Gary Shueng Han Chan

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of Global Positioning System (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

Fusing noisy fingerprints with distance bounds for indoor localization

Fusing fingerprints with mutual distance information potentially improves indoor localization accuracy. Such distance information may be spatial (e.g., via inter-node measurement) or temporal (e.g., via dead reckoning). Previous approaches on distance fusion often require exact distance measurement, assume the knowledge of distance distribution, or apply narrowly to some specific sensing technology or scenario. Due to random signal fluctuation, wireless fingerprints are inherently noisy and distance cannot be exactly measured. We hence propose Wi-Dist, a highly accurate indoor localization framework fusing noisy fingerprints with uncertain mutual distances (given by their bounds). Wi-Dist is a generic framework applicable to a wide range of sensors (peer-assisted, INS, etc.) and wireless fingerprints (Wi-Fi, RFID, CSI, etc.). It achieves low errors by a convex-optimization formulation which jointly considers distance bounds and only the first two moments of measured fingerprint signals. We implement Wi-Dist, and conduct extensive simulation and experimental studies based on Wi-Fi in our international airport and university campus. Our results show that Wi-Dist achieves significantly better accuracy than other state-of-the-art schemes (often by more than 40%).

Detecting malicious nodes in peer-to-peer streaming by peer-based monitoring

Current peer-to-peer (P2P) streaming systems often assume that nodes cooperate to upload and download data. However, in the open environment of the Internet, this is not necessarily true and there exist malicious nodes in the system. In this article, we study malicious actions of nodes that can be detected through peer-based monitoring. We require each node to monitor the data received and to periodically send monitoring messages about its neighbors to some trustworthy nodes. To efficiently store and search messages among multiple trustworthy nodes, we organize trustworthy nodes into a threaded binary tree. Trustworthy nodes also dynamically redistribute monitoring messages among themselves to achieve load balancing. Our simulation results show that this scheme can efficiently detect malicious nodes with high accuracy, and that the dynamic redistribution method can achieve good load balancing among trustworthy nodes.

Sectjunction: Wi-Fi indoor localization based on junction of signal sectors

In Wi-Fi fingerprint localization, a target sends its measured Received Signal Strength Indicator (RSSI) of access points (APs) to a server for its position estimation. Traditionally, the server estimates the target position by matching the RSSI with the fingerprints stored in database. Due to signal measurement uncertainty, this matching process often leads to a geographically dispersed set of reference points, resulting in unsatisfactory estimation accuracy. We propose a novel, efficient and highly accurate localization scheme termed Sectjunction which does not lead to a dispersed set of neighbors. For each selected AP, Sectjunction sectorizes its coverage area according to discrete signal levels, hence achieving robustness against measurement uncertainty. Based on the received AP RSSI, the target can then be mapped to the sector where it is likely to be. To further enhance its computational efficiency, Sectjunction partitions the site into multiple area clusters to narrow the search space. Through convex optimization, the target is localized based on the cluster and the junction of the sectors it is within. We have implemented Sectjunction, and our extensive experiments show that it significantly outperforms recent schemes with much lower estimation error.

Contour-based Trilateration for Indoor Fingerprinting Localization

Trilateration has been widely and successfully employed to locate outdoor mobile devices due to its accuracy. However, it cannot be directly applied for indoor localization due to issues such as non-line-of-sight measurement and multipath fading. Though fingerprinting overcomes these issues, its accuracy is often hampered by signal noise and the choice of similarity metric between signal vectors. We propose INTRI, a novel, simple and effective indoor localization technique combining the strengths of trilateration and fingerprinting. For a signal level received from an access point (AP) by the target, INTRI first forms a contour consisting of all the reference points (RPs) of the same signal level for that AP, taking into account the signal noise. The target is hence at the juncture of all the contours. With an optimization formulation following the spirit of trilateration, it then finds the target location by minimizing the distance between the position and all the contours. INTRI further uses an online algorithm based on signal correlation to efficiently calibrate heterogeneous mobile devices to achieve higher accuracy. We have implemented INTRI, and our extensive simulation and experiments in an international airport, a shopping mall and our university campus show that it outperforms recent schemes with much lower location error (often by more than 20%).

Coding and replication co-design for interactive multiview video streaming

Multiview video refers to the simultaneous capturing of multiple video views with an array of closely spaced cameras. In an interactive multiview video streaming (IMVS) system, a client can play back the content in time in a single view, and may observe a scene of interest by switching to different viewpoints. Users independently choose their own view navigation paths through the high-dimensional multiview data. Distributed servers are deployed to collaboratively replicate video content in order to support user scalability. Such a system typically presents challenges in both coding and content replication. In coding, the multiview video must be encoded in order to support efficient view-switching and distributed replication. In content replication, it is important to decide which data blocks to store at each server to facilitate view-switches at any time. In this paper, we co-design a coding structure and a distributed content replication strategy. First, we propose a coding structure based on redundant P-frames and distributed source coding (DSC) frames to achieve efficiency in coding, view switches and content replication. We then propose a heuristic-based distributed and cooperative replication strategy to take advantage of the correlation between the multiple views for resource-effective content delivery. Simulation results show that our coding and replication co-design is cost-effective in supporting IMVS services.

Calibration-free fusion of step counter and wireless fingerprints for indoor localization

In order to improve the accuracy of fingerprint-based localization, one may fuse step counter measurement with location estimation. Previous works on this often require a pre-calibrating the step counter with training sequence or explicit user input, which is inconvenient for practical deployment. Some assume conditional independence on successive sensor readings, which achieves unsatisfactory accuracy in complex and noisy environment. Some other works need a calibration process for RSSI measurement consistency if different devices are used for offline fingerprint collection and online location query. We propose SLAC, a fingerprint positioning framework which simultaneously localizes the target and calibrates the system. SLAC is calibration-free, and works transparently for heterogeneous devices and users. It is based on a novel formulation embedded with a specialized particle filter, where location estimations, wireless signals and user motion are jointly optimized with resultant consistent and correct model parameters. Extensive experimental trials at HKUST campus and Hong Kong International Airport further confirm that SLAC accommodates device heterogeneity, and achieves significantly lower errors compared with other state-of-the-art algorithms.

Optimal Bandwidth Assignment for Multiple-Description-Coded Video

In video streaming over multicast network, user bandwidth requirement is often heterogeneous possibly with orders of magnitude difference (say, from hundreds of kb/s for mobile devices to tens of Mb/s for high-definition TV). Multiple description coding (MDC) can be used to address this bandwidth heterogeneity issue. In MDC, the video source is encoded into multiple independent descriptions. A receiver, depending on its available bandwidth, joins different descriptions to meet their bandwidth requirements. An important but challenging problem for MDC video multicast is how to assign bandwidth to each description in order to maximize overall user satisfaction. In this paper, we investigate this issue by formulating it as an optimization problem, with the objective to maximize user bandwidth experience by taking into account the encoding inefficiency due to MDC. We prove that the optimization problem is NP-hard. However, if the description number is larger than or equal to a certain threshold (e.g., if the minimum and maximum bandwidth requirements are 100 kb/s and 10 Mb/s, respectively, such threshold is seven descriptions), there is an exact and simple solution to achieve maximum user satisfaction, i.e., meeting all the bandwidth requirements. For the case when the description number is smaller, we present an efficient heuristic called simulated annealing for MDC bandwidth assignment (SAMBA) to assign bandwidth to each description given the distribution of user bandwidth requirement. We evaluate our algorithm using simulations. SAMBA achieves virtually the same optimal performance based on exhaustive search. By comparing with other assignment algorithms, SAMBA significantly improves user satisfaction. We also show that, if the coding efficiency decreases with the number of descriptions, there is an optimal description number to achieve maximal user satisfaction.

Unstructured Peer-to-Peer Network Architectures

With the rapid growth of the Internet, peer-to-peer P2P networks have been widely studied and deployed. According to CacheLogic Research, P2P traffic has dominated the Internet traffic in 2006, by accounting for over 72% Internet traffic. In this chapter, we focus on unstructured P2P networks, one key type of P2P networks. We first present several unstructured P2P networks for the file sharing application, and then investigate some advanced issues in the network design. We also study two other important applications, i.e., media streaming and voice over Internet Protocol (VoIP). Finally, we discuss unstructured P2P networks over wireless networks.

Supporting Multiple-Keyword Search in A Hybrid Structured Peer-to-Peer Network

Most existing techniques for keyword search in structured peer-to-peer (P2P) networks only support single-keyword exact-match lookups. In practice, however, users often have fuzzy information for identifying these items and tend to submit broad queries. The support of searching based on multiple keywords is hence desirable. In multiple-keyword search, a data item is associated with multiple keywords for storage. A query may also contain multiple keywords. The search result for a query should include all the data items whose storage keywords contain all the query keywords. Traditional DHT-based approaches achieve this by storing a data item (or its index) multiple times, each time with one of its keywords. A query is processed by searching each of the query keywords once. Therefore, the storage cost and search cost are both linear wit the number of keywords. Clearly, it is not efficient in case of a large number of keywords. In this paper, we propose a hybrid structured network called MKey to address this problem. Its backbone is a structured network. Each node in the backbone is also the leader of a cluster formed by non-backbone nodes. Within a cluster, nodes form an unstructured network and cooperate to store data and answer queries. When inserting a data item, multiple copies of its index are stored in a few different clusters. A query is also mapped to multiple clusters, and a flooding search within these clusters is performed. The union of all the search results are returned to users as the final result.