The WHOI micromodem-2: A scalable system for acoustic communications and networking

Gallimore, Eric; Partan, Jim; Vaughn, Ian; Singh, Sandipa; Shusta, Jon; Freitag, Lee

doi:10.1109/oceans.2010.5664354

Cited by 103 publications

(49 citation statements)

References 20 publications

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“…From now on, we refer to this intertransmission duration as sleeping. We assume that the transmit power control is possible as it is already present in some acoustic modems [25].…”

Section: Scenario Description and Definitionsmentioning

confidence: 99%

Cross-Layer Energy Minimization for Underwater ALOHA Networks

Koseoglu

Karasan

Chen

2017

IEEE Systems Journal

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Abstract-Underwater networks suffer from energy efficiency challenges due to difficulties in recharging underwater nodes. In addition, underwater acoustic networks show unique transmission characteristics such as frequency-dependent attenuation, which causes the transmission power to significantly depend on the bandwidth and the distance. We here investigate the cross-layer energy minimization problem in underwater ALOHA networks considering the unique transmission properties of the underwater medium. We first analyze the separate optimization of the physical (PHY) and multiple access control (MAC) layers to minimize energy consumption. We analytically obtain the energy-optimum channel access rate for the ALOHA MAC layer, which minimizes the energy consumption per successfully transmitted bit. We then formulate a cross-layer optimization problem, which jointly optimizes PHY and MAC layers to minimize energy consumption. We show that such cross-layer optimization reduces the energy consumption per bit as much as 66% in comparison with separate optimization of both layers. Cross-layer optimization achieves this energy efficiency by assigning higher MAC-layer resources to the nodes that have a longer distance to the base station, i.e., which experience a less efficient PHY layer. Moreover, cross-layer optimization significantly increases the amount data transferred until first node failure since it results in a more homogeneous energy consumption distribution among the nodes.Index Terms-ALOHA, cross-layer design, multiple access control (MAC), underwater networks.

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“…From now on, we refer to this intertransmission duration as sleeping. We assume that the transmit power control is possible as it is already present in some acoustic modems [25].…”

Section: Scenario Description and Definitionsmentioning

confidence: 99%

Cross-Layer Energy Minimization for Underwater ALOHA Networks

Koseoglu

Karasan

Chen

2017

IEEE Systems Journal

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“…These two vehicles are featured in Figure 3-1 on board the R/V Shana Rae for the "Satellites to the Ocean Floor" research project [44]. Iver-106 was equipped with a Woods Hole Oceanographic Institution (WHOI) 25-kHz acoustic micro-modem [24], a SonTek Doppler Velocity Log (DVL) for speed estimation, a MEMS OceanServer compass for attitude estimation, a depth sensor, a GPS receiver, and a Pololu AltIMU-10 v5 MEMS IMU. This IMU consisted of a triaxial gyroscope, accelerometer, compass, and altimeter (LSM6DS33, LIS3MDL, and LPS25H Carrier).…”

Section: Vehiclesmentioning

confidence: 99%

MEMS IMU navigation with model based dead-reckoning and one-way-travel-time acoustic range measurements for autonomous underwater vehicles

Kepper¹

2017

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Recent advances in acoustic navigation methodologies are enabling the way for AUVs to extend their submerged mission time and maintain a bounded XY position error. Additionally, advances in inertial sensor technology have drastically lowered the size, power consumption, and cost of these sensors. Nonetheless, these sensors are still noisy and accrue error over time. This thesis builds on the research and recent developments in single beacon one-waytravel-time (OWTT) acoustic navigation and investigates the degree of bounding position error for small AUVs with a minimal navigation strap-down sensor suite, relying mostly on a consumer grade microelectromechanical system (MEMS) inertial measurement unit (IMU) and a vehicle's dynamic model velocity. An implementation of an Extended Kalman Filter (EKF) that includes IMU bias estimation and coupled with a range filter, is obtained in the field on two OceanServer Technology, Inc. Iver2 AUVs and one Bluefin Robotics SandShark AUV. Results from these field trials on Ashumet Pond of Falmouth, Massachusetts, the Charles River of Cambridge, Massachusetts, and Monterey Bay near Santa Cruz, California show a navigation solution accuracy comparable to current standard navigation techniques.

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“…Five sinks are floating on the surface of the sea. According to the specification of real underwater sensor WHOI [55] [52], the transmission range is set to at most 1000 m. The reception energy waste is 3 W, and the transmission energy consists of two parts: a 10-W power level to keep the transmission electronics turned on at their lowest level and a 40-W dynamic range for power control with respect to hop distance [7]. A total of 100 packets are generated from arbitrary nodes in each test (see Table 1).…”

Section: Parameter Setupmentioning

confidence: 99%