A wide variety of sensors and devices are used in indoor positioning scenarios to improve localization accuracy and overcome harsh radio propagation conditions. The availability of these individual sensors suggests the idea of sensor fusion to achieve a more accurate solution. This work aims to address, with the goal of improving localization accuracy, the fusion of two conventional candidates for indoor positioning scenarios: Ultra Wide Band (UWB) and Wireless Fidelity (WiFi). The proposed method consists of a Machine Learning (ML)-based enhancement of WiFi measurements, environment observation, and sensor fusion. In particular, the proposed algorithm takes advantage of Received Signal Strength (RSS) values to fuse range measurements utilizing a Gaussian Process (GP). The range values are calculated using the WiFi Round Trip Time (RTT) and UWB Two Way Ranging (TWR) methods. To evaluate the performance of the proposed method, trilateration is used for positioning. Furthermore, empirical range measurements are obtained to investigate and validate the proposed approach. The results prove that UWB and WiFi, working together, can compensate for each other’s limitations and, consequently, provide a more accurate position solution.
In this paper, a new hybrid routing protocol is presented for low power Wireless Sensor Networks (WSNs). T he new system uses an integrated piezoelectric energy harvester to increase the network lifetime. Power dissipation is one of the most important factors affecting the lifetime of a WSN. An innovative cluster head selection technique using Cuckoo optimization algorithm has been used in the designed protocol. T he residual energy of the nodes and the distances to the sink were used in the threshold calculations, besides taking advantage of the relay node for communication. A hybrid method using the optimized routing protocol and the integrated energy harvester results in 100% increase in the network lifetime compared to recent clustering-based protocols. The simulations results using MATLAB indicate that energy consumption has been decreased by more than 40%.
NOMENCLATURE( ) T hreshold value in LEACH protocol Number of bits for communication Desired percentage of cluster heads Distance for data communication r Number of round Eelec Required energy for sending and receiving a single bit G Set of the nodes ( ) Energy dissipated in transmitting l bits 0 Initial energy of the nodes ( ) Energy dissipated in receiving l bits _ Residual energy of the i th node in EECRP ( ) Residual energy of i th node in HYREP ( , ) Location of i th node in EECRP ( ) Distance from i th node to the sink ( ̅̅̅̅ , ̅̅̅̅ ) Energy centroid in EECRP Maximum residual energy of the nodes Energy required by the amplifier in free space channels Maximum distance between the nodes Emp Energy required by the amplifier in multipath fading channels α , β Optimization coefficients in HYREP 0 T hreshold distance in energy model
The advent of Artificial Intelligence (AI) has impacted all aspects of human life. One of the concrete examples of AI impact is visible in radio positioning. In this article, for the first time we utilize the power of AI by training a Convolutional Neural Network (CNN) using 5G New Radio (NR) fingerprints consisting of beamformed Channel State Information (CSI). By observing CSI, it is possible to characterize the multipath channel between the transmitter and the receiver, and thus provide a good source of spatiotemporal data to find the position of a User Equipment (UE). We collect ray-tracing-based 5G NR CSI from an urban area. The CSI data of the signals from one Base Station (BS) is collected at the reference points with known positions to train a CNN. We evaluate our work by testing: a) the robustness of the trained network for estimating the positions for the new measurements on the same reference points and b) the accuracy of the CNN-based position estimation while the UE is on points other than the reference points. The results prove that our trained network for a specific urban environment can estimate the UE position with a minimum mean error of 0.98 m.
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