Energy optimization with delay constraints in Underwater Acoustic Networks

Ponnavaikko, Poongovan; Yassin, Kamal; Wilson, Sarah Kate; Stojanovic, Milica; Holliday, Joanne

doi:10.1109/glocom.2013.6831129

Cited by 14 publications

(15 citation statements)

References 12 publications

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“…Some applications of optimization methods used in time‐critical UASNs include routing protocol development and optimal relay‐node deployment. The surface‐level gateway node‐deployment problem can be considered as an example of optimal relay‐node deployment.…”

Section: Related Workmentioning

confidence: 99%

“…We assume a slotted link‐layer model where the network operation time is divided into equal duration of rounds . Each round lasts for T R = 70 seconds . The active time slot of a link ( i , j ) is calculated as

t_{i j}^{s} = \frac{L_{P}}{R} + t_{i j}^{p} + \frac{L_{A}}{R}

.…”

Section: System Modelmentioning

confidence: 99%

“…Typical underwater acoustic sensor networks (UASNs) applications can be categorized as time‐critical applications (eg, coastline protection, submarine detection, military‐assisted navigation, tactical surveillance, leakage detection, and mine reconnaissance) and time‐uncritical applications (eg, underwater‐habitat monitoring and ocean‐temperature tracking). Regardless of the application category, UASNs are negatively affected by harsh impairments of the underwater environment such as long propagation delay, low bandwidth, high path loss, and frequent packet drops .…”

Section: Introductionmentioning

confidence: 99%

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Investigation of maximum lifetime and minimum delay trade‐off in underwater sensor networks

Yildiz

2019

Int J Communication

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Summary Underwater acoustic sensor networks (UASNs) are subjected to harsh characteristics of underwater acoustic channel such as severe path losses, noise, and high propagation delays. Among these constraints, propagation delay (more generally, end‐to‐end delay) is the most dominating limitation especially for time‐critical UASN applications. Although the minimization of end‐to‐end delay can be achieved by using the minimum hop routing, this solution cannot lead prolonged lifetimes since nodes consume excessive energy for transmission over long links. On the other hand, the maximization of network lifetime is possible by using energy efficient paths, which consist of relatively short links but high number of hops. However, this solution results in long end‐to‐end delays. Hence, there is a trade‐off between maximizing the network lifetime and minimizing the end‐to‐end delay in UASNs. In this work, we develop a novel multi‐objective–optimization (MOO) model that jointly maximizes the network lifetime while minimizing the end‐to‐end delay. We systematically analyze the effects of limiting the end‐to‐end delay on UASN lifetime. Our results reveal that the minimum end‐to‐end delay routing solution results in at most 72.93% reduction in maximum network lifetimes obtained without any restrictions on the end‐to‐end delay. Nevertheless, relaxing the minimum end‐to‐end delay constraint at least by 30.91% yields negligible reductions in maximum network lifetimes.

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Section: Related Workmentioning

confidence: 99%

t_{i j}^{s} = \frac{L_{P}}{R} + t_{i j}^{p} + \frac{L_{A}}{R}

.…”

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigation of maximum lifetime and minimum delay trade‐off in underwater sensor networks

Yildiz

2019

Int J Communication

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“…The papers selected for this review and tutorial are based on how well they covered the topics of interest and the degree to which they highlight the key design issues for WSN. In part III of this paper, we expose the reader to the following design problems in WSNs: routing for multi-hop WSNs with a single immobile sink [ 2 ], routing in a delay-tolerant WSN with a single mobile sink [ 3 ], joint routing, power and bandwidth allocation in FDMA WSNs [ 4 ], joint energy allocation and routing in WSNs with rechargeable batteries [ 5 ], routing in multi-hop single fixed sink with different objectives under distance uncertainties [ 6 ], joint routing and scheduling in WSNs with multiple sinks with different sink location possibilities [ 7 ], delay sensitive routing for underwater WSNs [ 8 ], using mobile radio frequency (RF) power charger to charge the batteries of sensors in a WSN [ 9 ], assignment of data processing tasks across the nodes in WSN [ 10 ], hierarchical clustering in a heterogeneous network [ 11 ], energy efficient co-operative broadcasting at the symbol level [ 12 ], dispatching of mobile sensor nodes in a WSN to sense a particular region of interest [ 13 ], fusion of delay sensitive noise perturbed data sensed by different nodes in a given cluster [ 14 ], Energy Optimization in Wireless Visual Sensor Networks While Maintaining Image Quality [ 15 ]. …”

Section: Part I Introductionmentioning

confidence: 99%

Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial

Ibrahim

Alfa

2017

Sensors

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This paper is intended to serve as an overview of, and mostly a tutorial to illustrate, the optimization techniques used in several different key design aspects that have been considered in the literature of wireless sensor networks (WSNs). It targets the researchers who are new to the mathematical optimization tool, and wish to apply it to WSN design problems. We hence divide the paper into two main parts. One part is dedicated to introduce optimization theory and an overview on some of its techniques that could be helpful in design problem in WSNs. In the second part, we present a number of design aspects that we came across in the WSN literature in which mathematical optimization methods have been used in the design. For each design aspect, a key paper is selected, and for each we explain the formulation techniques and the solution methods implemented. We also provide in-depth analyses and assessments of the problem formulations, the corresponding solution techniques and experimental procedures in some of these papers. The analyses and assessments, which are provided in the form of comments, are meant to reflect the points that we believe should be taken into account when using optimization as a tool for design purposes.

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“…The study showed that an optimal number of hops along a path exist and showed that increasing the number of hops by choosing closer relays is preferred with respect to keeping the route shorter. Ponnavaikkoy et al [14] studied the energy optimization problem and delay constraints. The authors [15] developed a relay selection criterion, called cooperative best relay assessment for underwater cooperative acoustic networks, to minimize the one-way packet transmission time.…”

Section: Related Workmentioning

confidence: 99%

Balance Transmission Mechanism in Underwater Acoustic Sensor Networks

Cao

Dou

Dong

2015

International Journal of Distributed Sensor Networks

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With the rapid development of underwater acoustic modem technology, underwater acoustic sensor networks (UWASNs) have more applications in long-term monitoring of the deployment area. In the underwater environment, the sensors are costly with limited energy. And acoustic communication medium poses new challenges, including high path loss, low bandwidth, and high energy consumption. Therefore, designing transmission mechanism to decrease energy consumption and to optimize the lifetime of UWASN becomes a significant task. This paper proposes a balance transmission mechanism, and divides the data transmission process into two phases. In the routing set-up phase, an efficient routing algorithm based on the optimum transmission distance is present to optimize the energy consumption of the UWASN. And then, a data balance transmission algorithm is introduced in the stable data transmission phase. The algorithm determines one-hop or multihop data transmission of the node to underwater sink according to the current energy level of adjacent nodes. Furthermore, detailed theoretical analysis evaluates the optimum energy levels in the UWASNs with different scales. The simulation results prove the efficiency of the BTM.

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Energy optimization with delay constraints in Underwater Acoustic Networks

Cited by 14 publications

References 12 publications

Investigation of maximum lifetime and minimum delay trade‐off in underwater sensor networks

Investigation of maximum lifetime and minimum delay trade‐off in underwater sensor networks

Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial

Balance Transmission Mechanism in Underwater Acoustic Sensor Networks

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