Muhammad Ayoub Kamal^1,3, Muhammad Mansoor Alam^1,2,4,6, Aznida Abu Bakar Sajak¹, Mazliham Mohd Su’ud^2,5,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1927-1945, 2024, DOI:10.32604/cmc.2024.052169 - 15 August 2024

Abstract In situations when the precise position of a machine is unknown, localization becomes crucial. This research focuses on improving the position prediction accuracy over long-range (LoRa) network using an optimized machine learning-based technique. In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology, this study proposed an optimized machine learning (ML) based algorithm. Received signal strength indicator (RSSI) data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building. The More >

Junyang Liu¹, Yuan Li², Yulu Zhang², Shuai Ma², Gui Li³, Yi He¹, Haiwen Yi¹, Yue Liu¹, Xiaotao Xu⁴, Xu Zhang¹, Jinyao He¹, Guangjun Wen¹, Jian Li^1,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 913-930, 2024, DOI:10.32604/cmc.2024.049999 - 18 July 2024

Abstract Addressing the challenges of passive Radio Frequency Identification (RFID) indoor localization technology in Non-Line-of-Sight (NLoS) and multipath environments, this paper presents an innovative approach by introducing a combined technology integrating an improved Kalman Filter with Space Domain Phase Difference of Arrival (SD-PDOA) and Received Signal Strength Indicator (RSSI). This methodology utilizes the distinct channel characteristics in multipath and NLoS contexts to effectively filter out interference and accurately extract localization information, thereby facilitating high precision and stability in passive RFID localization. The efficacy of this approach is demonstrated through detailed simulations and empirical tests conducted on… More >

Khaldon Azzam Kordi¹, Mardeni Roslee^1,*, Mohamad Yusoff Alias¹, Abdulraqeb Alhammadi², Athar Waseem³, Anwar Faizd Osman⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3261-3298, 2024, DOI:10.32604/cmc.2024.044890 - 15 May 2024

Abstract This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning. It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this research explores the integration of multiple sensor modalities (e.g., Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expand indoor localization methods, particularly in obstructed environments. It addresses the challenge of precise object localization, introducing a novel hybrid DL approach using received signal information (RSI), Received Signal Strength (RSS), and Channel State Information (CSI) data… More >

Yan Wang^*, You Lu, Yuqing Zhou, Zhijian Zhao

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2673-2703, 2024, DOI:10.32604/cmes.2023.046743 - 11 March 2024

Abstract Indoor positioning is a key technology in today’s intelligent environments, and it plays a crucial role in many application areas. This paper proposed an unscented Kalman filter (UKF) based on the maximum correntropy criterion (MCC) instead of the minimum mean square error criterion (MMSE). This innovative approach is applied to the loose coupling of the Inertial Navigation System (INS) and Ultra-Wideband (UWB). By introducing the maximum correntropy criterion, the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise, thus enhancing its adaptability to diverse environmental localization requirements. Particularly in… More >

Amr Abozeid^1,*, Ahmed I. Taloba^1,2, Rasha M. Abd El-Aziz^1,3, Alhanoof Faiz Alwaghid¹, Mostafa Salem³, Ahmed Elhadad^1,4

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2637-2650, 2023, DOI:10.32604/csse.2023.037761 - 09 February 2023

Abstract Indoor localization methods can help many sectors, such as healthcare centers, smart homes, museums, warehouses, and retail malls, improve their service areas. As a result, it is crucial to look for low-cost methods that can provide exact localization in indoor locations. In this context, image-based localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object. Image-based localization faces many issues, such as image scale and rotation variance. Also, image-based localization’s accuracy and speed (latency) are two critical factors. This paper proposes an efficient 6-DoF deep-learning… More >

Gengyu Ge^1,2,*, Zhong Qin¹, Xin Chen¹

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6351-6369, 2023, DOI:10.32604/cmc.2023.035832 - 28 December 2022

Abstract Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for the following path planning task. In an indoor environment where the global positioning system signal fails or becomes weak, the wireless sensor network (WSN) or simultaneous localization and mapping (SLAM) scheme gradually becomes a research hot spot. WSN method uses received signal strength indicator (RSSI) values to determine the position of the target signal node, however, the orientation of the target node is not clear. Besides, the distance error is large when the indoor signal receives interference. The… More >

Yun Fen Yong^1,*, Chee Keong Tan¹, Ian Kim Teck Tan², Su Wei Tan¹

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1801-1818, 2023, DOI:10.32604/cmc.2023.032710 - 22 September 2022

Abstract A robust radio map is essential in implementing a fingerprint-based indoor positioning system (IPS). However, the offline site survey to manually construct the radio map is time-consuming and labour-intensive. Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys. This paper presents a novel fingerprint interpolator using a multi-path loss model (M-PLM) to create the virtual fingerprints from the collected sample data based on different signal paths from different access points (APs). Based on the historical signal data, the poor signal paths are… More >

Edgar Scavino^1,*, Mohd Amiruddin Abd Rahman¹, Zahid Farid²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 379-397, 2023, DOI:10.32604/cmc.2023.023824 - 22 September 2022

Abstract Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting… More >

Samir Salem Al-Bawri^1,*, Mohammad Tariqul Islam², Mandeep Jit Singh^1,2, Mohd Faizal Jamlos³, Adam Narbudowicz⁴, Max J. Ammann⁴, Dominique M. M. P. Schreurs⁵

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5437-5452, 2022, DOI:10.32604/cmc.2022.020781 - 11 October 2021

Abstract The paper proposes an Indoor Localization System (ILS) which uses only one fixed Base Station (BS) with simple non-reconfigurable antennas. The proposed algorithm measures Received Signal Strength (RSS) and maps it to the location in the room by estimating signal strength of a direct line of sight (LOS) signal and signal of the first order reflection from the wall. The algorithm is evaluated through both simulations and empirical measurements in a furnished open space office, sampling 21 different locations in the room. It is demonstrated the system can identify user’s real-time location with a maximum More >

Imran Ashraf, Soojung Hur, Yousaf Bin Zikria, Yongwan Park^*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2597-2620, 2021, DOI:10.32604/cmc.2021.016214 - 13 April 2021

Abstract Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors: Smartphone heterogeneity and smaller data lengths. The use of multifarious smartphones cripples the performance of such approaches owing to the variability of the magnetic field data. In the same vein, smaller lengths of magnetic field data decrease the localization accuracy substantially. The current study proposes the use of multiple neural networks like deep neural network (DNN), long short term memory network (LSTM), and gated recurrent unit network (GRN) to perform indoor localization based on the embedded magnetic sensor of… More >