking and Collaborative Systems, INCoS 2013, 2013, doi: 10.1109/INCoS.2013.151.
[17] S. Haruta, H. Asahina, and I. Sasase, “Visual Similarity-Based Phishing Detection Scheme
Using Image and CSS with Target Website Finder,” in 2017 IEEE Global Communications
Conference, GLOBECOM 2017 - Proceedings, 2017, doi: 10.1109/GLOCOM.2017.8254506.
[18] H. Zhang, G. Liu, T. W. S. Chow, and W. Liu, “Textual and visual content-based anti-phishing:
A Bayesian approach,” IEEE Trans. Neural Networks, 2011, doi: 10.1109/TNN.2011.2161999.
[19] E. Medvet, E. Kirda, and C. Kruegel, “Visual-similarity-based phishing detection,” Proc. 4th
Int. Conf. Secur. Priv. Commun. Networks, Secur., no. September 2008, 2008, doi:
10.1145/1460877.1460905.
[20] S. G. Selvaganapathy, M. Nivaashini, and H. P. Natarajan, “Deep belief network based detection
and categorization of malicious URLs,” Inf. Secur. J., vol. 27, no. 3, pp. 145–161, 2018, doi:
10.1080/19393555.2018.1456577.
[21] Y. Ding, N. Luktarhan, K. Li, and W. Slamu, “A keyword-based combination approach for
detecting phishing webpages,” Comput. Secur., vol. 84, pp. 256–275, 2019, doi:
10.1016/j.cose.2019.03.018.
[22] H. huan Wang, L. Yu, S. wei Tian, Y. fang Peng, and X. jun Pei, “Bidirectional LSTM Malicious
webpages detection algorithm based on convolutional neural network and independent recurrent
neural network,” Appl. Intell., vol. 49, no. 8, pp. 3016–3026, 2019, doi: 10.1007/s10489-019-01433-
4.
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similarity index,” Human-centric Comput. Inf. Sci., vol. 7, no. 1, pp. 1–13, 2017, doi:
10.1186/s13673-017-0098-1.
[24] S. Parekh, D. Parikh, S. Kotak, and S. Sankhe, “A New Method for Detection of Phishing
Websites: URL Detection,” Proc. Int. Conf. Inven. Commun. Comput. Technol. ICICCT 2018, no.
Icicct, pp. 949–952, 2018, doi: 10.1109/ICICCT.2018.8473085.
[25] H. Le, Q. Pham, D. Sahoo, and S. C. H. Hoi, “URLNet: Learning a URL Representation with
Deep Learning for Malicious URL Detection,” no. i, 2018, [Online]. Available:
http://arxiv.org/abs/1802.03162.
[26] J. Saxe and K. Berlin, “eXpose: A Character-Level Convolutional Neural Network with
Embeddings For Detecting Malicious URLs, File Paths and Registry Keys,” 2017, [Online].
Available: http://arxiv.org/abs/1702.08568.
[27] K. Shima et al., “Classification of URL bitstreams using bag of bytes,” 21st Conf. Innov. Clouds,
Internet Networks, ICIN 2018, pp. 1–5, 2018, doi: 10.1109/ICIN.2018.8401597.

[28] R. Vinayakumar, K. P. Soman, and P. Poornachandran, “Evaluating deep learning approaches
to characterize and classify malicious URL’s,” J. Intell. Fuzzy Syst., vol. 34, no. 3, pp. 1333–1343,
2018, doi: 10.3233/JIFS-169429.
[29] O. K. Sahingoz, E. Buber, O. Demir, and B. Diri, “Machine learning based phishing detection
from URLs,” Expert Syst. Appl., vol. 117, pp. 345–357, 2019, doi: 10.1016/j.eswa.2018.09.029.
[30] W. Wang, F. Zhang, X. Luo, and S. Zhang, “PDRCNN: Precise Phishing Detection with
Recurrent Convolutional Neural Networks,” Secur. Commun. Networks, 2019, doi:
10.1155/2019/2595794.
[31] S. Khan, H. Rahmani, S. A. A. Shah, and M. Bennamoun, “A Guide to Convolutional Neural
Networks for Computer Vision,” Synth. Lect. Comput. Vis., 2018, doi:
10.2200/s00822ed1v01y201712cov015.
[32] V. Karthikeyani and S. Nagarajan, “Machine Learning Classification Algorithms to Recognize
Chart Types in Portable Document Format (PDF) Files,” Int. J. Comput. Appl., 2012, doi:
10.5120/4789- 6997.
[33] M. A. Adebowale, K. T. Lwin, and M. A. Hossain, “Deep learning with convolutional neural
network and long short-term memory for phishing detection,” 2019 13th Int. Conf. Software,
Knowledge, Inf. Manag. Appl. Ski. 2019, no. March 2019, doi:
10.1109/SKIMA47702.2019.8982427.
[34] C. Opara, B. Wei, and Y. Chen, “HTMLPhish: Enabling Phishing Web Page Detection by
Applying Deep Learning Techniques on HTML Analysis,” no. October 2018, 2019, [Online].
Available: http://arxiv.org/abs/1909.01135.

IMPROVEMENTS IN ROUTING ALGORITHMS TO ENHANCE
LIFETIME OF WIRELESS SENSOR NETWORKS
D. Naga Ravikiran1
and C.G. Dethe2
1
Research Scholar, ECE Department, Priyadarshini Institute of Engineering and Technology
(PIET), Nagpur, Maharashtra.
2
Director, UGC-Human Resource Development Centre, RTM Nagpur University, Nagpur, India
ABSTRACT
Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with
limited computation, communication, memory, and energy resources that are being used fora huge
range of applications. Clustering in WSNs is an effective way to minimize the energy consumption
of sensor nodes. In this paper improvements in various parameters are compared for three different
routing algorithms. First, it is started with Low Energy Adaptive Cluster Hierarchy (LEACH)which
is a famed clustering mechanism that elects a CH based on the probability model. Then, work
describes a Fuzzy logic system initiated CH selection algorithm for LEACH. Then Artificial Bee
Colony (ABC)which is an optimisation protocol owes its inspiration to the exploration behaviour
of honey bees. In this study ABC optimization algorithm is proposed for fuzzy rule selection. Then,
the results of the three routing algorithms are compared with respect to various parameters
KEYWORDS
Wireless Sensor Network (WSN), LEACH, Clustering, Artificial Bee Colony (ABC), Fuzzy logic
system.

REFERENCES
[1] Abad, M.F.K. and Jamali, M.A.J. (2011) ‘Modify LEACH algorithm for wireless sensor network’,
IJCSI International Journal of Computer Science Issues, Vol. 8, No. 5.
[2] Abraham, A., Jatoth, R.K. and Rajasekhar, A. (2012) ‘Hybrid differential artificial bee colony
algorithm’, Journal of Computational and Theoretical Nanoscience, Vol. 9, No. 2, pp.249–257.
[3] Selvakumar, K., &Selvi, M. S. (2014). Efficient Load Balanced Routing Algorithm Based On Genetic
And Particle Swarm Optimization.
[4] Manjusha, M. S., &Kannammal, K. E. (2014). Efficient Cluster Head Selection Method For Wireless
Sensor Network
[5] Bee-Sensor-C: An Energy-Efficient and Scalable Multipath Routing Protocol for Wireless Sensor
Networks.Celik, F., Zengin, A. and Tuncel, S. (2010)
[6] ‘A survey on swarm intelligence based routing protocols in wireless sensor networks’, International
Journal of Physical Sciences, Vol. 5, No. 14, pp.2118–2126.
[7] Saini, M., &Saini, R. K. (2013). Solution of Energy-Efficiency of sensor nodes in Wireless sensor
Networks. International Journal of Advanced Research in Computer Science and Software Engineering,
3(5), 353-357.
[8] Han, L. (2010, October). LEACH-HPR: An energy efficient routing algorithm for Heterogeneous
WSN. In Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on
(Vol. 2, pp. 507-511).IEEE.
[9] Gou, H., &Yoo, Y. (2010, April). An energy balancing LEACH algorithm for wireless sensor
networks. In Information Technology: New Generations (ITNG), 2010 Seventh International Conference
on (pp. 822-827). IEEE.
[10] Farooq, M. O., Dogar, A. B., & Shah, G. A. (2010, July). MR-LEACH: multi-hop routing with low
energy adaptive clustering hierarchy. In Sensor Technologies and Applications (SENSORCOMM), 2010
Fourth International Conference on(pp. 262-268). IEEE.
[11] El-Saadawy, M., &Shaaban, E. (2012, May). Enhancing S-LEACH security for wireless sensor
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