Delay constrained relay node placement in two-tiered wireless sensor networks: A set-covering-based algorithm

Ma, Chaofan; Liang, Wei; Zheng, Meng

doi:10.1016/j.jnca.2017.05.004

Cited by 27 publications

(38 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%

“…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 . This 2‐tier topology is seen as a clustering scheme of the network, where each cluster has an RN acting as a cluster‐head, and several SNs considered as a cluster members.…”

Section: Overview On the Use Of Rnsmentioning

confidence: 99%

“…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) . Having an extended energy autonomy and a communication range often greater than that of SNs, 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.…”

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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Node placement optimization using particle swarm optimization and iterated local search algorithm in wireless sensor networks

Chelbi

Dhahri

Bouaziz

2021

Int J Communication

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Summary Coverage and connectivity are the two most challenging issues in target‐based wireless sensor networks (WSNs). For that, node placement is one of the fundamental concerns that affect the performance of coverage and connectivity in WSN. This paper introduces a new approach by combining particle swarm optimization and iterated local search (PSO‐ILS) to have an optimum coverage and connectivity rate with the minimum number of nodes. In one side, to maintain the full coverage of targets, the PSO‐ILS is used to deploy the minimum number of sensor nodes. In other side, to achieve the full connectivity, the optimal position determination (OPD) algorithm was conceived to identify the optimal candidate positions which can be used by the PSO‐ILS to place the minimum number of relay nodes. The obtained results considered over a number of runs are compared with canonical PSO, differential evolution (DE), and genetic algorithms (GAs). The outcomes derived from this comprehensive analysis determine that PSO‐ILS provides an effectual improvement in contrary to the methods PSO, DE, and GA in terms of the selected potential positions to ensure full coverage of target points and the number of relay nodes required to achieve full connectivity.

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Fault‐tolerant data transport backbone for 3D wireless sensor networks

Zafer¹,

Senouci²,

Aissani³

2019

Trans Emerging Tel Tech

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Existing approaches for clustering wireless sensor networks by using relay nodes (RNs) suffer from two major problems: impracticability for realistic scenarios and lack of fault tolerance. The first problem is due to the assumption of a two-dimensional Region of Interest (RoI). The second problem stems from the fact that these approaches do not address the construction of disjoint routing paths, where multiple routing paths have common intermediate RNs, which create a bottleneck for the data transport and points of failure. In this paper, we tackle these two problems by proposing a new RNs deployment approach based on more realistic assumptions. Indeed, a more realistic communication model is considered, which takes into account the impact of the RoI topography on wireless communications. Additionally, a practical position constraint is adopted, where the RNs are placed only on the RoI crest points, to minimize the topography impact on the wireless communications. By relying on an efficient algorithm that constructs from a connected graph many Steiner trees, covering the same subset of vertices, the proposed RNs deployment approach selects the appropriate positions of the RNs and ensures a reliable data transport from the sensor nodes to the collector node, according to a two-tier topology while satisfying other secondary objectives related to the cost and the quality of the communication links. The simulation results demonstrate the feasibility of the proposed approach and its ability to minimize cost and improve communication links quality.

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Delay constrained relay node placement in two-tiered wireless sensor networks: A set-covering-based algorithm

Cited by 27 publications

References 30 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

Node placement optimization using particle swarm optimization and iterated local search algorithm in wireless sensor networks

Fault‐tolerant data transport backbone for 3D wireless sensor networks

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