We perceive vibrotactile stimuli from smart devices such as smartphones when we use various applications. However, vibrators in these devices can present only a specific nearby resonant frequency with enough intensity to perceive, making it challenging to offer various vibrotactile stimuli. In this study, we propose a method to realize a vibrotactile presentation in a wide range of frequencies using airflow vibration generated by a built-in audio speaker of a smart device. We implemented a system based on the proposed method using a smartphone and experimented with measuring the airflow pressure. Moreover, we also propose the application of texture presentation using airflow.

Humans can perceive tactile sensation through multimodal stimuli. To demonstrate realistic pseudo tactile sensation for the users, a tactile display is needed that can provide multiple tactile stimuli. In this paper, we have explicated a novel printed tactile display that can provide both the electrical stimulus and the electrostatic force. The circuit patterns for each stimulus were fabricated by employing the technique of double-sided conductive ink printing. Requirements for the fabrication process were analyzed and the durability of the tactile display was evaluated. Users' perceptions of a single tactile stimulus and multiple tactile stimuli were also investigated. The obtained experimental results indicate that the proposed tactile display is capable of exhibiting realistic tactile sensation and can be incorporated by various applications such as tactile sensation printing of pictorial illustrations and paintings. Furthermore, the proposed hybrid tactile display can contribute to accelerated prototyping and development of new tactile devices.

The study of human activity patterns traditionally relies on the continuous tracking of user location. We approach the problem of activity pattern discovery from a new perspective which is rapidly gaining attention. Instead of actively sampling increasing volumes of sensor data, we explore the participatory sensing potential of multiple mobile social networks, on which users often disclose information about their location and the venues they visit. In this paper, we present automated techniques for filtering, aggregating, and processing combined social networking traces with the goal of extracting descriptions of regularly-occurring user activities, which we refer to as “user routines”. We report our findings based on two localized data sets about a single pool of users: the former contains public geotagged Twitter messages, the latter Foursquare check-ins that provide us with meaningful venue information about the locations we observe. We analyze and combine the two datasets to highlight their properties and show how the emergent features can enhance our understanding of users' daily schedule. Finally, we evaluate and discuss the potential of routine descriptions for predicting future user activity and location.

To seek and collect useful sensor data in a participatory sensing environment, participants should be able to coordinate their activities in a timely manner. However, existing systems deal separately with the "preparation time" to define the goal and method of sensing, and the "sensing time" in the field. Therefore, participants cannot easily modify the sensing method, including the collaboration setting, once they are out in the field. In this demonstration, we will present a system called Kitokito, which allows participants to easily create small sensing tasks, and iteratively modify the collaboration and sensing method during the "sensing time."

Camera networks have been paid much attention in sensor networks since those network are useful to visualize a phenomenon sensed by other sensors for temperature, humidity, and light. When a user selects a point of an area deployed cameras in an application, a geographic routing is best used as a routing of the camera network. In conventional geographic routing, the destination of a message should be the location of a node. However, this is not suitable for sensors that cover a wide or directed sensing area such as cameras. To cope with this problem, we propose a modified geographic routing scheme called SenriGan. SenriGan accommodates specifying a sensed point as a destination instead of using a location of a node. In this demonstration, we show a prototype system of SenriGan.

Querying in heterogeneous sensor networks is a challenging research issue due to a variety of real-world queries depending on users' preferences. Examples of queries are weather, nearby restaurants, navigation, etc. Users may ask for a breezy path starting from distinct points to distinct train stations. Sensing data collected from wide areas (city or country level) are needed to provide the real-world search as a service for users. Therefore, another challenge is a collaboration of heterogenous sensor networks because deploying sensors in wide areas is impractical. SensorMap [1] collects sensing data from independent sensor networks by allowing data owners to publish their data on the web. Sensing data from all owners are sent to a centric storage in SensorMap. However, distributed data storage is likely to be an efficient solution comparing to a centric storage in which bottleneck always occurs.

Recently, Technology of sensor networks develops rapidly. In addition, people have started utilizing many kinds of sensors. Sensors independently collect environmental information all over the world. Unfortunately those sensed data are not shared and open to the public. Therefore, we have constructed super-large-scale sensor network system to share sensed data collected from sensors all over the world. We call such a project Live E!. In a part of Live E!, To sense a public area in whole, we assume public objects(mailbox, bus stop, dumpster) uniformly allocate in a district evenly is equipped with a sensor and a wireless device. The public objects often dose not have connectivity to a network. Those are Isolated Wireless Sensor Nodes (ISNs). .. Then, we need to consider the way of collecting from the ISNs. Accordingly, we utilize a Patrol Node (PN) that moves around ISNs, and collects sensed data from ISNs. The ISN stores the sensed data until the time PN comes back. However, because the communication time to the PN depends on the speed of the PN, ISNs can not necessarily transmit all the maintained sensed data to the PN. Therefore, we suggest that the ISN should send adaptive data according to the speed of PN. We propose a technique for transmitting adaptive data depending on the movement of the PN. In addition, we have implemented a prototype of our proposal and verified the effectiveness of our proposed system. Finally, we show that our system improves the performance of the sensor network.