All Management Events

Wireless Sensor Networks (WSNs) and their derivatives such as Internet of Things (IoT) and the Internet of Industrial Things (IIOT) are no longer confined to traditional applications such as smart homes and transportation. It has already marked its presence in Industrial applications and extended even to wildlife conservation. The impending concerns associated with such wireless networks are their privacy and security. One such menace afflicting wildlife is cyber poaching. Taking this into consideration, Dr Manjula R, Assistant Professor, and her student Mr Tejodbhav Koduru, from the Department of Computer Science and Engineering, have published a paper, “Position-independent and Section-based Source Location Privacy Protection in WSN” in the journal, ‘IEEE Transactions on Industrial Informatics’ having an Impact Factor of 10.215. The article is published in collaboration with Ms Florence Mukamanzi from the University of Rwanda, Rwanda, Africa and Prof Raja Datta from IIT Kharagpur, West Bengal, India.

The sensors collect data about these endangered animals and report it to the central controller which is connected to the Internet. Over the period, the hunters have also evolved and are equipped with smart devices that help them to easily locate the animal with minimal effort. In the simplest form, the attacker or the hunter just eavesdrops on the communication links to know the message’s origin and backtrack to the source of information. Once the source of information i.e., the location is identified then the endangered animal is captured. To overcome such backtracking issues, their work aims at delaying the information disclosure to the attacker through traffic obfuscation.

Although it may not act as an ultimate solution, the research work focuses on contextual privacy, unlike traditional content privacy. The attacker collects only contextual information such as packet rate, traffic intensities, routing paths, time correlations etc., to determine the source of information. The work focuses on mitigating traffic correlation i.e., hop-by-hop backtrack attacks and protecting the assets that are monitored using WSNs. The performance metrics include safety period and network lifetime amongst other metrics. The proposed random-walk-based routing solution achieves an improved safety period and network lifetime compared to the existing schemes. The work was simulated using a custom-designed simulation tool and was validated with the numerical results obtained using mathematical models.

The proposed solutions could be seamlessly used in monitoring endangered animals such as rhinoceros or in military applications to track soldiers. In addition, the routing algorithm could also be used in delaying tolerant networks to improve the efficiency and lifetime of the network, in designing the random trajectories of bio-nano bots for intrabody monitoring etc. Their future research plan includes developing improved source location privacy preservation techniques for terrestrial and underwater wireless sensor networks using the benefits of Artificial Intelligence and Machine Learning. In addition, they also aims at the development of data collection and routing protocols for intrabody nanonetwork operating at tera hertz frequencies— next-generation networks, envisioned networks.

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The Department of Electronics and Communication Engineering is glad to announce that Dr V Udaya Sankar, Assistant Professor has published the patent (App no. 202141056542), ‘A system and method with Matrix enabled Road distress classification with reduced computational complexity and reduced memory requirements’, in collaboration with Dr Siva Sankar Yellampalli and Ms Gayathri Lakshmi Chinthakrindi.

This work has applications related to visual inspection systems. While this research considers road crack detection application, the same can be extended to various applications such as leaf disease prediction, covid prediction etc. This invention provides an alternative approach instead of using traditional machine learning algorithms that has less computational complexity as opposed to deep neural networks that take more complex operations. This method will also lead to further research in matrix-based machine learning applications related to image processing and image classification.

The research team is planning to collaborate with Efftronics Systems Pvt ltd. for PCB defect detection and discussions are initiated with some start-ups for visual inspection applications. Their future research plan is to look deeper into these algorithms in combination with some of the deep neural networks to reduce computational complexity. In addition, Dr Udaya Sankar is also looking forward to establishing his own start-up in the incubation centre soon.

Abstract of the Research

A method for image classification is provided, wherein, the pre-processed gray scale image is first sent to the feature extraction block, and the said feature extraction block considers every image as a matrix and computes the metrics for features, viz., 1) EMD distance which is popularly known as Wassertain distance/Earth movers distance and is computed with respect to block image and 2) Frobenius Norm which is the square root of the sum of the absolute squares of its elements and finally, 3) Condition Number, which measures the ratio of the maximum relative stretching to the maximum relative shrinking that matrix does to any non-zero vectors. This method is preferred over the existing methods due to the drastic reduction in computational complexities and, utilizing lesser memory. Also, with this method and system, the communicational complexities too are significantly reduced and also, and the results yielded are far more significantly accurate.

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The Department of Electrical and Electronics Engineering is proud to announce that the estimable book of Springer Nature, ‘Soft Computing: Theories and Applications’ has featured three publications by Dr Tousif Khan, Assistant Professor. His publications are part of the book series, Lecture Notes in Networks and Systems (LNNS), Volume 425. The book stimulates discussions on various emerging trends, innovations, practices, and applications in the field of soft computing, ranging from data mining, prediction analysis, control systems, image processing, health care, medicine, agriculture analysis, supply chain management and cryptanalysis etc.

The first chapter titled “Design of Fast Battery Charging Circuit for Li-Ion Batteries” was co-authored by Dr Khan along with the final year EEE students; P Manoj Sai, G Nithin Sai, Puja Manohari, and P Gopi Krishna. In this chapter, a battery charging topology has been designed and developed for the fast charging of Li-ion batteries. The charging circuitry comprises a Proportional-Integral-Derivative (PID) controlled DC-DC buck converter system for reducing the charging time in Li-ion batteries. Battery charging time depends on several factors and the charging current is one of the major criteria. In this work, the buck converter is used to attain a high charging current, besides providing the regulated voltage to the battery. Initially, the AC supply obtained from the mains is converted to DC using an AC-DC rectifier. The rectifier output is further fed to the buck converter to increase the output current of the circuit. The buck converter reduces the output voltage and increases through it.

The circuit parameters are designed by considering the commercially available Lithium-ion battery LIR18650 as the load with a capacity of 2600 mAh and a nominal voltage of 3.7 V. The considered battery requires a standard charging current of 0.5 A, however, the circuit is designed to provide the rapid charge current of 1.3 A as the output by using the buck converter. The converter is operated in continuous conduction mode and helps in charging the battery under constant current mode. To avoid interruption to the charging current when there is a simultaneous discharge of the battery, further improvement in the closed-loop control action is made by employing a PID controller. Extensive simulation work has been conducted using the MATLAB/Simulink tool. The results obtained suggest there is a significant reduction in charging time under different conditions compared to the conventional method of battery charging.

In the chapter, “Global Horizontal Solar Irradiance Forecasting Based on Data-Driven and Feature Selection Techniques”, Dr Khan discusses the need for an accurate solar prediction. It has become an essential part of the renewable energy sector with the rapidly expanding infrastructure of the solar energy system. Over the past decade, various machine learning (ML) algorithms have been used for this purpose. Although the prediction of solar irradiance forecasting has been discussed in many studies, the use of meta-heuristic optimization techniques has not been explored to select features for the forecasting model. This study comprises two meta-heuristic optimization techniques such as simulated annealing (SA) and ant colony optimization (ACO) for feature selection. The results show that feature selection based on meta-heuristics gave better results than models without feature selection.

Amongst the two optimization methods, ACO outperformed SA with some exceptions. For SA, the declining order of performance observed is extreme gradient boosting (XGBoost), random forest (RF), multilayer perceptron (MLP), decision tree (DT) and support vector regression (SVR), while for ACO the declining order observed is XGBoost followed by MLP, RF, DT and SVR. This manuscript indicates the potential capability of meta-heuristic techniques for accurate prediction of global horizontal irradiance (GHI) given a wide array of feature variables.

In yet another chapter, “Exhaustive Search Approach to Place PV in Radial Distribution Network for Power Loss Minimization”, co-authored with Dr Shubh Lakshmi, Assistant Professor, and the final year students; P Manoj Sai and M Dhana Sai Baji from the Department of Electrical and Electronics Engineering, an exhaustive search approach to determine the best location and size of PV placement for power loss minimization of radial distribution networks is discussed. In this approach, the network power loss is determined by placing PV in each location, one at a time, and the size of PV in the same location is varied between 10 and 300 kW with an increment of 10 kW.

The combination of location and size of PV which provides the minimum network power loss can be the best location and size of PV for power loss minimization of radial distribution networks. The forward–backward sweep load flow algorithm is used to incorporate the PV model. The 33-bus radial distribution network is used to demonstrate the approach. The simulation results show that the placement of a suitable size of PV in some specific locations significantly reduces the network power loss.

Publishing the latest advancements in Networks and Systems, The LNNS series will serve as an edifying read for all the researchers and scientists across the globe. Volumes published in LNNS give a deep insight into all aspects and subfields of, as well as new challenges in, Networks and Systems. The series encompasses the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them.

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Yoga essentially refers to exploring one’s highest self through the unison of mind and body. The 3000-year-old tradition of yoga is hailed worldwide as a panacea to all human ailments. Yoga promotes holistic well-being through a combination of mindful and muscular practices. As the society is caught in an endless chase for worldly happiness, the philosophy of yoga gains immense importance to regulate and moderate human lives. It reveals the magic of remaining happy and tranquil in being oneself. The ultimate goal of yoga is to reach a sustained state of pure awareness.

The Department of Student Affairs is celebrating International Yoga Day 2022 to pay respect to this invaluable gift of India’s ancient tradition and to illuminate students on the need to make yoga an essential part of their day-to-day lives.

Date: June 21, 2022

Time: 3.30 pm

Venue: ALC 4

The resource persons for the day are Mr Venkata Krishna Kumar S, Associate Professor, Department of Architecture, School of Planning and Architecture-Vijayawada and Mr Ram Pavan, Broadcast Engineer, Doordarshan Kendra, Vijayawada. Mr Venkata Krishna Kumar is a practitioner of Heartfulness for 19 years and is also a certified trainer of Heartfulness since 2021. Mr Ram Pavan has also been practising Heartfulness yoga for the past 15 years and is serving as a trainer with Heartfulness institute with a mission to uplift the standards of life.

Join the celebration to disseminate the power of #yogaforself and #yogaforhumanity.

Stories of innovation from SRM AP are not something new! M Tanveer, G Sai Venkat, and Divyansh Awasthi, three vibrant students from the Department of Mechanical Engineering at SRM University-AP, have built a multi-utility buggy car as a part of their final year project work.

The students initially planned to build a completely new multi-utility vehicle. Later they slightly modified the idea to make a buggy car from scrap materials, i.e. by gathering the efficiently working components from old and unused cars and making them compatible with the chassis.

Motivated by their admiration for the automotive industry, students had great enthusiasm for building the buggy car. They had an urge to convert theoretical knowledge gained from classrooms into practical working machines. Consequently, the trio developed an off-road diesel buggy to show their prowess in the automobile industry, especially on and off-road cars. A Diesel motor is utilised, which is competent in conveying a speed of 50 kilometres per hour.

The chassis of the buggy is designed and manufactured by the students themselves. The spare parts and connection are from the TATA ZIP car model and incorporated into the buggy fitting to the design. The necessary fitting and binding of spare parts are done according to the driving comfort.

The students expressed their gratitude for the support of their project guide Dr Jasvinder Singh, co-guide Prof Venkata N Nori, and HOD Prof Prakash Jadhav. “We received constant mentorship and guidance throughout our project tenure from them”, said the students. “Whenever we were stuck or were not able to solve a particular problem, or at times were mentally stressed and worn out, our professors gave us proper guidance to warded off our problems and stress, ” they added.