Yunhao Liu

LANDMARC: indoor location sensing using active RFID

Growing convergence among mobile computing devices and embedded technology sparks the development and deployment of “context-aware” applications, where location is the most essential context. In this paper we present LANDMARC, a location sensing prototype system that uses Radio Frequency Identification (RFID) technology for locating objects inside buildings. The major advantage of LANDMARC is that it improves the overall accuracy of locating objects by utilizing the concept of reference tags. Based on experimental analysis, we demonstrate that active RFID is a viable and cost-effective candidate for indoor location sensing. Although RFID is not designed for indoor location sensing, we point out three major features that should be added to make RFID technologies competitive in this new and growing market.

Big Data: A Survey

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

Locating in fingerprint space: wireless indoor localization with little human intervention

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1600m2, and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey.

AnySee: Peer-to-Peer Live Streaming

Efficient and scalable live-streaming overlay construction has become a hot topic recently. In order to improve the performance metrics, such as startup delay, source-to-end delay, and playback continuity, most previous studies focused on intra-overlay optimization. Such approaches have drawbacks including low resource utilization, high startup and source-to-end delay, and unreasonable resource assignment in global P2P networks. Anysee is a peer-to-peer live streaming system and adopts an inter-overlay optimization scheme, in which resources can join multiple overlays, so as to (1) improve global resource utilization and distribute traffic to all physical links evenly; (2) assign resources based on their locality and delay; (3) guarantee streaming service quality by using the nearest peers, even when such peers might belong to different overlays; and (4) balance the load among the group members. We compare the performance of our design with existing approaches based on comprehensive trace driven simulations. Results show that AnySee outperforms previous schemes in resource utilization and the QoS of streaming services. AnySee has been implemented as an Internet based live streaming system, and was successfully released in the summer of 2004 in CERNET of China. Over 60,000 users enjoy massive entertainment programs, including TV programs, movies, and academic conferences. Statistics prove that this design is scalable and robust, and we believe that the wide deployment of AnySee will soon benefit many more Internet users.

Underground coal mine monitoring with wireless sensor networks

Environment monitoring in coal mines is an important application of wireless sensor networks (WSNs) that has commercial potential. We discuss the design of a Structure-Aware Self-Adaptive WSN system, SASA. By regulating the mesh sensor network deployment and formulating a collaborative mechanism based on a regular beacon strategy, SASA is able to rapidly detect structure variations caused by underground collapses. We further develop a sound and robust mechanism for efficiently handling queries under instable circumstances. A prototype is deployed with 27 mica2 motes in a real coal mine. We present our implementation experiences as well as the experimental results. To better evaluate the scalability and reliability of SASA, we also conduct a large-scale trace-driven simulation based on real data collected from the experiments.

Underground structure monitoring with wireless sensor networks

Environment monitoring in coal mines is an important application of wireless sensor networks (WSNs) that has commercial potential. We discuss the design of a structure-aware self-adaptive WSN system, SASA. By regulating the mesh sensor network deployment and formulating a collaborative mechanism based on a regular beacon strategy, SASA is able to rapidly detect structure variations caused by underground collapses. A prototype is deployed with 27 Mica2 motes. We present our implementation experiences as well as the experimental results. To better evaluate the scalability and reliability of SASA, we also conduct a large-scale trace-driven simulation based on real data collected from the experiments.

A Survey of Green Mobile Networks: Opportunities and Challenges

The explosive development of Information and Communication Technology (ICT) has significantly enlarged both the energy demands and the CO2 emissions, and consequently contributes to make the energy crisis and global warming problems worse. However, as the main force of the ICT field, the mobile networks, are currently focusing on the capacity, variety and stability of the communication services, without paying too much severe concerns on the energy efficiency. The escalating energy costs and environmental concerns have already created an urgent need for more energy-efficient “green” wireless communications. In this paper, we survey and discuss various remarkable techniques toward green mobile networks to date, mainly targeting mobile cellular networks. We also summarize the current research projects related to green mobile networks, along with the taxonomy of energy-efficiency metrics. We finally discuss and elaborate future research opportunities and design challenges for green mobile networks.

Canopy closure estimates with GreenOrbs: sustainable sensing in the forest

Motivated by the needs of precise forest inventory and real-time surveillance for ecosystem management, in this paper we present GreenOrbs [2], a wireless sensor network system and its application for canopy closure estimates. Both the hardware and software designs of GreenOrbs are tailored for sensing in wild environments without human supervision, including a firm weatherproof enclosure of sensor motes and a light-weight mechanism for node state monitoring and data collection. By incorporating a pre-deployment training process as well as a distributed calibration method, the estimates of canopy closure stay accurate and consistent against uncertain sensory data and dynamic environments. We have implemented a prototype system of GreenOrbs and carried out multiple rounds of deployments. The evaluation results demonstrate that GreenOrbs outperforms the conventional approaches for canopy closure estimates. Some early experiences are reported in this paper.

From RSSI to CSI: Indoor localization via channel response

The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

Tagoram: real-time tracking of mobile RFID tags to high precision using COTS devices

In many applications, we have to identify an object and then locate the object to within high precision (centimeter- or millimeter-level). Legacy systems that can provide such accuracy are either expensive or suffering from performance degradation resulting from various impacts, e.g., occlusion for computer vision based approaches. In this work, we present an RFID-based system, Tagoram, for object localization and tracking using COTS RFID tags and readers. Tracking mobile RFID tags in real time has been a daunting task, especially challenging for achieving high precision. Our system achieves these three goals by leveraging the phase value of the backscattered signal, provided by the COTS RFID readers, to estimate the location of the object. In Tagoram, we exploit the tags mobility to build a virtual antenna array by using readings from a few physical antennas over a time window. To illustrate the basic idea of our system, we firstly focus on a simple scenario where the tag is moving along a fixed track known to the system. We propose Differential Augmented Hologram (DAH) which will facilitate the instant tracking of the mobile RFID tag to a high precision. We then devise a comprehensive solution to accurately recover the tags moving trajectories and its locations, relaxing the assumption of knowing tags track function in advance. We have implemented the Tagoram system using COTS RFID tags and readers. The system has been tested extensively in the lab environment and used for more than a year in real airline applications. For lab environment, we can track the mobile tags in real time with a millimeter accuracy to a median of 5mm and 7.29mm using linear and circular track respectively. In our year- long large scale baggage sortation systems deployed in two airports, our results from real deployments show that Tagoram can achieve a centimeter-level accuracy to a median of 6.35cm in these real deployments.