In Underwater Wireless Sensor Networks (UWSNs), data should be transmitted to data centers reliably and efficiently. However, due to the harsh channel conditions, reliable data transmission is a challenge for large-scale UWSNs. Thus, opportunistic routing (OR) protocols with high reliability, strong robustness, low end-to-end delay, and high energy efficiency are widely applied. However, OR in UWSNs is vulnerable to routing attacks. For example, sinkhole attack nodes can attract traffic from surrounding nodes by forging information such as the distance to the sink node. In order to reduce the negative impact of malicious nodes on data transmission, we propose an intrusion detection scheme (IDS) based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm for OR (DOIDS) in this paper. DOIDS is based on small-sample IDS and is suitable for UWSNs with sparse node deployment. In DOIDS, the local monitoring mechanism is adopted. Every node in the network running DOIDS can select the trusted next hop. Firstly, according to the behavior characteristics of common routing attack nodes and unreliable underwater acoustic channel characteristics, DOIDS selected the energy consumption, forwarding, and link quality information of candidate nodes as the detection feature values. Then, the collected feature information is used to detect potential abnormal nodes through the DBSCAN clustering algorithm. Finally, a decision function is defined according to the time decay function to reduce the false detection rate of DOIDS. It makes a final judgment on whether the potential abnormal node is malicious. The simulation results show that the algorithm can effectively improve the detection accuracy rate (3% to 15% for different scenarios) and reduce the false positive rate, respectively.

Spectrum-sensing algorithms are one of the effective solutions to the problem of the underwater spectrum resource constraint. However, because the underwater acoustic channel is one of the most complex channels, it has many characteristics, such as a limited communication bandwidth, multipath effect, and ocean noise, all of which render the spectrum detection more difficult. As basic spectrum-sensing algorithms, energy detection algorithms are widely used in underwater acoustic communication and radio. However, most of the existing dual-threshold energy detection methods do not judge the signals with energy values between the thresholds or discard them directly. In this paper, a double-threshold centralized co-operative detection algorithm is proposed to solve this problem. In this algorithm, each sensing user makes a judgment independently, and if the historical energy statistics are between the thresholds, the number of sampling points is increased, and the judgment is made again. In the centralized collaborative sensing algorithm, each sensing user’s results are sent to the fusion center, which uses the OR judgment criterion to make decisions. Simulation results show that this algorithm can improve the detection performance and reduce the error rate.

The enrichment of social media expression makes multimodal sentiment analysis a research hotspot. However, modality heterogeneity brings great difficulties to effective cross-modal fusion, especially the modality alignment problem and the uncontrolled vector offset during fusion. In this paper, we propose a bimodal multi-head attention network (BMAN) based on text and audio, which adaptively captures the intramodal utterance features and complex intermodal alignment relationships. Specifically, we first set two independent unimodal encoders to extract the semantic features within each modality. Considering that different modalities deserve different weights, we further built a joint decoder to fuse the audio information into the text representation, based on learnable weights to avoid an unreasonable vector offset. The obtained cross-modal representation is used to improve the sentiment prediction performance. Experiments on both the aligned and unaligned CMU-MOSEI datasets show that our model achieves better performance than multiple baselines, and it has outstanding advantages in solving the problem of cross-modal alignment.