A neural network approach for Radio Frequency based indoors localization

Azenha, Abílio; Peneda, Luis; Carvalho, Adriano

doi:10.1109/iecon.2012.6389103

Cited by 11 publications

(8 citation statements)

References 17 publications

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“…13, No. 12, December 2018 ©2018 Journal of Communications [20], [25], [31]- [33], [40], [58]- [64] have been compared with the current work in terms of MAE. These studies are similar to our work on indoor environments, adopted wireless technologies (i.e., ZigBee), and artificial intelligent or optimization algorithm.…”

Section: E Comparison Results Between Hybrid Pe-pso and Lnsmmentioning

confidence: 99%

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Path Loss Model-Based PSO for Accurate Distance Estimation in Indoor Environments

Hashim¹,

Mohammed²,

Gharghan³

2018

jcm

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Wireless sensor networks (WSNs) and their applications have received considerable interest in the last few years. In WSNs, accurate path loss models should be considered to achieve a successful distribution of several nodes. In this work, two path loss models are proposed to evaluate the distance between two ZigBee WSNs. First, a path loss model based on conventional Log-Normal Shadowing Model (LNSM) is derived using the collected received signal strength indicator (RSSI) of the ZigBee in real time. Second, a new path loss model based on Particle Swarm Optimization (PSO) algorithm hybridized with Polynomial Equation (PE) is proposed. The PSO algorithm is used to select the optimum coefficients of PE. These coefficients can be utilized to optimize the distance estimation error based on the curve fitting. Therefore, the new path loss model called hybrid PE-PSO is innovated in this work. The hybrid PE-PSO model considerably improves the distance estimation accuracy compared with the LNSM. Results show that the hybrid PE-PSO achieves 85% improvement in distance error compared with the traditional LNSM. The mean absolute error of 0.77 m is obtained for distance estimation, which outperforms that by state of the arts.

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Section: E Comparison Results Between Hybrid Pe-pso and Lnsmmentioning

confidence: 99%

“…Azenha et al [33] adopted the ANN method for localization in indoor quasi-structured environments. This method uses radio frequency trilateration based on ANN (MLP type).…”

Section: Related Workmentioning

confidence: 99%

Path Loss Model-Based PSO for Accurate Distance Estimation in Indoor Environments

Hashim¹,

Mohammed²,

Gharghan³

2018

jcm

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“…The hybrid GSA–ANN algorithm can be compared with previous works [ 9 , 30 , 39 , 46 , 51 , 53 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ] in terms of localization or distance error to validate our proposed system. Similar studies based on different soft computing techniques were considered for the purposes of comparison.…”

Section: Results Comparisonmentioning

confidence: 99%

A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

Gharghan

Nordin

Ismail

2016

Sensors

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In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively.

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“…Figure 10 shows a comparison of the proposed hybrid PSO-ANN algorithm with other artificial intelligent algorithms employed for outdoor and indoor wireless SN localization in other studies. The results reveal that the location estimated using the hybrid PSO-ANN algorithm outperforms the algorithms of previous studies [33,[51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] in terms of MAE.…”

Section: Hybrid Pso-ann Algorithm For Distance Estimationmentioning

confidence: 94%

Energy Efficiency of Ultra-Low-Power Bicycle Wireless Sensor Networks Based on a Combination of Power Reduction Techniques

2016

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In most wireless sensor network (WSN) applications, the sensor nodes (SNs) are battery powered and the amount of energy consumed by the nodes in the network determines the network lifespan. For future Internet of Things (IoT) applications, reducing energy consumption of SNs has become mandatory. In this paper, an ultra-low-power nRF24L01 wireless protocol is considered for a bicycle WSN. The power consumption of the mobile node on the cycle track was modified by combining adjustable data rate, sleep/wake, and transmission power control (TPC) based on two algorithms. The first algorithm was a TPC-based distance estimation, which adopted a novel hybrid particle swarm optimization-artificial neural network (PSO-ANN) using the received signal strength indicator (RSSI), while the second algorithm was a novel TPC-based accelerometer using inclination angle of the bicycle on the cycle track. Based on the second algorithm, the power consumption of the mobile and master nodes can be improved compared with the first algorithm and constant transmitted power level. In addition, an analytical model is derived to correlate the power consumption and data rate of the mobile node. The results indicate that the power savings based on the two algorithms outperformed the conventional operation (i.e., without power reduction algorithm) by 78%.

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A neural network approach for Radio Frequency based indoors localization

Cited by 11 publications

References 17 publications

Path Loss Model-Based PSO for Accurate Distance Estimation in Indoor Environments

Path Loss Model-Based PSO for Accurate Distance Estimation in Indoor Environments

A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

Energy Efficiency of Ultra-Low-Power Bicycle Wireless Sensor Networks Based on a Combination of Power Reduction Techniques

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