Advances in Microelectronics, Embedded Systems and IoT

Logic locking is an emerging technique for securing the IP from hardware security threats at the IC manufacturing supply chain. Over a decade, the research studies have investigated the metrics to assess the efficacy, the impact of locking at different levels of abstraction, threat model definition, resiliency against physical attacks and tampering. In this survey paper, we classify the existing defences and attacks to capture benefits from the logic locking techniques for IP protection. This survey paper serves as a guide to quickly navigate and identify the state-of-the-art studies on logic locking techniques.

The proposed article provides the design, analysis of low-noise amplifier (LNA) for sub-6 cellular LTE/5G NR application (Comer DJ and Comer DT, IEEE Trans Circuits Syst 51(I):8–14, 2004; Mou et al., in IEEE Trans Circuits Syst 52:784–788, 2005; Liao and Chuang in IEEE Microwave Wirel Compon Lett 13:526–528, 2003) programs operating in the 2.4–3.5 GHz frequency band using CMOS 110 nm SOI process technology. In this article, the choice of the cascode inductive source degeneration topology for the LNA design is based on its ability to offer high gain, a low-noise figure and exceptional linearity, as previously mentioned. The main focus of this research is to decrease the noise factor while simultaneously enhancing power gain, all while operating at a lower power consumption level compared to prior outcomes under a 1.2 V power supply. Low-noise amplifiers (LNAs) are of paramount importance in receiver systems due to the inherently weak nature of incoming signals, which are vulnerable to interference from environmental noise. The primary objective of LNAs is to achieve superior performance while operating at lower voltage levels. This is accomplished by amplifying the received signal and optimizing both gain and noise figure, which are distinctive characteristics of the LNA design. The comprehensive analysis of this amplifier predominantly revolves around evaluating various factors, including noise figure, forward gain, impedance matching, stability, return loss and linearity. Cadence virtuoso tool is used for design, simulation and optimization. The LNA has achieved a gain exceeding 20 dB while maintaining a noise figure of approximately 1.5 dB. In terms of stability, it exhibits unconditional stability across the entire frequency range from 0 GHz up to fmax, aligning with established RF design principles. It’s important to note that the LNA is designed to operate at a supply voltage of 1.2 V and utilizes an operational current of 9.5 milliamps. Additionally, the third-order intercept point stands at 4 dBm, and the LNA is tailored for a source and load impedance of 50 Ω.

In recent years, there have been remarkable advancements in Integrated Circuit (IC) technology, enabling the development of highly sophisticated computer systems on a single chip. Custom System on Chip (SoC) designs, where the processor core(s) and cache represent a smaller portion of the overall chip, have gained widespread popularity. Nowadays, it is challenging to come across an electronic product of any size that does not incorporate a processor. Open-source instruction set architecture, such as RISC-V-based processors, has gained traction in custom SoC design. A processor is the core of an electronic system. In a five-stage pipelined RISC-V processor, instructions are executed in the sequence that they are given. In this work, the architecture suggests an out-of-order execution of instructions when the resources are available yet all instructions are to be blocked owing to a multi-cycle instruction. In this architecture, in comparison with an in-order execution, we notice a difference of 120 ns, i.e., six clock cycles being used efficiently in an out-of-order execution when a multi-cycle instruction is executed.

Fog computing is backed by widely dispersed fog nodes; from the cloud, it extends and is distributed, bringing networking power and processing, closer to IoT devices. The data majorly needs to be processed, inferred, stored, and sent to the cloud servers that negatively affect latency, security, mobility, and dependability in cloud-only systems. The cloud supports the ultra-low latency needs of delay-sensitive applications. However, the fog layer’s proximity to IoT devices can significantly reduce latency and satisfy these requirements. A new generation of applications and services is produced as a result of fog computing’s constant interaction with and support of the cloud. As load balancing is a crucial issue, a load balancing approach can considerably improve QoS parameters in a fog network. In fog networks, load balancing divides the incoming load among the available fog nodes using a technique in order to prevent over- or under-loading of the fog nodes. This method can reduce reaction time, cost, and latency while maximizing throughput, performance, and resource utilization.

The authors present an analysis of various clock gating techniques and its power requirements considering an application. Whenever the number of transistors to be mounted on single chip enhances, need for power optimization increases in the same pace. In the design of SoC, the reduction of power plays a vital role. When considered in sequential design source of power consumption is dynamic power. The clock gating technique reduces this dynamic clock to larger extent. In this paper, authors have implemented various clock gating techniques on test circuit. Power analysis is carried out using X-power analyzer to study the power optimization. From experimental results, it is observed that autogated clock gating technique consumes 68.63% power when compared with design under test without clock gating technique.

Improvement in technology has enriched the popularity of different sectors of society. Health care is one of the sectors where people can monitor the physical activity of the body remotely without taking help from a doctor or caretaker. Nowadays, in most of the developing countries, health care is much more expensive. Hence, it is not affordable for human beings. This paper presents the design and development of a remote health monitoring system using wearable sensors. Hence, we have considered three different wearable sensors like temperature sensor, spo2, and heart rate sensor to sense human vital activity. The acquired raw data has been sent to the data acquisition system and displayed in LCD. Moreover, we have done a comparative study of the human physiological parameters according to different ranges of ages. In our future work, the received data will be sent to the doctor through Wi-Fi for further analysis.

This paper proposes an energy-productive improvement to the Advance Encryption Standard (AES) module inside the Randshift strategy. Supplanting the ordinary S-box with registers lessens energy utilization and further develops asset utilization. The adjusted AES module exhibits remarkable benefits. It accomplishes a smoothed-out equipment execution, bringing about diminished boundaries like postponement, region, and time contrasted with the first AES module. The straightforwardness of the plan empowers critical energy reserve funds, making it especially appropriate for energy-obliged environments. The execution of the better High-Level Encryption Standard plan is led in Verilog HDL and approved utilizing the Xilinx ISE 14.7 apparatus. Through exhaustive assessment and testing, the proposed methodology can prove that the adjusted AES module keeps up with similar safety while consuming less energy than the first execution and other ongoing methodologies.

This work presents a fiber designed by solid-core hexagonal photonic crystal fiber (PCF) with Zinc-Germanium Diphosphide (ZnGeP2). The aim of this study is to generate an efficient mid-infrared supercontinuum (SC). Here, we have numerically investigated all the optical properties of the fiber by finite-difference time-domain (FDTD). To achieve this goal, a structure has been obtained by optimizing the fiber properties by selecting unequal air hole diameters in the six layers. The design proposed here has non-uniformity in the diameter of the air holes, thereby optimizing important features such as flat dispersion, effective mode area, and low attenuation for efficient spectral broadening. The PCF provides an anomalous dispersion profile with a maximum power of 3 kW for the pump pulse at 3.5 μm by which a spectral range from 1 to 15 μm has been achieved. The obtained results have significant applications in high-speed nonlinear imaging, optical communication, and frequency measurement.

The rapid proliferation of the Internet of Things (IoT) has ushered in a new era of connectivity and automation across various industries. However, the widespread adoption of IoT devices has also introduced significant security challenges, necessitating novel approaches to safeguard sensitive data and combat emerging threats. Traditional security mechanisms may prove inadequate in coping with the dynamic nature of IoT ecosystems, calling for intelligent and adaptive solutions to counter evolving risks effectively. This research presents an innovative Adaptive Learning-based IoT Security Framework that harnesses the power of recurrent neural networks (RNNs) to fortify the security of IoT ecosystems. By leveraging the immense volume of IoT-generated data, machine learning algorithms integrated within the framework can discern patterns, detect anomalies, and make informed decisions to enhance security measures. RNNs, known for their proficiency in sequential data analysis, are well-suited for IoT security applications, enabling real-time detection of anomalies and timely mitigation of potential security threats. The research encompasses designing, implementing, and evaluating the framework’s adaptive learning capabilities through real-world IoT scenarios. By formulating an adaptive security solution underpinned by RNNs, this research aims to contribute a novel approach to address the ever-evolving security challenges posed by IoT environments. The proposed framework promises to enhance the security posture of IoT ecosystems, safeguarding against data breaches, unauthorized access, and device manipulation. Ultimately, implementing the Adaptive Learning-based IoT Security Framework is poised to foster a safer and more secure IoT landscape, enabling seamless IoT deployment across diverse domains.

This paper introduces a radial basis function or RBF neural network (NN) for designing a supervisory controller (SRBF) with the combination of discrete-time proportional-integral (PI) controller for a laboratory scale single tank and a coupled tank liquid level system. Here, an RBF-NN is used as a feed forward controller with the combination of discrete-time PI controller in a closed loop structure to develop this SRBF-PI controller. This neural controller updates its weight by applying the gradient descent method to track the set level of water. The simulation results from MATLAB show that this SRBF-PI controller furnishes better performance to track the level than the discrete-time PI controller for the single and coupled tank system. To overcome the slow response of MATLAB for handling a large amount of data, the Google Research Colaboratory platform is used here to design the discrete-time conventional controller for both plants.

For making a smart city, there is a need of a system with smart underground infrastructure that includes underground water pipelines, communication cables, gas pipelines, electric flow, etc. In the context of India, where many cities have already embraced underground drainage systems, ensuring the effective operation of this system is of paramount importance to uphold the city’s cleanliness, safety, and overall public health. Neglecting the maintenance of the system could result in the contamination of clean water sources by sewage leading to the spread of infectious diseases. Hence, considerable efforts have been directed toward the detection, maintenance, and management of these subterranean systems. This paper depicts the implementation and design for monitoring the underground drainage system with different approaches using IoT. Using Raspberry Pi, the data received from the flow sensor is first displayed on the monitor. When there will be a water overflow, the system captures image via camera attached to it and sends the image as an E-mail attachment to the sender. After that, the system alerts nearby people with an alarming sound with the help of a buzzer.

The frequency of riverbank landslides in the Mekong Delta, Vietnam, has recently increased, causing serious damages to people and properties. The increasing density of infrastructure and roads along the riverbanks has an impact on the changes of ultimate stresses and horizontal displacements of the limited ground causing riverbank landslides. However, there has been no specific study on evaluating the horizontal pressure in the sliding mass under the effect of the load and the horizontal displacements of soil mass causing riverbank landslides. In this paper, the authors present the design and implementation of an IoT-based system to monitor the displacement of riverbank soil mass under the influence of load. Inclinometers, soil moisture sensors, and soil pressure transducers were employed to monitor the soil mechanical parameters in real time at the experimental site. Preliminary results show that the measured data of the horizontal ground pressure in the sliding mass agree well with those measured by the Kyowa measuring instrument. The results obtained in this study can be used to build landslide prediction models and early warning systems for riverbank failure in the Mekong Delta region.

Traditional storage management techniques are becoming less effective for handling this enormous volume of data due to the development of data centers and the unexpected rise in storage needs. By removing the storage control activities from the actual data storage media and putting them in the software layer of a centralized controller, software-defined storage offers a solution to this problem. An intelligent software stack titled “software-defined infrastructure” may manage any type of affordable commodity hardware. A computer program known as software-defined storage (SDS) controls data storage resources without the use of actual hardware. Making the storage architecture of a true software-defined storage system without any simulation or emulation is a costly and risky method. As a result, the system needs to be simulated before being used in real life. Therefore, virtualized test beds for such systems must be simulated before actual implementation and deployment. With the aid of the software-defined network (SDN) simulator Mininet, which is constructed over it for the distributed storage system, we address the evaluation of software-defined storage architecture in this article. The primary Mininet components have been modified to meet the needs of data distribution in the current SD storage and to scale storage appropriately in this simulated environment.

Non-isolated DC-DC converter based on cascaded structure of interleaved boost circuit, voltage lift circuit, and boost circuit configuration is presented in this paper to achieve high voltage gain. The circuit operation of different modes and different switching operations is analyzed. The mathematical analysis is obtained for different modes. The dual-loop control technique using PI controller is implemented, and proposed converter is examined by MATLAB Simulink.

This paper presents a comparative analysis of two step-up DC-DC converters, the interleaved boost converter and self-lift SEPIC converter, for Fuel Cell Electric Vehicles (FCEVs). The study explores key components, specifications and topologies of the converters. The fuel cell is regulated using a fuel flow regulator, while Maximum Power Point Tracking (MPPT) controls the DC-DC converters, supplying PWM pulses. The converters step-up DC voltage for the three-phase inverter linked to a Hall effect sensor-controlled BLDC motor. MATLAB Simulink simulations assess the converters’ and motor’s performance. The findings aim to improve power conversion and motor control in FCEVs, enhancing overall efficiency and encouraging sustainable transportation integration.

This research presents an all-in-one portable vital sign (PVS) measurement device integrated with a cloud platform for remote patient monitoring in a home isolation setting. The study aims to evaluate the device's usability by comparing its vital sign measurements with clinical vital sign simulators and conducting trials with mild symptom COVID-19 patients. The PVS device successfully demonstrates a high level of agreement with the clinical vital sign simulators, signifying its reliability in providing accurate data based on Pearson’s correlation coefficient. Furthermore, when assessed by mild symptom COVID-19 patients, the device exhibits 95% confidence interval agreement with standard home use vital sign devices in measuring blood pressure, pulse rate, SpO2, and temperature using the Bland–Altman method. The findings suggest that the conceptual PVS device may be used in diverse healthcare settings, including elderly community healthcare and acute respiratory infection (ARI) units. Future work will involve integrating edge computing to enhance connectivity reliability and reduce data transmission delays, further improving the system’s efficiency. Overall, this innovative research lays the groundwork for broader applications of the PVS cloud-based system, contributing to advancements in remote patient monitoring and effective healthcare resource management in challenging healthcare scenarios.

In recent years, perovskite solar cells’ power conversion efficiency has increased significantly. Perovskite solar cells, on the other hand, experience continual light and thermal stress as a result of the p-dopants and other additives in the HTLs. We created a model that uses reduced graphene oxide (rGO) as an HTL to enhance stability, and we used SCAPS-1D to analyse how efficiency changed when parameters were changed. The effect of perovskite layer thickness, perovskite layer doping, TCO layer thickness and reduced graphene oxide (rGO) on the model’s performance was examined. Taking into account the negative consequences it imposes, the perovskite material utilised in the model is tin-based (CH3NH3SnIBr2) as opposed to the deadly lead-based perovskites. Our simulation shows that we can produce high-performance perovskite solar cells with an efficiency of above 15% at a substantially reduced cost by optimising the rGO doping.

Based on Moore’s law, the number of transistors is widely increasing on a chip as it leads to the formation of complex SoC design. Transistor nanotechnology will depend on the length of the channel between the source-drain terminals of a transistor. The proposed work depicts the design of Fully Depleted Silicon On Insulator FinFET with the channel doping concentration of 1 × 1019 cm−3 and the placing of dual-dielectric spacer combinations between source and drain terminals. The FinFETs are three-dimensional transistors built with certain characteristics and properties since by use of dielectric materials in the design will result in the reduction of leakage current and improve short-channel effects (SCEs). Hence, the designed FinFET performance is analyzed by using Visual TCAD tool.

In this communication, Nd-doped bismuth iron titanate [(Bi0.9Nd0.1)(Fe0.5Ti0.5)O3] material was synthesized with the traditional solid-state reaction method. Structural analysis and phase identification were done with X-ray diffraction. Morphological analysis was carried out by field emission scanning electron microscope (FE-SEM). FE-SEM image shows that the grains are uniformly distributed over the surface. Also, the polycrystalline nature of the sample is confirmed by FE-SEM analysis. EDX study shows the presence of all essential elements with appropriate purity. Dielectric and impedance analyses were done in a wide range of frequencies and temperatures. NTCR behaviour of the sample was confirmed by impedance analysis. The electrical and dielectric properties have been substantially enhanced, and the synthesized material’s frequency-dependent AC conductivity plot agrees with Jonscher’s universal power law. Based on the dielectric and electrical features of [(Bi0.9Nd0.1)(Fe0.5Ti0.5)O3], the synthesized material could be useful for electronic device applications.

There is a shift in digital payment system from digital money to virtual digital currency called Central Bank Digital Currency (CBDC). CBDC is programmable currency issued by central banks and sovereign authorities. CBDC is having combined features of cryptocurrency and fiat currency. The main objective of introducing digital virtual currency to minimize the operation cost of currency management which includes currency printing, distribution and storage. It also increases the accountability and traceability of currency. CBDC addresses the country major issues of the money laundering, counterfeit currency and terrier finance. Using CBDC, we can provide the transparent and traceable government welfare schemas and subsidiary benefits to the common people. In this paper, we propose a four stage model including initiation, innovation, implementation and integration of CBDC to make effective, efficient, secure, transparent and traceable global payment systems. The initiation model describes the need of digital virtual currency, introducing novelty to existing payment channels or processes, leading to increase the digitization. The innovation model enhances, upgrades and transforms the payment systems to the global standard payment systems to solve the cross-border payments. The implementation model describes the underlying technologies, distribution models and different forms of CBDC. The integration model describes how we can integrate to existing payment systems or channels to enhance the performance, efficiency, security and privacy of user services.

Prediction of development effort of software is an important prerequisite for its actual development. However, the complexities involved in the creation process make it a stiff challenge to make viable prediction that is adequately precise. This study reveals a smart estimation approach for the present-day applications in various domains. The applied approach first extracts the details represented in the Unified Modeling Language (UML) Activity models. These details are then fed to a number of regression analysis procedures written for this study that includes: ridge (LRR), lasso (LLR), support vector (SVR), extreme gradient boosting (XGBR), decision tree (DTR), K-nearest neighbors (KNNR) and Bayesian ridge regression (BRR). The findings from the experimentation suggested that the BRR delivered a superior accuracy in train-test split as well as fivefold cross-validation.

Recently, Indian government is promoting the concept of smart cities. This concept focuses on improving the quality of people’s life by making different things autonomous using the technologies of wireless networks, IoT, Cloud, mobile computing, AI, and machine learning. Finding the parking slot in malls and public places with less time and fuel consumption in heavy floating times is becoming an important issue as car has become the necessary transportation means for a family these days. Smart parking system is one of the applications of smart cities for which different researchers are trying to find the solution. This paper has presented a good review on different technologies of smart parking system along with implementation steps and results of IOT-based smart parking system mobile application.

Pregnancy is a crucial phase in a woman's life that demands constant monitoring and timely medical intervention when needed. This abstract introduces a pioneering technological solution—the Smart Pregnancy Watch—designed to cater to the needs of expectant mothers. The proposed watch is equipped with hardware and software components that enable real-time health monitoring, automatic emergency messaging, and accurate location tracking to ensure the well-being of pregnant women. The Smart Pregnancy Watch is equipped with an array of sensors capable of monitoring vital signs such as heart rate, blood pressure, and temperature. One of the standout features of the Smart Pregnancy Watch is its location-based emergency messaging system. The watch integrates GPS and advanced location-based algorithms to determine the user's precise location. In the event of a health emergency or any distressing situation, the watch can accurately identify the nearest medical facility and send an automatic distress message to a pre-selected contact list, which may include family members, friends, and medical professionals. This feature ensures that medical assistance can be dispatched promptly, potentially saving lives in critical situations. To implement this solution, a blend of hardware and software technologies is employed. The hardware components encompass a combination of physiological sensors, GPS and GSM modules. These components work in synergy to collect data and communicate with external devices. On the software side, a custom mobile application is developed, providing a user-friendly interface for monitoring health metrics, setting emergency contacts, and configuring personalized settings.

Automated vehicle is one which is equipped for visualizing the current circumstances and taking the decisions by itself on the movement and control with the assistance of the human. Human driver is not needed always and takes the responsibility in all decision making of driving since it is self-driven. It mimics the actions of the driver by the predefined sets of rules and self-learning on the decisions to be taken dynamically. It will depend on sensors, actuators, calculations, complex decision and Artificial Intelligent frameworks. Specialized processors are available on the design and programming aspects now. Radar sensors are useful in screening the situations nearby to the vehicles. Camcorders will identify traffic signals, digitize street signs, monitor different vehicles and keep the people updated without their request. Light Identification and Ranging (LIDAR) sensors skip beats of light off the environmental factors of the vehicles to gauge distances, recognize markings of the path and distinguish street edges. Ultrasonic sensors in the wheels of the vehicle distinguish controls and different vehicles on the fly of the vehicle. This research deals the automation of the car by finding the path and avoiding the obstacles automatically in 360 degrees.

Internet of Things (IoT) connects various devices of networks, enhances services used to protect against attacks and provide privacy of the user related to all the types of security. This paper analyzes the methods and techniques used in IoT systems with artificial intelligence approach to enhance security. Applying AI algorithms to IoT security allows us to develop smart systems which can detect and block security attacks in real time. Due to the lack of powerful and unified security standards in IoT, an increasing number of IoT devices is vulnerable to threats from malicious attackers and bots. In order to detect attacks and identify abnormal behaviors of smart devices and networks, ML techniques can be used to overcome the issues and challenges. The IoT environment gathers data and analyzes it, and can be done effectively using machine learning, which has the ability to access data, analyze data, and perform decision-making based on data received from IoT devices. This paper addresses the issues which need to be investigated and addressed while implementing the machine learning schemes of security in IoT systems. Respectability, confirmation, and privacy are major principles to be considered to ensure the correspondence between IoT devices. AI offers us a new to solve traditional problems and help us reveal new insights on the field of IoT.

Here, the automatic screw jack using IoT technology shared is used as a light weight component to access a heavy weight car and other four wheeler automobiles. Here, we used Bluetooth technology to send signals using mobile app for the uplift of car for the screws in jack to rotate in clockwise and anticlockwise direction for the uplift and release of the automatic screw jack. Here, we used input 12 V from the car battery and Arduino devices are connected with relay and it is connected to a wiper motor to perform the screw rotation in both the sides, namely clockwise and anticlockwise direction. Arduino Uno is used with Bluetooth technology to connect with android devices so that we can perform the rotation using GSM models. Here, few coding to implement relay was written using Arduino IDE which was used to perform automatic screw jack using IoT devices with android technology by using car battery itself.

Recent advancements in sensor technology, including innovations like Micro-electromechanical Systems (MEMS), wireless communication, embedded systems, distributed processing, and the deployment of wireless sensor applications, have brought about a significant transformation in the realm of wireless sensor network (WSN). In recent times, WSN has witnessed widespread adoption across various domains, with a particular emphasis on applications in agricultural and environmental surveillance and monitoring. Environmental monitoring has emerged as a critical field for observation and validation, enabling real-time interaction and control with the physical environment. This article delves into the exploration and evaluation of the myriad applications of wireless sensor networks in the context of environmental monitoring. To establish an effective monitoring system, certain prerequisites must be met. Research indicates that this approach has become a feasible alternative to traditional manual monitoring methods. Additionally, these methodologies have demonstrated the potential to improve system performance, offer convenience and efficiency, and fulfill functional requirements.

Greenhouse automation is the remote management and control of household appliances for energy consumption. Smart devices are used in smart greenhouse systems that are intended to enhance users’ lives by automating greenhouse security and safety and introducing extra features like remote greenhouse surveillance. This research proposed a greenhouse monitoring system with Internet of Things (IoT) to monitor energy consumption. The infrared (IR) sensor helps to identify the presence of human inside the greenhouse. NodeMCU in the proposed system helps in sending data/signal to ThingSpeak for the storage of information. The threshold value set on each appliance helps in energy consumption. During abnormal conditions in any of the greenhouse automation system, an alert is shared to the concerned person through Global System for Mobile communication (GSM) Module. A Blynk application developed helps in controlling the greenhouse appliance remotely. The monitoring and controlling temperature and soil moisture is provided accurately.

Water monitoring and controlling smart system is proposed to address the limitations of traditional manual irrigation methods in farmlands. These conventional procedures result in waste of resources, including labor and water. To overcome these drawbacks, the proposed system incorporates a comprehensive monitoring approach using soil samples collected from six different locations. Various parameters such as moisture, temperature, humidity, phosphorous, potassium, and nitrogen are monitored using a two-level sensor setup, along with a moisture sensor. The sensors in the proposed work are interfaced with the NodeMCU esp8266 microcontroller. The pump motor’s operation is dependent on inputs from the field water level sensor, well water level sensor, and soil moisture sensor. When the field water level is detected and the well water level is not detected, the pump motor remains off. However, if soil moisture is detected and the well water level is within the desired range, the pump motor is activated accordingly. All the data generated throughout the process are stored and displayed on the Blynk IoT cloud platform. This proposed automated irrigation system offers cost-effectiveness and ecological friendliness and enhances agricultural output while reducing the need for manual labor.

The goal of this project is to create a web-based monitoring service that uses much less space and power while maintaining the same level of efficiency. Web server-based monitoring solutions are becoming more used in many businesses. However, setting up a server on a personal computer requires a lot of space and resources. Using an ESP 32 WI-FI Module and a web server application (Thing Speak Cloud), this study aims to create a system for remote data collecting. Not only can the devices be monitored, but they can also be controlled using this technology.

In the heart of bustling urban landscapes, the critical arteries of power distribution remain hidden beneath the earth's surface, connecting substations to vital endpoints. However, the reliability of these subterranean transmission lines can be compromised by elusive issues that are challenging to detect and rectify promptly. Our mission is to revolutionize our approach to this problem by harnessing the innovative potential of the Internet of Things (IoT). Our primary goal is to develop a robust system capable of precisely locating defects or anomalies within kilometers-long underground power transmission cables, starting from the substation. The motivation behind this endeavor is to ensure the uninterrupted flow of electricity in major cities, safeguarding against potential power outages and disruptions with far-reaching societal consequences. Our IoT innovation strategically deploys sensors along these critical underground routes. These sensors continuously monitor the cable's condition. When an issue arises, the system swiftly identifies the fault's location. The resulting data is relayed in real-time to our website and displayed on an LCD screen, offering immediate visibility to operators and maintenance personnel. The societal impact of this initiative is profound. Firstly, it bolsters the resilience of urban infrastructure by minimizing downtime and enhancing the power grid's overall reliability. This ensures that essential services, including hospitals and public transportation, continue to operate seamlessly, even during adverse conditions. Secondly, by proactively identifying and addressing cable faults, our system contributes to reducing energy wastage and carbon emissions. This aligns with the global drive toward sustainable practices and energy efficiency. Our project draws inspiration from the fundamental principles of Ohm's law. It utilizes low DC voltage and precise resistance measurements to accurately locate cable faults. By harnessing IoT technology, we aim to transform the management of underground power transmission cables, ultimately creating a more resilient and sustainable urban environment. With this innovative solution, we empower cities to thrive, adapt, and embrace a greener and more dependable energy future, safeguarding the vitality of urban life.

Personal medical scribes are used to be a reliable option for doctors who had too many patients. Commonly medical students who were applicants for this role often moved on quickly, costing providers time and money in recruitment. But modern AI-powered scribe software can reduce physicians’ workloads by diminishing data entry and EHR organization, allowing them to focus instead on patient care. This gives doctors back the joy of practicing medicine. Physicians are meant to provide care to those in need, not to be bogged down with documentation. With our AI-assisted scribe, doctors can rest easy knowing that their notes will be accurate and complete. PenBOT uses natural language processing, machine learning, and speech recognition technology to filter small conversations, handle multiple speakers, and work through interruptions. This technology will enable physicians to focus on providing the best patient care possible and reduce the stress caused by tedious documentation tasks while ensuring patient safety is maintained at all times.

This paper proposes a solution: “Landmine Detection Robotic Vehicle with GPS Positioning Using STM32.” In this, a metal detecting coil is used to detect buried mines, mitigating the risk of human casualties. Its tracked design enables it to navigate through rough terrain effortlessly. Additionally, an ultrasonic sensor has been incorporated for obstacle detection, enabling autonomous mine scanning in specific areas. This GSM-based vehicle permits the user to locate the exact position of the mine through SMS to his registered mobile number and is both simple and cost-effective.

The issue of water shortage by using the Raspberry Pi with a LoRa module to foster a brilliant water the executives system. The proposed framework expects to save the water and the groundwater assets by effectively observing and controlling water use in the fields. The system worked with a various complete arrangement of sensors, including precipitation, dampness, water level, temperature, and moistness sensors, to gather continuous information from the fields. This information is used to robotize the activity of an on–off engine, which controls the water stream in light of predefined values, improving water use and forestalling wastage. In the perspective on distant regions, the proposed framework is utilized with the long-range (LoRa) module, which empowers long-range, low-power remote correspondence, working with consistent availability and information trade among different framework parts. Also, the whole framework is fueled by solar energy, empowering supportability and decreasing reliance on customary energy sources. The point of the exploration is to contribute the headway of water protection endeavors and give a useful answer for relieving water shortage in both metropolitan and rustic regions. The framework’s capacity to screen and control water use in a financially savvy and economical way makes it reasonable for sending in rural fields.

Agriculture is a field where automation is becoming increasingly prevalent, particularly in India. Traditional methods of manual sowing can be time-consuming and require a high level of precision as a result, automatic sowing machines are preferred. To ensure the well-being of plants and support their full life cycle, robots and devices are now being employed. In the world of precision farming, multi-tasking agriculture robots are becoming more and more common. These cost-efficient robots are highly effective and accurate at carrying out a variety of tasks, including spraying, weeding, harvesting, and sowing. Faster seed planting is made possible by the use of this robot for large cultivating areas.

Billions of accidents happen due to drowsiness. For a variety of reasons, drivers are more likely to become tired or distracted under certain circumstances. Accidents caused by drowsy driving are significantly on the rise in the modern world. Most drivers report feeling short on energy as a result of their weariness or fatigue from all the effort. They consequently frequently experience sleepiness throughout the driving. These fatigues greatly enhance the likelihood of accidents occurring. Therefore, the goal of this research is to create a model that can recognise driver tiredness and emit an alarm anytime the driver exhibits signs of being sleepy or drowsy. The majority of expensive automobiles have these models built right in; however, the majority of cars on the road today lack this technology. Python is being used as the implementation language for this project. In this research, our main goal is to develop a sleepiness detection model that is both effective and inexpensive in terms of price and availability. This project’s primary focus is facial detection using the ROI of both eyes rather than the entire face.

The proposed approach provides the fixing of server in suitable manner without loss of capacity and with utmost maximum number of nodes in a linear topological network. Optimization for four different types of linear models in appropriate logical methodology is discussed after observation of the domination principles in discrete mathematics along with existed tool in communication engineering.

The advancement of the Internet of Things (IoT) has enabled the deployment of health monitoring systems in various healthcare settings. However, the large-scale adoption of IoT-based health monitoring systems has raised concerns regarding privacy and security. The distributed nature of IoT devices makes them vulnerable to unauthorized access, cyberattacks, and data breaches. Therefore, there is a need for a secure and efficient mechanism to protect sensitive healthcare data in IoT-based health monitoring systems. This paper proposes a distributed ledger technology (DLT)-based solution for enhancing privacy and security in IoT-based health monitoring systems. DLT provides a tamper-evident and decentralized framework for storing and sharing data, thereby ensuring data integrity, confidentiality, and availability. The proposed solution also incorporates encryption techniques to safe transmission of sensitive information and storage. The system’s performance is evaluated in terms of latency, throughput, and security using the Hyperledger Caliper benchmarking tool. The results of the evaluation demonstrate that the proposed solution provides a secure and efficient mechanism for health data exchange in IoT-based health monitoring systems. The proposed solution provides granular access control and data privacy, making it suitable for deployment in sensitive healthcare environments. This paper presents an efficient approach for enhancing privacy and security in IoT-based health monitoring systems using distributed ledger technology. The proposed solution provides a secure and efficient mechanism for health data exchange, which can be deployed in various healthcare settings to improve patient outcomes while protecting patient privacy and security.

In healthcare environments, edge computing has emerged as a significant tool for providing real-time data processing and analysis. Yet, due to the sensitive nature of healthcare data, sophisticated security measures, including encryption methods, are required. While encryption techniques provide high-level security, they can also be computationally demanding and have an influence on edge device performance. As a result, this research study provides a survey of the literature on strategies for improving encryption algorithms in edge computing for smart hospitals. The research evaluates the performance of numerous encryption methods, including AES, DES, RSA, ECC, and HE, in edge computing. The research also looks at optimization strategies such protocol optimization, hardware acceleration, and cloud-assisted encryption. This paper’s results will be valuable for healthcare companies trying to utilize edge computing technologies while guaranteeing patient data security and privacy.

In today’s digitally advanced world and competitive business environment, inventory management plays an important role in improving the profitability of any retail store or business enterprise. With new technologies and strategies entering the market, it is essential to conduct thorough inventory analysis to prevent and reduce customer dissatisfaction due to stock unavailability. Inventory management systems were developed for performing inventory analysis efficiently considering the high cost involved in maintaining inventories. Our software is a cross-platform inventory management and billing system built upon ionic framework that offers inventory synchronized operations across the shop. It provides an intuitive and easy to use platform for shop owners to manage their shop’s inventory. This research paper discusses the features, technology, and methodology used in designing and developing the system, while also incorporating the best industry practices.

This study uses bibliometric methods (Biblioshiny 4.0 software) and VOSviewer 1.1.16 to analyse the specialised literature on the role of IT in the field of building energy efficiency, including the Web of Sciences database between the years 2007 and 2022, to locate the most pertinent authors, publications, research teams and groups, the development of the theme through time, journals, geographical regions, and ultimately the data analysis methods used. The USA, China, and Australia are the biggest contributors, where the Negev Ben-Gurion University, China-Science and Technology University, and Hunan University in Changsha, Hunan, were the three organisations that have the most scholarly works in this field. Engineering, energy, and environmental sciences make up the majority of the categories for the three most significant publications as Energy and Buildings, Building and Environment, and Applied Energy. According to the authors primarily the essential terms can be separated into the following main clusters: Energy Efficiency, Thermal Performance, Building Energy Efficiency and Optimization, Thermal Mass, Building Energy Simulation, and Energy plus. The study makes use of bibliometric analysis and the Web of Science core collection to examine how IT affects building envelope energy efficiency in a hot climate. The data will be relevant to the research scholars to identify the hotspots in the field of building energy efficiency with the help of information technology based on a specific research timeline and publications.

Hydroponic pots are being used to manage agriculture in the most effective way. Hydroponics is a model of advancement in agriculture. When a farmer uses an effective hydroponic system, plants produce thirty to forty percent more than they would under conventional agriculture. We have created a hydroponic system using the Nutrient Film Technology (NFT) and used a more sophisticated IOT network that allows anyone, anywhere, at any time to use a smartphone to monitor an NFT hydroponic system. Hydroponic system characteristics including temperature, humidity, water level, water quality, and soil moisture are collected using an ESP32 microcontroller. Here, we used the Message Queuing Telemetry Transport (MQTT) App to show the hydroponic system sensor information. To send and receive messages, a MQTT-enabled mobile app uses a MQTT library. Utilizing the MQTT Protocol, all acquired sensor data is sent to the MQTT App, where the values are also shown on an LCD.

Technology known as the Industrial Internet of Things (IIoT) is becoming more important to businesses as a means of boosting productivity and making more educated decisions about their operations. Despite the fact that connected environments provide significant advantages to companies, users of IIoT are especially susceptible to incursions on account of a few key characteristics. To begin, there will be an increase in the number of entry points into industrial systems when there are more linked endpoints. There is also the problem of functioning equipment being modified for applications it was never intended for, communication methods that are vulnerable, and software-hardware configurations that considerably vary across businesses and industries. The most current research in the area of Internet of Things (IIoT) cybersecurity is thoroughly reviewed in this article. Data protection through encryption techniques, using deep learning and machine learning approaches to detect attacks on the IIoT network, and implementing honeypot designs as a preventative measure to safeguard against data loss during potential cyberattacks are the main areas of focus for the research.

The accurate forecasting of solar power is an essential component of both the effective incorporation of solar power into the existing electrical grid and the improvement of overall energy management. Using a Soft Voting based Ensemble Machine Learning Classifier is the innovative strategy that we present here for estimating the amount of electricity that will be generated by the sun. The Adaboost, KNN, Logistic Regression, and SVC machine learning classifiers are just few of the examples that are included in the ensemble model, which integrates the best aspects of numerous machine learning classifiers into one reliable and precise prediction system. The ensemble model is able to successfully represent the variability of solar energy production in response to changing weather conditions because it makes use of the many learning methodologies that these classifiers have to offer. The Soft Voting process makes certain that the predictions from the many individual classifiers are given the proper amount of weight, which in turn improves the overall predictive performance of the ensemble. Comprehensive tests are run on datasets collected from actual solar power production in the real world in order to evaluate the efficacy of the proposed technique. The findings show that the ensemble model is superior than individual classifiers. The Soft Voting based Ensemble Machine Learning Classifier is a promising and practical approach for increasing the accuracy of solar power prediction, which contributes to the smooth integration of renewable energy sources and to the management of sustainable energy. The accuracy of the model that was proposed was 91%.