Multi-Agent Deep Reinforcement Learning for Packet Routing in Tactical Mobile Sensor Networks

Tactical wireless sensor networks (T-WSNs) are used in critical data-gathering military operations, such as battlefield surveillance, combat monitoring, and intrusion detection. These networks have unique challenges, such as jamming attacks, which are not normally encountered in traditional WSNs. Jamming attacks on the networks' links disrupt data communication and make packet routing in T-WSNs a difficult task. Consequently, T-WSN routing aims to find the most reliable routes, while meeting the stringent delay and energy requirements. To this end, we propose a distributed multi-agent deep reinforcement learning (MADRL)-based routing solution for multi-sink tactical mobile sensor networks to overcome link layer jamming attacks. Our proposed routing scheme captures the hop count to the nearest sink, the one-hop delay, the next hop's packet loss rate (PLR), and the energy cost of packet forwarding in the action reward estimation. Furthermore, the proposed scheme outperforms benchmark algorithms in terms of the packet delivery ratio (PDR), packet delivery time, and energy efficiency.