LEMOn: Wireless Localization for IoT Employing a Location-Unaware Mobile Unit

Nguyen, Cam Ly; Raza, Usman

doi:10.1109/access.2019.2904731

Cited by 10 publications

(4 citation statements)

References 42 publications

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“…Throughout the network training stage, the method generates a dataset via sampling the channel fingerprinting of an environment at numerous identified locations. The user location estimates of the network are based on received signals of unknown locations and rely on a reference database [6][7][8]. Directed Acyclic Graph residual network of Deep Learning was widely used for image classification [9] and for improving noisy images that are already filtered by the bilateral process via a multi-scale context aggregation network as discussed in [10].…”

Section: Introductionmentioning

confidence: 99%

Developing three dimensional localization system using deep learning and pre-trained architectures for IEEE 802.11 Wi-Fi

Hamza

Hussein

Ismaeel

et al. 2022

EEJET

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The performance of Wi-Fi fingerprinting indoor localization systems (ILS) in indoor environments depends on the channel state information (CSI) that is usually restricted because of the fading effect of the multipath. Commonly referred to as the next positioning generation (NPG), the Wi-Fi™, IEEE 802.11az standard offers physical layer characteristics that allow positioning and enhanced ranging using conventional methods. Therefore, it is essential to create an indoor environment dataset of fingerprints of CIR based on 802.11az signals, and label all these fingerprints by their location data estimate STA locations based on a portion of the dataset for fingerprints. This work develops a model for training a convolutional neural network (CNN) for positioning and localization through generating IEEE® 802.11data. The study includes the use of a trained CNN to predict the position or location of several stations according to fingerprint data. This includes evaluating the performance of the CNN for multiple channel impulses responses (CIRs). Deep learning and Fingerprinting algorithms are employed in Wi-Fi positioning models to create a dataset through sampling the fingerprints channel at recognized positions in an environment. The model predicts the locations of a user according to a signal acknowledged of an unidentified position via a reference database. The work also discusses the influence of antenna array size and channel bandwidth on performance. It is shown that the increased training epochs and number of STAs improve the network performance. The results have been proven by a confusion matrix that summarizes and visualizes the undertaking classification technique. We use a limited dataset for simplicity and last in a short simulation time but a higher performance is achieved by training a larger data.

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Section: Introductionmentioning

confidence: 99%

Developing three dimensional localization system using deep learning and pre-trained architectures for IEEE 802.11 Wi-Fi

Hamza

Hussein

Ismaeel

et al. 2022

EEJET

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“…Measures such as optimizing the flight routes [ 9 , 12 , 14 , 15 , 16 , 19 ] and locations [ 15 , 19 , 21 ] of UAVs, designing clustering mechanisms [ 17 , 18 , 22 ], media access control mechanisms [ 10 , 18 , 23 , 24 ], and sleeping schedules of WSN [ 14 ] and the like have been adopted to achieve these targets. Assisting in locating the WSN nodes [ 25 , 26 , 27 ]. Performing wireless charging for the WSN nodes [ 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

WSN-Assisted UAV Trajectory Adjustment for Pesticide Drift Control

Tuan

Yang

et al. 2020

Sensors

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Unmanned Aerial Vehicles (UAVs) have been widely applied for pesticide spraying as they have high efficiency and operational flexibility. However, the pesticide droplet drift caused by wind may decrease the pesticide spraying efficiency and pollute the environment. A precision spraying system based on an airborne meteorological monitoring platform on manned agricultural aircrafts is not adaptable for. So far, there is no better solution for controlling droplet drift outside the target area caused by wind, especially by wind gusts. In this regard, a UAV trajectory adjustment system based on Wireless Sensor Network (WSN) for pesticide drift control was proposed in this research. By collecting data from ground WSN, the UAV utilizes the wind speed and wind direction as inputs to autonomously adjust its trajectory for keeping droplet deposition in the target spraying area. Two optimized algorithms, namely deep reinforcement learning and particle swarm optimization, were applied to generate the newly modified flight route. At the same time, a simplified pesticide droplet drift model that includes wind speed and wind direction as parameters was developed and adopted to simulate and compute the drift distance of pesticide droplets. Moreover, an LSTM-based wind speed prediction model and a RNN-based wind direction prediction model were established, so as to address the problem of missing the latest wind data caused by communication latency or a lack of connection with the ground nodes. Finally, experiments were carried out to test the communication latency between UAV and ground WSN, and to evaluate the proposed scheme with embedded Raspberry Pi boards in UAV for feasibility verification. Results show that the WSN-assisted UAV trajectory adjustment system is capable of providing a better performance of on-target droplet deposition for real time pesticide spraying with UAV.

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“…12 With the location available, a range of IoT mobility applications can be met. 13 Based on the constraints imposed by LLN devices and the challenges inherent in operating these networks, it is necessary to emphasize the importance of routing protocols, which help provide the underlying logic of all network communication. Therefore, it is crucial to develop successive improvements to meet the requirements necessary for efficient network operation.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, information produced by a node can be limited in use without being aware of its location 12 . With the location available, a range of IoT mobility applications can be met 13 …”

Section: Introductionmentioning

confidence: 99%

A mobility solution for low power and lossy networks using the LOADng protocol

Gonçalves

Rabêlo

Rodrigues

et al. 2020

Trans Emerging Tel Tech

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Many devices on an Internet of Things (IoT) network have severe constraints on computing resources in terms of memory, processing, and power, which make these networks generally known as Low power and Lossy Networks (LLNs). Device mobility in these scenarios has several negative impacts on the network, particularly in terms of connectivity, as node movement affects the routes established by the routing protocol. The network performance is highly related to the adjacent routing protocol. The protocol considered as standard for IoT networks was designed for static networks, and it has a low reactivity in mobility scenarios, besides other limitations. Lightweight On-demand Ad hoc Distance-vector Routing-Next Generation (LOADng) is currently the primary reactive protocol for LLNs. Considering that mobility is a crucial feature for the future of IoT, this article proposes Extended Kalman Filter (EKF)-LOADng, a solution built from LOADng for LLNs in the mobility context. The solution aims to make the mobile nodes aware of its location, as well as to predict the nonlinear trajectory of these nodes through the EKF. The location obtained is used to improve the connectivity of the mobile nodes and to shorten paths while messages are exchanged on the network. EKF-LOADng was compared with the standard version and an enhanced version of the LOADng protocol in two different scenarios in the Contiki OS. Simulation results show the proposed solution is capable of locating mobile nodes with an average error of 1.46 m while maintaining connectivity to other nodes, as well as outperforms the compared proposals in some network metrics in terms of packet delivery ratio, latency, power consumption, and control bit overhead.

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LEMOn: Wireless Localization for IoT Employing a Location-Unaware Mobile Unit

Cited by 10 publications

References 42 publications

Developing three dimensional localization system using deep learning and pre-trained architectures for IEEE 802.11 Wi-Fi

Developing three dimensional localization system using deep learning and pre-trained architectures for IEEE 802.11 Wi-Fi

WSN-Assisted UAV Trajectory Adjustment for Pesticide Drift Control

A mobility solution for low power and lossy networks using the LOADng protocol

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