This paper considers the age-optimal joint sampling and transmitting scheduling design for wireless sensor networks with energy harvesting. The optimization problem is modeled as a Markov decision process (MDP), and a low-complexity online policy is proposed based on the formulation, which has two main steps, i.e., “separate deciding” and “joint scheduling”. Specifically, each sensor's action is determined by the double-threshold policy first and then the scheduled sensor is chosen from the set of non-idle sensors by comparing the expected reward gain. The proposed policy can be extended to the case of partial state information knowledge, where the state information at the sensors' side can only be delivered through packet updatings. Numerical results show that our proposed policy outperforms the existing benchmarks under general settings.

This paper considers the design of age-optimal packet scheduling policies in networks with delayed feedback, long-term resource constraint and various packet arrival models. The problem is formulated as a constrained partial observable Markov decision process (CPOMDP) in general. We first derive the achievable age of information (AoI) by the random and determined transmission policies, which works in the absence of feedback. Then, we propose a greedy policy that traces the expected receiver AoI based on the delayed feedback, and selects the action that minimizes the expected immediate Lagrange cost defined in the formulated CPOMDP. The proposed policy outperforms the existing policies for a wide range of feedback delay, even in the absence of feedback, yet the implementation complexity is still low since the decision threshold is derived in the closed-form, as a function of the network parameters.