In Cognitive Radio Networks (CRNs), network fault analysis, traffic tracing, and resource optimization are challenging tasks. With the increasing number of wireless applications and the conflict with limited wireless spectrum resources, the Sniffers Channel Assignment problem in CRNs has become particularly important. To address this issue, we propose a Value Decomposition Networks-based channel selection (CSVDN) algorithm. During centralized training, the Monitoring Quality Network (MQN) is trained based on observed data, using global information to calculate the Quality of Monitoring (QoM), which is then used as a reward to guide sniffers in selecting the optimal channels. During decentralized execution, sniffers share model parameters and independently run the MQN, sequentially selecting the optimal channels. This process ensures that sniffers collectively maximize network coverage while maintaining distributed control, thereby improving efficiency and scalability in dynamic environments. The results from NS-3 simulations show that CSVDN provides a distributed and implementable channel selection solution with high scalability and practicality, making it particularly suitable for large-scale CRNs.

Due to the low bandwidth, high latency, and high communication energy cost of acoustic channel communication, data collection in underwater sensor networks is still severely limited, and it is particularly important to design an efficient and reliable routing protocol. Depth-based opportunistic routing has garnered significant attention due to its lack of reliance on 3-D geographic coordinates. Nevertheless, it suffers from the problems of void region and high energy consumption. In this paper, we propose an opportunistic routing protocol for underwater sensor networks based on the bargaining game. The protocol regards source and relay nodes as buyers and sellers, respectively, and establishes a multi-stage bargaining game model. In the phase of selecting the optimal relay set, the source and relay nodes make the decision of maximum payoff based on subgame perfect equilibrium to balance the energy consumption of the nodes. Subsequently, we introduce a recovery model to effectively mitigate the void region and long detour problems. In addition, an associated dynamic timing forwarding mechanism is set up to calculate timing based on node pricing and packet delivery probability, reducing end-to-end delay. Simulation results show that the proposed method significantly improves the network performance when compared with other representative underwater routing protocols.

To improve the reliability and efficiency of underwater acoustic sensor networks (UASNs) under limited node energy and network attacks, a joint scheme of enhancing robustness and extending network lifetime is proposed in this paper. Scale-free networks exhibit strong robustness against random failures, and because of the lower number of link connections, nodes consume energy more slowly. We first introduce a new scale-free topology evolution model that adjusts the initial number of nodes according to the characteristics of UASNs. This model incorporates factors such as flow load, energy consumption, and distance into the preferential attachment mechanism, balancing the network load and enhancing its resistance to attacks. Further, based on this topology, we propose a network flow adjustment algorithm that features the joint selection of paths and corresponding power levels. Energy consumption is balanced among nodes in proportion to their residual energy, rather than by minimizing the absolute consumed power. Numerical simulations show that different topologies significantly affect network lifetime, which is the longest when the number of links added is 2. The network lifetime of the proposed scheme surpasses the state-of-the-art schemes, such as ETFLA, Initial-BA, and VODA, by up to 37.18%, 46.23%, and 128.80%, respectively.