Yutaka Yanagisawa

An anonymous communication technique using dummies for location-based services

Recently, highly accurate positioning devices enable us to provide various types of location-based services. On the other hand, because such position data include deeply personal information, the protection of location privacy is one of the most significant problems in location-based services. In this paper, we propose an anonymous communication technique to protect the location privacy of the users of location-based services. In our proposed technique, such users generate several false position data (dummies) to send to service providers with the true position data of users. Because service providers cannot distinguish the true position data, user location privacy is protected. We also describe a cost reduction technique for communication between a client and a server. Moreover, we conducted performance study experiments on our proposed technique using practical position data. As a result of the experiments, we observed that our proposed technique protects the location privacy of people and can sufficiently reduce communication costs so that our communication techniques can be applied in practical location-based services.

Protection of Location Privacy using Dummies for Location-based Services

Recently, highly accurate positioning devices enable us to provide various types of location-based services. On the other hand, because position data obtained by such devices include deeply personal information, protection of location privacy is one of the most significant issues of location-based services. Therefore, we propose a technique to anonymize position data. In our proposed technique, the psrsonal user of a location-based service generates several false position data (dummies) sent to the service provider with the true position data of the user. Because the service provider cannot distinguish the true position data, the user’s location privacy is protected. We conducted performance study experiments on our proposed technique using practical trajectory data. As a result of the experiments, we observed that our proposed technique protects the location privacy of users.

Object-based activity recognition with heterogeneous sensors on wrist

This paper describes how we recognize activities of daily living (ADLs) with our designed sensor device, which is equipped with heterogeneous sensors such as a camera, a microphone, and an accelerometer and attached to a users wrist. Specifically, capturing a space around the users hand by employing the camera on the wrist mounted device enables us to recognize ADLs that involve the manual use of objects such as making tea or coffee and watering plant. Existing wearable sensor devices equipped only with a microphone and an accelerometer cannot recognize these ADLs without object embedded sensors. We also propose an ADL recognition method that takes privacy issues into account because the camera and microphone can capture aspects of a users private life. We confirmed experimentally that the incorporation of a camera could significantly improve the accuracy of ADL recognition.

Shape-Based Similarity Query for Trajectory of Mobile Objects

In this paper, we describe an efficient indexing method for a shape-based similarity search of the trajectory of dynamically changing locations of people and mobile objects. In order to manage trajectories in database systems, we define a data model of trajectories as directed lines in a space, and the similarity between trajectories is defined as the Euclidean distance between directed discrete lines. Our proposed similarity query can be used to find interested patterns embedded into the trajectories, for example, the trajectories of mobile cars in a city may include patterns for expecting traffic jams. Furthermore, we propose an efficient indexing method to retrieve similar trajectories for a query by combining a spatial indexing technique (R+-Tree) and a dimension reduction technique, which is called PAA (Piecewise Approximate Aggregate). The indexing method can efficiently retrieve trajectories whose shape in a space is similar to the shape of a candidate trajectory from the database.

Object-Blog System for Environment-Generated Content

The object-blog service application automatically converts raw sensor data to environment-generated content (EGC), including texts, graphs, and figures. This conversion facilitates data searching and browsing. Generated content can serve several purposes, including memory aids, security, and communication media. In object-blog, personified objects automatically post entries to a Weblog about sensor data obtained from sensors attached to the objects. Feedback thus far from participants working with object-blog in an experimental environment has been positive.

Clustering Multidimensional Trajectories based on Shape and Velocity

Recently, the analysis of moving objects has become one of the most important technologies to be used in various applications such as GIS, navigation systems, and locationbased information systems, Existing geographic analysis approaches are based on points where each object is located at a certain time. These techniques can extract interesting motion patterns from each moving object, but they can not extract relative motion patterns from many moving objects. Therefore, to retrieve moving objects with a similar trajectory shape to another given moving object, we propose queries based on the similarity between the shapes of moving object trajectories. Our proposed technique can find trajectories whose shape is similar to a certain given trajectory. We define the shape-based similarity query trajectories as an extension of similarity queries for time series data, and then we propose a new clustering technique based on similarity by combining both velocities of moving objects and shapes of objects. Moreover, we show the effectiveness of our proposed clustering method through a performance study using moving rickshaw data.

WristSense: Wrist-worn sensor device with camera for daily activity recognition

This demo paper describes our daily activity sensing and recognition system with a wrist-worn sensor device called WristSense. The wrist-worn device is equipped with an accelerometer and camera, and can send the sensor data to a Bluetooth-enabled smart phone. With the accelerometer, we capture the wearers hand postures and hand movements. With the camera, we capture visual information related to an object that the wearer is holding. An object that the wearer is using relates strongly to the activity that the wearer is performing. For example, an image (frame) including a coffee maker that is captured when the wearer is making coffee can be useful for recognizing the activity of making coffee. Also, a logger application on a phone records sounds related to daily activities. We use the sensor data in an attempt to recognize high-level daily activities that involve object use in real time.

A Real-World Event Search System in Sensor Network Environments

Assuming an environment in which a sensor network always collects data produced by sensors attached to physical objects, the demonstration system presented here searches sensor data for corresponding real-world events using a natural language (NL) words in a query. The system translates each query into a physical quantity representation also introduced here, searches for a sensor data segment that satisfies the description by the representation, and returns the information about the event that is reflected in time series of values from sensor readings.

Recognizing handheld electrical device usage with hand-worn coil of wire

This paper describes the development of a new finger-ring shaped sensor device with a coil of wire for recognizing the use of handheld electrical devices such as digital cameras, cellphones, electric toothbrushes, and hair dryers by sensing time-varying magnetic fields emitted by the devices. Recently, sensing the usage of home electrical devices has emerged as a promising area for activity recognition studies because we can estimate high-level daily activities by recognizing the use of electrical devices that exist ubiquitously in our daily lives. A feature of our approach is that we can recognize the use of electrical devices that are not connected to the home infrastructure without the need to equip them with sensors. We evaluated the performance of our approach by using sensor data obtained from real houses. We also investigated the portability of training data between different users.

Reconfigurable hardware architecture for saving power consumption on a sensor node

We propose the use of a reconfigurable hardware architecture to reduce the power consumption of small sensor node that has various sensors and wireless communication facilities, that were the result of an adaptive function specialization mechanism. Traditional sensor nodes must have had a powerful and multi functional Micro-Controller Unit (MCU) to satisfy the requirements for processing any kinds of application. However, most of these systems only use a part of the functions provided by an MCU. In other words, such a unit often consumes a great dead of power for unused circuits. To avoid this situation, we propose the use of a reconfigurable architecture based on a Field Programmable Gate Array (FPGA) instead of an MCU because this array dynamically changes the circuit to the optimal one that is just used for the calculation required by an application. Moreover, we implemented a prototype system to do a preliminary evaluation of our proposed mechanism. In this evaluation, we show the performance of our proposed reconfigurable architecture by comparison with traditional architecture that uses processing time and power consumption. The experimental result shows that our proposed mechanism reduces enough power of its sensor nodes to prolong the lifetime of nodes without decreasing the processing time.