In ubiquitous sensor networks (USN), sensor node localization is normally performed with global positioning system (GPS) or radio signal strength (RSS) between a target node and reference nodes. Because GPS is not available in indoor environments, RSS-based approach is commonly used for indoor localization. However, RSS-based approach is hard to be applied to real indoor environments (e.g., home) because of signal interferences with various indoor obstacles such as walls, doors, furniture, and electric appliances. In this paper, we propose an efficient indoor localization method for Zigbee sensor nodes by classifying link quality indicator (LQI) patterns between a target node and multiple reference nodes rather than using calculation with RSS values. And we also present the results of indoor localization experiments in our ubiquitous home network test bed using the proposed localization method.

Sensor location estimation is important for many location-based systems in ubiquitous environments. Sensor location is usually determined using a global positioning system. For indoor localization, methods that use the received signal strength (RSS) of wireless sensors are used instead of a global positioning system because of the lack of availability of a global positioning system for indoor environments. However, there is a problem in determining sensor locations from the RSS: radio signal interference occurs because of the presence of indoor obstacles. To avoid this problem, we propose a novel localization method that uses environmental data recorded at each sensor location and a data classification technique to identify the location of sensor nodes. In this study, we used a wireless sensor node to collect data on various environmental parameters-temperature, humidity, sound, and light. We then extracted some features from the collected data and trained the location data classifier to identify the location of the wireless sensor node.

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