A novel scheduling strategy for priority-aware iot networks for age of information optimization

As the need for wireless communication grows, the need to increase spectral efficiency and reduce latency in communication has become more critical. In this study a novel scheduling strategy is proposed based on solving the Knapsack problem in Internet of Thing networks. The main goal is to minimize the difference between Age of Information (AoI) values of sensors and Peak AoI (PAoI) constraints subject to average and peak transmission power constraints given the communication resources are scarce. At first, we formulated the problem according to the specified PAoI constraint, transmission power, and frequency band constraints.The proposed approach involves reformulating the original problem as a Knapsack problem. This is done by assigning a value as the decrease in AoI of a sensor if a status update transmission is successful. In addition, a weight is specified as the frequency band amount that a sensor requires to transmit its update. Additionally, Fully Polynomial-Time Approximation scheme (FPTAS) is proposed to decrease the computational complexity while preserving the quality of results. Secondly, algorithms of benchmark methods were created together with the proposed method. Finally, the results of the simulations completed using the adapted algorithms are given. The proposed scheduling method is shown to outperform the benchmarks which are Multi-Armed-Bandit Q-learning and Whittle's Index strategies.