In particular, predicting the temperature and humidity information plays a crucial role in plantation, estimating rainfalls and climate change, and predicting air quality via specified geographical regions. The temperature and humidity forecasting information is occasionally presented with low accuracy due to uncertain techniques and vast methods that employ different sensors and models. For this reason, this work proposes an Internet of Things (IoT) temperature and humidity forecasting model based on an improved whale optimization algorithm with long short-term memory (IWOA-LSTM) technique. To increase the convergence speed processing time and overcome the local optimization problem, the IWOA is introduced. The number of hidden layers, learning rate momentum, and weight decay of the LSTM optimized using the IWOA. The actual temperature and humidity data are collected using DHT11 and ESP8266 NodeMCU practical model and processed using the ThingSpeak platform. The processing data stage depends on filling the missing data gaps using the rolling average technique (RAT). The performance evaluation of the proposed IWOA-LSTM forecasting model is assessed using some statistical functions, namely known as mean square error, mean absolute error, root mean square error, and mean absolute percentage error. The IWOA-LSTM techniques were also assessed using throughput, latency, and power consumption. The developed IWOA-LSTM model shows high accuracy, leading to better forecasting information than other forecasting models. 

Building an IoT temperature and humidity forecasting model based on long short-term memory (LSTM) with improved whale optimization algorithm

In industries and other locations gas leakage causes number of negative health effects .so an early detection of gas leakage and alertness will reduce the damage and save human life’s. Gas leakage techniques, trends and sensors are constantly evolving, and it is important for developers and researchers to stay up-to-date on the latest advancements. This paper conducts a systematic literature review on current state of gas leakage detection using Internet of Things (IOT) and Cloud technology. It explores the various sensor-based and non-sensor based IOT systems available for gas leakage detection, and their relative advantages and disadvantages. Additionally, this review summarizes current trends and challenges in the field of gas leakage detection, and discusses future research directions for improving the reliability and accuracy of these systems. This literature review highlights the need for more efficient, cost effective, and scalable IOT-based solutions for gas leakage detection.

https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01063.pdf

Gas Leakage Detection System Using IoT And cloud Technology: A Review

The MD5 algorithm is widely employed for generating fixed-size hash values to ensure data integrity. By incorporating MD5 into the UVM verification framework, designers can efficiently verify the algorithm's functionality within digital designs. The UVM framework provides a structured environment with standardized components, including agents, sequences, and checkers, facilitating the development and verification of complex designs. Dedicated UVM checkers designed for MD5 verification automate and streamline the verification process, ensuring the accurate operation of the MD5 algorithm. These checkers monitor input and output data, comparing the generated hash value with the expected outcome, enabling thorough verification of the algorithm's integrity. Integrating MD5 into the UVM verification framework with checkers improves the verification process's efficiency, reducing manual efforts and enhancing reliability. This integration allows designers and verification engineers to effectively validate the implementation and accuracy of the MD5 hash algorithm in digital designs.

Designing and validating an MD5 Hash Algorithm using the UVM Verification Framework and Checkers

A wireless remote-control lock system is used for pedestrian entrances to both homes and businesses. To engage and disengage a door latch component, this locking mechanism is powered by electricity and consists of a striker plate assembly with a movable striker plate element A radio frequency receiver and a circuit that responds to signals from an operator-controlled radio emitter power the striker assembly. The striker assembly has the ability to unlock the door either temporarily or permanently. The circuit in the control unit is made up of a self-latching relay, a selection switch, and a second relay. The door will stay open for up to 3.5 seconds, or until the emitter transmits a new signal to the receiver. Because everyone is accustomed to using physical keys to lock and unlock doors, this is the most natural and prevalent method. The physical key is a well-known and dependable mechanism, but it has limitations. It is impossible to have multiple distinct keys for the same lock. Several of the locks work with multiple key types.

A Novel Hybrid Approach to Smart Door Locking System based on Real Time Data Transmission

This study aims to set up a system that will watch for leaks of liquid petroleum gas in both homes and businesses. This data will update through IoT connectivity to the user's web browser in the case of a gas leak. Using IoT, the user may keep a constant eye on the data. The MQ06 sensor is a gas detector that is sensitive to LPG. LPG gas is detected using an Arduino-based Internet-of- Things (IoT) leak detection device. Interfacing an LPG gas sensor with an Arduino is the main focus of this research study. This is the sensor that sends the signal to the Arduino microcontroller. The microcontroller is in link to an indicator light (LED), an alarm (buzzer), and the Internet of things (ESP8266) through an IoT module. The ESP8266 chip is to make this Internet of Things (IoT) LPG leak detector work. This Wi-Fi module may link microcontrollers to a wireless network, allowing them to access the Internet and transmit and receive data through TCP/IP. Afterwards, the IoT receives emotional data. Using the IoT module, the web may send information. When the detector picks up a gas leak and turns on the device, the site will show a “leak” message. Installations of an IoT and Arduino-powered LPG leak monitoring system are suitable for dwellings, lodging establishments, and LPG cylinder storage locations. The main benefits of this project are that leakage can be found during monitoring, and corrective action data can send to a website for analysis. IoT reports may prevent injuries and property damage if appropriate measures take promptly.

LPG Cylinder Leakage Monitoring by IoT

IoT enabled smart solar water heater system using real time ThingSpeak IoT platform

Now a day’s lot of home fires have been taking place frequently due to the leakage of gases such as LPG, etc. The LPG is highly inflammable and it can easily cause explosion. Liquefied petroleum gas consists of butane and propane. Its versatile nature enables it to get used in various needs like industrial fuel, domestic fuel etc. Most of the fire accidents are caused due to poor quality regulators, human careless and failing to turn off regulators when they are unused. Therefore, the detection of gas detection system is essential. This paper presents the gas detections model using Arduino UNO on the basis of IOT (internet of things). The gas sensor is used to identify the smell of gas leakage. LCD is turned on, when the gas gets leaked. If gas leakage takes place, both the LCD and GSM modem will be turned ON. With the help of GSM modem, messages notifying gas leakage will be send as SMS to user. Then user can take a proper action to avoid any incidents from happening. To avoid fire accidents due to gas leakage, this proposed gas leakage system using IoT has been developed and presented in this paper.

Smart Detection System for LPG Gas Leakage using IoT

In modern days, health monitoring plays a vital role in monitoring the patients' health. It is advised that hospitals integrate health monitoring systems into their existing medical infrastructure so that doctors can monitor the vital signs of their patients. The Internet of Things (IoT), recently undergone technological improvements, which interconnects everything to one another and subsequently regarded as new technology. The data taken from the patient needs internet connections to transmit, but not all patients can access good internet connectivity. With these modern technologies, the healthcare sector improves on rapidly with new innovations. With the help of edge technologies like wearables, wireless networks, and other remote instruments, health technologists and professionals have created a great, affordable healthcare monitoring system for people dealing with a variety of conditions. IoT based health monitoring systems are used to collect and exchange patient data from hospital sensors. Here, this study use temperature sensor, pulse sensor, tilt sensor and flex sensors. All these sensors are combined in a single kit of Raspberry pi Pico W. When an input is drawn from the patients all the needed data will be obtained by using these sensors. Specially it also be used for bedridden patients which help in monitoring their movement.

Patient Health Monitoring using Fog and Edge Computing

The concerned workers ensure that vehicles are parked in the appropriate spaces. Employees must repeatedly poll their coworkers via personal surveys or the company's internal phone system to ensure that everything is correct. If there is an available parking space, the driver will manually move the vehicle there, regardless of any obstacles. If there is no available space, the car must return and try again later. The proposed intelligent parking system, if implemented, would solve all parking problems. This will take less time and fuel than other options. Intelligent parking solutions will fundamentally alter automobile-centric cities. It may make parking more convenient by bringing order to the chaos. People are always concerned that finding a parking spot will take too long, particularly in densely populated cities. This study focuses on developing a new system in which residents in high-traffic areas can earn extra money by renting out their unused parking spaces to those in need. This strategy could help malls to manage parking more effectively during peak shopping hours. A customer can save time by reserving a parking space ahead of time.

An IoT based Low Cost E-Parking System in Smart Cities

Experimental Possibilities of Decentralized Health Information System Interoperability Using Blockchain Technology

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