In this paper, we present an overview of a system that we are developing for the continuous monitoring of water distribution systems in Singapore. We discuss the design and implementation of the system that enables real-time detection of pipe burst events.

The rapid developments in wireless communications, embedded systems and MEMS sensor technologies have boosted the research and development on large-scale wireless sensor networks (WSNs). WSNs have very broad application areas such as security surveillance, environmental monitoring and healthcare monitoring. The paper presents a novel simulation framework called WSN middleware service modules simulation platform (WISDOM). The WISDOM simulator is written in Java and it uses the recursive porous agent simulation toolkit (Repast)O as the simulation engine to perform discrete event-driven simulations. WISDOM was used in WSN research (Wang et al.,2007) to simulate and verify middleware services for routing, sensing activity scheduling, group formation and management, target detection and tracking, and collaborative classification and fusion in a wireless sensor network. The versatility and performance of WISDOM for middleware service protocol development and evaluation have proven to be valuable.

In recent years, wireless sensor networks have received great interest with rapid developments in both hardware and software aspects. However, there is always a problem with limited energy supply for the tiny sensor node. In wireless imaging sensor network in which imaging devices are utilized, the bottleneck of energy consumption efficiency is even more serious. The main activities of such imaging networks are related to image data transmission over wireless channel. Therefore it is necessary to seek a good balance between the image quality and energy conservation. We propose an energy-efficient control scheme based on recent dynamic range coding techniques. The simulation results are presented with emphasis on prolonging the sensor network lifetime.

High dynamic range (HDR) images require a higher number of bits per color channel than traditional images. This brings about problems to storage and transmission, especially in distributed wireless sensor networks in which transmission capability and energy consumption are critical issues. The paper proposes a structure of compressing multiple dynamic range images, which is called multiple dynamic range (MDR) coding. MDR imaging system is attractive due to the capability of reconstructing lower dynamic range images from partial bitstream. MDR coding shows its advantages in terms of energy efficiency in distributed wireless sensor networks. The efficacy of MDR coding is also illustrated by presenting the results of encoding a series of synthetic and natural MDR images

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.

In this paper, the family of fastest ternary linearly independent arithmetic transforms, which possesses forward and inverse butterfly diagrams with lowest computational complexity have been identified. This family is recursively defined and has consistent formulas relating forward and inverse transform matrices. Computational costs of the calculation for presented transforms are also discussed.