LAUSR

Age of Information (AoI), a metric measuring the information freshness, has drawn increased attention due to its importance in monitoring applications in which nodes send timestamped status updates to interested recipients, and timely updates about phenomena are important. In this work, we consider the AoI minimization scheduling problem in multihop energy harvesting (EH) wireless sensor networks (WSNs). We design the generation time of updates for nodes and develop transmission schedules under both protocol and physical interference models, aiming at achieving minimum peak AoI and average AoI among all nodes for a given time duration. We prove that it is an NP-Hard problem and propose an energy-adaptive, distributed algorithm called the minimizing AoI scheduling algorithm for general network (MAoIG). We derive its theoretical upper bounds for the peak and average AoI and a lower bound for peak AoI. The numerical results validate that MAoIG outperforms all of the baseline schemes in all scenarios and that the experimental results tightly track the theoretical upper bound optimal solutions while the lower bound tightness decreases with the number of nodes.