The selection of optimal camera configurations (camera locations, orientations, etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we propose a statistical framework of the problem as well as propose a trans-dimensional simulated annealing algorithm to effectively deal with it. We compare our approach with a state-of-the-art method based on binary integer programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than two alternative heuristics designed to deal with the scalability issue of BIP. Last, we show the versatility of our approach using a number of specific scenarios.

We investigate the problem of scheduling sensor node up-times to maximize the utility of the data they collect while operating within their resource constraints. We show that the optimal scheduling algorithm can improve data utility by more than 70% compared to naive schedules. We consider a suite of sensors with different capabilities and resource demands and represent their subsets as virtual sensors. For each virtual sensor, we calculate its optimal data fusion parameters and evaluate the sensors' performance in a given environment. The selection of virtual sensors best suited to collect data in a given environment can be modeled as an Integer Linear programming problem, and we study three different algorithms to solve the problem efficiently. We evaluate the performance of virtual sensor scheduling algorithms by extensive simulation. We show that even though the naive greedy scheduling approaches work well in some scenarios, none of them are able to match our best scheduling algorithm consistently, under varying environmental conditions and sensor resources.

This paper examines efficient and decentralized monitoring of objects moving in a transportation network. Previous work in moving object monitoring has focused primarily on centralized information systems, like moving object databases and geographic information systems. In contrast, in this paper monitoring is in-network, requiring no centralized control and allowing for substantial spatial constraints to the movement of information. The transportation network is assumed to be augmented with fixed checkpoints that can detect passing mobile objects. This assumption is motivated by many practical applications, from traffic management in vehicle ad hoc networks to habitat monitoring by tracking animal movements. In this context, this paper proposes and evaluates a family of efficient decentralized algorithms for capturing, storing and querying the movements of objects. The algorithms differ in the restrictions they make on the communication and sensing constraints to the mobile nodes and the fixed checkpoints. The performance of the algorithms is evaluated and compared with respect to their scalability (in terms of communication and space complexity), and their latency (the time between when a movement event occurs, and when all interested nodes are updated with records about that event). The conclusions identify three key principles for efficient decentralized monitoring of objects moving past checkpoints: structuring computation around neighboring checkpoints; taking advantage of mobility diffusion and separating the generation and querying of movement information.

In this paper we consider a sensor network deployed far away from the base station. Each sensor in the network is equipped with two radio interfaces: the low-power IEEE 802.15.4 radio and the high-bandwidth 3G radio. The low-power radios are used on all sensors to transmit data within the network, while the high-bandwidth radios are activated only on a subset of sensors, referred to as gateways, for sending data to the base station. We assume that not all sensors are required to transmit their sensed data to the base station, yet the base station does have a network throughput requirement. A high throughput requirement would cause more energy consumption on sensors and shorten the network lifetime. We investigate the problem of maximizing the network lifetime subject to the network throughput being guaranteed and the data delivery latency from the network to the base station being bounded. We first formulate a novel optimization problem, namely, the throughput guaranteed network lifetime maximization problem. We then devise a heuristic for it, with the key ingredients of gateway assignment technique and energy-efficient routing forest establishment strategy. We finally conduct extensive experiments through simulation to evaluate the performance of the proposed heuristic and show that it outperforms another two algorithms in terms of network lifetime.

We describe our current work towards a framework that establishes a hierarchy of devices (sensors and actuators) within a wireless multimedia node and uses frequent sampling of cheaper devices to trigger the activation of more energy-hungry devices. Within this framework, we consider the suitability of servos for Wireless Multimedia Sensor Networks (WMSNs) by examining their functional characteristics and energy consumption [2].

This paper proposes an intuitive wireless sensor/actuator based communication network for human animal interaction for a digital zoo. In order to enhance effective observation and control over wild life, we have built a wireless sensor network. 25 video transmitting nodes are installed for animal behavior observation and experimental vibrotactile collars have been designed for effective control in an animal park.

This paper is concerned with the application of wireless sensor network (WSN) technology to long-duration and large-scale environmental monitoring. The holy grail is a system that can be deployed and operated by domain specialists not engineers, but this remains some distance into the future. We present our views as to why this field has progressed less quickly than many envisaged it would over a decade ago. We use real examples taken from our own work in this field to illustrate the technological difficulties and challenges that are entailed in meeting end-user requirements for information gathering systems. Reliability and productivity are key concerns and influence the design choices for system hardware and software. We conclude with a discussion of long-term challenges for WSN technology in environmental monitoring and outline our vision of the future.

This paper considers the use of servo-mechanisms as part of a tightly integrated homogeneous wireless multimedia sensor network (WMSN). We describe the design of our second generation WMSN node platform, which has increased image resolution, in-built audio sensors, PIR sensors, and servomechanisms. These devices have a wide disparity in their energy consumption and in the information quality they return. As a result, we propose a framework that establishes a hierarchy of devices (sensors and actuators) within the node and uses frequent sampling of cheaper devices to trigger the activation of more energy-hungry devices. Within this framework, we consider the suitability of servos for WMSNs by examining the functional characteristics and by measuring the energy consumption of 2 analog and 2 digital servos, in order to determine their impact on overall node energy cost. We also implement a simple version of our hierarchical sampling framework to evaluate the energy consumption of servos relative to other node components. The evaluation results show that: (1) the energy consumption of servos is small relative to audio/image signal processing energy cost in WMSN nodes; (2) digital servos do not necessarily consume as much energy as is currently believed; and (3) the energy cost per degree panning is lower for larger panning angles.

This paper describes the development and deployment of a wireless sensor network for monitoring human motion and position in an indoor environment. Mobile sensor nodes comprising mote-type devices, along with inertial sensors are worn by persons moving inside buildings. Motion data is preprocessed onboard mobile nodes and transferred to a static network of seed nodes using a delay tolerant protocol with minimal radio packet overhead. A Monte Carlo based localisation algorithm is implemented, which uses a person's pedometry data, indoor map information and seed node positions to provide accurate, real-time indoor location information. The performance of the network protocols and localisation algorithm are evaluated using simulated and real experimental data.

This paper describes our demonstration of a wireless sensor network for monitoring human motion and position in an indoor environment. Mobile sensor nodes comprising mote-type devices, along with inertial sensors are worn by persons moving inside buildings. Motion data is pre-processed onboard mobile nodes and transferred to a static network of seed nodes using a delay tolerant protocol with minimal radio packet overhead. A Monte Carlo based localisation algorithm is implemented, which uses a person's pedometry data, indoor map information and seed node positions to provide accurate, real-time indoor location information.

We present the design and deployment results for PosNet - a large-scale, long-duration sensor network that gathers summary position and status information from mobile nodes. The mobile nodes have a fixed-sized memory buffer to which position data is added at a constant rate, and from which data is downloaded at a non-constant rate. We have developed a novel algorithm that performs online summarization of position data within the buffer, where the algorithm naturally accommodates data input and output rate mismatch, and also provides a delay-tolerant approach to data transport. The algorithm has been extensively tested in a large-scale long-duration cattle monitoring and control application.

A technique known as fused hidden Markov models (FHMMs) was recently proposed as an alternative multi-stream modelling technique for audio-visual speaker recognition. In this paper we show that for audio-visual speech recognition (AVSR), FHMMs can be adopted as a novel method of training synchronous MSHMMs. MSHMMs, as proposed by several authors for use in AVSR, are jointly trained on both the audio and visual modalities. In contrast our proposed FHMM adaptation method can be used to adapt the multi-stream models from single-stream audio HMMs, and in the process, better model the video speech in the final model when compared to jointly-trained MSHMMs. By experiments conducted on the XM2VTS database we show that the improved video performance of the FHMM-adapted MSHMMs results in an improvement in AVSR performance over jointly-trained MSHMMs at all levels of audio noise, and provide significant advantage in high noise environments.

This paper investigates a mobile, wireless sensor/actuator network application for use in the cattle breeding industry. Our goal is to prevent fighting between bulls in on-farm breeding paddocks by autonomously applying appropriate stimuli when one bull approaches another bull. This is an important application because fighting between high-value animals such as bulls during breeding seasons causes significant financial loss to producers. Furthermore, there are significant challenges in this type of application because it requires dynamic animal state estimation, real-time actuation and efficient mobile wireless transmissions. We designed and implemented an animal state estimation algorithm based on a state-machine mechanism for each animal. Autonomous actuation is performed based on the estimated states of an animal relative to other animals. A simple, yet effective, wireless communication model has been proposed and implemented to achieve high delivery rates in mobile environments. We evaluated the performance of our design by both simulations and field experiments, which demonstrated the effectiveness of our autonomous animal control system.

This short-paper presents the design and experimental validation of model-based, mobile routing protocol for a delay tolerant network (DTN), where herds of animals are utilised as message ferries. We develop a novel routing protocol that utilises knowledge of the predicted behaviour of each ferry in order to choose optimal ferries for carrying messages from source to sink nodes, as well as minimise routing overhead of the network via adaptive beaconing based on current behaviour.

This paper presents research that is being conducted by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) with the aim of investigating the use of wireless sensor networks for automated livestock monitoring and control. It is difficult to achieve practical and reliable cattle monitoring with current conventional technologies due to challenges such as large grazing areas of cattle, long time periods of data sampling, and constantly varying physical environments. Wireless sensor networks bring a new level of possibilities into this area with the potential for greatly increased spatial and temporal resolution of measurement data. CSIRO has created a wireless sensor platform for animal behaviour monitoring where we are able to observe and collect information of animals without significantly interfering with them. Based on such monitoring information, we can identify each animal's behaviour and activities successfully

Investigates the use of temporal lip information, in conjunction with speech information, for robust, text-dependent speaker identification. We propose that significant speaker-dependent information can be obtained from moving lips, enabling speaker recognition systems to be highly robust in the presence of noise. The fusion structure for the audio and visual information is based around the use of multi-stream hidden Markov models (MSHMM), with audio and visual features forming two independent data streams. Recent work with multi-modal MSHMMs has been performed successfully for the task of speech recognition. The use of temporal lip information for speaker identification has been performed previously (T.J. Wark et al., 1998), however this has been restricted to output fusion via single-stream HMMs. We present an extension to this previous work, and show that a MSHMM is a valid structure for multi-modal speaker identification.

This paper investigates the use of lip information, in conjunction with speech information, for robust speaker verification in the presence of background noise. It has been previously shown in our own work, and in the work of others, that features extracted from a speaker's moving lips hold speaker dependencies which are complementary with speech features. We demonstrate that the fusion of lip and speech information allows for a highly robust speaker verification system which outperforms the performance of either sub-system. We present a new technique for determining the weighting to be applied to each modality so as to optimize the performance of the fused system. Given a correct weighting, lip information is shown to be highly effective for reducing the false acceptance and false rejection error rates in the presence of background noise.

This paper presents a novel technique for the tracking and extraction of features from lips for the purpose of speaker identification. In noisy or other adverse conditions, identification performance via the speech signal can significantly reduce, hence additional information which can complement the speech signal is of particular interest. In our system, syntactic information is derived from chromatic information in the lip region. A model of the lip contour is formed directly from the syntactic information, with no minimization procedure required to refine estimates. Colour features are then extracted from the lips via profiles taken around the lip contour. Further improvement in lip features is obtained via linear discriminant analysis (LDA). Speaker models are built from the lip features based on the Gaussian mixture model (GMM). Identification experiments are performed on the M2VTS database, with encouraging results.

This paper presents a novel technique for the tracking of moving lips for the purpose of speaker identification. In our system, a model of the lip contour is formed directly from chromatic information in the lip region. Iterative refinement of contour point estimates is not required. Colour features are extracted from the lips via concatenated profiles taken around the lip contour. Reduction of order in lip features is obtained via principal component analysis (PCA) followed by linear discriminant analysis (LDA). Statistical speaker models are built from the lip features based on the Gaussian mixture model (GMM). Identification experiments performed on the M2VTS/sup 1/ database, show encouraging results.