## 2 Related Work  
This section provides an overview of the most recent advancements that have been made in the application of WSNs. Precision agriculture, industrial, forestry, and weather monitoring systems can all benefit from the suggested method. Industry 4.0 categorises numerous wireless sensor networks [7; 8] coverage approaches, deployment issues, sensing models, and research obstacles, including sensor networks, fog, edge computing, distributed control systems, digital twins, and cyber-physical systems. Due to the size and adaptability of our system, it is able to support the rapid deployment of IoT devices on conveyor belts for the purpose of monitoring the process of production lines or various areas for environmental monitoring. The IoT application for forestry management involves data transfer to cover locations where 3G/4G signals are unavailable. The authors used low power wide area network (LPWAN), NB-IoT (NB--narrow band), LoRa (long-range), and appropriate sensors to convey data [9]. Using that approach, they employ two repeaters to cover the more considerable distance. In addition, the sensor network that they have works well for measuring air pressure, UV light, and carbon dioxide on hillsides. In light of the findings from earlier research, the decision was made to install retransmitters to cover the distance inaccessible to communication channels. Atmospheric environmental monitoring plays a major role in monitoring the aerosol, particulate matter, haze, dust storms, straw burning, and gaseous pollutants that contribute to global warming. Three types of atmospheric monitoring use remote satellite sensing, and improved retrieval methods, product validation, local application, air quality monitoring, and control are all part of the network [10]. However, according to experts, the current monitoring precision is insufficient to meet the management demands of the atmospheric environment. A portable weather-monitoring system for greenhouses to measure temperature, humidity, and moisture is presented in [11]. The solution provides a simple gadget that could measure such characteristics reliably and practically. The gathered sensor values are regularly updated using the ThingSpeak IoT's web service. However, this approach is unreliable in a larger region. The proposed system in our work using the PHP application and MySQL database results in easy time-series management and can forecast future sensor values in the web server, and the real-time charts can display a maximum of 150 data points. Traditional farmers gain a valuable technological tool by using the IoT to monitor and control environmental variables in a coffee crop via a wireless sensor network. This allows them to increase their economic benefits, reduce environmental impact, and improve the quality of life. The Zigbee protocol by Zigbee Alliance was suggested in [12] over other wireless technologies with equivalent capabilities because of its low cost. However, due to the excessive humidity in coffee plantations, the failure to execute all of the anticipated measurements causes difficulties with the sensors and the readings of this monitoring system. The energy optimisation of the quality of service (QoS) in wireless sensor networks is highly essential and depends on different routing protocols such as the RPAR (real-time power-aware routing) protocol, GRPAR (greedy real-time power-aware routing) protocol [13], and the low-energy adaptive clustering hierarchy (LEACH) protocol. The latter is based on clustering, and the nodes can only send to the cluster head (CH). Since WSNs interact with the environment, their characteristics are expected to differ from conventional data networks. QoS support for the network has to consider a few of the unique challenges such as severe resource constraints, unbalanced traffic, data redundancy, network dynamics, energy balance and scalability, multiple sinks, traffic types, and packet criticality.