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Chun Chou

The University of New South Wales, School of Computer Science and Engineering, Faculty Member

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Papers by Chun Chou

Ear-Phone: A context-aware noise mapping using smart phones

Pervasive and Mobile Computing, 2015

A noise map facilitates the monitoring of environmental noise pollution in urban areas. It can ra... more A noise map facilitates the monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (for months or even years), as they rely on population and traffic models rather than on real data. Smart phone based urban sensing can be leveraged to create an open and inexpensive platform for rendering up-todate noise maps. In this paper, we present the design, implementation and performance evaluation of an end-to-end, context-aware, noise mapping system called Ear-Phone. Ear-Phone investigates the use of different interpolation and regularization methods to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95, N97 and HP iPAQ, HTC One mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. A major challenge of using smart phones as sensors is that even at the same location, the sensor reading may vary depending on the phone orientation and user context (for example, whether the user is carrying the phone in a bag or holding it in her palm). To address this problem, Ear-Phone leverages context-aware sensing. We develop classifiers to accurately determine the phone sensing context. Upon context discovery, Ear-Phone automatically decides whether to sense or not. Ear-phone also implements in-situ calibration which performs simple calibration that can be carried out without any technical skills whatsoever required on the user's part. Extensive simulations and outdoor * Corresponding author.

An Adaptive Algorithm for Compressive Approximation of Trajectory (AACAT) for Delay Tolerant Networks

Lecture Notes in Computer Science, 2011

Highly efficient compression provides a promising approach to address the transmission and comput... more Highly efficient compression provides a promising approach to address the transmission and computation challenges imposed by moving object tracking applications on resource constrained Wireless Sensor Networks (WSNs). In this paper, we propose and design a Compressive Sensing (CS) based trajectory approximation algorithm, Adaptive Algorithm for Compressive Approximation of Trajectory (AACAT), which performs trajectory compression, so as to maximize the information about the trajectory subject to limited bandwidth. Our extensive evaluation using "real" trajectories of three different object groups (animals, pedestrians and vehicles) shows that CS-based trajectory compression reduces up to 30% transmission overheads, for given information loss bounds, compared to the state-of-the-art trajectory compression algorithms. We implement AACAT on the resource-impoverished sensor nodes, which shows that AACAT achieves high compression performance with very limited resource (computation power and energy) overheads.

Energy efficient information collection in wireless sensor networks using adaptive compressive sensing

2009 IEEE 34th Conference on Local Computer Networks, 2009

We consider the problem of using Wireless Sensor Networks (WSNs) to measure the temporal-spatial ... more We consider the problem of using Wireless Sensor Networks (WSNs) to measure the temporal-spatial field of some scalar physical quantities. Our goal is to obtain a sufficiently accurate approximation of the temporal-spatial field with as little energy as possible. We propose an adaptive algorithm, based on the recently developed theory of adaptive compressive sensing, to collect information from WSNs in an energy efficient manner. The key idea of the algorithm is to perform "projections" iteratively to maximise the amount of information gain per energy expenditure. We prove that this maximisation problem is NPhard and propose a number of heuristics to solve this problem. We evaluate the performance of our proposed algorithms using data from both simulation and an outdoor WSN testbed. The results show that our proposed algorithms are able to give a more accurate approximation of the temporal-spatial field for a given energy expenditure.

Efficient background subtraction for real-time tracking in embedded camera networks

Background subtraction is often the first step of many computer vision applications. For a backgr... more Background subtraction is often the first step of many computer vision applications. For a background subtraction method to be useful in embedded camera networks, it must be both accurate and computationally efficient because of the resource constraints on embedded platforms. This makes many traditional background subtraction algorithms unsuitable for embedded platforms because they use complex statistical models to handle subtle illumination changes. These models make them accurate but the computational requirement of these complex models is often too high for embedded platforms. In this paper, we propose a new background subtraction method which is both accurate and computational efficient. The key idea is to use compressive sensing to reduce the dimensionality of the data while retaining most of the information. By using multiple datasets, we show that the accuracy of our proposed background subtraction method is comparable to that of the traditional background subtraction methods. Moreover, real implementation on an embedded camera platform shows that our proposed method is at least 5 times faster, and consumes significantly less energy and memory resources than the conventional approaches. Finally, we demonstrated the feasibility of the proposed method by the implementation and evaluation of an end-to-end real-time embedded camera network target tracking application.

Poster abstract: Distributed sparse approximation for frog sound classification

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN), 2012

Sparse approximation has now become a buzzword for classification in numerous research domains. W... more Sparse approximation has now become a buzzword for classification in numerous research domains. We propose a distributed sparse approximation method based on 1 minimization for frog sound classification, which is tailored to the resource constrained wireless sensor networks. Our pilot study demonstrates that 1 minimization can run on wireless sensor nodes producing satisfactory classification accuracy.

Ear-Phone assessment of noise pollution with mobile phones

A hybrid optical network architecture consisting of optical cross connects and optical burst switches

by Farzad Safaei and Chun Chou

Optical burst switches (OBSes) have been proposed to improve the utilization of a network of opti... more Optical burst switches (OBSes) have been proposed to improve the utilization of a network of optical cross connect (OXCs). Current studies on OBS assume a network consisting of OBSes alone. While this is a reasonable assumption for evaluating a new technology, the question of how a network of OXCs can be evolved to a network of OBSes has not been studied. In this paper, we propose a hybrid architecture consisting of OBSes at the network edge and OXCs in the network core. This architecture allows carriers to gradually migrate from an OXC-based network to an OBS-based network with an improved network utilization. In addition, we use queueing analysis to study the performance of this new architecture.

Deploying long-lived and cost-efiective hybrid sensor networks

by Nirupama Bulusu and Chun Chou

In this paper, we consider the problem of network deployment in hybrid sensor net-works, consisti... more In this paper, we consider the problem of network deployment in hybrid sensor net-works, consisting of both resource-rich and resource-impoverished sensor devices. The resource-rich devices, called micro-servers, are more expensive but have signif-icantly greater bandwidth and energy capabilities compared to the low-cost, low-powered sensors. Such hybrid sensor networks have the potential to support the higher bandwidth communications of broadband sensor

Two adaptive AQM algorithms for quantitative differentiated services

IEEE Global Telecommunications Conference, 2004. GLOBECOM '04., 2004

The DiffServ Assured Forwarding (AF) service provides a scalable solution to QoS guarantee. Curre... more The DiffServ Assured Forwarding (AF) service provides a scalable solution to QoS guarantee. Currently, AF only provides qualitative differentiation of delay or loss between classes of service, but not quantitative guarantee. By studying the quantitative behaviour of the steady state operating point for RIO, we propose two adaptive RIO algorithms for per-hop QoS provisioning in AF service. These two algorithms, ARIO-D and ARIO-L, work by dynamically adjusting the RIO parameters so that the packet delay and the packet loss are, respectively, kept at their target level. Simulation results show that they can provide both high link utilization as well as stable and differentiated delay or loss for different AF classes on a single router.

by Nirupama Bulusu, Chun Chou, and Rajib Rana (rajibrana.org)

Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks - IPSN '10, 2010

A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise ... more A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps.

Ear-Phone: An End-to-End Participatory Urban Noise Mapping System

by Chun Chou, Nirupama Bulusu, and Rajib Rana (rajibrana.org)

A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise ... more A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. In this paper, we present the design, implementation and performance eval- uation of an end-to-end participatory urban noise mapping system called Ear- Phone. Ear-Phone, for the first time, leverages Compressive Sensing to ad- dress the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95 and HP iPAQ mobile devices, also addres...

Presentation Slides from Urbansense 2008: "Participatory Sensing in Commerce: Using Mobile Phones to Track Market Price Dispersion

by Nirupama Bulusu and Chun Chou

In economics, price dispersion refers to the price difference of a homogeneous good across differ... more In economics, price dispersion refers to the price difference of a homogeneous good across different vendors. According to Baye et al, “The empirical evidence suggests that price dispersion in both online and offline markets is sizable, pervasive, and persistent.” Not surprisingly, there exist several popular web commerce sites such as Froogle that enable users to track consumer pricing information in online markets. In this paper, we present and explore our vision that participatory sensing can be employed in this new application domain to track price dispersion in homogeneous consumer goods even in offline markets. We discuss two proof-of-concept participatory mobile camera-phone sensing systems that we have built: (1) automating fuel price collection, and (2) semi-automated scanning of receipts.

Protecting Multicast Sessions in Wireless Mesh Networks

Proceedings. 2006 31st IEEE Conference on Local Computer Networks, 2006

To support reliable multicast routing in wireless mesh networks, it is important to protect multi... more To support reliable multicast routing in wireless mesh networks, it is important to protect multicast sessions against link or node failures. The issue of protecting multicast sessions in wireless mesh networks is a new problem to the best of our knowledge. In this paper, we propose a resilient forwarding mesh approach for protecting a multicast session in wireless mesh networks. Utilizing the wireless broadcast advantage, a resilient forwarding mesh effectively establishes two node disjoint paths for each sourcedestination pair. This allows a multicast session to be immune from any single link or intermediate node failure. We introduce four heuristic algorithms to obtain approximate solutions that seek to minimize the number of required broadcast transmissions. We evaluate the performance of these heuristic algorithms against the optimal resilient forwarding mesh (ORFM) obtained by solving an integer linear programming (ILP) formulation of the problem. Experimental results demonstrate that one of these heuristic algorithms, which we call the minimal disjoint mesh algorithm (MDM), performs sufficiently close to ORFM. Besides, we find that the resilient forwarding mesh approach provides efficient 1+1 protection to the multicast session without incurring much additional overhead on a single minimal cost multicast tree.

Nonuniform compressive sensing for heterogeneous wireless sensor networks

by Chun Chou and Rajib Rana (rajibrana.org)

In this paper, we consider the problem of using wireless sensor networks (WSNs) to measure the te... more In this paper, we consider the problem of using wireless sensor networks (WSNs) to measure the temporal-spatial profile of some physical phenomena. We base our work on two observations. First, most physical phenomena are compressible in some transform domain basis. Second, most WSNs have some form of heterogeneity. Given these two observations, we propose a nonuniform compressive sensing method to improve the performance of WSNs by exploiting both compressibility and heterogeneity. We apply our proposed method to real WSN data sets. We find that our method can provide a more accurate temporal-spatial profile for a given energy budget compared with other sampling methods.

Non-uniform compressive sensing in wireless sensor networks: Feasibility and application

by Chun Chou, Rajib Rana (rajibrana.org), and W. Hu

In this paper, we consider the problem of using wireless sensor networks (WSNs) to measure the te... more In this paper, we consider the problem of using wireless sensor networks (WSNs) to measure the temporal-spatial profile of some physical phenomena. We base our work on two observations. Firstly, most physical phenomena are compressible in some transform domain basis. Secondly, most WSNs have some form of heterogeneity. Given these two observations, we propose a non-uniform compressive sensing method to improve the performance of WSNs by exploiting both compressibility and heterogeneity. We apply our proposed method to a real WSN data set. We find that our method can provide a more accurate temporal-spatial profile for a given energy budget compared with other sampling methods.

Real-time classification via sparse representation in acoustic sensor networks

by Mingrui Yang, Chun Chou, and Rajib Rana (rajibrana.org)

Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems - SenSys '13, 2013

Acoustic Sensor Networks (ASNs) have a wide range of applications in natural and urban environmen... more Acoustic Sensor Networks (ASNs) have a wide range of applications in natural and urban environment monitoring, as well as indoor activity monitoring. In-network classification is critically important in ASNs because wireless transmission costs several orders of magnitude more energy than computation. The main challenges of in-network classification in ASNs include effective feature selection, intensive computation requirement and high noise levels. To address these challenges, we propose a sparse representation based featureless, low computational cost, and noise resilient framework for in-network classification in ASNs. The key component of Sparse Approximation based Classification (SAC), 1 minimization, is a convex optimization problem, and is known to be computationally expensive. Furthermore, SAC algorithms assumes that the test samples are a linear combination of a few training samples in the training sets. For acoustic applications, this results in a very large training dictionary, making the computation infeasible to be performed on resource constrained ASN platforms. Therefore, we propose several techniques to reduce the size of the problem, so as to fit SAC for in-network classification in ASNs. Our extensive evaluation using two real-life datasets (consisting of calls from 14 frog species and 20 cricket species respectively) shows that the proposed SAC framework outperforms conventional approaches such as Support Vector Machines (SVMs) and k-Nearest Neighbor (kNN) in terms of classification accuracy and robustness. Moreover, our SAC approach can deal with multi-label classification which is common in ASNs. Finally, we explore the system design spaces and demonstrate the real-time feasibility of the proposed framework by the implementation and evaluation of an acoustic classification application on an embedded ASN testbed.

Provisioning content distribution networks over shared infrastructure

IEEE International Conference on Networks, 2003

The current paradigm of content distribution networks (CDN) deployment requires heavy infrastruct... more The current paradigm of content distribution networks (CDN) deployment requires heavy infrastructure investment since a large number of servers have to be deployed over a wide area. To overcome this difficulty, we propose a new paradigm where future CDNs are to be deployed over a leased server infrastructure. This paradigm shift changes the object placement problem in current CDNs to

Frame rate control for video streaming

2011 IEEE 36th Conference on Local Computer Networks, 2011

This paper aims to reduce the amount of prebuffering required to ensure a maximum video continuit... more This paper aims to reduce the amount of prebuffering required to ensure a maximum video continuity in streaming. Current approaches do this by slowing the playout frame rate of the decoder, this is known as adaptive media playout (AMP). However, doing this introduces playout distortion to the viewers as the video is played slower than its natural playout rate. We

A hybrid sensor network for cane-toad monitoring

by Nirupama Bulusu and Chun Chou

Proceedings of the 3rd international conference on Embedded networked sensor systems - SenSys '05, 2005

This demonstration shows a wireless, acoustic sensor network application -monitoring amphibian po... more This demonstration shows a wireless, acoustic sensor network application -monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our system uses automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the Cane Toad (see ). This is a challenging application because it requires high frequency acoustic sampling, complex signal processing and wide area sensing coverage [2]. Our prototype consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica motes operating in concert. The Mica motes are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks.