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

Wireless sensor networks: a 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

A wireless network consisting of a large number of small sensors with low-power transceivers can be an effective tool for gathering data in a variety of environments. The data collected by each sensor is communicated through the network to a single processing center that uses all reported data to determine characteristics of the environment or detect an event. The communication or message passing process must be designed to conserve the limited energy resources of the sensors. Clustering sensors into groups, so that sensors communicate information only to clusterheads and then the clusterheads communicate the aggregated information to the processing center, may save energy. In this paper, we propose a distributed, randomized clustering algorithm to organize the sensors in a wireless sensor network into clusters. We then extend this algorithm to generate a hierarchy of clusterheads and observe that the energy savings increase with the number of levels in the hierarchy. Results in stochastic geometry are used to derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the network when all sensors report data through the clusterheads to the processing center.

/pdf/an-energy-efficient-hierarchical-clustering-algorithm-for-574o6gyna1.pdf

An energy efficient hierarchical clustering algorithm for wireless sensor networks

We present nesC, a programming language for networked embedded systems that represent a new design space for application developers. An example of a networked embedded system is a sensor network, which consists of (potentially) thousands of tiny, low-power "motes," each of which execute concurrent, reactive programs that must operate with severe memory and power constraints.nesC's contribution is to support the special needs of this domain by exposing a programming model that incorporates event-driven execution, a flexible concurrency model, and component-oriented application design. Restrictions on the programming model allow the nesC compiler to perform whole-program analyses, including data-race detection (which improves reliability) and aggressive function inlining (which reduces resource consumption).nesC has been used to implement TinyOS, a small operating system for sensor networks, as well as several significant sensor applications. nesC and TinyOS have been adopted by a large number of sensor network research groups, and our experience and evaluation of the language shows that it is effective at supporting the complex, concurrent programming style demanded by this new class of deeply networked systems.

/pdf/the-nesc-language-a-holistic-approach-to-networked-embedded-3nbdmnogzq.pdf

The nesC language: a holistic approach to networked embedded systems

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

Model integrated computing (MIC) is gaining increased attention as an effective and efficient method for developing, maintaining, and evolving large-scale, domain-specific software applications for computer-based systems. MIC is a model-based approach to software development, allowing the synthesis of application programs from models created using customized, domain-specific model integrated program synthesis (MIPS) environments. Until now, these MIPS environments have been handcrafted. Analysis has shown that it is possible to "model the modeling environment" by creating a metamodel that specifies both the syntactic and semantic behavior of the desired domain-specific MIPS environment (DSME). Such a metamodel could then be used to synthesize the DSME itself allowing the entire design environment to safely and efficiently evolve in the face of changing domain requirements. This paper discusses the use of the Unified Modeling Language and the Object Constraint Language to specify, such metamodels, and describes a method for incorporating these metamodels into the MultiGraph Architecture, a MIPS creation toolset.

https://www.isis.vanderbilt.edu/sites/default/files/Nordstrom_GG_4_0_1999_Metamodeli.pdf

Metamodeling-rapid design and evolution of domain-specific modeling environments

Computer-based systems (CBS) development integrates various disciplines, such as hardware design, software engineering, and performance modeling, as well as the "base" engineering discipline in which the CBS will operate. As such, use of a "non-native" modeling language is not acceptable when performing CBS design, and rapid specification and development of domain-specific modeling languages (DSMLs) is necessary. We advocate a UML-based metamodeling technique to DSML specification and generation. A key feature of our approach is the composition of new metamodels from existing metamodels through the use of three newly defined UML operators-equivalence, implementation inheritance, and interface inheritance. The paper describes the development of these new operators, details how they are used in metamodel composition, and presents examples of metamodel composition.

On metamodel composition

Embedded computer-based systems are becoming highly complex and difficult to implement due to the large number of concerns designers must address. These systems are tightly coupled to their environments, requiring an integrated view that encompasses both the information system and its physical surroundings. Mathematical analysis of such systems requires formal modeling of both "sides", including their interaction. There exist a number of suitable modeling techniques for describing both the information system component and physical environment, but the best choice changes from domain to domain. We propose a two-level approach to modeling that introduces a meta-level representation. Meta-level models define modeling languages, but they can also be used to capture subtle interactions between domain level models. We show how the two-level approach can be supported with computational tools, and what kinds of novel capabilities are offered.

/pdf/specifying-graphical-modeling-systems-using-constraint-based-1qclck6djj.pdf

G. Nordstrom

Papers

Smart Dust: communicating with a cubic-millimeter computer

The Generic Modeling Environment

Metamodeling-rapid design and evolution of domain-specific modeling environments

On metamodel composition

Specifying graphical modeling systems using constraint-based meta models