An intelligent approach for deploying applications in WSN's

Wireless sensor networks have been identified as one of the key technologies for the 21st century. They consist of tiny devices with limited processing and power capabilities, called motes that can be deployed in large numbers of useful sensing capabilities. Even though, they are flexible and easy to deploy, there are a number of considerations when it comes to their fault tolerance, conserving energy and re-programmability that need to be addressed before we draw any substantial conclusions about the effectiveness of this technology. In order to overcome their limitations, we propose a middleware solution. The proposed scheme is composed based on two main methods. The first method involves the creation of a flexible communication protocol based on technologies such as Mobile Code/Agents and Linda-like tuple spaces. In this way, every node of the wireless sensor network will produce and process data based on what is the best for it but also for the group that it belongs too. The second method incorporates the above protocol in a middleware that will aim to bridge the gap between the application layer and low level constructs such as the physical layer of the wireless sensor network. A fault tolerant platform for deploying and monitoring applications in real time offers a number of possibilities for the end user giving him in parallel the freedom to experiment with various parameters, in an effort towards the deployed applications running in an energy efficient manner inside the network. The proposed scheme is evaluated through a number of trials aiming to test its merits under real time conditions and to identify its effectiveness against other similar approaches. Finally, parameters which determine the characteristics of the proposed scheme are also examined.

High performance liquid level monitoring system based on polymer fiber Bragg gratings embedded in silicone rubber diaphragm

Liquid-level sensing technologies have attracted great prominence, because such measurements are essential to industrial applications, such as fuel storage, flood warning and in the biochemical industry. Traditional liquid level sensors are based on electromechanical techniques; however they suffer from intrinsic safety concerns in explosive environments. In recent years, given that optical fiber sensors have lots of well-established advantages such as high accuracy, costeffectiveness, compact size, and ease of multiplexing, several optical fiber liquid level sensors have been investigated which are based on different operating principles such as side-polishing the cladding and a portion of core, using a spiral side-emitting optical fiber or using silica fiber gratings. The present work proposes a novel and highly sensitive liquid level sensor making use of polymer optical fiber Bragg gratings (POFBGs). The key elements of the system are a set of POFBGs embedded in silicone rubber diaphragms. This is a new development building on the idea of determining liquid level by measuring the pressure at the bottom of a liquid container, however it has a number of critical advantages. The system features several FBG-based pressure sensors as described above placed at different depths. Any sensor above the surface of the liquid will read the same ambient pressure. Sensors below the surface of the liquid will read pressures that increase linearly with depth. The position of the liquid surface can therefore be approximately identified as lying between the first sensor to read an above-ambient pressure and the next higher sensor. This level of precision would not in general be sufficient for most liquid level monitoring applications; however a much more precise determination of liquid level can be made by linear regression to the pressure readings from the sub-surface sensors. There are numerous advantages to this multi-sensor approach. First, the use of linear regression using multiple sensors is inherently more accurate than using a single pressure reading to estimate depth. Second, common mode temperature induced wavelength shifts in the individual sensors are automatically compensated. Thirdly, temperature induced changes in the sensor pressure sensitivity are also compensated. Fourthly, the approach provides the possibility to detect and compensate for malfunctioning sensors. Finally, the system is immune to changes in the density of the monitored fluid and even to changes in the effective force of gravity, as might be obtained in an aerospace application. The performance of an individual sensor was characterized and displays a sensitivity (54 pm/cm), enhanced by more than a factor of 2 when compared to a sensor head configuration based on a silica FBG published in the literature, resulting from the much lower elastic modulus of POF. Furthermore, the temperature/humidity behavior and measurement resolution were also studied in detail. The proposed configuration also displays a highly linear response, high resolution and good repeatability. The results suggest the new configuration can be a useful tool in many different applications, such as aircraft fuel monitoring, and biochemical and environmental sensing, where accuracy and stability are fundamental. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Fiber Bragg Grating sensors for railway systems

Fiber Bragg grating (FBG) sensor technology has been attracting substantial industrial interests for the last decade. FBG sensors have seen increasing acceptance and widespread use for structural sensing and health monitoring applications in composites, civil engineering, aerospace, marine, oil & gas, and smart structures. One transportation system that has been benefitted tremendously from this technology is railways, where it is of the utmost importance to understand the structural and operating conditions of rails as well as that of freight and passenger service cars to ensure safe and reliable operation. Fiberoptic sensors, mostly in the form of FBGs, offer various important characteristics, such as EMI/RFI immunity, multiplexing capability, and very long-range interrogation (up to 230 km between FBGs and measurement unit), over the conventional electrical sensors for the distinctive operational conditions in railways. FBG sensors are unique from other types of fiber-optic sensors as the measured information is wavelength-encoded, which provides self-referencing and renders their signals less susceptible to intensity fluctuations. In addition, FBGs are reflective sensors that can be interrogated from either end, providing redundancy to FBG sensing networks. These two unique features are particularly important for the railway industry where safe and reliable operations are the major concerns. Furthermore, FBGs are very versatile and transducers based on FBGs can be designed to measure a wide range of parameters such as acceleration and inclination. Consequently, a single interrogator can deal with a large number of FBG sensors to measure a multitude of parameters at different locations that spans over a large area.

Creating gold nanoprisms directly on quartz crystal microbalance electrodes for mercury vapor sensing

A novel electrochemical route is used to form highly {111}-oriented and size-controlled Au nanoprisms directly onto the electrodes of quartz crystal microbalances (QCMs) which are subsequently used as mercury vapor sensors. The Au nanoprism loaded QCM sensors exhibited excellent response–concentration linearity with a response enhancement of up to ~ 800% over a non-modified sensor at an operating temperature of 28 °C. The increased surface area and atomic-scale features (step/defect sites) introduced during the growth of nanoprisms are thought to play a significant role in enhancing the sensing properties of the Au nanoprisms toward Hg vapor. The sensors are shown to have excellent Hg sensing capabilities in the concentration range of 0.123–1.27 ppmv (1.02–10.55 mg m − 3), with a detection limit of 2.4 ppbv (0.02 mg m − 3) toward Hg vapor when operating at 28 °C, and 17 ppbv (0.15 mg m − 3) at 89 °C, making them potentially useful for air monitoring applications or for monitoring the efficiency of Hg emission control systems in industries such as mining and waste incineration. The developed sensors exhibited excellent reversible behavior (sensor recovery) within 1 h periods, and crucially were also observed to have high selectivity toward Hg vapor in the presence of ethanol, ammonia and humidity, and excellent long-term stability over a 33 day operating period.

Characterisation of humidity dependence of a metal oxide semiconductor (MOS) sensor array using Partial Least Squares (PLS)

Metal oxide semiconductor (MOS) sensors are a class of chemical sensor that have potential for being a practical core sensor module for an electronic nose system in various environmental monitoring applications. However, the responses of these sensors may be affected by changes in humidity and this must be taken into consideration when developing calibration models. This paper characterises the humidity dependence of a sensor array which consists of 12 MOS sensors. The results were used to develop calibration models using partial least squares. Effects of humidity on the response of the sensor array and predictive ability of partial least squares are discussed. It is shown that partial least squares can provide proper calibration models to compensate for effects caused by changes in humidity.

Characterisation of humidity dependence of a metal oxide semiconductor sensor array using partial least squares

Metal oxide semiconductor (MOS) sensors are a class of chemical sensors that have potential for being a practical core sensor module for an electronic nose system in various environmental monitoring applications. However, the responses of these sensors may be affected by changes in humidity and this must be taken into consideration when developing calibration models. This paper characterises the humidity dependence of a sensor array which consists of 12 MOS sensors. The results were used to develop calibration models using partial least squares (PLS). Effects of humidity on the response of the sensor array and predictive ability of partial least squares are discussed. It is shown that partial least squares can provide proper calibration models to compensate for effects caused by changes in humidity. Special Issue: Selected Paper from the 12th International Symposium on Olfaction and Electronic Noses - ISOEN 2007, International Symposium on Olfaction and Electronic Noses.

Near infrared spectroscopy as a rapid non-invasive tool for agricultural and industrial process management with special reference to avocado and sandalwood industries

Near infrared spectroscopy (NIRS) can play a vital role as a cost effective, rapid, non-invasive, reproducible diagnostic tool for many environmental management, agricultural and industrial waste water monitoring applications. In this paper we highlight the ability of NIRS technology to be used as a diagnostic tool in agricultural and environmental applications through the successful assessment of Fourier Transform NIRS to predict α santalol in sandalwood chip samples, and maturity of ‘Hass’ avocado fruit based on dry matter content. Presented at the Third International Conference on Challenges in Environmental Science & Engineering, CESE-2010. 26 September – 1 October 2010, The Sebel, Cairns, Queensland, Australia.

A novel interrogation system for fiber Bragg grating sensor array

Interrogation techniques for fiber Bragg grating sensor arrays need particular attention in the case of structural health monitoring applications involving dynamic strain measurement. Typically the performance of the sensing system is dependent on both the sensor type and the interrogation method employed. A novel interrogation system is proposed here that consists of different interrogation units for each sensor in the array, each unit comprising of a circulator, chirped grating and a Mach-Zehnder interferometer. We present an analysis that consists of tracking the spectral changes as the light passes through various elements in the interrogation system. This is expected to help in the optimization of sensor and interrogation elements leading to improved performance of the health monitoring system.

Ultrasonic performance of the PVDF thin film sensors under thermal fatigue

In the present work, the ultrasonic strain sensing performance of the large area PVDF thin film subjected to the thermal fatigue is studied. The PVDF thin film is prepared using hot press and the piezoelectric phase (beta-phase) has been achieved by thermo-mechanical treatment and poling under DC field. The sensors used in aircrafts for structural health monitoring applications are likely to be subjected to a wide range of temperature fluctuations which may create thermal fatigue in both aircraft structures and in the sensors. Thus, the sensitivity of the PVDF sensors for thermal fatigue needs to be studied for its effective implementation in the structural health monitoring applications. In present work, the fabricated films have been subjected to certain number of thermal cycles which serve as thermal fatigue and are further tested for ultrasonic strain sensitivity at various different frequencies. The PVDF sensor is bonded on the beam specimen at one end and the ultrasonic guided waves are launched with a piezoelectric wafer bonded on another end of the beam. Sensitivity of PVDF sensor in terms of voltage is obtained for increasing number of thermal cycles. Sensitivity variation is studied at various different extent of thermal fatigue. The variation of the sensor sensitivity with frequency due to thermal fatigue at different temperatures is also investigated. The present investigation shows an appropriate temperature range for the application of the PVDF sensors in structural health monitoring.

Energy Harvesting WSNs for Accurately Estimating the Maximum Sensor Reading: Trade-Offs and Optimal Design

Computing the maximum of sensor readings arises in several environmental, health, and industrial monitoring applications of wireless sensor networks (WSNs). We characterize the several novel design trade-offs that arise when green energy harvesting (EH) WSNs, which promise perpetual lifetimes, are deployed for this purpose. The nodes harvest renewable energy from the environment for communicating their readings to a fusion node, which then periodically estimates the maximum. For a randomized transmission schedule in which a pre-specified number of randomly selected nodes transmit in a sensor data collection round, we analyze the mean absolute error (MAE), which is defined as the mean of the absolute difference between the maximum and that estimated by the fusion node in each round. We optimize the transmit power and the number of scheduled nodes to minimize the MAE, both when the nodes have channel state information (CSI) and when they do not. Our results highlight how the optimal system operation depends on the EH rate, availability and cost of acquiring CSI, quantization, and size of the scheduled subset. Our analysis applies to a general class of sensor reading and EH random processes.

Production of positive controls for calcivirus-specific PCR using recombinant baculiovirus technology

Recent advances in our knowledge of the genetic structure of human caliciviruses (HuCVs) and small round-structured viruses (SRSVs) have led to the development of polymerase chain reaction (PCR)-based molecular tests specific for these viruses. These methods have been developed to detect a number of human pathogenic viruses in environmental samples including water, sewage and shellfish. HuCVs and SRSVs are not culturable, and no animal model is currently available. Therefore there is no convenient method of preparing viruses for study or for reagent production. One problem facing those attempting to use PCR-based methods for the detection of HuCVs and SRSVs is the lack of a suitable positive control substrate. This is particularly important when screening complex samples in which the levels of inhibitors present may significantly interfere with amplificiation. Regions within the RNA polymerase regions of two genetically distinct human caliciviruses have been amplified and used to produce recombinant baculoviruses which express RNA corresponding to the calicivirus polymerase. This RNA is being investigated as a positive control substrate for PCR testing, using current diagnostic primer sets. Recombinant baculovirus technology will enable efficient and cost-effective production of large quantities of positive control RNA with a specific known genotype. We consider the development of these systems as essential for successful screening and monitoring applications.

Service Provisioning in Mobile Networks Through Distributed Coordinated Resource Management

The pervasiveness of personal computing platforms offers an unprecedented opportunity to deploy large-scale services that are distributed over wide physical spaces. Two major challenges face the deployment of such services: the often resource-limited nature of these platforms, and the necessity of preserving the autonomy of the owner of these devices. These challenges preclude using centralized control and preclude considering services that are subject to performance guarantees. To that end, this thesis advances a number of new distributed resource management techniques that are shown to be effective in such settings, focusing on two application domains: distributed Field Monitoring Applications (FMAs), and Message Delivery Applications (MDAs). In the context of FMA, this thesis presents two techniques that are well-suited to the fairly limited storage and power resources of autonomously mobile sensor nodes. The first technique relies on amorphous placement of sensory data through the use of novel storage management and sample diffusion techniques. The second approach relies on an information-theoretic framework to optimize local resource management decisions. Both approaches are proactive in that they aim to provide nodes with a view of the monitored field that reflects the characteristics of queries over that field, enabling them to handle more queries locally, and thus reduce communication overheads. Then, this thesis recognizes node mobility as a resource to be leveraged, and in that respect proposes novel mobility coordination techniques for FMAs and MDAs. Assuming that node mobility is governed by a spatio-temporal schedule featuring some slack, this thesis presents novel algorithms of various computational complexities to orchestrate the use of this slack to improve the performance of supported applications. The findings in this thesis, which are supported by analysis and extensive simulations, highlight the importance of two general design principles for distributed systems. First, a-priori knowledge (e.g., about the target phenomena of FMAs and/or the workload of either FMAs or DMAs) could be used effectively for local resource management. Second, judicious leverage and coordination of node mobility could lead to significant performance gains for distributed applications deployed over resource-impoverished infrastructures.

Use of MIR Spectroscopy for Optimisation of Biogas Plants

MIR spectroscopy is an established technique which has process monitoring applications in the chemical and pharmaceutical industries. Previous attempts to utilise the technology for monitoring of AD plants were of limited success, with operation hindered by severe clogging of the probe.

Novel fittings, which allow a probe to be withdrawn from the process fluid, cleaned and recalibrated in situ have now been developed to combat this clogging problem. This has allowed a spectroscopic probe to be used successfully in lab scale digesters for real time measurement of VFA concentration, a key parameter to the stability of AD plants.

This project will demonstrate the technology at a farm scale AD plant for the first time. Both real-time measurements of VFA concentrations and parameters currently measured by plant operators will be available, leading to state-of-the-art monitoring and control of the AD plant. With the improved monitoring that this probe will deliver, it is hoped to realise a 10% increase in biogas production without compromising the stability of the process. This will deliver both economic and environmental benefits.

A quality-scalable and energy-efficient approach for spectral analysis of heart rate variability

Today there is a growing interest in the integration of health monitoring applications in portable devices necessitating the development of methods that improve the energy efficiency of such systems. In this paper, we present a systematic approach that enables energy-quality trade-offs in spectral analysis systems for bio-signals, which are useful in monitoring various health conditions as those associated with the heart-rate. To enable such trade-offs, the processed signals are expressed initially in a basis in which significant components that carry most of the relevant information can be easily distinguished from the parts that influence the output to a lesser extent. Such a classification allows the pruning of operations associated with the less significant signal components leading to power savings with minor quality loss since only less useful parts are pruned under the given requirements. To exploit the attributes of the modified spectral analysis system, thresholding rules are determined and adopted at design- and run-time, allowing the static or dynamic pruning of less-useful operations based on the accuracy and energy requirements. The proposed algorithm is implemented on a typical sensor node simulator and results show up-to 82% energy savings when static pruning is combined with voltage and frequency scaling, compared to the conventional algorithm in which such trade-offs were not available. In addition, experiments with numerous cardiac samples of various patients show that such energy savings come with a 4.9% average accuracy loss, which does not affect the system detection capability of sinus-arrhythmia which was used as a test case. 

Online audio background determination for complex audio environments

We present a method for foreground/background separation of audio using a background modelling technique. The technique models the background in an online, unsupervised, and adaptive fashion, and is designed for application to long term surveillance and monitoring problems. The background is determined using a statistical method to model the states of the audio over time. In addition, three methods are used to increase the accuracy of background modelling in complex audio environments. Such environments can cause the failure of the statistical model to accurately capture the background states. An entropy-based approach is used to unify background representations fragmented over multiple states of the statistical model. The approach successfully unifies such background states, resulting in a more robust background model. We adaptively adjust the number of states considered background according to background complexity, resulting in the more accurate classification of background models. Finally, we use an auxiliary model cache to retain potential background states in the system. This prevents the deletion of such states due to a rapid influx of observed states that can occur for highly dynamic sections of the audio signal. The separation algorithm was successfully applied to a number of audio environments representing monitoring applications.