Cross-Domain Lifelong Reinforcement Learning for Wireless Sensor Networks

Wireless sensor networks (WSNs) with energy harvesting (EH) are expected to play a vital role in intelligent 6G systems, especially in industrial sensing and control, where continuous operation and sustainable energy use are critical. Given limited energy resources, WSNs must operate efficiently to ensure long-term performance. Their deployment, however, is challenged by dynamic environments where EH conditions, network scale, and traffic rates change over time. In this work, we address system dynamics that yield different learning tasks, where decision variables remain fixed but strategies vary, as well as learning domains, where both decision space and strategies evolve. To handle such scenarios, we propose a cross-domain lifelong reinforcement learning (CD-L2RL) framework for energy-efficient WSN design. Our CD-L2RL algorithm leverages prior experience to accelerate adaptation across tasks and domains. Unlike conventional approaches based on Markov decision processes or Lyapunov optimization, which assume relatively stable environments, our solution achieves rapid policy adaptation by reusing knowledge from past tasks and domains to ensure continuous operations. We validate the approach through extensive simulations under diverse conditions. Results show that our method improves adaptation speed by up to 35% over standard reinforcement learning and up to 70% over Lyapunov-based optimization, while also increasing total harvested energy. These findings highlight the strong potential of CD-L2RL for deployment in dynamic 6G WSNs.

Optimal Routing and Link Configuration for Covert Heterogeneous Wireless Networks

Nodes in contemporary radio networks often have multiple interfaces available for communication: WiFi, cellular, LoRa, Zigbee, etc. This motivates understanding both link and network configuration when multiple communication modalities with vastly different capabilities are available to each node. In conjunction, covertness or the hiding of radio communications is often a significant concern in both commercial and military wireless networks. We consider the optimal routing problem in wireless networks when nodes have multiple interfaces available and intend to hide the presence of the transmission from attentive and capable adversaries. We first consider the maximization of the route capacity given an end-to-end covertness constraint against a single adversary and we find a polynomial-time algorithm for optimal route selection and link configuration. We further provide optimal polynomial-time algorithms for two important extensions: (i) statistical uncertainty during optimization about the channel state information for channels from system nodes to the adversary; and, (ii) maintaining covertness against multiple adversaries. Numerical results are included to demonstrate the gains of employing heterogeneous radio resources and to compare the performance of the proposed approach versus alternatives.