Optimization of delay of data delivery in Wireless Sensor Network using Genetic Algorithm

Khan, Ajaz Ahmed; Agrawal, Himani

doi:10.1109/iccpeic.2016.7557241

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…It is possible that full observation of f is impossible, or even the full graph structure is unavailable. For example, in a sensor network, it is possible the overall sensor network is unknown or readings from some sensors are missing, due to reasons such as processing delay [18], damage, energy conservation [19], or lack of access because of privacy [20]- [22]. As another example, full information of a mega size social network may not be readily available, and one can only perform inference and learning with partial signal as well as graph information.…”

Section: Subgraph Signal Processingmentioning

confidence: 99%

On semi shift invariant graph filters

Feng¹,

Hian²,

Tay³

2022

Preprint

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In graph signal processing, one of the most important subject is the study of filters, i.e., linear transformations that capture relations between graph signals. One of the most important families of filters is the space of shift invariant filters, defined as transformations commute with a preferred graph shift operator. Shift invariant filters have a wide range of applications in graph signal processing and graph neural networks. A shift invariant filter can be interpreted geometrically as an information aggregation procedure (from local neighborhood), and can be computed easily using matrix multiplication. However, there are still drawbacks to use solely shift invariant filters in applications, such as being restrictively homogeneous. In this paper, we generalize shift invariant filters by introducing and study semi shift invariant filters. We give an application of semi shift invariant filters with a new signal processing framework, the subgraph signal processing. Moreover, we also demonstrate how semi shift invariant filters can be used in graph neural networks.

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Section: Subgraph Signal Processingmentioning

confidence: 99%

On semi shift invariant graph filters

Feng¹,

Hian²,

Tay³

2022

Preprint

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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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Optimization of delay of data delivery in Wireless Sensor Network using Genetic Algorithm

Cited by 12 publications

References 7 publications

On semi shift invariant graph filters

On semi shift invariant graph filters

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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