Hock Beng Lim

Future Mobility Survey

The Future Mobility Survey (FMS) is a smartphone-based prompted-recall travel survey that aims to support data collection initiatives for transport-modeling purposes. This paper details the considerations that have gone into the surveys development, including the smartphone apps for iPhone and Android platforms, the online activity diary and user interface, and the background intelligence for processing collected data into activity locations and travel traces. The various trade-offs concerning user comprehension, resource use, and participant burden, including findings from usability tests and a pilot study, are discussed. Close attention should be paid to the simplicity of the user interaction, determinations of activity locations (such as the false positive and false negative trade-off in their automatic classification), and the clarity of interactions in the activity diary. The FMS system design and implementation provide pragmatic, useful insights into the development of similar platforms and approaches for travel and activity surveys.

Sensor grid: integration of wireless sensor networks and the grid

Wireless sensor networks have emerged as an exciting technology for a wide range of important applications that acquire and process information from the physical world. Grid computing has evolved as a standards-based approach for coordinated resource sharing. Sensor grids combine these two promising technologies by extending the grid computing paradigm to the sharing of sensor resources in wireless sensor networks. There are several issues and challenges in the design of sensor grids. In this paper, we propose a sensor grid architecture, called the scalable proxy-based architecture for sensor grid (SPRING), to address these design issues. We also developed a sensor grid testbed to study the design issues of sensor grids and to improve our sensor grid architecture design

In-network execution of monitoring queries in sensor networks

Sensor networks are widely used in many applications for collecting information from the physical environment. In these applications, it is usually necessary to track the relationships between sensor data readings within a time window to detect events of interest. However, it is difficult to detect such events by using the common aggregate or selection queries. We address the problem of processing window self-join in order to detect events of interest. Self-joins are useful in tracking correlations between different sensor readings, which can indicate an event of interest. We propose the Two-Phase Self-Join (TPSJ) scheme to efficiently evaluate self-join queries for event detection in sensor networks. Our TPSJ scheme takes advantage of the properties of the events and carries out data filtering during in-network processing. We discuss TPSJ execution with one window and we extend it for continuous event monitoring. Our experimental evaluation results indicate that the TPSJ scheme is effective in reducing the amount of radio transmissions during event detection.

Two-Tier Multiple Query Optimization for Sensor Networks

When there are multiple queries posed to the resource-constrained wireless sensor network, it is critical to process them efficiently. In this paper, we propose a two-tier multiple query optimization (TTMQO) scheme. The first tier, called base station optimization, adopts a cost-based approach to rewrite a set of queries into an optimized set that shares the commonality and eliminates the redundancy among the queries in the original set. The optimized queries are then injected into the wireless sensor network. In the second tier, called in-network optimization, our scheme efficiently delivers query results by taking advantage of the broadcast nature of the radio channel and sharing the sensor readings among similar queries over time and space at a finer granularity. Our experimental results indicate that our proposed TTMQO scheme offers significant improvements over the traditional single query optimization technique.

A Coverage-Aware Clustering Protocol for Wireless Sensor Networks

In energy-limited wireless sensor networks, network clustering and sensor scheduling are two efficient techniques for minimizing node energy consumption and maximizing network coverage lifetime. When integrating the two techniques, the challenges are how to select cluster heads and active nodes. In this paper, we propose a coverage-aware clustering protocol. In the proposed protocol, we define a cost metric that favors those nodes being more energy-redundantly covered as better candidates for cluster heads and select active nodes in a way that tries to emulate the most efficient tessellation for area coverage. Our simulation results show that the network coverage lifetime can be significantly improved compared with an existing protocol.

WaterWiSe@SG : a testbed for continuous monitoring of the water distribution system in Singapore

This paper describes the development of WaterWiSe@SG, a wireless sensor network to enable real-time monitoring of a water distribution network in Singapore. The overall project is directed towards three main goals: 1) the application of a low cost wireless sensor network for high data rate, on-line monitoring of hydraulic parameters within a large urban water distribution system; 2) the development of systems to enable remote detection of leaks and prediction of pipe burst events; 3) the integrated monitoring of hydraulic and water quality parameters. In this paper we will describe the current state of the WaterWiSe@SG testbed, and report on experimentation we have performed with respect to leak detection and localization. Furthermore, we describe how we have assimilated real time pressure and flow measurements from the sensor network into hydraulic models that are used to improve state estimation for the network. Finally, we discuss the future plans for the project.

Wavelet-based Burst Event Detection and Localization in Water Distribution Systems

In this paper we present techniques for detecting and locating transient pipe burst events in water distribution systems. The proposed method uses multiscale wavelet analysis of high rate pressure data recorded to detect transient events. Both wavelet coefficients and Lipschitz exponents provide additional information about the nature of the signal feature detected and can be used for feature classification. A local search method is proposed to estimate accurately the arrival time of the pressure transient associated with a pipe burst event. We also propose a graph-based localization algorithm which uses the arrival times of the pressure transient at different measurement points within the water distribution system to determine the actual location (or source) of the pipe burst. The detection and localization performance of these algorithms is validated through leak-off experiments performed on the WaterWiSe@SG wireless sensor network test bed, deployed on the drinking water distribution system in Singapore. Based on these experiments, the average localization error is 37.5 m. We also present a systematic analysis of the sources of localization error and show that even with significant errors in wave speed estimation and time synchronization the localization error is around 56 m.

Improving Positioning Accuracy Using GPS Pseudorange Measurements for Cooperative Vehicular Localization

Accurate positioning is a key factor for enabling innovative applications in intelligent transportation systems. Cutting-edge communication technologies make cooperative localization a promising approach for accurate vehicle positioning. In this paper, we first propose a ranging technique called weighted least squares double difference (WLS-DD), which is used to detect intervehicle distances based on the sharing of GPS pseudorange measurements and a weighted least squares method. It takes the carrier-to-noise ratio (CNR) of raw pseudorange measurements into consideration for mitigating noises so that it can improve the accuracy of the distance detection. We show the superiority of WLS-DD by conducting a series of field experiments. Based on intervehicle distance detection, we propose a distributed location estimate algorithm (DLEA) to improve the accuracy of vehicle positioning. The implementation of DLEA only relies on inaccurate GPS pseudorange measurements and the obtained intervehicle distances without using any reference points for positioning correction. Moreover, to evaluate the joint effect of WLS-DD and DLEA, we derive a data fitting model based on the observed distance detection bias from field experiments, which generates parameters in a variety of environments for performance evaluation. Finally, we demonstrate the effectiveness of the proposed solutions via a comprehensive simulation study.

Layered Diffusion-based Coverage Control in Wireless Sensor Networks

Coverage is an important performance metric for many applications such as surveillance in wireless sensor networks (WSNs). Coverage control is used to select as few active nodes as possible from all deployed sensor nodes such that sufficient coverage of the monitored area can be guaranteed, while reducing the energy consumption of each individual sensor node to prolong the network lifetime. This paper presents a distributed and localized technique called the layered diffusion based coverage control (LDCC). The LDCC protocol does not require information on the node location coordinates when selecting active nodes. Instead, it exploits the hop count information, which is easily obtained in a WSN, to select active sensor nodes. Furthermore, the LDCC protocol is very simple and does not require any sophisticated computation such as distance or covered area computation. Our simulation results show that the LDCC protocol achieves high coverage ratio while incurring very low message overhead compared with other existing protocols.

Water Main Burst Event Detection and Localization

In this paper we present a technique for detecting and locating burst events in pipelines. The proposed method uses wavelet analysis of the high-rate pressure data to detect pipe burst events. Multiscale wavelet analysis of the pressure signal will be shown to be robust to impulsive noise encountered in the physical phenomena under observation. The wavelet coefficients also allow us to obtain additional information about the nature of the signal feature detected, which can used for further feature classification. A local search method is also proposed to accurately determine the arrival time of the pressure front associated with the burst event. The detection performance of these algorithms is verified through leak-off experiments performed on the WaterWiSe@SG test bed deployed on the water distribution system in Singapore. We also propose a graph-based search algorithm which uses the arrival times of the pressure front at different locations within the water distribution system to determine the actual location of the pipe burst event.