In order to improve crop production and efficiency of farming operations, an IoT (Internet of Things) system for remote monitoring has been attracting a lot of attention. The existing studies have proposed agricultural sensing systems such that environmental information is collected from many sensor nodes installed in farmland through wireless communications (e.g., Wi-Fi, ZigBee). Especially, Low-Power Wide-Area (LPWA) is a focus as a candidate for wireless communication that enables the support of vast farmland for a long time. However, it is difficult to achieve long distance communication even when using the LPWA because a clear line of sight is difficult to keep due to many obstacles such as crops and agricultural machinery in the farmland. In addition, a sensor node cannot run permanently on batteries because the battery capacity is not infinite. On the other hand, an Unmanned Aerial Vehicle (UAV) that can move freely and stably in the sky has been leveraged for agricultural sensor network systems. By utilizing a UAV as the gateway of the sensor network, the gateway can move to the appropriate location to ensure a clear line of sight from the sensor nodes. In addition, the coverage area of the sensor network can be expanded as the UAV travels over a wide area even when short-range and ultra-low-power wireless communication (e.g., Bluetooth Low Energy (BLE)) is adopted. Furthermore, various wireless technologies (e.g., wireless power transfer, wireless positioning) that have the possibility to improve the coverage area and the lifetime of the sensor network have become available. Therefore, in this study, we propose and develop two kinds of new agricultural sensing systems utilizing a UAV and various wireless technologies. The objective of the proposed system is to provide the solution for achieving the wide-area and long-term sensing for the vast farmland. Depending on which problem is in a priority, the proposed system chooses one of two designs. The first design of the system attempts to achieve the wide-area sensing, and so it is based on the LPWA for wireless communication. In the system, to efficiently collect the environmental information, the UAV autonomously travels to search for the locations to maintain the good communication properties of the LPWA to the sensor nodes dispersed over a wide area of farmland. In addition, the second design attempts to achieve the long-term sensing, so it is based on BLE, a typical short-range and ultra-low-power wireless communication technology. In this design, the UAV autonomously flies to the location of sensor nodes and supplies power to them using a wireless power transfer technology for achieving a battery-less sensor node. Through experimental evaluations using a prototype system, it is confirmed that the combination of the UAV and various wireless technologies has the possibility to achieve a wide-area and long-term sensing system for monitoring vast farmland.

High-frequency wireless communication is drawing attention because of its potential to actualize huge transmission capacity in the next generation wireless system. The use of high-frequency bands requires dense deployment of access points to compensate for significant distance attenuation and diffraction loss. Dense deployment of access points in a mobility environment triggers an increase in the frequency of handover because the number of candidate access points increases. Therefore, simple handover schemes are needed. High-frequency wireless systems enable station position to be determined using their wideband and highly directional communication signals. Thus, simple handover based on position information estimated using the communication signal is possible. Interruptions caused by handover are also a huge barrier to actualizing stable high-frequency wireless communications. This paper proposes a seamless handover scheme using multiple radio units. This paper evaluates the combination of simple handover and the proposed scheme based on experiments using a formula racing car representing the fastest high-speed mobility environment. Experimental results show that seamless handover and high-speed wireless transmission over 200Mbps are achieved over a 400-m area even at station velocities of greater than 200km/h.

We propose GConvLoc, a WiFi fingerprinting-based indoor localization method utilizing graph convolutional networks. Using the graph structure, we can consider the fingerprint data of the reference points and their location labels in addition to the fingerprint data of the test point at inference time. Experimental results show that GConvLoc outperforms baseline methods that do not utilize graphs.

In this paper, we report the recent progress in visible light positioning and communication systems using light-emitting diodes (LEDs). Due to the wide deployment of LEDs for indoor illumination, visible light positioning (VLP) and visible light communication (VLC) using existing LEDs fixtures have attracted great attention in recent years. Here, we review our recent works on visible light positioning and communication, including image sensor-based VLP, photodetector-based VLP, integrated VLC and VLP (VLCP) systems, and heterogeneous radio frequency (RF) and VLC (RF/VLC) systems.

Cloud-based Global Navigation Satellite Systems (CB-GNSS) positioning architecture that offloads part of GNSS positioning computation to cloud/edge infrastructure has been studied as an architecture that adds valued functions via the network. The merits of CB-GNSS positioning are that it can take advantage of the abundant computing resources on the cloud/edge to add unique functions to the positioning calculation and reduce the cost of GNSS receiver terminals. An issue in GNSS positioning is the degradation in positioning accuracy in unideal reception environments where open space is limited and some satellite signals are blocked. To resolve this issue, we propose a satellite selection algorithm that effectively removes the multipath components of blocked satellite signals, which are the main cause of drop in positioning accuracy. We build a Proof of Concept (PoC) test environment of CB-GNSS positioning architecture implementing the proposed satellite selection algorithm and conduct experiments to verify its positioning performance in unideal static and dynamic conditions. For static long-term positioning in a multipath signal reception environment, we found that CB-GNSS positioning with the proposed algorithm enables a low-end GNSS receiver terminal to match the positioning performance comparable to high-end GNSS receiver terminals in terms of the FIX rate. In an autonomous tractor driving experiment on a farm road crossing a windbreak, we succeeded in controlling the tractor's autonomous movement by maintaining highly precise positioning even in the windbreak. These results indicates that the proposed satellite selection algorithm achieves high positioning performance even in poor satellite signal reception environments.

In March 2020, the 5th generation mobile communication system (5G) was launched in Japan. Frequency bands of 3.7GHz, 4.5GHz and 28GHz were allocated for 5G services, and the 5G use cases fall into three broad categories: Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC). The use cases and services that take advantage of the characteristics of each category are expected to be put to practical use, and experiments of practical use are underway. This paper introduces and demonstrates a touchless gate that can identify, authenticate and allow passage through the gate by using these features and 5G beam tracking to estimate location by taking advantage of the low latency of 5G and the straightness of the 28GHz band radio wave and its resistance to spreading. Since position estimation error due to reflected waves and other factors has been a problem, we implement an algorithm that tracks the beam and estimates the user's line of movement, and by using an infrared sensor, we made it possible to identify the gate through which the user passes with high probability. We confirmed that the 5G touchless gate is feasible for gate passage. In addition, we demonstrate that a new service based on high-speed high-capacity communication is possible at gate passage by taking advantage of the wide bandwidth of the 28GHz band. Furthermore, as a use case study of the 5G touchless gate, we conducted a joint experiment with an airline company.

Machine learning is becoming an attractive topic for researchers and industrial firms in the area of computational intelligence because of its proven effectiveness and performance in resolving real-world problems. However, some challenges such as precise search, intelligent discovery and intelligent learning need to be addressed and solved. One most important challenge is the non-steady performance of various machine learning models during online learning and operation. Online learning is the ability of a machine-learning model to modernize information without retraining the scheme when new information is available. To address this challenge, we evaluate and analyze four widely used online machine learning models: Online Sequential Extreme Learning Machine (OSELM), Feature Adaptive OSELM (FA-OSELM), Knowledge Preserving OSELM (KP-OSELM), and Infinite Term Memory OSELM (ITM-OSELM). Specifically, we provide a testbed for the models by building a framework and configuring various evaluation scenarios given different factors in the topological and mathematical aspects of the models. Furthermore, we generate different characteristics of the time series to be learned. Results prove the real impact of the tested parameters and scenarios on the models. In terms of accuracy, KP-OSELM and ITM-OSELM are superior to OSELM and FA-OSELM. With regard to time efficiency related to the percentage of decreases in active features, ITM-OSELM is superior to KP-OSELM.

In this paper, we clarify the importance of performance evaluation using a plurality of smartphones in a positioning system based on radio waves. Specifically, in a positioning system using bluetooth low energy, the positioning performance of two types of positioning algorithms is performed using a plurality of smartphones. As a result, we confirmed that the fingerprint algorithm does not always provide sufficient positioning performance. It depends on the model of the smartphone used. On the other hand, the hybrid algorithm that the authors have already proposed is robust in the difference of the received signal characteristics of the smartphone. Consequently, we spotlighted that the use of multiple devices is essential for providing high-quality location-based services in real environments in the performance evaluation of radio wave-based positioning systems using smartphones.

The phaseless antenna measurement technique is advantageous for high-frequency near-field measurements in which the uncertainty of the measured phase is a problem. In the phaseless measurement, which is expected to be used in the frequency band with a short wavelength, a slight positional deviation error of the probe greatly deteriorates the measurement result. This paper proposes a phase retrieval method that can compensate the measurement errors caused by misalignment of a probe and its jig. And this paper proposes a far-field estimation method by phase resurrection that incorporated the compensation techniques. We find that the positioning errors are due to the random errors occurring at each measurement point because of minute vibrations of the probe; in addition, we determine that the stationary depth errors occurring at each measurement surface as errors caused by improper setting of the probe jig. The random positioning error is eliminated by adding a low-pass filter in wavenumber space, and the depth positioning error is iteratively compensated on the basis of the relative residual obtained in each plane. The validity of the proposed method is demonstrated by estimating the far-field patterns using the results from numerical simulations, and is also demonstrated using measurement data with probe-positioning error. The proposed method can reduce the probe-positioning error and improve the far-field estimation accuracy by more over than 10 dB.

In this paper, we propose a WiFi-based indoor positioning system using a fingerprint method, whose database is constructed with estimated reference locations. The reference locations and their information, called data sets in this paper, are obtained by moving reference devices at a constant speed while gathering information of available access points (APs). In this approach, the reference locations can be estimated using the velocity without any precise reference location information. Therefore, the cost of database construction can be dramatically reduced. However, each data set includes some errors due to such as the fluctuation of received signal strength indicator (RSSI) values, the device-specific WiFi sensitivities, the AP installations, and removals. In this paper, we propose a method to merge data sets to construct a consistent database suppressing such undesired effects. The proposed approach assumes that the intervals of reference locations in the database are constant and that the fingerprint for each reference location is calculated from multiple data sets. Through experimental results, we reveal that our approach can achieve an accuracy of 80%. We also show a detailed discussion on the results related parameters in the proposed approach.

In the typical unmanned aircraft system (UAS), several unmanned aerial vehicles (UAVs) traveling at a velocity of 40-100km/h and with altitudes of 150-1,000m will be used to cover a wide service area. Therefore, Doppler shifts occur in the carrier frequencies of the transmitted and received signals due to changes in the line-of-sight velocity between the UAVs and the terrestrial terminal. By observing multiple Doppler shift values for different UAVs or observing a single UAV at different local times, it is possible to detect the user position on the ground. We conducted computer simulations for evaluating user position detection accuracy and Doppler shift distribution in several flight models. Further, a positioning accuracy index (PAI), which can be used as an index for position detection accuracy, was proposed as the absolute value of cosine of the inner product between two gradient vectors formed by Doppler shifts to evaluate the relationship between the location of UAVs and the position of the user. In this study, a maximum positioning error estimation method related to the PAI is proposed to approximate the position detection accuracy. Further, computer simulations assuming a single UAV flying on the curved routes such as sinusoidal routes with different cycles are conducted to clarify the effectiveness of the flight route in the aspects of positioning accuracy and latency by comparing with the conventional straight line fight model using the PAI and the proposed maximum positioning error estimation method.

Automatic disassembly of railway fasteners is of great significance for improving the efficiency of replacing rails. The accurate positioning of fastener is the key factor to realize automatic disassembling. However, most of the existing literature mainly focuses on fastener region positioning and the literature on accurate positioning of fasteners is scarce. Therefore, this paper constructed a visual inspection system for accurate positioning of fastener (VISP). At first, VISP acquires railway image by image acquisition subsystem, and then the subimage of fastener can be obtained by coarse-to-fine method. Subsequently, the accurate positioning of fasteners can be completed by three steps, including contrast enhancement, binarization and spike region extraction. The validity and robustness of the VISP were verified by vast experiments. The results show that VISP has competitive performance for accurate positioning of fasteners. The single positioning time is about 260ms, and the average positioning accuracy is above 90%. Thus, it is with theoretical interest and potential industrial application.

This manuscript discusses a new indoor positioning method and proposes a multi-distance function trilateration over k-NN fingerprinting method using radio signals. Generally, the strength of radio signals, referred to received signal strength indicator or RSSI, decreases as they travel in space. Our method employs a list of fingerprints comprised of RSSIs to absorb interference between radio signals, which happens around the transmitters and it also employs multiple distance functions for conversion from distance between fingerprints to the physical distance in order to absorb the interference that happens around the receiver then it performs trilateration between the top three closest fingerprints to locate the receiver's current position. An experiment in positioning performance is conducted in our laboratory and the result shows that our method is viable for a position-level indoor positioning method and it could improve positioning performance by 12.7% of positioning error to 0.406 in meter in comparison with traditional methods.

This paper proposes a dual-band dual-rectangular-loop circular polarization antenna for Global Navigation Satellite Systems (GNSSs). The proposed antenna combines two large outer rectangular loops with two small inner loops. Each large outer loop is connected to its corresponding small inner rectangular loop. Each loop has gaps located symmetrically with respect to a feed point to produce Right Handed Circular Polarization (RHCP). The gap position and the shape of the rectangular loops are very important to adjust both the impedance matching and circular polarization characteristics. The proposed antenna offers dual-band Voltage Standing Wave Ratio (VSWR) and Axial Ratio (AR) frequency characteristics that include the L1 (1575.42 MHz) and L2 (1227.60 MHz) bands. The antenna gains exceed 8.7 dBi. Broad AR elevation patterns are obtained. These antenna characteristics are well suited to precise positioning.

Positioning information plays a significant role in multi-unmanned aerial vehicles (UAVs) applications. Traditionally, the positioning information is widely provided by Global Navigation Satellite System (GNSS) due to its good performance and global coverage. However, owing to complicated flight environment or signal blockage, jamming and unintentional interference, the UAVs may fail to locate themselves by using GNSS alone. As a new method to resolve these problems, cooperative positioning, by incorporating peer-to-peer range measurements and assisted information, has attracted more and more attentions due to its ability to enhance the accuracy and availability of positioning. However, achieving good performance of cooperative positioning of multi-UAVs is challenging as their mobility, arbitrary nonlinear state-evolution, measurement models and limited computation and communication resources. In this paper, we present a factor graph (FG) representation and message passing methodology to solve cooperative positioning problem among UAVs in 3-dimensional environment where GNSS cannot provide services. Moreover, to deal with the nonlinear state-evolution and measurement models while decreasing the computation complexity and communication cost, we develop a distributed algorithm for dynamic and hybrid UAVs by means of Spherical-Radial Cubature Rules (CR) method with belief propagation (BP) and variational message passing (VMP) methods (CRBP-VMP) on the FG. The proposed CRBP deals with the highly non-linear state-evolution models and non-Gaussian distributions, the VMP method is employed for ranging message, gets the simpler message representation and can reduce communication cost in the joint estimation problem. Simulation results demonstrate that the higher positioning accuracy, the better convergence as well as low computational complexity and communication cost of the proposed CRBP-VMP algorithm, which can be achieved compared with sum-product algorithm over a wireless network (SPAWN) and traditional Cubature Kalman Filters (CKF) method.

This letter proposes a new positioning method for WLAN (Wireless Local Area Network) systems based on a principle of the RTK-GPS (Real Time Kinematic-Global Positioning System). The proposed method collects observations of the carrier phase at access points for a double phase difference of the RTK-GPS. We show a numerical example for evaluations of the proposed method considering the measurement error by computer simulations.

Unmanned aircraft systems (UASs) have been developed and studied as temporal communication systems for emergency and rescue services during disasters, such as earthquakes and serious accidents. In a typical UAS model, several unmanned aerial vehicles (UAVs) are used to provide services over a large area. The UAV is comprised of a transmitter and receiver to transmit/receive the signals to/from terrestrial stations and terminals. Therefore, the carrier frequencies of the transmitted and received signals experience Doppler shifts due to the variations in the line-of-sight velocity between the UAV and the terrestrial terminal. Thus, by observing multiple Doppler shifts from different UAVs, it is possible to detect the position of a user that possesses a communication terminal for the UAS. This study aims to present a methodology for position detection based on the least-squares method to the Doppler shift frequencies. Further, a positioning accuracy index is newly proposed, which can be used as an index for measuring the position accurately, instead of the dilution-of-precision (DOP) method, which is used for global positioning systems (GPSs). A computer simulation was conducted for two different flight route models to confirm the applicability of the proposed positioning method and the positioning accuracy index. The simulation results confirm that the parameters, such as the flight route, the initial position, and velocity of the UAVs, can be optimized by using the proposed positioning accuracy index.

For capacity expansion of the Quasi-Zenith Satellite System (QZSS) safety confirmation system, frame slotted ALOHA with flag method has previously been proposed as an access control scheme. While it is always able to communicate in an optimum state, its maximum channel efficiency is only 36.8%. In this paper, we propose adding a reservation channel (R-Ch) to the frame slotted ALOHA with flag method to increase the upper limit of the channel efficiency. With an R-Ch, collision due to random channel selection is decreased by selecting channels in multiple steps, and the channel efficiency is improved up to 84.0%. The time required for accommodating 3 million mobile terminals, each sending one message, when using the flag method only and the flag method with an R-Ch are compared. It is shown that the accommodating time can be reduced to less than half by adding an R-Ch to the flag method.

An accurate but energy-efficient estimation of a position is important as the number of mobile computing systems grow rapidly. A challenge is to develop a highly accurate but energy efficient estimation method. A particle filter is a key algorithm to estimate and track the position of an object which exhibits non-linear movement behavior. However, it requires high usage of computation resources and energy. In this paper, we propose a scheme which can dynamically adjust the number of particles according to the accuracy of the reference signal for positioning and reduce the energy consumption by 37% on Cortex A7.

Cell phones with GPS function as well as GPS loggers are widely used and users' geographic information can be easily obtained. However, still battery consumption in these mobile devices is main concern and then obtaining GPS positioning data so frequently is not allowed. In this paper, a stayed location estimation method for sparse GPS positioning information is proposed. After generating initial clusters from a sequence of measured positions, the effective radius is set for every cluster based on positioning accuracy and the clusters are merged effectively using it. After that, short-time clusters are removed temporarily but measured positions included in them are not removed. Then the clusters are merged again, taking all the measured positions into consideration. This process is performed twice, in other words, two-stage short-time cluster removal is performed, and finally accurate stayed location estimation is realized even when the GPS positioning interval is five minutes or more. Experiments demonstrate that the total distance error between the estimated stayed location and the true stayed location is reduced by more than 33% and also the proposed method much improves F1 measure compared to conventional state-of-the-art methods.