Generic prediction assisted single-copy routing in underwater delay tolerant sensor networks

Guo, Zheng; Wang, Bing; Cui, Jun-Hong

doi:10.1016/j.adhoc.2012.11.012

Cited by 35 publications

(23 citation statements)

References 35 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%

“…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%

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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%

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

Yildiz

2019

Int J Communication

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“…A three-dimensional grid division method is proposed in [37], to support the packets forwarding in a grid-by-grid manner. A prediction assisted single-copy routing scheme is developed for efficient packet routing in UWSNs [38]. Intuitively, underlying network mobility patterns are characterized, and the features of the best routing paths are determined, which are applied as the guideline for selecting the most appropriate routing paths afterwards.…”

Section: Related Work and Comparisonmentioning

confidence: 99%

E-CARP: An Energy Efficient Routing Protocol for UWSNs in the Internet of Underwater Things

et al. 2016

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With the advance of the Internet of Underwater Things, smart things are deployed under the water and form the underwater wireless sensor networks (UWSNs), to facilitate the discovery of vast unexplored ocean volume. A routing protocol, which is not expensive in packets forwarding and energy consumption, is fundamental for sensory data gathering and transmitting in UWSNs. To address this challenge, this paper proposes E-CARP, which is an enhanced version of the C hannel-Aware Routing Protocol (CARP) developed by S. Basagni et al., to achieve the location-free and greedy hop-by-hop packet forwarding strategy. Generally, CARP does not consider the reusability of previously collected sensory data to support certain domain applications afterwards, which induces data packets forwarding which may not be beneficial to applications. Besides, the PING-PONG strategy in CARP can be simplified for selecting the most appropriate relay node at each time point, when the network topology is relatively steady. These two research problems have been addressed by our E-CARP. Simulation results validate that our technique can decrease the communication cost significantly and increase the network capability to a certain extent.

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“…In [26], [27], the depth of sensor nodes could be controlled and nodes moved as a group with water current, thus the mobility of nodes was essentially limited and regular. A generic prediction assisted single-copy routing (PASR) scheme [28], that can be configured for multiple mobility models, was investigated. PASR differentiated the network mobility patterns according to the short-duration trace, then depicted the features of best routing paths.…”

Section: Introductionmentioning

confidence: 99%

On Exploring Data Forwarding Problem in Opportunistic Underwater Sensor Network Using Mobility-Irregular Vehicles

Liu

2015

IEEE Trans. Veh. Technol.

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Data forwarding techniques in opportunistic underwater sensor networks (OUSNs) are significantly different from those of wireless sensor networks or delay-tolerant networks, due to the irregular mobility of nodes. To this end, an Opportunistic Forwarding Algorithm based on Irregular Mobility (OFAIM) is proposed hereby, which intermittently computes and chooses the appropriate paths of an end-to-end data session. In OFAIM, the contacting probabilities among nodes is estimated according to current nodes statuses. OFAIM is particularly suitable for heterogeneous networks composed of nodes with different communication and movement ranges. The performance of OFAIM is also analyzed through simulations, which indicate that OFAIM achieves a satisfactory delivery ratio (larger than 67% at the worst case) within a limited duration while the message cost is largely reduced (the number of forwarding copies at every slot is restricted to 2 or 3) compared to epidemic forwarding, motion vector forwarding and predict and spread forwarding.

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Generic prediction assisted single-copy routing in underwater delay tolerant sensor networks

Cited by 35 publications

References 35 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

E-CARP: An Energy Efficient Routing Protocol for UWSNs in the Internet of Underwater Things

On Exploring Data Forwarding Problem in Opportunistic Underwater Sensor Network Using Mobility-Irregular Vehicles

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