Relay Placement in Wireless Networks: Minimizing Communication Cost

Nikolov, Milen; Haas, Zygmunt J.

doi:10.1109/twc.2016.2523984

Cited by 21 publications

(22 citation statements)

References 30 publications

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“…The proposed methods for the deployment of RNs differ first and foremost in their objectives. As such, some of these methods aim to ensure only simple connectivity (1‐connectivity) . Other methods aim to reach k ‐connectivity ( k ≥ 2), which means, in the context of the problem at hand, that each SN has k disjoint paths to route its own data to the CNs, allowing the fault‐tolerance.…”

Section: Overview On the Use Of Rnsmentioning

confidence: 99%

“…Other methods aim to reach k ‐connectivity ( k ≥ 2), which means, in the context of the problem at hand, that each SN has k disjoint paths to route its own data to the CNs, allowing the fault‐tolerance. The connectivity objective is supported by two deployment modes: (i) reactive deployment, initiated after the loss of connectivity to re‐establish it, and (ii) proactive deployment, adopted to avoid any loss of connectivity …”

Section: Overview On the Use Of Rnsmentioning

confidence: 99%

“…In addition to connectivity, these methods aim to ensure other objectives, such as minimizing the cost, by minimizing the number deployed RNs, minimizing end‐to‐end delivery delay as well as the maximization of the network lifetime, through the preservation of the SNs energy and the RNs energy, or by maximizing the quality of communication links, which helps to minimize packet retransmissions. It is useful to specify that the communication links are modeled in these methods by binary, or probabilistic models, as a function of many parameters, which include among others the Euclidean distance between the communicating nodes, their transmission range (power), the obstacles, and the transmission medium (acoustic, radio, etc) …”

Section: Overview On the Use Of Rnsmentioning

confidence: 99%

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A practical data driven approach for the deployment of WSNs on realistic terrains

Zafer

Senouci

Aissani

2019

Trans Emerging Tel Tech

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Existing works on relay nodes placement in two-tiered wireless sensor networks (WSNs) assume two-dimensional terrains and ignore completely the impact of the topography on the communication quality. In contrast, this paper addresses constrained versions of the relay nodes placement problem while adopting more realistic assumptions. To ensure the connectivity of a two-tiered WSN deployed on a three-dimensional terrain, the proposed approach determines the relay nodes positions by analyzing the data extracted from the terrain topography. The main idea is to consider a concrete positions constraint, by requiring the placement of relay nodes only at the terrain crest points, which have a relatively wide visibility on the terrain and are well exposed to the sun. This allows minimizing the number of relay nodes required to ensure the connectivity, reduces the impact of the topography on the wireless communication quality, and offers the opportunity to equip the relay nodes with solar panels to extend their lifetimes. Based on this idea and inspired by the One-Step constrained Relay nodes Placement (OSRP) and Realistic constrained Relay nodes PLacement (RRPL) approaches, three algorithms, called 3D-OSRP (3D One-Step constrained Relay nodes Placement), 3D-RRPL-v1 (3D Realistic constrained Relay nodes PLacement version-1), and 3D-RRPL-v2 (3D Realistic constrained Relay nodes PLacement version-2), are designed, implemented, and evaluated by extensive simulations on realistic three-dimensional terrains. Obtained results demonstrate the relevance of considering positions constraint.Trans Emerging Tel Tech. 2019;30:e3558.wileyonlinelibrary.com/journal/ett network lifetime. Moreover, the limited communication range of the SNs requires the use of a multihop routing, in which the data passes through many intermediate nodes before reaching one of the CNs, which increases both packet loss probability and packet delivery delay. To overcome these challenges, recent works have suggested reinforcing this basic architecture, composed of SNs and CNs, by a third type of nodes, called relay nodes (RNs). [2][3][4] Having an extended energy autonomy 5,6 and a communication range often greater than that of SNs, 7,8 these RNs are designed to preserve or to restore the network connectivity by extensively participating in routing and driving data to the CNs, without contributing to the detection tasks assigned exclusively to the SNs. Nevertheless, the introduction of RNs generates new constraints that need to be taken into account. Indeed, the arrangement of these RNs in the RoI must be well examined according to several factors, which include, among others, the positions already occupied by the SNs, because the main role of the RNs is to reinforce or to restore network connectivity. In addition, because of their high-cost, 9 the number of RNs to be deployed must be minimized. Therefore, the deployment of RNs must be performed in a deterministic manner, 10 by precomputing their positions in the RoI, since the random deployment cannot respond ef...

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Section: Overview On the Use Of Rnsmentioning

confidence: 99%

Section: Overview On the Use Of Rnsmentioning

confidence: 99%

Section: Overview On the Use Of Rnsmentioning

confidence: 99%

See 1 more Smart Citation

A practical data driven approach for the deployment of WSNs on realistic terrains

Zafer

Senouci

Aissani

2019

Trans Emerging Tel Tech

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“…The time complexity is very high in largescale networks. In [20], it is proven that just listing a set of feasible sites for the relays is already at least APX-hard. Though many heuristic algorithms have been proposed, such as Genetic Algorithm [12,21], Artificial Bee Colony Algorithm [14], and Concentric Fermat Points [31], it is still extraordinarily time-consuming.…”

Section: Related Workmentioning

confidence: 99%

“…Every actor sends a message to notify its backup node to monitor its status and BestPosition. When a node detects a failure of its neighbors, it will trigger the cascading node motion (line [18][19][20][21][22][23][24][25]. The backup node will add BestPosition into its NEIGH_TABLE to tell whether it is critical.…”

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confidence: 99%

Hybrid connectivity restoration in wireless sensor and actor networks

Yan

Luo

Tian

et al. 2017

J Wireless Com Network

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Wireless sensor and actor networks are becoming more and more popular in the recent years. Each WSAN consists of numerous sensors and a few actors working collaboratively to carry out specific tasks. Unfortunately, actors are prone to failure due to harsh deployment environments and constrained power, which may break network connectivity resulting in disjoint components. Thus, maintaining the connectivity among actors is especially important. This paper proposes hybrid connectivity restoration (HCR), which integrates proactive selection and reactive motion. An actor protectively selects a backup node through its one-hop neighbor table and informs the backup node to supervise its stage. Once it fails, the backup node moves to the best position to restore the connectivity of the failed node's neighbors reactively. This triggers a local recovery process at the backup node, which is repeated until network connectivity is restored. In order to minimize travel distance, HCR selects the backup node which moves the shortest distance to restore connectivity. Furthermore, HCR opts to reduce the number of messages by just informing the failure to its backup node. The correctness and effectiveness of HCR are validated through both theoretical analysis and simulations.

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