Heurstic approaches for underwater sensing and processing deployment

Alhumyani, Hesham; Ammar, Reda A.; Elfouly, Raafat; Alharbi, Ayman

doi:10.1109/icenco.2015.7416330

Cited by 6 publications

(10 citation statements)

References 11 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. In this approach, ordinary sensor nodes convey their data to the nearest surface‐level gateway.…”

Section: Related Workmentioning

confidence: 99%

“…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}

. In this calculation,

t_{i j}^{p} = 2 \times \frac{d_{i j}}{c}

is the round‐trip propagation delay accounted for both data and ACK‐packet transmissions on link‐( i , j ) where c = 1500 m/s is the nominal speed of sound in water LPR and

\frac{L_{A}}{R}

are times required to transmit L P = 1024 bits of a data packet, and L A = 88 bits of an ACK packet where R = 5 kbps is the data rate of WHOI Micromodems .…”

Section: System Modelmentioning

confidence: 99%

“…We denote

l_{D, i j}^{o p t}

and

l_{A, j i}^{o p t}

to represent the optimal transmission power levels used on link ( i , j ) for data and ACK‐packet transmissions, respectively. Constraint limits the amount of energy dissipated at each sensor node to the initial battery energy (ie, ξ = 1 MJ). In this constraint,

E_{i j}^{t x} false(l_{D, i j}^{o p t}, l_{A, j i}^{o p t} false)

and

E_{j i}^{r x} false(l_{D, i j}^{o p t}, l_{A, j i}^{o p t} false)

values are obtained by plugging the optimal power levels

l_{D, i j}^{o p t}

and

l_{A, j i}^{o p t}

into Equations and , respectively.…”

Section: System Modelmentioning

confidence: 99%

“…A considerable amount of literature has been published on the usage of optimization methods for time-critical UASNs in recent years. In these works, the objective functions of the proposed optimization models are generally defined as (a) the minimization of end-to-end delay (eg, previous studies 4,5,[13][14][15][16][17][18] ), (b) the minimization of energy consumption (eg, previous studies 2,5,7,15,[18][19][20], and (c) the maximization of network lifetime (eg, previous studies 4, [21][22][23] ). Some constraints of these optimization models include the per-node flow-balance constraint (ie, flows are balanced at each node), the end-to-end flow conservation constraint (ie, every generated data are terminated at the sink node), the energy capacity constraint (ie, amount of energy consumed by nodes are limited), data-capacity constraint (ie, the capacity of each link is bounded), and the delay constraint (ie, end-to-end delay of the network is limited), which are either defined as separate constraints 2,7,9,20,[22][23][24] or incorporated into some constraints (eg, per-flow balance).…”

Section: Related Workmentioning

confidence: 99%

“…UASNs use acoustic (sound) waves for communications where the speed of sound is roughly 1500 m/s in underwater that is extremely slow as compared with electromagnetic waves . The low propagation speed of sound results in high propagation delays, which drastically increase the end‐to‐end delay (ie, time required for data packets to travel from source nodes to the sink node).…”

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%

“…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}

. In this calculation,

t_{i j}^{p} = 2 \times \frac{d_{i j}}{c}

is the round‐trip propagation delay accounted for both data and ACK‐packet transmissions on link‐( i , j ) where c = 1500 m/s is the nominal speed of sound in water LPR and

\frac{L_{A}}{R}

are times required to transmit L P = 1024 bits of a data packet, and L A = 88 bits of an ACK packet where R = 5 kbps is the data rate of WHOI Micromodems .…”

Section: System Modelmentioning

confidence: 99%

“…We denote