Underwater sensor networks (UWSNs) can be applied in sea resource reconnaissance, pollution monitoring and assistant navigation, etc., and have become a hot research field in wireless sensor networks. In open and complicated underwater environments, targets (events) tend to be highly dynamic and uncertain. It is important to deploy sensors to cover potential events in an optimal manner. In this paper, the underwater sensor deployment problem and its performance evaluation metrics are introduced. Furthermore, a particle swarm inspired sensor self-deployment algorithm is presented. By simulating the flying behavior of particles and introducing crowd control, the proposed algorithm can drive sensors to cover almost all the events, and make the distribution of sensors match that of events. Through extensive simulations, we demonstrate that it can solve the underwater sensor deployment problem effectively, with fast convergence rate, and amiable to distributed implementation.

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.

The sensors in water surface sensor networks are sparsely deployed, which means the distance between sensors is generally larger than the sensors' communication radius. So, it is difficult for sensors to aggregate their data by multihop routing. If water surface mobile sink is to be used to collect the sensors' data, its path forming method is a key issue. In this paper, the Voronoi diagram and dominating set theory are introduced, and a path forming method called path forming Voronoi diagram dominating set (PFVD) for water surface mobile sink is proposed. First, the water surface sensor network structure is converted into a Voronoi diagram and the adjacent sensors' data can be collected at the generated Voronoi vertexes position. Second, the dominating set theory is used to compute the minimum set of Voronoi vertexes that can gather all the sensors' data. Finally, based on the minimum set, the final path for mobile sink is formed. Theoretical analysis and extensive experiments demonstrate that PFVD has the advantages of short path and balanced energy consumption in different scale networks.

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.