An optimization framework for joint sensor deployment, link scheduling and routing in underwater sensor networks

Badia, Leonardo; Mastrogiovanni, M.; Petrioli, Chiara; Stefanakos, Stamatis; Zorzi, Michele

doi:10.1145/1161039.1161051

Cited by 47 publications

(12 citation statements)

References 16 publications

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“…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). [25][26][27] Some applications of optimization methods used in time-critical UASNs include routing protocol development 2,10,14,24,25 and optimal relay-node deployment. The surface-level gateway node-deployment problem 4,5,13,15,18,21,28 can be considered as an example of optimal relay-node deployment.…”

Section: Related Workmentioning

confidence: 99%

“…2,4,5,7,[17][18][19][20]22,25,26 However, there is a large volume of published works that ignore to model propagation mechanisms of underwater environment at the physical layer (eg, previous research [13][14][15][16]21,23,24,27,28 ). Moreover, frequent packet drops are considered in some works 2,7,16,17,19,20,22,25 but assumed to be neglected in many of the works (eg, other studies 4,5,[13][14][15]18,21,23,24,[26][27][28] ). There are only a few works that incorporate either retransmission or PDR thresholds for a reliable communication performance 15,17,20,22,25 in delay constrained UASNs.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, we include constraints (24) and (25) . Constraint (26) states that nodes cannot transmit farther than R max = 5 km. 27 Finally, constraint (27) states that the amount of data flows cannot be negative.…”

Section: Moo Modelmentioning

confidence: 99%

“…• When the optimization framework with the objective function (16) subject to the constraints (18) to (22), (26), and (27) is solved, we obtain N U R (ie, maximum network lifetime when there are no constraints on the end-to-end delay). • If the optimization problem with the objective (17) subject to (18) to (23), (26), and (27) is solved when N R is set as a parameter with N R = N U R , the solution of this problem yields D U (ie, upper bound on end-to-end delay when the network lifetime is maximum).…”

Section: Moo Modelmentioning

confidence: 99%

“…• In order to find D L (ie, the minimum end-to-end delay of the network), we solve the same optimization problem (ie, optimization problem with the objective (17) subject to (18) to (23), (26), and (27)), where each sensor node generates a single packet per round (ie, N R is treated as a parameter that is equal to 1). • Finally, N L R (ie, network lifetime for the minimum end-to-end delay routing) can be obtained by solving the optimization problem with the objective function (16) subject to the constraints (18) to (23), (26), and (27) where D is treated as a parameter with the value D = D L .…”

Section: Moo Modelmentioning

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%

Section: Related Workmentioning

confidence: 99%

Section: Moo Modelmentioning

confidence: 99%

Section: Moo Modelmentioning

confidence: 99%

Section: Moo Modelmentioning

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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Path unaware layered routing protocol (PULRP) with non‐uniform node distribution for underwater sensor networks

Gopi

Kannan

Chander

et al. 2008

Wireless Communications

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We propose path unaware layered routing protocol (PULRP) for 2D underwater sensor networks (UWSNs) with mobile nodes. The steady-state distribution of mobile nodes in UWSNs is nonuniform in general. Hence, we use a mobility model-dependent node distribution. The proposed PULRP algorithm consists of two phases. In the first phase (layering phase), a layering structure is presented which is a set of concentric circles, around a sink node. The radii of the concentric circles are chosen based on equal distribution of nodes in every layer. A distributed power control mechanism is also introduced. The power level of nodes in a particular layer is chosen such that communication occurs only with nodes in the next layer. In PULRP, we consider multihop communication from source to sink. Therefore, in the second phase (communication phase), we propose a method to choose the intermediate relay nodes and an on the fly routing algorithm for packet delivery from source node to sink node across the chosen relay nodes. The proposed algorithm, PULRP finds the routing path on the fly and hence, it does not require any fixed routing table, localization, or time synchronization processes. We demonstrate the performance of PULRP using random waypoint (RWP) mobility model in a simulated underwater environment. Our findings show that the proposed algorithm has considerably better throughput (successful packet delivery rate) compared to the underwater diffusion (UWD) algorithm for various node densities as well as node velocities. In addition, the delay performance of PULRP is also better than that of UWD.[2]. With the advent of wireless ad hoc sensor networking systems underwater research has recently received a second wind of attention. Sensor networks for underwater systems have huge potential for marine scientific exploration, commercial applications,

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PAS: probability and sub‐optimal distance‐based lifetime prolonging strategy for underwater acoustic sensor networks

Dou

Zhang

Cao³

2008

Wireless Communications

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Lifetime prolonging is one of the most significant issues in the research on underwater acoustic sensor networks (UASNs). Unbalanced energy consumption influences greatly the network lifetime. First this study discusses a probability-based energy balance (PEB) scheme. The sensor nodes report the data to the sink by single-hop direct transmission (DT) or by multi-hop transmission (MT) under the probabilities. A centralized probabilities finding algorithm (PFA) can find a set of transmission probabilities to better balance the energy consumption. Then, a suboptimal distance (SOD)-based data transmission scheme is proposed which is a distributed scheme and operates on each sensor node. It optimizes the slice width and selects the relays near the optimum transmission range. Simulations show that the two schemes can save more energy and prolong the network lifetime efficiently.

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An optimization framework for joint sensor deployment, link scheduling and routing in underwater sensor networks

Cited by 47 publications

References 16 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

Path unaware layered routing protocol (PULRP) with non‐uniform node distribution for underwater sensor networks

PAS: probability and sub‐optimal distance‐based lifetime prolonging strategy for underwater acoustic sensor networks

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