Research News

Agricultural development is one of the powerful tools to boost the economy of any developing country. The recent advancement of IoT-based smart agriculture systems helps to achieve more productivity with relatively less overhead. The Department of Computer Science and Engineering is glad to announce that their faculty; Dr Sobin CC, Associate Professor; Dr Sonam Maurya, Assistant Professor; and Dr Amit Kumar Singh, Assistant Professor; have published a patent titled “Smart Agriculture System using Delay Tolerant Internet of Things” (Application No. 20224102799), a framework for smart agricultural applications using Delay Tolerant Internet of Things (DT-IoT) which can handle the issues related to disruptions in network connectivity.

The inherent limitations of IoT-based smart agriculture systems majorly in terms of resource constraints, frequent network disconnections and vulnerability to many attacks may affect their advantages over the traditional systems. The application using DT-IoT, with access to greater network connectivity can deliver relevant data in real-time. Furthermore, the stored data can be processed and analysed to help farmers in making critical decisions related to their farm filed. Hence, their innovation focuses on designing and developing a prototype for a smart agricultural application using the Internet of Things (IoT).

One of the simplest outcomes of providing smart agricultural solutions for remote villages in India will be greater support to the farmers to improve their productivity and better decision-making in cultivation. But advanced technologies need Internet connectivity in the field to function, which is still a dream in many of the remote villages in India. The lack of proper communication facilities faces off the application of IoT networks. This fact has motivated them to propose a smart agricultural system to work on agricultural application issues using delay-tolerant characteristics. The use of delay-tolerant features in traditional IoT provides a solution for smart agriculture which can handle issues related to disruptions in connection to improve communications.

Another important aspect is that many of the applications, including IoT/Sensor networks, are either simulation-based or experimental. A very few of the applications are developed and implemented in the real-time field for the benefit of farmers in remote villages. In most of the remote villages in India, most of the farmers are poor, many of them are even without primary school education and they rely mostly on traditional agricultural practices which they received from their previous generations. Therefore, their study proposed to test and implement the smart agricultural system with real-time automated solutions related to irrigation, controlled fertilisation, cultivation, production quality, quantity, crop health etc. using IoT with delay-tolerant support. They are also in the process of collaborating with academia and industry to execute this project.

Social Implications

• Proposed smart agriculture system will assist in real-time monitoring of farm field conditions, like irrigation, soil quality, and nutrient deficiency.
• It provides support to farmers to improve their productivity and decision-making in crop cultivation.
• The proposed system will provide optimization in terms of seed selection, resource utilisation, planning cultivation, marketing, harvest quality, etc., using Machine Learning techniques.
• Agricultural field data analysis (for data collected by the large group of sensors) and its visualisation.
• Weather prediction (for better planning).
• Price prediction (for better marketing strategies).

Fig 1: Main components of the proposed Smart Agriculture System

Fig 2: Illustration of 3-level architecture implementation in the Smart Agriculture System

The Department of chemistry has always been a dynamic space for innovative and inspiring research. Recently, Assistant Professor Dr Nimai Mishra published his fifteenth research paper from SRM university-AP as a corresponding author. The paper is titled Post-synthesis Treatment with Lead Bromide for Obtaining Near Unity Photoluminescence Quantum Yield and Ultra-Stable Amine Free CsPbBr 3 Perovskite Nanocrystal and is published in the Q1 journal, The Journal of Physical Chemistry C with an impact factor of 4.2. The research group is comprised of Dr Mishra’s PhD students Mr Syed Akhil, Dr V G Vasavi Dutt, and Mr Rahul Singh. This is the group’s fifth consecutive article published in the American Chemical Society.

About the article

The article reports Ultra-Stable and Near Unity Photoluminescence Quantum Yield Amine Free CsPbBr 3 Perovskite Nanocrystal Post-synthesis Treatment with Lead Bromide. Herein, the researchers have introduced a simple lead bromide (PbBr 2 ) post-treatment process to achieve the near-unity PLQY (>95 %) in amine-free CsPbBr 3 PNCs. Furthermore, PbBr 2 treatment enables these materials to drastically improve stability in different environmental conditions (polar solvents, light, and heat). In addition, a green-emitting down- converted light-emitting diode was fabricated using PbBr 2 treated amine-free CsPbBr 3 PNCs, which shows its considerable prospects for display applications. Thus, the results of the research will promote these PbBr 2 treated amine-free inorganic perovskite nanocrystals for commercial development in optoelectronic applications.

Explanation of the research

Cesium lead halide perovskite nanocrystals (PNCs) have been the flourishing area of research in the field of photovoltaic and optoelectronic applications because of their excellent optical and electronic properties. Mainly, cesium lead bromide (CsPbBr 3 ) NCs with bright green photoluminescence (PL) and narrow full-width at half-maximum (FWHM) of < 25 nm is the most desirable for television displays and green-emitting LEDs. Improving the photoluminescence quantum yields (PLQYs) and optimizing the stability have been challenging tasks to promote cesium lead halide (CsPbX3; X=Cl, Br and I) perovskite nanocrystals (PNCs) for real optoelectronic applications. In recent years, the amine- free synthesis route has become an option for making stable CsPbX 3 PNCs.

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By publishing two papers in well-acclaimed research journals, Assistant Professor Dr M Durga Prakash of the Department of Electronics and Communication Engineering is expanding the possibilities of his research domain through innovative ideas. The first paper was published in the International Journal of Electronics Letters, an internationally renowned peer-reviewed rapid communication journal. It is titled Design of approximate reverse carry select adder using RCPA and has an impact factor of 1.5.

Abstract

An approximate carry select adder (CSLA) with reverse carry propagation (RCSLA) is shown in this work. This RCSLA was designed with a reverse carry propagate full adder (RCPFA). In the RCPFA structure, the carry signal propagates in the reverse direction, that is, from MSB part to LSB part, then the carry input has greater importance compared to the output carry. Three types of implementations were designed in RCPFA based on the design parameters. This method was applied to RCA & CSLA to design other types of approximate adders. These designs and simulations were done in CADENCE Software tool with 45 nm COMS technology. The design parameters of the three CSLA implementations with RCPFA are compared with the existing CSLA adders.

The other paper, A highly sensitive graphene-based field-effect transistor for detection of myoglobin, has been published in the Silicon Journal, an international and interdisciplinary journal, with an impact factor of 2.67.

Abstract

Biomedical applications adapt Nanotechnology-based transistors as a key component in the biosensors for diagnosing life-threatening diseases like Covid-19, Acute Myocardial Infarction (AMI), etc. The proposed work introduces a new biosensor, based on the Graphene Field Effect Transistor (GFET), which is used in the diagnosis of Myoglobin (Mb) in human blood. Graphene-based biosensors are faster, more precise, stronger, and more trustworthy. A GFET is created in this study for the detection of myoglobin biomarker at various low concentrations. Because graphene is sensitive to a variety of biomarker materials, it can be employed as a gate material. When constructed Graphene FET is applied to myoglobin antigens, it has a significant response. The detection level for myoglobin is roughly 30 fg/ml, which is quite high. The electrical behaviour of the GFET-based biosensor in detecting myoglobin marker is ideal for Lab-on-Chip platforms and Cardiac Point-of-Care Diagnosis.

Dr Pankaj Pathak from the Department of Environmental Science has been keenly involved in research studies involving solid waste management and the effective conversion of wastes to energy. Her latest research publication ‘A comprehensive review on integrative approach for sustainable management of plastic waste and its associated externalities’ in the journal Science of the Total Environment (Impact Factor: 10.973) proposes enhanced solution for the sustainable management of plastic wastes. The article was published in collaboration with her PhD Scholar MSSR Tejaswini, Prof Sreeram Ramakrishna from the Centre for Nanofibers and Nanotechnology, National University of Singapore and Dr P Sankar Ganesh from BITS Pilani, Hyderabad.

Abstract of the Research

The management of post-consumer discarded plastic wastes (PCPW) creates new challenges in developing countries due to the lack of amenities, technological interventions, and associated negative environmental externalities. The fate of untreated recyclable and non-recyclable plastic wastes lies in open dumping along with other solid waste, and improper management leads to environmental externalities such as pollution, global climate change, and health issues. Additionally, open dumping upsurges the emerging microplastics and nano plastics (MNPs) contaminants. The externalities depend on the waste generating sources (household, industries, commercial), waste composition, and its characteristics. However, urban mining can minimize environmental externalities where waste plastics can convert into potential anthropogenic resources and also helps in achieving the target of sustainable development goals (SDGs 11 & 12). Moreover, various treatment technologies that help in the sustainable utilization of plastic wastes are extensively reviewed in this study and evaluate the costs benefits arising during various stages of treating plastic waste through recycling (R), incineration (I), and landfilling (L). The recycling of plastic waste has demonstrated the lowest impact on global warming potential (GWP) and total energy use (TEU), followed by landfilling and incineration (R < L < I). Nevertheless, when energy is recovered from inert (non-recyclable) plastic waste in the form of fuel or by its utilization in construction purposes, the environmental impacts are more negligible (Incineration < Landfilling). Therefore, this study determines the significance of circular economy with legislative approach and standards on plastic waste management, which help in reducing environmental externalities besides yielding a secondary resource as energy and materials through urban mining. A sustainable plastic waste management (SPWM) model is proposed for developing countries to convert plastic waste into resources and use it as a sustainable tool in urban mining.

Yet another article, ‘Comprehensive technological assessment for different treatment methods of leather tannery wastewater’, co- published by Dr Pankaj Pathak along with a group of other researchers was featured in the journal Environmental Science and Pollution Research having an Impact Factor of 5.19. The work offers some exhaustive observations and recommendations that could be helpful in the industry to manage tannery wastewater and recirculate the water in a sustainable manner.

Abstract of the Research

The leather-making process necessitates large amounts of water and consequently generates tons of liquid waste as leather tannery wastewater (TWW) is disposed of directly in the open environment. Open disposal of untreated TWW into the natural environment causes an accumulation of various polluting compounds, including heavy metals, dyes, suspended solids inorganic matter, biocides, oils, tannins, and other toxic chemicals. It thus poses potential hazards to the environment and human health. This study primarily focuses on providing in-depth insight into the characteristics, treatment strategies, and regulatory frameworks for managing TWW in leather processing industries. Different technologies of conventional physico-chemical (equalization, coagulation, and adsorption), advanced approaches (Fenton oxidation, ozonation, cavitation), thermo-catalytic and biological treatments available to treat TWW, and their integrative approaches were also highlighted. This review also sheds light on the most frequently applied technologies to reduce contaminant load from TWW though there are several limitations associated with it such as being ineffective for large quantities of TWW, waste generation during treatment, and high operational and maintenance (O&M) costs. It is concluded that the sustainable alternatives applied in the current TWW technologies can minimize O&M costs and recirculate the treated water in the environment. The exhaustive observations and recommendations presented in this article are helpful in the industry to manage TWW and recirculate the water in a sustainable manner.

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The Department of Electronics and Communication Engineering has come out with yet another rewarding publication, “Energy-Efficient Hybrid Relay – IRS aided wireless IoT network for 6G communications”, in the Electronics Journal, with Impact Factor 2.4. The article was published by Mr Rajak Shaik, PhD Scholar, in collaboration with the faculty members; Dr Sunil Chinnadurai, Dr Karthikeyan Elumalai and Dr Inbarasan Muniraj. This research is the first of its kind, which examines and compares the impact of relay-aided, IRS-aided, and novel hybrid relay-IRS-aided wireless IoT networks for 6G communications in terms of Energy Efficiency.

The article examines Energy Efficiency as a function of user distance and various SNR (Signal-to-noise ratio) values. The Energy Efficiency with fixed and varying numbers of IRS elements is analysed for the proposed IoT network. The results show that the proposed hybrid relay-IRS-assisted IoT network outperforms both the conventional relay and IRS-aided wireless IoT networks. The hybrid relay-IRS-aided IoT network can fulfil the requirements of high data rate, reliable data transfer, and large bandwidth needed for 6G communications. The multiple IRS concept can also be used in 6G communications at high SNR values to reduce both the cost and additional power consumption of wireless IoT networks. Their future research plan also includes the real-time implementations to improve the energy efficiency for wireless IoT networks with IRS in 6G communications.

Abstract of the Research

Intelligent Reflecting Surfaces (IRS) have been recognized as presenting a highly energy-efficient and optimal solution for future fast-growing 6G communication systems by reflecting the incident signal towards the receiver. A large number of Internet of Things (IoT) devices are distributed randomly in order to serve users while providing a high data rate, seamless data transfer, and Quality of Service (QoS). The major challenge in satisfying the above requirements is the energy consumed by the IoT network. Hence, in this paper, we examine the energy efficiency (EE) of a hybrid relay-IRS-aided wireless IoT network for 6G communications. In our analysis, we study the EE performance of IRS-aided and DF relay-aided IoT networks separately, as well as a hybrid relay-IRS-aided IoT network. Our numerical results showed that the EE of the hybrid relay-IRS-aided system has better performance than both the conventional relay and the IRS-aided IoT network. Furthermore, we realized that the multiple IRS blocks can beat the relay in a high SNR regime, which results in lower hardware costs and reduced power consumption.

Two research papers from the Department of Computer Science and Engineering were presented at the 4th International Conference on Energy, Power, and Environment held from April 29 to May 1, 2022. Assistance professor V M Manikandan and three BTech students participated in the conference organised by NIT Meghalaya, India. The papers will be published in IEEE Xplore Digital Library (Scopus Indexed).

Third-year BTech CSE student Harshad Dhane presented the paper A Novel High Capacity Reversible Data Hiding through Encryption Scheme by Permuting Encryption Key and Entropy Analysis, co-authored by Palak Agarwal, third-year BTech student, and Dr V M Manikandan. The reversible data hiding scheme proposed by the research can be used in the healthcare sector to transmit electronic patient reports along with medical images. Improving the embedding rate of the reversible data hiding is the further plan of the researchers.

Explanation of the research

A Reversible Data Hiding Through Encryption (RDTE) scheme will consider an original image and a sequence of bits as the input and generate an encrypted image as the output. This encrypted image will be able to transmit through the network securely, and the authorized receiver can take out the hidden details along with the restoration of the actual image. This paper proposes a new RDTE scheme with a good rate of embedding without any issues during the restoration of the original image. The researchers used the well-known RC4 pseudo-random generator for the image encryption and performed data hiding during block-wise image encryption. In the proposed scheme, the original image is considered non-overlapping blocks of size BXB pixels, and these blocks will be encrypted using a sequence of pseudo-random integers. During the RDTE process, all the possible unique permutations of the encryption key, K, will be generated, say (K0, K1,…, KN). Further, the sender will be capable of embedding one integer value from the set {0, 1, …,N} in a selected image block. A selected block will be encrypted using the pseudo-random sequence of integers using the key K_Q to embed the integer Q in the selected block. The proposed scheme prefers to select keys with unique characters with sufficient length to ensure the maximum embedding capacity. The message extraction and image restoration are performed by analysing the entropy measure from each block after attempting the decryption.

The paper presented by second year BTech CSE student Sri Satya Maram is titled A Novel System for Automated Coloring of Neat Sketches and was co-authored by Dr V M Manikandan. The research introduces a new algorithm to colour a given neat sketch. The proposed algorithm can be used to colour drawings to create animated movies or to colour the designs developed by the designers. The researchers plan to develop artistic features on the coloured image for better visual appearance.

Explanation of the research

The process of colouring neat sketches is a significant activity when making animated movies or for better visualization in computer modelling. The colour filling tools are widely available in almost all the image/video editing software, which will help us pick a colour from a colour palette and can be filled in a selected region. This process is known as flat colouring. The flat colouring process has several challenges. One of the significant challenges is that the colour may leak from the selected regions to neighbouring regions if there are some small openings on the contours. The second concern while using flat colouring is that the designated areas will be filled entirely with the same colour, so the drawing will not have an artistic look. The research proposes a new software application that will take a neat sketch as the input, and the system will generate a coloured drawing as the output. In the proposed scheme, the researchers have converted the given sketch to a grayscale or binary image and applied image dilation operation to fill the small open spaces in the contours (if any). Further, the closed regions are identified and coloured with a predefined set of colours or random colour combinations. While colouring the regions, the proposed system will ensure that the adjacent regions will not be coloured with the same colours. A number of sketches have been considered during the experimental study, and the results are validated manually.

Computational and artificial intelligence is enjoying an unparalleled relevance in the modern world. They improve people’s lives and are highly anticipated in the healthcare industry. Research in this domain is hugely appreciated by the contemporary world, considering its potential to create revolutionary changes in the health care system. The Department of Computer Science and Engineering is delighted to inform you that the paper Robust, Reversible Medical Image Watermarking for Transmission of Medical Images over Cloud in Smart IoT Healthcare has been accepted for publishing as a chapter in the book Predictive Analysis in Cloud, Fog and Edge Computing and Practice of Blockchain, IoT and 5G.

The paper was submitted by Assistant Professor Dr Priyanka S, her PhD student Ms Jyothsna Devi, and MTech student Mr Jayant Krishna. The book chapter for the edited book is entitled Predictive Data Security using Ai – Insights and Issues of Blockchain, IoT, and DevOps and is published by Springer Nature. It is a part of the book series, Studies in Computational Intelligence, indexed by SCOPUS.

The book Studies in Computational Intelligence targets to bring together researchers and practitioners in computational intelligence and AI technology, especially those related to the areas of Machine learning, blockchain, multimedia using AI, smart IoT environment and email spam and online surveys, and many more recent emerging fields. The research work mainly provides highly secure, robust medical image transmission over the cloud in a smart IoT healthcare environment to ensure high embedding capacity and integrity.

This book’s target audience comprises professionals and researchers working in the field of computational intelligence and AI for health services in a smart environment. The book will attract Engineers (computer, industrial, software, and others), health care scholars, and information scientists since it caters to their interests.

The research paper, An under complete autoencoder for denoising computational 3D sectional images from the Department of Electronics and Communication Engineering has been accepted in a prestigious conference called Imaging and Applied Optics Congress to be held in Vancouver, Canada 2022. Assistant Professors; Dr Sunil Chinnadurai, Dr Karthikeyan Elumalai, Dr Inbarasan Muiraj, and the PhD students; Ms Vineela Chandra Dodda and Ms Lakshmi Kuruguntla are the authors who contributed to composing the paper.

Abstract

This paper proposes to use a deep-stacked under complete autoencoder to denoise the noisy 3D integral (sectional) images with a patch-based approach. In this process, the noisy input 3D sectional image is divided into multiple patches, which are then used to train the neural network. By using the patch-based approach, the time required to prepare the labeled training data is greatly reduced. Results demonstrate the feasibility of our proposed model in terms of the peak-signal-to-noise ratio.

Explanation of the research

Denoising is one of the preliminary processes in image processing that removes noise from an image of interest and restores a clean image. The noise which was generated during the image acquisition process is attenuated using deep learning techniques. The denoised image is further used in various tasks of image processing.

In any image acquisition system, noise is inevitable and needs to be attenuated before further processing for qualitative results. The medical field is an example of this (images acquired through CT, MRI, PET, etc.). The researchers further investigate various techniques in deep learning to improve the denoising performance along with the applicability of deep learning in various tasks such as object recognition etc.

The Department of Physics is proud to announce that Prof Ranjit Thapa and his PhD scholar Mr Samadhan Kapse have published an article titled “Lewis acid-dominated aqueous electrolyte acting as co-catalyst and overcoming N2 activation issues on catalyst surface” in the most prestigious and highly cited multidisciplinary research journal, ‘Proceedings of the National Academy of Sciences’ (PNAS), having an Impact Factor of 11.2. The research was done in collaboration with Ms Ashmita Biswas, Mr Bikram Ghosh, and Dr. Ramendra Sundar Dey from the Institute of Nano Science and Technology (INST), Punjab.

Abstract of the Research

The growing demands for ammonia in agriculture and transportation fuel stimulate researchers to develop sustainable electrochemical methods to synthesize ammonia ambiently, to get past the energy-intensive Haber Bosch process. But the conventionally used aqueous electrolytes limit N2 solubility leading to insufficient reactant molecules in the vicinity of the catalyst during electrochemical nitrogen reduction reaction (NRR). This hampers the yield and production rate of ammonia, irrespective of how efficient the catalyst is. Herein we introduce a new aqueous electrolyte (NaBF4), which not only acts as an N2-carrier in the medium but also works as a full-fledged “co-catalyst” along with our active material MnN4 to deliver high yield of NH3 (328.59 μg h-1 mgcat-1) at 0.0 V vs RHE. BF3-induced charge polarization shifts the metal d-band center of MnN4 unit close to the Fermi level, inviting N2 adsorption facilely. The Lewis acidity of the free BF3 molecules further propagates their importance in polarizing the N≡N bond of the adsorbed N2 and its first protonation. This push-pull electronic interaction has been confirmed from the change in d-band center values of MnN4 site as well as charge density distribution over our active model units, which turned out to be effective enough to lower the energy barrier of the potential determining steps of NRR. Resultantly, a high production rate of NH3 (7.37 × 10-9 mol s-1 cm-2) was achieved, approaching the industrial scale where the source of NH3 was thoroughly studied and confirmed to be chiefly from the electrochemical reduction of the purged N2 gas.

A Brief Summary of the Research

The widely highlighted problem of NRR is that the competitive HER is most likely worked upon with several catalyst development and electrolyte modifications, while the N2 solubility and activation issues in the aqueous medium are generally neglected. This work justifies our aim to contribute towards this troublemaker by using NaBF4 as a working electrolyte, which served as a “full-packaged co-catalyst” along with MnN4, reinforcing the NRR kinetics at the cost of low overpotential. The Lewis-acidic nature of BF3 induced adduct formation with the N2 molecules acted as a carrier of N2 gas into the medium in vicinity of the electrocatalyst. Simultaneously, the charge polarization over MnN4 active site due to BF3 delocalized the metal d-band centre, which triggered N2 adsorption on the catalyst site. Under this condition, free BF3 form the medium interacted with the adsorbed N2 and brought about the facile polarization of the N≡N bond and its first protonation at a much lower energy barrier. This push-pull charge transfer effect enormously helped to overcome the potential determining steps and this BF3 mediated NRR resulted in a huge production rate of NH3, which could be compared to that of industrial scale, which was not achieved so far with any aqueous or ionic liquid electrolytes. In short, this kind of user-friendly aqueous electrolyte is being investigated for the first time for NRR. Since BF3 displayed tremendous potential in triggering the kinetics of NRR, this new finding may encourage researchers to work more on aqueous electrolyte designing towards an even improved NRR performance of the electrocatalysts. Not only that, electrocatalysts could also be functionalized with BF3 derivatives, which could be one entirely new route of study in the field of NRR.

Social Implications

Ammonia is considered as the most abundant and widely used synthetic fertilizer in the world. The sole mean of large-scale ammonia production relies on the century-old Haber-Bosch process, which takes in more energy than it can produce, while the electrochemical nitrogen reduction reaction (NRR) offers a carbon-free and sustainable way of ammonia synthesis. However, electrochemical NH3 synthesis is often arrested by a few factors such as NH3 detection, contaminations from source gases, nitrogen-containing chemicals and the presence of labile nitrogen in the catalysts. In the recent past, several protocols have been proposed to correct the fallacious results. Recently, Choi et el. have concluded that it is difficult to believe from the too-low yield rate of NH3 that the reduction of N2 has actually occurred in the aqueous medium. It is noteworthy that the electrolyte plays a crucial role and offers a suitable environment for any electrochemical reactions to occur. However, the issue with the solubility of N2 in conventional aqueous electrolytes is a real troublemaker to achieve a high yield and production rate of NH3 during electrochemical synthesis. Therefore, it is necessary to solve the most important issue i.e., to solvate a promising concentration of N2 molecules into the electrolyte such that it becomes accessible to the catalyst surface for its subsequent reduction.

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Intelligent Transportation System (ITS) is on its way to becoming the biggest player in the coming-of-age transportation system. However, the sheer demand for the enormous amount of data to secure seamless connectivity and functioning with maximum speed and safety tends to increase the power consumption of the ITS. Dr Sunil Chinnadurai and his PhD scholar Mr Shaik Rajak from the Department of Electronics and Communication Engineering present Intelligent Reflecting Surfaces (IRS) as the key enabling technology to provide the data required by the ITS with less power consumption.

Their article “Deep Learning Enabled IRS for 6G Intelligent Transportation Systems: A Comprehensive Study” which makes a comprehensive study on the DL-enabled IRS-aided ITS was published in the esteemed journal ‘IEEE Transactions on Intelligent Transportation Systems’ having an Impact factor of 6.5. The article elucidates the ways and means to overcome the channel estimation, secrecy rate, and energy efficiency optimisation problems.

The research suggests that connecting ITS to wireless networks via IRS will help in reaching the destination within the stipulated time duration with enhanced safety and comfort. Besides highlighting the reduced power consumption and hardware cost of the DL-enabled IRS-aided ITS, the article also projects that IRS usage in 6G-ITS massively helps the traffic control system to precisely send and receive the information of school buses as well as healthcare vehicles like ambulances, fire safety vehicles, etc. Their future research plans also include the experimental analysis of energy efficiency for wireless networks and Intelligent Transportation Systems with IRS.

Abstract of the Research

Intelligent Transportation Systems (ITS) play an increasingly significant role in our life, where safe and effective vehicular networks supported by sixth generation (6G) communication technologies are the essence of ITS. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications need to be studied to implement ITS in a secure, robust, and efficient manner, allowing massive connectivity in vehicular communications networks. Besides, with the rapid growth of different types of autonomous vehicles, it becomes challenging to facilitate the heterogeneous requirements of ITS. To meet the above needs, intelligent reflecting surfaces (IRS) are introduced to vehicular communications and ITS, containing the reflecting elements that can intelligently configure incident signals from and to vehicles. As a novel vehicular communication paradigm at its infancy, it is key to understand the latest research efforts on applying IRS to 6G ITS as well as the fundamental differences with other existing alternatives and the new challenges brought by implementing IRS in 6G ITS. In this paper, we provide a big picture of deep learning enabled IRS for 6G ITS and appraise most of the important literature in this field. By appraising and summarizing the existing literature, we also point out the challenges and worthwhile research directions related to IRS aided 6G ITS.

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