Yi Shang, Markus P.J. Fromherz, Ying Zhang, and Lara S. Crawford
Future large-scale networks, such as sensor networks, will consist of hundreds and even thousands of wirelessly connected sensor and actuator nodes. The nodes are typically highly resource-constrained (processor, memory, and power), have limited communication range, and are prone to failure. Furthermore, there is no global information about the exact location and connectivity of the nodes. Consequently, the explicit consideration of network and task constraints and objectives will be an important part of routing algorithms for these networks. In this paper, we present a distributed constraint-based routing approach that represents destination conditions as well as routing constraints and objectives explicitly. We further present an efficient routing algorithm, CB-LRTA*, that extends traditional Learning Real-Time A* (LRTA*) with back-propagation. We evaluate CB-LRTA* using simulation and demonstrate that it improves convergence to the optimal route over LRTA*.