This dissertation aims to reuse the cold water which will initially present inside the water heater and reduce its wastage by sending it back to the water tank. The water heater temperature could be customized based on the number of users either manually or in an automated way using Internet of Things (IoT) techniques. The system is designed and operated using open‐source hardware and an IoT platform. The smart solar water heater with IoT capabilities continuously monitors the tank level, temperature, and pH level, and the data collected by the IoT node is uploaded/published to ThingSpeak. The IoT cloud analyses the sensor value and instructs the control unit of the smart solar system. The IoT enabled solar water heating system efficiency is high for smart homes and Industry 4.0. The main advantages of this model include handling multiple users, reducing the wastage of cold water, and purifying the water based on TDS level and this model aims to make it possible to control the proposed system from anywhere and at any time. The highest efficiency of the respective solar water heater could be well utilized.

This research presents a vehicle ID‐based congestion aware message (CAM) for beacon signals on the vehicle environment. At the MAC protocol of the vehicle environment, enhanced vehicle ID‐based analysis model is given first. With the automobile ID embedded in their separate CAMs, the model weights the randomized back‐off numbers chosen by cars engaging in the back‐off procedure. This leads to identifying a car ID‐based randomized back‐off code, which reduces the likelihood of a collision due to the identical back‐off number. A traffic density based‐congestion control algorithm (TDCCA) is suggested in this research. The revised mathematical approach surpasses previous work’s overall packet latency because just one‐fourth of the congestion window is employed during the experiment. The research includes a congestion management method that adjusts the rate of CAM transmitted over the host controller to improve the efficiency of the model parameters. The method considers various circumstances, from nonsaturated to substantially saturated networks (in terms of congestion probability) and sparsely dispersed and teemed networks (in the form of vehicular intensity). The technique is run across various automobile ID‐based back‐off values for high‐standard results analysis. The simulation outcomes in terms of packet delivery ratio, energy consumption, delay, success rate, and collision ensure the effectiveness of the TDCCA method. Even at high traffic densities, the automobile ID‐based CAM following information method outperforms the typical fixed CAM frequency IEEE 802.11p, according to simulation findings for all back‐off figures.