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Guest Editorial: Special issue on explainable AI empowered for indoor positioning and indoor navigation
- Author(s): Ki‐Il Kim ; Aswani Kumar Cherukuri ; Xue Jun Li ; Tanveer Ahmad ; Muhammad Rafiq ; Shehzad Ashraf Chaudhry
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p.
1101
–1103
(3)
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Optimal fusion‐based localization method for tracking of smartphone user in tall complex buildings
- Author(s): Harun Jamil and Do‐Hyeun Kim
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p.
1104
–1123
(20)
AbstractIn the event of a fire breaking out or in other complicated situations, a mobile computing solution combining the Internet of Things and wearable devices can actually assist tracking solutions for rescuing and evacuating people in multistory structures. Thus, it is crucial to increase the positioning technology's accuracy. The sequential Monte Carlo (SMC) approach is used in various applications such as target tracking and intelligent surveillance, which rely on smartphone‐based inertial data sequences. However, the SMC method has intrinsic flaws, such as sample impoverishment and particle degeneracy. A novel SMC approach is presented, which is built on the weighted differential evolution (WDE) algorithm. Sequential Monte Carlo approaches start with random particle placements and arrives at the desired distribution with a slower variance reduction, like in a high‐dimensional space, such as a multistory structure. Weighted differential evolution is included before the resampling procedure to guarantee the appropriate variety of the particle set, prevent the usage of an inadequate number of valid samples, and preserve smartphone user position accuracy. The values of the smartphone‐based sensors and BLE‐beacons are set as input to the SMC, which aids in fast approximating the posterior distributions, to speed up the particle congregation process in the proposed SMC‐based WDE approach. Lastly, the robustness and efficacy of the suggested technique more accurately reflect the actual situation of smartphone users. According to simulation findings, the suggested approach provides improved location estimation with reduced localization error and quick convergence. The results confirm that the proposed optimal fusion‐based SMC‐WDE scheme performs 9.92% better in terms of MAPE, 15.24% for the case of MAE, and 0.031% when evaluating based on the R2 Score.
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A higher prediction accuracy–based alpha–beta filter algorithm using the feedforward artificial neural network
- Author(s): Junaid Khan ; Eunkyu Lee ; Kyungsup Kim
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p.
1124
–1139
(16)
AbstractThe alpha–beta filter algorithm has been widely researched for various applications, for example, navigation and target tracking systems. To improve the dynamic performance of the alpha–beta filter algorithm, a new prediction learning model is proposed in this study. The proposed model has two main components: (1) the alpha–beta filter algorithm is the main prediction module, and (2) the learning module is a feedforward artificial neural network (FF‐ANN). Furthermore, the model uses two inputs, temperature sensor and humidity sensor data, and a prediction algorithm is used to predict actual sensor readings from noisy sensor readings. Using the novel proposed technique, prediction accuracy is significantly improved while adding the feed‐forward backpropagation neural network, and also reduces the root mean square error (RMSE) and mean absolute error (MAE). We carried out different experiments with different experimental setups. The proposed model performance was evaluated with the traditional alpha–beta filter algorithm and other algorithms such as the Kalman filter. A higher prediction accuracy was achieved, and the MAE and RMSE were 35.1%–38.2% respectively. The final proposed model results show increased performance when compared to traditional methods.
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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities
- Author(s): Mian Imtiaz ul Haq ; Ruhul Amin Khalil ; Muhannad Almutiry ; Ahmad Sawalmeh ; Tanveer Ahmad ; Nasir Saeed
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p.
1140
–1149
(10)
AbstractLocalisation of machines in harsh Industrial Internet of Things (IIoT) environment is necessary for various applications. Therefore, a novel localisation algorithm is proposed for noisy range measurements in IIoT networks. The position of an unknown machine device in the network is estimated using the relative distances between blind machines (BMs) and anchor machines (AMs). Moreover, a more practical and challenging scenario with the erroneous position of AM is considered, which brings additional uncertainty to the final position estimation. Therefore, the AMs selection algorithm for the localisation of BMs in the IIoT network is introduced. Only those AMs will participate in the localisation process, which increases the accuracy of the final location estimate. Then, the closed‐form expression of the proposed greedy successive anchorization process is derived, which prevents possible local convergence, reduces computation, and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement noise. The results are compared with the state‐of‐the‐art and verified through numerous simulations.
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Energy efficient indoor localisation for narrowband internet of things
- Author(s): Ismail Keshta ; Mukesh Soni ; Mohammed Wasim Bhatt ; Azeem Irshad ; Ali Rizwan ; Shakir Khan ; Renato R. Maaliw III ; Arsalan Muhammad Soomar ; Mohammad Shabaz
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p.
1150
–1163
(14)
AbstractThere are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly. The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices. To maximise the data rate fairness of Narrow Band IoT devices, a multi‐dimensional indoor localisation model is devised, consisting of transmission power, data scheduling, and time slot scheduling, based on a network model that employs non‐orthogonal multiple access via a relay. Based on this network model, the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors, while taking into account the Narrow Band IoT network: The multi‐dimensional indoor localisation optimisation model of equipment tends to minimize data rate, energy constraints and EH relay energy and data buffer constraints, data scheduling and time slot scheduling. As a result, each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised. We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion. The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay. However, the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference, which impacts NOMA's performance enhancement. Through simulation, the proposed approach is successfully shown. These improvements have boosted the network's energy efficiency by 44.1%, data rate proportional fairness by 11.9%, and spectrum efficiency by 55.4%.
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- Author(s): Xu Zhang ; Dengbing Huang ; Hanyu Li ; Youjia Zhang ; Ying Xia ; Jinzhuo Liu
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Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey
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