Osong Medical Innovation Foundation, Korea University Research and Business Foundation

Samsung Electronics Co., Ltd.

Korea University

Electronics and Telecommunications Research Institute

Korea University Industry and Academy Corporation Foundation

Lynn Choi

Hyo Hyun Choi

Sun Gi Kim

Han Jun Bae

Cheol Sig Pyo

Sang Hun Lee

Sang Hoon Lee

Jong Arm Jun

Seung Ki Hong

Ho Jun Jang

Jae Kyun Jung

Disclosed is a low power and low memory based indoor positioning method performed by a computing device including at least one processor. The method may include: determining an operation mode to be executed based on at least one of a motion of a target and movement of the target; loading a map segment related to a current location of the target from a memory when executing a specific mode related to performing indoor positioning; and acquiring positioning data by using sensing data collected in real time for the indoor positioning, and the map segment.

Low-power method and map data hierarchical segmentation load method for indoor positioning

Disclosed is a low memory based indoor positioning method performed by a computing device including at least one processor. The method may include: extracting coordinate information in which a sensing value is not recorded as empty space information from map data in which each of a plurality of sensing values measured in a plurality of coordinates is recorded; generating a sensing value array including the plurality of sensing values; and storing the empty space information and the sensing value array in a memory.

Map compression method for indoor positioning

A device that generates a location estimation model is provided. The location estimation model generator device includes a map generator part configured to generate a magnetic field map, which includes magnetic field values corresponding respectively to the coordinates of an indoor space; a data generator part configured to generate learning data by implementing the magnetic field map; and a learning part configured to generate a location estimation model by artificial neural network (ANN) learning implementing the learning data.

Device and method for generating geomagnetic sensor based location estimation model using artificial neural networks

A communication method and apparatus using a virtual sink node in a wireless sensor network, in which location information about a destination node is acquired by a source node. If the location of the destination node is out of a transmission range of the source node, data including the location information about the destination node are transmitted from the source node to one of nodes adjacent to the source node, where the one node is typically closest to the destination node. The data are transmitted from the one node to another one of nodes adjacent to the one node, where another node is closest to the destination node, until the destination node is located within a transmission range of the one node. If the destination node is located within the transmission range of the one node, the data are transmitted from the one node to the destination node.

Communication method and apparatus using virtual sink node in wireless sensor network

Provided are a communication method and system for sensor network. In reporting event occurrence in a real-time sensor network application, a cycle time is divided into an event announcement period and a data transmission period to provide real-time transmission performance and energy efficiency. Each of the periods is again divided into the number of slots equal to the maximum depth of a network. Respective nodes sequentially operate according to their depths in a routing tree during the event announcement period to receive the signal transmitted from the lower node. To remove sleep delay, a slot next to the signal reception slot becomes a signal transmission slot. A node sensed an event transmits a signal informing event occurrence during its signal transmission slot, and this signal includes only the address of the transmission node. When a signal does not exist in the event announcement period, no traffic occurs in the data transmission period and the upper nodes maintain a sleep state. When a signal is transmitted during the event announcement period, nodes transmitting/receiving a corresponding signal operate during the data transmission period to transfer data packets.

Sensor network system and communication method thereof

A communication method and apparatus is disclosed that uses a received signal strength indicator (RSSI). In a sensor network including a plurality of nodes, a first node of the plurality of nodes receives a signal transmitted from a second node of the plurality of nodes, at least once. The first node also manages a link state with the second node according to the RSSI of the received signal. Therefore, the first node can receive signals from the plurality of nodes and then predict whether the second node moves. The first node can also predict a link state after the second node is moved so that communication can be performed through a link in a good state. Accordingly, the communication quality and the network performance of a network are enhanced.

Communication method and apparatus using received signal strength indicator in wireless sensor network

A packet classification method through hierarchical rulebase partitioning. The packet classification method for searching for a rule having a highest priority and matching among a plurality of rules included in a specified rulebase with respect to a packet input from an external network and processing the packet according to the searched rule. The method includes: a preprocessing step of partitioning the rulebase into a plurality of independent sub-rulebases based on a specified condition and producing a hash table on the basis of the sub-rulebases; and a classification step of classifying the packet by searching the hash table using a hash key extracted from a packet header of the inputted packet and mapping the packet on the sub-rulebase corresponding to the packet.

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Seoul, South Korea

Lynn Choi