Lowpan kullanarak kablosuz sensör ağı için enerji farkındalığını artırın

There are many networks in remote areas to support all areas and applications. Networks That allow connectivity covering a range of square kilometers are critical for these remote deployments. The widely used star topology is not ideal for a rural environment as the coverage is limited by the central axis positioning which also contributes to being a single point of failure. It is clear that mesh networks are more attractive in this respect, but scalability has always been an issue for mesh networks, especially with regard to routing. Saving power very fundamental in remote IoT deployments, where devices can be left in isolated fields for an extended period of time. In this thesis we dealt with the steering problem of remote sensors by introducing reinforcement learning energy-aware routing algorithms. We determined the strength of the RL routing algorithm for remote sensing networks. This thesis also presents a step-by-step detailed analysis of the RL routing algorithm to To prove the effectiveness of the algorithm. We model the network operating in a region bounded by a finite number of randomly distributed nodes within the region in this algorithm. Hence, we have defined the service area of the target network assuming network limitations in the model.