i-manager Publications

i-manager's Journal on Electronics Engineering (JELE)

Current Issue Vol. 15 Issue 1

Most Cited

Volume 14 Issue 1 October - December 2023

Research Paper

An Intelligent Base Link Recovery Management and Path Stability in Clustered Randomly Distributed Mobile Ad Hoc Networks

Anil Kumar Bandani*

Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.

Bandani, A. K. (2023). An Intelligent Base Link Recovery Management and Path Stability in Clustered Randomly Distributed Mobile Ad Hoc Networks. i-manager’s Journal on Electronics Engineering, 14(1), 1-12. https://doi.org/10.26634/jele.14.1.20469

Abstract

MANETs are networks of mobile nodes that are randomly distributed and play a major role in data transmission, route discovery, and route maintenance. Connection recovery and path stability are significant issues in the MANET, making data transfer problematic. When mobile nodes move out of range or when the node lacks adequate energy to maintain connection or path stability, data packet loss happens. We suggest a Clustered Intermediate System-Clustered Intermediate System Local Connection Failure Recovery Algorithm (Clustered Intermediate System-Clustered Intermediate System LLFRA) routing migration technique that builds the node while simultaneously repairing the broken link. Also utilised to look at node energy, energy- drained nodes, and route stability is Dolphin Partner Optimisation (DPO). The suggested approach's primary goal is to guarantee route stability while reducing packet loss. The suggested method is put to the test using the NS-2 simulator. The proposed recovery protocol beats the current technique in charge of PDR, end to end delay, throughput and node failures, according to experimental outputs.

References

[1]. Allali, M. A., & Maaza, Z. M. (2017). Selection of Stable Paths in the MANET Network. Journal of Communications, 12(11), 611-616.

[2]. Bais, N. S., Rao, D. S., & Sriram, G. (2017). Network path stability based routing protocol for MANET. International Journal of Engineering and Advanced Technology (IJEAT), 6(5), 216-222.

[3]. Bandani, A. K., Subramanyam, M. V., & Prasad, K. S. (2022). An innovative prediction of link failure and node lifetime in mobile Ad Hoc networks using grey wolf optimization. Journal of Hunan University Natural Sciences, 49(9), 118-128.

[4]. Deepa, J., & Sutha, J. (2019). A new energy based power aware routing method for MANETs. Cluster Computing, 22(6), 13317-13324.

[5]. Gautam, N., & Kumar, T. (2019). Novel approach for path recovery in mobile Ad hoc networks. International Journal for Scientific Research & Development, 7(5), 270-273.

[6]. Gowtham, M. S., & Subramaniam, K. (2019). Congestion control and packet recovery for cross layer approach in MANET. Cluster Computing, 22(5), 12029-12036.

[7]. Goyal, A., Sharma, V. K., Kumar, S., & Kumar, K. (2020). Modified local link failure recovery multicast routing protocol for MANET. Journal of Information and Optimization Sciences, 41(2), 669-677.

[8]. Gupta, A. (2015). Maintenance of link for stability in MANET. International Journal of Software & Hardware Research in Engineering, 3(6), 1-8.

[9]. Jain, A., & Narang, K. (2018). Novel approach for link recovery in MANET. International Journal of Research in Electronics and Computer Engineering, 6(3), 1018-1021.

[10]. Javaid, A., & Singh, J. (2023). Link failure approach to maintain quality of service in mobile Ad Hoc network. International Journal for Research in Applied Science & Engineering Technology, 11(1), 505-513.

[11]. Jeon, J., Lee, K., & Kim, C. (2011, January). Fast route recovery scheme for mobile ad hoc networks. In the International Conference on Information Networking 2011 (ICOIN2011) (pp. 419-423). IEEE.

[12]. Kalita, C., & Thakurta, P. K. G. (2018). Energy efficient routing to improve lifetime in MANET: A clustering approach. Journal of Communications, 13(11), 679-684.

[13]. Kavitha, T., & Muthaiah, R. (2017). Instant route migration during link failure in MANETS. International Journal of Mechanical Engineering and Technology (IJMET), 8(8), 428-436.

[14]. Kavitha, V. R., & Moorthi, M. (2019). A quality of ser vice load balanced connected dominating set–stochastic diffusion search (CDS–SDS) network backbone for MANET. Computer Networks, 151, 124-131.

[15]. Khudayer, B. H., Anbar, M., Hanshi, S. M., & Wan, T. C. (2020). Efficient route discovery and link failure detection mechanisms for source routing protocol in mobile adhoc networks. IEEE Access, 8, 24019-24032.

[16]. Kumar, S. (2020). Prediction of node and link failures in mobile Ad Hoc network using hello based path recovery routing protocol. Wireless Personal Communications, 115(1), 725-744.

[17]. Mallapur, S., & Patil, S. R. (2014, April). Route stability based on demand multipath routing protocol for mobile Ad Hoc networks. In 2014 International Conference on Communication and Signal Processing (pp. 1859-1863). IEEE.

[18]. Narendra, B. Y., & Babu, C. S. (2013). Route recovery schemes for efficient MANET. International Journal of Emerging Trends in Engineering and Development, 1(3), 700-705.

[19]. Naushad, A., Abbas, G., Abbas, Z. H., & Pagourtzis, A. (2019). Novel strategies for path stability estimation under topology change using Hello messaging in MANETs. Ad Hoc Networks, 87, 76-99.

[20]. Pal, A., Dutta, P., Chakrabarti, A., Singh, J. P., & Sadhu, S. (2019). Biogeographic-based temporal prediction of link stability in mobile Ad Hoc networks. Wireless Personal Communications, 104, 217-233.

[21]. Robinson, Y. H., Julie, E. G., Saravanan, K., Kumar, R., & Son, L. H. (2019). FD-AOMDV: fault-tolerant disjoint adhoc on-demand multipath distance vector routing algorithm in mobile ad-hoc networks. Journal of Ambient Intelligence and Humanized Computing, 10, 4455-4472.

[22]. Selvi, M., & Balakrishna, R. (2019). A new method to recover the link failure and reliable data delivery in MANET. International Journal of Engineering Research & Technology, 8(1), 105-108.

[23]. Sengul, C., & Kravets, R. (2006). Bypass routing: An on-demand local recovery protocol for ad hoc networks. Ad Hoc Networks, 4(3), 380-397.

[24]. Singh, A., Kumar, R., & Pant, S. (2017, September). Advanced routing on AODV using link prediction in mobile Ad-Hoc network. In 2017 3rd International Conference on Advances in Computing, Communication & Automation (ICACCA)(Fall) (pp. 1-7). IEEE.

[25]. Singh, O., Singh, J., & Singh, R. (2018). Multi-level trust based intelligence intrusion detection system to detect the malicious nodes using elliptic curve cryptography in MANET. Cluster Computing, 21(1), 51-63.

[26]. Talawar, M. B., & Ashoka, D. V. (2020). Link failure detection in MANET: A survey. Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough: Latest Trends in AI (pp. 169-182).

[27]. Zhang, F., & Yang, G. (2020). A stable backup routing protocol for wireless Ad Hoc networks. Sensors, 20(23), 6743.

Full Article (HTML)

Pdf

Research Paper

A High-Performance Dual Classifier Based Packet Classification Engine on FPGA

Midde Adiseshaiah* , Maruvada Sailaja**

*-** Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.

Adiseshaiah, M., and Sailaja, M. (2023). A High-Performance Dual Classifier Based Packet Classification Engine on FPGA. i-manager’s Journal on Electronics Engineering, 14(1), 13-27. https://doi.org/10.26634/jele.14.1.20441

Abstract

Security is one of the key elements of real-time communication systems, especially with the developments in digital communication systems. One of the most crucial networking activities that guards against hacking and unintentional consequences on connected devices is packet classification. The router's and other linked devices' sluggish processing speeds are a problem for implementing real-time packet classification. This paper proposes a novel parallel architecture for packet classification in routers. The design that has been suggested uses the parallelism that modern hardware technologies offer to categorize several packets at once to increase throughput, lower energy usage, and drastically lower latency. The suggested design supports multiple data reads by utilizing Dual Port RAM, or DPRAM. They have low latency performance and allow for fast rate packet lookup, making them the preferable choice for stage memory. The DPRAM provides the data to the packet scheduling module. The packets are received concurrently by the two subsequent packet splitter units. The throughput of packet classification increases as a result. In this paper, a parallel packet categorization architecture using FPGA is explored for maximizing throughput and maintain allowable latency while preserving low energy consumption. The classifier has the lowest energy efficiency of 7.2nJ and less latency of 58 ns. Its throughput may reach 668 MPPS (million packets per second).

References

[1]. Abbasi, M., Vesaghati Fazel, S., & Rafiee, M. (2020). MBitCuts: Optimal bit-level cutting in geometric space packet classification. The Journal of Supercomputing, 76, 3105-3128.

[2]. Ahmadi, M., Ostadzadeh, S. A., & Wong, S. (2007, November). An analysis of rule-set databases in packet classification. In 18th Annual Workshop on Circuits, Systems and Signal Processing (ProRISC 2007) (pp. 29-30).

[3]. Aldiab, M., Garcia-Palacios, E., Crookes, D., & Sezer, S. (2008). Packet classification by multilevel cutting of the classification space: An algorithmic-architectural solution for IP packet classification in next generation networks. Journal of Computer Networks and Communications, 2008, 1-14.

[4]. Brack, S., Hager, S., & Scheuermann, B. (2015, October). JitVector: Just-in-time code generation for network packet classification. In 2015 IEEE 40th Conference on Local Computer Networks (LCN) (pp. 161-164). IEEE.

[5]. Chang, Y. K., & Chen, H. C. (2019). Fast packet classification using recursive endpoint-cutting and bucket compression on FPGA. The Computer Journal, 62(2), 198-214.

[6]. Chang, Y. K., & Chi, T. Y. (2019, July). Hash-based OpenFlow packet classification on heterogeneous system architecture. In 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 300-305). IEEE.

[7]. Chen, S., Zhong, J., Huang, T., Wei, Z., & Zhao, S. (2021). CMT: An efficient algorithm for scalable packet classification. The Computer Journal, 64(6), 941-959.

[8]. Cheng, Y. C., & Wang, P. C. (2013). Packet classification using dynamically generated decision trees. IEEE Transactions on Computers, 64(2), 582-586.

[9]. Cheng, Y., & Shi, Q. (2022). PCMIgr: A fast packet classification method based on information gain ratio. The Journal of Supercomputing, 79(7), 7414-7437.

[10]. Daly, J., Bruschi, V., Linguaglossa, L., Pontarelli, S., Rossi, D., Tollet, J., & Yourtchenko, A. (2019). Tuplemerge: Fast software packet processing for online packet classification. IEEE/ACM Transactions on Networking, 27(4), 1417-1431.

[11]. Dong, X., Qian, M., & Jiang, R. (2020). Packet classification based on the decision tree with information entropy. The Journal of Supercomputing, 76(6), 4117-4131.

[12]. Doshi, B. T., Dravida, S., Einstein, D. S., & Harshavardhana, P. (1994). Congestion Control for High Speed Packet Networks. United States Patent No. 5,291,481, Washington.

[13]. Ganegedara, T., & Prasanna, V. K. (2012, June). StrideBV: Single chip 400G+ packet classification. In 2012 IEEE 13th International Conference on High Performance Switching and Routing, (pp. 1-6). IEEE.

[14]. Gupta, P., & McKeown, N. (2001). Algorithms for packet classification. IEEE Network, 15(2), 24-32.

[15]. Guruprasad, S. P., & Chandrasekar, B. S. (2020). An efficient bridge architecture for NoC based systems on FPGA platform. In Advances in Intelligent Systems and Interactive Applications: Proceedings of the 4th International Conference on Intelligent, Interactive Systems and Applications (IISA2019) (pp. 377-383). Springer International Publishing.

[16]. Hatami, R., & Bahramgiri, H. (2019). Highperformance architecture for flow-table lookup in SDN on FPGA. The Journal of Supercomputing, 75(1), 384-399.

[17]. Huang, J., Lu, Y., & Guo, K. (2019, June). A hybrid packet classification algorithm based on hash table and geometric space partition. In 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC) (pp. 587-592). IEEE.

[18]. Irfan, M., Ullah, Z., & CC Cheung, R. (2019). Zi-CAM: A power and resource efficient binary contentaddressable memory on FPGAs. Electronics, 8(5), 584.

[19]. Irtija, N., Plusquellic, J., Tsiropoulou, E. E., Goldberg, J., Lobser, D., & Stick, D. (2023). Design and analysis of digital communication within an SoC-based control system for trapped-ion quantum computing. IEEE Transactions on Quantum Engineering, 4, 1-24.

[20]. Jamil, H., & Weng, N. (2020, May). Multibit tries packet classification with deep reinforcement learning. In 2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR) (pp. 1-6). IEEE.

[21]. Jamil, H., Yang, N., & Weng, N. (2022). Many field packet classification with decomposition and reinforcement learning. IET Networks, 11(3), 112-127.

[22]. Jiang, W., & Prasanna, V. K. (2009, August). Field-split parallel architecture for high performance multi-match packet classification using FPGAs. In Proceedings of the Twenty-First Annual Symposium on Parallelism in Algorithms and Architectures, (pp. 188-196).

[23]. Jiang, W., & Prasanna, V. K. (2011). Scalable packet classification on FPGA. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 20(9), 1668-1680.

[24]. Joseph, D. A. (2009). Packet Classification as a Fundamental Network Primitive (Doctoral Dissertation, University of California, Berkeley).

[25]. Karkvandi, N. R., Asgharian, H., Kusedghi, A., & Akbari, A. (2014). Hardware network packet classifier for high speed intrusion systems. International Journal of Engineering and Technology, 4(3).

[26]. Kennedy, A., Liu, Z., Wang, X., & Liu, B. (2009, August). Multi-engine packet classification hardware accelerator. In 2009 Proceedings of 18th International Conference on Computer Communications and Networks, (pp. 1-6). IEEE.

[27]. Khan, A. U., Chawhan, M., Suryawanshi, Y., & Kakde, S. (2017). Design of high performance packet classification architecture for communication networks. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(4), 109-115.

[28]. Li, C., Li, T., Li, J., Li, D., Yang, H., & Wang, B. (2019). Memory optimization for bit-vector-based packet classification on FPGA. Electronics, 8(10), 1159.

[29]. Li, C., Zeng, X., Song, L., & Jiang, Y. (2020a). A fast, smart packet classification algorithm based on decomposition. Journal of Control Science and Engineering, 2020, 1-11.

[30]. Li, W., Yang, T., Rottenstreich, O., Li, X., Xie, G., Li, H., & Lin, H. (2020b). Tuple space assisted packet classification with high performance on both search and update. IEEE Journal on Selected Areas in Communications, 38(7), 1555-1569.

[31]. Li, Y., Wang, J., Chen, X., & Wu, J. (2021). ITOC: An improved trie-based algorithm for online packet classification. Applied Sciences, 11(18), 8693.

[32]. Li, Y., Wang, J., Chen, X., & Wu, J. (2022). SplitTrie: A fast update packet classification algorithm with trie splitting. Electronics, 11(2), 199.

[33]. Lin, H. T., & Wang, P. C. (2023). TCAM-based packet classification for many-field rules of SDNs. Computer Communications, 203, 89-98.

[34]. Neves, F., Soares, S., & Assuncao, P. A. A. (2018). Optimal voice packet classification for enhanced VoIP over priority-enabled networks. Journal of Communications and Networks, 20(6), 554-564.

[35]. Ponnuswamy, A., & Devi, M. (2022). Range enhanced packet classification to improve computational performance on field programmable gate array. International Journal of Electrical & Computer Engineering, 12(6), 5840-5847.

[36]. Qu, Y. R., & Prasanna, V. K. (2015). Highperformance and dynamically updatable packet classification engine on FPGA. IEEE Transactions on Parallel and Distributed Systems, 27(1), 197-209.

[37]. Reviriego, P., Ullah, A., & Pontarelli, S. (2019). PRTCAM: Efficient TCAM emulation on Xilinx FPGAs using partial reconfiguration. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 27(8), 1952-1956.

[38]. Sanny, A., Ganegedara, T., & Prasanna, V. K. (2013, May). A comparison of ruleset feature independent packet classification engines on FPGA. In 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, (pp. 124-133). IEEE.

[39]. Shen, T., Xie, G., Wang, X., Li, Z., Zhang, X., Zhang, P., & Zhang, D. (2019). RVH: Range-vector hash for fast online packet classification. arXiv preprint arXiv:1909.07159.

[40]. Turčaník, M. (2015, May). Packet filtering by artificial neural network. In International Conference on Military Technologies (ICMT) 2015 (pp. 1-4). IEEE.

[41]. Wee, J., & Pak, W. (2018). Integrated packet classification to support multiple security policies for robust and low delay V2X services. Mobile Information Systems, 2018, 1-10.

[42]. Wee, J., Choi, J. G., & Pak, W. (2019). Wildcard fieldsbased partitioning for fast and scalable packet classification in vehicle-to-everything. Sensors, 19(11), 2563.

[43]. Yazdinejad, A., Parizi, R. M., Bohlooli, A., Dehghantanha, A., & Choo, K. R. (2020). A highperformance framework for a network programmable packet processor using P4 and FPGA hardware. Journal of Network and Computer Applications, 156, 102564.

[44]. Yu, W., Sivakumar, S., & Pao, D. (2019). Pseudo-TCAM: SRAM-based architecture for packet classification in one memory access. IEEE Networking Letters, 1(2), 89-92.

[45]. Zane, F., Narlikar, G., & Basu, A. (2003, March). CoolCAMs: Power-efficient TCAMs for forwarding engines. In IEEE INFOCOM 2003: Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No. 03CH37428), 1, 42-52. IEEE.

[46]. Zhong, J., & Chen, S. (2021). Efficient multi-category packet classification using TC AM. Computer Communications, 169, 1-10.

Full Article (HTML)

Pdf

Research Paper

An Overview of Smart Grid Smart Meters with Fault Detection

Shawanti Roy* , Jayanti Sarker, Sayan Paramanik*, Krishna Sarker****

, Department of Electrical Engineering, St. Thomas' College of Engineering & Technology, West Bengal, India.

Department of Electrical Engineering, Techno Main Salt Lake, West Bengal, India.

Department of Servicing, Statcon Electronics India Ltd, Noida, India.

Roy, S., Sarker, J., Paramanik, S., and Sarker, K. (2023). An Overview of Smart Grid Smart Meters with Fault Detection. i-manager’s Journal on Electronics Engineering, 14(1), 28-38. https://doi.org/10.26634/jele.14.1.20196

Abstract

Smart meters (SM) arrangement play an essential role in Smart Grid (SG), serving as advanced energy meters crucial for monitoring and managing energy consumption. This paper provides an in-depth exploration of the significance, functions, and technological aspects of smart meters within the context of the SG. Smart meters acquire data from endusers' load devices, offering a comprehensive measurement of consumers' energy consumption. The obtained information is then communicated to utility companies and system operators, facilitating efficient energy management. The integration of control devices and a number of sensors, supported by a dedicated communication infrastructure, enhances the reliability and functionality of smart meters. The paper summarizes hardware model key facets and functions of smart meters, offering a comprehensive view of their capabilities.

References

[1]. Ashna, K., & George, S. N. (2013, March). GSM based automatic energy meter reading system with instant billing. In 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s) (pp. 65-72). IEEE.

[2]. BC Hydro. (2011). Smart Metering and Infrastructure Program Business Case. BC Hydro, Burnaby, Canada.

[3]. GRID, S. (2013). Smart Grid Research: Control Systems - IEEE Vision for Smart Grid Controls: 2030 and Beyond Reference Model. IEEE Standards Association, USA.

[4]. Hao, Q., & Song, Z. (2005). The status and development of the intelligent automatic meter reading system. China Science and Technology Information, (19), 72.

[5]. Jägerlind, C. (2006). Improvements for the automatic meter reading process in electricity distribution companies (Master thesis, Department of Industrial Info and Control Systems, Royal Institute of Technology, Stockholm, Sweden).

[6]. Kesav, O. H., & Rahim, B. A. (2012). Automated wireless meter reading system for monitoring and controlling power consumption. International Journal of Recent Technology and Engineering, 1(2), 66-69.

[7]. Luan, W., Sharp, D., & Lancashire, S. (2010). Smart grid communication capacity planning for power utilities. In Proceedings IEEE Power Energy Transmission and Distribution Conference and Exposition (pp. 1-4).

[8]. Luan, W., Sharp, D., & LaRoy, S. (2013, July). Data traffic analysis of utility smart metering network. In 2013 IEEE Power & Energy Society General Meeting (pp. 1-4). IEEE.

[9]. Maitra, S. (2008, October). Embedded Energy Meter-A new concept to measure the energy consumed by a consumer and to pay the bill. In 2008 Joint International Conference on Power System Technology and IEEE Power India Conference (pp. 1-8). IEEE.

[10]. Malik, N. S., Kupzog, F., & Sonntag, M. (2010, October). An approach to secure mobile agents in automatic meter reading. In 2010 International Conference on Cyberworlds (pp. 187-193). IEEE.

[11]. Prapasawad, C., Pornprasitpol, K., & Pora, W. (2012, December). Development of an automatic meter reading system based on ZigBee PRO Smart Energy Profile IEEE 802.15. 4 standard. In 2012 IEEE International Conference on Electron Devices and Solid State Circuit (EDSSC) (pp. 1-3). IEEE.

[12]. Prashanthi, G. L., & Prasad, K. V. (2014). Wireless power meter monitoring with power theft detection and intimation system using GSM and Zigbee networks. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), 9(6), 4-8.

[13]. Zheng, G., & Zhang, Z. (2011, December). Intelligent wireless electric power management and control system based on ZigBee technology. In Proceedings 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE) (pp. 1120-1124). IEEE.

Full Article (HTML)

Pdf

Research Paper

Shopping Trolley for Automated Billing System with Pick and Place Mechanism using Image Processing

Deepti S. Hegde* , Divyashree M., K. S. Tanushree, Koushik M., Nithin H. V.

*-***** Department of Electronics & Communication Engineering, PES Institute of Technology and Management, India.

Hegde, D. S., Divyashree, M., Tanushree, K. S., Koushik, M., and Nithin, H. V. (2023). Shopping Trolley for Automated Billing System with Pick and Place Mechanism using Image Processing. i-manager’s Journal on Electronics Engineering, 14(1), 39-44. https://doi.org/10.26634/jele.14.1.20454

Abstract

In the realm of individual E-business transactions, many people engage in purchasing various items from markets and shopping centers to fulfill their basic needs. Despite the numerous challenges faced by customers, a prevalent issue is the time-consuming process of waiting in line for billing, even when the purchase involves only a couple of items. This traditional method of queuing up, handling cash transactions, and the average time spent on each customer becomes particularly burdensome in crowded grocery stores and shopping malls .Customers, especially those inclined towards embracing new technologies, are eager for innovations that streamline the billing process, reducing waiting times and overall transaction duration. The primary objective is to meet the essential needs of customers and, importantly, alleviate the time spent on the billing process. The proposal is to transition from the conventional waiting-in-line approach to a system where the billing is completed within the shopping trolley itself. This paper introduces a technological solution that facilitates a seamless shopping experience, utilizing Raspberry Pi and other advancements. The proposed system is wellsuited for implementation in general shopping centers such as supermarkets, D-mart, Big Bazaar, and Walmart. The technology aims to significantly reduce time consumption, offering an enhanced shopping experience for customers.

References

[1]. Kowshika, S., Varshini, G. M., Megha, V., & Lakshmi, K. (2021, March). IoT based smart shopping trolley with mobile cart application. In 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) 1, 1186-1189. IEEE.

[2]. Maner, T., Benake, G., Bhosale, M., Mali, S., & Pukale, S. (2019). Smart trolley for shopping malls. International Journal of Research in Engineering, Science and Management, 2(4), 111-115.

[3]. Sarala, T., Sudha, Y. A., Sindhu, K. V., Suryakiran, C. H., & Nithin, B. N. (2018, May). Smart electronic trolley for shopping mall. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2422-2427). IEEE.

[4]. Shahroz, M., Mushtaq, M. F., Ahmad, M., Ullah, S., Mehmood, A., & Choi, G. S. (2020). IoT-based smart shopping cart using radio frequency identification. IEEE Access, 8, 68426-68438.

[5]. Shankar, S. K., Balasubramani, S., Basha, S. A., Ahamed, S. A., & Reddy, N. S. K. (2021, April). Smart trolley for smart shopping with an advance billing system using IoT. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 390-394). IEEE.

Full Article (HTML)

Pdf

Research Paper

Flexing Frontiers: Pioneering Advances in Biosensors for Instant Health Insights

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

* Department of ECE, Indira Institute of Technology and Sciences, Markapur, AP, India.

Senior Software Engineer, Tech Lead at Medallia, Austin, Texas, United States.

* Site Reliability Engineer II (SRE) at Dell EMC | CKAD | AWS CSAA.

**** Senior Data Engineer at CVS Health Centre, Dallas, Texas, United States.

Eswaran, U., Eswaran, V., Murali, K., and Eswaran, V. (2023). Flexing Frontiers: Pioneering Advances in Biosensors for Instant Health Insights. i-manager’s Journal on Electronics Engineering, 14(1), 45-55. https://doi.org/10.26634/jele.14.1.20191

Abstract

Biosensors that incorporate a biological recognition element with a transducer offer a rapid, simple approach for selective detection of analytes. However, conventional rigid biosensors have limited capabilities for continuous on-body monitoring applications. The emergence of flexible electronics has opened new possibilities for developing conformal biosensing platforms that can intimately interface with soft, curvilinear surfaces like skin for non-invasive health tracking. This review covers the fundamentals of biosensor technologies and recent progress in flexible biosensors aimed at pointof- care diagnostics and medical monitoring. Key developments in materials, electrochemical sensing interfaces, microfluidics integration and wireless connectivity are highlighted. Examples of flexible biosensors for metabolites, electrolytes, proteins and nucleic acids are presented along with capabilities and limitations. Current challenges include improving biocompatibility, correlation with standard assays, wireless range, manufacturability, and regulatory requirements. Further research on translating flexible biosensor devices from initial proof-of-concepts to validated medical products is warranted. Overall, flexible biosensors hold immense promise to enable continuous personalized health monitoring.

References

[1]. Anand, S., & Eswaran, D.U.G.P.G. (2018). Design and Analysis of high sensitive microcantilever based biosensor for ca 15-3 biomarker detection. Journal of Applied Science and Computations, 5(7), 682-698.

[2]. Arugula, M. A., & Simonian, A. (2014). Novel trends in affinity biosensors: current challenges and perspectives. Measurement Science and Technology, 25(3), 032001.

[3]. Bandodkar, A. J., Hung, V. W., Jia, W., Valdés-Ramírez, G., Windmiller, J. R., Martinez, A. G., & Wang, J. (2013). Tattoo-based potentiometric ion-selective sensors for epidermal pH monitoring. Analyst, 138(1), 123-128.

[4]. Bandodkar, A. J., Jia, W., Yardımcı, C., Wang, X., Ramirez, J., & Wang, J. (2015). Tattoo-based noninvasive glucose monitoring: a proof-of-concept study. Analytical Chemistry, 87(1), 394-398.

[5]. Bandodkar, A. J., & Wang, J. (2014). Non-invasive wearable electrochemical sensors: A review. Trends in Biotechnology, 32(7), 363-371.

[6]. Eswaran, U., Eswaran, V., & Sudharshan, V. B. (2013, April). Human like biosensor disease simulator, disease analyzer and drug delivery system. In 2013 IEEE Conference on Information & Communication Technologies (pp. 1033-1038). IEEE.

[7]. Eswaran, U., & Eswaran, V. (2012). Neural network architecture based software reference model for automated disease classification and detection. UNIASCIT, 2(1), 183-192.

[8]. Eswaran, V., & Ushaa, S M. (2012). Modeling and simulation of nanosensor arrays for automated disease detection and drug delivery unit. International Journal of Advances in Engineering & Technology, 2(1), 564-577.

[9]. Eswaran, U., Madhavilatha, M., & Ganji, M. R. (2006). A proposal for design of hybrid multi-functional implantable biochip using bio-intelligent expert system. In The 2006 International Conference on Artificial Intelligence (ICAI'06) (pp. 26-29).

[10]. Kim, J., Jeerapan, I., Imani, S., Cho, T. N., Bandodkar, A., Cinti, S., & Wang, J. (2016). Noninvasive alcohol monitoring using a wearable tattoo-based iontophoretic-biosensing system. ACS Sensors, 1(8), 1011-1019.

[11]. Huang, X., Liu, Y., Hwang, S. W., Kang, S. K., Patnaik, D., Cortes, J. F., & Rogers, J. A. (2014). Biodegradable materials for multilayer transient printed circuit boards. Advanced Materials, 26(43), 7371-7377.

[12]. Mostafalu, P., Lenk, W., Dokmeci, M. R., Ziaie, B., Khademhosseini, A., & Sonkusale, S. R. (2015). Wireless flexible smart bandage for continuous monitoring of wound oxygenation. IEEE Transactions on Biomedical Circuits and Systems, 9(5), 670-677.

[13]. Tao, H., Marelli, B., Yang, M., An, B., Onses, M. S., Rogers, J. A., & Omenetto, F. G. (2015). Inkjet printing of regenerated silk fibroin: From printable forms to printable functions. Advanced Materials, 27(29), 4273-4279.

[14]. Yetisen, A. K., Akram, M. S., & Lowe, C. R. (2013). Paper based microfluidic point-of-care diagnostic devices. Lab on a Chip, 13(12), 2210-2251.

[15]. Yetisen, A. K., Martinez Hurtado, J. L., Ünal, B., Khademhosseini, A., & Butt, H. (2018). Wearables in medicine. Advanced Materials, 30(33), 1706910.

i-manager's Journal on Electronics Engineering (JELE)

Current Issue Vol. 15 Issue 1

Most Read

Most Cited

Volume 14 Issue 1 October - December 2023

An Intelligent Base Link Recovery Management and Path Stability in Clustered Randomly Distributed Mobile Ad Hoc Networks

Anil Kumar Bandani*

Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.

Bandani, A. K. (2023). An Intelligent Base Link Recovery Management and Path Stability in Clustered Randomly Distributed Mobile Ad Hoc Networks. i-manager’s Journal on Electronics Engineering, 14(1), 1-12. https://doi.org/10.26634/jele.14.1.20469

Abstract

References

Full Article (HTML)

Pdf

A High-Performance Dual Classifier Based Packet Classification Engine on FPGA

Midde Adiseshaiah* , Maruvada Sailaja**

*-** Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.

Adiseshaiah, M., and Sailaja, M. (2023). A High-Performance Dual Classifier Based Packet Classification Engine on FPGA. i-manager’s Journal on Electronics Engineering, 14(1), 13-27. https://doi.org/10.26634/jele.14.1.20441

Abstract

References

Full Article (HTML)

Pdf

An Overview of Smart Grid Smart Meters with Fault Detection

Shawanti Roy* , Jayanti Sarker**, Sayan Paramanik***, Krishna Sarker****

*,**** Department of Electrical Engineering, St. Thomas' College of Engineering & Technology, West Bengal, India. ** Department of Electrical Engineering, Techno Main Salt Lake, West Bengal, India. *** Department of Servicing, Statcon Electronics India Ltd, Noida, India.

Roy, S., Sarker, J., Paramanik, S., and Sarker, K. (2023). An Overview of Smart Grid Smart Meters with Fault Detection. i-manager’s Journal on Electronics Engineering, 14(1), 28-38. https://doi.org/10.26634/jele.14.1.20196

Abstract

References

Full Article (HTML)

Pdf

Shopping Trolley for Automated Billing System with Pick and Place Mechanism using Image Processing

Deepti S. Hegde* , Divyashree M.**, K. S. Tanushree***, Koushik M.****, Nithin H. V.*****

*-***** Department of Electronics & Communication Engineering, PES Institute of Technology and Management, India.

Hegde, D. S., Divyashree, M., Tanushree, K. S., Koushik, M., and Nithin, H. V. (2023). Shopping Trolley for Automated Billing System with Pick and Place Mechanism using Image Processing. i-manager’s Journal on Electronics Engineering, 14(1), 39-44. https://doi.org/10.26634/jele.14.1.20454

Abstract

References

Full Article (HTML)

Pdf

Flexing Frontiers: Pioneering Advances in Biosensors for Instant Health Insights

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

Eswaran, U., Eswaran, V., Murali, K., and Eswaran, V. (2023). Flexing Frontiers: Pioneering Advances in Biosensors for Instant Health Insights. i-manager’s Journal on Electronics Engineering, 14(1), 45-55. https://doi.org/10.26634/jele.14.1.20191

Abstract

References

Full Article (HTML)

Pdf

Shawanti Roy* , Jayanti Sarker, Sayan Paramanik*, Krishna Sarker****

, Department of Electrical Engineering, St. Thomas' College of Engineering & Technology, West Bengal, India.

Department of Electrical Engineering, Techno Main Salt Lake, West Bengal, India.

Department of Servicing, Statcon Electronics India Ltd, Noida, India.

Deepti S. Hegde* , Divyashree M., K. S. Tanushree, Koushik M., Nithin H. V.

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