This paper proposes an improved Hopfield neural network (I-HNN) algorithm to optimize the slot assignment scheme in wireless sensor networks. The key advantage of the proposed algorithm is to increase the convergence probability under different traffic loads. To achieve this, nodes can adjust their slot demands according to the traffic load, slots number, and demand history. Various aspects of the network performances with the proposed I-HNN algorithm are evaluated via simulation. The results indicate that I-HNN is suitable for wireless sensor networks with dynamically varying traffic. In particular, it can increase the convergence probability and slot utilization under the heavy traffic load.
To address the problem with varied traffic loads, an Improved Hopfield Neural Network (I-HNN) REF was designed for the main purpose of assigning slot in wireless sensor networks.
A Dynamical Slot Assignment Method for Wireless Sensor Networks Based on Hopfield Network
International Journal of Distributed Sensor Networks
Computer Science