Luby Transform (LT) code is considered as an efficient erasure fountain code. The construction of the coded symbols is based on the formation of the degree distribution which played a significant role in ensuring a smooth decoding process. In this paper, we propose a new encoding scheme for LT code generation. This encoding presents a deterministic degree generation (DDG) with time hoping pattern which found to be suitable for the case of short data length where the well-known Robust Soliton Distribution (RSD) witnessed a severe performance degradation. It is shown via computer simulations that the proposed (DDG) has the lowest records for unrecovered data packets when compared to that using random degree distribution like RSD and non-uniform data selection (NUDS). The success interpreted in decreasing the overhead required for data recovery to the order of 25% for a data length of 32 packets.

The localization for indoor environments is faced by problems related to high error rates. To address this problem, a hyperbolic method is chosen which is a suitable for such environments. Also, such methods give a significant error when depending on RSS values, which suffer from unstable reading. To overcome this phenomenon, a proposed method is presented in this research to choose a suitable value of RSS. The idea is to install 4TXs in the chosen case study building and select the best two reading of 2TXs lying on the same side of the square case study building among four values of RSS measured from the 4TXs. The results show that, a significant reduction of error is achieved using the proposed method as the range of achieved error is between (0.1091, 0.0061) to (1.7162, 1.6593). While, the such range are from (0.0114, 0.0472) to (1.2685, 2.3207) by using 2TXs. Finally, a validation of achieved error is applied for the results of proposed method applied by real measurements. The results of such validation show that a close measurement to the simulation result is achieved.

Designing a localization system for an indoor environment faces more challenges because of multipath and interference problems. In this field, the most important techniques used for such environment, are RSS and ToA which need to be improved especially from more interference because of the huge multipath problems. In this paper, a case study of a selected building is chosen in order to apply the proposed technique of this research. Such proposal is based on the PT ‎ of the area in the case study into MZ. Each zone is allocated special values for the parameters used to estimate the target positions. WI package is used to simulate the case study area and apply such proposal based on RSS and ToA. The results confirm that the estimated locations are close to the real locations by the average error of (2.8) meter and (0.192) meter for ToA corresponding one zone and four zones ‎respectively. ‎ In contrast, the results of our experiment show that the accuracy is improved from an average error of (2.4) meter and (0.217) meter for RSS corresponding one zone and four zones ‎respectively‎. Such results confirm that dividing the case study area into more zones leads to more accuracy.

Indoor positioning plays a pivotal role in a wide range of applications, from smart homes to industrial automation. In this paper, we propose a comprehensive approach for accurate positioning in indoor environments through the integration of existing Wi-Fi and Bluetooth Low Energy (BLE) devices. The proposed algorithm involves acquiring the received signal strength indicator (RSSI) data from these devices and capturing the complex interactions between RSSI and positions. To enhance the accuracy of the collected data, we first use a Kalman filter for denoising RSSI values, then categorize them into distinct classes using the K-nearest neighbor (KNN) algorithm. Incorporating the filtered RSSI data and the class information obtained from KNN, we then introduce a recurrent neural network (RNN) architecture to estimate the positions with a high precision. We further evaluate the accuracy of our proposed algorithm through testbed experiments using ESP32 system on chip with integrated Wi-Fi and BLE. The results show that we can accurately estimate the positions with an average error of 61.29 cm, which demonstrates a 56% enhancement compared to the state-of-the-art existing works.

Implementing a limiting framework for an indoor climate has been complicated by multipath and blockage difficulties. The Angle of Arrival (AoA) is the most commonly used method for such situations in the area, and it should be strengthened, especially for additional obstructions due to massive multipath issues. A contextual investigation of a chosen fabricating is chosen in this paper to use the offered approach of this analysis. This concept is based on the triangulation approach. The Wireless Insite Software (WIS) was used to reconstruct the contextual analysis territory, with 30 Receivers (RXs) and 2 Transmitters (TXs) placed in appropriate locations. Following the estimation of AoA and saving them into the database, the coordinates of RXs are determined using MATLAB programming. The findings show that the estimated locations are reasonably near to the genuine locations, with an average error of (0.24) metre for the X-coordinate and (0.205857) metre for the Y-coordinate. As a result, we find that under the Triangulation method, the AoA approach is more effective in evaluating indoor localisation.