In this paper, we propose a reinforcement learning-based adaptive modulation and coding scheme for underwater communications. More specifically, based on the network states such as the quality of service requirement of the sensing message, the previous transmission quality and the energy consumption. This scheme applies reinforcement learning to choose the modulation and coding policy in a dynamic underwater communication system. We provide the performance bound of this scheme and perform experiments in both pool and sea environments. The experimental data were collected and post-processed. Compared with the benchmark schemes, this scheme can improve the throughputs and reduce the BER with less energy consumption.

Litopenaeus vannamei, as the main target of shrimp farming in China, has a great market prospect and high economic value. However, domestic Litopenaeus vannamei farming and feeding efficiency are low, mainly through the use of hand tools by farmers to complete the feed throwing work, and the feed cost is high. Therefore, in view of the high density and low visibility of culture ponds in Litopenaeus vannamei aquaculture industry, it is impossible to judge the quantity and time of feed feeding by visual way. In this study, based on the analysis of acoustic feeding signal characteristics of Litopenaeus vannamei, accurate and intelligent control of feed amount is proposed, so as to reduce the breeding cost and obtain environmental benefits. This study designed a system for collecting and analyzing the acoustic signals of the feeding motion of Litopenaeus vannamei. Based on passive acoustic technology, the motion acoustic signal of Litopenaeus vannamei is extracted when starving. Through time domain and frequency domain research, the characteristic parameters of the motion acoustic signal of the specific body type of Litopenaeus vannamei are determined. The signal duration range is 8-10ms, and the average range of the resonance band is 2-10KHz. Furthermore, the dual-threshold method is further used to detect the effective range of the feeding signals, and then the number of acoustic signals is obtained, and the corresponding magnitude relationship between the characteristic parameters and the starvation degree of Litopenaeus vannamei is established, which can be used as a basis for judging intelligent feeding on demand. The research provides theoretical support for the feasibility of studying the feeding patterns of Litopenaeus vannamei through underwater passive acoustic detection.

Electricity and appliance usage information can often reveal the nature of human activities in a home. For instance, sensing the use of vacuum cleaner, a microwave oven, and kitchen appliances can give insights into a person's current activities. Instead of putting a sensor on each appliance, our technique is based on the idea that appliance usage can be sensed by their manifestations in an environment's existing electrical infrastructure. Prior approaches using this technique could only detect an appliance's on-off states; that is, they only sense “what” is being used, but not “how” it is used. In this paper, we introduce DOSE, a significant advancement for inferring operating states of electronic devices from a single sensing point in a home. When an electronic device is in operation, it generates time-varying Electromagnetic Interference (EMI) based upon its operating states (e.g., vacuuming on a rug vs. hardwood floor). This EMI noise is coupled to the power line and can be picked up from a single sensing hardware attached to the wall outlet in a house. Unlike prior data-driven approaches, we employ domain knowledge of the device's circuitry for semi-supervised model training to avoid tedious labeling process. We evaluated DOSE in a residential house for 2 months and found that operating states for 16 appliances could be estimated with an average accuracy of 93.8%. These fine-grained electrical characteristics affords rich feature sets of electrical events and have the potential to support various applications such as in-home activity inference, energy disaggregation and device failure detection.

Underwater data collection is a key component of ocean observation systems, providing reliable data provenance through sensor networks deployed across diverse marine environments. Timely and energy-efficient data collection mechanisms are critical for ensuring the continuity and integrity of the collected data. For the sake of jointly optimizing the age of information (AoI) and energy consumption, we propose a multi-autonomous underwater vehicle (AUV)-assisted underwater data collection scheme in this paper. The optimization problem is formulated as a mixed integer linear programming (MILP) problem, which is non-deterministic polynomial (NP)-hard. In order to address the problem efficiently, we propose a two-stage method. First, considering the balance of energy consumption and the limitation of sensor communication ranges, we design a cluster-based AUV scheduling data transmission protocol to find a series of hovering points at which the AUVs hover to receive data and schedule data transmissions between the AUVs and sensors. Based on that, a combined heuristic algorithm is proposed to plan AUVs’ trajectories so that the data collection task can be completed properly with a low cost. Simulation results show that the proposed algorithm outperforms three benchmark algorithms in terms of average AoI and collection utility. Specifically, the freshness of information and the collection utility improved by 10.23% and 33.36% on average, respectively. Additionally, the energy consumption of sensor nodes is more balanced, and the network lifetime is extended.

Underwater localization is an important and fundamental part of the Underwater Acoustic Networks (UANs). The problem we must face is that radio waves and optical waves are heavily attenuated underwater, so acoustic signals become the most common form of communication. However, the speed of sound wave is not constant and will be affected by the environmental factors. The inaccurate sound velocity will have a serious impact on the traditional positioning results. Therefore, the symmetry correction based on least square estimation (SC-LSE) is proposed in this paper. SC-LSE mitigates the influence of the imprecise velocity estimation on the localization by means of a special symmetrical relationship. We consider a realistic case, where the actual speed of sound is uncertain and the unknown nodes may be moving. The simulation results exhibit that our algorithm can achieve good performance and is not sensitive to the change of sound velocity and node movement.