Therefore, an awake/sleep algorithm was used to decrease the power consumption of the PEPLS. These radio frequency modules increase power consumption. In this system, location information was acquired via global positioning system (GPS) and transmitted via global system for mobile (GSM) and ZigBee protocols. In this paper, a power-efficient portable localization system (PEPLS) based on WSN was implemented. Therefore, saving power in localization systems is a vital requirement. Most localization system buildings use wireless sensor network (WSN) technology, but WSNs are a major source of energy consumption. Several techniques can use in the localization for the blind. According to the World Health Organization (WHO), approximately 1 billion people suffer from blindness or poor vision that cannot be treated. The mobility of blind individuals is restricted by their inability to perceive their surroundings. The experimental findings indicate that the DAECNN methodology works better than the existing classification approaches. The proposed method is compared with the existing deep learning approaches such as deep autoencoder, sparse autoencoder, CNN, multilayer perceptron, radial basis function neural network, and the performances are analyzed. The proposed approach uses the de-noising autoencoder to reconstruct the noisy image and the convolution neural network (CNN) to classify the users' current position. This paper presents a denoising auto encoder based on the convolutional neural network (DAECNN) to identify the present location of the users. This work presents a detailed analysis of the recent user positioning techniques and methodologies on the indoor navigation system based on the parameters, such as techniques, cost, the feasibility of implementation, and limitations. The entire selection of the navigation path depends upon the current location of the user. Identifying the current location of the users can be a difficult task for those with visual impairments. Experiments show that the algorithm proposed in this article can achieve significant results in the fingerprint localization task of real indoor scenes and effectively improve the localization accuracy.Ī challenging area of research is the development of a navigation system for visually impaired people in an indoor environment such as a railway station, commercial complex, educational institution, and airport. Finally, the suppression of these duplicate regions is achieved in the original image fingerprint database by introducing weighted suppression coefficients. Then the similarity of these duplicate regions in Euclidean space is defined, and the degree of influence of these regions on the distinguishability of the fingerprint database is measured. First, duplicate regions (confusion subregions) are extracted from the fingerprint database using an appropriate salient region detection method. To solve this problem, this article proposes a confusion subregion weighted suppression strategy in the image fingerprint database. However, indoor image information from different locations is characterized by high content repetition, and these repetitive regions can cause the problem of insufficient differentiation of adjacent image fingerprints or even fingerprint misclassification. Image data can provide rich content information, which has attracted a lot of attention in the field of indoor fingerprint positioning.
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