Underwater Data Collection Using Robotic Sensor Networks

Hollinger, Geoffrey A.; Choudhary, Sunav; Qarabaqi, Parastoo; Murphy, Christopher; Mitra, Urbashi; Sukhatme, Gaurav S.; Stojanovic, Milica; Singh, Hanumant; Hover, Franz S.

doi:10.1109/jsac.2012.120606

Cited by 171 publications

(73 citation statements)

References 32 publications

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“…References [17], [7] discuss two specific MicroModem datasets which are supportive of the mean power model in Eqn. 2, and have a path loss variance in decibels which is constant with distance.…”

Section: Acoustic Communications Modelmentioning

confidence: 90%

“…In addition, considering large-scale ocean missions, data assimilation and planning are typically centralized today and the marine assets are expensive and tracked carefully [6]. These aspects of acomms and ocean missions motivate optimization methods which can take into account motion plans, global channel information, and operator input [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust minimum energy wireless routing for underwater acoustic communication networks

Reed¹,

Stojanovic

Mitra

et al. 2012

2012 IEEE Globecom Workshops

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Abstract-Marine robots are an increasingly attractive means for observing and monitoring the ocean, but underwater acoustic communications remain a major challenge. The channel exhibits long delay spreads with frequency-dependent attenuation; moreover, it is time-varying. We consider the minimum energy wireless transmission problem [MET], augmented by the practical condition that constraints on link power must be satisfied in probability. For this, we formulate the robust counterpart of the multicommodity mixed-integer linear programming (MILP) model from Haugland and Yuan [1], and derive scaled power levels that account for uncertainty. Our main result is that the deterministic formulation with these scaled power levels recovers exactly the optimal robust solution in the absence of correlations, and therefore allows for efficient solution via MILP. This approach achieves significant power improvements over heuristics, and naturally lends itself to vehicle networks.

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Section: Acoustic Communications Modelmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Robust minimum energy wireless routing for underwater acoustic communication networks

Reed¹,

Stojanovic

Mitra

et al. 2012

2012 IEEE Globecom Workshops

Self Cite

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show abstract

“…It is shown that an AUV acting as a mobile sink can effectively reduce the transmission range of sensors [5], which leads to energy saving of transmission. In this way, an AUV travels a specified path and stops at location called "tour-point" for data gathering.…”

Section: Introductionmentioning

confidence: 99%

An Energy-Aware Data-Gathering Protocol Based on Clustering using AUV in Underwater Sensor Networks

MotahariNasab¹,

Bohlooli²,

Moghim³

2016

IJCNIS

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Abstract-Underwater Wireless Sensor Networks (UWSNs) consist of certain number of sensors and vehicles interacting with each other to collect data. In recent years, the use of Autonomous Underwater Vehicle (AUV) has improved the data delivery ratio and maximized the energy efficiency in UWSNs. Clustering is one of the effective techniques in energy management which increases the lifetime of these networks. One of the most important parameters in creating optimized clusters is the choice of appropriate cluster head (CH), which not only increases the lifetime of the network and the received data in the sink, but also reduces energy consumption. Clustering of networks was primary done via distributed methods in previous researches. It spends too much energy and also involves too many nodes in the clustering process and fades their main functionality, which is gathering data in sensor networks. It also causes more damping of the network. However, in the proposed protocol, instead of having them distributed by the network and the nodes, the stages of clustering and selecting the appropriate CH is the task of the AUV (Autonomous Underwater Vehicle). Since all the necessary measures to cluster in the network will be carried out by the AUV by this method, many control overheads in the process of clustering the network will be removed and energy consumption caused by nodes reduces significantly. With this method, the network scalability will also be manageable and under control. For simulating and implementing our method we mainly used the OPNET software. The results show that energy consumption of nodes in the proposed algorithm has been significantly improved compared to previous results.

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“…Considering large-scale ocean missions, data assimilation and planning are typically centralized today and the marine assets are expensive and tracked carefully [195]. These aspects of acomms and ocean missions motivate optimization methods which can take into account motion plans, global channel information, and operator input [119].…”

Section: Introduction and Prior Workmentioning

confidence: 99%

Controller design for underwater vehicle systems with communication constraints

Reed¹

2015

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Real-time cooperation between autonomous vehicles can enable time-critical missions such as tracking and pursuit of a dynamic target or environmental feature, but relies on wireless communications. Underwater, communication over distances beyond about one hundred meters is almost exclusively accomplished through acoustics, which bring challenges such as propagation delays, low data rates, packet loss, and scheduling constraints due to interference and limited bandwidth. These limitations make underwater pursuit missions preeminent applications of networked control. Motivated by such applications, this thesis presents contributions towards multi-vehicle feedback control in the presence of severe communication constraints.The first major area of work considers the formulation and solution of new underwater multi-vehicle tracking and pursuit problems using closed-loop control. We begin with a centralized robust optimization approach for multicast routing and power control which is suitable for integration with vehicle control. Next, we describe field experiments in range-based target pursuit at high tracking bandwidths in a challenging shallow-water environment. Finally, we present a methodology for pursuit of dynamic ocean features such as fronts, which we validate using hindcast ocean model data. The primary innovation is a projection algorithm which carries out linearization of ocean model forecast dynamics and uncertainty directly in vehicle coordinates via a forward model technique. The resulting coupled linear stochastic system is suitable for networked control.The second area of work presents a unified formalism for multi-vehicle control and estimation with measurement, control, and acknowledgment packets all subject to scheduling, delays and packet loss. The modular framework we develop is built around a jump linear system description incorporating receding horizon optimization and buffering at actuators. Integration of these elements enables synthesis of a novel technique for estimation using delayed and lossy control acknowledgments-a desirable and practical capability of fielded systems that has not been considered to date.Simulations and field experiments demonstrate the effectiveness of our approach.

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Underwater Data Collection Using Robotic Sensor Networks

Cited by 171 publications

References 32 publications

Robust minimum energy wireless routing for underwater acoustic communication networks

Robust minimum energy wireless routing for underwater acoustic communication networks

An Energy-Aware Data-Gathering Protocol Based on Clustering using AUV in Underwater Sensor Networks

Controller design for underwater vehicle systems with communication constraints

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