Improved Many-Objective Optimization Algorithms for the 3D Indoor Deployment Problem

Mnasri, Sami; Nasri, Nejah; Bossche, Adrien van den; Val, Thierry

doi:10.1007/s13369-018-03712-7

Cited by 21 publications

(8 citation statements)

References 33 publications

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“…Ayinde and Hashim [15] employed two evolutionary techniques, gravitational search algorithm (GSA) and differential evolution (DE), as a hybrid single-objective optimisation algorithm to maximise the lifetime. Mnasri et al [16] proposed a many-objective optimisation algorithm for the 3D indoor deployment problem. They attempted to minimise the number of deployed nodes and energy consumption, and maximise the localisation rate, coverage and network lifetime.…”

Section: Literature Reviewmentioning

confidence: 99%

Node deployment in wireless sensor networks using the new multi‐objective Levy flight bee algorithm

Nahid

Javidan

Shamsinejad

et al. 2020

IET wirel. sens. syst.

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Wireless sensor networks (WSNs) play a prominent role in the world of computer networks. WSNs rely on deployment as a basic requirement and an effective factor on the basic network services. In deployment, creating a balance between conflicting optimisation factors, e.g. connectivity and coverage, is a challenging and sophisticated issue, so that deployment turns into an NP-complete problem. The majority of existing researches has attempted to tackle this problem by applying classic single-objective metaheuristic algorithms in 2D small-scale uniform environments. In this study, a new hybrid multi-objective optimisation algorithm, which is constructed by the combination of multi-objective bee algorithms and Levy flight (LF) random walk is proposed to deal with the deployment problem in WSNs. For this purpose, two of the most important criteria, connectivity and coverage, have been considered as objectives. A series of experiments are carried out in large-scale non-uniform 3D environments, despite the fact that most of the present methods are applicable in small-scale uniform 2D environments. This study completely takes into account the stochastic behaviour of swarms, something that other papers do not consider. The evaluation results show that the multi-objective LF bee algorithm, in most cases, surpasses NSGAII, IBEA and SPEA2 algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Node deployment in wireless sensor networks using the new multi‐objective Levy flight bee algorithm

Nahid

Javidan

Shamsinejad

et al. 2020

IET wirel. sens. syst.

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“…A. "Non-Dominated Genetic Sort Algorithm II" (NSGA-II) NSGA-II is a genetic algorithm where it is considered in the literature as one of the most efficient algorithms for solving a multi-objective optimization problem using the Pareto approach [12]. The NSGA-II consists of the following steps:…”

Section: Proposed Approachmentioning

confidence: 99%

3D Deployment Problem in Wireless Sensor Networks resolved by Genetic and Ant Colony Algorithms

Mnasri

Nasri

Alrashidi

et al. 2020

2020 International Conference on Computing and Information Technology (ICCIT-1441)

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Wireless Sensor Networks (WSN) is an evolving field of research with various applications. Deploying sensors is a decisive operation that greatly influences the performance and the functioning of the network. Comparing the 2D deployment with the 3D one, the latter is more complex because of its ability to attend more constraints imposed by real problems. On this article, we discuss the 3D node positioning problem, while minimizing the global number of sensors, the consumption levels of energy, and maximizing the covered area and the hybrid localization rate based on the 3D DV-HOP protocol and the RSSI. We seek to solve our problem by choosing the ideal 3D locations to add nomad nodes to achieve the mentioned objectives. An approach based on variants of optimization algorithms based on the NSGA-II algorithm [1] and the ACO algorithm [2] is proposed. The simulation model considers the implementation of the IEEE 802.15.4 standard, and a reactive AODV based routing layer. The results of simulations with OMNeT++ indicate the sustainability of the tested algorithms (NSGA-II, ACO).

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“…Qasim et al [24] used ant colony optimization (ACO) to deploy a WSN in a 3D grid with the objective of minimizing cost. Mnasri et al [25] used a multiobjective optimization algorithm based on dominance and decomposition (MOEA/DD) to solve problems regarding the deployment of WSNs in indoor 3D virtual spaces.…”

Section: B Three-dimensional Deployment Problemsmentioning

confidence: 99%

Three-Dimensional Internet-of-Things Deployment With Optimal Management Service Benefits for Smart Tourism Services in Forest Recreation Parks

Lin

Liu

2019

IEEE Access

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In a large-scale national forest recreation park, it is difficult for park managers to maintain facilities and provide timely or emergency services to visitors in need (e.g., elderly adults, children, or lost visitors without communication devices). To implement smart tourism services, park managers have introduced the Internet of things (IoT) to their parks, which has enabled the provision of diversified intelligent information services. However, most of the previous studies focused on 2D deployment of the IoT system to cover all targets, ignoring actual 3D topographic differences; their objectives rarely included the effectiveness of services generated from introducing the IoT; they assumed targets to be equally crucial, regardless of differences in tourism attractiveness (e.g., tourist attractions, view trails, and recreation facilities). Therefore, this study investigates 3D deployment of an IoT system to cover targets with different scores based on tourism attractiveness in a forest recreation park with optimal management service benefits, represented as a weighted sum of service quality index (SQI) and managerial setting attributes index (MSAI), so that the system collects the data from visitors equipped with wearable devices, and applies it to tasks such as physiological detection and positioning. This problem belongs to deployment and coverage problems, which have been shown to be NP-hard, and thus is also NP-hard. Therefore, this study further solves this problem by improved simulated annealing (ISA), including three neighborhood searching operators and the dynamic probability adjustment scheme. Experimental results under various parameter settings indicate that ISA has an excellent optimization ability.INDEX TERMS Smart tourism service, Internet of Things, simulated annealing, three-dimensional deployment, national forest recreation park.

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Improved Many-Objective Optimization Algorithms for the 3D Indoor Deployment Problem

Cited by 21 publications

References 33 publications

Node deployment in wireless sensor networks using the new multi‐objective Levy flight bee algorithm

Node deployment in wireless sensor networks using the new multi‐objective Levy flight bee algorithm

3D Deployment Problem in Wireless Sensor Networks resolved by Genetic and Ant Colony Algorithms

Three-Dimensional Internet-of-Things Deployment With Optimal Management Service Benefits for Smart Tourism Services in Forest Recreation Parks

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