Data fusion in wireless sensor networks

University of Technology, Sydney. Faculty of Engineering and Information Technology.Wireless Sensor Networks (WSNs) are deployed for the purpose of monitoring an area of interest. Even when the sensors are properly calibrated at the time of deployment, they develop drift in their readings leading to erroneous network inferences. Traditionally, such errors are corrected by site visits where the sensors are calibrated against an accurately calibrated sensor. For large scale sensor networks, the process is manually intensive and economically infeasible. This imposes finding automatic procedures for continuous calibration. Noting that a physical phenomenon in a certain area follows some spatio-temporal correlation, we assume that the sensors readings in that area are correlated. We also assume that measurement errors due to faulty equipment are likely to be uncorrelated. Based on these assumptions, we follow a Bayesian framework to solve the drift and bias problem in WSNs. In the case of densely deployed WSN, neighbouring sensors are assumed to be close to each other that they observe the same phenomenon. Hence, the average of their corrected readings is taken as a basis for each sensor to self-assess its measurement, estimate its drift and to correct the measurement using a Kalman Filter (KF) in the case of smooth drift, and the Interacting Multiple Model algorithm (IMM) in the case of unsmooth drift. The solutions are computationally simple, decentralised and also scalable. Any new node joining the neighbourhood needs only to obtain the corrected readings of its neighbours to find the average and apply the KF iterative procedure. On the other hand, when the sensors are not densely deployed, Support Vector Regression (SVR) is used to model the interrelationships of sensor measurements in a neighbourhood. This enables the incorporation of the spatio-temporal correlation of neighbouring sensors, to predict future measurements. The SVR predicted value is used by a KF to estimate the actual drift and correct the measurement. Unfortunately, the KF introduces some system errors when used with nonlinear systems. The use of Unscented Kalman filter (UKF) instead, considerably reduces the system error and results in a better drift correction. The use of IMM with the SVRUKF framework allows for reducing the sampling rate which eventually reduces the communication overhead among the sensors and saves the communication energy. In this thesis, we present several solutions for the random and systematic (drift and bias) errors in sensors measurements, for different sensor deployment scenarios. We also consider two drift scenarios, namely smooth and unsmooth drifts. We evaluate the presented algorithms on simulated and real data obtained from the Intel Berkeley Research Laboratory sensor deployment. The results show that our algorithms successfully detect and correct systematic errors (drift and bias) developed in sensors and filters out the noise. Thereby, prolonging the effective lifetime of the network

Program Synthesis using Natural Language

Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks. We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively

Relativistic Compact Objects in Isotropic Coordinates

We present a matrix method for obtaining new classes of exact solutions for Einstein's equations representing static perfect fluid spheres. By means of a matrix transformation, we reduce Einstein's equations to two independent Riccati type differential equations for which three classes of solutions are obtained. One class of the solutions corresponding to the linear barotropic type fluid with an equation of state $p=\gamma \rho$ is discussed in detail.Comment: 9 pages, no figures, accepted for publication in Pramana-Journal of Physic

Stakeholder Analysis

In common with other projects in the RESAS programme, Natural Capital -Galvanising Change (D5.3) follows a Responsible Research and Innovation approach to co-produce research with stakeholders and publics, emphasising openness, transparency, diversity, inclusiveness and adapting the research as far as possible to changing contexts and needs (Owen et al., 2012). Broadly speaking, there are two reasons for this (Reed, 2008). First, the pragmatic argument is that working with stakeholders can facilitate more relevant research than is more likely to yield beneficial impacts in a given context. By engaging relevant stakeholders in the research from the outset, D5.3 will be able to consider the range of individuals, groups and organisations that might benefit from the research, whether directly or indirectly, and increase the likelihood that the project delivers tangible benefits that are relevant across as many of these groups as possible, rather than just benefiting formal project partners and ‘easy to reach’ groups. Second, there is the normative argument that the research team should engage stakeholders in a project that seeks to shape the delivery of outcomes from natural capital policies and facilitate highintegrity ecosystem markets in Scotland. There is evidence that decisions about who engages in research can have a strong bearing on the legitimacy of decisions and the likelihood that those decisions are actually implemented and deliver benefits rather than unintended negative consequences (de Vente et al., 2016). This finding is because decisions benefit from more comprehensive information inputs and ownership over outcomes (de Vente et al., 2016; Reed, 2018a). As such, the goal is to enable broad participation from relevant publics and stakeholders in D5.3 research. For definitions of publics, stakeholders, participation and impact, see Box 1. Based on this approach, engagement with publics and stakeholders in D5.3 will beguided by a stakeholder analysis to systematically assess the relative interest, influence and impacts associated with different organisations and groups who are affected by, or have the capacity to affect, decisions pertaining natural capital policy and ecosystem markets in Scotland. This report starts by explaining stakeholder analysis and the methods used to analyse stakeholders for D5.3, followed by the presentation of results from the analysis