Improving AoI via Learning-based Distributed MAC in Wireless Networks

Abstract

In this work, we consider a remote monitoring scenario in which multiple sensors share a wireless channel to deliver their status updates to a process monitor via an access point (AP). Moreover, we consider that the sensors randomly arrive and depart from the network as they become active and inactive. The goal of the sensors is to devise a medium access strategy to collectively minimize the long-term mean network Age of Information (AoI) of their respective processes at the remote monitor. For this purpose, we propose specific modifications to ALOHA-QT algorithm, a distributed medium access algorithm that employs a Policy Tree (PT) and Reinforcement Learning (RL) to achieve high throughput. We provide the upper bound on the mean network AoI for the proposed algorithm along with pointers for selecting its key parameter. The results reveal that the proposed algorithm reduces mean network AoI by more than 50 percent for state of the art stationary randomized policies while successfully adjusting to a changing number of active users in the network. The algorithm needs less memory and computation than ALOHA-QT while performing better in terms of AoI.

Survey of Reinforcement-Learning-Based MAC Protocols for Wireless Ad Hoc Networks with a MAC Reference Model

“…When nodes become active and inactive in the WSN as nodes randomly join and leave the network, the long-term average network age of information (AoI) of their respective processes can be collectively minimized at the remote monitors. An optimized modification of the ALOHA-QT algorithm was proposed in [35], which employed a policy tree (PT) and RL to achieve high throughput. A PT is a prediction model, which represents a mapping relationship between attributes and values.…”

“…Similarly, there are some works for underwater acoustic networks (UANs) [10][11][12], the Internet of things (IoT) [13][14][15][16], unmanned aerial vehicle (UAV) formations [17][18][19][20][21], vehicular ad hoc networks [22], flying ad hoc networks (FANET) [23][24][25], heterogeneous networks (HetNet) [26][27][28][29] and next-generation wireless communication [30][31][32]. In addition, there are some works aimed at security [33], robustness [34], energy saving [35,36], adaptability [37][38][39][40][40][41][42] and stability [43][44][45][46][47][48][49][50][51]. As recent WNs become more complex, more demands are placed on learning systems [52].…”