ABSTRACT

The energy consumption of the routing protocol can affect the lifetime of a wireless sensor network (WSN) because tiny sensor nodes are usually difficult to recharge after they are deployed. Generally, to save energy, data aggregation is used to minimize and/or eliminate data redundancy at each node and reduce the amount of the overall data transmitted in a WSN. Furthermore, energy-efficient routing is widely used to determine the optimal path from the source to the destination, while avoiding the energy-short nodes, to save energy for relaying the sensed data. In most conventional approaches, data aggregation and routing path selection are considered separately. In this project, we consider the degrees of the possible data aggregation of neighbor nodes when a node needs to determine the routing path. We propose a novel Q-learning-based data-aggregation-aware energy-efficient routing algorithm. The proposed algorithm uses reinforcement learning to maximize the rewards, defined in terms of the efficiency of the sensor-type-dependent data aggregation, communication energy and node residual energy, at each sensor node to obtain an optimal path. We used sensor-type-dependent aggregation rewards. Finally, we performed simulations to evaluate the performance of the proposed routing method and compared it with that of the conventional energy-aware routing algorithms. Our results indicate that the proposed protocol can successfully reduce the amount of data and extend the lifetime of the WSN.