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i-manager's Journal on Mobile Applications and Technologies (JMT)

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Volume 9 Issue 2 July - December 2022

Research Paper

Exploring Machine Learning-Based Traffic Prediction in 5G Networks using a QualNet Simulator and STLSTM

Rathna R.* , D. Vinod**

* Department of Information Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India.

** Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.

Rathna, R., and Vinod, D. (2022). Exploring Machine Learning-Based Traffic Prediction in 5G Networks using a QualNet Simulator and STLSTM. i-manager’s Journal on Mobile Applications & Technologies, 9(2), 1-6. https://doi.org/10.26634/jmt.9.2.19317

Abstract

This paper aims to explore Machine Learning-based traffic prediction in 5G networks using the QualNet simulator and the Spatio-Temporal Long Short-Term Memory (STLSTM) model. The study evaluated the performance of the STLSTM model by comparing it with other models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN). The evaluation metrics used for the simulation experiments included Packet Delivery Ratio (PDR), throughput, end-to-end delay, and jitter. The results showed that the STLSTM model outperformed the other models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared, and achieved improved accuracy in predicting traffic in 5G networks. The findings of this study can help network operators to effectively manage traffic and optimize network performance.

References

[1]. Dinesh, S., & Sonal, G. (2014). Qualnet simulator. International Journal of Information & Computation Technology, 4(13), 1349-1354.

[2]. Gao, Z. (2022). 5G traffic prediction based on deep learning. Computational Intelligence and Neuroscience, 1-5. https://doi.org/10.1155/2022/3174530

[3]. Luo, F. L. (Ed.). (2020). Machine Learning for Future Wireless Communications. Wiley & Sons, Ltd.

[4]. Panwar, N., Sharma, S., & Singh, A. K. (2016). A survey on 5G: The next generation of mobile communication. Physical Communication, 18, 64-84. https://doi.org/10.1016/j.phycom.2015.10.006

[5]. Paul, U., Liu, J., Troia, S., Falowo, O., & Maier, G. (2019). Traffic-profile and machine learning based regional data center design and operation for 5G network. Journal of Communications and Networks, 21(6), 569-583. https://doi.org/10.1109/JCN.2019.000055

[6]. Selvamanju, E., & Shalini, V. B. (2021, December). Machine learning based mobile data traffic prediction in 5g cellular networks. In 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1318-1324). IEEE. https://doi.org/10.1109/ICECA52323.2021.9675887

[7]. Xie, F., Wei, D., & Wang, Z. (2021). Traffic analysis for 5G network slice based on machine learning. EURASIP Journal on Wireless Communications and Networking, 2021(1), 108. https://doi.org/10.1186/s13638-021-01991-7

[8]. Zhang, C., Patras, P., & Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE Communications Surveys & Tutorials, 21(3), 2224-2287.

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Research Paper

Smart Antenna Design for Mobile Application

Sachin Bandewar* , Virendra Singh Chaudhary**

*-** Ram Krishna Dharmarth Foundation University, Bhopal, Madhya Pradesh, India.

Bandewar, S., and Chaudhary, V. S. (2022). Smart Antenna Design for Mobile Application. i-manager’s Journal on Mobile Applications & Technologies, 9(2), 7-15. https://doi.org/10.26634/jmt.9.2.19065

Abstract

Digitalization is becoming more and more important. Building smart houses and industries to offer humans longer lives is one of the main goals of the digitization movement. The main aim of this work is to make those verbal exchange modules more powerful with the aid of either improving the antennas to have a higher layout or changing it with new varieties of antennas that are better able to facilitate powerful verbal exchange. This paper proposes an antenna that can be applied to a verbal exchange module to be able to perform inside the Industrial, Scientific, and Medical (ISM) band at 2.4 GHz. After designing and simulating a complete Printed Circuit Board (PCB) antenna, it found that a few designs produced advanced simulation effects in terms of gain, reflection coefficient, voltage standing wave ratio (VSWR), and band width. Always-pointing antennae are protected by the layout of the antenna, allowing it to protect features inside the Global Positioning System (GPS) and ISM (Industrial, Scientific, and Medical) bands for Wi-Fi devices.

References

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[20]. Mandal, A., Ghosal, A., Majumdar, A., Ghosh, A., Das, A., & Das, S.K. (2012). Analysis of feeding techniques of rectangular microstrip antenna. 2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2012), 26-31.

[21]. Mulla, M., Sohail, M., Ulusoy, A. H., Uyguroğlu, R., Rizaner, A., & Amca, H. (2021, June). A single user millimeter wave massive MIMO system using defected ground structure and metasurface antenna arrays. In 2021 29th Signal Processing and Communications Applications Conference (SIU), (pp. 1-4). IEEE. https://doi.org/10.1109/SIU53274.2021.9477798

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[28]. Ren, K., Ma, R., & Behdad, N. (2021). Performance- Enhancement of platform-based, hf direction-finding systems using dynamic mode selection. IEEE Open Journal of Antennas and Propagation, 2, 793-806. https://doi.org/10.1109/OJAP.2021.3095223

[29]. Roges, R., Malik, P. K., & Sharma, S. (2021, June). A compact CPW-fed log-periodic antenna for IoT applications. In 2021 International Conference on Communication, Control and Information Sciences (ICCISc), 1, 1-5. https://doi.org/10.1109/ICCISc52257.2021.9484975

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[34]. Seven, Z. S., & Can, S. (2021, June). Calculation of resonant frequency and bandwidth of rectangular microstrip antennas via genetic algorithm. In 2021 29th Signal Processing and Communications Applications Conference (SIU), (pp. 1-4). IEEE. https://doi.org/10.1109/SIU53274.2021.9478054

[35]. Sohail, M., & Uyguroğlu, R. (2021, June). Near field focused microstrip patch antenna array characteristics enhancement with parasitic patch elements. In 2021 29th Signal Processing and Communications Applications Conference (SIU), (pp. 1-4). IEEE. https://doi.org/10.1109/SIU53274.2021.9477974

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[40]. Zhang, D., Li, A., Pradhan, C., Li, J., Vucetic, B., & Li, Y. (2021). Training beam sequence design for multiuser millimeter wave tracking systems. IEEE Transactions on Communications, 69(10), 6939-6955. https://doi.org/10.1109/TCOMM.2021.3096543

[41]. Zhong, S. S., Yang, X. X., Gao, S. C., & Cui, J. H. (2002). Corner-fed microstrip antenna element and arrays for dual-polarization operation. IEEE Transactions on Antennas and Propagation, 50(10), 1473-1480. https://doi.org/10.1109/TAP.2002.801289

[42]. Zhou, P., Liu, Y., Zhao, H., Shi, Y., & Yin, X. (2021). Differentially fed dual-mode patch antenna with wideband common-mode absorption and its array application. IEEE Transactions on Antennas and Propagation, 69(12), 8937-8942. https://doi.org/10.1109/TAP.2021.3097438

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Research Paper

Revolutionizing Hospital Dietary with an Android - Based Food Automation System

Sheryl Radley* , T. Jaya**

* Meenakshi College of Engineering, Chennai, Tamil Nadu, India.

** Department of Electronics and Communication Engineering, C.S.I. Institute of Technology, Thovalai, Tamil Nadu, India.

Radley, S., and Jaya, T. (2022). Revolutionizing Hospital Dietary with an Android - Based Food Automation System. i-manager’s Journal on Mobile Applications & Technologies, 9(2), 16-21. https://doi.org/10.26634/jmt.9.2.19315

Abstract

The advent of mobile technology has brought about significant changes in many areas of life, including the management of food intake and dietary habits. This paper proposes an Android-based food automation and dietary management system that leverages the power of mobile technology to streamline the process of ordering and preparing meals in a hospital setting. The system includes a mobile app that allows users to place food orders and track their nutrient intake, as well as a dietary plan generator module that utilizes third-party Application Programming Interfaces (APIs) to create personalized meal plans based on individual dietary needs and preferences. It discusses the benefits of this system, including improved efficiency, accuracy, and patient satisfaction. Overall, this paper demonstrates the potential of mobile technology to transform the way it approaches food and nutrition in healthcare settings.

References

[1]. Caldwell, D. G., Davis, S., Moreno Masey, R. J., & Gray, J. O. (2009). Automation in food processing. Springer Handbook of Automation, 1041-1059. https://doi.org/10.1007/978-3-540-78831-7_60

[2]. DiCE. (2021). New Nutrition App Available to Help Cancer Patients on a Daily Basis. Retrieved from https://digestivecancers.eu/new-nutrition-app-available-tohelp-cancer-patients-on-a-daily-basis/

[3]. Doupis, J., Festas, G., Tsilivigos, C., Efthymiou, V., & Kokkinos, A. (2020). Smartphone-based technology in diabetes management. Diabetes Therapy, 11, 607-619. https://doi.org/10.1007/s13300-020-00768-3

[4]. Kankanhalli, A., Shin, J., & Oh, H. (2019). Mobilebased interventions for dietary behavior change and health outcomes: Scoping review. JMIR mHealth and uHealth, 7(1), e11312. https://doi.org/10.2196/11312

[5]. Moore, C. A. (2012). Automation in the Food Industry. Springer Science & Business Media.

[6]. Okamoto, K., & Yanai, K. (2016, October). An automatic calorie estimation system of food images on a smartphone. In Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management (pp. 63-70). https://doi.org/10.1145/2986035.2986040

[7]. Rehman, F., Khalid, O., Bilal, K., & Madani, S. A. (2017). Diet-right: A smart food recommendation system. KSII Transactions on Internet and Information Systems (TIIS), 11(6), 2910-2925. https://doi.org/10.3837/tiis.2017.06.006

[8]. Silva, B. V. R., Rad, M. G., Cui, J., McCabe, M., & Pan, K. (2018). A mobile-based diet monitoring system for obesity management. Journal of Health & Medical Informatics, 9(2), 307. https://doi.org/10.4172/21577420.1000307

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Research Paper

Two-Level Data Packet Security Mechanism – Secure Location-Aided Routing (SLAR)

S. Christy* , L. Mary Gladence**

* Saveetha University, Chennai, Tamil Nadu, India.

** School of Computing, Sathyabama Institute of Science and Technology, Chennai, India.

Christy, S., and Gladence, L. M. (2022). Two-Level Data Packet Security Mechanism – Secure Location-Aided Routing (SLAR). i-manager’s Journal on Mobile Applications & Technologies, 9(2), 22-27. https://doi.org/10.26634/jmt.9.2.19316

Abstract

Secure data transmission is one of the most difficult challenges of Mobile Ad hoc Networks (MANET), it is a group of wireless mobile nodes that creates a temporary network without the help of a centralized system, infrastructure, or access point. Location-Aided Routing (LAR) protocols limit the ad hoc network's search for a new route to a limited "request zone." For safe message transmission in the current Location Aided Routing protocol, the Secure Location Aided Routing algorithm (SLAR) is proposed in this paper. The LAR is a geographic routing protocol that establishes the route discovery region between the source and distance before forwarding the route request packets. SLAR is an extension of LAR where the performance of LAR is compared in the presence and absence of malicious nodes, and the security of LAR is improved by putting cryptographic features in it. SLAR has significantly improved throughput, end-to-end delay, and packet delivery ratio compared to LAR.

References

[1]. Al-Omari, S. A. K., & Sumari, P. (2010). An overview of mobile ad hoc networks for the existing protocols and applications. International Journal on Applications of Graph Theory in Wireless Ad Hoc Networks and Sensor Networks (Graph-Hoc), 2(1), 87-110. https://doi.org/10.5121/jgraphhoc.2010.2107

[2]. Basagni, S., Chlamtac, I., Syrotiuk, V. R., & Woodward, B. A. (1998, October). A distance routing effect algorithm for mobility (DREAM). In Proceedings of the 4th Annual ACM/IEEE International Conference on Mobile Computing and Networking (pp. 76-84).

[3]. Bellur, B., Ogier, R. G., & Templin, F. L. (2003). Topology broadcast based on reverse-path forwarding routing protocol (TBRPF). Internet Draft, Draft-Ietf-Manet-Tbrpf-06. Txt, Work in Progress.

[4]. Johnson, D. B., & Maltz, D. A. (1996). Dynamic source routing in ad hoc wireless networks. In Mobile Computing (pp. 153-181). Springer, Boston, MA. https://doi.org/10.1007/978-0-585-29603-6_5

[5]. Ko, Y., & Vaidya, N. H. (2000). Location-aided routing (LAR) in mobile ad hoc networks. Wireless Networks, 6, 307–321. https://doi.org/10.1023/A:1019106118419

[6]. Liao, W. H., Sheu, J. P., & Tseng, Y. C. (2001). GRID: A fully location-aware routing protocol for mobile ad hoc networks. Telecommunication Systems, 18(1), 37-60. https://doi.org/10.1023/A:1016735301732

[7]. Radhakrishnan, S., Racherla, G., Sekharan, C. N., Rao, N. S., & Batsell, S. G. (1999, September). DST-a routing protocol for ad hoc networks using distributed spanning trees. In WCNC. 1999 IEEE Wireless Communications and Networking Conference (Cat. No. 99TH8466) (Vol. 3, pp. 1543-1547). IEEE. https://doi.org/10.1109/WCNC.1999.796997

[8]. Woo, S. C. M., & Singh, S. (2001). Scalable routing protocol for ad hoc networks. Wireless Networks, 7(5), 513-529. https://doi.org/10.1023/A:1016726711167

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Research Paper

Gradify: Analysis of Twitter Account using Classification Algorithm

Gunjan Gaur* , Naman Bangad, Siddhi Jain, Manish Rana, Pranita Kanchan

*-***** Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, India.

Gaur, G., Bangad, N., Jain, S., Rana, M., and Kanchan, P. (2022). Gradify: Analysis of Twitter Account using Classification Algorithm. i-manager’s Journal on Mobile Applications & Technologies, 9(2), 28-35. https://doi.org/10.26634/jmt.9.2.19147

Abstract

With technology's increasing capabilities, social media has become the largest pool of data from which it can extract public opinion and begin to gather informative data on the success or failure of a brand, product, or marketing campaign in the eyes of the public. People share their experiences, opinions, and daily activities on social media, which results in enormous amounts of online data that attract developers to carry out data mining and analysis. Thus, there is a necessity for social media screening to obtain results that can be used for analysis. Twitter is an online networking site driven by tweets, which are 140-character limited messages. Thus, the character limit enforces the use of hashtags for text classification. Currently, around 5500–6000, tweets are published every second, which results in approximately 561.6 million tweets per day. Performing sentiment analysis of tweets can help us to determine the polarity and inclination of a vast population toward a specific topic, term, or entity. The applications of such analysis can easily be observed during public elections, movie promotions, brand endorsements, and many other fields. This proposed system uses a Naïve Bayes classifier to determine the tweets based on sentiment. In the implemented system, tweets are collected, and sentiment analysis is performed on them. Based on the sentiment analysis results, a few suggestions can be provided to the user. The primary aim is to provide a method for analyzing sentiment scores based on grades. This paper reports on the design of sentiment analysis, extracting vast numbers of tweets. Results classify users' perceptions via tweets into positive and negative categories. Secondly, it discusses various techniques to carry out a sentiment analysis on Twitter data in detail.

References

[1]. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. J. (2011, June). Sentiment analysis of twitter data. In Proceedings of the Workshop on Language in Social Media (LSM 2011) (pp. 30-38).

[2]. Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data, 2(1), 1-14. https://doi.org/10.1186/s40537-015-0015-2

[3]. Ingle, A., Kante, A., Samak, S., & Kumari, A. (2015). Sentiment analysis of twitter data using hadoop. International Journal of Engineering Research and General Science, 3(6), 144-147.

[4]. Karatzoglou, A., Meyer, D., & Hornik, K. (2006). Support vector machines in R. Journal of Statistical Software, 15(9), 1–28.

[5]. Kiruthika, M., Woonna, S., & Giri, P. (2016). Sentiment analysis of twitter data. International Journal of Innovation in Engineering and Technology (IJIET), 6(4), 246-273.

[6]. Pak, A., & Paroubek, P. (2010, May). Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10).

[7]. Pancham, A., Sharma, D., Sharma, A., & Patil, H. (2020). Implementing system for rating generation by analysis of social media. International Journal of Engineering and Advanced Technology, 9(3), 638- 643.

[8]. Rangu, C., Chatterjee, S., & Valluru, S. R. (2017). Text mining approach for product quality enhancement. In 2017 IEEE 7th International Advance Computing Conference. https://doi.org/10.1109/IACC.2017.0100

[9]. Sahayak, V., Shete, V., & Pathan, A. (2015). Sentiment analysis on twitter data. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 2(1), 178-183.

[10]. Sangeeta. (2016). Twitter data analysis using FLUME & HIVE on hadoop frame work. International Journal of Recent Advances in Engineering & Technology (IJRAET), (pp. 119-123).

[11]. Singh, V. K., Piryani, R., Uddin, A., & Waila, P. (2013, March). Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification. In 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (Imac4s) (pp. 712-717). https://doi.org/10.1109/iMac4s.2013.6526500

[12]. Wang, H., Can, D., Kazemzadeh, A., Bar, F., & Narayanan, S. (2012, July). A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In Proceedings of the ACL 2012 System Demonstrations (pp. 115-120).

i-manager's Journal on Mobile Applications and Technologies (JMT)

Current Issue Vol. 11 Issue 1

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Volume 9 Issue 2 July - December 2022

Exploring Machine Learning-Based Traffic Prediction in 5G Networks using a QualNet Simulator and STLSTM

Rathna R.* , D. Vinod**

* Department of Information Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. ** Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.

Rathna, R., and Vinod, D. (2022). Exploring Machine Learning-Based Traffic Prediction in 5G Networks using a QualNet Simulator and STLSTM. i-manager’s Journal on Mobile Applications & Technologies, 9(2), 1-6. https://doi.org/10.26634/jmt.9.2.19317

Abstract

References

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Smart Antenna Design for Mobile Application

Sachin Bandewar* , Virendra Singh Chaudhary**

*-** Ram Krishna Dharmarth Foundation University, Bhopal, Madhya Pradesh, India.

Bandewar, S., and Chaudhary, V. S. (2022). Smart Antenna Design for Mobile Application. i-manager’s Journal on Mobile Applications & Technologies, 9(2), 7-15. https://doi.org/10.26634/jmt.9.2.19065

Abstract

References

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Revolutionizing Hospital Dietary with an Android - Based Food Automation System

Sheryl Radley* , T. Jaya**

* Meenakshi College of Engineering, Chennai, Tamil Nadu, India. ** Department of Electronics and Communication Engineering, C.S.I. Institute of Technology, Thovalai, Tamil Nadu, India.

Radley, S., and Jaya, T. (2022). Revolutionizing Hospital Dietary with an Android - Based Food Automation System. i-manager’s Journal on Mobile Applications & Technologies, 9(2), 16-21. https://doi.org/10.26634/jmt.9.2.19315

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Two-Level Data Packet Security Mechanism – Secure Location-Aided Routing (SLAR)

S. Christy* , L. Mary Gladence**

* Saveetha University, Chennai, Tamil Nadu, India. ** School of Computing, Sathyabama Institute of Science and Technology, Chennai, India.

Christy, S., and Gladence, L. M. (2022). Two-Level Data Packet Security Mechanism – Secure Location-Aided Routing (SLAR). i-manager’s Journal on Mobile Applications & Technologies, 9(2), 22-27. https://doi.org/10.26634/jmt.9.2.19316

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Gradify: Analysis of Twitter Account using Classification Algorithm

Gunjan Gaur* , Naman Bangad**, Siddhi Jain***, Manish Rana****, Pranita Kanchan*****

*-***** Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, India.

Gaur, G., Bangad, N., Jain, S., Rana, M., and Kanchan, P. (2022). Gradify: Analysis of Twitter Account using Classification Algorithm. i-manager’s Journal on Mobile Applications & Technologies, 9(2), 28-35. https://doi.org/10.26634/jmt.9.2.19147

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* Department of Information Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India.

** Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.

* Meenakshi College of Engineering, Chennai, Tamil Nadu, India.

** Department of Electronics and Communication Engineering, C.S.I. Institute of Technology, Thovalai, Tamil Nadu, India.

* Saveetha University, Chennai, Tamil Nadu, India.

** School of Computing, Sathyabama Institute of Science and Technology, Chennai, India.

Gunjan Gaur* , Naman Bangad, Siddhi Jain, Manish Rana, Pranita Kanchan