RSSI Based Trilatertion for Outdoor Localization in Zigbee based Wireless Sensor Networks (WSNs)

Arif, Syed A.; Niaz, Muhammad H.; Shabbir, Noman; Zafar, Muhammad Qasim; Hassan, Syed Rizwan; Rehman, Ateeq Ur

doi:10.1109/cicn.2018.8864943

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…The problem of target localization and tracking has been intensively addressed by the researchers regarding WSNs [32]- [34]. These models present a PF technique to solve radio source localization by applying the measurements of only received signal strength indicator (RSSI), [35]. These models also take into account the antennae geometry, RF propagation, and ground reflection are presented.…”

Section: Related Workmentioning

confidence: 99%

A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms

et al. 2020

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Localization plays an important role in the field of Wireless Sensor Networks (WSNs) and robotics. Currently, localization is a very vibrant scientific research field with many potential applications. Localization offers a variety of services for the customers, for example, in the field of WSN, its importance is unlimited, in the field of logistics, robotics, and IT services. Particularly localization is coupled with the case of human-machine interaction, autonomous systems, and the applications of augmented reality. Also, the collaboration of WSNs and distributed robotics has led to the creation of Mobile Sensor Networks (MSNs). Nowadays there has been an increasing interest in the creation of MSNs and they are the preferred aspect of WSNs in which mobility plays an important role while an application is going to execute. To overcome the issues regarding localization, the authors developed a framework of three algorithms named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF) Localization algorithms. In our previous study, the authors only focused on EKF-based localization. In this paper, the authors present a modified Kalman Filter (KF) for localization based on UKF and PF Localization. In the paper, all these algorithms are compared in very detail and evaluated based on their performance. The proposed localization algorithms can be applied to any type of localization approach, especially in the case of robot localization. Despite the harsh physical environment and several issues during localization, the result shows an outstanding localization performance within a limited time. The robustness of the proposed algorithms is verified through numerical simulations. The simulation results show that proposed localization algorithms can be used for various purposes such as target tracking, robot localization, and can improve the performance of localization. INDEX TERMS Extended Kalman filter, localization, particle filter, robot, unscented Kalman filter, wireless sensor networks.

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Section: Related Workmentioning

confidence: 99%

A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms

et al. 2020

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“…This system has been shown to save water and be feasible for precision farming [8][9][10][11][12]. With this solution, the authors recommended an improved automated irrigation method [13][14][15]. A sprinkler head was fitted to a water supply under load by a conduit with a valve.…”

Section: Introductionmentioning

confidence: 99%

“…No destructive sampling of the crop Expensive for small areas [13] Cyber interactive networks lightweight safety compliance Not limited to specific IoT services Stable water supply need [14] Advanced and modern agricultural real-time automation and control system Ability to save water Rural areas cannot fulfill that requirements [15] Wireless vehicle speed with crop tracking and routing system sensor network…”

Section: Introductionmentioning

confidence: 99%

Design and development of an Irrigation Mobile Robot

Hassan

Asif

Rehman³

et al. 2021

IJRA

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Water plays a significant role among other existing natural resources. The daily demand for water supplies is increasingly on the rise as the population grows. To minimize the consumption of water in irrigation, several proposals were suggested. The currently existing system known as the automated irrigation system for effective water resource use with the prediction of the weather (AISWP) functions with a single farm that lacks the reliability in the precision of weather forecasting. So, a robot-based irrigation system has been proposed to improve the performance of the system. To minimize the water usage for crops, an automated irrigation system has been developed which irrigates the field in acres. An additional characteristic of the system has also been given for the soil pH measurement to allow the use of fertilizers accordingly. The solar-powered robot is managed wirelessly by a designated application. The robot is attached with various sensors and with a highresolution camera that tests crop conditions and senses the soil state. The application has been created to provide information about the soil’s condition such as temperature level, humidity level, water level, and level of nutrients to the PC/Laptop with the real-time values via the GSM module.

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“…At this stage, the more common indoor localization technologies include Bluetooth localization (Kuxdorf-Alkirata & Brückmann, 2016), Ultra wide band (UWB) indoor localization technology (Xu, Shi, & Wu, 2007), Radio frequency identification(RFID) localization (Buffi, Nepa, & Cioni, 2017), ZigBee indoor localization technology (Arif, Niaz, Shabbir, Zafar, Hassan, & ur Rehman, 2018), ultrasonic localization (Li, Ji, & Zhang, 2010), infrared localization (De-La-Llana-Calvo, Lázaro-Galilea, Gardel-Vicente, Rodríguez-Navarro, & Bravo-Muñoz, 2018) and Wi-Fi localization (Sun, Xue, Yu, Tang, & Lin, 2018;Zegeye, Amsalu, Astatke, & Moazzami, 2016), etc. Although there are many types of indoor localization technologies, in fact, many algorithms are universal.…”

Section: Introductionmentioning

confidence: 99%

Indoor Localization Based on Fingerprint Clustering

Chen¹

2020

NCT

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With the rapid development of the huge promotion of the Internet and artificial intelligence, the demand for location-based services in indoor environments has grown rapidly. At present, for the localization of the indoor environment, researchers from all walks of life have proposed many indoor localization solutions based on different technologies. Fingerprint localization technology, as a commonly used indoor localization technology, has led to continuous research and improvement due to its low accuracy and complex calculations. An indoor localization system based on fingerprint clustering is proposed by this paper. The system includes offline phase and online phase. We collect the RSS signal in the offline phase. We preprocess it with the Gaussian model to build a fingerprint database, and then we use the K-Means++ algorithm to cluster the fingerprints and group the fingerprints with similar signal strengths into a clustering subset. In the online phase, we classify the measured received signal strength (RSS), and then use the weighted K-Nearest neighbor (WKNN) algorithm to calculate the localization error. The experimental results show that we can reduce the localization error and effectively reduce the computational cost of the localization algorithm in the online phase, and effectively improve the efficiency of real-time localization in the online phase.

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RSSI Based Trilatertion for Outdoor Localization in Zigbee based Wireless Sensor Networks (WSNs)

Cited by 12 publications

References 6 publications

A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms

A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms

Design and development of an Irrigation Mobile Robot

Indoor Localization Based on Fingerprint Clustering

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