Abstract: A sensor device allocation apparatus that allocates a sensor device to an IoT service of the present invention includes a scoring unit that performs ranking of sensor devices satisfying a service requirement by an evaluation function based on specification conditions of the sensor devices and a scheduling unit that determines a control law of the sensor devices ranked by the scoring unit, in which the scheduling unit determines whether a service is satisfied based on collected data of the sensor device collected based on the control law, and the scoring unit corrects the evaluation function and repeats ranking of the sensor devices when the scheduling unit determines that the service is not satisfied.

Abstract: A multi device coordination control device includes: a degree-of-service-achievement calculator configured to acquire sensor information from one or more sensor devices disposed on the same physical space, and convert the acquired sensor information to a degree of service achievement; a target control amount calculation part configured to make a learning device allow an input of the degree of service achievement and compute therefrom an actuator control amount for each of the actuator devices, to thereby obtain the actuator control amount as an output value; and an actuator controller configured to convert the actuator control amount obtained as the output value to a control instruction in accordance with each of the actuator devices, and transmit the control instruction to each of the actuator devices, to thereby make the each of the actuator devices execute the control instruction.

Abstract: A device identification apparatus includes: a communication information collection unit configured to acquire communication information of existing devices and an identification target device; a feature amount generation unit configured to make the communication information of the existing devices' feature amounts and assign labels to generate first training data, make the communication information of the identification target device feature amounts and assign a dummy label to generate second training data, and further acquire communication information of the identification target device to generate identification data; a machine learning unit configured to cause a learning engine to learn the training data, and input the identification data to classify the identification data into the labels; a degree-of-similarity calculation unit configured to calculate a degree of similarity for each label; and a device identification unit configured to use a new type determination threshold to determine whether the devic

Abstract: A device control apparatus includes: a classification unit which refers to a classification table and assigns a classification label to sensor information of this time acquired from a sensor device of this time and a control unit which refers to a model table, selects, as an enhancement model, a learning model corresponding to a classification label provided by the classification unit, controls an actuator device using the enhancement model, and enhances and learns the enhancement model on the basis of the sensor information acquired from the sensor device in accordance with the control.

Abstract: Provided is an optimally deploying an application on a node of a network. An application deployment apparatus 10 is an apparatus that deploys an application in a node N in a network 4, the network 4 including a plurality of the nodes N communicably connected by links L, and includes a deployment determination unit 14 configured to determine, as a deployment destination of the application, a node of the nodes N having a minimum cost including a computation cost of the node N and a communication cost of a links of the links L, and a deployment execution unit 15 configured to distribute the application to the determined node N.

Abstract: When selecting a combination of application software, a computer, and a device in executing various services, even in a situation in which an environment greatly varies, the probability to achieve the purpose of the service is increased to shorten the duration time required to achieve the purpose of the service. Data effectiveness expectation value J(x, z) regarding a combination of a specific application x and a specific device z on a communication network is calculated, an evaluation value F(x, y, z) including the data effectiveness expectation value and a specific computer y as parameters is calculated, and an appropriate combination (x, y, z) is selected based on the result. The data effectiveness expectation value is calculated from actual record data. The data effectiveness expectation value is calculated by weighting old data and new data. The data effectiveness expectation value is calculated by using actual record values of other applications similar to the specific application x.

Abstract: An inter-device positional relationship estimation apparatus includes an analysis information acquisition unit that acquires time-series analysis information, a recommendation processing unit that estimates unanalyzed detailed information in time-series analysis information applying a predetermined recommendation algorithm, an initial map creation unit that calculates a neighborhood determination point indicating a degree of correlation of detailed information between fixed devices to create an initial map, and a device relationship map creation unit that calculates neighborhood points indicating a degree of correlation of detailed information between respective devices including a moving device or/and a new fixed device to calculate a detection probability and a movement time between devices and create a device relationship map.

Abstract: An imaging range estimation device includes an image data processor configured to acquire image data imaged by a camera device and generate image data with an object name label added, a reference data generator configured to set, by using geographic information, a region within a predetermined distance that is imageable from an estimated position at which the camera device is installed and generate reference data with an object name label added, and an imaging range estimator configured to calculate a concordance rate by comparing a feature indicated by a region of an object name label of the image data with a feature indicated by a region of an object name label of the reference data, and estimate the imaging range of the camera device to be a region of the reference data that corresponds to the image data.

Abstract: A software placement device of a software placement system includes an information acquiring unit configured to acquire a finally-found location and a finally-found time, a deployment destination determining unit configured to calculate a data presence probability indicating a probability of a search target being detected at a certain time using the finally-found location and the finally-found time, to calculate a total cost using the data presence probability calculated and a computational cost and a network cost of each of base computers, and to select a base computer and a time interval for which the calculated total cost is a minimum, and a software distributing unit configured to distribute software to the selected base computer and transmit an analysis instruction for data of the selected time interval to the selected base computer.

Abstract: A device identification apparatus (1) includes: a communication information collection unit (101) configured to acquire communication information of existing devices and an identification target device; a feature amount generation unit (102) configured to make the communication information of the existing devices feature amounts and assign labels to generate first training data, make the communication information of the identification target device feature amounts and assign a dummy label to generate second training data, and further acquire communication information of the identification target device to generate identification data; a machine learning unit (103) configured to cause a learning engine (3) to learn the training data, and input the identification data to classify the identification data into the labels; a degree-of-similarity calculation unit (104) configured to calculate a degree of similarity for each label; and a device identification unit (105) configured to use a new type determination thr

Abstract: An offloading server includes: a data transfer designation section configured to analyze reference relationships of variables used in loop statements in an application and designate, for data that can be transferred outside a loop, a data transfer using an explicit directive that explicitly specifies a data transfer outside the loop; a parallel processing designation section configured to identify loop statements in the application and specify a directive specifying application of parallel processing by an accelerator and perform compilation for each of the loop statements; and a parallel processing pattern creation section configured to exclude loop statements causing a compilation error from loop statements to be offloaded and create a plurality of parallel processing patterns each of which specifies whether to perform parallel processing for each of the loop statements not causing a compilation error.

Abstract: When selecting a combination of application software, a computer, and a device in executing various services, even in a situation in which an environment greatly varies, the probability to achieve the purpose of the service is increased to shorten the duration time required to achieve the purpose of the service. Data effectiveness expectation value J(x, z) regarding a combination of a specific application x and a specific device z on a communication network is calculated, an evaluation value F(x, y, z) including the data effectiveness expectation value and a specific computer y as parameters is calculated, and an appropriate combination (x, y, z) is selected based on the result. The data effectiveness expectation value is calculated from actual record data. The data effectiveness expectation value is calculated by weighting old data and new data. The data effectiveness expectation value is calculated by using actual record values of other applications similar to the specific application x.

Abstract: [Problem] Provided is an application deployment apparatus capable of optimally deploying an application on a node of a network. [Solution] An application deployment apparatus 10 is an apparatus that deploys an application in a node N in a network 4, the network 4 including a plurality of the nodes N communicably connected by links L, and includes a deployment determination unit 14 configured to determine, as a deployment destination of the application, a node of the nodes N having a minimum cost including a computation cost of the node N and a communication cost of a links of the links L, and a deployment execution unit 15 configured to distribute the application to the determined node N.

Abstract: A multi device coordination control device includes: a degree-of-service-achievement calculator configured to acquire sensor information from one or more sensor devices disposed on the same physical space, and convert the acquired sensor information to a degree of service achievement; a target control amount calculation part configured to make a learning device allow an input of the degree of service achievement and compute therefrom an actuator control amount for each of the actuator devices, to thereby obtain the actuator control amount as an output value; and an actuator controller configured to convert the actuator control amount obtained as the output value to a control instruction in accordance with each of the actuator devices, and transmit the control instruction to each of the actuator devices, to thereby make the each of the actuator devices execute the control instruction.

Abstract: An offloading server includes: a data transfer designation section configured to analyze reference relationships of variables used in loop statements in an application and designate, for data that can be transferred outside a loop, a data transfer using an explicit directive that explicitly specifies a data transfer outside the loop; a parallel processing designation section configured to identify loop statements in the application and specify a directive specifying application of parallel processing by an accelerator and perform compilation for each of the loop statements; and a parallel processing pattern creation section configured to exclude loop statements causing a compilation error from loop statements to be offloaded and create a plurality of parallel processing patterns each of which specifies whether to perform parallel processing for each of the loop statements not causing a compilation error.