With the advancement of sensing technology, the use of spatial information from LiDAR and similar measurement devices such as a depth camera is rapidly expanding. However, 3D spatial data contains trade secrets such as facility layouts and equipment configurations, making direct sharing a significant business risk. Additionally, from the perspective of data distribution between companies, a mechanism to prove the value of data utilization before purchase is essential. Existing approaches using trusted third parties or conventional encryption require data disclosure for utility verification, failing to achieve both confidentiality and value assessment simultaneously. Therefore, this study proposes a distributed platform that enables secure data exchange between organizations while ensuring confidentiality of 3D spatial information using cryptographic methods. The system operates on a Hyperledger Fabric-based permissioned blockchain to establish trust through immutable proof verification records for data distribution, and enables verification of data utility without disclosing any original data through zero-knowledge proof technology. Specifically, we implement a proprietary algorithm that generates feature values with concealed coordinates while preserving the geometric characteristics of the spatial information. Each participating organization generates feature values from spatial information and records proofs of the validity of this process on the blockchain, allowing other organizations not only to search for useful spatial information based on the feature values but also to verify the reliability of the feature values themselves. This enables previously difficult applications such as collaborative digital twin construction with competitors in manufacturing and logistics industries. Through empirical experiments, we clarify practical processing speeds in a consortium of multiple organizations, confirming the applicability in enterprise environments.

In order to realize beyond visual line of sight (BVLOS) unmanned aerial vehicle (UAV) flights for agricultural support in mountainous areas, it is necessary to estimate the flight range of UAVs where the stable communication with ground control terminals can be maintained for observing the condition of the flight path. Existing research proposes methods for estimating wireless communication qualities using radio wave propagation simulations based on ray tracing technology. However, when incorporating complex structures of the field such as vegetation in the mountainous areas, the computational load of the ray tracing markedly increases, making it difficult to perform frequent simulations in agricultural areas where terrain structures change continuously. Therefore, this study proposes a new wireless communication quality estimation method that integrates lightweight radio wave propagation simulation and machine learning technology. The proposed method first roughly estimates communication quality using the lightweight simulation that considers only coarse terrain structures such as elevation. And then, the estimation results are applied to a machine learning model trained to predict communication quality incorporating accurate 3D terrain structures. Through field experiments, we demonstrate that utilizing machine learning models enables the accurate prediction of radio wave propagation in the field by reflecting detailed terrain features.

This paper focuses on a wildlife monitoring system with wireless video transfer, which enables users to remotely monitor and control the status of cages deployed to capture wild animals. We introduce a system employing a wireless technology called diversified range IoT (DR-IoT), which is a private radio operating in the VHF band. The usage of VHF band allows us to achieve a long-range transmission, however, it also poses several challenges due to its low achievable data rates. In this paper, we investigate how to increase the capacity (number of supported cages) and realize video transfer over a low-rate wireless link with experimental results.

In recent years, Ethernet is attracting attention as a technology for constructing in-vehicle networks. However, there is a concern that bottlenecks may occur due to concentration of a large amount of data on specific parts of the network, resulting in the situation that critical data related to vehicle control does not reach the ECU in real-time. Therefore, in this study, we propose a new automated design system of in-vehicle Ethernet utilizing a machine learning technology for deriving appropriate QoS control parameter.

In order to prevent the lifestyle diseases, it is clarified that the recording of lifelog data related with the location and motions of people is effective. However, the power consumption of the sensor device measuring the lifelog data should be reduced to observe users in a wide area for a long time. On the other hand, an energy harvesting technology that can obtain electric power from nearby energy sources such as solar light, wind, heat, and vibration is attracting attention as a viable alternative to conventional batteries. In addition, to achieve wide-area communication, the mesh network technology is attracting attention because the wide-area sensor network can be constructed even if the communication distance of each sensor device should be short to reduce the power consumption. However, if a power supply from the energy harvesting is very limited, prolonged operation of the wide-area mesh network is challenging because the sensor node can work as a part of the mesh network only in a very limited time. Therefore, in this study, we propose a new method for constructing a mesh sensor network system by controlling the timing of wake-up of sensor devices based on the states of power generation by energy harvesting.

With each new iteration of mobile wireless networks, demands for low latency and real-time processing continue to grow. Traditional cloud-based processing suffers from latency and bandwidth issues, hindering real-time applications. To address this, we propose an architecture that distributes computation to edge servers in order to reduce latency and improve the user experience. Using the OMNeT++ platform with Simu5G and INET libraries, we simulate a detailed 5G network with MEC integration. Central to our architecture is the MEC Orchestrator, which manages edge computing resources and interacts with the MEC Hosts which are small edge-located servers that process data and deliver services closer to the user, minimizing latency and improving the user experience. We then simulate the dynamic interactions between a mobile user's User Equipment (UE) and the MEC infrastructure. To further enhance the efficiency of our proposed architecture, we propose to integrate an algorithm that can predict the UE's future location. This algorithm will enable optimal MEC Host selection for user tasks, ensuring data processing occurs closest to the user's predicted future location.

Internet of Things (IoT) systems using real world data are making our lives more convenient and efficient by using environmental information. The IoT system consists of an IoT device that obtains real-world data, a server that processes and stores the data obtained by the IoT device, and actuators that adjust environmental information. If the server is compromised by an attacker, the data stored on the server may be tampered, and cause the actuator to operate incorrectly based on the tampered data. Existing studies focus on the possibility that multiple IoT devices may also exist near the in a proximity to detect the occurence of the data tampering by the malicious device or attackers. In this existing study, unique data is shared among the multiple IoT devices in close proximity using BLE (Bluetooth Low Energy) and sent to a server along with the obtained real-world data. The server compares and analyzes the data sent from the IoT devices to verify whether the real-world data is tampered or not by the malicious IoT device or attackers. However, the unique data may be recorded by an attacker and shared via BLE to a distant IoT device, which misleads the IoT system into believing the proximity of an IoT device that is not originally in proximity, leading to a false sense of data integrity. Therefore, in this study, we propose a method to verify the proximity of an IoT device to other IoT devices based on the similarity of environmental information to ensure the integrity of data before it is stored in the blockchain. To solve the existing problem, this study proposes the development of a data management infrastructure equipped with the capability to identify devices in physical proximity by recording and verifying environmental information dependent on time and location, thereby enhancing security against such attacks. The approach further addresses attacks that attempt to tamper with the data before it is stored on the blockchain, enhancing security measures against these threats.

With the spread of ICT technology, it has become common for people to spend long hours at their desks in the office, which hurts their health. In addition, this effect leads to stress, which in turn reduces concentration and makes it difficult to produce good results at work. Consequently, it is necessary to observe regular stress and encourage breaks to prevent prolonged sitting at the desk. To solve this problem, sensing systems have been researched and developed to measure heartbeats by mounting sensors on desk chairs and user interfaces of the computer and to analyze the measurement results. The existing methods require many sensors to be installed on various devices and thus cause large costs. On the other hand, the existing methods measure heartbeats without contacting the device. For example, existing research has constructed a fatigue estimation system using a non-contact Doppler sensor that can measure heartbeats and breathing from a distance. However, existing Doppler sensors are directional, meaning that they can only observe a specific direction, making it impossible to observe a large number of people at the same time with only a limited number of sensors. Therefore, in this study, we combine an omnidirectional camera and a Doppler sensor to develop a system that analyzes and records the stress values of each individual by estimating the position of each individual from the images captured by the camera and adjusting the orientation of the Doppler sensor in that direction. In the proposed system, by combining an omnidirectional directional camera and a directional sensor, a large number of people can be placed in the observation range with a small number of sensors.

In recent years, Japan has a steady increase in fitness enthusiasts. However, it is accompanied by a rise in injury rate due to improper fitness knowledge. To address this issue, IoT-based fitness assessment systems are gaining attention. However, many existing systems can not simultaneously assess fitness movements and vital signals, and they have challenges in monitoring people in large spaces. Therefore, in this study, we propose a remote fitness monitoring system that employs a collaborative approach with various sensors, including fisheye camera, RGB camera, infrared sensor, and millimeter wave radar. This system enables non-contact monitoring of users' physiological information during fitness, while also evaluating the quality of their movements. Through location detection via image analysis and actuation by servo motor, this system achieves wide- range and continuous monitoring of trainees using a small number of devices. Furthermore, we conduct experiments to validate the system's effectiveness in terms of scalability of the monitoring range and the effectiveness of vital signs monitoring.

As society ages, the workload of desk workers is increasing, leading many to experience overwork and heightened job-related stress. However, due to busy schedules, they often neglect their health and cannot regularly undergo health checks. Consequently, many desk workers suffer from various health issues, including the risk of overwork-related death. To address these issues, existing research has explored real-time health monitoring using medical devices and cameras, but this approach is costly and raises privacy concerns. Therefore, we propose a new system that utilizes low-cost, non-contact multi-sensors to monitor the vital signs of office workers in real-time. The system employs infrared sensors to monitor body and environmental temperature, analyzes temperature images to identify sitting posture, and uses millimeter-wave sensors to track heart and respiration rates. Additionally, a multi-modal deep learning model with an attention mechanism extracts key features from these vital signs to assess individual health status and psychological stress levels. The system delivers real-time monitoring results to desk workers' devices and provides advice when potential health issues are detected, such as suggesting rest or seeking medical attention.

IoT data includes Spatio-temporal data (STD) that is needed only at a specific time and place. In our previous research, we have proposed the STD data retention system (STD-RS), aiming to construct a novel architecture for STD distributing in a retention area by vehicles. However, since vehicles may distribute the STD in unexpected locations due to GPS errors or malicious behavior in a real environment. Therefore, we propose a reliability audit mechanism based on multi-layered blockchain managing and analyzing the history of STD distribution and vehicles' behavior in each retention area. Through simulation experiment, we demonstrated that our mechanism detects the malfunction of STD-RS effectively.

In recent years, low-cost 3D printing has rapidly spread among general consumers. On the other hand, the fact that once an adversary obtains the data, he can produce any number of reproductions has been pointed out as one of the critical issues confronting 3D printing. There is existing research in which cavities that express embedded information are made in the interior of 3D objects. This method aims at embedding information into a legitimately created object, however, it is not robust against an attack where an adversary obtains the external shape of the object by 3D scanning and reproduces the object, or an attack where an adversary simply makes a mold of the object and produces copies by casting. In this paper, we propose a scheme whereby information is embedded into the external shapes of objects. Attackers cannot change the information without changing the external shapes of the objects when they make copies of the original 3D objects. In the proposed scheme, we define the pseudo rotation axis for a 3D object and cutting planes that are orthogonal to the axis, and then we embed information into the maximum values of the radii of the cross sections. To modify the maximum values of the radii, we scale x coordinates and y coordinates of vertices of polygons. The scale factor continuously varies according to the z coordinate of the vertex in order to reduce the distortion of the object. We conduct experiments in which actual 3D objects are made and into which information is embedded using the proposed method and extracting the information from only the object without any metadata. We show that transmission of information with only the printed 3D object is possible using the proposed scheme without notable distortion of the object.

We propose algorithms that uses fast graph mining for resolving the network topology to a locally regulated area defined as a component of network topology. Though the IP backbone network should be reconfigured periodically in accordance with environmental changes, reconfiguration results in heavy workload, such as routing re-designs or testing these re-designs for network operators. If a network can be reconfigured within the component, however, we can drastically reduce the operation workload for network reconfiguration. For finding the components from the network topology, we should solve the subgraph isomorphism problem, which is NP-hard. We propose the heuristic graph mining algorithm that reduces the size of search space by considering network operating conditions. We visualize the results of components analysis, and show that the computation finishes within the practical time.

Timing synchronization is essential in cases where multiple sensor nodes with ultrasonic sensors are deployed in the same place. This is because ultrasonic sensors should be powered at a precise timing so as to prevent ultrasonic interference. A harmful-birds scaring system utilizing a new timing-synchronization method for a Zig Bee sensor network is proposed. In this method, the processing load for synchronizing the timing of ultrasonic sensors is concentrated at the server. Accordingly, the amount of calculation on each node can be minimized and is unaffected by increasing the number of nodes. Experimental evaluation of the proposed method, applied in an ecology observation by a sensor array, was shown that it sufficiently fulfills the timing requirement for the proposed harmful-birds scaring system.

In this paper, we propose a new real-time guidance system which is equipped with an indoor positioning technology for visitors in hospitals. In order to estimate a user's position without affecting operation of medical equipment, our proposed system utilizes a combination of RSSI of Personal Handy-phone System (PHS) and Dead-Reckoning based on sensors of an Android device. As a first step of the study, feasibility of indoor RSSI of PHS and sensors of the Android device for the position estimating is evaluated. Through the evaluation, we have clarified that the position estimation of the user is possible by confirming the RSSI of signals transmitted from multiple PHS base stations, and a gyroscope of the Android device can be utilized to detect moving direction of the user.

In the environmental and ecology observation, the sensor network has been commonly used, but it is difficult to deploy it in a vast field or some special environments such as a forest or a mountainous area. To solve this problem, we propose a new sensor network system based on a DTN technology to collect environmental observation data automatically using an autonomous air vehicle. In particular, an autonomous flight algorithm for the air vehicle using GPS sensor is developed. We have also performed some experiments to evaluate practicality of the proposed system.

In this paper, we propose a new browser synchronization system, which supports elderly people and IT shortfalls to learn how to use on-line shopping services from a support person on a remote site. A key feature of the proposed system is to synchronize browsers without a centralized server, and it works even if user terminals are placed behind a NAT device. In order to traverse the NAT, the proposed system uses a Simple Traversal of UDP through NATs (STUN) server and a mail server. In addition, a packet loss compensation method is implemented in order for the proposed system to avoid a mis-synchronization due to UDP packet losses. We have implemented the proposed system to Linux/Windows and Android tablets. From an evaluation of the proposed methods, it has been concluded that contents on browsers can be synchronized within a sufficiently short time.

Timing synchronization is essential in case where multiple sensor nodes with ultrasonic sensors are deployed on the same place. This is because the ultrasonic sensors should be powered at an appropriate timing so as to prevent ultrasonic interference. In this paper, we propose a new timing synchronization method for ZigBee sensor network for ecology observation. In this method, a sensor node has been configured as a ZigBee router in order to exchange time information among nodes in real-time. Moreover, by concentrating the processing load to the server, the amount of calculation at each node can be minimized and is unaffected by the increase in nodes. By using the proposal method, it is concluded that the time synchronization should be executed per every few minutes in order to detect moving objects by the ultrasonic sensors.

With a wide spread of smartphones and tablets, a mobile network becomes frequently congested when many users concentrate to the same place. Especially when a large-scale event is held, a heavy network congestion interferes with the communication of the participants and local residents. In order to detect the network congestion, a large amount of traffic log should be analyzed in real time. In this paper, the proposed system attempts to detect a sign of the congestion by using a Complex Event Processing (CEP). We analyze network status when the large-scale event, Nagaoka Fireworks festival, is held. As a result, it is concluded that a sign of the network congestion can be detected by combination of the RTT and the abnormal termination of TCP sessions.

By utilizing a new network protocol, Locator/Identifier Separation Protocol (LISP), a global IP multicasting system can be achieved without modifying protocol stacks of routers on the Internet. A mapping system which manages relationship between each multicast group and the group members is a core component of the system, but the centralized system requires costly resources for handling a large-scale network. In this study, a new decentralized mapping system for LISP- based information multicasting system is proposed. The proposed system utilizes a structured P2P network consisting of LISP routers to manage the membership information of multicast groups, and collaborates with two proximity-aware methods in order to shorten the start-up time needed for newly-arrived multicast members to start communicating with other members. The first method enables LISP routers to connect to lower- latency neighbors, and the second one replicates the membership information on a router which is close to all group members. This paper considers the performance of the proposed system by using a large-scale computer simulation and clarifies that the proposed mapping system with both methods can halve the start- up time compared with the simple P2P-based system.

With the wide-spread use of high-speed network connections and high performance computing resources available over the Internet, live video broadcasting services have become available on the Internet. In this service, anyone who has a laptop, desktop, or mobile terminal with a camera can broadcast his own video to any user who is interested in watching it in real time. Such a system must be able to accommodate a large number of simultaneous video broadcasts and allow the user to locate the desired content quickly. Furthermore, it should minimize the end-to-end streaming delay between the video broadcaster and the viewer to support the real-time video broadcasting. In order to satisfy these requirements, the advantages of peer-to-peer network technology must be leveraged to build a completely distributed live video broadcasting system. Peer-to-peer technology makes it possible for the system to efficiently match video broadcasting to the interested viewers, and to select the appropriate resources that can distribute the streamed content to all viewers in real time. This paper considers the performance of the proposed system using a realistic computer simulation to show that the system has scalability for a large-scale distributed system that attracts a very large number of users.

This paper shows the decreased effectiveness of on-chip decoupling capacitance in high-frequency operation. On-chip decoupling capacitance is often used to decrease the variation of the propagation delay caused by power/ground noise, i.e., dynamic IR-drop and/or delta-I noise. However, it is shown in this paper that decoupling capacitance is only effective for coping with dynamic IR-drop if the recharging time between switching events is sufficient. In other words, the effectiveness of decoupling capacitance for dynamic IR-drop in high-frequency operation is less than that of a fully-charged decoupling capacitor. The recharging time and the effectiveness of a decoupling capacitor depend on the propagation delay of the average circuit path which is used to determine the total switching current of a given macro/chip and clock cycle time. If the propagation delay of the critical paths is approximately equal to that of the average circuit path, then it is shown in this paper that adding decoupling capacitance never improves the maximum frequency of the system due to dynamic IR-drop limitations. On the other hand, if the propagation delay of the critical paths is larger than that of the average circuit path, then the maximum frequency is improved by adding decoupling capacitance. In both cases, a new metric, called the apparent capacitance, can be used to help make correct decisions about decoupling capacitance planning.

When the Web services are used, there is a case that they need not identify who is the user if the user is the regular user. In this paper, we propose the method that the service based on authority can be used by public-key certificate and attribute certificate without identifying. As a result, the user can take the services though the user with a regular authority is not identified by the service server. Moreover, the service server confirms user is a regular user, and can provide the services without identifying the user. Our method makes it possible to protect the user's usage information.

In wireless sensor networks, if the local sensor node transmits wrong decided data to a fusion center, the energy-efficiency of the network is degraded. In this paper, we propose wireless sensor networks with local fusion to reduce the degradation caused by observation noise, channel noise, and fading. In the proposed systems, first, information transmitted from local sensor nodes are decided by a local fusion center allocated in the neighborhood of local sensor nodes. Here, we consider two signal decision schemes (local fusion) for use at the local fusion center: majority decision and likelihood decision. By using local fusion, even if the local sensor node makes an error in the decision of the event, the probability that the local fusion center makes an error in the decision of the event becomes small. Thus, the probability that the local fusion center transmits wrong decided data becomes small. Then, from the local fusion center to a destination node (a global fusion center), error correcting coded bits are transmitted in multihop communication. We show that the proposed systems can achieve good energy-efficiency as compared with the system without local fusion. We also show that the total energy consumption of the system with likelihood decision is smaller than that of the system with majority decision when the influence of observation noise is large. Moreover, we show that the optimal number of hops minimizing the total energy consumption of the proposed systems depends on the signal attenuation parameter k and a long-haul transmission distance between the local fusion center and the global fusion center.

Optical communications through clear atmospheric links can provide high-speed communications where cables cannot be installed easily. However, on atmospheric optical links, the turbulence-induced intensity fluctuations deteriorate the quality of received optical signals. In this paper, we propose two atmospheric optical subcarrier modulation systems using space-time block codes (STBC) with coherent and differential detection to realize high-speed communications on atmospheric optical links. In the proposed systems STBC is used to overcome the influence of the turbulence-induced fading and to get the space diversity gain. We show that the proposed systems can achieve the better bit error rate (BER) than the atmospheric optical subcarrier modulation systems with the single transmit aperture (antenna) in the presence of the scintillation. Moreover, we prove that the differential STBC scheme is 3 dB worse than coherent STBC scheme.

N-gram indexing method is an algorithm for the full text search system where each index consists of serial N characters. To save the volumes of index file, the system has 2-gram index as base and to improve the performance, it has higher-gram index as supplement. This paper describes the performance of the incremental 3-gram index for the first step based on the search term intensive approach. Experimentation includes four index patterns and four search cases.

N-gram indexing method is an algorithm for the full text search system where each index consists of serial N words or characters. While the system for Japanese text has the 2-gram characters index as base in order to save the volumes of the index file, the additional higher-gram index is expected to improve the performance. This paper presents the entropy-based method for selecting additional higher-gram index. The basic idea comes from the fact that the Katakana words (they have often the same prefix such as "in-" and "ex-" in English) are suitable for the incremental index.

A new method is proposed to evaluate the quality of pellet component. Unlike other methods which only rely on the binarized image to solve this problem, ours rely on both the binarized image for measuring and other features of the image. In this paper, first of all, a model is constructed. The size of the model depends on the designed requirement of dimension and its allowable tolerance. By using this model, the sizes of the component are inspected roughly. Then, we propose the backpropagation (BP) neural networks for evaluating the ring of central part based on the features of the tested component. Because there are measurement errors such as systematic and random errors, so the dimension of the component has to be measured several times. Finally, the final decision method is proposed. This method can evaluate the quality of pellet component more precisely than the traditional methods.

This paper presents an efficient trellis-based maximum-likelihood decoding algorithm for binary linear block codes. This algorithm is recursive in nature and is devised based on the structural properties and optimum sectionalization of a code trellis. The complexity of the proposed decoding algorithm is analyzed. Numerical results show that the proposed decoding algorithm significantly reduces the decoding complexity. A recursive method for finding the optimum sectionalization of a trellis in terms of computational complexity is given.

Widely distributed systems are constructed step-by-step over a long lime. These systems must permit on-line testing. On-line testing verifies newly added application software by receiving the real data in the real environment without disrupting the operating subsystems. To enable testing during system operation, an on-line test technique based on autonomous decentralized system structure was proposed. In this paper, the functions to verify various types of application software are proposed: (1) for non-real-time application software, checking whether it communicates with every operating application software. (2) for real-time application software, checking whether timing of output data is within the timing-deadline or not. (3) for new version software, verifying whether the version of all functions is newer than that off functions in the present software. An example of application software applied to the on-line test and the effectiveness of the technique is shown in a real system.

In a large scale mission critical real time control system, such as the Tokyo metropolitan area train traffic system, it is very difficult to construct the entire system at once. Moreover economic considerations makes year by year investment inevitable for developing the system. The new Tokyo metropolitan area train traffic computer control system, supplying service to 19 train lines, 300 stations and 6200 trains per day, has been constructed step by step over 8 years without halting train operations. Here the computer system is required to be expandable online during system operation and without disrupting already constructed subsystems. To enable testing during system operation, an autonomous decentralized online expansion technique is proposed. Then the effectiveness of the technique is demonstrated in a real system.>

The number of precision bits for operations and data are limited in the hardware implementations of backpropagation (BP). Reduction of rounding error due to this limited precision is crucial in the implementation. The new learning algorithm is based on overestimation of significant error in order to alleviate underflow and omission of weight updating for correctly recognized patterns. While the conventional BP algorithm minimizes the squared error between output signals and supervising data, the new learning algorithm minimizes the weighted error function. In the learning simulation of multifont capital recognition, this algorithm converged recognition accuracy to 100% with only 8-b precision. In addition, the recognition accuracy for characters that did not appear in the training data reached 94.9%. This performance is equivalent to that of a conventional BP with 12-b precision. Moreover, it is found that the performance of the weighted error function is high even when only a small number of hidden neurons is used. Consequently, the algorithm reduces the required amount of weight memory.