Authors: Chavan Prathmesh Sambhaji, Dhakne Vaibhav Balasaheb, Giram Devandra Rajandra, Prof. S. D. Chougule, Prof. M. S. Nalawade, Dr. V. M. Dhede

Abstract: Electric fans are used for personal comfort and for cooling equipment such as power supplies, boilers, incubators and ovens. A fan that always runs at full speed wastes energy and produces unnecessary noise, while manual speed selection depends on the user noticing a change in temperature. Automatic fan speed control solves this by measuring temperature and adjusting fan speed continuously. This paper reviews the design of such controllers built around low-cost microcontrollers of the AVR family, such as the ATmega8, ATmega168 and ATmega328 used on Arduino boards. The review covers temperature sensing with the LM35 and alternative sensors, analog-to-digital conversion, pulse width modulation (PWM) drive through a transistor stage, display of set and measured values on a 16×2 LCD, and the power supply. Control strategies ranging from on–off and stepped control to proportional, PID and fuzzy logic control are explained and compared. A reference architecture and control flow are presented for a stand-alone controller using a 12 V, 2 A supply, together with design calculations. Applications in air-conditioning, water heaters, incubators and industrial process equipment, as well as limitations and possible improvements such as wireless monitoring, are discussed.

DOI: https://doi.org/10.5281/zenodo.23881620

Published by: vikaspatanker

Authors: Dnyaneshwari Babasaheb Niwdunge, Rasika Jijabhau Gunjal, Priti Vilas Hadawale, Dr. V. M. Dhede, Prof. M. S. Nalawade

Abstract: Many personal safety devices depend on the user pressing a panic button or unlocking a smartphone, yet in a fall, a sudden medical event, an assault or harassment the person may be unable to do either. A wearable that recognises such situations by itself and calls for help silently can close this gap. This review examines the technologies needed for such a device and organises them around SilentSOS, a multimodal context-aware wearable concept. It surveys physiological sensing of heart rate and blood oxygen saturation by photoplethysmography, motion sensing and fall detection with inertial sensors, skin and ambient temperature monitoring, acoustic analysis of surrounding sounds, satellite positioning and cellular alerting. It then reviews how machine learning combines these signals so that the decision does not rest on a single sensor, which reduces both missed events and false alarms. A layered architecture is presented in which an ESP32 microcontroller acquires data from MAX30102, MPU6050, temperature and microphone sensors, extracts features, classifies the situation as normal, suspicious or high-risk, and sends an SMS with location and readings through a GSM module, with a discreet gesture available for manual activation. Design trade-offs including battery life, privacy, on-device inference and validation are discussed, together with future extensions such as ECG and blood-pressure estimation.

DOI: https://doi.org/10.5281/zenodo.23863454

Published by: vikaspatanker

Authors: Kapadi Lalita Arvind, Kurhe Yashomati Satish, Kute Ruchika Santosh, Dr. V. M. Dhede, Prof. M. S. Nalawade

Abstract: The Internet of Things (IoT) links sensors, machines, vehicles and everyday objects so that they can exchange data and act with little human intervention. Fifth-generation (5G) networks introduced dedicated support for massive machine-type and ultra-reliable low-latency communication, but emerging applications such as autonomous transport, remote surgery, immersive extended reality and large-scale digital twins demand still higher data rates, lower latency, greater reliability, precise positioning and far denser connectivity. Sixth-generation (6G) networks, now being defined under the IMT-2030 framework, aim to meet these needs and to make artificial intelligence a native part of the network. This survey reviews 6G-enabled IoT. It summarises the requirements and key performance indicators, and explains the enabling technologies: terahertz and sub-terahertz communication, intelligent reflecting surfaces, integrated sensing and communication, edge intelligence, AI-native network management and non-terrestrial networks. A five-layer architecture for 6G-enabled IoT is presented, linking devices, access, edge, core and applications under a cross-layer security and energy plane. Applications in smart cities, connected vehicles, healthcare, industrial automation, precision agriculture and environmental monitoring are discussed. Finally, open challenges including infrastructure cost, spectrum regulation, energy consumption, security and privacy, and the management of billions of devices are identified.

DOI: https://doi.org/10.5281/zenodo.23881407

Published by: vikaspatanker

Authors: Dhavale Priyal Sandesh, Dhavale Pranjal Sandesh, Gaikar Aditi Bhausaheb, Dr. V. M. Dhede, Prof. M. S. Nalawade

Abstract: The rapid growth of online services, digital education and large-scale artificial intelligence has increased the electricity demand of cloud data centres, where servers, storage, networks and cooling equipment run around the clock. Rule-based management policies, which react to fixed thresholds, often leave servers underused and cooling plants over-provisioned. Artificial intelligence (AI) offers a different approach: models learn from operational data, anticipate demand and choose actions that save energy while protecting service quality. This review examines how AI is applied to four tasks in a green data centre: forecasting workload, allocating and consolidating virtual machines, scheduling tasks with awareness of energy and carbon, and controlling cooling systems. Statistical forecasting, recurrent neural networks, reinforcement learning and data-driven cooling control are explained and compared through representative studies. An AI-based green-cloud framework is then described that combines a learning-based demand predictor, an energy- and carbon-aware scheduler, a cooling optimiser and a renewable-supply monitor in a closed control loop. A simulation-based evaluation plan using energy, response time, utilization and carbon emission as metrics is outlined. Challenges including data quality, model interpretability, safety of autonomous control and the energy cost of the AI models themselves are discussed.

DOI: https://doi.org/10.5281/zenodo.23879779

Published by: vikaspatanker

Authors: Chaskar Siddhi Dattatray, Chinchawade Tanvi Santosh, Bhore Tanvi Santosh, Prof. P. S. Dumbre, Prof. M. S. Nalawade, Dr. V. M. Dhede

Abstract: Forest fires destroy trees, wildlife habitats and natural resources, release large amounts of smoke and threaten nearby communities. Their impact depends strongly on how quickly they are detected, yet traditional methods based on watchtowers and ground patrols require continuous human effort and are slow to cover remote terrain. Satellites observe large areas but revisit a location only a few times a day and cannot see small fires under cloud or canopy. Unmanned aerial vehicles, commonly called drones, fill this gap by carrying cameras and sensors over the forest on demand. This survey reviews forest fire detection methods and focuses on drone-based systems. It discusses the sensing payload, including visible and thermal cameras and gas and temperature sensors, the detection algorithms, from colour-rule methods to convolutional neural networks, and the communication links that deliver alerts with GPS coordinates to forest authorities. Representative studies and datasets are compared. A layered architecture for a smart drone-based fire detection and alert system is then presented, covering sensing, onboard processing, communication, ground-station verification and alert dissemination. Practical challenges such as limited flight time, false alarms, communication range and regulation are discussed along with future directions such as drone swarms and integration with satellite data.

DOI: https://doi.org/10.5281/zenodo.23862991

Published by: vikaspatanker

Authors: Bhise Pallavi Ramdas, Ganjale Tanishka Jayram, Ghule Srushti Suresh, Prof. M. S. Nalawade, Dr. V. M. Dhede

Abstract: The temperature of tap water changes with the seasons. In summer, water stored in rooftop tanks and exposed pipes can become uncomfortably warm, while in winter it can feel too cold for washing or drinking. Users often wait for the water to change temperature or depend on separate coolers and heaters, which wastes water and energy. A smart adaptive tap addresses this by sensing the ambient conditions, deciding a comfortable water temperature and conditioning the water automatically. This paper reviews the technologies required for such a device. It covers temperature sensors for air and water, flow sensing to detect when the tap is in use, cooling and heating methods with emphasis on thermoelectric Peltier modules and resistive heaters, and control strategies from hysteresis to PID and fuzzy logic. Basic heat-transfer relations are used to estimate the power needed to change the temperature of flowing water, showing why a small conditioned reservoir is more practical than instantaneous cooling for most household flows. A system architecture and control flow are proposed in which a microcontroller maps ambient temperature to a target water temperature, compares it with the measured value and drives cooling or heating only while water is drawn. Applications, safety requirements, limitations and future enhancements such as user profiles and IoT monitoring are discussed.

DOI: https://doi.org/10.5281/zenodo.23879037

Published by: vikaspatanker

Authors: Nakirekanti Srinu, A.Upendar

Abstract: Earthquakes produce ground motions with varying frequency characteristics that influence the seismic response of reinforced concrete buildings. This study examines regular and irregular G+9 and G+19 reinforced concrete structures subjected to low-, intermediate- and high-frequency ground motions across different seismic zones. Nonlinear time-history analysis is performed using ETABS to compare base shear, natural time period, storey displacement and storey drift. The study evaluates the effects of building height, structural irregularity, seismic zone and earthquake ground-motion characteristics on structural response. Comparative results are presented through the original numerical tables, structural models and graphical outputs. Keywords: reinforced concrete; seismic response; regular and irregular buildings; ETABS; nonlinear time-history analysis; ground motion frequency.

DOI: https://doi.org/10.5281/zenodo.23861405

Published by: vikaspatanker

Authors: Karnati surya teja, A.upendar

Abstract: A lot of research work has been going on in the field of concrete and constant efforts are going on to improve its performance with respect to strength and durability. One of major considerations in concrete making is gaining control on water. The water supplied during mixing in concrete is needed to hydrate the cement and to achieve the required rheology properties of various concrete materials in their mixing, transportation, placing and compacting. During hydration, there is a need to maintain a correct relative humidity and temperature for developing good concrete. Decrease in relative humidity will cause self desiccation or chemical shrinkage at micro level. To prevent the above problem, it is advised to make embedded water available for curing. There are a number of methods for providing embedded water for curing viz., using light weight aggregate, super absorbent polymers and hydrophilic materials.

DOI: https://doi.org/10.5281/zenodo.23858878

Published by: vikaspatanker

Authors: Sphoorthy Reddy Surakanti, Dr. S. Ramachandram

Abstract: The use of IoT devices in huge numbers results in massive amounts of sensitive data being outsource into cloud storage systems, making it necessary to design efficient search schemes over such encrypted data. Phrase Search allows users to retrieve their documents by matching exact sequences of keywords, but existing solutions have not found a good balance between search time, storage space, and privacy loss due to their static design. In this work, we propose an efficient Dynamic Adaptive Bloom Filter based Privacy-Preserving Phrase Search (DABF-PPS) scheme for encrypted IoT cloud data. We introduce a dynamic parameter optimization method to adaptively adjust Bloom filters according to document set properties and queries. A hierarchical n-gram Bloom filter index is designed to allow phrase searches without exposing keyword locations. Furthermore, our solution supports dynamic updating of indices with the help of counting Bloom filter structures and lazy deletion. Our security analysis proves that our solution provides forward and limited backward privacy against chosen-keyword attacks. Our experiments on real-world IoT datasets show that our approach decreases search time and storage space while maintaining acceptable false-positive rates.

DOI: https://doi.org/10.5281/zenodo.23834235

Published by: vikaspatanker

Authors: Vinutha R

Abstract: The emergence of advanced cyber-attacks targeting the latest network infrastructure has highlighted the inefficiencies of signature-based systems employed to detect such attacks, which are unable to recognize new threats due to their lack of adaptability. The current paper offers a machine learning framework for cyber threat detection and network security improvement through real-time application of streaming data analysis techniques together with ensemble learning approaches. The system is based on a hybrid framework comprising of the Random Forest and XGBoost classifiers for detection of known attacks and autoencoder-based anomaly detection method for identifying unknown threats. Feature engineering is used to extract static and behavioral features of the network traffic. In addition, the system includes an adaptive response strategy, which allows the system to dynamically modify the network security policy depending on the threat level. It was shown that the offered system is capable of providing efficient threat detection for different types of attacks, while being computationally efficient enough to be applied in a real-time scenario.

DOI: https://doi.org/10.5281/zenodo.23834126

Published by: vikaspatanker