An Efficient Node Localization Approach with RSSI for Randomly Deployed Wireless Sensor Networks

Zhang, Xihai; Fang, Junlong; Meng, Fanfeng

doi:10.1155/2016/2080854

Cited by 13 publications

(5 citation statements)

References 28 publications

(33 reference statements)

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“…Depending on the technique used to estimate distances in a localization algorithm, the localization algorithms can be divided into two main groups [18]: Range-based localization algorithm and Rangefree localization algorithms. Range-Based localization algorithms [19] are those algorithms that use distance or angle measurements techniques such as RSSI [19], [23], Time Difference of Arrival (TDoA) [11], Time of Arrival (ToA) [22], and Angle of Arrival (AoA) [14]. While range-free localization algorithms use connectivity information [7], i.e.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Anchor-Free Localization Algorithm for all Cluster Topologies in a Wireless Sensor Network

Aka,

Atta,

Keupondjo

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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A wireless sensor network is a type of network, which has many application domains such as military, civil security, industrial and environmental. Localization of fixed or mobile wireless sensors in wireless sensor networks is a delicate issue that has attracted the attention of many researchers. Indeed, a good estimation of the distances between different wireless sensors allows to derive their precise locations in the network. An ideal solution for locating these wireless sensors is to equip them with localization devices such as GPS. However, this solution is not an energy-efficient. Indeed, GPS is very energy-consuming, and the deployment environment is not easily accessible to humans. So, it is not possible to replace the batteries of these wireless sensors when they are discharged. Therefore, it is necessary to propose an energy-saving anchor-free localization algorithm. Despite the grouping of nodes into clusters (or sub-networks), existing anchor-free localization algorithms suffer from a low rate of node localization, low localization accuracy, and high energy consumption. To improve the proposed solutions in the literature, an Efficient Anchor Free Localization Algorithm (EAFLA) was proposed. Regardless the topology of each cluster, our algorithm allows localization of all wireless sensors with a very low localization error rate and consumes less energy.

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Section: Related Workmentioning

confidence: 99%

An Efficient Anchor-Free Localization Algorithm for all Cluster Topologies in a Wireless Sensor Network

Aka,

Atta,

Keupondjo

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…Advanced trilateration localization algorithms were proposed by Yan et al [19] and Monta et al [20], both offering improvements on distance estimation. Liu and Cai [21] and Zhang et al [22] also reported enhanced trilateration localization methods, with the latter proposing an iterative trilateration approach to maintain localization accuracy. A novel improvement for the trilateration technique using fuzzy logic was developed by El Samadony et al [23], adding power level as an additional parameter for distance estimation between anchors and nodes.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Wildfire Localization Using a Trilateration-Based Nelder-Mead Algorithm in a Wireless Sensor Network

Hassan,

Mahmood

2023

MMEP

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Wildfires pose significant threats globally, affecting human life, wildlife, and ecosystems. The detrimental effects of these fires are often exacerbated by inaccuracies in fire positioning systems and delays in alarm response, leading to rapid and uncontrolled fire spread, consequently affecting emergency response times. This paper presents a novel, real-time wildfire localization system that employs an optimized trilateration technique. The technique leverages anchor node technology for the localization process. The Nelder-Mead (NM) optimization algorithm is utilized to augment the trilateration technique, thereby enhancing the accuracy of the estimated coordinates for unidentified nodes. The proposed localization algorithm is deployed using cloud computing and the Internet of Things (IoT) MQTT communication protocol. Simulation results demonstrate that the proposed method maintains accurate localization performance, with an enhancement in fire localization accuracy of up to 83% with the optimized trilateration based on the NM algorithm. As a result, the proposed approach offers potential time-savings in the early detection of wildfires, thus contributing to more efficient emergency response measures.

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“…Iterative multi lateralization technique and algorithm start conditions are also provided to avoid loss of localization accuracy during the iterative phase, according to the study. X. Zhang, et al [26] suggested a numerous localization technique using the TDOA has provided information for UWB MIMO cognitive radar. If there are many targets, different correlation peaks show the TDAs of those targets.…”

Section: Literature Surveymentioning

confidence: 99%

Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization

et al. 2022

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The localization of Wireless sensor networks (WSNs) has been recognized as one of the most challenging problems to overcome. Thus, much work has been given to solving this difficult problem. In emergency services, navigational systems, civil/military surveillance etc., locating the signal source in a WSN is essential. A novel approach for sensor node localization using range-based localization methodology has been proposed to overcome this issue. The problem is expressed in the form of a maximum probability distribution function. The use of an RSSI-based Time Difference of Arrival (TDOA) measurement model, along with the Chan algorithm, is used to find the coordinates of unknown nodes has been proposed. With the help of ultra-wideband, this research aims to develop new and precise localization algorithms for wireless sensor networks (WSNs). This work offers localization using two-hybrid localization algorithms, i.e., ELPSO (Ensemble learning particle swarm optimization) and PSO-BPNN (Back-propagation neural network optimized by particle swarm optimization). Further, the error optimization accuracy has been compared between those algorithms using simulations. The proposed techniques consistently offer a better localization accuracy than the conventional algorithms available in the literature. The new localization methods with optimal techniques reduce the error value to a minimal distance. The distance value of localization error is nearly2.7cms compared to other designs from the literature. It is noted as significantly less.

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An Efficient Node Localization Approach with RSSI for Randomly Deployed Wireless Sensor Networks

Cited by 13 publications

References 28 publications

An Efficient Anchor-Free Localization Algorithm for all Cluster Topologies in a Wireless Sensor Network

An Efficient Anchor-Free Localization Algorithm for all Cluster Topologies in a Wireless Sensor Network

Optimization of Wildfire Localization Using a Trilateration-Based Nelder-Mead Algorithm in a Wireless Sensor Network

Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization

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