Spiking Neural Networks (SNNs) become popular due to excellent energy efficiency, yet facing challenges for effective model training. Recent works improve this by introducing knowledge distillation (KD) techniques, with the pre-trained artificial neural networks (ANNs) used as teachers and the target SNNs as students. This is commonly accomplished through a straightforward element-wise alignment of intermediate features and prediction logits from ANNs and SNNs, often neglecting the intrinsic differences between their architectures. Specifically, ANN's outputs exhibit a continuous distribution, whereas SNN's outputs are characterized by sparsity and discreteness. To mitigate this issue, we introduce two innovative KD strategies. Firstly, we propose the Saliency-scaled Activation Map Distillation (SAMD), which aligns the spike activation map of the student SNN with the class-aware activation map of the teacher ANN. Rather than performing KD directly on the raw features of ANN and SNN, our SAMD directs the student to learn from saliency activation maps that exhibit greater semantic and distribution consistency. Additionally, we propose a Noise-smoothed Logits Distillation (NLD), which utilizes Gaussian noise to smooth the sparse logits of student SNN, facilitating the alignment with continuous logits from teacher ANN. Extensive experiments on multiple datasets demonstrate the effectiveness of our methods.

In the Internet of Things, openness significantly increases security risks. Therefore, detecting such attacks through anomaly traffic monitoring is crucial for ensuring network security. Although existing Intrusion Detection Systems (IDS) have made some progress in detection performance using deep learning techniques (e.g., graph learning and meta-learning), the class imbalance problem remains a major challenge that needs to be addressed. To this end, we propose a Double-Anchor Contrastive Learning (DACL) framework. Firstly, we introduce a positive-negative sample construction module. Compared to traditional contrastive learning methods, this module directly constructs sample pairs, preserving more flow information and thereby enhancing the effectiveness. Secondly, we design a feature embedding and extraction module. By utilizing convolutional neural networks to extract spatial correlations among raw features, we generate more discriminative feature representations. Subsequently, we propose the double-anchor contrastive learning module, introducing a double-anchor mechanism to impose double constraints on inter-class distances, further enhancing feature representation capabilities and clustering effects. Finally, we map the learned features to a low-dimensional space via a classification network, achieving precise detection of abnormal traffic. Extensive experiments demonstrate that DACL outperforms existing works in terms of accuracy on datasets such as CIC-DDoS2017, CIC-IDS2018, and CIC-DDoS2019.

Node failures in underwater acoustic sensor networks (UASNs), caused by network attacks or energy depletion, can trigger cascading failures and compromise service quality. To address this, we propose a joint optimization scheme to enhance network robustness and prolong network lifetime. Network topology optimization is reformulated as an integer nonlinear problem, and a centralized control framework is developed based on software-defined networking (SDN). Within this framework, a scale-free topology evolution model is introduced to generate the initial topology, aiming to enhance the network’s robustness against random attacks. Subsequently, a robustness enhancement algorithm based on actor–critic deep reinforcement learning is developed for the resulting topology. The agent interacts with the topological environment, learns and evaluates robustness under various topologies, and intelligently adjusts node links to enhance resistance to malicious attacks. Furthermore, we develop an SDN-enabled flow scheduling algorithm that balances traffic over multiple paths by minimizing the maximum energy pressure, thereby maximizing network lifetime. Numerical simulations show that the cascading robustness of our approach significantly outperforms state-of-the-art schemes, including DDLP, FA-SFTC, ETFLA, and Initial-BA, by 17.88%, 38.6%, 113.62%, and 117.71%, respectively. In terms of network lifetime, our scheme consistently improves upon FA-SFTC, DDLP, ETFLA, FAA, and VODA by 6%, 58.21%, 41.95%, 27.19%, and 149.65%, respectively.

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.

In wireless networks, utilizing sniffers for fault analysis, traffic traceback, and resource optimization is a crucial task. However, existing centralized algorithms cannot be applied to high-density wireless networks. Therefore, distributed optimization of channel selection to maximize the monitoring rate of sensors in Wireless Monitoring Networks (WMNs) is a challenge. This paper proposes a joint double auction-based distributed channel selection algorithm (J2A-CS) to maximize overall quality of monitoring (QoM). First, sniffers are redundantly deployed in WMNs, and an initial channel allocation strategy is formulated. Subsequently, sniffers collectively act as buyers and sellers at different stages. Finally, buyers bid asynchronously, and sellers settle synchronously to maximize the seller’s marginal revenue and update the channel selection scheme. As a distributed channel selection algorithm, J2A-CS addresses the highest overall QoM issue in WMNs, demonstrating high scalability and fault tolerance. Simulation results show that J2A-CS significantly improves QoM compared to existing distributed algorithms and outperforms centralized algorithms in high-density scenarios.

The Audio-visual Classification (AVC) task aims to determine video categories by integrating audio and visual signals. Traditional methods for AVC leverage Artificial Neural Networks (ANNs) that operate on floating-point features, affording large parameter counts and consuming extensive energy. Recent research has shifted towards brain-inspired Spiking Neural Networks (SNNs), which transmit audiovisual information through sparser 0/1 spike features allowing for better energy efficiency. However, a byproduct of such sparsity is the increased difficulty in effectively encoding and utilizing these spike features. Moreover, the spike firing characteristics based on neuron membrane potential cause asynchronous spike activations due to the heterogeneous distributions of different modalities in the AVC task, resulting in cross-modal asynchronization. This issue is often overlooked by prior SNN models, resulting in lower classification accuracy compared to traditional ANN models. To address these challenges, we present a new Multimodal Interaction Spiking Network (MISNet), the first to successfully balance both accuracy and efficiency for the AVC task. As the core of MISNet, we propose a Multimodal Leaky Integrate-and-fire (MLIF) neuron, which coordinates and synchronizes the spike activations of audiovisual signals within a single neuron, distinguishing it from the prior paradigm of SNNs that relies on multiple separate processing neurons. As a result, our MISNet enables to generate audio and visual spiking features with effective cross-modal fusion. Additionally, we propose to add extra loss regularizations before fusing the obtained audio-visual features for final classification, thereby benefiting unimodal spiking learning for multimodal interaction. We evaluate our method on five audio-visual datasets, demonstrating advanced performance in both accuracy and energy consumption.

To address the problems of node deployment schemes in existing underwater sensor networks that lack consideration of network connectivity and high deployment costs, this article constructs an optimization model that maximizes network coverage and minimizes deployment costs while ensuring full connectivity. For the NP-hard property of this optimization model, an improved moth flame optimization node deployment algorithm based on fuzzy operators (IMF 2 O 2 ) is proposed. First, comprehensively considering the two performance metrics of network coverage and network connectivity, a multi-objective selection mechanism based on fuzzy operators is proposed to improve network coverage while ensuring full connectivity. Second, a fixed number of nodes are used to monitor the target event points, transforming the node deployment of sensors into an optimal problem and proposing an improved moth flame optimization algorithm to solve this problem. Finally, the two metrics of coverage and deployment cost are measured and the fuzzy operator is used to select the optimal number of nodes to be deployed. Numerical results showed that the proposed algorithm improved network coverage rate by 10%, 22%, and 25%, and improved network connectivity rate by 12%, 20%, and 8% as compared to PSSD, RAWS, and VODA, respectively, while ensuring full connectivity.

Underwater pollution incidents occur frequently, and obtaining accurate information about their exact location and real-time situation is helpful for promptly formulating plans to contain and mitigate the situation. Autonomously adjusting the position of sensors for optimal coverage and monitoring of regions of interest (e.g., oil spill zones, chemical contamination areas) in real time is a significant challenge. To this end, this article proposes a hunting-style underwater sensor deployment based on the level set method. This method uses a gateway-like role to calculate the boundary and other parameters of the interest region based on an energy function used for positioning sensors. Subsequently, the sensors use these parameters as the basis to complete their migration toward the boundary of the interest region. This sensor migration can gradually evolve into a hunting deployment for the interest region. This article also proposes two novel performance evaluation metrics–structural similarity and network energy balance–to evaluate the comprehensive performance of the proposed hunting-style deployment of underwater sensors. Extensive simulation experiments demonstrate the effectiveness of the proposed method.

Underwater sensor networks (UWSNs) have been investigated in a variety of applications such as sea resources reconnaissance, pollution monitoring and tactical monitoring. In 3D underwater environments, it is a key topic to judge the localizability of sensor nodes given known locations of a small set of anchor nodes. In this paper, a novel localizability judgment method for UWSNs is proposed based on rigidity theory. A UWSN is modelled as an undirected graph based on acoustic connectivity. The graph is then reduced to a subgraph with global rigidity, called skeleton, from which the set of localizable sensors can determined. Furthermore, the Analytic Hierarchy Process (AHP) is used to evaluate the localization confidence of localizable sensors. Extensive simulations demonstrate that the proposed localizability judgment method can achieve low false negative rate and high efficiency networks of different sensor numbers and sensor densities. It is also shown to perform well in dynamic networks with relatively low waterflow speed.

Wireless monitoring employing distributed sniffers has been shown to complement wire side monitoring using SNMP and base station logs since it reveals detailed PHY (e.g., signal strength, spectrum density) and MAC behaviors (e.g, collision, retransmissions), as well as timing information (e.g., back-off time), which are often essential for network diagnosis. Due to hardware limitations, wireless sniffers typically can only collect information on one channel at a time. Thus, it is important to determine the optimal channel allocation of sniffer nodes to maximize the information collected. In this paper, we propose a Gibbs sampler approach for optimal distributed monitoring of multi-channel wireless networks with provable convergence. Simulation studies show that in general the proposed method has low computation complexity while achieving optimal or near optimal solutions.

Distributed active sensing is a new sensing paradigm, where active sensors and passive sensors are distributed in a field, and collaboratively detect and track the objects. "Exposure" of distributed active sensing networks (DASNs) quantifies the dimension limitations in detectability. It is important to deploy the sensors such that the exposure is minimized. Exposure minimization is shown to be NP-hard, and thus efficient heuristic algorithms are needed. In this paper, we propose a Genetic Algorithm (GA)-based solution that aims at achieving low exposure, scalability, and fast convergence. A novel flat binary chromosome encoding scheme and corresponding crossover and mutation operators are devised. Geometric knowledge is incorporated to significantly improve the convergence rate. Through extensive simulations, we demonstrate that the proposed algorithm outperforms a simple heuristic algorithm by up to 75%. The simulation results show that this algorithm is robust, self-adaptive and efficient under irregular boundary conditions.