Advances on Broad-Band Wireless Computing, Communication and Applications

In this work, we focus on the transmission of messages by vehicles for multiple users in a disaster area. Previously, we have discussed the effectiveness of message transmission by vehicles based on the Delay Tolerant Networking (DTN). In this paper, we evaluate the network performance considering the impact of closed roads for resilient disaster network. We use Epidemic with recovery function and the proposed Enhanced Dynamic Timer (EDT) as the message delivery protocols. From the simulation results, we found that the delay of proposed EDT is good for both normal and disaster situations.

Wireless Mesh Networks (WMNs) have many advantages such as: easy maintenance, low upfront cost and high robustness. The connectivity and stability affect directly the performance of WMNs. In our previous work, we implemented a simulation system considering Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Distributed Genetic Algorithm (DGA), called WMN-PSOSA-DGA. In this paper, we evaluate the performance of Constriction Method (CM) and Random Inertia Weight Method (RIWM) for WMNs using WMN-PSOSA-DGA hybrid simulation system considering Chi-square distribution of mesh clients. Simulation results show that a good performance is achieved for CM compared with the case of RIWM.

In the fog computing model to realize the IoT, each fog node supports application processes to calculate output data on input data received from a fog node and sends the output data to another fog node. In our previous studies, types of the TBFC (Tree-Based Fog Computing) models are proposed to reduce the electric energy consumption and execution time of fog nodes and servers and to be tolerant of node faults. In the TBFC models, the tree structure of fog nodes is not changed even if some fog node is overloaded and underloaded. In this paper, we consider the DNFC (Dynamic Network-based Fog Computing) model. Here, there is one or more than one possible target fog node for each fog node and also one or more than one possible source node for each target node. A pair of a source node and target node which exchange data have to be selected. In this paper, we propose an MSMT (Multi-Source and Multi-Target node selection) protocol among multiple source and target nodes. Here, a pair of a source node and a target node are selected so that the total energy consumption of the nodes can be reduced. In the evaluation, we show the total energy consumption and total execution time by target nodes can be more reduced in the MSMT protocol.

In the fog computing (FC) models, a fog node supports application processes to calculate output data on input data from sensors and other fog nodes and sends the output data to target fog nodes which can calculate on the output data. In this paper, we consider the MPSFC (Mobile topic-based PS (publish/subscribe) FC) model where mobile fog nodes communicate with one another by publishing and subscribing messages in wireless networks. Subscription topics of a fog node denote input data on which the fog node can calculate and publication topics of a message show data carried by the message. A fog node only receives a message whose publication topics shares a common topic with the subscription topics. In the TBDT protocol proposed in our previous studies, a fog node only publishes a message of the output data to a target fog node in the communication range. Here, while a fewer number of messages are transmitted, the delivery ratio of messages in the TBDT protocol is smaller than the epidemic routing protocol. In this paper, we propose an ETBDT (Epidemic and Topic-Based Data Transmission) protocol in order to increase the delivery ratio, where mobile fog nodes forward messages to not only target nodes but also non-target nodes in the communication range. In the evaluation, we show the delivery ratio in the ETBDT protocol is larger than the TBTD protocol.

In order to realize various types of distributed application services, various kinds of data are gathered from various types of devices. Each gathered data unit is stored in an object like database systems. Each object is replicated on multiple virtual machines in a system to provide reliable and available application services. However, a large mount of electric energy is consumed since replicas of each object are manipulated in multiple virtual machines in a system. In this paper, the EIEEQS-VM (Extended IEEQS-VM) algorithm is newly proposed to reduce the total electric energy consumption of servers by omitting meaningless read and write methods.

In recent years, the need for programming has increased. Self-study in programing is important for mastering it. However, many sites for learning programming only support personal computers. JavaScript Development Environment (JDE) was developed in our previous research. JDE is a single-page application that allows a user to learn programming on a smartphone. It imposes a small load on the server, thus many people can use it at the same time. Because it does not use a computer, it can be used in a classroom. Furthermore, JDE is suitable for distance learning (or e-learning). The number of remote classes has increased in Japan in 2020, as a result of the coronavirus pandemic. In this paper, we introduce an example of distance learning using JDE and verify the effectiveness of JDE.

The Novel Coronavirus disease 2019 (COVID-19) is raging around the world and is seriously affecting daily lives and economic activities for people. For example, the work and class are now taking place online, and major lifestyle changes are also taking place. It is possible that COVID-19 will continue to change lifestyle habits in the future. To cope with this change, COVID-19 needs to be taken into account in daily life and in economic activities, and measures need to be taken based on a variety of information. A variety of approaches are currently being tried and tested around the world to assist in combating COVID-19. This paper focuses on measures taken in the tourism industry and aims to propose a tourist spot collection method that takes into account COVID-19.

Distracted driving on roads is a problem that is common across the world now. With increasing use of smarter and connected devices, coupled with their miniature form factors, humans are now increasingly using these devices under mobility. When operating a vehicle, using smart devices can pose serious threats to road safety. Another contributing factor to distracted driving today stems from the urge to eat, drink and fall asleep while driving. In this paper, we use a popular and publicly available image dataset captured from embedded cameras inside cars that indicate instances of distracted driving or not. Different from existing works that look at the entire image to classify distracted driving, we first localize objects within the image that impact distracted driving. There are three broad categories we localize, namely, external entities (smartphones and bottles); entities within the car (steering wheel) and human-centered entities (left and right hand). Our approach to localize objects is based on Regional-Convolutional Neural Networks (R-CNNs). Once we localize these objects, we then design simpler machine learning techniques to process the relative locations of these objects within the image to detect instances of distracted driving. Our resulting performance evaluations demonstrate the validity of our approach. To the best of our knowledge, our work in this paper is unique, and we believe, provides more contextual relevance towards detecting instances of distracting driving, and could possibly yield newer approaches to educate drivers on safe driving.

Mobile ad-hoc network consists of a collection of mobile devices that are interconnected each other. It has attracted attention in various areas, such as information sharing and intelligent transportation. In particular, information sharing on MANET in an emergency is crucial for victims. In this situation, information on relief supplies and safety confirmation should be replicated to keep it on the network as long as possible. On the other hand, the cost of replicated information should be taken into account since the storage of each mobile device is limited. In this paper, we evaluate the performance of the replication protocol based on the cuckoo search that is a meta-heuristic algorithm inspired by the egg-laying habits of cuckoos to improve the availability of low demand data in MANET. We also discuss the tradeoff between file availability and the costs for storage and communication associated with the replication protocol .

Efficient and precise relief activities in disaster affected areas are required immediately after disasters. In order to deal with disaster situations, delay/disruption-tolerant network is a good way to deliver messages to destination by use of multihop forwarding by mobile nodes. Hence, a method called MRD [6], Message Relay Decision has been proposed to reduce the number of transmissions. However, it can not cope with the existence of obstacles and this will degrades message delivery rate. This paper proposes a new routing protocol, MRDAI (MRD with Area Increase) which increases message relay area to avoid negative effect of obstacle. The evaluation results confirm that the MRDAI achieves higher performance under the existence of obstacle.

Network traffic data generally comprise a major amount of normal traffic data and a minor amount of attack data. Such an imbalance in the amounts of the two types of data leads to issues such as low prediction performance including misclassifications owing to the estimation bias toward minority data and anomalies. To address this problem, several minority data synthesis models based on the synthetic minority oversampling technique algorithm have been developed. However, in recent years, studies have been actively conducted to synthesize minority data using the newly developed generative adversarial network (GAN) model. In this paper, we examine a GAN based oversampling model to address the data imbalance problem associated with intrusion detection data and compares the performance of the oversampling models. Therefore, the GAN based oversampling model can generate data of a class which has a small number of data so that the problem induced by imbalanced class distribution can be mitigated, and classification performance can be improved. Simulation results using KDD Cup 99 dataset show that the oversampling method using GAN algorithm is effective and that it is superior to existing oversampling methods.

In multihop wireless sensor networks, packets are periodically generated at each node in every cycle period time and forwarded along lossy wireless links between adjacent nodes toward one of the gateways through which the packets can reach a central data collection server. To cope with frequent packet losses, we consider a TDMA-based packet scheduling with redundant transmissions. In our previous work, we propose an optimal scheduling for the tandemly-arranged network topology with two gateways at the both edges of the network; which is not always realistic. Therefore, in this paper, we extend the research to the T-shaped network topology with three gateways. We derive a static time-slot allocation for T-shaped topology, which maximizes the theoretical probability that all packets are successfully delivered to the server with the basic redundant transmission scheme in a limited cycle period; and show its benefit through numerical results. This extension significantly increases the applicability of our optimal scheduling scheme.

Autonomous driving vehicle is designed with multiple types of onboard sensor and depended on delivered multiple types of information from outside of the vehicle. In order to deliver these information, vehicle-to-everything (V2X) is an essential technical element to realize connected car. Since these vehicles require larger amount of data which are generated by increasing of number of sensors or output data, higher data rate wireless communication is also required. So far we have already obtained a result of performance evaluation for IEEE 802.11ac standard based V2X communication. However, the result suggested some bottlenecks due to hardware limitation of the configuration. This paper reports performance evaluation on V2X wireless network over IEEE 802.11ac wireless LAN (WLAN). We have reconsidered the system configuration which can solve those hardware limitations. Through the performance evaluation at actual road environment, V2R communication has no significant difference to previous configuration. On V2V communication, we have confirmed a part of improvement for the end-to-end performance between inter-vehicle communications.

Students in a dental course have to pay a large amount of money for their training to become a dentist because they have to use expensive dentures in the dental treatment training that are discarded after the training. If we can provide virtual objects realized by 3D CG instead of the physical dentures, dental students can reduce their training costs drastically because such virtual objects can be used repeatedly and there are not any physical loss. Then, this paper treats a dental treatment training system. The research group of the authors have already proposed a dental treatment training system using a haptic device called Phantom. In this system, the treatment accuracy was not enough. So, the group tried to improve the system by adopting polygonal mesh subdivision. However, there still remain several problems. Therefore, the authors solved these problems and improved the treatment accuracy. This paper describes how the authors improved the system and also shows simple evaluation result to justify the improvement.

Nowadays, global warming has considered a key environmental issue associated with the pursuit of sustainable societies around the world. Therefore, it appears to be critical how to achieve the best usage of an air conditioner (AC) to reduce unnecessary energy consumption. In addition, it would be beneficial to elders who tend to suffer from heatstroke in hot summers. However, the AC can be overused or underused, while the indoor temperature is adjusted without considering the outdoor condition. If the weather is cool outside, the AC should be turned off and the windows in the room should be opened. On the contrary, if the outdoor temperature is high, the AC should be turned on. In this paper, we propose an air-conditioning guidance system (AC-Guide) to optimize the use of the AC. This system periodically, 1) samples the temperature and humidity of the room with the sensor, 2) obtains the outdoor weather data from an API, 3) calculates the discomfort index (DI) for the indoor and outdoor, 4) detects the on/off state of the AC using the image, and 5) sends a message of requesting turning on/off the AC when the DI is outside/inside the comfortable range. For evaluations, we implement the AC-Guide using Raspberry Pi, and verify the effectiveness through simulations and applications in two rooms at Okayama University.

In recent years, content sharing via mobile terminals has become more and more popular, but there is a concern that the network load will be getting too much higher. Besides, in the system that uses fog servers in addition to cloud, the cost of the server operation occurs. In this paper, we propose dynamic fog, that is, a method in which multiple mobile edge terminals form a logical fog server function in ad-hoc, thereby replacing the function of providing the content replicas and replica sharing metadata, executed by the physical fog server. Furthermore, computer simulations we executed using a model of actual human movements in a city in Tokyo show the tendency of substituting physical fog server functions when dynamic fog is used, thus the potential effectiveness of this method is revealed.

The threats of cyber-attacks caused by malicious JavaScript are becoming more and more dangerous recently. In order to be able to detect new malicious JavaScript with low cost, methods with machine learning techniques have been proposed and gave positive results. These methods focus on achieving a light-weight filtering model that can quickly and precisely filter out malicious data for dynamic analysis. A method constructs a language model to represent the data in vector form from the source code, the represented data will then be used for machine learning. This method has high testing score with the balanced dataset, but the experiment with an imbalanced dataset has not been done. To construct an efficient language model, and to deal with the data imbalance problem, we focus on oversampling techniques and Natural Language Processing techniques. In our research, our method is the first to use oversampling and machine learning to detect malicious JavaScript. We use Natural Language Processing techniques for extracting unique words from source code and converting them into vectors The experimental result shows that our method can detect new malicious JavaScript more accurately and efficiently. Our model can quickly filter out malicious data for dynamic analysis. The best F-measure achieves 0.78.

Existing DTN routing protocols such as MaxProp and PRoPHETV2 are Routing based on network states. However, those protocols consider only specific parameters for routing. The DTN routing protocol is designed to improve the delivery ratio of a message to its destination. Therefore, we consider several parameters that affect the message delivery ratio. In this paper, we propose a DTN routing protocol that uses reinforcement learning considering multiple parameters.

In this paper will be presented a new class of security protocols dedicated to Cloud infrastructures. Fusion technology in different types of data and management processes, will be described. Especially, will be presented an impact of description, interpretation and analysis processes as well as securing processes. Security processes, which are based on fusion methodology also include data sharing technologies. Data fusion protocols can be used in different infrastructures, so the impact of these processes will be analysed and presented.

This paper describes the distribution of processing and process migration in a PC-FPGA hybrid system. Here, the proposed hybrid system is constructed by a bus network using Ethernet, and communication between the nodes of the system is achieved by a proprietary protocol that is defined on the basis of Ethernet frames. For performance evaluation, we compare the processing time by distributed processing with that in the previous work. In addition, the execution process is migrated to FPGA. Experimental results show that the proposed system is capable of distributed processing using both PC and FPGA, and has the ability to migrate PC processing to FPGA.

The graph coloring problem (GCP) is one of the most important combinatorial optimization problems that has many practical applications. DSATUR and RLF are well known as typical solution construction algorithms for GCP. It is necessary to update the vertex degree in the subgraph when selecting vertices to be colored in both DSATUR and RLF. There is an issue that the higher the edge density of a given graph, the longer the processing time. In this paper, we propose a subgraph degree updating method to improve this issue. Experimental results show that the proposed method is faster than the conventional method and LazyRLF, especially for graphs with higher edge density.

This study provides a forward-looking usage-based automobile blockchain platform on the Internet of Vehicles network, describing the possible stakeholders, services, and their interaction modes. Most existing UBI (Usage-Based Insurance) products use the drivers’ driving distance, driving time, or driving area as premium calculating standard. If driving data are recorded on the blockchain to take usage-based automobile insurance, it could bring more additional services such as automatic claiming, improvement of traffic adjudication’s quality, and preventive maintenance. The feasibility of this platform will discuss the configuration of the blockchain and related management or privacy issues to explain how this research can improve the existing limitation of usage-based automobile insurance.

In this paper, we present an integrated Fuzzy-based Simulation System for Driver Risk Management (FSSDRM) in Vehicular Ad hoc Networks (VANETs). FSSDRM considers the current condition of different parameters which have an impact on the driver and vehicle performance to assess the risk level. The considered parameters include vehicle’s Environment Temperature (ET), Relative Humidity (RH), Noise Level (NL), Driver’s Health Condition (DHC), Weather Condition (WC), Road Condition (RC) and Vehicle Speed (VS). FSSDRM is composed of three Fuzzy Logic Controllers (FLCs): FLC1, FLC2 and FLC3. FLC1 has the following inputs: RH, NL and ET, while WC, RC and VS are the inputs of FLC2. Both outputs of these two FLCs together with DHC, serve as input parameters for FLC3. The input parameters’ data can come from different sources, such as on-board and on-road sensors and cameras, sensors and cameras in the infrastructure and from the communications between the vehicles. Based on the system’s final output i.e., driving risk level, a smart box informs the driver for a potential risk/danger and provides assistance. We show through simulations the effect of the considered parameters on the determination of the driving risk and demonstrate a few actions that can be performed accordingly.

Recently, many Internet of Things (IoT) related applications have been designed to assist industrial development and human beings’ lives. Among these IoT applications, many of them require small amount of data but long distant communication with the features of high-reliability and high level of security. Moreover, since many IoT user equipments (UEs) are powered by batteries, their energy management is an important design consideration. In order to support low-power communication, the 3GPP has proposed two energy-saving mechanisms, i.e., extended Discontinuous Reception (eDRX) and Power Save Mode (PSM). However, how to effectively integrate these two mechanisms to adjust UE’s parameters so that the UE’s energy consumption can be optimal is not an easy task. In this paper, the UE’s energy consumption model by combining eDRX with PSM is investigated and the energy consumption of a UE is also analyzed. According to the Poisson distribution, the uplink and downlink data transmission rates are fused in the energy consumption model. Our simulation results show that for an IoT with small amount of data transmission, the energy consumption can be significantly reduced when setting proper eDRX and PSM parameters.

The continuous changes environment and the rapid evolution of technology make that information system needs continuously and effectively maintain. The research report of the Standish group showed that the success rate of agile projects far exceeds the traditional projects. However, the agile software project has ignored many maintenance issues and characteristics. This paper combines the agile software development advantages and the maintenance benefits of traditional development, and proposes the CMQM model and CMQI process to strengthen the four critical quality characteristics which are documentation quality, test management quality, CM quality, and continuous operations quality characteristics. High critical quality characteristics can concretely improve the efficiency and quality of maintenance operations, and effectively extend the life of information systems.

Segmenting documents or conversation threads into semantically coherent segments have been one of the challenging tasks in natural language processing. In this work, we introduce three new text segmentation models that employ BERT for post-training. Extensive experiments are conducted based on benchmark datasets to demonstrate that our BERT-based models show significant improvements over the state-of-the-art text segmentation algorithms.

The wireless sensor networks have lots of popular applications for years. The efficiency of a wireless sensor network is subject to the monitoring accuracy and limited energy. Thus, event detection and energy efficiency are two important issues in Wireless Sensor Networks. In order to overcome the limit of yield rate for sensor hardware, some studies built other monitoring system to support monitoring for enhancing monitoring accuracy. According to limited energy, some studies designed energy efficient mechanisms to extend the lifetime of monitoring. In this paper, we propose a distributed cooperative mechanism that neighboring sensors mutual confirm the event occurrence for improving the monitoring accuracy and reducing the total numbers and energy consumption of transmissions. The result of simulations reveals that using the proposed mechanism, the monitoring lifetime is extended and the monitoring accuracy of proposed mechanism is much better than other existed mechanisms.

A digital signature is essential in verifying the reliability of people and data over networks, such as through web server certificates, authentication, and blockchain technologies. In blockchain, multi-signature signature schemes have recently attracted attention for reducing the amount of data in transactions. While such schemes support only a single message, Interactive Aggregate Signatures (IAS), an extended Schnorr multi-signature scheme, supports some messages under the plain public key model. However, there are three problems with this scheme in certain use cases.We propose a key aggregatable IAS scheme called KAIAS. In contrast to the previous works, KAIAS solves these problems which means that KAIAS (1) includes a verification algorithm using only a single aggregated public key, (2) dynamically signature aggregation, and (3) requires signers to sign only their own messages.Recently, the Schnorr multi-signature scheme has been discussed mainly from its advantages of reducing the size of the signatures in the implementation of Bitcoin. Thus, we also propose a practical application of KAIAS that takes advantage of its feature to aggregate both signatures and public keys with low computational complexity of signing.

Wireless sensor networks can be used for long-term operation and they can collect data effectively from huge volumes of sensed data. There are many applications of wireless sensor networks. In this paper, we propose a fuzzy-based transmission control system of sensed data for resilient wireless sensor networks in disaster situations. From the evaluation results, we found that our proposed system can reduce the transmission interval and extend the lifetime of network for disaster situations.

RFID is very useful as an automatic recognition technology because it can contactless access to the information. However, the communication performance of RFID is easily changed by metal, water and other surrounding environments because this system use the electromagnetic fields for communication. In this paper, we evaluate the effect of the parasitic coil placed on the table type RFID reader on the communication performance between the reader and the tag by changing the number of turns of the parasitic coil.

In two-dimensional photonic crystal waveguide with passive switching function depending on the input light wavelength, the output characteristic is simulated by using frequency dependent FDTD method. For the wide range of input optical wavelength, distribution ratio to output ports is calculated from the electric field profile. For add/drop circuit of wavelength division multiplexing (WDM) optical communication systems, it was found that tuning of add/drop signal wavelength is possible by designing duplexer at branching point with its radius of pillars. Finally, a configuration of drop circuit with simulated structure is proposed.

The Deep Q-Network (DQN) is a method of deep reinforcement learning algorithm. DQN is a deep neural network structure used for the estimation of Q value of the Q-learning technique. The authors have previously developed a simulation system on DQN-based behavioral control methods for actuator nodes in Wireless Sensor Actor Networks (WSANs). In this paper, an Autonomous Aerial Vehicle (AAV) testbed is designed and implemented for DQN-based mobility control. We evaluate the performance of the AAV testbed for a indoor single-path environment. For simulation results show that the DQN can control the AAV.

In order to decrease the energy consumption of the IoT (Internet of Things), the TBFC (Tree-based Fog Computing) model is proposed in our previous studies. Here, fog nodes are hierarchically structured where a root node shows a cluster of servers and a leaf node indicates an edge node which communicates with sensors and actuators. Each fog node supports a subsequence of subprocesses of an application process. As a volume of sensor data increases and decreases, some fog node gets overloaded and underloaded, respectively. In the DTBFC (Dynamic TBFC) model, the tree structure is dynamically changed by creating and dropping fog nodes so that the energy consumption of fog nodes can be reduced. A fog node is splitted, i.e. some of the subprocesses move to another fog node and is replicated, i.e. the subprocesses are supported by multiple fog nodes. We newly propose a DFC (Dynamic FC) algorithm to dynamically change the tree structure of the fog nodes to reduce the energy consumption and execution time of the fog nodes in a DTBFC model.

It is critical to reduce electric energy consumed by servers in clusters to reduce carbon dioxide emission. Here, application processes issued by clients are performed on servers in clusters. In this paper, we take the migration approach that application processes on a server migrate to a guest server by using the live migration technologies of virtual machines. By making a virtual machine where application processes are performed migrate from a host server to a guest server, the energy consumption of the servers to perform the application processes may be reduced as discussed in our previous studies. On the other hand, it takes time for a virtual machine to migrate from a host server to a guest server, since it may take a longer time to perform application processes on the virtual machine. In this paper, we first measure migration time of a virtual machine. Then, we propose an MDMG (Maximum energy consumption Difference by virtual machine MiGration) algorithm to select a virtual machine on a host server and a guest server to which the virtual machine to migrate in order to reduce the total energy consumption of servers in a cluster. In the evaluation, we show the energy consumption of servers and the average execution time of application processes can be reduced in the MDMG algorithm.

Wireless mesh networks (WMNs) are one of the wireless network technologies that have received much attention in recent years, and as the name implies, routers can provide a stable network over a wide area by configuring the network like a mesh. In order to provide a lower cost and more stable network, various methods for optimizing the placement of mesh routers are being studied. In a previous work, we proposed a Coverage Construction Method (CCM) for this mesh router placement problem. In this paper, we propose a CCM based Hill Climbing (HC) for mesh router placement optimization problem.

In recent years, machine learning, deep learning, neural network and other artificial intelligence technologies have made significant development and breakthroughs. Thus more mature and reliable technologies start to be applied in the field of data analysis. Data analysis is the process of studying and summarizing data in detail in order to extract useful information, that is, collecting, sorting, processing and analyzing data. Moreover, in order to make the analysis results easier to understand, data visualization technology is widely employed. This paper tries to review the state-of-the-art data analysis methods based on artificial intelligence. Besides, the mainstream data visualization technology and cases are elaborated. Finally, some open problems and challenges are also put forward for the future research.

In the past few years, the automatic construction of large-scale knowledge graphs has received extensive attention from academia and industry. In this paper, we show the specific applications and typical cases of knowledge graphs in data analysis, and summarize the overall process of knowledge graphs from construction to data analysis. Moreover, some open problems and challenges are also presented for the future research.

The Fifth Generation (5G) network is expected to be flexible to satisfy user requirements and the Software-Defined Network (SDN) with Network Slicing will be the constructive paradigm for admission control. The Quality of Service (QoS) is influential point in the 5G wireless networks. In this paper, we propose a Fuzzy-based scheme to evaluate the QoS considering three parameters: Slice Throughput, Slice Delay and Slice Loss. We carried out the simulation for evaluating the performance of our proposed scheme. From simulation results, we conclude that the considered parameters have different effect on the QoS performance. When Slice Throughput (ST) is increasing, the QoS parameter is increased. But, when Slice Delay (SD) and Slice Loss (SL) are increasing, the QoS is decreased.

As there have been a growing number of introductions to integrated management service on the ICS (industrial control systems) and advancements of smart factories which link to various forms of network, cyber attacks targeting the ICS have been on the rise. To defend against them, a full set of data is needed when attacks take place in the actual ICS. Collecting data from attacks is a difficult task to perform in the actual environment due to the characteristic of the ICS that requires high-level security. To resolve this issue, some studies are in progress as to building ICS testbeds and collecting attack data, but they face a problem of not reflecting requirements for the collection of attack data set. This paper describes considerations to take when building testbeds for security research on the ICS and suggests subsequent implementations of the testbeds for collecting a set of data on the ICS.

Recently, NDN (Named Data Networking) [1] which is a new content communication architecture, attracts a lot of attentions from the network research community. NDN routes the Interest/Data according to both of its content name and route information on each relay router. Then, NDN routing is categorized as hop-by-hop routing. Incidentally, due to the rapid development of recent ICT technology, current mobile networks allow a mobile terminal to publish/delivery its content even if it is in migration. NDN, however, could not operate correctly because of old information of publisher’s location information on relay router when we implement original NDN on such mobile networks. The literature [2] has proposed the method for solving the problem, but it requires publisher’s destination information. Hence, in this paper, we propose new method solving the Interest misleading problem include by mobile publisher. In order to clarify the advances of our proposal, this paper reports the results of performance evaluation in terms of cache hit ratio and content acquisition ratio.

As V2X system has developed, some vehicles that are recently released have been designed to meet the requirements of cooperative automated driving technology. Cooperative automated driving is the method of communication between the car and the surrounding infrastructure, and it predicts the surrounding situation through the V2X communication system to prevent unexpected accidents and situations that external sensors of the car are not aware of. In this environment, external security threats are also possible through each contact points while V2X (Vehicle to Everything) system communicates with Ethernet-based WAVE (Wireless Access in Vehicle Environment) through antenna, GPS module, and OBU (On Board Unit). Those all can generate false messages, for example, which are modulating location data by disturbing the GPS, manipulating the WAVE messages inside the OBU, or injecting internal CAN messages to cause propagation of wrong situation. Based on these potential threats, this paper presents possible cyber-attack scenarios in V2X environment.

Convenient systems and equipments to support humans are in great need for ever-growing populations of the elderly and those with disabilities caused by illness or injury. One of these system is the wheelchair, which can provide the user with many benefits such as maintaining mobility, continuing or broadening community and social activities, conserving strength and energy, and enhancing quality of life. However, when users use wheelchairs, they have to frequently stand and sit. This increases the physical burden on the user. For this reason, we proposed a self-standing omnidirectional robot. In order to support the user, the robot body must be able to flexibly make different movements and should be capable to deal with various control methods to meet diverse needs. In this paper, we present implementation of a user finger movement capturing device for control of self-standing omnidirectional robot.

Recently, various communication technologies have been developed in order to satisfy the user requirements. Especially, mobile communication technology continues to develop rapidly and Wireless Mesh Networks (WMNs) are attracting attention from many researchers in order to provide cost efficient broadband wireless connectivity. The main issue of WMNs is to improve network connectivity and stability in terms of user coverage. In this paper, we introduce a moving omnidirectional access point robot (called MOAP robot) and implementation of control interfaces for the robot. In order to realize moving Access Points (APs), the MOAP robot should move omnidirectional in 2 dimensional space. It is important that the MOAP robot moves to an accurate position in order to have a good connectivity. Thus, MOAP robot can provide good communication and stability for WMNs. The experimental evaluation show that the MOAP robot was controlled correctly by the implemented interfaces.

In Japan, vocational schools, technical high schools and junior high schools offer classes on soldering with a soldering iron. The use of a soldering iron can be difficult and dangerous for first-time learners. Teachers are limited in their ability to keep track of each practice of students. So, there is a need for a support system for hands-on practice in Japan. In this paper, we propose an ambient intelligence based support system to reduce the risk of soldering practice in the educational field.

Because of the diversification of network service requirements, agent-oriented computing is attracting attention as a means of realizing flexible systems. A learning agent adds “learnability”, which produces optimal behavior based on results of past behaviors. The learning agent can perform flexible and efficient actions by learning. Learning agents often deal with problems where uncertain factors such as stochastic events affect the outcome. This uncertain factor adversely affects the learning opportunities to educate the learnability of agents. Furthermore, such problems present obstacles to the training of learning agent system developers. Therefore, we investigated whether a tool that enables efficient education considering uncertain factors such as probabilistic events is effective for the education of learning agent developer.