On Self-Content Localization
|關鍵字:||自我修正;訊號地圖;慣性元件;位置感知服務;感測網路;擴增實境;self calibration/adaptation;radio map;inertial sensors;location-based service;wireless sensor network;augmented reality|
的定位方式。所謂self-content，有幾個層次：(i)具有自我修正radio map 之能力；
的sensors，無需外在輔助基地台(如WiFi Access Points)，即可由手機自行定位。我
以 WiFi 基地台相互監測訊號強度，建立具有自我修正能力的訊號地圖：
行新一代的WiFi 與Zigbee 所允許的功能，據此我們將開發數項具有自
self-content localization 議題上，可以成為國際一流的團隊。|
Location-based services (LBSs) have been recognized as a killer application in the mobile computing and wireless communications fields. Therefore, the demand for better location-tracking and localization techniques also increases. In outdoor environments, GPS is the widest used localization technique so far. In indoor environments, pattern-matching localization techniques have been intensively discussed and most of the works have adopted WiFi networks as the infrastructure to serve as transmitters. In pattern-matching localization, we need a training phase to collect the RSS (receive signal strength) of each nearby WiFi access point (AP) at each training location. These RSSs are collected in a database called radio map. Then, during the on-line localization phase, a user device also collects nearby APs' RSSs and sends this characteristic vector to a location server. The location server then compares this characteristic vector against its pre-trained radio map. For example, the location with the most similar characteristic is retrieved as the device's current location. However, there are several bottlenecks in current pattern-matching techniques: (1) The radio maps usually change as the environments change. If we do not frequently calibrate the radio maps, the localization accuracy will be significantly impacted. However, the change of radio maps is always time-variant. (2) The collection of radio maps, which usually involves a large volume of training data, is very labor-intensive. In this proposal, we are going to develop a new concept, called self-content localization. There are several levels of challenges for a localization system to be self-content: (i) How can a localization system calibrate its radio maps automatically? (ii) How can we reduce, or even totally remove, the efforts in collecting radio maps in the training phase? (iii) Is it possible that a user device can determine its own location without using any auxiliary signal transmitted from any infrastructure network (such as WiFi network)? For example, a big challenge is: Can a smart phone use its own sensors and camera to calculate its location without relying on any external signal source. The goal of this project is to enhance the self-calibration capability of pattern-matching techniques and even gradually relieve the dependence on any infrastructure in typical pattern-matching techniques when conducting localization. This is what we mean by "self-content" localization. Based on these goals, we plan to investigate in three issues: Self-adaptive radio maps: We will develop enabling techniques to allow APs to detect each other's RSS and use such information as indices to self-calibrate radio maps. First, we will switch an AP to the receive mode from time to time. Under the receive mode, an AP will be able to overhear nearby APs' RSSs. We call this inter-beacon measurement. An important observation is: If we conducted such inter-beacon measurement when collecting training data during the training phase, these measurements can be used as important indices of the environment factors when collecting our training data. Then, at the on-line localization phase, we can also ask APs to collect current inter-beacon measurements and use them as indices to select a "proper" radio map for comparison. As far as we know, both WiFi and ZigBee are able to support such capability. Based on this novel idea, we will develop several pattern-matching localization methods with self-training, self-adaptive, and self-calibrating capabilities. Semi-automatic or fully automatic radio maps collection: We shall develop two ways to collect radio maps to reduce the collection overheads. The semi-automatic solution counts on community users or volunteers to contribute training data. However, volunteers are not always fully trustable. Therefore, we need a credit accounting mechanism to evaluate a volunteer's accountability. Also, the possibility of manual errors and even attackers needs to be taken care of. The fully automatic solution relies on robots to collect radio maps in a daily manner. We will use iRobot to collect radio maps. Our past work has shown how to program an iRobot. Note that an iRobot also needs to localize itself when going out and collecting training data. A novel RFID-based landmark solution will be proposed. Basically, iRobots will follow RFID tags sticking on the ground to trace itself. Then on its way roaming around, it collects training data. The fully automatic solution will completely eliminate manual data collection. Self-content localization without auxiliary signals: The previous solutions all rely on some sort of training data. In this part, we will develop a self-content concept for localization. We plan to use the camera and sensors on a smart phone to realize the goal. The basic idea is to use the augmented reality (AR) concept to identify objects captured by a smart phone. In the meantime, the angle relative to the phone is calculated by the e-compass of the smart phone. By identifying at least three objects and calculating their angles relative to the smart phone, we will show how to compute the user's current location. Note that the above process does not rely on any auxiliary signal transmitted by external infrastructure. Therefore, this process is fully self-content. In addition, we will further extend our model to mobile cases, where the user may move around while identifying objects. The movement of the user will be computed by other sensors, such as accelerometer. Then, we will show how to conduct mobile self-content localization. All these ideas will be prototyped on existing smart phones. The above three goals are all very challenging. In this project, we plan to use three years to achieve these goals, through designs as well as prototyping systems. We will design location systems that can self-calibrate and self-adapt themselves to environment changes by semi-automatically or fully automatically collect training data. In addition, we will further develop self-content and even mobile self-content solutions via existing smart phones by utilizing the camera and inertial sensors on them. Such self-content localization solutions greatly improve over existing systems because no auxiliary signals and no infrastructure are needed. We believe that through the success of these developments, our team will become worldwide well-known team in the LBS society.