Rapid technical advancements in recent times have made it possible to produce a class of affordable unmanned aerial vehicles (UAVs), which can serve a wide range of public and private users for different industrial or non-industrial requirements. It is very complex to set up a communication network among the UAVs, which is very essential for completing any task like collecting data in extreme weather conditions. For UAV placement, we have suggested a distributed approach using PPO-based reinforcement learning; here, UAVs are utilized as aerial access points to create a mesh network that connects to ground nodes positioned in a designated area and can collect data from ground nodes to predict weather conditions. Our objective is to fulfill each ground node’s data rate requirements while deploying the fewest number of UAVs possible to cover it. The convex hull formation of swarm uavs satisfy our objective, which arranges the n uavs on a convex hull’s vertex. In this study, we present a deep reinforcement learning-based method for building swarm uavs with convex hull patterns in the euclidean plane. The existing research on convex hull pattern generation uses heuristic and combinatorial optimization techniques and evaluates performance in terms of time and space consumed. Convex hull patterns increase the mutual visibility of swarm UAVs, which helps in the cooperation of UAVs. In our research for the first time, convex hull pattern formation around a given target point is accomplished using PPO-based Deep Reinforcement Learning (DRL).

Infrastructure for managing and distributing water in the traditional manner is somewhat outdated. As a result of incorporating information and communication technologies into the current system, a smart water management system has been developed. The smart water management system uses a number of different technologies to monitor and sense water leaks, theft, and delivery. The supervisory control and data acquisition system SCADA, which shows that the distribution side is being monitored less, has very little impact at the pump house level. The development of the Internet of Things (IoT) has made it possible to connect a large number of monitoring devices to the Internet, which will be more beneficial for automating water delivery and leak detection. As a result, a fog-integrated IoT-based smart water distribution and monitoring system for a smart city is proposed. This system will handle customer utilization, water theft detection, water quality control, fault prediction, fault localization, and rainfall prediction. Fog and cloud computing enable these system processes in order to achieve effective control action. In this work first, we apply the transformer for the rainfall prediction and then use ant colony optimization for optimization of the water distribution process.