Synchronization protocol for dynamic environment: Design and prototype experiments

Bizagwira, Honore; Toussaint, Joël; Misson, Michel

doi:10.1109/ict.2016.7500369

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…Same as with the teaching strategy it includes higher learning enthusiasm for learners with good grades and those learners lie on high probability region for getting knowledge from others and vice versa. Based on the performance of grades all learners are sorted in the case of learning enthusiasm inspired learner phase as defined in Equation (5).…”

Section: Learning Enthusiasm Based Learner Phasementioning

confidence: 99%

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Optimized localization of sensor nodes in 3D WSNs using modified learning enthusiasm‐based teaching learning based optimization algorithm

Singh

Mittal

2021

IET Communications

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Localization in wireless sensor networks (WSNs) is used to determine the coordinates of the sensor nodes deployed in the sensing field. It is the process that determines the location of the target nodes relative to the location of deployed anchor nodes. These anchor nodes are deployed at known locations having GPS installed in them. However, mostly in all 3D applications, the area under observation may have a complexity in the sensing environment. In this work, a modified learning enthusiasm-based teaching learning based optimization algorithm (LebTLBO) is proposed to deal with the 3D localization problem using single anchor and moving target nodes in anisotropic network with DOI 0.01. LebTLBO is a metaheuristic inspired by the classroom teaching and learning method of teaching learning based optimization algorithm. An improved LebTLBO algorithm aims to achieve enhanced performance by balancing the exploration and exploitation capabilities of conventional LebTLBO to improve its global performance. On the CEC2019 benchmark functions, the suggested technique is assessed, and computational findings show that it provides promising outcomes over other competitive algorithms. Also, mLebTLBO outperforms well in terms of localization error in 3D environment. The proposed technique is useful to cope up in case of rescue operations. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Learning Enthusiasm Based Learner Phasementioning

confidence: 99%

“…The main challenges in WSNs consist of localization problems, routing, network topology, memory and power constraints etc. [5][6][7]. Amongst all, the localization is a huge impacted problem, because, the reporting event location must be known; otherwise, the information will be useless.…”

Section: Introductionmentioning

confidence: 99%

Optimized localization of sensor nodes in 3D WSNs using modified learning enthusiasm‐based teaching learning based optimization algorithm

Singh

Mittal

2021

IET Communications

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“…There are various problems faced by WSNs, such as localization, node deployment, routing, energy consumed by sensor nodes, and short lifetime of sensor nodes [12][13][14][15][16]. Enhancing the network lifetime is another issue in WSN which is associated with energy consumed by sensor nodes, and the lifetime of WSNs can be improved by using clustering-based routing protocols [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics

2022

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Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have been collected is revealed. Localization of sensor nodes in WSNs plays a significant role in several applications such as detecting the enemy movement in military applications. The major aim of localization problem is to find the coordinates of all target nodes with the help of anchor nodes. In this paper, two variants of bat optimization algorithm (BOA) are proposed to localize the sensor nodes in a more efficient way and to overcome the drawbacks of original BOA, i.e. being trapped in local optimum solution. The exploration and exploitation features of original BOA are modified in the proposed BOA variants 1 and 2 using improved global and local search strategies. To validate the efficiency of the proposed BOA variants 1 and 2, several simulations have been performed for various numbers of target nodes and anchor nodes, and the results are compared with original BOA and other existing optimization algorithms applied to node localization problem. The proposed BOA variants 1 and 2 outperform the other algorithms in terms of mean localization error, number of localized nodes and computing time. Further, the proposed BOA variants 1 and 2 and original BOA are also compared in terms of various errors and localization efficiency for several values of target and anchor nodes. The simulations results signify that the proposed BOA variant 2 is superior to the proposed BOA variant 1 and existing BOA in terms of several errors. The node localization based on the proposed BOA variant 2 is more effective as it takes less time to perform computations and has less mean localization error than the proposed BOA variant 1, BOA and other existing optimization algorithms.

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“…4 The main challenges in WSNs consist of localization problem, routing, network topology, memory, power constraints and so on. [5][6][7] Among all, the localization is a huge impacted problem, because the reporting event location must be known; otherwise, the information will be useless. Further, localization itself has several challenges that include node failure and self-localization within the nodes.…”

Section: Introductionmentioning

confidence: 99%

Optimized localization using naked mole‐rat algorithm in dynamic wireless sensor networks

Singh

Mittal

2021

Int J Communication

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Localization in wireless sensor networks (WSNs) becomes an essential and important part of research in past few years. Finding location of the occurring event is essential in most of the applications, namely, monitoring of objects, tracking of animals and various resources placed in the indoor and outdoor environments. WSN consists of sensors placed in huge amounts across large regions of globe to perform various tasks such as data sensing and communicating with other devices. The aim of this research is to analyse various location optimization techniques to address a WSN localization problem and assigning appropriate coordinates to the unknown sensor nodes. In this paper, the authors have introduced the concept of sensor node localization in a 2D dynamic environment using a single anchor node (static) along with virtual anchors to locate target nodes (dynamic) using hexagonal projection technique, and further, the estimated target node coordinates are optimized using novel naked mole-rat algorithm (NMRA). This paper discusses the performance of various location optimization techniques available in literature with NMRA-based technique in terms of localization error and number of localized nodes.

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Synchronization protocol for dynamic environment: Design and prototype experiments

Cited by 10 publications

References 7 publications

Optimized localization of sensor nodes in 3D WSNs using modified learning enthusiasm‐based teaching learning based optimization algorithm

Optimized localization of sensor nodes in 3D WSNs using modified learning enthusiasm‐based teaching learning based optimization algorithm

Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics

Optimized localization using naked mole‐rat algorithm in dynamic wireless sensor networks

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