The indoor positioning system comprises portable wireless devices that aid in finding the location of people or objects within the buildings. Identification of the items is through the capacity level of the signal received from various access points (i.e., Wi-Fi routers). The positioning of the devices utilizing some algorithms has drawn more attention from the researchers. Yet, the designed algorithm still has problems for accurate floor planning. So, the accuracy of position estimation with minimum error is made possible by introducing Gaussian Distributive Feature Embedding based Deep Recurrent Perceptive Neural Learning (GDFE-DRPNL), a novel framework. Novel features from the dataset are through two processing stages dimensionality reduction and position estimation. Initially, the essential elements selection using the Gaussian Distributive Feature Embedding technique is the novel framework. The feature reduction process aims to reduce the time consumption and overhead for estimating the location of various devices. In the next stage, employ Deep Recurrent multilayer Perceptive Neural Learning to evaluate the device position with dimensionality reduced features. The proposed Deep-learning approach accurately learns the quality and the signal strength data with multiple layers by applying Deming Regressive Trilateral Positioning Model. As a result, the GDFE-DRPNL framework increases the positioning accuracy and minimizes the error rate. The experimental assessments with various factors such as positioning accuracy minimized by 70% and 60%, computation time minimized by 45% and 55% as well as overhead by 11% and 23% compared with PFRL and two-dimensional localization algorithm. Through the experiment and after analyzing the data, verify that the proposed GDFE-DRPNL algorithm in this paper is better than the previous methods.

The industrial paradigm is widely utilized in indoor positioning research. The Indoor positioning systems use various technologies that consist of distance measurement to neighbouring anchor nodes such as Wi-Fi access points, Bluetooth beacons or Ultra-Wideband beacons. The positioning system has many applications ranging from commercial, military, retail to stock tracking industries. Many techniques and devices aid in locating the indoor positioning system. In this regard, for an accurate wireless indoor positioning system, a Particle Filter based Reinforcement Learning (PFRL) technique was designed [

In order to decrease the location error and execution time, a new deep-learning-based indoor fingerprinting system was designed in [

A deep-learning-based approach was presented in [

A Back Propagation Neural Network optimized with Particle Swarm Optimization (BPNN-PSO) was employed in [

The issues reviewed from the above existing floor positioning works are computed overhead that did not minimize during the localization process—the machine learning technique like PFRL technique introduced for a robust wireless indoor positioning system. PFRL technique comprises a particle filter component and a reinforcement learning-based re-sampling method. The zone prediction method combines dissimilar individual predictors in a Hidden Markov Model (HMM) by an ensemble learning algorithm. A particle filter approach was developed to provide accurate localization of failure problems with a reinforcement on learning-based re-sampling method. Yet, it did not enhance the computation overhead. So, a Two-dimensional Localization Algorithm was introduced to accomplish more accurate parameter estimation by applying particle swarm optimization. The parametric model is used to strengthen the two-dimensional (2D) positioning to position the users with the weighted K-Nearest Neighbour algorithm. Still, it did not improve positioning accuracy. The existing method underwent many hitches to perform continuous positioning with high precision. Accurate position estimation was not attained during the designed scheme and it is not considered a path loss to achieve higher accuracy for the indoor positioning system. Further, the existing localization techniques did not reduce accurate dimensionality. Hence, there is a need to introduce the GDFE-DRPNL framework to address these problems

The paper presents the development of a novel GDFE-DRPNL framework and summarizes their significant contributions, introduces a novel deep-learning framework GDFE-DRPNL by combining feature selection and position estimation process. To achieve the goal, GDFE-DRPNL framework is created through Gaussian Distributive Embedding Feature and Deep Recurrent multilayer Perceptive Neural Learning. By applying Gaussian distributive feature embedding technique [GDFE-DRPNL] framework, the overall computation time and overhead is reduced. Then, it minimizes the dimensionality of the dataset by selecting principle features. DRPNL is used to increase the positioning accuracy as well as to minimize the error. In the proposed deep recurrent learning, the Deming Regressive Trilateral Positioning Model [

To facilitate the process of cell planning that involves locating and configuring infrastructure for mobile networks a system using cluster techniques was proposed in [

This article is divided into various sections. In the second section, the novel indoor positioning framework and its description are presented with a neat diagram. The third section presents different tests conducted using the dataset and the results are compared with related works—finally, the fourth section offers conclusion.

With the extensive growth of information technology, the indoor positioning system has rapidly increased to identify mobile devices, people, and equipment. The Wi-Fi-based dynamic environment is not perfect and robust as the device is not capable of adapting signal oscillations, noises, and radio signal instabilities. Therefore, the proposed work has introduced deep learning as an innovative strategy to handle traditional learning problems. The main aim of the indoor positioning and localization is based on the strength of the wireless signals. The Gaussian distributive feature embedding technique increases dimensionality reduction performance. Further, it combines the Gaussian distributive function and Kernel Principal Component Analysis concept to select the principle features precisely with lower time complexity. Besides, the Deep Recurrent Multilayer Neural Network and Deming Regressive Trilateral Positioning Model are used in the proposed technique to improve the performance of indoor floor planning with higher positioning accuracy. On the contrary, Deep Recurrent Multilayer Neural Network handles a large number of Wi-Fi devices simultaneously. The overall system for Wi-Fi-based positioning and localization of various devices consists of fixed wireless access points. They are displayed in

The proposed GDFE-DRPNL framework initially performs dimensionality reduction. It then removes the random variables and selects a set of essential variables. Then, to improve learning performance, appropriate principle features are selected for data modeling that reduces computational time or required resources, and high-dimensional input to decrease the curse of dimensionality. The feature selection is to choose a set of principle features that provide the best positioning estimation with a classifier. Based on this motivation, the proposed GDFE-DRPNL framework uses the Gaussian distributive feature embedding technique to perform a dimension reduction by selecting the fewest features from the dataset.

Consider the feature vectors from the given dataset ‘

The proposed technique initially calculates the probabilities using the Gaussian distribution function relative to the similarity of objects. The probability is expressed as follows:

From _{i}, if_{j})’ stands for the probability of identifying the principal features from the given feature vector, ‘D’ symbolizes Gaussian standard deviation and ||if_{i} − if_{j}|| designates Euclidean distance similarity between the two features if_{i} and if_{j} in the feature vector. The estimated probability value lies between zero and one. Then, the predefined threshold sets to map the feature vector. If the calculated probability result is higher than the predefined threshold (φ = 0.5) then, it is mapped into the principle-feature subset; otherwise, the redundant features map into the other subgroup. Based on the probability value, at first maps input features vectors ‘if_{i} = {if_{i} ∈ if^{N}|i = 1, …, N}’ into dimensional space ‘Z’ by way of nonlinear mapping ‘φ’ associated with kernel function ‘ω’. The mapping process perform as,

By using

In _{i}. Subsequently, non-linear PCA [

From _{i})’. After that, eigenvectors are determined utilizing a linear combination of ‘φ(if_{i})’ with the support of the following expression,

By applying _{i})^{T} on both sides, the following equation is obtained:

In ^{N×N}’ refers to the Gram matrix where ‘a’ stands for normalized Eigenvectors of ‘ω’. The gram matrix is determined as an inner product form to identify principle-feature subset using the equation below:

Next, the proposed technique identifies eigenvectors with greater Eigen values to find out principal feature subset α_{p} ⊂ α with the support of kernel function using the following:

From _{N}’ and ‘a_{p} ⊂ a’, where ‘n’ designates the set of principle features that provide the best positioning estimation.

Once the principle features are identified from the dataset, the proposed GDFE-DRPNL framework begins to perform the positioning of Wi-Fi devices in the indoor floors. A high positioning accuracy is obtained with the proposed GDFE-DRPNL framework and employs DRPNL and considers the building of floors and walls that have influence on RSSI for the corresponding MAC address of the devices. The indoor environmental aspects such as multi-path, path loss and person movement affects the collected RSSI samples. So, the received RSSI values are dissimilar even in the same device. Similarly, location and different times, also considerably affect the accuracy of localization. Therefore, the proposed deep recurrent multilayer perceptive neural learning also measures the path loss between the floor and the wall for the accurate localization of devices.

The Deep recurrent multilayer Perceptive neural network is a machine learning technique. This technique uses cascading of layers to acquire the principle features and their information directly from the dataset. The structure of a deep recurrent multilayer perceptive neural network includes three layers, namely input, two, or more hidden layers, and output layer. The function of the input layer is to collect the series’ type of information with no predetermined size from the dataset and transfer it to the hidden layer. The feature learning process is carried out repetitively in the hidden layers, and it provides accurate results at the output layer. The proposed deep-learning model uses a unit delay for repetitively learning the features that are fed back into its input layer. The input layer in deep-learning techniques is fully linked with the output layer by adjustable weight connections.

The Deep recurrent multilayer Perceptive neural network is schematically presented in _{1}(t − 1), δ_{2}(t − 1), δ_{3}(t − 1)’. The weighted values between the input and hidden layers are represented by v_{ih}. Then, the deal between the invisible and output is referred to as v_{ho}. In the network architecture, each layer has its own set of weights and biases which indicates that each of the layers is independent of others. The input and their value are denoted as follows:

From _{i}’ denotes input, where ‘v_{i}’ refers to weight and ‘ b’ represents the bias that is used to adjust the output with the weighted sum of the information to the neuron. In the first hidden layer, the received signal strength of various devices is collected from the access point AP_{i}. The signal strength [

As given in the above equation, the two antennae possess different heights, the signal strength ss_{R} posses two components, ‘G_{t}’ and ‘G_{r}’ symbolize a transmitter and receiver gain. Besides, the distance ‘_{r} from ‘AP’. Besides, the path loss focuses on the floors and walls influences of RSS and gives as follows:

From the above _{fw}. The path loss between the floor and wall is based on the penetration loss. It is at a distance of ‘1’ meter and denotes by PL_{0} and PL_{fk} which stands for the attenuation. It is of the floor ‘f’ to the ‘_{wk} denotes the attenuation due to the wall ‘w’ to the ‘kth’ traversed wall. The dataset comprises the measurement of several inbuilt sensors of smartphones. These smartphones are used to position the devices. Therefore, the signal strength of smartphones [

_{ih}, _{hh} denotes the weight of the hidden layers, ‘v_{ih}’ indicates adjustable value between input and hidden layer and w_{i}(t) represents the input. The recurrent process of deep learning repeats until the error gets minimized. The hidden layers with recurrent results are fed into the output layers. The exact coordinate for the positioning device is correctly identified at the output layer ‘y(t)’.

The goal of algorithmic process of deep recurrent multilayer perceptive neural network is to achieve accuracy and minimum error. The deep-learning network system receives the principle features and gives it to the next layer where the signal strength of various devices are analyzed utilizing the Deming regressive positioning model. The regression function minimizes the error and finds the exact coordinate for positioning multiple devices. For each result obtained, regression function determines the error and consequently updates all the weights on the network and thereby finds out the minimal error using gradient descends function. The process mentioned below iterates until the error gets minimized. Finally, the exact output is displayed in the output layer.

The algorithmic steps of the proposed deep recurrent multilayer perceptive neural learning are described as follows:

The experimental evaluation of the proposed GDFE-DRPNL framework without GDFE, PFRL, and a two-dimensional localization algorithm are possible through Java. The indoor floor planning is through the IPIN 2016 competition dataset [

Multiple data are gathered from four different buildings with different time stamps. In the dataset, 2971 Wi-Fi fingerprints are constructed by associating every Wi-Fi fingerprint with the nearest (in terms of timestamps) ground truth. The dataset comprises the measurement of several inbuilt sensors of smartphones (i.e., Wi-Fi, Magnetometer, Accelerometer, Barometer, Gyroscope, etc., with timestamps).

The IPIN dataset includes the tuples containing many grounds truth for particular timestamps due to those different sensors has different sampling rates. The samples were collected with their timestamps and also obtained from various sensors and synchronized with each other. The IPIN dataset comprises a total of 816 access points (APs). The dataset includes 26 log files. Among these, 17 log files and 9 log files were used for training and evaluate the work. Here, the first column comprises the sequence number of the log files. The second column denotes the building-id, where the data of log files are collected. Similarly, the third and fourth columns indicate the number of floors and landmarks involved in managing the data of these log files. Finally, the fifth column denotes the smartphone used to capture the data of specific log files.

In this section, the performance of the GDFE-DRPNL framework and the other three related approaches, namely without GDFE, PFRL and two-dimensional localization algorithm are analyzed with different quantitative metrics such as positioning accuracy, positioning error, computational time and computational overhead.

It is used to find how the proposed framework accurately estimates the position for different iterations. In other words, the positioning accuracy is calculated as the ratio of successful trials to the total number of shots taken as input. The formula is expressed as follows:

The performance results of GDFE-DRPNL frameworks and the other three related approaches, namely without GDFE, PFRL and two-dimensional localization algorithm are discussed in this subsection with table and graphical representation. Initially, the positioning accuracy measures are concerned with the number of trials. Totally ten iterations are considered for fair computation of accuracy using the three methods.

While considering a simulation environment with 50 Wi-Fi devices and trials, the proposed GDFE-DRPNL framework attains nine successful trials and one failed trial, whereas the one existing Without GDFE gets to earn eight successful trials and two failed trials. Besides, conventional PFRL gains seven successful trials and three failed trials, whereas the existing Two-dimensional localization algorithm acquires six successful trials and four failed trials. Based on the simulation result of successful practices and failed attempts are determined for ten iterations, positioning accuracy measures are presented in

In GDFE-DRPNL Framework, the experimental results of the Wi-Fi device positioning accuracy and the other three related approaches, namely without GDFE, PFRL and two-dimensional localization algorithm are reported in

Number of iterations | Number of Wi-Fi devices | No. of trials | Positioning accuracy (%) | |||
---|---|---|---|---|---|---|

GDFE- |
Without GDFE | PFRL | Two-dimensional localization algorithm | |||

1 | 10 | 10 | 90.2 | 80.2 | 70.5 | 60.2 |

2 | 20 | 20 | 90.6 | 80.8 | 70.25 | 60.4 |

3 | 30 | 30 | 90.2 | 80.6 | 70.8 | 60.8 |

4 | 40 | 40 | 80.8 | 70.4 | 60.5 | 50.8 |

5 | 50 | 50 | 80.6 | 70.4 | 60.6 | 50.5 |

6 | 60 | 60 | 80.4 | 70.6 | 60.4 | 50.6 |

7 | 70 | 70 | 80.4 | 70.2 | 60.5 | 50.5 |

8 | 80 | 80 | 70.25 | 62.5 | 60.4 | 50.6 |

9 | 90 | 90 | 70.5 | 65.5 | 60.2 | 50.8 |

10 | 100 | 100 | 70.8 | 60.5 | 50.2 | 50.5 |

In the first iteration, ten trials are considered while computing the Wi-Fi device’s positioning accuracy. For each test has various sizes of floors view, the positioning of devices are accurately found in different sizes of the floors. As shown in

Number of iterations | Number of Wi-Fi devices | No. of trials | Number of successful trials | |||
---|---|---|---|---|---|---|

GDFE- |
Without GDFE | PFRL | Two-dimensional localization algorithm | |||

1 | 10 | 10 | 9 | 8 | 7 | 6 |

2 | 20 | 20 | 18 | 16 | 14 | 12 |

3 | 30 | 30 | 27 | 24 | 21 | 18 |

4 | 40 | 40 | 32 | 28 | 24 | 20 |

5 | 50 | 50 | 40 | 35 | 30 | 25 |

6 | 60 | 60 | 48 | 42 | 36 | 30 |

7 | 70 | 70 | 56 | 49 | 42 | 35 |

8 | 80 | 80 | 56 | 50 | 48 | 40 |

9 | 90 | 90 | 63 | 59 | 54 | 45 |

10 | 100 | 100 | 70 | 60 | 50 | 50 |

The computational time measures the amount of time consumed by object localization within the building. Accordingly, computational time (from 17) counts as follows:

Number of trials | Number of Wi-Fi devices | Computational time (ms) | |||
---|---|---|---|---|---|

GDFE-DRPNL | Without GDFE | PFRL | Two-dimensionallocalization algorithm | ||

1 | 10 | 0.13 | 0.14 | 0.16 | 0.18 |

2 | 20 | 0.21 | 0.32 | 0.43 | 0.61 |

3 | 30 | 0.29 | 0.45 | 0.56 | 0.72 |

4 | 40 | 0.35 | 0.56 | 0.75 | 0.95 |

5 | 50 | 0.49 | 0.65 | 0.84 | 1.15 |

6 | 60 | 0.56 | 0.85 | 1.20 | 1.45 |

7 | 70 | 0.76 | 1.06 | 1.42 | 1.75 |

8 | 80 | 0.81 | 1.21 | 1.60 | 1.80 |

9 | 90 | 0.97 | 1.41 | 1.75 | 1.95 |

10 | 100 | 1.15 | 1.70 | 2.09 | 2.25 |

Computational overhead refers to the amount of memory consumed for object localization in indoor floor planning. The overall computational overhead (from 18) is mathematically defined using the following expression:

This work has proposed the GDFE-DRPNL framework for efficient wireless indoor positioning system. The GDFE-DRPNL framework is validated on distributed machine learning-based network architecture that comprises feature selection and position estimation. The feature selection achieves better positioning accuracy using the Gaussian Distributive feature Embedding technique. Followed by this, an efficient deep recurrent learning approach achieves high positioning system performance by integrating the selected features with the Deming regression learning technique through RSS signal measure. The results obtained demonstrate that the proposed framework is suitable for a robust indoor positioning system with minimum error. The localization framework is evaluated with two existing approaches in terms of different performance metrics. The observed evaluation results show that our proposed GDFE-DRPNL framework gives more accurate localization results with minimum positioning accuracy by 70% and 60%, time consumption is minimized by 45% and 55%, as well as overhead is reduced by 11% and 23% than PFRL and two-dimensional localization algorithms. In future, to enhance the accuracy and also reduce computational overhead the positioning of Wi-Fi devices for indoor floor planning could be carried out using ensemble learning techniques.