RLT Code Based Handshake-Free Reliable MAC Protocol for Underwater Sensor Networks

The characteristics of underwater acoustic channels such as long propagation delay and low bit rate cause the medium access control (MAC) protocols designed for radio channels to either be inapplicable or have low efficiency for underwater sensor networks (UWSNs). Meanwhile, due to high bit error, conventional end-to-end reliable transfer solutions bring about too many retransmissions and are inefficient in UWSN. In this paper, we present a recursive LT (RLT) code. With small degree distribution and recursive encoding, RLT achieves reliable transmission hop-by-hop while reducing the complexity of encoding and decoding in UWSN. We further propose an RLT code based handshake-free (RCHF) reliable MAC protocol. In RCHF protocol, each node maintains a neighbor table including the field of state, and packages are forwarded according to the state of a receiver, which can avoid collisions of sending-receiving and overhearing. The transmission-avoidance time in RCHF decreases data-ACK collision dramatically. Without RTS/CTS handshaking, the RCHF protocol improves channel utilization while achieving reliable transmission. Simulation results show that, compared with the existing reliable data transport approaches for underwater networks, RCHF can improve network throughput while decreasing end-to-end overhead

An Environment-Friendly Multipath Routing Protocol for Underwater Acoustic Sensor Network

Underwater Acoustic Sensor Network (UASN) is a promising technique by facilitating a wide range of aquatic applications. However, routing scheme in UASN is a challenging task because of the characteristics of the nodes mobility, interruption of link, and interference caused by other underwater acoustic systems such as marine mammals. In order to achieve reliable data delivery in UASN, in this work, we present a disjoint multipath disruption-tolerant routing protocol for UASN (ENMR), which incorporates the Hue, Saturation, and Value color space (HSV) model to establish routing paths to greedily forward data packets to sink nodes. ENMR applies the mechanism to maintain the network topology. Simulation results show that, compared with the classic underwater routing protocols named PVBF, ENMR can improve packet delivery ratio and reduce network latency while avoiding introducing additional energy consumption

Intention Understanding in Human-Robot Interaction Based on Visual-NLP Semantics

With the rapid development of robotic and AI technology in recent years, human-robot interaction has made great advancement, making practical social impact. Verbal commands are one of the most direct and frequently used means for human-robot interaction. Currently, such technology can enable robots to execute pre-defined tasks based on simple and direct and explicit language instructions, e.g., certain keywords must be used and detected. However, that is not the natural way for human to communicate. In this paper, we propose a novel task-based framework to enable the robot to comprehend human intentions using visual semantics information, such that the robot is able to satisfy human intentions based on natural language instructions (total three types, namely clear, vague, and feeling, are defined and tested). The proposed framework includes a language semantics module to extract the keywords despite the explicitly of the command instruction, a visual object recognition module to identify the objects in front of the robot, and a similarity computation algorithm to infer the intention based on the given task. The task is then translated into the commands for the robot accordingly. Experiments are performed and validated on a humanoid robot with a defined task: to pick the desired item out of multiple objects on the table, and hand over to one desired user out of multiple human participants. The results show that our algorithm can interact with different types of instructions, even with unseen sentence structures

A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs) have become a hot research topic. In UASNs, nodes can be affected by ocean currents and external forces, which could result in sudden link disruption. Therefore, designing a flexible and efficient link disruption restoration mechanism to ensure the network connectivity is a challenge. In the paper, we propose a glider-assisted restoration mechanism which includes link disruption recognition and related link restoring mechanism. In the link disruption recognition mechanism, the cluster heads collect the link disruption information and then schedule gliders acting as relay nodes to restore the disrupted link. Considering the glider’s sawtooth motion, we design a relay location optimization algorithm with a consideration of both the glider’s trajectory and acoustic channel attenuation model. The utility function is established by minimizing the channel attenuation and the optimal location of glider is solved by a multiplier method. The glider-assisted restoration mechanism can greatly improve the packet delivery rate and reduce the communication energy consumption and it is more general for the restoration of different link disruption scenarios. The simulation results show that glider-assisted restoration mechanism can improve the delivery rate of data packets by 15–33% compared with cooperative opportunistic routing (OVAR), the hop-by-hop vector-based forwarding (HH-VBF) and the vector based forward (VBF) methods, and reduce communication energy consumption by 20–58% for a typical network’s setting

EFPC: An Environmentally Friendly Power Control Scheme for Underwater Sensor Networks

In oceans, the limited acoustic spectrum resource is heavily shared by marine mammals and manmade systems including underwater sensor networks. In order to limit the negative impact of acoustic signal on marine mammals, we propose an environmentally friendly power control (EFPC) scheme for underwater sensor networks. EFPC allocates transmission power of sensor nodes with a consideration of the existence of marine mammals. By applying a Nash Equilibrium based utility function with a set of limitations to optimize transmission power, the proposed power control algorithm can conduct parallel transmissions to improve the network’s goodput, while avoiding interference with marine mammals. Additionally, to localize marine mammals, which is a prerequisite of EFPC, we propose a novel passive hyperboloid localization algorithm (PHLA). PHLA passively localize marine mammals with the help of the acoustic characteristic of these targets. Simulation results show that PHLA can localize most of the target with a relatively small localization error and EFPC can achieve a close goodput performance compared with an existing power control algorithm while avoiding interfering with marine mammals

PC-MAC: A Prescheduling and Collision-Avoided MAC Protocol for Underwater Acoustic Sensor Networks

The impact of the acoustic modem with long preamble characteristic on the collision feature of the media access control scheme in underwater acoustic sensor networks (UANs) is evaluated. It is observed that the collision probability is relatively high due to the extremely long duration of preamble. As a result, UANs generally have much lower network throughput. To address this problem, a prescheduling MAC protocol named PC-MAC for UANs is proposed, which leverages a novel prescheduling scheme for the exchange of control packet to alleviate the collision probability among control packets. PC-MAC is a reservation-based channel access scheme. In the proposed protocol, an extra guard time is introduced to avoid the influence of dynamic spatial-temporal uncertainty of the sender and receiver positions. Simulation results show that PC-MAC outperforms classic reservation-based MAC protocol named SFAMA in terms of network goodput and end-to-end delay and lowers collision probability among control packets in two representative network scenarios

Comprehensive Ocean Information-Enabled AUV Motion Planning Based on Reinforcement Learning

Motion planning based on the reinforcement learning algorithms of the autonomous underwater vehicle (AUV) has shown great potential. Motion planning algorithms are primarily utilized for path planning and trajectory-tracking. However, prior studies have been confronted with some limitations. The time-varying ocean current affects algorithmic sampling and AUV motion and then leads to an overestimation error during path planning. In addition, the ocean current makes it easy to fall into local optima during trajectory planning. To address these problems, this paper presents a reinforcement learning-based motion planning algorithm with comprehensive ocean information (RLBMPA-COI). First, we introduce real ocean data to construct a time-varying ocean current motion model. Then, comprehensive ocean information and AUV motion position are introduced, and the objective function is optimized in the state-action value network to reduce overestimation errors. Finally, state transfer and reward functions are designed based on real ocean current data to achieve multi-objective path planning and adaptive event triggering in trajectorytracking to improve robustness and adaptability. The numerical simulation results show that the proposed algorithm has a better path planning ability and a more robust trajectory-tracking effect than those of traditional reinforcement learning algorithms