Zhaoyan Fan

Architectural design of a sensory node controller for optimized energy utilization in sensor networks

The increasing complexity of manufacturing machines and the continued demand for high productivity have led to growing applications of sensor networks to enable more reliable, timely, and comprehensive information gathering from the machines being monitored. An effective and efficient utilization of sensor networks requires new sensor designs that enable adaptive event-driven information gathering based on the condition of the machines, as well as a coordinated information distribution adjusted to the available communication bandwidth of the network. This paper investigates several fundamental aspects regarding the architectural design of a sensory node controller (SNOC). The SNOC is the key element in a large-scale sensor network that coordinates the operation of individual sensors and the communication among various sensing clusters to realize distributed intelligent sensing. A parametric SNOC design that dynamically adjusts the power supply and the data-acquisition procedure to reduce the overall energy consumption of the sensor network is presented. Considerations on both the hardware and software aspects of the design to achieve energy efficiency are described, and analytical formulations are derived. Simulation results for a sensor network consisting of 40 SNOCs, each coordinating eight physical sensors, have shown that the design is able to reduce the energy consumption by about 43%, as compared to traditional techniques. A prototype SNOC was designed and implemented, based on the platform of a commercially available microcontroller, and experimentally tested for its ability to dynamically adjust the power consumption. The study has provided a concrete input to the design optimization and experimental realization of an SNOC-based sensor network for machine-system monitoring.

Occupancy and indoor environment quality sensing for smart buildings

This paper presents a technique to determine the occupancy and indoor environment quality (IEQ) in buildings by enhancing physical measurements from a distributed sensor network with a statistical estimation method. The research is motivated by the increasing demand for improving energy efficiency while maintaining healthy and comfortable environment in buildings. Features representing the occupancy level and the relative changes are extracted from a suite of sensors: passive infra-red (PIR) sensors, Carbon Dioxide (CO2) concentration sensors, and relative humidity (RH) sensors, which are networked and installed in a laboratory. An Autoregressive Hidden Markov Model (ARHMM) has been developed to model the occupancy pattern, based on the measurements, given its ability to establish correlations among the observed variables. The result is compared with that obtained from the classical Hidden Markov Model (HMM) and Support Vector Machines (SVM), which indicates that the ARHMM estimation method performed better than the other two methods, with an average estimation accuracy of 80.78%.

Occupancy estimation for smart buildings by an auto-regressive hidden Markov model

One of the primary energy consumers in buildings are the Heating, Ventilation, and Air-Conditioning (HVAC) systems, which usually operate on a fixed schedule, i.e., running from early morning until late evening during the weekdays. This fixed operation schedule does not take the dynamics of occupancy level in the building into consideration, therefore may lead to waste of energy. An estimate of the number of occupants in the building with time can contribute to improving the control policy of the buildings HVAC system by reducing energy consumption. In this paper, the auto-regressive hidden Markov model (ARHMM), is investigated to estimate the number of occupants in a research laboratory in a building using a wireless sensor network deployed. The network is composed of stand-alone sensing nodes with wireless data transmission capability, a base station that collects data from the sensing nodes, and a server to analyze the data from the base station. Experimental results and numerical simulation demonstrate that the ARHMM is more effective in estimating the number of occupants in the laboratory than the HMM algorithm, especially when the occupancy level fluctuates frequently.

Enhancement of Measurement Efficiency for Electrical Capacitance Tomography

This paper presents a new sensing method to improve the efficiency of electrical capacitance tomography. Instead of applying one excitation signal to one electrode at a time, the multiple excitation capacitance polling (MECaP) method progressively applies an increasing number of multiple excitations to multiple electrodes and simultaneously measures the capacitance values, thereby significantly increasing the image scanning speed. The performance of a MECaP-based sensor system is numerically simulated and analyzed using the finite element method. Experimental evaluation of the numerical results demonstrates the effectiveness and efficiency of the new sensing technique and its applicability to a broad range of commercial and industrial applications where permittivity determination through capacitance measurement provides an effective means for noninvasive dynamic processes monitoring in an enclosed environment.

Energy-Aware Data Acquisition in Wireless Sensor Networks

This paper presents an energy-aware data acquisition scheme for Wireless Sensor Networks (WSNs) that integrates Dynamic Voltage Scaling (DVS) and Dynamic Modulation Scaling (DMS) techniques to minimize the total energy consumption within the networks. Applications of data acquisition with real-time constraints are considered in the study. The new scheme achieves energy savings by trading energy against both computation and communication time. The objective is to design a strategy that optimally allocates the limited processing time to computation and communication by adjusting the processors supply voltage and the radios modulation level. This leads to a resource allocation problem which exhibited three characteristics: (1) mono-increasing, (2) mono-decreasing, and (3) convex functions which depend on the in-situ condition. By finding the boundaries that distinguish the three cases, a strategy is built that used a simple policy for the first two monotone cases and an optimization approach for the third case. By applying this strategy as the energy-aware scheme, the simulation results demonstrate an average 60 percent energy reduction when compared to a node where no energy-aware technique is used. The ineffectiveness of DVS in high communication tasks is also rectified by incorporating DMS into DVS.

Energy efficient wireless sensor network for dynamic system monitoring

This paper presents a systematic approach to the design and implementation of an energy-efficient multi-sensor network. The nodes of the sensor network form the basis of a sectioned Bayesian network that can be used to determine the state of the system being monitored. A key issue in the design of Bayesian networks for monitoring engineering systems is to ensure that reliable inference scheme about the health state of the system can be made by combining information acquired from each sensor in the system into a single Bayesian network. As the size of the network increases, aggregating information made by all the sensors becomes computationally intractable. Hence, sectioning of the Bayesian network based on functional or logical constraints allows computational efficiency in aggregating information and reduces overall communication requirements. Furthermore, an in-network data processing scheme, motivated by the concept of Dynamic Voltage Scheduling, has been investigated to minimize computation energy consumption through dynamically adjusting the voltage supply and clock frequency of the individual sensors. As a result, the processor idle time can be better utilized for prolonged computation latency, leading to significantly reduced energy cost and increased computational efficiency.

A new sensing method for Electrical Capacitance Tomography

A new sensing method, termed Multiple Excitation Capacitance Polling (MECaP), has been developed to improve the efficiency of Electrical Capacitance Tomography (ECT). Instead of applying one excitation signal to an electrode at a time, the MECaP technique progressively applies an increasing number of excitation signals to multiple electrodes, and measures the capacitance values simultaneously, thereby increasing the scanning speed significantly. A numerical model of a MECaP-based sensor system has been developed using the Finite Element method. Results obtained from simulation and experiment demonstrated the effectiveness and efficiency of the new sensing technique and its applicability to a broad range of commercial and industrial applications where capacity measurement provides an efficient means for non-invasive monitoring of dynamic processes.

Self-energized acoustic wireless sensor for pressure-temperature measurement in injection molding cavity

This paper presents a self-energized acoustic wireless sensing method for simultaneous measurement of pressure and temperature within the cavity of an injection mold and wireless transmission of the measured parameters out of such a metallically shielded environment through ultrasound pulse trains. Two attributes of the ultrasound pulses have been explored to enable dual-parameter sensing: the number of pulses serves as a direct measure for the magnitude of the pressure, and the carrier frequency of the pulses accounts for the polymer melt temperature. A piezoceramic stack serves as an energy harvester that scavenges energy from the pressure differential of the molding process itself to power the sensor electronics. Systematic comparative experiments on a real-world injection molding machine with commercial wired sensors as the reference verified the accuracy and robustness of the new sensor. Small in size, requiring no battery and no holes to be drilled through the mold, the new sensor is suited for embedded monitoring in a wide range of industrial applications characterized by low accessibility and harsh environmental conditions.

CLOUD-BASED PROGNOSIS : PERSPECTIVE AND CHALLENGE

Comprehensive acquisition, distribution, and utilization of information about machine equipment and/or processes across spatial boundaries for improved productivity and decision have increasingly become the hallmark of advanced manufacturing. The emergence of cloud computing has created ample opportunities to achieve this goal. This paper presents a review of the state-of-the-art of prognosis technique for manufacturing and its future development motivated by the cloud infrastructure. Prevailing methods of prognosis are summarized, and their respective performance is comparatively evaluated. Basic principles and recent advances in cloud computing, as well as its application to cloud manufacturing, are introduced. Based on the survey, the concept of cloud-based prognosis is proposed, and its architecture as well as associated challenges are discussed.

Design of a self-energized wireless sensor for simultaneous pressure and temperature measurement

On-line monitoring of polymer melt state is critical to ensuring part quality in injection molding. This paper presents the design of a dual-parameter acoustic wireless sensor for simultaneous measurement of pressure and temperature variations within the mold cavity. The sensor consists of three major functional components: a piezoceramic energy harvester, a modulator circuit, and an electric-acoustic signal converter. It discretizes pressure and temperature variations during the molding cycle into a number of acoustic pulses with varying carrier frequencies, and transmits them to an remote receiver for data retrieval. To minimize sensor dimension and optimize functionality, the mechanical and electronic portions of the sensor are concurrently designed as a mechatronic system. The sensor is prototyped and evaluated on a production grade injection molding machine. Good agreement is found between the new, wireless sensor and traditional wired sensors.