Yafeng Yin

Managing RFID Data: Challenges, Opportunities and Solutions

The advances of Radio-Frequency Identification (RFID) technology have significantly enhanced the capability of capturing data from pervasive space. It becomes a great challenge in the information era to effectively understand human behavior, mobility and activity through the perceived RFID data. Focusing on RFID data management, this article provides an overview of current challenges, emerging opportunities and recent progresses in RFID. In particular, this article has described and analyzed the research work on three aspects: algorithm, protocol and performance evaluation. We investigate the research progress in RFID with anti-collision algorithms, authentication and privacy protection protocols, localization and activity sensing, as well as performance tuning in realistic settings. We emphasize the basic principles of RFID data management to understand the state-of-the-art and to address directions of future research in RFID.

CrowdSensing: A crowd-sourcing based indoor navigation using RFID-based delay tolerant network

Abstract As a supporting technology for most pervasive applications, indoor localization and navigation has attracted extensive attention in recent years. Conventional solutions mainly leverage techniques like WiFi and cellular network to effectively locate the user for indoor localization and navigation. In this paper, we investigate the problem of indoor navigation by using the RFID-based delay tolerant network. Different from the previous work, we aim to efficiently locate and navigate to a specified mobile user who is continuously moving within the indoor environment. As the low-cost RFID tags are widely deployed inside the indoor environment and acting as landmarks, the mobile users can actively interrogate the surrounding tags with devices like smart phones and leave messages or traces to the tags. These messages or traces can be carried and forwarded to more tags by other mobile users. In this way, the RFID-based infrastructure forms a delay tolerant network. By using the crowd-sourcing technology in RFID-based delay tolerant network, we respectively propose a framework, namely CrowdSensing, to schedule the tasks and manage the resources in the network. We further propose a navigation algorithm to locate and navigate to the moving target. We verify the performance of proposed framework and navigation algorithm on mobility model built on real-world human traceset. Experiment results show that our solution can efficiently reduce the average searching time for indoor navigation.

CamK: A camera-based keyboard for small mobile devices

Due to the smaller size of mobile devices, on-screen keyboards become inefficient for text entry. In this paper, we present CamK, a camera-based text-entry method, which uses an arbitrary panel (e.g., a piece of paper) with a keyboard layout to input text into small devices. CamK captures the images during the typing process and uses the image processing technique to recognize the typing behavior. The principle of CamK is to extract the keys, track the users fingertips, detect and localize the keystroke. To achieve high accuracy of keystroke localization and low false positive rate of keystroke detection, CamK introduces the initial training and online calibration. Additionally, CamK optimizes computation-intensive modules to reduce the time latency. We implement CamK on a mobile device running Android. Our experiment results show that CamK can achieve above 95% accuracy of keystroke localization, with only 4.8% false positive keystrokes. When compared to on-screen keyboards, CamK can achieve 1.25X typing speedup for regular text input and 2.5X for random character input.

Focus and Shoot: Exploring Auto-Focus in RFID Tag Identification Towards a Specified Area

With the rapid proliferation of RFID technologies, RFID has been introduced into applications such as inventory and sampling inspection. Conventionally, in RFID systems, the reader usually identifies all the RFID tags in the interrogation region with the maximum power. However, some applications may only need to identify the tags in a specified area, which is usually smaller than the readers default interrogation region. An example could be identifying the tags in a box, while ignoring the tags out of the box. In this paper, we respectively present two solutions to identify the tags in the specified area. The principle of the solutions can be compared to the picture-taking process of an auto-focus camera, which firstly focuses on the target automatically and then takes the picture. Similarly, our solutions first focus on the specified area and then shoot the tags. The design of the two solutions is based on the extensive empirical study on RFID tags. Realistic experiment results show that our solutions can reduce the execution time by 44 percent compared to the baseline solution, which identifies the tags with maximum power. Furthermore, we improve the proposed solutions to make them work well in more complex environments.

Focus and Shoot: Efficient Identification Over RFID Tags in the Specified Area

In RFID systems, the reader usually identifies all the RFID tags in the interrogation region with the maximum power. However, some applications may only need to identify the tags in a specified area, which is usually smaller than the reader’s default interrogation region. In this paper, we respectively present two solutions to identify the tags in the specified area. The principle of the solutions can be compared to the picture-taking process of a camera. It first focuses on the specified area and then shoots the tags. The design of the two solutions is based on the extensive empirical study on RFID tags. Realistic experiment results show that our solutions can reduce the execution time by \(46\,\%\) compared to the baseline solution.

An efficient indoor navigation scheme using RFID-based delay tolerant network

As a supporting technology for most pervasive applications, indoor localization and navigation has attracted extensive attention in recent years. Conventional solutions mainly leverage techniques like WiFi, cellular network etc. to effectively locate the user for indoor localization and navigation. In this paper, we investigate into the problem of indoor navigation by using the RFID-based delay tolerant network. Being different from the previous work, we aim to efficiently locate and navigate to a specified mobile user who is continuously moving within the indoor environment. We respectively propose a framework to schedule the tasks and manage the resources in the network and a navigation algorithm to locate and navigate to the moving target. Experiment results show that our solution can efficiently reduce the average searching time for indoor navigation.

A Context Aware Energy-Saving Scheme for Smart Camera Phones Based on Activity Sensing

Nowadays more and more users tend to take photos with their smart phones. However, energy-saving continues to be a thorny problem for smart camera phones, since smart phone photographing is a very power hungry function. In this paper, we propose a context aware energy-saving scheme for smart camera phones, by accurately sensing the users activities in the photographing process. Our solution is based on the observation that during the process of photographing, most of the energy are wasted in the preparations before the shooting. By leveraging the embedded sensors like the accelerometer and gyroscope, our solution is able to extract representative features to perceive the users current activities including body movement, arm movement and wrist movement. Furthermore, by maintaining an activity state machine, our solution can accurately determine the users current activity states and make the corresponding energy saving strategies. Experiment results show that, our solution is able to perceive the users activities with an average accuracy of 95.5% and reduce the overall energy consumption by 46.5% for smart camera phones compared to that without energy-saving scheme.

Efficient localization based on imprecise anchors in RFID system

With the rapid proliferation of RFID-based applications, RFID tags have been deployed into pervasive spaces in increasingly large numbers, e.g., the shelves of super markets are filled with tag-labeled items. Conventional localization schemes usually leverage precise anchor nodes to help compute the position of objects. However, it is usually difficult to find or deploy enough anchor nodes for accurate localization. In this paper, we propose solutions to locate the mobile users based on imprecise anchors in RFID systems. A large number of tags with approximate locations are used as anchor nodes to compute the users locations. We thus present a time-efficient localization scheme to continuously tracking the mobile users. Experimental results indicate that our solutions can accurately locate the mobile users in a real-time approach. The improved methods accuracy is more than 30% better than the base solution.

Check out the Rules: Towards Time-Efficient Rule Checking over RFID Tags

With the rapid proliferation of RFID technologies, RFID has been introduced to the applications like safety inspection and warehouse management. Conventionally a number of deployment rules are specified for these applications. This paper studies a practically important problem of rule checking over RFID tags, i.e., checking whether the specified rules are satisfied according to the RFID tags within the monitoring area. This rule checking function may need to be executed frequently over a large number of tags and therefore should be made efficient in terms of execution time. Aiming to achieve time efficiency, we respectively propose two protocols, CRCP and ECRCP. CRCP works based on collision detection, while ECRCP combines the collision detection and the logical features of the rules. Simulation results indicate that our protocols achieve much better performance than other solutions in terms of time efficiency.

I Am the UAV: A Wearable Approach for Manipulation of Unmanned Aerial Vehicle

Nowadays, Unmanned Aerial Vehicles(UAVs) have been widely applied in our life. However, the existing approach of interacting with UAVs, i.e., using a remote controller with control sticks, is not a natural and intuitive way. In this paper, we present a novel approach for users to interact with personal UAVs using wearable devices. The basic idea of our approach is to manipulate UAVs based on human activity sensing, including motion recognition and pedestrian dead- reckoning. We have implemented the proposed approach on a DJI drone, and evaluated its performance in the real- world environment. Realistic experiment results show that our solution can replace the remote controller to manipulate the UAV.