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In applications such as seismic acquisition, the position information of sensor nodes, that are deployed in a linear topology, is desired with sub-meter accuracy in the presence of a limited number of anchor nodes. This can be achieved with antenna arrays via mm-wave cooperative localization, whose performance limits are derived in this letter. The number of anchor nodes is seen to have a stronger impact than the number of antenna elements in the anchor nodes. Succinct closed-form expressions for the position error bound are also obtained for 1-hop and 2-hop cooperative localization, where sub-meter accuracy is perceived over several hundred nodes.

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Oil and gas companies consider transforming conventional cable-based seismic acquisition to wireless acquisition as a promising step for cost and weight reduction in reservoir exploration. Wireless seismic acquisition requires large number of wireless geophone (WG) sensors to be deployed in the field. The locations of the WG sensors must be known when processing the collected data. The application of direction of arrival (DOA) estimation helps in localizing WGs and improves received signal level through beam steering and interference avoidance. Conventional DOA algorithms require high computational complexity which renders them inefficient for real-time response. In this paper, deep neural network (DNN) is proposed for DOA estimation of WGs at wireless gateway node (WGN) under different channel conditions. The estimated angle and corresponding coordinates of WGNs are used in least square estimation (LSE) to estimate the position of the WGs. The simulation results depict reasonable estimation and position accuracy in real-time.

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The next frontier of maritime networking will see the deployment of large-scale buoybased mesh networks, with an equal emphasis on both high-speed data transfer and energy-efficiency. One such challenging application is the operation of maritime seismic surveys for oil/gas exploration and academic studies. Large amounts of seismic data are generated at a rate of several Gigabits per second, by nearly 10,000-30,000 seismic sensors that are deployed on the seabed across an area of several square kilometers in offshore oceanic environments. The task of monitoring existing reservoirs and identifying new oil and gas deposits require subsurface images of superior quality, which in turn are dependent on high-quality data for processing. Wireless technology can unlock real-time data transfer for rapid image-viewing, enhanced productivity, and reduced logistical costs. This interdisciplinary article outlines the challenges of marine seismic acquisition and the design of a buoy-based wireless backhaul network for high-rate data transfer over the ocean surface. Based on off-the-shelf IEEE 802.11 systems, a standards-compliant wireless buoy network architecture called Wi-buoy is proposed for real-time, scalable, and energy-efficient data delivery. In order to attain optimal power conservation, a Buoy-Based Power-Saving Backhaul (B-PSB) scheme is also proposed for specifying the operating parameters across all layers of the protocol stack. Essential aspects of the marine propagation environment are reviewed, and the performance of the proposed system is evaluated as a function of the antenna height, wind speed, compression ratio, and various flavors of the IEEE 802.11 standard. Furthermore, the use of Autonomous Underwater Vehicles (AUVs) is analyzed as an integral component of upcoming high-speed buoy-based networks in the maritime environment.

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