Distributed Underwater Sensor Research Articles

Underwater Backscatter: What is its Practical Communication Distance?

Recent years have seen mounting interest in low-power, distributed subsea internet-of-things (IoT) networks because of their potential in environmental, industrial, and defense applications [1,2]. Energy-efficient ocean IoT networks can enable long-term sensing of ocean variables (temperature, pH, pressure, salinity, etc.) to create more accurate climate and weather prediction models and monitor the impact of climate change on the ocean [3]. Similarly, low-cost and efficient ocean IoT networks can help boost the growth of the world's blue economy by enabling active monitoring of marine infrastructures ranging from oil/gas pipelines to underwater tunnels [4]. Real-time distributed underwater sensor networks can help boost aquaculture (seafood farm) production by monitoring the farm vitals (water temperature, dissolved nutrients, pH, etc.) and detecting environmental hazards (such as harmful algae blooms) early [5].

A Traffic Load-Aware OFDMA-Based MAC Protocol for Distributed Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UW-ASNs) have received increasing attention in the civil and military fields. However, due to the unique features of the underwater environment, designing a suitable media access control (MAC) protocol for UW-ASNs is an immense challenge. While orthogonal frequency division multiple access (OFDMA) is a multiple access technology based on orthogonal frequency division multiplexing (OFDM), allocating different subchannels to different users for parallel transmission. In this paper, we propose a traffic load-aware OFDMA-based MAC protocol for UW-ASNs, called TLAO-MAC, that can suit the operational requirements of distributed underwater sensor networks. It uses the exchange of control packets to collect data transmission requests and update the channel resource list. Neighboring nodes rely on receiving nodes to conduct channel negotiation and resource allocation in a distributed manner. In addition, we define the channel busyness index to sense the dynamic traffic load in distributed networks. The proposed TLAO-MAC includes two key schemes: an adaptive channel grouping algorithm based on channel busyness, and a distributed subcarrier and power allocation algorithm. By adaptively adjusting the channel grouping in consideration of the dynamics of the traffic load, TLAO-MAC allows simultaneous transmission between neighboring nodes and avoids conflict in the data channel. Under the premise of meeting the transmission rate requirement of the sender, the transmission power is saved to the greatest extent. The simulation results show that TLAO-MAC can improve network goodput and reduce end-to-end delay, showing better adaptability in distributed networks.

Processing of aircraft signatures with significant Doppler, simultaneously observed on microphones and distributed underwater sensors

An experiment was performed with a source mounted on an aircraft with a microphone array rigidly mounted on a research vessel, and with sonobuoys and a vertical line array deployed in the ocean underneath. Programmed waveforms, including tones, linearly frequency modulated tones, and m-sequences, were transmitted from the airplane. The Doppler in this configuration was substantial, due to the sound speed in air being 1/5 that of water, with much of the acoustic path passing through the air. We will present results of matched filtering the transmitted waveforms, using various combinations of observed signals, including using replicas augmented by modeling, to compensate for the Doppler in order to enhance the coherent integration gain in this configuration.

A Distributed Data-Gathering Protocol Using AUV in Underwater Sensor Networks.

In this paper, we propose a distributed data-gathering scheme using an autonomous underwater vehicle (AUV) working as a mobile sink to gather data from a randomly distributed underwater sensor network where sensor nodes are clustered around several cluster headers. Unlike conventional data-gathering schemes where the AUV visits either every node or every cluster header, the proposed scheme allows the AUV to visit some selected nodes named path-nodes in a way that reduces the overall transmission power of the sensor nodes. Monte Carlo simulations are performed to investigate the performance of the proposed scheme compared with several preexisting techniques employing the AUV in terms of total amount of energy consumption, standard deviation of each node’s energy consumption, latency to gather data at a sink, and controlling overhead. Simulation results show that the proposed scheme not only reduces the total energy consumption but also distributes the energy consumption more uniformly over the network, thereby increasing the lifetime of the network.

Deployment Strategy in Distributed Underwater Sensor Networks

Distributed underwater sensor network coverage is divided into two main categories: deterministic coverage and stochastic coverage. A strategy is put forward to deploy determinate area by using a triangular-grid method. When a coverage ratio is known, the distance between nodes can be adjusted to meet the coverage ratio in the monitored area, and the least number of sensor nodes can be calculated. Also a heuristic method is proposed for stochastic area deployment strategy. It is under the premise that the initial node location randomly deployed is given, using Voronoi diagram, the not easiest monitored path is searched, and the network coverage performance is improved by configuring the new nodes in the path. Finally it is proved that network performance is more improved by the simulation experiments, when one to four nodes are configured in the easiest breach path.

Acoustic mapping of ocean currents using networked distributed sensors

Distributed underwater sensors are expected to provide oceanographic monitoring over large areas. As fabrication technology advances, low cost sensors will be available for many uses. The sensors communicate to each other and are networked using acoustic communications. This paper first studies the performance of such systems for current measurements using tomographic inversion approaches to compare with that of a conventional system which distributes the sensors on the periphery of the area of interest. It then proposes two simple signal processing methods for ocean current mapping (using distributed networked sensors) aimed at realtime in-buoy processing, Tomographic inversion generally requires solving a challenging high dimensional inverse problem, involving substantial computations. Given distributed sensors, currents can be constructed locally based on data from neighboring sensors. It is shown using simulated data that results obtained using distributed processing are similar to those obtained from conventional tomographic approaches. The advantage for distributed systems is that by increasing the number of nodes, one gains a much more improved performance. Furthermore, distributed systems use much less energy than conventional tomographic system for the same area coverage. Experimental data from an acoustic communication and networking experiment are used to demonstrate the feasibility of acoustic current mapping.

Acoustic mapping of ocean currents using networked distributed sensors

Distributed underwater sensors are expected to provide oceanographic monitoring over large areas. As fabrication technology advances, low cost sensors will be available for many uses. The sensors communicate to each other and are networked using acoustic communications. This paper first studies the performance of such systems for current measurements using tomographic inversion approaches to compare with that of a conventional system which distributes the sensors on the periphery of the area of interest. It then proposes two simple signal processing methods for ocean current mapping (using distributed networked sensors) aimed at real-time in-buoy processing. Tomographic inversion generally requires solving a challenging high dimensional inverse problem, involving substantial computations. Given distributed sensors, currents can be constructed locally based on data from neighboring sensors. It is shown using simulated data that similar results are obtained using distributed processing as using conventional tomographic approaches. The advantage for distributed systems is that by increasing the number of nodes, one gains a much more improved performance. Furthermore, distributed systems use much less energy than a conventional tomographic system for the same area coverage. Experimental data from an acoustic communication and networking experiment are used to demonstrate the feasibility of acoustic current mapping.

Ocean surface degradation of shallow water acoustic communication.

The accurate prediction of underwater acoustic communication system performance at high frequencies is essential to system risk mitigation prior to deployment. The deterioration of acoustic coherence by ocean surface dynamics will be predicted via three dimensional (3‐D) ray tracing with the inclusion of interaction with a realistic dynamic ocean surface. Acoustic communication performance limits at high frequencies for most fixed platform systems are dominated by multipath interaction with the spectrally rich heaving ocean. The high‐fidelity Wave‐watch III surface spectra model is used here with a wave‐number integration approach for surface time series modeling. A fully 3‐D ray tracing model is used to model the propagation of sound under a dynamic surface boundary at the time and spatial scales that dominate coherence degrading effects for underwater acoustic communications. The proposed method will serve the rapid development of operating regimes for high‐frequency underwater acoustic communication systems. Proposed applications include distributed underwater sensor networks and persistent deployable low‐cost systems that meet a wide range of underwater sensing needs. This work seeks to solve problems in deploying advanced communication systems by predicting the degradation of signals due to ocean variability and automatically selecting appropriate signaling rates, schemes, and constants. [This work was supported by ONR.]

Guest editors' introduction

Wireless information transmission underwater is one of the enabling technologies for the development of future ocean-observation systems, whose applications include gathering of scientific data, pollution control, climate monitoring, detection of objects on the ocean floor, and transmission of images from remote sites. Implicitly, wireless signal transmission is crucial for control of autonomous underwater vehicles (AUVs) which will serve as mobile nodes in the future information networks of distributed underwater sensors. Wireless communication provides advantages of collecting data without the need to retrieve instruments, and maneuvering underwater vehicles and robots without the burden of cables. It is most often accomplished using acoustic transmission, although other approaches including optics and electromagnetic fields are also being investigated for short-range high-bandwidth links.