It suggests a design that makes sensor networks in the Internet of Things (IoT) more energy-efficient by connecting them to LoRaWAN and combining edge computing. The main purpose of the framework is to tackle the main issues of energy use, slow response times and repeating data found in large-scale IoT projects, mostly in smart cities and remote monitoring. LoRaWAN’s abilities in ultra-low-power and long-range connection, in addition to the processing power at the edge, ensures that critical services can be managed directly on-site, while also making sure the system is not overly reliant on the cloud and reacts promptly. Smart data filtering is used to process and compress data from sensors at the edge which reduces the pointless transmission of large amounts of data. It also relies on local algorithms to pick out and handle critical situations occurring near the edge without needing approval from the cloud. Also, adaptive duty cycling is used so that nodes can change activity level based on the situation and what needs to be done, leading to much more efficient usage of energy and an extended network life. Reviews are carried out by running simulations and installing the system in smart agriculture, monitoring the environment and servicing industry situations. The amount of power used, how fast commands are executed, and the rate of delivered packets and responsiveness are monitored and measured against traditional approaches using a centralized system. Based on the results, energy efficiency is much better, bringing about a 40% lower electricity demand, plus greater accuracy in data and faster speed in functions. With this work, IoT networks focused on sustainability and real-time intelligence can now rely on a reliable and scalable option, encouraging more autonomous, dependable and responsive sensing systems. Using this approach, people can set up energy-aware intelligent IoT systems in new smart communities.
S. et al. (Mon,) studied this question.