Relay Node Placement Under Budget Constraint

Zhou, Chenyang; Mazumder, Anisha; Das, Arun; Basu, Kaustav; Matin-Moghaddam, Navid; Mehrani, Saharnaz; Sen, Arunabha

doi:10.1145/3154273.3154302

Cited by 8 publications

(15 citation statements)

References 17 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%

“…Additionally, these methods target mainly two topologies, namely, the 1‐tier topology (see Figure A), in which the SNs participate, beside the RNs, in the routing of data to the CNs, in addition to their task of data collection; and the 2‐tier topology (see Figure B), in which each SN collects data and transmits it to one of the CNs, if it is in its communication range, or to one of the neighboring RNs. Subsequently, the RNs handle the routing of the collected data to the CNs .…”

Section: Overview On the Use Of Rnsmentioning

confidence: 99%

“…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, the number of RNs to be deployed must be minimized. Therefore, the deployment of RNs must be performed in a deterministic manner, by precomputing their positions in the RoI, since the random deployment cannot respond effectively to these considerations.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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%

Section: Overview On the Use Of Rnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Properties, Extensions and Application of Piecewise Linearization for Euclidean Norm Optimization in $$\mathbb {R}^2$$

Duguet

Artigues

Houssin

et al. 2022

J Optim Theory Appl

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This work considers non-convex mixed integer nonlinear programming where nonlinearity comes from the presence of the two-dimensional euclidean norm in the objective or the constraints. We build from the euclidean norm piecewise linearization proposed by Camino et al. [2019], that allows to solve such non-convex problems via mixed-integer linear programming with an arbitrary approximation guarantee. Theoretical results are established that prove that this linearization is able to satisfy any given approximation level with the minimum number of pieces. An extension of the piecewise linearization approach is proposed. It shares the same theoretical properties for elliptic constraints and/or objective. An application shows the practical appeal of the elliptic linearization on a non-convex beam layout mixed optimization problem coming from an industrial application.

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MILP formulation improvement with k-means clustering for the beam layout optimization in multibeam satellite systems

Camino

Artigues

Houssin

et al. 2021

Computers & Industrial Engineering

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This paper addresses an NP-hard design optimization problem in a multibeam satellite communication system. This problem consists in designing irregular beam layouts to satisfy non-uniform user traffic demands over the satellite service area, under antenna constraints, satellite payload constraints and a telecommunication mission criterion. Efficiently solving this problem is of crucial importance due to its impact on the system performance and cost. We compare three mixed-integer linear programming formulations. The first one, issued from previous work, is based on a linearization of both convex and non-convex Euclidean distance constraints. The two other aim at reducing the solution space size and at breaking symmetry inherent to the first formulation. For that purpose, we introduce a new process to interface k-means clustering with mixed-integer linear programming. We examine an exact and a heuristic approach for exploiting these principles that yield two new formulations. The heuristic approach outperforms the others based on our tests on a set of large-scale realistic problem instances, allowing to use mixed-integer linear programming in the industrial context.

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Relay Node Placement Under Budget Constraint

Cited by 8 publications

References 17 publications

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

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

Properties, Extensions and Application of Piecewise Linearization for Euclidean Norm Optimization in $$\mathbb {R}^2$$

MILP formulation improvement with k-means clustering for the beam layout optimization in multibeam satellite systems

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