Performance Analysis of IP based WSNs in real time systems

Wireless sensor networks (WSNs) are the key enablers of the internet of things (IoT) paradigm. Traditionally, sensor network research has been to be unlike the internet, motivated by power and device constraints. The IETF 6LoWPAN draft standard changes this, defining how IPv6 packets can be efficiently transmitted over IEEE 802.15.4 radio links. Due to this 6LoWPAN technology, low power, low cost micro- controllers can be connected to the internet forming what is known as the wireless embedded internet. Another IETF recommendation, CoAP allows these devices to communicate interactively over the internet. The integration of such tiny, ubiquitous electronic devices to the internet enables interesting real-time applications. This thesis work attempts to evaluate the performance of a stack consisting of CoAP and 6LoWPAN over the IEEE 802.15.4 radio link using the Contiki OS and Cooja simulator, along with the CoAP framework Californium (Cf). Ultimately, the implementation of this stack on real hardware is carried out using a raspberry pi as a border router with T-mote sky sensors as slip radios and CoAP servers relaying temperature and humidity data. The reliability of the stack was also demonstrated during scalability analysis conducted on the physical deployment. The interoperability is ensured by connecting the WSN to the global internet using different hardware platforms supported by Contiki and without the use of specialized gateways commonly found in non IP based networks. This work therefore developed and demonstrated a heterogeneous wireless sensor network stack, which is IP based and conducted performance analysis of the stack, both in terms of simulations and real hardware.

A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.

Reasoning over knowledge-based generation of situations in context spaces to reduce food waste

With the ever-growing amount of connected sensors (IoT), making sense of sensed data becomes even more important. Pervasive computing is a key enabler for sustainable solutions, prominent examples are smart energy systems and decision support systems. A key feature of pervasive systems is situation awareness which allows a system to thoroughly understand its environment. It is based on external interpretation of data and thus relies on expert knowledge. Due to the distinct nature of situations in different domains and applications, the development of situation aware applications remains a complex process. This thesis is concerned with a general framework for situation awareness which simplifies the development of applications. It is based on the Situation Theory Ontology to provide a foundation for situation modelling which allows knowledge reuse. Concepts of the Situation Theory are mapped to the Context Space Theory which is used for situation reasoning. Situation Spaces in the Context Space are automatically generated with the defined knowledge. For the acquisition of sensor data, the IoT standards O-MI/O-DF are integrated into the framework. These allow a peer-to-peer data exchange between data publisher and the proposed framework and thus a platform independent subscription to sensed data. The framework is then applied for a use case to reduce food waste. The use case validates the applicability of the framework and furthermore serves as a showcase for a pervasive system contributing to the sustainability goals. Leading institutions, e.g. the United Nations, stress the need for a more resource efficient society and acknowledge the capability of ICT systems. The use case scenario is based on a smart neighbourhood in which the system recommends the most efficient use of food items through situation awareness to reduce food waste at consumption stage.

Biofouling protection by electro-chlorination on optical windows for oceanographic sensors and imaging devices

Oceans environmental monitoring and seafloor exploitation need in situ sensors and optical devices (cameras, lights) in various locations and on various carriers in order to initiate and to calibrate environmental models or to operate underwater industrial process supervision. For more than 10 years Ifremer deploys in situ monitoring systems for various seawater parameters and in situ observation systems based on lights and HD Cameras. To be economically operational, these systems must be equipped with a biofouling protection dedicated to the sensors and optical devices used in situ. Indeed, biofouling, in less than 15 days [1] will modify the transducing interfaces of the sensors and causes unacceptable bias on the measurements provided by the in situ monitoring system. In the same way biofouling will decrease the optical properties of windows and thus altering the lighting and the quality fot he images recorded by the camera.

Sensor for cysteine based on oxovanadium(IV) complex of Salen modified carbon paste electrode

The electrochemical behavior of a carbon paste electrode modified (CPEM) with N,N′-ethylenebis(salicylideneiminato)oxovanadium(IV) complex ([(VO)-O-IV(Salen)]) was investigated as a new sensor for cysteine. Cyclic voltammetry at the modified electrode in 0.1 mol L-1 KCl Solution (pH 5.0) showed a single-electron reduction/oxidation of the Couple VO3+/VO2+. The CPEM with [VO(Salen)] presented good electrochemical stability in a wide pH range (4.0-10.0) and an ability to electrooxidate cysteine at 0.65 V versus SCE. These results demonstrate the viability of the use of this modified electrode as an amperometric sensor for cysteine determination. © 2004 Elsevier B.V. All rights reserved.

Fluidic flow delay by ionogel passive pumps in microfluidic paper-based analytical devices

[EN] A new concept for fluid flow manipulation in microfluidic paper-based analytical devices (m-PADs) is presented by introducing ionogel materials as passive pumps. m-PADs were fabricated using a new doubleside contact stamping process and ionogels were precisely photopolymerised at the inlet of the m-PADs.The ionogels remain mainly on the surface of the paper and get absorbed in the superficial paper-fibers allowing for the liquid to flow from the ionogel into the paper easily. As a proof of concept the fluid flowand mixing behaviour of two different ionogels mPADs were compared with the non-treated mPADs.It was demonstrated that both ionogels highly affect the fluid flow by delaying the flow due to their different physical and chemical properties and water holding capacities.

Adaptive coatings based on polyaniline for direct 2D observation of diffusion processes in microfluidic systems

[EN] Therefore the understanding and proper evaluation of the flow and mixing behaviour at microscale becomes a very important issue. In this study, the diffusion behaviour of two reacting solutions of HCI and NaOH were directly observed in a glass/polydimethylsiloxane microfluidic device using adaptive coatings based on the conductive polymer polyaniline that are covalently attached to the microchannel walls. The two liquid streams were combined at the junction of a Y-shaped microchannel, and allowed to diffuse into each other and react. The results showed excellent correlation between optical observation of the diffusion process and the numerical results. A numerical model which is based on finite volume method (FVM) discretisation of steady Navier-Stokes (fluid flow) equations and mass transport equations without reactions was used to calculate the flow variables at discrete points in the finite volume mesh element. The high correlation between theory and practical data indicates the potential of such coatings to monitor diffusion processes and mixing behaviour inside microfluidic channels in a dye free environment.

Wireless-based sleep technology for monitoring sleep apnea

Abstract: This paper covers the technical aspects of the wireless-based sleep technology for monitoring sleep apnea, which is a sleep disorder that can be detected via continuous monitoring. In this paper, a wireless system testbed is designed to monitor the patients for the signs of sleep apnea. The testbed is comprised of a number of biomedical sensors, which are used to monitor the related biological parameters related to the patient's sleeping mechanism, such as: nasal airflow, snoring, abdominal, leg, chest, and eye movements, blood oxygen level, blood pressure, and body position. The goal of this paper is to characterize the and model the data traffic generated from the biomedical sensors used in the sleep apnea study and find the network-centric lower traffic limits; minimum frequency deployment and minimum bandwidth required.

Market-based sensor relocation by robot team inwireless sensor networks

Randomly scattered sensors may cause sensing holes and redundant sensors. In carrier-based sensor relocation, mobile robots (with limited capacity to carry sensors) pick up additional or redundant sensors and relocate them at sensing holes. In the only known localized algorithm, robots randomly traverse field and act based on identified pair of spare sensor and coverage hole. We propose a Market-based Sensor Relocation (MSR) algorithm, which optimizes sensor deployment location, and introduces bidding and coordinating among neighboring robots. Sensors along the boundary of each hole elect one of them as the representative, which bids to neighboring robots for hole filling service. Each robot randomly explores by applying Least Recently Visited policy. It chooses the best bid according to Cost over Progress ratio and fetches a spare sensor nearby to cover the corresponding sensing hole. Robots within communication range share their tasks to search for better possible solutions. Simulation shows that MSR outperforms the existing competing algorithm G-R3S2 significantly on total robot traversed path and energy, and time to cover holes, slightly on number of sensors needed to cover the hole and number of sensor messages for bidding and deployment location sharing.

An adaptive orientation misalignment calibration method for shoulder movements using inertial sensors : a feasibility study

Qualitative assessment of the progress in physical rehabilitation largely depends on accurate measurement of the range of movements and other kinematic parameters. In clinical practice, wearable inertial sensors have proved to be a potential candidate for such measurements, over the traditional marker based optical systems due to cost and space considerations. The accuracy of wearable sensors have a significant dependence on the initial orientation calibration and the assumption that the sensor will not slip or move with respect to the attached limb. This article introduces a novel calibration algorithm to correct initial orientation misalignment, as well as to track and correct subsequent alignment errors progressively throughout the experiment. The theoretical assertions are validated through controlled experiments with simulated accelerometer and gyroscope measurements.

A machine-driven process for human limb length estimation using inertial sensors

The computer based human motion tracking systems are widely used in medicine and sports. The accurate determination of limb lengths is crucial for not only constructing the limb motion trajectories which are used for evaluation process of human kinematics, but also individually recognising human beings. Yet, as the common practice, the limb lengths are measured manually which is inconvenient, time-consuming and requires professional knowledge. In this paper, the estimation process of limb lengths is automated with a novel algorithm calculating curvature using the measurements from inertial sensors. The proposed algorithm was validated with computer simulations and experiments conducted with four healthy subjects. The experiment results show the significantly low root mean squared error percentages such as upper arm - 5.16%, upper limbs - 5.09%, upper leg - 2.56% and lower extremities - 6.64% compared to measured lengths.

The remote exercise monitoring trial for exercise-based cardiac rehabilitation (REMOTE-CR): a randomised controlled trial protocol

BACKGROUND: Exercise is an essential component of contemporary cardiac rehabilitation programs for the secondary prevention of coronary heart disease. Despite the benefits associated with regular exercise, adherence with supervised exercise-based cardiac rehabilitation remains low. Increasingly powerful mobile technologies, such as smartphones and wireless physiological sensors, may extend the capability of exercise-based cardiac rehabilitation by enabling real-time exercise monitoring for those with coronary heart disease. This study compares the effectiveness of technology-assisted, home-based, remote monitored exercise-based cardiac rehabilitation (REMOTE) to standard supervised exercise-based cardiac rehabilitation in New Zealand adults with a diagnosis of coronary heart disease. METHODS/DESIGN: A two-arm, parallel, non-inferiority, randomised controlled trial will be conducted at two sites in New Zealand. One hundred and sixty two participants will be randomised at a 1:1 ratio to receive a 12-week program of technology-assisted, home-based, remote monitored exercise-based cardiac rehabilitation (intervention), or an 8-12 program of standard supervised exercise-based cardiac rehabilitation (control).The primary outcome is post-treatment maximal oxygen uptake (V̇O2max). Secondary outcomes include cardiovascular risk factors (blood lipid and glucose concentrations, blood pressure, anthropometry), self-efficacy, intentions and motivation to be active, objectively measured physical activity, self-reported leisure time exercise and health-related quality of life. Cost information will also be collected to compare the two modes of delivery. All outcomes are assessed at baseline, post-treatment, and 6 months, except for V̇O2max, blood lipid and glucose concentrations, which are assessed at baseline and post-treatment only. DISCUSSION: This novel study will compare the effectiveness of technology-supported exercise-based cardiac rehabilitation to a traditional supervised approach. If the REMOTE program proves to be as effective as traditional cardiac rehabilitation, it has potential to augment current practice by increasing access for those who cannot utilise existing services. TRIAL REGISTRATION: Australian New Zealand Clinical Trials RegistryStudy ID number: ACTRN12614000843651. Registered 7 August 2014.

Spiking neural network-based target tracking control for autonomous mobile robots

In this paper, a target tracking controller based on spiking neural network is proposed for autonomous robots. This controller encodes the preprocessed environmental and target information provided by CCD cameras, encoders and ultrasonic sensors into spike trains, which are integrated by a three-layer spiking neural network (SNN). The outputs of SNN are generated based on the competition between the forward/backward neuron pair corresponding to each motor, with the weights evolved by the Hebbian learning. The application to target tracking of a mobile robot in unknown environment verifies the validity of the proposed controller.

A single cation or anion dendrimer-based liquid electrolyte

We propose here a novel liquid dendrimer-based single ion conductor as a potential alternative to conventional molecular liquid solvent-salt solutions in rechargeable batteries, sensors and actuators. A specific change from ester (-COOR) to cyano (-CN) terminated peripheral groups in generation-one poly(propyl ether imine) (G1-PETIM)-lithium salt complexes results in a remarkable switchover from a high cation (tLi+ = 0.9 for -COOR) to a high anion (tPF6- = 0.8 for -CN) transference number. This observed switchover draws an interesting analogy with the concept of heterogeneous doping, applied successfully to account for similar changes in ionic conductivity arising out of dispersion of insulator particle inclusions in weak inorganic solid electrolytes. The change in peripheral group simultaneously affects the effective ionic conductivity, with the room temperature ionic conductivity of PETIM-CN (1.9 × 10-5 Ω-1 cm-1) being an order of magnitude higher than PETIM-COOR (1.9 × 10-6 Ω-1 cm-1). Notably, no significant changes are observed in the lithium mobility even following changes in viscosity due to the change in the peripheral group. Changes in the peripheral chemical functionality directly influence the anion mobility, being lower in PETIM-COOR than in PETIM-CN, which ultimately becomes the sole parameter controlling the effective transport and electrochemical properties of the dendrimer electrolytes.

Condition assessment of timber utility poles based on a hierarchical data fusion model

This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyzes stress wave data from multisensor and multiexcitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition, and decision fusion algorithms. The researchers validate the proposed technique using guided wave tests of a sample of in situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT), and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimize information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the Dempster–Shafer (D-S) evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles, demonstrating the effectiveness of the new method.