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  • Takuro Yonezawa · Hiroshi Sakakibara · Jin Nakazawa · Kazunori Takashio · Hideyuki Tokuda

    Towards a better understanding of association between sensor nodes and daily objects

    Received: date / Accepted: date

    Abstract This paper introduces uAssociator, an image-based association tool for realizing sensor-attachment type smart object services that enable end-users to associate everyday objects to tiny sensor nodes easily. By attaching sensor nodes to everyday objects, users can augment the objects digitally and take the objects into various services intuitively. When using such smart object services, a semantic connection be- tween sensor nodes and objects must be made before ser- vices initiate properly. At home, however, professional as- sistance with such installation may be either unavailable or too costly. This paper explores the design choices to realize an easy association method, describing trade-offs inherent in each choice. In addition, we show a spotlight-and-camera based association tool which can reduce association costs significantly.

    Keywords Association · Interaction · Smart Object · Deployment · Application Model

    1 Introduction

    Our life is filled with everyday objects, and we often have troubles with them (e.g. lost property). In order to achieve the pervasive computing environment, it is important to take everyday objects into pervasive service. Sensor nodes, when attached to everyday objects, enable us to gather real-world information as context. Recently many researchers are fo- cusing on the services with these smart objects [5, 11]. With smart objects, users would be able to enjoy the privilege of pervasive technology anytime anywhere in their lives.

    We consider that smart objects can be classified into the two: the sensor-builtin type and the sensor-attachment type. The difference of these two kinds is their origins. While the builtin-type smart objects is well-configured at the time of shipment, the attachment-type smart objects is configured

    Takuro Yonezawa Keio University, Delta S213 Endou 5322, Fujisawa, Kanagawa, Japan Tel.: +081-466-47-0836 Fax: +081-466-47-0835 E-mail:

    by users (i.e., users attach sensor nodes to their belongings). Each type of smart objects has both advantages and disad- vantages. For example, builtin type smart objects require no complex configuration to users; once users buy these smart objects, they can leverage smart object services instantly. In addition, builtin type has good looks. However, the users should buy and use pre-configured products only. In con- trast, attachment type smart objects can provide freedom to users; users can use many ordinary (i.e., not smart) belong- ings that already exist in our daily life. However, users must make software configurations for adjusting those objects to run various applications. In addition, objects may look bad with sesor nodes attached, because sensor nodes are still big in the present MEMS technology. However, we con- sider the problem of bad looks will be solved in the future because sensor nodes will become tiny as technology ad- vances. Therefore, if the configuration cost can be reduced, the attachment-type smart objects plays an important role to realize ubiquitous computing environment.

    The goal of our research is reducing this configuration cost for realizing the attachment-type smart object services. Successful installation for attachment-type smart object ser- vices requires a three-step process: 1) attaching sensor nodes to objects, 2) making semantic associations between the sen- sor nodes and the objects, and 3) configuring each applica- tion to a preferred setting. Of these, this paper focuses to reduce association costs. We assume sensor nodes have lim- ited computation power only enough to transport sensor data for reducing the cost, so that application software are imple- mented on high performance machines such as desktop or laptop PCs. From this point of view, applications, which pro- vide smart objects services, need to know what object each sensor node is monitoring. This, in turn, requires associa- tion, or making a semantic relationship between the sensor node ID and its object information.

    This paper explores the design choices to realize intu- itive association method, describing trade-offs inherent in each choice (in section 2). After that, we propose our scheme called uAssociator, a spotlight-and-camera based associa- tion tool (in section 3). The users can achieved association task by following process: obtaining sensor node ID and ob-

  • ject image simultaneously by using digital camera, and en- ter the optional information through graphical user interface. Our association scheme can be used in various smart object services to monitor, notify the status of, or generate cooper- ative functions among smart objects. In addition, we discuss pros and cons of our approach comparing related work (in section 4). Finally, we conclude this paper and show future work (in section 5 ).

    2 Design space for association

    Before discussing design space for association, the applica- tion domain that we target should be made clear. As most common context-aware applications are described as a col- lection of rule-based conditions, applications we target adapts if-then rule for providing smart object services. The differ- ence between common context-aware applications and ap- plications in attachment-type smart object is that users can choose any domestic object as the target of applications. As the scenario, ”if a secret diary which mounts sensor node is removed from drawer, alert by sounds” or ”if a brush which mounts sensor node is not moved after a meal, tell the child to brush his teeth” are simple examples. The major require- ment in the scenario is Do-It-Yourself (DIY) style of ser- vice usage; non-expert users must be able to register their preferred belongings to preferred services. It can be divided into the following three according to the operations needed for a registration.

    – Coping with variety of items: The user needs to attach sensor nodes to his/her belongings to use them in a ser- vice. The sensor node must be small enough to be at- tached to a wide range of items. In addition, it must have features to use in daily life (e.g., a sensor cover for wa- terproof). These are a physical requirement to the sensor node itself, which this paper does not focus.

    – Easy association: The user needs to tell the system which object each sensor node is attached to. To do so, the user first needs to specify the sensor node that the user wants to associate with an object. The user then needs to spec- ify the object. These specifications can be done by dif- ferent methods, each of which entails pros and cons that affect the system’s intuitiveness and ease of use.

    – Reusability of smart objects: To leverage smart objects in various services, the user needs to load the association information into the services. While the above simple scenario involves only one service at one object, there may be multiple different services in operation simulta- neously in a home. Therefore, the system needs to enable the user to use a smart object in those different services.

    Based on our experiences in creating smart object ser- vices framework, the most important considerations to achieve the easy sensor node-object association can be captured by two dimensions. A point along each of these dimensions em- bodies its own tradeoffs. This section explores these dimen- sions, and the pros and cons associated with each.

    2.1 Sensor node specification

    One key dimension concerns how a sensor node, which a user wants to associate to an object, is specified. More pre- cisely, it concerns how a user specifies the sensor node’s ID to the system, since we assume that each sensor node has a unique identifier. The first approach is the manual input from keyboard. Tiny sensor nodes have no space for attach- ing label or bar-code, let alone display which their IDs can be shown. Thus, consequently, the users are forced to rely on professional identification tools or simply estimate the ID based on the sensor data packet sent by the node to the net- work. Either way, the procedure could be highly inhibiting to end-users. this approach assumes that users can somehow acquire the sensor node’s ID. Second approach is to mount a special chip for identifying sensor nodes. For example, if sensor nodes has IrDA, Bluetooth or Near Field Communi- cation (e.g., RFID) chips, users can obtain sensor node ID by using special communication device that has the same chip. However, it is impractical to attach these chips on ev- ery kind of sensor nodes. In addition, mounting these chips would only increase the cost.

    Another approach for identifying sensor node is using signal strength of sensor nodes. However, there is a disad- vantage to applying this method to association between sen- sor nodes and objects. The problem is a lack of general ver- satility. Proximity interaction [6] is an example of using sig- nal strength. In the study, a series of experiments have been conducted using Mote [1]: proximity was monitored based on radio frequency. For that purpose, each sensor node has to use a different frequency to avoid radio interference. Since sensor nodes of the same type emit the same radio frequency, we cannot use more than one sensor nodes of the same type with this method. This could be a major obstacle when all different sensor nodes co-exist in an environment.

    The final approach is to characterize the sensor data transmitted by the node that the user wants to associate to an object. The system detects the characteristic among data received from sensor nodes in a network, and determines the node that th