A Survey of Localization using RSSI and TDoA Techniques in Wireless Sensor Network: System Architecture

Azmi, Nur Aisyah; Samsul, Shafiqa; Yamada, Yoshıhıde; Yakub, Fitri; Ismail, Mohd Ismifaizul Mohd; Dziyauddin, Rudzidatul Akmam

doi:10.1109/tafgen.2018.8580464

Cited by 29 publications

(15 citation statements)

References 10 publications

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“…The estimate distance will be determined using the (2) [7], but we could convert this formula equation to a slope-intercept formula equation in this section (3).…”

Section: Distance Estimationmentioning

confidence: 99%

“…Bluetooth low energy technology is a new standard for wireless personal area network technology developed by bluetooth special interest group (SIG) [6]. Due to the low cost, low power consumption, easyto-deploy solution and the vast majority of portable devices in the, BLE beacon has been used for indoor localization [7]. BLE beacon is low power consumption device and its coverage area will become smaller compared to WI-FI due to short signal reachable distance [8].…”

Section: Introductionmentioning

confidence: 99%

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Indoor positioning utilizing bluetooth low energy (BLE) RSSI on LoRa system

Suseenthiran

Ja'afar

Heng

et al. 2021

IJEECS

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Indoor positioning systems has become popular in this era where it is a network of devices used to locate people or object especially in indoor environment instead of satellite-based positioning. The satellite-based positioning global positioning system (GPS) signal is affected and loss incurred by the wall of the building causes the GPS lack of precision which leads to large positioning error. As a solution to the indoor area coverage problem, an indoor positioning based on bluetooth low energy (BLE) and long range (LoRa) system utilising the receive signal strength indicator (RSSI) is proposed, designed and tested. In this project, the prototype of indoor positioning system is built using node MCU ESP 32, LoRa nodes and BLE beacons. The node MCU ESP 32 will collect RSSI data from each BLE beacons that deployed at decided position around the area. Then, linear regression algorithm will be used in distance estimation. Next, particle filteris implemented to overcome the multipath fading effect and the trilateration technique is applied to estimate the user’s location. The estimated location is compared to the actual position to analyze the root mean square error (RMSE) and cumulative distribution function (CDF). Based on the experiment result, implementing the particle filter reduces the error of location accuracy. The particle filter achieves accuracy with 90% of the time the location error is lower than 2.6 meters.

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“…The estimate distance will be determined using the (2) [7], but we could convert this formula equation to a slope-intercept formula equation in this section (3).…”

Section: Distance Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Indoor positioning utilizing bluetooth low energy (BLE) RSSI on LoRa system

Suseenthiran

Ja'afar

Heng

et al. 2021

IJEECS

View full text Add to dashboard Cite

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“…When the temperature sensor reached beyond the normal range, it sent an alert message to the parents' mobile number. According to [54], localisation study has become a central attraction for tracking objects in the era of a new global economy. The object environment has a substantial effect on accuracy offered by the localisation algorithms.…”

Section: Wsns Localisation and Positioningmentioning

confidence: 99%

“…The object environment has a substantial effect on accuracy offered by the localisation algorithms. Correspondingly, the proposed system in [54] can be deployed for a small low-cost and low-power LoRa tracking device with no GPS dependency, improving the localisation even when the Tx node is covered in shadows or under a building. Nevertheless, this method needs at least three Rx nodes to identify the location of the Tx node.…”

Section: Wsns Localisation and Positioningmentioning

confidence: 99%

A Review of Energy Harvesting in Localization for Wireless Sensor Node Tracking

et al. 2021

Self Cite

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The localisation and positioning in Wireless Sensor Node (WSN) are prone to tracking loss because of battery depletion resulted from high power consumption. Considering this, Energy Harvesting (EH) is a significant factor to ensure the sustainability of WSN trackers. Therefore, the key objective of the paper is to review the existing EH approaches, specifically for WSN trackers. An overview of WSNs including the underlying wireless communication technologies is initially presented. We compared the communication range, data rates, power consumption and also the cost across wireless technologies used for WSN. This paper further discussed the localisation and positioning techniques using the Global Positioning System (GPS) and other types of sensors exploiting signal parameters like Received Signal Strength Indicator (RSSI) and Angle of Arrival (AoA). Subsequently, we reviewed the energy harvesting approaches in terms of power density, efficiency and also highlighted their advantages and disadvantages. The EH components such as energy storage and energy-combining circuit for active monitoring are presented as well. Finally, this paper outlined the key challenges and future regards EH for WSN.

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“…During the methodology devising process, issues of positioning accuracy, scalability, and energy consumption must be evaluated to establish the system baseline. The sensor node localization algorithm is therefore of great significance for network deployment [17]. In this paper, a sensor node localization method called the distance mapping algorithm (DMA), which is distinctive from the aforementioned approaches, is devised and developed.…”

Section: Introductionmentioning

confidence: 99%

Distance Mapping Algorithm for Sensor Node Localization in WSNs

Tan

Shao

et al. 2019

Int J Wireless Inf Networks

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The localization of sensing nodes is most pronounced in the application of wireless sensor networks. To address this issue, a node localization algorithm called the DMA is proposed in this paper. This algorithm identifies the node position by using the estimation matrix and distance matrix together with the optimized linear transforming function. With the integration of GA, the position of the node can be accurately determined. The conducted simulation outcomes and the corresponding analysis verify the high accuracy and low energy consumption of the proposed algorithm, which can outperform other widely used approaches. This study designs and deploys the proposed algorithm and shows its sensor node localization theory, which makes it a promising basis for the realization of positioning in WSNs.Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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A Survey of Localization using RSSI and TDoA Techniques in Wireless Sensor Network: System Architecture

Cited by 29 publications

References 10 publications

Indoor positioning utilizing bluetooth low energy (BLE) RSSI on LoRa system

Indoor positioning utilizing bluetooth low energy (BLE) RSSI on LoRa system

A Review of Energy Harvesting in Localization for Wireless Sensor Node Tracking

Distance Mapping Algorithm for Sensor Node Localization in WSNs

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