i-manager Publications

i-manager's Journal on Wireless Communication Networks (JWCN)

Current Issue Vol. 12 Issue 1

5G Wireless Communication Systems: Performance Analysis and Optimization using Massive MIMO Technology
Performance Optimization of 5G NR Technology for Industrial Automation
A Comparative Analysis of SDON and Traditional Optical Networks in Elastic Optical Environments
A Review of Techniques for Minimizing Energy Consumption and Enhancing Lifespan in Wireless Sensor Networks
NodeMCU and Wireless Sensor Networks in Agriculture: A Review of Environmental Parameter Sensing

Most Cited

Volume 12 Issue 1 July - December 2023

Research Paper

5G Wireless Communication Systems: Performance Analysis and Optimization using Massive MIMO Technology

Ushaa Eswaran* , Vivek Eswaran, Keerthna Murali*, Vishal Eswaran****

* Department of Electronics and Communications Engineering, Indira Institute of Technology and Sciences, Idupur, Andhra Pradesh, India.

Medallia Inc, Austin, Texas, United States.

* Dell Technologies, Austin, Texas, United States.

**** CVS Health Centre, Dallas, Texas, United States.

Eswaran, U., Eswaran, V., Murali, K., and Eswaran, V. (2023). 5G Wireless Communication Systems: Performance Analysis and Optimization using Massive MIMO Technology. i-manager’s Journal on Wireless Communication Networks, 12(1), 1-12. https://doi.org/10.26634/jwcn.12.1.20271

Abstract

Fifth-Generation (5G) cellular networks target substantial gains over 4G networks to meet the increasing data rate, capacity and low-latency demands of emerging mobile applications. This research investigates the potential of largescale antenna system architectures named Massive Multiple-Input Multiple-Output (MIMO) to enhance the spectral efficiency and reliability of 5G new radio (NR) wireless access. Comprehensive system-level modeling and simulations quantify the throughput, capacity and transmit power savings realizable under various realistic propagation environments. Results demonstrate significant performance gains over contemporary network configurations, supporting Massive MIMO as a key enabler on the road to 5G.

References

[1]. 3GPP TR 38.802. (2017). Study on New Radio Access Technology Physical Layer Aspects (Release 14). Technical Report, ETSI (pp. 1-144).

[2]. 3GPP TR 38.901. (2020). Study on channel model for frequencies from 0.5 to 100 GHz (3GPP TR 38.901 version 16.1.0 Release 16). Technical Report, ETSI (pp. 1-103).

[3]. Björnson, E., Hoydis, J., & Sanguinetti, L. (2017). Massive MIMO networks: Spectral, energy, and hardware efficiency. Foundations and Trends® in Signal Processing, 11(3-4), 154-655.

[4]. Hoydis, J., Brink, S.T., & Debbah, M. (2013). Massive MIMO in the UL/DL of cellular networks: How many antennas do we need? IEEE Journal on selected Areas in Communications, 31(2), 160-171.

[5]. Hoydis, J., Hoek, C., Wild, T., & Brink, S.T. (2012, August). Channel measurements for large antenna arrays. In 2012 International Symposium on Wireless Communication Systems (ISWCS) (pp. 811-815). IEEE.

[6]. Interdonato, G., Frenger, P., & Larsson, E. G. (2019, May). Scalability aspects of cell-free massive MIMO. In ICC 2019-2019 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.

[7]. Larsson, E. G., Edfors, O., Tufvesson, F., & Marzetta, T. L. (2014). Massive MIMO for next generation wireless systems. IEEE Communications Magazine, 52(2), 186-195.

[8]. Shepard, C., Yu, H., Anand, N., Li, E., Marzetta, T., Yang, R., & Zhong, L. (2012, August). Argos: Practical many-antenna base stations. In Proceedings of the 18th Annual International Conference on Mobile Computing and Networking (pp. 53-64).

[9]. Sun, S., Rappaport, T. S., Rangan, S., Thomas, T. A., Ghosh, A., Kovacs, I. Z., ... & Jarvelainen, J. (2016, May). Propagation path loss models for 5G urban micro-and macro-cellular scenarios. In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring) (pp. 1-6). IEEE.

[10]. WHO. (2010). Health Effects of RF Fields. Retrieved fro.

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Research Paper

Performance Optimization of 5G NR Technology for Industrial Automation

Pankaj Kumar*

Institute of Engineering & Technology, Ayodhya, Uttar Pradesh, India.

Kumar, P. (2023). Performance Optimization of 5G NR Technology for Industrial Automation. i-manager’s Journal on Wireless Communication Networks, 12(1), 13-19. https://doi.org/10.26634/jwcn.12.1.19966

Abstract

Communication over the wireless has significantly evolved to the level where it is capable of supporting the essential controlling mechanisms of the high-definition automated industrial process. The Fifth-Generation (5G) mobile networking air-based interface, named New Radio (5GNR), is presently capable of adopting scalable control and subband data transmission for small data packets, which has made it efficiently compatible for the critical controlling application of industrial automation processes. The minimum cycle time is used as a reliable parameter for an indication of the healthy techniques for industrial wireless telecommunication, but it has not undergone deep investigation within the framework of modern 5G technology. For addressing such types of challenges, this paper has taken into consideration 5G-based industrial automation process control through wireless networking by using a delay optimization approach associated with data-base channel control using the Ant Colony Optimization (ACO) approach for solving the proposed evaluation of a reliable minimum cycle time over 5G network communication.

References

[1]. Amendola, S., Lodato, R., Manzari, S., Occhiuzzi, C., & Marrocco, G. (2014). RFID technology for IoT-based personal healthcare in smart spaces. IEEE Internet of Things Journal, 1(2), 144-152.

[2]. Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C., & Zhang, J. C. (2014). What will 5G be? IEEE Journal on Selected Areas in Communications, 32(6), 1065-1082.

[3]. Ashraf, N., Haraz, O., Ashraf, M. A., & Alshebeili, S. (2015, May). 28/38-GHz dual-band millimeter wave SIW array antenna with EBG structures for 5G applications. In 2015 International Conference on Information and Communication Technology Research (ICTRC) (pp. 5-8). IEEE.

[4]. Basta, A., Kellerer, W., Hoffmann, M., Morper, H. J., & Hoffmann, K. (2014, August). Applying NFV and SDN to LTE mobile core gateways, the functions placement problem. In Proceedings of the 4th Workshop on all Things Cellular: Operations, Applications, & Challenges (pp. 33-38).

[5]. Berardinelli, G., Mahmood, N. H., Rodriguez, I., & Mogensen, P. (2018, December). Beyond 5G wireless IRT for industry 4.0: Design principles and spectrum aspects. In 2018 IEEE Globecom Workshops (GC Wkshps) (pp. 1-6). IEEE.

[6]. Chataut, R., & Akl, R. (2020). Massive MIMO systems for 5G and beyond networks-Overview, recent trends, challenges, and future research direction. Sensors, 20(10), 2753.

[7]. Ding, Z., Yang, Z., Fan, P., & Poor, H. V. (2014). On the performance of non-orthogonal multiple access in 5G systems with randomly deployed users. IEEE Signal Processing Letters, 21(12), 1501-1505.

[8]. Kiani, A., & Ansari, N. (2018). Edge computing aware NOMA for 5G networks. IEEE Internet of Things Journal, 5(2), 1299-1306.

[9]. Niu, Y., Li, Y., Jin, D., Su, L., & Vasilakos, A. V. (2015). A survey of millimeter wave communications (mmWave) for 5G: Opportunities and challenges. Wireless Networks, 21, 2657-2676.

[10]. Ojanen, P., & Yrjölä, S. (2019). Assessment of spectrum management approaches to private industrial networks. In Cognitive Radio-Oriented Wireless Networks: 14th EAI International Conference, CrownCom 2019, Poznan, Poland (pp. 277-290). Springer International Publishing.

[11]. Ojanen, P., Yrjölä, S., & Matinmikko-Blue, M. (2020, March). Assessing the feasibility of the spectrum sharing concepts for private industrial networks operating above 5 GHz. In 2020 14th European Conference on Antennas and Propagation (EuCAP) (pp. 1-5). IEEE.

[12]. Pandey, P., & Pompili, D. (2017). Exploiting the untapped potential of mobile distributed computing via approximation. Pervasive and Mobile Computing, 38, 381-395.

[13]. Peng, M., Wang, C., Li, J., Xiang, H., & Lau, V. (2015). Recent advances in underlay heterogeneous networks: Interference control, resource allocation, and selforganization. IEEE Communications Surveys & Tutorials, 17(2), 700-729.

[14]. Prasad, K. S. V., Hossain, E., & Bhargava, V. K. (2017). Energy efficiency in massive MIMO-based 5G networks: Opportunities and challenges. IEEE Wireless Communications, 24(3), 86-94.

[15]. Qiao, J., Shen, X. S., Mark, J. W., Shen, Q., He, Y., & Lei, L. (2015). Enabling device-to-device communications in millimeter-wave 5G cellular networks. IEEE Communications Magazine, 53(1), 209-215.

[16]. Rappaport, T.S. (1990). A single-hop F2 propagation model for frequencies above 30 MHz and path distances greater than 4000 km. IEEE Transactions on Antennas and Propagation, 38(12) 1967–1968.

[17]. Timotheou, S., & Krikidis, I. (2015). Fairness for nonorthogonal multiple access in 5G systems. IEEE Signal Processing Letters, 22(10), 1647-1651.

[18]. Wang, P., Xiao, J., & Ping, L. (2006). Comparison of orthogonal and non-orthogonal approaches to future wireless cellular systems. IEEE Vehicular Technology Magazine, 1(3), 4-11.

[19]. Zhang, H., Dong, Y., Cheng, J., Hossain, M. J., & Leung, V. C. (2016). Fronthauling for 5G LTE-U ultra-dense cloud small cell networks. IEEE Wireless Communications, 23(6), 48-53.

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Review Paper

A Comparative Analysis of SDON and Traditional Optical Networks in Elastic Optical Environments

Jatin Garg* , Ashutosh Kumar Singh, Chandan*, Manoj Kumar Jain****

- Dr. Rammanohar Lohia Avadh University, Ayodhya, Uttar Pradesh, India.

** Faculty of Engineering and Technology, Lucknow University, Uttar Pradesh, India.

Garg, J., Singh, A. K., Chandan, and Jain, M. K. (2023). A Comparative Analysis of SDON and Traditional Optical Networks in Elastic Optical Environments. i-manager’s Journal on Wireless Communication Networks, 12(1), 20-28. https://doi.org/10.26634/jwcn.12.1.20150

Abstract

The advent of Software-Defined Optical Networking (SDON) and its integration into Elastic Optical Networks (EON) have significantly advanced the evolution of communications networks. In the context of elastic optical environments, this paper provides a thorough comparison of SDON and conventional optical networks. The goal of the study is to examine the unique traits, benefits, and difficulties of these two network technologies in order to highlight their individual potentials in contemporary communication infrastructures. This paper consists of comparison of SDON, SDN and NFV technologies. The paper concludes by looking at potential directions for future research and development in SDON and EON. The results of this comparative analysis highlight the growing role that SDON is playing in reshaping the telecom industry and present a vision for a more flexible and effective communication infrastructure.

References

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[3]. Das, S., Parulkar, G., & McKeown, N. (2012, September). Why OpenFlow/SDN can succeed where GMPLS failed. In European Conference and Exhibition on Optical Communication (pp. Tu-1). Optica Publishing Group.

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[5]. Gerstel, O., Jinno, M., Lord, A., & Yoo, S. B. (2012). Elastic optical networking: A new dawn for the optical layer? IEEE Communications Magazine, 50(2), s12-s20.

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[9]. Leytón, G. V. (2019, May). Software-defined optical network (SDON), new generation networks. In IOP Conference Series: Materials Science and Engineering, 519(1), 012013. IOP Publishing.

[10]. Mckeown, N. (2013). Software Defined Transport. In OFC Plenary/Keynote.

[11]. Mukherjee, B. (2006). Optical WDM Networks. Springer, New York.

[12]. Nsafoa-Yeboah, K., Tchao, E. T., Yeboah-Akowuah, B., Kommey, B., Agbemenu, A. S., Keelson, E., & Monirujjaman Khan, M. (2022). Software-Defined Networks for Optical Networks Using Flexible Orchestration: Advances, Challenges, and Opportunities. Journal of Computer Networks and Communications, 2022.

[13]. Nsafoa-Yeboah, K., Tchao, E. T., Yeboah-Akowuah, B., Kommey, B., Agbemenu, A. S., Keelson, E., & Monirujjaman Khan, M. (2022). Software-Defined Networks for Optical Networks Using Flexible Orchestration: Advances, Challenges, and Opportunities. Journal of Computer Networks and Communications.

[14]. Roslyakov, A. (2021, June). Virtualization in optical networks. In Optical Technologies for Telecommunications 2020, 11793, 1179302. SPIE.

[15]. Santos Boada, G., de Almeida Amazonas, J. R., & Solé Pareta, J. (2016). Quality of network economics optimisation using service level agreement modelling. Transactions on Emerging Telecommunications Technologies, 27(5), 731-744.

[16]. Singh, A. K., Arun, V., Singh, P., & Upadhayay, K. K. (2020). Novel approach to jointly optimize working and spare capacity of survivable optical networks. Journal of Optical Communications, 000010151520200228.

[17]. Thyagaturu, A. S., Mercian, A., McGarry, M. P., Reisslein, M., & Kellerer, W. (2016). Software defined optical networks (SDONs): A comprehensive survey. IEEE Communications Surveys & Tutorials, 18(4), 2738-2786.

[18]. Zhang, S., Martel, C., & Mukherjee, B. (2012). Dynamic traffic grooming in elastic optical networks. IEEE Journal on Selected Areas in Communications, 31(1), 4-12.

[19]. Zhou, Y., Yin, S., Guo, B., Huang, H., Li, W., Zhang, M., & Huang, S. (2016). Experimental demonstration of software-defined optical network for heterogeneous packet and optical networks. Photonic Network Communications, 32, 329-335.

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Review Paper

A Review of Techniques for Minimizing Energy Consumption and Enhancing Lifespan in Wireless Sensor Networks

Bharti P.*

Department of Electronic and Telecommunication, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India.

Bharti, P. (2023). A Review of Techniques for Minimizing Energy Consumption and Enhancing Lifespan in Wireless Sensor Networks. i-manager’s Journal on Wireless Communication Networks, 12(1), 29-36. https://doi.org/10.26634/jwcn.12.1.20318

Abstract

WSNs play a crucial role in monitoring physical or environmental conditions in scenarios where traditional wired networks are impractical. The study focuses on various clustering techniques and energy-efficient protocols, emphasizing their impact on Packet Drop Ratio (PDR) and overall network efficiency. The paper evaluates the strengths and limitations of protocols such as LEACH, DEEC, SEP, and EDEEC. The evaluation of energy-aware schemes in WSNs includes a thorough comparison of clustering algorithm techniques based on parameters like energy load, algorithm efficiency, and balancing complexity. The presented research not only consolidates existing knowledge but also identifies avenues for future improvements. As the field of WSNs continues to evolve, advancements in clustering algorithms and energyefficient protocols are crucial for meeting the diverse demands of applications.

References

[1]. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393-422.

[2]. Awad, F., Taqieddin, E., & Seyam, A. (2012). Energyefficient and coverage-aware clustering in wireless sensor networks. Wireless Engineering and Technology, 2012(3), 142-151.

[3]. Cheikh, E., & Abderrahmane, H. (2014). Energy efficient enhancement of TDEEC wireless sensors network protocol based on passive RFID implementation. International Journal of Advanced Research in Computer and Communication Engineering, 3(5), 6647- 6653.

[4]. Elbhiri, B., Saadane, R., & Aboutajdine, D. (2010, September). Developed Distributed Energy-Efficient Clustering (DDEEC) for heterogeneous wireless sensor networks. In 2010 5th International Symposium on I/V Communications and Mobile Network (pp. 1-4). IEEE.

[5]. Hasan, S., Hussain, Z., & Singh, R. K. (2013). A survey of wireless sensor network. International Journal of Emerging Technolgy and Advanced Engineering, 3(3), 487-492.

[6]. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000, January). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences (pp. 10-pp). IEEE.

[7]. Jan, M. A., Nanda, P., He, X., & Liu, R. P. (2014). PASCCC: Priority-based application-specific congestion control clustering protocol. Computer Networks, 74, 92-102.

[8]. Kaur, A., & Kaur, H. (2017). A review on a hybrid approach using mobile sink and fuzzy logic for region based clustering in WSN. International Journal, 16(2), 7586-7590.

[9]. Krishnamachari, B. (2005, January). An introduction to wireless sensor networks. In Second International Conference on Intelligent Sensing and Information Processing (ICISIP), Chennai, India (pp. 1-101).

[10]. Kumar, R., & Kaur, R. (2014). Evaluating the Performance of DEEC variants. International Journal of Computer Applications, 97(7).

[11]. Kumar, S., Prateek, M., Ahuja, N. J., & Bhushan, B. (2014). DE-LEACH: Distance and energy aware LEACH. arXiv.

[12]. Liu, M., Cao, J., Chen, G., & Wang, X. (2009). An energy-aware routing protocol in wireless sensor networks. Sensors, 9(1), 445-462.

[13]. Mahajan, S., Malhotra, J., & Sharma, S. (2014). An energy balanced QoS based cluster head selection strategy for WSN. Egyptian Informatics Journal, 15(3), 189-199.

[14]. Maraiya, K., Kant, K., & Gupta, N. (2011). Efficient cluster head selection scheme for data aggregation in wireless sensor network. International Journal of Computer Applications, 23(9), 10-18.

[15]. Nadeem, Q. (2013). A New Energy Efficient Protocol for Wireless Body Area Networks (Master thesis, MS Comsats Institute of Information Technology, Islamabad, Pakistan).

[16]. Nadeem, Q., Rasheed, M. B., Javaid, N., Khan, Z. A., Maqsood, Y., & Din, A. (2013, October). M-GEAR: Gateway-based energy-aware multi-hop routing protocol for WSNs. In 2013 Eighth International Conference on Broadband and Wireless Computing, Communication and Applications (pp. 164-169). IEEE.

[17]. Perillo, M. A., & Heinzelman, W. B. (2005). Wireless Sensor Network Protocols. In Handbook of Algorithms for Wireless Networking and Mobile Computing, CRC Hall, New York (pp. 1-35).

[18]. Prajapat, M., & Barwar, N. C. (2014). Performance analysis of energy dissipation in WSNs using multi-chain PEGASIS. International Journal of Computer Science Information Technolgy, 5(6), 8033-8036.

[19]. Preethi, A., Pravin, E., & Sangeetha, D. (2016, April). Modified balanced energy efficient network integrated super heterogeneous protocol. In 2016 International Conference on Recent Trends in Information Technology (ICRTIT) (pp. 1-6). IEEE.

[20]. Rawat, P., Singh, K. D., Chaouchi, H., & Bonnin, J. M. (2014). Wireless sensor networks: A survey on recent developments and potential synergies. The Journal of Supercomputing, 68, 1-48.

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[22]. Saini, P., & Sharma, A. K. (2010, October). E-DEECenhanced distributed energy efficient clustering scheme for heterogeneous WSN. In 2010 First international conference on parallel, distributed and grid computing (PDGC 2010) (pp. 205-210). IEEE.

[23]. Shah, T., Javaid, N., & Qureshi, T. N. (2012, December). Energy efficient sleep awake aware (EESAA) intelligent sensor network routing protocol. In 2012 15th International Multitopic Conference (INMIC) (pp. 317-322). IEEE.

[24]. Weil, S. A., Brandt, S. A., Miller, E. L., & Maltzahn, C. (2006, November). CRUSH: Controlled, scalable, decentralized placement of replicated data. In Proceedings of the 2006 ACM/IEEE Conference on Supercomputing (pp. 122-es).

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Review Paper

NodeMCU and Wireless Sensor Networks in Agriculture: A Review of Environmental Parameter Sensing

P. Paul Shalini*

Immanuel Arasar JJ College of Engineering, Marthandam, Tamil Nadu, India.

Shalini, P. P. (2023). NodeMCU and Wireless Sensor Networks in Agriculture: A Review of Environmental Parameter Sensing. i-manager’s Journal on Wireless Communication Networks, 12(1), 37-42. https://doi.org/10.26634/jwcn.12.1.20317

Abstract

In the agricultural environment, there is an increasing need for advanced techniques and systems to identify and prevent crop diseases. Various challenges such as groundwater depletion, soil erosion, emergence of new pests and diseases, land fragmentation, rural-urban migration, and the availability of power supply for farming are currently impacting the agricultural sector. To address these challenges, a system has been developed to monitor field conditions at farms in real-time, focusing on parameters such as temperature, soil moisture, humidity, and water levels. Sensors collect data, and the real-time values are stored in the cloud. The NodeMCU Microcontroller Unit serves as the main component, acting as a gateway between the field and the internet and facilitating communication of parameter values to the cloud. This system enables farmers to access critical field parameters remotely, promoting the adoption of smart agriculture practices.

References

[1]. Akshay, C., Karnwal, N., Abhfeeth, K. A., Khandelwal, R., Govindraju, T., Ezhilarasi, D., & Sujan, Y. (2012, December). Wireless sensing and control for precision Green house management. In 2012 Sixth International Conference on Sensing Technology (ICST) (pp. 52-56). IEEE.

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[3]. Bhange, M. A., & Hingoliwala, H. A. (2015). A review of image processing for pomegranate disease detection. International Journal of Computer Science and Information Technologies, 6(1), 92-94.

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[11]. More, S., & Nighot, M. (2016, September). An agro advisory for pomegranate field using wireless sensor network. In 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT) (pp. 557-562). IEEE.

[12]. Nikhade, R. K., & Nalbalwar, S. L. (2013). Monitoring greenhouse using wireless sensor network. International Journal of Advanced Computer Research, 3(2), 79.

[13]. Ojha, T., Misra, S., & Raghuwanshi, N. S. (2015). Wireless sensor networks for agriculture: The state-of-theart in practice and future challenges. Computers and Electronics in Agriculture, 118, 66-84.

[14]. Tripathy, A. K., Adinarayana, J., Vijayalakshmi, K., Merchant, S. N., Desai, U. B., Ninomiya, S., ... & Kiura, T. (2014). Knowledge discovery and leaf spot dynamics of groundnut crop through wireless sensor network and data mining techniques. Computers and Electronics in Agriculture, 107, 104-114.

[15]. Vanaja, K. J., Suresh, A., Srilatha, S., Kumar, K. V., & Bharath, M. (2018). IOT based agriculture system using node MCU. International Research Journal of Engineering and Technology, 5(3), 3025-3028.

i-manager's Journal on Wireless Communication Networks (JWCN)

Current Issue Vol. 12 Issue 1

Most Read

Most Cited

Volume 12 Issue 1 July - December 2023

5G Wireless Communication Systems: Performance Analysis and Optimization using Massive MIMO Technology

Ushaa Eswaran* , Vivek Eswaran**, Keerthna Murali***, Vishal Eswaran****

* Department of Electronics and Communications Engineering, Indira Institute of Technology and Sciences, Idupur, Andhra Pradesh, India. ** Medallia Inc, Austin, Texas, United States. *** Dell Technologies, Austin, Texas, United States. **** CVS Health Centre, Dallas, Texas, United States.

Eswaran, U., Eswaran, V., Murali, K., and Eswaran, V. (2023). 5G Wireless Communication Systems: Performance Analysis and Optimization using Massive MIMO Technology. i-manager’s Journal on Wireless Communication Networks, 12(1), 1-12. https://doi.org/10.26634/jwcn.12.1.20271

Abstract

References

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Performance Optimization of 5G NR Technology for Industrial Automation

Pankaj Kumar*

Institute of Engineering & Technology, Ayodhya, Uttar Pradesh, India.

Kumar, P. (2023). Performance Optimization of 5G NR Technology for Industrial Automation. i-manager’s Journal on Wireless Communication Networks, 12(1), 13-19. https://doi.org/10.26634/jwcn.12.1.19966

Abstract

References

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A Comparative Analysis of SDON and Traditional Optical Networks in Elastic Optical Environments

Jatin Garg* , Ashutosh Kumar Singh**, Chandan***, Manoj Kumar Jain****

*-*** Dr. Rammanohar Lohia Avadh University, Ayodhya, Uttar Pradesh, India. **** Faculty of Engineering and Technology, Lucknow University, Uttar Pradesh, India.

Garg, J., Singh, A. K., Chandan, and Jain, M. K. (2023). A Comparative Analysis of SDON and Traditional Optical Networks in Elastic Optical Environments. i-manager’s Journal on Wireless Communication Networks, 12(1), 20-28. https://doi.org/10.26634/jwcn.12.1.20150

Abstract

References

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A Review of Techniques for Minimizing Energy Consumption and Enhancing Lifespan in Wireless Sensor Networks

Bharti P.*

Department of Electronic and Telecommunication, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India.

Bharti, P. (2023). A Review of Techniques for Minimizing Energy Consumption and Enhancing Lifespan in Wireless Sensor Networks. i-manager’s Journal on Wireless Communication Networks, 12(1), 29-36. https://doi.org/10.26634/jwcn.12.1.20318

Abstract

References

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NodeMCU and Wireless Sensor Networks in Agriculture: A Review of Environmental Parameter Sensing

P. Paul Shalini*

Immanuel Arasar JJ College of Engineering, Marthandam, Tamil Nadu, India.

Shalini, P. P. (2023). NodeMCU and Wireless Sensor Networks in Agriculture: A Review of Environmental Parameter Sensing. i-manager’s Journal on Wireless Communication Networks, 12(1), 37-42. https://doi.org/10.26634/jwcn.12.1.20317

Abstract

References

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Ushaa Eswaran* , Vivek Eswaran, Keerthna Murali*, Vishal Eswaran****

* Department of Electronics and Communications Engineering, Indira Institute of Technology and Sciences, Idupur, Andhra Pradesh, India.

Medallia Inc, Austin, Texas, United States.

* Dell Technologies, Austin, Texas, United States.

**** CVS Health Centre, Dallas, Texas, United States.

Jatin Garg* , Ashutosh Kumar Singh, Chandan*, Manoj Kumar Jain****

- Dr. Rammanohar Lohia Avadh University, Ayodhya, Uttar Pradesh, India.

** Faculty of Engineering and Technology, Lucknow University, Uttar Pradesh, India.