Most of the urban areas in India depend on Government water supply for their daily usage. The amount of water supply will be limited and provided in a short period of time by the Government. According to the climate and environmental changes the amount of water will be varied. Now many private companies have emerged in the field of water supply to sell water to gain huge profit. But still Government water supply is the main source for common people in urban areas of India. Most of the water connections are given to the individual home by plastic or steel pipes through underground or overground. It is very easy for a person to steal the water from the pipeline illegally. Others cannot identify the theft easily and they will be cheated. Consequently, water availability/supply for the homes in that area will be reduced. A system has been proposed in the paper to find and stop the water theft in an area using the sensors and IoT technology. This system can also identify the leakage of pipes anywhere in the line.
With the emergence of the Internet of things (IoT), embedded systems have now changed its dimensionality and it is applied in various domains such as healthcare, home automation and mainly Industry 4.0. These Embedded IoT devices are mostly battery-driven. It has been analyzed that usage of Dynamic Random-Access Memory (DRAM) centered core memory is considered the most significant source of high energy utility in Embedded IoT devices. For achieving the low power consumption in these devices, Non-volatile memory (NVM) devices such as Parameter Random Access Memory (PRAM) and Spin-Transfer Torque Magnetic Random-Access Memory (STT-RAM) are becoming popular among main memory alternatives in embedded IoT devices because of their features such as high thickness, byte addressability, high scalability and low power intake. Additionally, Non-volatile Random-Access Memory (NVRAM) is widely adopted to save the data in the embedded IoT devices. NVM, flash memories have a limited lifetime, so it is mandatory to adopt intelligent optimization in managing the NVRAM-based embedded devices using an intelligent controller while considering the endurance issue. To address this challenge, the paper proposes a powerful, lightweight machine learning-based workload-adaptive write schemes of the NVRAM, which can increase the lifetime and reduce the energy consumption of the processors. The proposed system consists of three phases like Workload Characterization, Intelligent Compression and Memory Allocators. These phases are used for distributing the write-cycles to NVRAM, following the energy-time consumption and number of data bytes. The extensive experimentations are carried out using the IoMT (Internet of Medical things) benchmark in which the different endurance factors such as application delay, energy and write-time factors were evaluated and compared with the different existing algorithms.
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