Optimal sub-tree scheduling for wireless sensor networks with partial coverage

Adasme, Pablo

doi:10.1016/j.csi.2018.04.002

“…Intelligent communication systems will be mandatorily required in the next decades to provide low-cost connectivity within many application domains involving network structures in the form of ring, tree, and star topologies [1][2][3], for instance, when designing network structures in telecommunications, facility location, electrical power systems, water and transportation networks, and for networks to be constructed under the Internet of ings (IoT) paradigm [2][3][4][5][6][7][8][9][10]. A particular example related with wireless sensor networks is due to density control methods [1,11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Intelligent communication systems will be mandatorily required in the next decades to provide low-cost connectivity within many application domains involving network structures in the form of ring, tree, and star topologies [1][2][3], for instance, when designing network structures in telecommunications, facility location, electrical power systems, water and transportation networks, and for networks to be constructed under the Internet of ings (IoT) paradigm [2][3][4][5][6][7][8][9][10]. A particular example related with wireless sensor networks is due to density control methods [1,11,12]. ese methods allow reducing energy consumption in sensor networks by means of activation or deactivation mechanisms which mainly consist of putting into sleep mode some of the nodes of the network while ensuring sensing operations, communication, and connectivity [1][2][3][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…A particular example related with wireless sensor networks is due to density control methods [1,11,12]. ese methods allow reducing energy consumption in sensor networks by means of activation or deactivation mechanisms which mainly consist of putting into sleep mode some of the nodes of the network while ensuring sensing operations, communication, and connectivity [1][2][3][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Degree-Constrained $k$ -Minimum Spanning Tree Problem

Adasme

¹

,

Firoozabadi

²

2020

Complexity

Self Cite

1

0

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Let G V , E be a simple undirected complete graph with vertex and edge sets V and E , respectively. In this paper, we consider the degree-constrained k -minimum spanning tree (DC k MST) problem which consists of finding a minimum cost subtree of G formed with at least k vertices of V where the degree of each vertex is less than or equal to an integer value d ≤ k − 2 . In particular, in this paper, we consider degree values of d ∈ 2,3 . Notice that DC k MST generalizes both the classical degree-constrained and k -minimum spanning tree problems simultaneously. In particular, when d = 2 , it reduces to a k -Hamiltonian path problem. Application domains where DC k MST can be adapted or directly utilized include backbone network structures in telecommunications, facility location, and transportation networks, to name a few. It is easy to see from the literature that the DC k MST problem has not been studied in depth so far. Thus, our main contributions in this paper can be highlighted as follows. We propose three mixed-integer linear programming (MILP) models for the DC k MST problem and derive for each one an equivalent counterpart by using the handshaking lemma. Then, we further propose ant colony optimization (ACO) and variable neighborhood search (VNS) algorithms. Each proposed ACO and VNS method is also compared with another variant of it which is obtained while embedding a Q-learning strategy. We also propose a pure Q-learning algorithm that is competitive with the ACO ones. Finally, we conduct substantial numerical experiments using benchmark input graph instances from TSPLIB and randomly generated ones with uniform and Euclidean distance costs with up to 400 nodes. Our numerical results indicate that the proposed models and algorithms allow obtaining optimal and near-optimal solutions, respectively. Moreover, we report better solutions than CPLEX for the large-size instances. Ultimately, the empirical evidence shows that the proposed Q-learning strategies can bring considerable improvements.

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“…If two facility nodes are located near a certain distance, then at most, one of them can be selected for the tree backbone. Finally, users can connect to the resulting subset S. Notice that forming a tree backbone with facilities is an efficient strategy to ensure connectivity in a network [5,[8][9][10][11][12]. A unit disk graph is the resulting graph obtained by intersecting several unit disks in the Euclidean plane where each vertex corresponds to a center of each disk and with edges connecting them whenever the corresponding vertices lie within a constant (unit) distance of each other.…”

Section: Introductionmentioning

confidence: 99%

Facility Location with Tree Topology and Radial Distance Constraints

Adasme

¹

,

Firoozabadi

²

2019

Self Cite

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Let G d (V, E d ) be an input disk graph with a set of facility nodes V and a set of edges E d connecting facilities in V. In this paper, we minimize the total connection cost distances between a set of customers and a subset of facility nodes S ⊆ V and among facilities in S, subject to the condition that nodes in S simultaneously form a spanning tree and an independent set according to graphs G d and G d , respectively, where G d is the complement of G d . Four compact polynomial formulations are proposed based on classical and set covering p-Median formulations. However, the tree to be formed with S is modelled with Miller-Tucker-Zemlin (MTZ) and path orienteering constraints. Example domains where the proposed models can be applied include complex wireless and wired network communications, warehouse facility location, electrical power systems, water supply networks, and transportation networks, to name a few. e proposed models are further strengthened with clique valid inequalities which can be obtained in polynomial time for disk graphs. Finally, we propose Kruskal-based heuristics and metaheuristics based on guided local search and simulated annealing strategies. Our numerical results indicate that only the MTZ constrained models allow obtaining optimal solutions for instances with up to 200 nodes and 1000 users. In particular, tight lower bounds are obtained with all linear relaxations, e.g., less than 6% for most of the instances compared to the optimal solutions. In general, the MTZ constrained models outperform path orienteering ones. However, the proposed heuristics and metaheuristics allow obtaining near-optimal solutions in signi cantly short CPU time and tight feasible solutions for large instances of the problem.

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“…Comparative performance measure includes Network lifetime, total energy consumed, energy consumption per packet, dead gateways, Inactive sensor nodes, and standard deviation. Similar papers which address the problem of coverage in target-based WSN are in[22][23][24][25].…”

mentioning

confidence: 99%

Binary Monkey-King Evolutionary Algorithm for single objective target based WSN

Balasubramanian¹,

Govindasamy²

2019

EAI Endorsed Trans IoT

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INTRODUCTION: Target based WSN faces coverage issue in which many targets could not be efficiently covered by static deployed sensors. OBJECTIVES: This paper covers the issue of coverage problems by deploying the sensors to cover all the targets with minimized sensors in number. METHODS: This paper proposes a Binary based Monkey King Evolutionary Algorithm for solving target based WSN problem, the proposed model consist a Binary method for converting the continuous values into binary form to solve the choice of potential position to place the sensors. RESULTS: The proposed algorithm is evaluated in a 50x50 grid and 100x100 grid to track the performance and the performance of the proposed is compared with GA and PSO. CONCLUSION: This paper utilized the MKE algorithm for improving the efficiency of the target coverage problem in WSN. It mainly focused on a single objective-based solution providing for small scale problems. From the simulation results, it is provided that the proposed MKE algorithm obtained 1.86 % of the F-value, which is higher than the other optimization algorithms such as GA and PSO.

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Optimal sub-tree scheduling for wireless sensor networks with partial coverage

Cited by 9 publications

References 28 publications

Degree-Constrained $k$ -Minimum Spanning Tree Problem

Degree-Constrained $k$ -Minimum Spanning Tree Problem

Facility Location with Tree Topology and Radial Distance Constraints

Binary Monkey-King Evolutionary Algorithm for single objective target based WSN

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Optimal sub-tree scheduling for wireless sensor networks with partial coverage

Cited by 9 publications

References 28 publications

Degree-Constrained k -Minimum Spanning Tree Problem

Degree-Constrained k -Minimum Spanning Tree Problem

Facility Location with Tree Topology and Radial Distance Constraints

Binary Monkey-King Evolutionary Algorithm for single objective target based WSN

Contact Info

Product

Resources

About

Degree-Constrained $k$ -Minimum Spanning Tree Problem

Degree-Constrained $k$ -Minimum Spanning Tree Problem