Chung-Ta King

Design of a Multifunctional Wireless Sensor for In-Situ Monitoring of Debris Flows

Debris flows carrying saturated solid materials in water flowing downslopes often cause severe damage to the lives and properties in their path. Close monitoring and early warning are imperative to save lives and reduce damage. Current debris-flow-monitoring systems usually install sensor equipment along the riverbanks and mountain slopes to detect debris flows and track their data. Unfortunately, most of this equipment indirectly collects data only from a distance. So far, there is no way to understand what is happening inside a debris flow and to collect its internal parameters, not to mention doing this in real time. To answer this challenge, this paper presents a novel multifunctional wireless sensor for monitoring debris flows. The core idea is to let these sensors drift with the debris flow, to collect flow information as they move along, and to wirelessly transmit the collected data to base stations in real time. The design of such a sensor needs to address many challenging issues, including cost, deployment efforts, long-term standby, and fast reaction. This paper addresses these issues and reports our evaluation results.

Pipelined data parallel algorithms-II: design

For pt.I see ibid., p.470-85. A methodology for designing pipelined data-parallel algorithms on multicomputers is studied. The design procedure starts with a sequential algorithm which can be expressed as a nested loop with constant loop-carried dependencies. The procedures main focus is on partitioning the loop by grouping related iterations together. Grouping is necessary to balance the communication overhead with the available parallelism and to produce pipelined execution patterns, which result in pipelined data-parallel computations. The grouping should satisfy dependence relationships among the iterations and also allow the granularity to be controlled. Various properties of grouping are studied, and methods for generating communication-efficient grouping are given. Given a grouping and an assignment of the groups to the processors, an analytic model is combined with the grouping results to describe the behavior and to estimate the performance of the resultant parallel program. Expressions characterizing the performance are derived. >

On maximizing the throughput of convergecast in wireless sensor networks

A primary task of wireless sensor networks is collecting environmental information. In most cases, a sink node is responsible for collecting data from all sensor nodes. The radio congestion around the sink becomes the main bottleneck to maximizing the throughput of the convergecast. A general strategy to alleviating the problem is to schedule the communications among sensors to avoid interference. In this paper, we consider both routing structures and communication schedules for optimizing the scheduling length. We show that the shortest-length conflict-free scheduling is equivalent to finding a minimal vertex coloring. To solve the schedule problem, a virtual-node expansion is proposed to handle the relay operations in convergecast, and then coloring algorithms are used to obtain the communication schedule. However, scheduling only solves part of the problem. Routing structures will also affect the scheduling quality. The disjoint-strips routing is thus proposed to leverage possible parallel transmissions in convergecast. The proposed algorithms are evaluated through simulations.

Pipelined data parallel algorithms-I: concept and modeling

The basic concept of pipelined data-parallel algorithms is introduced by contrasting the algorithms with other styles of computation and by a simple example (a pipeline image distance transformation algorithm). Pipelined data-parallel algorithms are a class of algorithms which use pipelined operations and data level partitioning to achieve parallelism. Applications which involve data parallelism and recurrence relations are good candidates for this kind of algorithm. The computations are ideal for distributed-memory multicomputers. By controlling the granularity through data partitioning and overlapping the operations through pipelining, it is possible to achieve a balanced computation on multicomputers. An analytic model is presented for modeling pipelined data-parallel computation on multicomputers. The model uses timed Petri nets to describe data pipelining operations. As a case study, the model is applied to a pipelined matrix multiplication algorithm. Predicted results match closely with the measured performance on a 64-node NCUBE hypercube multicomputer. >

Using integer linear programming for instruction scheduling and register allocation in multi-issue processors☆

Abstract Instruction scheduling and register allocation are two very important optimizations in modern compilers for advanced processors. These two optimizations must be performed simultaneously in order to maximize the instruction-level parallelism and to fully utilize the registers [1]. In this paper, we solve register allocation and instruction scheduling simultaneously using integer linear programming (ILP). We have successfully worked out the ILP formulations for the problem with and without register spilling. Two kinds of optimizations are considered: 1. (1) fix the number of free registers and then solve the minimum number of cycles to execute the instructions, or 2. (2) fix the maximum execution cycles for the instructions and solve the minimum number of registers needed. Besides being theoretically interesting, our solution serves as a reference point for other heuristic solutions. The formulations are also applicable to high-level synthesis of ASICs and designs for embedded processors. In these application domains, the code quality is more important than the compilation time.

MULTIPAR: behavioral partition for synthesizing multiprocessor architectures

In this paper, we present methods for scheduling and partitioning behavioral descriptions (e.g., CDFGs) in order to synthesize application-specific multiprocessor systems. Our target application domain is digital signal processing (DSP). In order to meet the user given constraints (such as timing), maximizing the system throughput and minimizing the amount of communication between processors are important. A model of a target processor and the communication device (i.e., bus, FIFO and delay element) is defined as a basis for the synthesis. We use an integer linear programming formulating to solve the partitioning and scheduling problems simultaneously. The optimization complexity for large applications can be reduced by using a simplified formulation. For even larger applications, we propose an iterative partitioning heuristic to solve. Finally, the formulations are extended to take into account of conditional branches, loops, and critical signals. >

Similarity discovery in structured P2P overlays

Peer-to-peer (P2P) overlays are appealing, since they can aggregate resources of end systems without relying on sophisticated infrastructures. Services can thus be rapidly deployed over such overlays. Primitive P2P overlays only support searches with single keywords. For queries with multiple keywords, presently only unstructured P2P systems can support by extensively employing message flooding. We propose a similarity information retrieval system called Meteorograph for structured P2P overlays without relying on message flooding. Meteorograph is fault-resilient, scalable, responsive and self-administrative, which is particularly suitable for an environment with an explosion of information and a large number of dynamic entities. An information item stored in Meteorograph is represented as a vector. A small angle between two vectors means that the corresponding items are characterized by some identical keywords. Meteorograph further stores similar items at nearby locations in the P2P overlay. To retrieve similar items, only nodes in nearby locations are located and consulted. Meteorograph is evaluated with simulation. The results show that Meteorograph can effectively distribute loads to the nodes. Discovering a single item and a set (in size k) of similar items takes O(log N) and (k/c)middotO(log N) messages and hops respectively, where N is the number of nodes in the overlay and c is the storage capacity of anode

Pipeline architectures for recursive morphological operations

Introduces efficient pipeline architectures for the recursive morphological operations. The standard morphological operation is applied directly on the original input image and produces an output image. The order of image scanning in which the operator is applied to the input pixels is irrelevant. However, the intent of the recursive morphological operations is to feed back the output at the current scanning pixel to overwrite its corresponding input pixel to be considered into computation at the following scanning pixels. The resultant output image by recursive morphology inherently depends on the image scanning sequence. Two pipelined implementations of the recursive morphological operations are presented. The design of an application-specific systolic array is first introduced. The systolic array uses 3 n cells to process an nxn image in 6 n-2 cycles. The cell utilization rate is 100%. Second, a parallel program implementing the recursive morphological operations and running on distributed-memory multicomputers is described. Performance of the program can be finely tuned by choosing appropriate partition parameters.

Bristle: a mobile structured peer-to-peer architecture

Most hash-based structured peer-to-peer (HS-P2P) architectures proposed previously focus on stationary peer nodes. When peers move to new locations in the network, these schemes become cumbersome and inefficient in delivering messages. This paper proposes a mobile HS-P2P architecture, called Bristle, which allows nodes to move freely. A node can disseminate its location information through the P2P network, and other nodes can learn its movement or query its location also through the P2P network. Consequently, an overlay infrastructure based on Bristle can be efficiently maintained over mobile nodes. The scalability, reliability and performance of the HS-P2P are thus leveraged. We believe that the concept proposed in Bristle can be applied to existing HS-P2P overlays.

Using mobile wireless sensors for in-situ tracking of debris flows

Most debris flow monitoring systems deployed today use indirect means to track information regarding debris flows. In this work, we introduce a novel debris flow monitoring system for in-situ and direct tracking of debris flows in real-time. The core idea is to throw wireless sensors into the debris flows and collect flow information as they move along. We will describe the design of the wireless sensors and present some preliminary performance results.