Multiple Sources Localization by the WSN Using the Direction-of-Arrivals Classified by the Genetic Algorithm

Zhang, Yuan; Yu-hua, WU

doi:10.1109/access.2019.2956825

Cited by 19 publications

(10 citation statements)

References 33 publications

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“…(1) e layer number of the sink node is initialed as 0. e sink node broadcasts the networking packets to the whole network for discovering child nodes. All nodes that have received packets are regarded as child nodes of the sink node and automatically estimate individual physical coordinates by measuring the distances and orientations from the sink node [29]. Afterwards, the physical coordinates of each child node are added into the corresponding routing table (Table 1).…”

Section: Networking Methodologymentioning

confidence: 99%

Bioinspired Mitigation Scheme for Cascading Failures in Farmland Wireless Sensor Networks

Wang

et al. 2020

Complexity

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Existing mitigation strategies on wireless sensor networks (WSNs) against cascading failures cannot appropriately adapt the particular characteristics of farmland WSNs. Spider web provides a new reference for improving network invulnerability. In this study, a bionic network scheme is built based on symmetric analysis of a series of spider-web vibration transmission trials, which include networking methodology, communication rules, and load capacity model. The basic idea of this scheme is to apply the cascading-failure coping mechanism inspired by spider web into the construction and operation of farmland WSNs. We found that the link backup contributed by a topological structure and communication rules had positive effects on suppressing the spread of cascading failures. The study showed that the damages of cascading failures can be efficiently lowered by regulating the adjustment coefficient of the load capacity model. The difference between the inner-layer node failures and outer-layer node failures for network invulnerability was verified under deliberate attack circumstances. Based on these results, the proposed network scheme can be utilized to enhance the invulnerability performance of farmland WSNs.

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Section: Networking Methodologymentioning

confidence: 99%

Bioinspired Mitigation Scheme for Cascading Failures in Farmland Wireless Sensor Networks

Wang

et al. 2020

Complexity

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“…In order to reduce energy consumption in the network, a multi-objective energy-aware routing protocol is introduced to achieve the best path selection, namely, multi-objective fractional particle lion algorithm (MOFPL) [ 32 ]. In this paper, to increase the lifetime of the network the author developed a hybrid genetic algorithm which is the combination of greedy initialization and bidirectional mutation [ 33 , 34 ].…”

Section: Related Workmentioning

confidence: 99%

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Ajmi

Helali

Lorenz

et al. 2021

Sensors

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Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput.

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“…In addition, coordinates can be obtained from those in the relative coordinate system via a simple linear transformation and some reference nodes in the absolute coordinate system. As stated by Z h a n g and Wu [51] the Genetic Algorithm classifies the Direction-of Arrival into different phases like initialisation, fitness evaluation, selection, cross over, mutation and optimization and this method decreases the computational load and increases the location accuracy. B r i d a, D u h a and K r a s n o v s k y [2] gives the three main components of localization techniques:…”

Section: Localizationmentioning

confidence: 99%

Localization in Wireless Sensor Networks: A Review

Sneha¹,

Nagarajan²

2020

Cybernetics and Information Technologies

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Wireless Sensor Network (WSN) has been a source of attraction for many researchers as well as common people for the past few years. The use of WSN in various environmental applications like monitoring of weather, temperature, humidity, military surveillance etc. is not limited. WSN is built on hundreds to thousands of nodes where each node is a sensor whose main role is to sense data. These nodes are restricted to various constraints like power, energy, efficiency and deployment. The location of deployment influences the efficiency of data transmission. In this paper we briefly discuss on localization process in WSN and the classification of localization methodologies, namely centralized localization and distributed localization. The various techniques like ToA, TDoA, AoA and RSSI that are used to estimate the distance among the nodes are studied in detail. The localization issues categorized under proximity-based, range-based and range-free localization are discussed in detail. This paper also focuses on how the nodes with GPS can contribute to the localization process. The merits and demerits of using GPS have also been looked into. The various approaches of range-based techniques like Bounding box, SumDistMinMax, geometric methods, general techniques have been discussed briefly. We will also discuss on how the factors like path loss, noise, propagation, device measurements, connectivity, power control and tracking can influence the measurements in localization. In the tracking process we have briefly discussed about the variants of Kalman filter that can be used in detecting the direct path, strongest path and undirected path. This paper as a whole is just a brush up of the localization methodologies used in wireless sensor networks. This paper may give idea to the researchers to develop efficient algorithms to localize nodes with accuracy adapting to different techniques with respect to the environment and applications to be designed.

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Multiple Sources Localization by the WSN Using the Direction-of-Arrivals Classified by the Genetic Algorithm

Cited by 19 publications

References 33 publications

Bioinspired Mitigation Scheme for Cascading Failures in Farmland Wireless Sensor Networks

Bioinspired Mitigation Scheme for Cascading Failures in Farmland Wireless Sensor Networks

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Localization in Wireless Sensor Networks: A Review

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