Edge computing for connected vehicles is expected to become a reality. Compared to public clouds, the computing resources available on individual edge servers are limited: therefore, dynamically allocating computing resources as needed based on load is effective for the efficient use of edge server resources, and the auto-scale functionality using containers and Kubernetes is widely used. However, if the edge servers are overloaded when requests exceed their maximum processing capacity, long response delays and service outages may occur. Cooperative load balancing among neighboring edge servers is known as a method to prevent edge servers from overload, but standard Kubernetes does not have a control function based on server location or distance between servers, making it difficult to implement this. This paper proposes a service mesh controller to achieve cooperative load balancing among neighboring edge servers in an environment where container execution infrastructure controlled by Kubernetes runs on edge servers. Experimental results show that the proposed method with the service mesh controller reduces the median of response time by 96.3% compared with a conventional method without load balancing while also preventing overloads.

The performance of human workers can be fluctuated due to changes in the cognitive state during sustained work. Though past researches have made human performance monitoring possible by utilizing physiological signals, little attention has been paid to the context of office works. This research proposes a multimodal approach to estimate office task performance. A transcription typing experiment was conducted to simulate the real working environment while typing speed and error rate represented as performance metrics. Physiological data collected during the experiment, together with conventional machine learning algorithms showed feasibility to accurately predict two levels (good/bad) of task performance. More importantly, a comprehensive comparison between choices of modality suggests that using data from particular sources could gain predictive performance comparable to the complete set of modalities.

In this demonstration, we show that on a device with multiple network interfaces, our proposed network selection method can select a proper interface based on measured delays and information flags with an ICMP reply packet in an edge computing (EC) environment. The methods mainly provide the following two features: the resolution of FQDN through multiinterfaces and the insertion of EC and congestion flags into an ICMP reply packet. These features enable the practical usage of multi-interfaces on a device to select a proper network interface for advanced services and applications based on EC. This paper first explains the features and the procedures of our method. Then our demonstration shows that the device can select a proper interface based on the flag information.

As signature-based techniques have ever more difficulty detecting increasing and varying malicious activities through network traffic, machine learning has become a promising approach in network security. Many previous studies have aggregated traffic data into groups by hosts or flows for generating features and training detection models. However, two problems degrade detection performance. One is the scarcity of training sets due to the rarity of new types of malicious traffic, and the other is variations in feature values generated from incomplete data due to limited observed traffic. In this paper, we propose a stochastic method called Alchemy that regenerates a set of feature vectors by randomly resampling raw traffic data of each bag into several subsets. Alchemy can increase training sets and represent raw traffic robustly to correct the influence of variations in feature vectors, regardless of types of traffic data and classifiers. We evaluated Alchemy with real-world traffic data of network flows, passive DNS records, and HTTP logs, and demonstrated that it improves detection performance of various classifiers more effectively than the conventional methods in all three types of traffic data.

Whereas various things are connected via networks thanks to IoT technology, and the era of the 4th industrial revolution which realizes optimization and efficiency by utilizing AI and big data is emerging, manufacturing systems are also significantly transforming globally. In addition to the robot revolution, along with progressing digitalization, manufacturing sites in Japan are changing, aiming at establishment of “Connected Industries” that is a solution-oriented industrial society, based on the high technological capabilities. In order to respond timely to diversifying demands of customers, it is necessary to build a next-generation manufacturing systems that realizes flexible and high productivity, such as human-robot collaboration, and it is becoming difficult to respond to the necessity by conventional safety concept. Therefore, in order to realize the 4th industrial revolution, the robot revolution, and “Connected Industries,” it is essential to establish a new safety concept corresponding to the next-generation manufacturing systems to ensure safety. This paper introduces the safety concept to be changed along with the evolution of manufacturing sites, and proposes a new safety concept, which realizes collaboration safety of humans and robots, and an outline of its safety level, for the first time in the world.

As is the case for recently networked systems, traffic analysis that detects malicious hosts is one of the most important issues in achieving better security. Many methods for detecting cyber attacks have been proposed; however, methods that specialize in detecting and identifying specific attacks will be obsolete in the near future. Instead of identifying types of cyber attacks, we propose a novel method to detect malicious hosts based on their behavior characteristics when they send SYN packets. Our method does not identify types of cyber attacks. It detects malicious hosts suspiciously sending SYN packets. This paper shows that (1) we have developed a method to convert a series of SYN packets to a visual image as input for neural networks, (2) the image represents distinctive features of the behavior of the hosts and (3) our convolutional neural network model successfully distinguishes malicious host from normal ones. Our preliminary evaluation using real-word traffic shows that the detection accuracy for identifying malicious hosts is over 98% regardless of types of attack.

Service chaining is an emerging architecture from the trend of Software Defined Networking and Network Functions Virtualization. This architecture is expected to improve network flexibility and reduce equipment and operational costs. However, recent approaches for service chaining require new networking technologies that need to be standardized, implemented and developed. In this paper, we present and demonstrate a scalable and deployable service chaining architecture, called FlowFall. FlowFall does not require any novel technologies, only requires OpenFlow and current Virtual Machines as Virtual Network Function. Moreover, we focus on the NFV backbone network and how to achieve scale-out its performance. We describe the approach of FlowFall and an evaluation in which our method achieves 10-gigabit wire-rate throughput with 128-byte packets on a single Hyper Visor and scale-out performance among multiple Hyper Visors. Finally, we demonstrate a proof-of-concept of FlowFall in an actual service network at Interop Tokyo 2015 ShowNet.

In this paper, we propose a method for estimating the camera pose for an environment in which the intrinsic camera parameters change dynamically. In video see-through augmented reality (AR) technology, image-based methods for estimating the camera pose are used to superimpose virtual objects onto the real environment. In general, video see-through-based AR cannot change the image magnification that results from a change in the camera׳s field-of-view because of the difficulty of dealing with changes in the intrinsic camera parameters. To remove this limitation, we propose a novel method for simultaneously estimating the intrinsic and extrinsic camera parameters based on an energy minimization framework. Our method is composed of both online and offline stages. An intrinsic camera parameter change depending on the zoom values is calibrated in the offline stage. Intrinsic and extrinsic camera parameters are then estimated based on the energy minimization framework in the online stage. In our method, two energy terms are added to the conventional marker-based method to estimate the camera parameters: reprojection errors based on the epipolar constraint and the constraint of the continuity of zoom values. By using a novel energy function, our method can accurately estimate intrinsic and extrinsic camera parameters. We confirmed experimentally that the proposed method can achieve accurate camera parameter estimation during camera zooming.

In general, video see-through based augmented reality (AR) cannot change the magnification of camera zooming parameter due to the difficulty of dealing with changes in intrinsic camera parameters. To realize the usage of camera zooming in AR, we propose a novel simultaneous intrinsic and extrinsic camera parameter estimation method based on an energy minimization framework. Our method is composed of the online and offline stages. An intrinsic camera parameter change depending on the zoom values is calibrated in the offline stage. Intrinsic and extrinsic camera parameters are then estimated based on the energy minimization framework in the online stage. In our method, two energy terms are added to the conventional marker-based camera parameter estimation method. One is reprojection errors based on the epipolar constraint. The other is the constraint of continuity of zoom values. By using a novel energy function, our method can estimate accurate intrinsic and extrinsic camera parameters. In an experiment, we confirmed that the proposed method can achieve accurate camera parameter estimation during camera zooming.