Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

http://www.eecs.ucf.edu/~dcm/Teaching/COT4810-Spring2011/Literature/SensorNetworksSurvey.pdf

Wireless sensor networks: a survey

https://www.science.smith.edu/~jcardell/Courses/EGR328/Readings/WSNSurvey2.pdf

Wireless sensor network survey

The term ‘‘Internet-of-Things’’ is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internetof-Things.

/pdf/internet-of-things-vision-applications-and-research-4xfstaiv80.pdf

Internet of things: Vision, applications and research challenges

This paper reviews the state-of-the art in vibration energy harvesting for wireless, self-powered microsystems. Vibration-powered generators are typically, although not exclusively, inertial spring and mass systems. The characteristic equations for inertial-based generators are presented, along with the specific damping equations that relate to the three main transduction mechanisms employed to extract energy from the system. These transduction mechanisms are: piezoelectric, electromagnetic and electrostatic. Piezoelectric generators employ active materials that generate a charge when mechanically stressed. A comprehensive review of existing piezoelectric generators is presented, including impact coupled, resonant and human-based devices. Electromagnetic generators employ electromagnetic induction arising from the relative motion between a magnetic flux gradient and a conductor. Electromagnetic generators presented in the literature are reviewed including large scale discrete devices and wafer-scale integrated versions. Electrostatic generators utilize the relative movement between electrically isolated charged capacitor plates to generate energy. The work done against the electrostatic force between the plates provides the harvested energy. Electrostatic-based generators are reviewed under the classifications of in-plane overlap varying, in-plane gap closing and out-of-plane gap closing; the Coulomb force parametric generator and electret-based generators are also covered. The coupling factor of each transduction mechanism is discussed and all the devices presented in the literature are summarized in tables classified by transduction type; conclusions are drawn as to the suitability of the various techniques.

/pdf/energy-harvesting-vibration-sources-for-microsystems-46t885pwdp.pdf

Energy harvesting vibration sources for microsystems applications

Wireless sensor network applications, similarly to other distributed systems, often require a scalable time synchronization service enabling data consistency and coordination. This paper describes the Flooding Time Synchronization Protocol (FTSP), especially tailored for applications requiring stringent precision on resource limited wireless platforms. The proposed time synchronization protocol uses low communication bandwidth and it is robust against node and link failures. The FTSP achieves its robustness by utilizing periodic flooding of synchronization messages, and implicit dynamic topology update. The unique high precision performance is reached by utilizing MAC-layer time-stamping and comprehensive error compensation including clock skew estimation. The sources of delays and uncertainties in message transmission are analyzed in detail and techniques are presented to mitigate their effects. The FTSP was implemented on the Berkeley Mica2 platform and evaluated in a 60-node, multi-hop setup. The average per-hop synchronization error was in the one microsecond range, which is markedly better than that of the existing RBS and TPSN algorithms.

/pdf/the-flooding-time-synchronization-protocol-2j5x8h86k4.pdf

The flooding time synchronization protocol

Domain-specific integrated development environments can help capture specifications in the form of domain models. These tools support the design process by automating analysis and simulating essential system behavior. In addition, they can automatically generate, configure, and integrate target application components. The high cost of developing domain-specific, integrated modeling, analysis, and application-generation environments prevents their penetration into narrower engineering fields that have limited user bases. Model-integrated computing (MIC), an approach to model-based engineering that helps compose domain-specific design environments rapidly and cost effectively, is particularly relevant for specialized computer-based systems domains-perhaps even single projects. The authors describe how MIC provides a way to compose such environments cost effectively and rapidly by using a metalevel architecture to specify the domain-specific modeling language and integrity constraints. They also discuss the toolset that implements MIC and describe a practical application in which using the technology in a tool environment for the process industry led to significant reductions in development and maintenance costs.

Smart Dust: communicating with a cubic-millimeter computer

An ad-hoc wireless sensor network-based system is presented that detects and accurately locates shooters even in urban environments. The system consists of a large number of cheap sensors communicating through an ad-hoc wireless network, thus it is capable of tolerating multiple sensor failures, provides good coverage and high accuracy, and is capable of overcoming multipath effects. The performance of the proposed system is superior to that of centralized countersniper systems in such challenging environment as dense urban terrain. In this paper, in addition to the overall system architecture, the acoustic signal detection, the most important middleware services and the unique sensor fusion algorithm are also presented. The system performance is analyzed using real measurement data obtained at a US Army MOUT (Military Operations in Urban Terrain) facility.

/pdf/sensor-network-based-countersniper-system-25jl0s8w8i.pdf

Sensor network-based countersniper system

The Generic Modeling Environment (GME) is a con- figurable toolset that supports the easy creation of d o- main-specific modeling and program synthesis environ- ments. The primarily graphical, domain-specific models can represent the application and its environment includ- ing hardware resources, and their relationship. The mod- els are then used to automatically synthesize the applica- tion and/or generate inputs to COTS analysis tools. In addition to traditional signal processing problems, we have applied this approach to tool integration and struc- turally adaptive systems among other domains. This pa- per describes the GME toolset and compares it to other similar approaches. A case study is also presented that illustrates the core concepts through an example.

/pdf/the-generic-modeling-environment-4ptjlz090g.pdf

The Generic Modeling Environment

The paper describes a model-integrated approach for embedded software development that is based on domain-specific, multiple-view models used in all phases of the development process. Models explicitly represent the embedded software and the environment it operates in, and capture the requirements and the design of the application, simultaneously. Models are descriptive , in the sense that they allow the formal analysis, verification, and validation of the embedded system at design time. Models are also generative, in the sense that they carry enough information for automatically generating embedded systems using the techniques of program generators. Because of the widely varying nature of embedded systems, a single modeling language may not be suitable for all domains; thus, modeling languages are often domain-specific. To decrease the cost of defining and integrating domain-specific modeling languages and corresponding analysis and synthesis tools, the model-integrated approach is applied in a metamodeling architecture, where formal models of domain-specific modeling languages-called metamodels-play a key role in customizing and connecting components of tool chains. This paper discusses the principles and techniques of model-integrated embedded software development in detail, as well as the capabilities of the tools supporting the process. Examples in terms of real systems will be given that illustrate how the model-integrated approach addresses the physical nature, the assurance issues, and the dynamic structure of embedded software.

/pdf/model-integrated-development-of-embedded-software-1rbhpt7dwl.pdf

Akos Ledeczi

Papers

The flooding time synchronization protocol

Smart Dust: communicating with a cubic-millimeter computer

Sensor network-based countersniper system

The Generic Modeling Environment

Model-integrated development of embedded software