Neural Network-Based Alzheimer’s Patient Localization for Wireless Sensor Network in an Indoor Environment

Munadhil, Zainab; Gharghan, Sadik Kamel; Mutlag, Ammar Hussein; Al-Naji, Ali; Chahl, Javaan

doi:10.1109/access.2020.3016832

Cited by 43 publications

(33 citation statements)

References 64 publications

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“…Other IoMT and EIoT applications include medical bleeder [40], emergency & disaster monitoring [11], Wearable Body Area Networks (WBAN) [41]- [43], and Device-to-Device (D2D) & Device-to-Machine (D2M) based communication devices to monitor and track devices during Search and Rescue (SAR) operations [44], as listed in Fig. 2.…”

Section: B Internet Of Medical Things (Iomt) and Emergency Iot (Eiot)mentioning

confidence: 99%

Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

et al. 2022

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The Internet of Things (IoT) has rapidly expanded for a wide range of applications towards a smart future world by connecting everything. As a result, new challenges emerge in meeting the requirements of IoT applications while retaining optimal performance. These challenges may include power consumption, quality of service, localization, security, and accurate modeling and characterization of wireless channel propagation. Among these challenges, the latter is critical to establishing point-to-point wireless communication between the sensors. Channel modeling also varies depending on the features of the surrounding area, which have a direct impact on the propagation of wireless signals. This presents a difficult task for network planners to efficiently design and deploy IoT applications without understanding the appropriate channel model to analyze coverage and predict optimal deployment configurations. As a result, this challenge has attracted considerable interest in academic and industrial communities in recent years. Therefore, this review presents an overview of current breakthroughs in wireless IoT technologies. The challenges in such applications are then briefly reviewed, focusing on wireless channel propagation modeling and characterization. Finally, the study gives a generalized form of commonly used channel models and a summary of recent channel modeling developments for wireless IoT technology. The outcome of this review is expected to provide a new understanding of the propagation behavior of present and future wireless IoT technologies, allowing network engineers to undertake correct planning and deployment in any environment. Additionally, the study may serve as a guideline for future channel modeling and characterization studies.

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Section: B Internet Of Medical Things (Iomt) and Emergency Iot (Eiot)mentioning

confidence: 99%

Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

et al. 2022

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“…In previous similar investigations, authors often use Wi-Fi, ZigBee, and Bluetooth technologies for indoor localization [7]- [12]. In [7] the authors combined the received signal strength indicator (RSSI) fingerprint and kernel-SVM learning approach for indoor localization based on Wi-Fi technology.…”

Section: Related Workmentioning

confidence: 99%

“…Their simulation result showed that by the fingerprinting-based method, the localization accuracy above 90% was achieved. In [12], the design and implementation of a wearable device for localization of Alzheimer's patients, based on the RSSI and ZigBee technology were presented. To improve the localization accuracy, a back propagation-based artificial neural network (BP-ANN) algorithm was used.…”

Section: Related Workmentioning

confidence: 99%

Improving Indoor Localization Using Mobile UWB Sensor and Deep Neural Networks

2022

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Accurate localization in indoor environments with ultra-wideband (UWB) technology has long attracted much attention. However, due to the presence of multipath components or non-line of sight (NLOS) propagation of the radio signals, it has been converted to a critical challenge. Existing solutions use many fixed anchors in the indoor environment. Particularly, large areas require many anchor points and in the case of unexpected events that lead to the destruction of existing infrastructures, the fixed anchor points cannot be used. In this paper, a novel localization framework based on the transmitting signal from a mobile UWB sensor outside of the building and its received signal regarding the modified Saleh Valenzuela (SV) channel model is presented. After preprocessing the received signals, two new procedures to reduce the ranging error caused by multipath components are proposed. In the first procedure, two machine learning algorithms including multi-layer perceptron (MLP) and support vector machine (SVM) using the extracted features from the received UWB signal time and power vectors are implemented. Moreover, in the second procedure, two deep learning algorithms including MLP and convolutional neural networks (CNNs) using the received UWB signal time and power vectors are implemented to improve the performance of the indoor localization system. The simulation results show that the architecture designed for the convolutional neural network based on the hybrid dataset (the combination of the dataset related to received UWB signal time and power vectors) provides a mean absolute error (MAE) of about 3 cm.

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“…The most used techniques are the Received Signal Strength Indicator (RSSI) [11], Angle of Arrival (AOA) [12], Time of Arrival (TOA) [13], and Time Difference of Arrival (TDOA) [14]. Also, several localization techniques based on artificial intelligence and meta-heuristic algorithms such as fuzzy logic and neural network, PSO and GA have been used for WSN routing and localization optimization [15], [16]. Due to the great interference and the electromagnetic pollution related to the environment, the resulted localization error is comparatively large and there are additional costs for hardware measuring equipment.…”

Section: Introductionmentioning

confidence: 99%

Regularized Least Square Multi-Hops Localization Algorithm for Wireless Sensor Networks

Liouane

Messous²,

Cheikhrouhou

et al. 2021

IEEE Access

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Location awareness is very crucial for almost existing sensor network applications. However, using Global Positioning System (GPS) receivers to every node is very expensive. Therefore, anchor based localization techniques are proposed. The lack of anchors in some WSNs lead to the appearance of multi-hop localization, which permits to localize nodes even if they are far from anchors. One of the well-known multi-hop localization algorithms is the Distance Vector-Hop algorithm (DV-Hop). Although its simplicity, DV-Hop presents some deficiencies in terms of localization accuracy. Therefore, to deal with this issue, we propose in this paper an improvement of DV-Hop algorithm, called Regularized Least Square DV-Hop Localization Algorithm for multihop wireless sensors networks. The proposed solution improves the location accuracy of sensor nodes within their sensing field in both isotropic and anisotropic networks. We used the double Least Square localization method and the statistical filtering optimization strategy, which is the Regularized Least Square method. Simulation results prove that the proposed algorithm outperforms the original DV-Hop algorithm with up to 60%, as well as other related works, in terms of localization accuracy.

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Neural Network-Based Alzheimer’s Patient Localization for Wireless Sensor Network in an Indoor Environment

Cited by 43 publications

References 64 publications

Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

Improving Indoor Localization Using Mobile UWB Sensor and Deep Neural Networks

Regularized Least Square Multi-Hops Localization Algorithm for Wireless Sensor Networks

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