164 research outputs found

    Data gathering approach in dense sensor networks

    Get PDF
    Sensor/actuator networks promised to extend automated monitoring and control into industrial processes. Avionic system is one of the prominent technologies that can highly gain from dense sensor/actuator deployments. An aircraft with smart sensing skin would fulfill the vision of affordability and environmental friendliness properties by reducing the fuel consumption. Achieving these properties is possible by providing an approximate representation of the air flow across the body of the aircraft and suppressing the detected aerodynamic drags. To the best of our knowledge, getting an accurate representation of the physical entity is one of the most significant challenges that still exists with dense sensor/actuator network. This paper offers an efficient way to acquire sensor readings from very large sensor/actuator network that are located in a small area (dense network). It presents LIA algorithm, a Linear Interpolation Algorithm that provides two important contributions. First, it demonstrates the effectiveness of employing a transformation matrix to mimic the environmental behavior. Second, it renders a smart solution for updating the previously defined matrix through a procedure called learning phase. Simulation results reveal that the average relative error in LIA algorithm can be reduced by as much as 60% by exploiting transformation matrix

    Liberalising Deployment of Internet of Things Devices and Services in Large Scale Environments

    Get PDF

    Extending Cyber-Physical Systems to Support Stakeholder Decisions Under Resource and User Constraints: Applications to Intelligent Infrastructure and Social Urban Systems

    Full text link
    In recent years, rapid urbanization has imposed greater load demands on physical infrastructure while placing stressors (e.g., pollution, congestion, social inequity) on social systems. Despite these challenges, opportunities are emerging from the unprecedented proliferation of information technologies enabling ubiquitous sensing, cloud computing, and full-scale automation. Together, these advancements enable “intelligent” systems that promise to enhance the operation of the built environment. Even with these advancements, the ability of professionals to “sense for decisions” —data-driven decision processes based on sensed data that have quantifiable returns on investment—remains unrealized for an entire class of problems. In response, this dissertation builds a rigorous foundation enabling stakeholders to use sensor data to inform decisions in two applications: infrastructure asset management and community-engaged decision making. This dissertation aligns sensing strategies with decisions governing infrastructure management by extending the role of reliability methods to quantify system performance. First, the reliability index is used as a scalar measure of the safety (i.e., failure probability) that is extracted from monitoring data to assess structural condition relative to a failure limit state. As an example, long-term data collected from a wireless sensing network (WSN) installed on the Harahan Bridge (Memphis, TN) is used in a reliability framework to track the fatigue life of critical eyebar assemblies. The proposed reliability-based SHM framework is then generalized to formally and more broadly link SHM data with condition ratings (CRs) because inspector-assigned CRs remain the primary starting point for asset management decisions made in practice today. While reliability methods historically quantify safety with respect to a single failure limit state, this work demonstrates that there exist measurable reliability index values associated with “lower” limit states below failure that more richly characterize structural performance and rationally map to CR scales. Consequently, monitoring data can be used to assign CRs based on quantitative information encompassing the measurable damage domain, as opposed to relying on visual inspection. This work reflects the first-ever SHM framework to explicitly map monitoring data to actionable decisions and is validated using a WSN on the Telegraph Road Bridge (TRB) (Monroe, MI). A primary challenge faced by solar-powered WSNs is their stringent energy constraints. For decision-making processes relying on statistical estimation of performance, the utility of data should be considered to optimize the data collection process given these constraints. This dissertation proposes a novel stochastic data collection and transmission policy for WSNs that minimizes the variance of a measured process’ estimated parameters subject to constraints imposed by energy and data buffer sizes, stochastic models of energy and event arrivals, the value of measured data, and temporal death. Numerical results based on one-year of data collected from the TRB illustrate the gains achieved by implementing the optimal policy to obtain response data used to estimate the reliability index. Finally, this dissertation extends the work performed in WSN and sense-for-decision frameworks by exploring their role in community-based decision making. This work poses societal engagement as a necessary entry point to urban sensing efforts because members of under-resourced communities are vulnerable to lack of access to data and information. A novel, low-power WSN architecture is presented that functions as a user-friendly sensing solution that communities can rapidly deploy. Applying this platform, transformative work to “democratize” data is proposed in which members of vulnerable communities collect data and generate insights that inform their decision-making strategies.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162898/1/kaflanig_1.pd

    Raspberry Pi Technology

    Get PDF
    • …
    corecore