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

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

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

We present a novel radio interference based sensor localization method for wireless sensor networks. The technique relies on a pair of nodes emitting radio waves simultaneously at slightly different frequencies. The carrier frequency of the composite signal is between the two frequencies, but has a very low frequency envelope. Neighboring nodes can measure the energy of the envelope signal as the signal strength. The relative phase offset of this signal measured at two receivers is a function of the distances between the four nodes involved and the carrier frequency. By making multiple measurements in an at least 8-node network, it is possible to reconstruct the relative location of the nodes in 3D. Our prototype implementation on the MICA2 platform yields an average localization error as small as 3 cm and a range of up to 160 meters. In addition to this high precision and long range, the other main advantage of the Radio Interferometric Positioning System (RIPS) is the fact that it does not require any sensors other than the radio used for wireless communication.

/pdf/radio-interferometric-geolocation-1t27bc3ate.pdf

Radio interferometric geolocation

6 15-322-3 162 Abstr-act-The design of reliable, dynamic, fault-tolerant services in wireless sensor networks is a big challenge and a hot research topic. In this paper an optimization method is proposed that can be used to tune parameters of the middleware services and applications to provide optimal performance. The optimization method is based on simulation, and is capable of handling 'noisy' error surfaces. The proposed optimization algorithm is illustrated by a new spanning-tree formation algorithm, which can effectively operate even if links between nodes are asymmetrical. In the near future large-scale sensor networks will be the key elements of embedded systems used in space and aviationrelated challenges, e.g. monitoring and control of safety critical systems [l], Smart Surfaces, Smart Dust [2], or can be used to make everyday life more comfortable, e.g. Intelligent Spaces [3]. These sensor networks often use distributed operating system-like services (called middleware) over wireless communication protocols, which must be fault tolerant and adaptive because of the dynamic network topology and changing mission objectives. The design of such middleware services is not straightforward, since the sensors have limited resources, and thus the used protocols are usually very simple compared to ones used in wired communication schemes. The nondeterministic nature of the environment is another factor making the design more difficult. This paper presents a simulation-based optimization method that can be used to tune the algorithms used in the middleware layer. Also some results are presented that were gained by the proposed method. The hardware structure of the wireless sensors may vary greatly, but invariably each of the intelligent sensors is a compact device with its own power source, it contains a processing unit (a small microprocessor), a communication unit and the sensor itself. The widely used Berkeley fieldnodes (or motes) have similar structure containing an 8-bit microcontroller, a 916.5 MHz radio and several interchangeable sensors. These tiny units have a simple local operating system called TinyOS. Application-specific middleware services can be added to provide an interface between the application and the primitive services of the local operating system. The middleware can also be considered as a distributed operating system that establishes network-wide resources and functions that the applications can utilize, e.g. leader election, spanning tree formation, distributed consensus and mutual exclusion, distributed transactions, group communication services, clock synchronization, etc. Typical applications may include hundreds or thousands of motes with often unknown or random distribution (e.g. motes dropped from an airplane to a hostile environment). The communication services must be reliably established to achieve the overall goal of the distributed sensor system. During the operation of the sensor network different metrics for the quality of service (QoS) are required, a dynamic tradeoff is necessary between accuracy, response time, power consumption, and other qualities of interest. Thus, the middleware services must be prepared to adapt to the actual circumstances and the QoS metric. To design such middleware services, the highly random nature of the environment (wireless communication with possible disturbances, random layout, possibly damaged motes, etc.) must be taken into consideration. The proposed design method is a probabilistic simulationbased optimization that can help the designer choose the right algorithm with an optimal parameter set. The MATLAB-based simulator is capable of simulating the important aspects of the communication scheme: local OS services including the network protocol stack, and also the radio transmission phenomena (signal power vs. distance, fading, collision, disturbances). In the simulation

Simulation-based optimization of communication protocols for large-scale wireless sensor networks

An ad-hoc wireless sensor network-based system is presented that detects and accurately locates shooters even in urban environments. The localization accuracy of the system in open terrain is competitive with that of existing centralized countersniper systems. However, the presented sensor network-based solution surpasses the traditional approach because it can mitigate acoustic multipath effects prevalent in urban areas and it can also resolve multiple simultaneous shots. These unique characteristics of the system are made possible by employing novel sensor fusion techniques that utilize the spatial and temporal diversity of multiple detections. In this article, in addition to the overall system architecture, the middleware services and the unique sensor fusion algorithms are described. An analysis of the experimental data gathered during field trials at US military facilities is also presented.

/pdf/countersniper-system-for-urban-warfare-klva8kb7rb.pdf

Peter Volgyesi

Papers

Smart Dust: communicating with a cubic-millimeter computer

The Generic Modeling Environment

Radio interferometric geolocation

Simulation-based optimization of communication protocols for large-scale wireless sensor networks

Countersniper system for urban warfare