WiFi based Indoor Positioning System using Machine Learning and Multi-Node Triangulation Algorithms

Jahagirdar, Sumit; Ghatak, Aditya; Kumar, Abhiram Ajith

doi:10.1109/icccnt49239.2020.9225350

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…The materialization of coverage in equation (7) depends on the number of covered TPs (RSS max count). Finally, AP deployment fitness obtains the optimized deployment the algorithm from our WOAIP algorithm as explained in equation (9).…”

Section: Figure 7 the Ap-matrixmentioning

confidence: 99%

“…Additionally, the Multipath Propagation (MP) describes the process of receiving several copies of the original signal distributed over varying time delays and attenuation caused by the reflection, diffraction and diffusion of these signals 8 . The ongoing signal from multiple paths can be easily interrupted by different moving objects, obstacles, furniture, and the body of the device holder 9 . Therefore, the need for efficient optimization techniques suggested to optimize the AP position and to cover the entire target area with a minimum number of AP devices in order to control the cost and interferences besides the reduction of the dead zones.…”

Section: Introductionmentioning

confidence: 99%

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WOAIP: Wireless Optimization Algorithm for Indoor Placement Based on Binary Particle Swarm Optimization (BPSO)

Naif

Mohammed

2022

Baghdad Sci.J

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Optimizing the Access Point (AP) deployment has a great role in wireless applications due to the need for providing an efficient communication with low deployment costs. Quality of Service (QoS), is a major significant parameter and objective to be considered along with AP placement as well the overall deployment cost. This study proposes and investigates a multi-level optimization algorithm called Wireless Optimization Algorithm for Indoor Placement (WOAIP) based on Binary Particle Swarm Optimization (BPSO). WOAIP aims to obtain the optimum AP multi-floor placement with effective coverage that makes it more capable of supporting QoS and cost-effectiveness. Five pairs (coverage, AP deployment) of weights, signal thresholds and received signal strength (RSS) measurements simulated using Wireless InSite (WI) software were considered in the test case study by comparing the results collected from WI with the present wireless simulated physical AP deployment of the targeted building - Computer Science Department at University of Baghdad. The performance evaluation of WOAIP shows an increase in terms of AP placement and optimization distinguished in order to increase the wireless coverage ratio to 92.93% compared to 58.5% of present AP coverage (or 24.5% coverage enhancement on average).

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Section: Figure 7 the Ap-matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

WOAIP: Wireless Optimization Algorithm for Indoor Placement Based on Binary Particle Swarm Optimization (BPSO)

Naif

Mohammed

2022

Baghdad Sci.J

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“…When utilizing the triangulation method, it may be beneficial to consider using the existing infrastructure of Wi-Fi routers and mobile hotspots. A precise positioning system can be achieved by using a multi-node triangulation algorithm that takes into account the correlation between RSSI values and transmitter–receiver distance, along with a coordinate system model to determine the optimal receiver coordinates [ 32 ]. If measurements in a localization system are obtained discontinuously, such as in laser localization, an extended Kalman filter can be used.…”

Section: Introductionmentioning

confidence: 99%

Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method

Ciężkowski,

Kociszewski

2024

Sensors

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One of the important issues being explored in Industry 4.0 is collaborative mobile robots. This collaboration requires precise navigation systems, especially indoor navigation systems where GNSS (Global Navigation Satellite System) cannot be used. To enable the precise localization of robots, different variations of navigation systems are being developed, mainly based on trilateration and triangulation methods. Triangulation systems are distinguished by the fact that they allow for the precise determination of an object’s orientation, which is important for mobile robots. An important feature of positioning systems is the frequency of position updates based on measurements. For most systems, it is 10–20 Hz. In our work, we propose a high-speed 50 Hz positioning system based on the triangulation method with infrared transmitters and receivers. In addition, our system is completely static, i.e., it has no moving/rotating measurement sensors, which makes it more resistant to disturbances (caused by vibrations, wear and tear of components, etc.). In this paper, we describe the principle of the system as well as its design. Finally, we present tests of the built system, which show a beacon bearing accuracy of Δφ = 0.51°, which corresponds to a positioning accuracy of ΔR = 6.55 cm, with a position update frequency of fupdate = 50 Hz.

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Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method

Ciężkowski,

Kociszewski

2024

Preprint

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One of the important issues being explored in Industry 4.0 is a collaborative mobile robots. This collaboration requires precise navigation systems, especially indoor navigation systems where GNSS (Global Navigation Satellite System) cannot be used. To enable precise localization of robots, different variations of navigation systems are being developed, mainly based on trilateration and triangulation methods. Triangulation systems are distinguished by the fact that they allow precise determination of an object’s orientation, which is important for mobile robots. An important feature of positioning systems is the frequency of position updates based on measurements. For most systems it is 10-20 Hz. In our work, we propose a high-speed 50 Hz positioning system based on the triangulation method with infrared transmitters and receivers. In addition, our system is completely static, i.e. it has no moving/rotating measurement sensors, which makes it more resistant to disturbances (caused by vibrations, wear and tear of components, etc.). In this paper we describe the principle of the system as well as its design. Finally, we present tests of the built system, which show centimeter positioning accuracy with a position update frequency of 50 Hz.

show abstract

WiFi based Indoor Positioning System using Machine Learning and Multi-Node Triangulation Algorithms

Cited by 3 publications

References 12 publications

WOAIP: Wireless Optimization Algorithm for Indoor Placement Based on Binary Particle Swarm Optimization (BPSO)

WOAIP: Wireless Optimization Algorithm for Indoor Placement Based on Binary Particle Swarm Optimization (BPSO)

Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method

Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method

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