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Home-Explorer: Search, Localize and Manage the Physical Artifacts Indoors

Bin Guo, Michita ImaiKeio University

3-14-1 Hiyoshi, Kohokuku, Yokohama, 223-0061, Japan {bingo, michita} @ayu.ics.keio.ac.jp

Abstract

A new system named Home-Explorer is proposed to search and localize physical artifacts in smart indoor environment. Our view is object-centered and sensors are attached to several objects (named smart objects) in the space. Different from others’ research, our system tackles not only smart objects but also hidden objects (e.g. no sensor attached objects). Home-Explorer resolves the hidden object problem by reasoning the physical context from smart objects. A series of inference rules are presented for context reasoning. Moreover, in order to deal with the uncertainty problem when estimating the identity of the hidden objects, we present two effective ways: attribute matching mechanism and associated relation method. Besides, to enhance user-friendly, multiple search modes are provided.

1. Introduction As the rapid development of wireless network

technology such as sensor network, all the devices, software agents, and physical artifacts are all expected to seamlessly integrate and coordinate to perform various tasks, which will inarguably revolutionize the way of our life. Such a smart environment makes it possible to search the real world. Different from Google search in the virtual world, a real world search system can save us much time and effort to organize and manage our physical belongings. This paper describes an indoor artifacts search system with some novel ideas.

Several problems exist when to build a physical world search system. One is how to deploy sensors to detect objects. Related work include the object-centered strategy used in the MediaCup project [1], in which sensors are embedded in the objects; the hierarchical architecture described in the MAX project [2], which separates sensors and actuators into three hierarchies to make search efficient. The second problem is how to localize objects, which has also been studied in lots of work. The Ubisense system (www.ubisense.net) obtains real-time location of assets and people by utilizing ultra-wideband technology; the 3D-iD system aims to realize a

local positioning system with the concepts of GPS [3]. Other problems relate to the search engine. An introduction about the probable issues when querying the real world is given in [4], while how to select optimal query protocol by considering hardware device parameters is investigated in [2].

To realize a physical world search system, further researches are required. Although the object-centered sensor deployment strategy has been the subject of many studies, little attention has been paid to the “hidden” object problem. We know that in ideal cases, the object attached with sensors can be directly detected. However, in some circumstances, the hidden object problem may come into being. For instance when Book-A is placed on Book-B, then Book-B’s sensor will become ineffective for its signal is blocked. Thus how to detect the hidden object like Book-B is one challenge that we should confront. Additionally, when a hidden object is detected, how to estimate its identity so as to list it in the relevant search result is the other problem that we will face to. Though the uncertainty problem in object recognition has been broadly studied in a great deal of work, little has been discussed in a search system.

In the present paper, an indoor physical artifacts search and localization system named Home-Explorer is proposed. Our view is object-centered and we attach wireless sensor nodes to several everyday objects. Different from other studies, our system handles not only smart sensor attached objects but also the hidden objects. Particularly, we use the physical context from smart objects to derive the information of hidden objects. A specific module named Sixth-Sense, based on our previous work [5], is used to fulfill this task. Besides, we developed several search modes to facilitate the search task, especially two novel ways to deal with the uncertainty problem when search the hidden objects.

2. Overview of Home-Explorer

2.1. System Goals

Before delving into the detailed system, we first note that our system is the result of the following design goals:

21st International Conference on Advanced Networking and Applications(AINA'07)0-7695-2846-5/07 $20.00 © 2007

• Object-oriented Design: as our system is object-centered, we import several concepts from object-oriented design. Each category (e.g. key) stands for a class; while a concrete object (e.g. room key) represents an instance of its class.

• Security and Authorization: Security, on one hand denotes that not everyone can access the system without authorization; on the other hand, it means that not every object could be tracked by an arbitrary user (for it may be privately owned). In view of this, we classified the smart objects into three levels: private, protected and public. Correspondingly, we provide three authorization types: master, family and friend. Master can track all the smart objects, and family user can’t visit the private objects, while friend user can only access the public ones. Besides, hidden objects can only be tackled by master.

• Multiple Search Modes: We seek to provide multiple search modes to satisfy various requirements from human, involving special ways for hidden objects search. When a search result consists of numerous items, a ranking algorithm is indispensable. Especially to the unknown hidden objects, measures should be taken to order them based on their degree of belief.

2.2. System Architecture

A whole architecture of Home-Explorer is shown in Figure 1. We can see that the design of this system is database-centric and it can be divided into four modules.

Figure 1. Home-Explorer Architecture

Figure 2. MOTE and Smart Objects

Physical and Sensor Deployment: This module involves various artifacts. Most of them are easily found in a typical home (e.g. a book). Sensors are part of this module as well. Left of Figure 2 shows the wireless

sensor nodes named MOTE, which includes acceleration sensor, light sensor and sound sensor. Besides, pressure sensors and ultrasonic 3D location sensors (U3D) are also used in our system. Some sensor attached object samples are given in right of Figure 2.

Smart Object Localization: How to find smart objects is the main focus of this module, details of this will be mentioned in Section 3.

Hidden Objects Detection: This module mainly deals with hidden objects. Section 4 will explain this in detail.

Security Search Engine and Display: This module’s chief task is to provide safe and efficient query operation to authorized users, and we will clarify this in Section 5.

3. Finding Indoor Smart Objects The focus of this section is how to find smart objects.

Firstly we have to make definite what smart objects are.

3.1. Smart Object Definition

Definition 1 (Smart Objects and Hidden Objects): In our smart indoor space, the detectable sensor attached objects are called smart objects; while the undetectable objects, involve the inactive sensor attached and no sensor attached objects, are called hidden objects. A smart object O can be described as , , ,O Nid Sid S T=< > .

Where: Nid and Sid separately denote the unique ID of O and sensor ID that O attached; S is an attribute set, which records the various attributes of O, main attributes are listed in Table 1; T denotes time.

Table 1. Smart Object Attributes

Static Attributes Shape {rectangle, circle, triangle, strip, irregular} Size Length, Width, Height or Diameter Value

Weight {very light, light, normal, heavy} Soundable {Y, N} Luminous {Y, N}

Auto-movable {Y, N} Category {Book, Pen, Cup, CD, …} Comment An additional note from user, such as “This

is a present for my 18th birthday”. Dynamic Attributes

Location 3D Position: (x, y, z) Slope Angle The range is [0, / 2]π , shown in Figure 3

Motion {Move, Rest} and Direction of Movement Light/Audition Numeric Value Force Analysis {Pressure, Pulling, ...} and Action of Force

Figure 3. Slope angle range of smart objects

Table 1 can be viewed as a common “ontology” of our system, which lists the main factors that we take account


of when defining an object (or an object category). Besides, it can be seen that the attribute set is divided into static attributes and dynamic attributes. The static attributes don’t make any change with time, which can be viewed as intrinsic attributes; while the dynamic ones could vary with time. To some attributes, such as weight, we don't use numeric data to describe them; instead we separate the whole span to several intervals (e.g. “light”, the weight between 10g and 100g). It provides a natural way similar to human cognition. Definition 2 gives two more concepts refer to the state change of smart objects.

Definition 2 (State-Stable and State-Alter): A smart object O is defined as 1 1 1 1, , ,Nid Sid S T< > at T1, and

1 1 2 2, , ,Nid Sid S T< > at T2. If S1 and S2 are the same, then the state of O can be described as State-Stable during this period; otherwise described as State-Alter.

3.2. Smart Object Registration

As an object search platform, one of the most important things is to register the object information into the central database. A smart object’s information can be derived from three sources: Observation and Thought (e.g. Shape, Soundable, Category); Simple Measure (e.g. Size, Weight); and Sensor (e.g. Location). Only the third information kind is obtained in real time, while the other two kinds could be registered before the system is run.

3.3. Skeleton Strategy and Symbol Expression

In order to provide intelligible location information to human, we prefer to use symbol expression rather than original sensor data. For this purpose, a skeleton strategy is proposed. We picked out several large and mostly static artifacts in our indoor space to serve as skeleton objects, i.e., the so called “indoor landmarks”. One more common character of these landmark objects is that they seldom shift positions. Tables, a bed, or the floor, are all able to meet these requirements. Undoubtedly, it’s much easier for human to find a landmark artifact (e.g. a table) than other small artifacts (e.g. a key). Thus the symbol expression “on (key, table)” will provide an easy way for human to find the key. Moreover, the skeleton strategy is highly approached to human’s cognition about the physical world. Similar to the smart objects, all the skeleton artifacts should be registered firstly. In Home-Explorer, such information is stored at Skeleton Base. Relative registration items are listed in Table 2.

Table 2. Skeleton Objects Registration

Item Description Name The identified ID for this object. Room The room it was laid (e.g. kitchen, bedroom) Shape Rectangle or Circle (shape of surface)

Position Area Coverage and Height.

3.4. Localization Algorithm

As discussed in section 3.3, we’d like to provide object’s location information by symbol expression. In reality, symbol expression denotes the spatial relation between two objects, and a lot of research has been done on this subject, such as [6]. Here, we mainly consider one general situation, that one object is placed upon another object’s surface. More clearly, we represent the localization problem to be on which skeleton object the object is laid. The following decision rule will be used to determine this kind of relation.

Decision Rule 1: To smart object A and skeleton object B, A doesn’t receive any upwards pulling force, if the downwards projection of A is within the range of B, and there is no other smart object between them, then we consider there exists a relation between A and B, described as ( , , ) ( , , )i iR A B t On A B t= ( it denotes time).

Since there are several skeleton objects stored at the skeleton base, when localizing a smart object, we have to compare it with all the skeleton objects by using this rule.

4. Hidden Objects Detection by Reasoning The hidden objects detection is based on our prior

work [5]. Here, some new advances are presented.

4.1. Category Base and Abstract Object Class

It’s known that people always use the concept of category to distinguish different objects. When a certain category is concerned, it should have one or several particular attributes that differ from others. Here, a definition about category attributes for artifacts is given.

Definition 3 (Category Standard): Each object category has its product standards, such as shape, size, color or weight, which are all endowed with specific or unified definitions (e.g., the size of a paper can be A4, B5; while the shape of a cup bottom is mostly circular). We call this category standard.

According to Definition 3, we preliminary divide the artifacts into several categories and build a category base for them. However, when a hidden object is concerned, as its unknown identity, it’s not easy to categorize it to some concrete category; thus, an abstract class is needed to register it. Similar to the smart object definition, the abstract class holds the whole attributes listed in Table 1.

Above all, when a hidden object is detected, some correlative attributes can also be derived by context reasoning, which will then be used to register this object.

4.2. Detection by Context Reasoning

In order to use context reasoning to detect hidden objects, firstly we give a clear definition about context.


Definition 4 (Physical Context): The physical context includes two levels of information: the first level is the state of the object itself (e.g. what is the object’s motion state...?); the second level consists of the possible physical relation between this object and others (e.g. is this object located on the table?).

Sixth-Sense module, which’s based on common sense reasoning, is developed to be in charge of hidden objects detection. Figure 4 illustrates how common sense works in our life. By analyzing the physical relation between smart object Book-B and skeleton object Table, we can infer that Book-B is not flatly placed on Table for there is an slope angle to it, then common sense tells us that there must exist something (Book-A) located between them.

Figure 4. A Sample for Hidden Object Detection In Sixth-Sense, the common sense knowledge is

represented as inference rules stored at IRB (Inference Rule Base). According to different knowledge the rules concern, we classified them into 4 classes. Hereafter, one or two instances of each class will be given.

(1) Location Relation Rule 3D-L1: A relation ),,( itBAOn exists between

smart object A and skeleton object B at ti; the slope angle of A is 0 or 2π ; besides, no touch point exist between A and B, then we infer that an object C between them (illustrated by left of Figure 5, formulated as below).

( , , , ) ( _ ( ) {0, 2})( _ ( , )) ( , ( , ))R On A B ti gets G A

no touch A B exist C between A Bπ< > ∧ ∈

∧ → (1)

Where: _ ( )gets G Obj is used to get the slope angle of object Obj; 1 2_ ( , )no touch O O denotes there is no touch point between 1O and 2O ; ( , )exist Obj Scope indicates the information of the new inferred object Obj.

Rule 3D-L2: A relation ),,( itBAOn exists between smart object A and skeleton object B at ti, and the slope angle of A is within (0, / 2)π , then we infer that there is an object C between A and B (illustrated by Figure 4).

( , , , ) ( _ ( ) (0, 2))( , ( , ))

iR On A B t gets G Aexist C between A B

π< > ∧ ∈→

(2)

(2) Force Relation Rule 3D-F1: To skeleton object A, if it suffers from

downward pressure in scope Sp (action zone of the force); Moreover, there is no smart object x in scope Sp can form the relation , , ,On x A t< > with A, then there will be an object B on A (illustrated by right of Figure 5).

_ ( ) ( ( _ ( )) )(( , , , )( _ ( ) _ ( ))

( ,on( _ ( )))

State Stable A direction gets P A downOn x A T gets L x gets R A

exist B get R A

∧ ==∧ ∀ < > ∉→

(3)

Where: _ ( )gets P O is to get the force that O suffered (gravity excluded); _ ( )gets L O is to get O’s location;

_ ( )gets R O is to get the action zone of the force that O suffered; ( )idirection F is to get the direction of force Fi.

Figure 5. Inference rule samples for 3D-L2, 3D-F1 (3) Motion State Change Rule 4D-M1: If there is a State-Alter to smart object A

between time T1 and T2 (from rest to horizontal motion), then there will be an object B in the inverse direction of A’s movement (illustrated by left of Figure 6).

1 2

2

( _ ( , ) ) ( _ ( , ) )( , ( , _ ( , )))

gets M A T rest gets M A T motionexist B after A gets D A T

== ∧ ==→

(4)

Where: _ ( , )gets M O T and _ ( , )gets D O T are separately used to get the motion state and direction of O at T.

Figure 6. Inference rule samples for 4D-M1, 4D-SR

(4) Senders and Receivers Rule 4D-SR: To smart objects A and B, B is a stable

light source and A can sense the light from B. If there is a State-Alter of A’s light sense attribute from T1 to T2 (from bright to dark, or dark to bright), then there is an object C between A and B (illustrated by right of Figure 6).

1 2_ ( ) ( _ ( , )! _ ( , ))( , ( , ))

State Stable B gets V A T gets V A Texist C between A B

∧ =→ (5) Where: _ ( , )gets V O T is to get O’s light sense attribute.

4.3. Hidden Object Attributes Registration

By using inference rules, the hidden objects are detected. However, in order to list them in a relative search result, we have to identify what the objects are. One common way to estimate an unknown object’s identity is based on its attributes. Review the inference rules, we can derive more information about the hidden objects from the contexts, which are listed in Table 3. All


the derived information will be used to register the hidden objects, which can be used to assist the search work (this will be described in next section).

Table 3. Information Derived from Inference Rules

Rule ID Derived Hidden Objects Info Rule 3D-L1 Location, Height Rule 3D-L2 Location Rule 3D-F1 Location, Weight, Shape, Size

Rule 4D-M1,4D-SR Location, Movable

5. Query Engine and Ranking Mechanism

5.1. Multiple Search Modes

Having obtained the information from both smart and hidden objects, the next step is to think over how to display them by human’s input. In order to provide more friendly and intelligent human-centric service, we developed five ways to search a target, shown in Table 4.

Table 4. List of Search Modes

Mode Target Remarks Name/C. All Search of a specific object. Location All List all placed on the same place. Category Smart List all belong to the same class. Attribute Matching

Hidden Objects

Search and ranking the hidden objects by attribute matching.

Associated Relation

Hidden Objects

Search the hidden objects by the associated relation.

The first three modes are basic search methods, which provide more precisely and direct search results that fit to the human’s inputs; while the last two modes provide indirect search ways for the hidden objects. The following sections will discuss these modes in detail. A search interface of our system is given in Figure 7.

Figure 7. Home-Explorer Search Interface

5.2. Basic Search Modes

Basic search modes involve the first three modes listed in Table 4, of which the first two ones can search both smart and hidden objects; while the third one is designed only for smart objects.

Search by Name and Comment: The most common case in our daily life is to find an individual object, such

as a bicycle key. In Home-Explorer, when an object is registered, whether a smart one or hidden one, the name and comment information of it will also be provided, which is helpful for this kind of search. Table 5 lists some typical registration objects, of which when human inputs “artificial intelligence”, the first two objects will be listed; while when the keyword is “present”, the second and the third one can be selected; besides, when a “movable” object becomes the target, the last object “Hidden08” will be displayed.

Table 5. Name and Comment Examples

Name Type Comment Artificial

Intelligence Smart Object

A famous book about intelligent agents.

Artificial Intelligence

Smart Object

A science fiction movie, a present from Bob.

Bicycle’s Key

Smart Object

The key for my bicycle, a birthday present from Jerry.

Hidden08 Hidden Object

An object detected on the floor, which is movable.

Search by Location: Sometimes people would like to know what is placed on a specific place, such as on a study table. As in our system the localization of objects is based on the skeleton objects, if a skeleton object’s name is offered, we can list all the objects placed on it, which will be helpful for people to make some decision or eliminate some dangerous factors (e.g. a cup of water and some books and files are placed on the same desk, which may cause the wet danger).

Search by Category: In some cases people may not be in need of finding an individual object, oppositely, he may want to find a series of objects that belong to the same category (e.g. Bob wants to choose an interesting book to read from all his books). Since the registration of each smart object also involves the category information, we can fulfill this task by querying it. However, this search method can only be used for smart objects, for the category information of the hidden ones is unknown. In section 5.3, another way to search the hidden objects by category will be given.

5.3. Attribute Matching and Ranking

In some circumstances, our search target might be a hidden object. For instance, when we want to find the bedroom key and unfortunately it happens to be a hidden object. In such case, when we input “bedroom key” and use the basic search methods, it is impossible to find it out. Therefore, some new ways should be considered to resolve this problem. Go on with the “bedroom key” problem, as it’s not easy to search it in smart objects, we may change our search focus to the hidden ones. Instead of using “bedroom key”, this time we’d like to use “key” (a category name) as the search keyword. Concretely speaking, in this way we expect to list all the “key” likely


hidden objects, among which we hope to find out our target. Following we’ll explain how this can be realized.

In section 4.1, we have talked about category base and abstract object class, the former one is to define the specific standard for each object category, which can be in reality regarded as a knowledge base about category; while the latter one is used to register the hidden objects with the derived information. These two concepts offer us a novel way to recognize the hidden objects. Just look at Figure 8, it contains two entities: The key category and a hidden object(Hidden06), of which the definition of key category consists of 5 specific attributes, while the hidden object has 4 registered attributes. We can also see that Hidden06’s attributes all drop in the corresponding intervals or have the same value in comparison with the key category, through which we can assign a degree of belief (or match rate) to Hidden06 that its probability to be a key is 0.8(4/5). In terms of this view, all the detected hidden objects can be assigned a match rate to a given category, from which we can select all those whose match rate is above 0. As there may be several selected objects, a ranking algorithm named RANK based on the match rate is needed. In RANK, the hidden object with higher degree of belief will be listed ahead than the lower ones. Figure 9 illustrates the whole process of attribute matching method.

Figure 8. Example for Attribute Matching

Figure 9. Search based on Attribute Matching

Above all, based on the category base and abstract object class, an effective hidden object search method is proposed. Different from the basic search modes, this kind of search is under uncertainty, in which we assign degree of belief to each hidden object. That’s to say, this method only supplies us the possibility of each hidden object to be the search target, while the real truth should finally be derived from human’s decision with the results.

5.4. Search based on Associated Relation

Last section discussed about a hidden object search mechanism based on attribute matching, in this section, we will present another way to tackle this problem. It’s known that we have abundant information about the smart objects; however, up to present, they haven’t been used sufficiently. Can the information of smart objects help us to recognize the hidden objects? This section’s work is based on this consideration, in which we’ll study the relationship between objects, rather than merely consider the attributes of individual objects.

The objects are not isolated existent in the world. Contrarily, there are various relations among them. Besides the physical relation such as location that we talked, there are still natural or social relations among them, such as the associated relation defined below.

Definition 5(Associated Relation between Objects): A specific relation exists among objects. According to their natural qualities and functions, or the daily habits or intention from human, these objects always emerge near each other, which we called the “associated relation”.

A great number of such examples exist in our daily life, such as: Telephone-Telephone Book, Tape-Tape recorder, Book-Pen. What’s more, associated relation also exists among the objects with the same category (e.g. piles of books placed together on the desk).

Taking account of our system, since the hidden objects are detected by the nearby smart objects, according to Definition 5, there may be associated relation between them. As the identity of smart object is clearly known, we can make an estimate of what the hidden object might be. What should be noted is that this kind of estimate is of low possibility (lower than attribute matching method); however, as its simplicity and efficiency, we can still treat it as an effective way to solve the hidden object search problem.

6. Implementation and Experiment

Figure 10 illustrates the implementation of our system, involves the Sensor Network Server, the Web Server, Central Database, multiple embedded sensors and human clients. Sensor Network Server mainly takes charge of the infrastructure development with multiple sensors, whereas the web server is mainly responsible for web related work and database maintenance (for the MySQL

Figure 10. System Implementation


database is installed in it). Besides, as these two servers use different programming languages, we use socket programming to transmit data between them.

We select two experiment situations shown in Figure 11 and Figure 13. Both of them relate to the use of inference rules, however, the first one mainly concerns the hidden object search by associated relation, while the second one emphasizes more on attribute matching.

6.1. Experiment-1

In the first situation, at first there are two objects placed on Desk (a skeleton object): Book-1 and Book-2(shown in left of Figure 11). Book-2 is a smart object, to which an ultrasonic 3D location sensor (U3D) and a MOTE sensor are attached. Though there is still an U3D sensor attached to Book-1, its signal is blocked by Book-2 and becomes a hidden object. At one moment, another book named Book-3 is laid against Book-2(shown in right of Figure 11), which has no sensor attached (maybe a new bought one). Then one problem occurs: when human wants to search an interesting book to read, how to list all these 3 books? It’s sure that Book-2 is easy to be detected for it’s a smart object. Therefore, how can the other two books be inferred based on Book-2’s context.

The solution is as follows: Firstly, by analyzing the sensor data from U3D and comparing with all the skeleton objects using Decision Rule-1, we can infer that Book-2 is on the desk. Through the acceleration sensor data from MOTE, we conclude that Book-2 is flatly deployed (how to use acceleration sensor to determine slope angle can be found in [5]). However, by comparing the height data of Book-2 and Desk, it can be known that Book-2 is placed higher than desktop. By these contexts we infer that there is no touch point between Book-2 and Desk. So far, all conditions of inference rule 3D-L1 are satisfied, thus Book-1 is then detected. When Book-3 is laid against Book-2, the light sensor of MOTE can sense the light change for the shade of Book-3, then inference rule 4D-SR will tell us the existence of Book-3.

Figure 11. Experiment-1 Situation

By computing and reasoning the physical context from Book-2, we detect the other two hidden objects. However, besides the information of existence and location, we don’t know more about these two objects, thus it’s impossible to use attribute matching to fulfill the search task. Now let’s consider about the other way: associated relation. Since Book-2 belongs to the Book

category, the associated relation tells us that the other two objects detected by Book-2 have probability to be the same category. Therefore, under this search mode, all the three books will be listed for human’s selection. The experiment data is given in Figure 12.

Figure 12. Experiment-1 result, where “Height

Difference” and “Light Value Change” can be used to derive the other two books.

6.2. Experiment-2

In the second situation, there are three objects laid on the desk: Mobile Phone, Room Key and Ball Pen. Unluckily, all of them are hidden objects. Our target is to display them correctly according to human’s desire (e.g., when the input is “key”, then Room Key is displayed, rather than the Cell Phone or Ball Pen). Different from Experiment-1, there are no smart objects involved, so the associated relation can’t be used and we can only focus on the attribute matching method. Here, a pressure sensor named Kinotex is used for this task. From left of Figure 14 we can see that Kinotex is formed by sixty ( 10 6rows lines× ) sensor cells (size: 2.4 3.2cm cm× ). When an object A is placed on it, the corresponding cells under A will generate sensory value for the pressure, by which we can get some valuable attributes of A, such as shape and size. However, as Kinotex is slightly coarse, some light objects can’t be detected by it. Any way, this defect can be eliminated by employing a pressure sensor with higher performances. Here, on purpose of research, we can still fulfill the task by adding a little pressure upon the light objects. Right of Figure 14 shows the output of Kinotex. By analyzing the value distribution of the sixty cells, we can use inference rule 3D-F1 to infer the existence of those three objects. Besides, additional information listed in Table 6 can also be derived.

Figure 13. Example-2 Situation

By Table 6’s information, we can fulfill the search task using attribute matching method. In case the human


input is “Pen”, then we’ll compare the three hidden objects with the “Pen” category standard from the category base (given in Table 7). The matching result in the form of match rate is as follows: Hidden01 (0.67), Hidden02 (0.33), Hidden03 (0.0). Thus by the RANK mechanism, Hidden01 and Hidden02’ll be listed firstly and secondly in the search result.

Figure 14. KINOTEX and Experiment-2 Output

Table 6. Inferred Attribute Information

Name(Location) Attribute Hidden01

(Right of Kinotex) Shape: Strip Size: 12.8 (4 ) 2.4cm cells cm×

Hidden02 (Middle of Kinotex)

Shape: Strip Size: 6.4 (2 ) 2.4cm cells cm×

Hidden03 (Left of Kinotex)

Shape: Rectangle Size: 9.6 (3 ) 4.8 (2 )cm cells cm cells×

Table 7. Pen Category Standard

Shape Weight Size Strip Light (11 ~ 14) (0.5 ~ 2.5)cm cm×

6.3. Experiment Discussion

In the second experiment, we use pressure sensor to obtain the shape and size attributes from objects. In general, there are other ways to handle this problem, such as the image recognition using camera [7] and the optical method based on structured lights [8]. However, though the above two methods can generate more precise results, there are still many problems exist, such as how to deploy camera in the smart space, how to posit the light source to get accurate object projection. Moreover, the usage of them is much difficult. As to our search system, the measurement result needn’t to be much precise and the deployment of sensor should be simple, hence the method based on pressure sensor is a much easier, efficient and effective way for object attribute acquisition.

7. Conclusion This paper proposed an object-oriented indoor search

system named Home-Explorer, which can be used to seek and localize artifacts in the smart sensor rich space. Different from others’ work, our system tackles not only smart objects but also hidden objects. For the smart objects, we use the skeleton strategy to position them. Besides, the ample physical contexts from smart objects

are used to infer the existence of hidden objects. A series of inference rules are built to perform the context reasoning task. What’s more, during the inference process, a few attributes about the hidden object can also be derived, which are used to register the hidden objects. For the sake of designing a user-friendly search system, we provide five search modes, which include three basic search modes and two special ways for hidden object search. As to hidden object search, attribute matching and associated relation method are based on different views of human cognition about the physical objects. At last, two experiments are presented to evaluate our ideas.

However, as the relationship contexts between objects are complex and changeful, currently Home-Explorer can deal with only a few situations for hidden object search. Moreover, when a hidden object is repeated detected during a period of time, how to make sure it’s the same object while not several different ones is not considered. As future works, we’d like to study more about common sense and human cognition, so as to enrich our inference rules base and acquire more useful physical context from sensor network. The relationship between physical event and time are also need to be seriously studied.

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