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Recently, Internet of Things (IoT) technologies have been progressed. To overcome of the high cost of developing IoT services by vertically integrating devices and services, open IoT enables various IoT services to be developed by integrating horizontally separated devices and services. For open IoT, we have proposed Tacit Computing technology to discover the devices that have data users need on demand and use them dynamically. However, existing Tacit Computing does not consider performance. Therefore, in this paper, we propose an automatic graphics processing unit (GPU) offloading technology as a new elementary technology of Tacit Computing that uses genetic algorithm to extract appropriate offloading areas from parallelizable loop statements automatically. This can improve performance of IoT applications. We evaluate our proposed GPU offloading technology by applying it to five C/C++ applications of image processing, matrix manipulation, and so on to verify its effectiveness and find that it can process them more than ten times as quickly as only using central processing units within 1 h tuning time.

IoT technologies have been progressed. Now Open IoT concept has attracted attentions which achieves various IoT services by integrating horizontal separated devices and services. For Open IoT era, we have proposed the Tacit Computing technology to discover the devices with necessary data for users on demand and use them dynamically. However, existing Tacit Computing does not care about performance and operation cost. Therefore in this paper, we propose an automatic GPU offloading technology as an elementary technology of Tacit Computing which uses Genetic Algorithm to extract appropriate offload loop statements to improve performances. We evaluate a C/C++ image processing to verify effectiveness of GPU offloading and confirm more than 16 times performances within 1 hour calculation time.

A wide variety of devices is being installed in various environments such as homes, factories, and streets with the rapid expansion of the Internet of Things (IoT). To properly and securely use IoT devices, the states of various devices that have different properties and protocols must be managed. However, it is difficult to understand the consistency of an individual device when its installation place or software changes, since many individual IoT devices do not have unique identifiers. In this paper, we propose an architecture that estimates individual devices by analyzing and combining multiple pieces of information that can be obtained from the device. This architecture estimates individual device identity based on the time change pattern of the feature amount extracted from a signal transmitted by a device. Results of a simulation revealed that this architecture could identify the individual devices to find the correct device.

New services can be developed by combining some of the huge number of heterogeneous devices connected to a network in the era of the Internet of Things (IoT). However, to develop such services, a mechanism is required to detect devices with appropriate attributes such as functions or locations and to communicate with the devices. In this paper, we propose architecture that enables us to communicate with devices that have desired attributes in the IoT world by reducing the size of tables of devices' attributes by distributing the tables over the whole network. Moreover, we quantitatively describe how small the tables can be compared with centralized management at places such as datacenters and then discuss techniques and problems to make distributed tables a reality.

Recently, IoT (Internet of Things) technologies have been progressed. To overcome of the high cost of developing IoT services by vertically integrating devices and services, Open IoT enables various IoT services to be developed by integrating horizontally separated devices and services. For Open IoT, we have proposed Tacit Computing technology to discover the devices that have data users need on demand and use them dynamically and an automatic GPU (graphics processing unit) offloading technology as an elementary technology of Tacit Computing. However, it can improve limited applications because it only optimizes parallelizable loop statements extraction. Therefore, in this paper, to improve performances of more applications automatically, we propose an improved method with reduction of data transfer between CPU and GPU. This can improve performance of many IoT applications. We evaluate our proposed GPU offloading method by applying it to Darknet which is general large application for CPU and find that it can process it 3 times as quickly as only using CPUs within 10 h tuning time.

Recently, IoT technologies have been progressed, and many sensors and actuators are connected to networks. Previously, IoT services were developed by vertical integration style. But now Open IoT concept has attracted attentions which achieves various IoT services by integrating horizontal separated devices and services. For Open IoT era, we have proposed the Tacit Computing technology to discover the devices with necessary data for users on demand and use them dynamically. We also implemented elemental technologies of Tacit Computing. In this paper, we propose three layers optimizations to reduce operation cost and improve performance of Tacit computing service, in order to make as a continuous service of discovered devices by Tacit Computing. In optimization process, appropriate function allocation or offloading specific functions are calculated on device, network and cloud layer before full-scale operation., 3 pages, 1 figure, 2017 Fifth International Symposium on Computing and Networking (CANDAR2017), Nov. 2017