Toward a Robust Sparse Data Representation for Wireless Sensor Networks

Compressive sensing has been successfully used for optimized operations in wireless sensor networks. However, raw data collected by sensors may be neither originally sparse nor easily transformed into a sparse data representation. This paper addresses the problem of transforming source data collected by sensor nodes into a sparse representation with a few nonzero elements. Our contributions that address three major issues include: 1) an effective method that extracts population sparsity of the data, 2) a sparsity ratio guarantee scheme, and 3) a customized learning algorithm of the sparsifying dictionary. We introduce an unsupervised neural network to extract an intrinsic sparse coding of the data. The sparse codes are generated at the activation of the hidden layer using a sparsity nomination constraint and a shrinking mechanism. Our analysis using real data samples shows that the proposed method outperforms conventional sparsity-inducing methods.Comment: 8 page

Deep learning approach to control of prosthetic hands with electromyography signals

Natural muscles provide mobility in response to nerve impulses. Electromyography (EMG) measures the electrical activity of muscles in response to a nerve's stimulation. In the past few decades, EMG signals have been used extensively in the identification of user intention to potentially control assistive devices such as smart wheelchairs, exoskeletons, and prosthetic devices. In the design of conventional assistive devices, developers optimize multiple subsystems independently. Feature extraction and feature description are essential subsystems of this approach. Therefore, researchers proposed various hand-crafted features to interpret EMG signals. However, the performance of conventional assistive devices is still unsatisfactory. In this paper, we propose a deep learning approach to control prosthetic hands with raw EMG signals. We use a novel deep convolutional neural network to eschew the feature-engineering step. Removing the feature extraction and feature description is an important step toward the paradigm of end-to-end optimization. Fine-tuning and personalization are additional advantages of our approach. The proposed approach is implemented in Python with TensorFlow deep learning library, and it runs in real-time in general-purpose graphics processing units of NVIDIA Jetson TX2 developer kit. Our results demonstrate the ability of our system to predict fingers position from raw EMG signals. We anticipate our EMG-based control system to be a starting point to design more sophisticated prosthetic hands. For example, a pressure measurement unit can be added to transfer the perception of the environment to the user. Furthermore, our system can be modified for other prosthetic devices.Comment: Conference. Houston, Texas, USA. September, 201

Improved wind turbine monitoring using operational data

With wind energy becoming a major source of energy, there is a pressing need to reduce all associated costs to be competitive in a market that might be fully subsidy-free in the near future. Before thousands of wind turbines were installed all over the world, research in e.g. understanding aerodynamics, developing new materials, designing better gearboxes, improving power electronics etc., helped to cut down wind turbine manufacturing costs. It might be assumed, that this would be sufficient to reduce the costs of wind energy as the resource, the wind itself, is free of costs. However, it has become clear that the operation and maintenance of wind turbines contributes significantly to the overall cost of energy. Harsh environmental conditions and the frequently remote locations of the turbines makes maintenance of wind turbines challenging. Just recently, the industry realised that a move from reactive and scheduled maintenance towards preventative or condition-based maintenance will be crucial to further reduce costs. Knowing the condition of the wind turbine is key for any optimisation of operation and maintenance. There are various possibilities to install advanced sensors and monitoring systems developed in recent years. However, these will inevitably incur new costs that need to be worthwhile and retro-fits to existing turbines might not always be feasible. In contrast, this work focuses on ways to use operational data as recorded by the turbine's Supervisory Control And Data Acquisition (SCADA) system, which is installed in all modern wind turbines for operating purposes -- without additional costs. SCADA data usually contain information about the environmental conditions (e.g. wind speed, ambient temperature), the operation of the turbine (power production, rotational speed, pitch angle) and potentially the system's health status (temperatures, vibration). These measurements are commonly recorded in ten-minutely averages and might be seen as indirect and top-level information about the turbine's condition. Firstly, this thesis discusses the use of operational data to monitor the power performance to assess the overall efficiency of wind turbines and to analyse and optimise maintenance. In a sensitivity study, the financial consequences of imperfect maintenance are evaluated based on case study data and compared with environmental effects such as blade icing. It is shown how decision-making of wind farm operators could be supported with detailed `what-if' scenario analyses. Secondly, model-based monitoring of SCADA temperatures is investigated. This approach tries to identify hidden changes in the load-dependent fluctuations of drivetrain temperatures that can potentially reveal increased degradation and possible imminent failure. A detailed comparison of machine learning regression techniques and model configurations is conducted based on data from four wind farms with varying properties. The results indicate that the detailed setup of the model is very important while the selection of the modelling technique might be less relevant than expected. Ways to establish reliable failure detection are discussed and a condition index is developed based on an ensemble of different models and anomaly measures. However, the findings also highlight that better documentation of maintenance is required to further improve data-driven condition monitoring approaches. In the next part, the capabilities of operational data are explored in a study with data from both the SCADA system and a Condition Monitoring System (CMS) based on drivetrain vibrations. Analyses of signal similarity and data clusters reveal signal relationships and potential for synergistic effects of the different data sources. An application of machine learning techniques demonstrates that the alarms of the commercial CMS can be predicted in certain cases with SCADA data alone. Finally, the benefits of having wind turbines in farms are investigated in the context of condition monitoring. Several approaches are developed to improve failure detection based on operational statistics, CMS vibrations or SCADA temperatures. It is demonstrated that utilising comparisons with neighbouring turbines might be beneficial to get earlier and more reliable warnings of imminent failures. This work has been part of the Advanced Wind Energy Systems Operation and Maintenance Expertise (AWESOME) project, a European consortium with companies, universities and research centres in the wind energy sector from Spain, Italy, Germany, Denmark, Norway and UK. Parts of this work were developed in collaboration with other fellows in the project (as marked and explained in footnotes)

Disaggregated Imaging Spacecraft Constellation Optimization with a Genetic Algorithm

This research is an extension of work by Major Robert Thompson, who uses a genetic algorithm to optimize certain parameters of a disaggregated constellation for most cost-effective coverage. This work looks at imaging sensor coverage of a specific target deck assumed to exist in the Middle East. Parameters varied in this optimization affect Walker constellation characteristics, orbital elements, and sensor size. Walker parameter variables are number of planes, number of satellites per plane, true anomaly spread, and RAAN increment. All classical orbital elements are variable, although a circular, low-Earth orbit is assumed. Sensor size is varied dependent upon sensor diameter. These parameters are applied to constellations of small satellites and large satellites. The Unmanned Spacecraft Cost Model (USCM) and the Small Spacecraft Cost Model (SSCM) are used to roughly determine the cost of each proposed mission. The sensor effectiveness is determined by the General Imaging Quality Equation (GIQE)

Novel security mechanisms for wireless sensor networks

Wireless Sensor Networks (WSNs) are used for critical applications such as health care, traffic management or plant automation. Thus, we depend on their availability, and reliable, resilient and accurate operation. It is therefore essential that these systems are protected against attackers who may intend to interfere with operations. Existing security mechanisms cannot always be directly transferred to the application domain of WSNs, and in some cases even novel methods are desirable to give increased protection to these systems. The aim of the work presented in this thesis is to augment security of WSNs by devising novel mechanisms and protocols. In particular, it contributes to areas which require protection mechanisms but have not yet received much attention from the research community. For example, the work addresses the issue of secure storage of data on sensor nodes using cryptographic methods. Although cryptography is needed for basic protection, it cannot always secure the sensor nodes as the keys might be compromised and key management becomes more challenging as the number of deployed sensor nodes increases. Therefore, the work includes mechanisms for node identification and tamper detection by means other than pure cryptography. The three core contributions of this thesis are (i) Methods for confidential data storage on WSN nodes. In particular, fast and energy-efficient data storage and retrieval while maintaining the required protection level is addressed. A framework is presented that provides confidential data storage in WSNs with minimal impact on sensor node operation and performance. This framework is further advanced by combining it with secure communication in WSNs. With this framework, data is stored securely on the flash file system such that it can be directly used for secure transmission, which removes the duplication of security operations on the sensor node. (ii) Methods for node identification based on clock skew. Here, unique clock drift patterns of nodes, which are normally a problem for wireless network operation, are used for non-cryptographic node identification. Clock skew has been previously used for device identification, requiring timestamps to be distributed over the network, but this is impractical in duty-cycled WSNs. To overcome this problem, clock skew is measured locally on the node using precise local clocks. (iii) Methods for tamper detection and node identification based on Channel State Information (CSI). Characteristics of a wireless channel at the receiver are analysed using the CSI of incoming packets to identify the transmitter and to detect tampering on it. If an attacker tampers with the transmitter, it will have an effect on the CSI measured at the receiver. However, tamper-unrelated events, such as walking in the communication environment, also affect CSI values and cause false alarms. This thesis demonstrates that false alarms can be eliminated by analysing the CSI value of a transmitted packet at multiple receivers

DEEP REINFORCEMENT LEARNING AND MODEL PREDICTIVE CONTROL APPROACHES FOR THE SCHEDULED OPERATION OF DOMESTIC REFRIGERATORS

Excess capacity of the UK’s national grid is widely quoted to be reducing to around 4% over the coming years as a consequence of increased economic growth (and hence power usage) and reductions in power generation plants. There is concern that short term variations in power demand could lead to serious wide-scale disruption on a national scale. This is therefore spawning greater attention on augmenting traditional generation plants with renewable and localized energy storage technologies, and consideration of improved demand side responses (DSR), where power consumers are incentivized to switch off assets when the grid is under pressure. It is estimated, for instance, that refrigeration/HVAC systems alone could account for ~14% of the total UK energy usage, with refrigeration and water heating/cooling systems, in particular, being able to act as real-time ‘buffer’ technologies that can be demand-managed to accommodate transient demands by being switched-off for short periods without damaging their outputs. Large populations of thermostatically controlled loads (TCLs) hold significant potential for performing ancillary services in power systems since they are well-established and widely distributed around the power network. In the domestic sector, refrigerators and freezers collectively constitute a very large electrical load since they are continuously connected and are present in almost most households. The rapid proliferation of the ‘Internet of Things’ (IoT) now affords the opportunity to monitor and visualise smart buildings appliances performance and specifically, schedule the operation of the widely distributed domestic refrigerator and freezers to collectively improve energy efficiency and reduce peak power consumption on the electrical grid. To accomplish this, this research proposes the real-time estimation of the thermal mass of individual refrigerators in a network using on-line parameter identification, and the co-ordinated (ON-OFF) scheduling of the refrigerator compressors to maintain their respective temperatures within specified hysteresis bands—commensurate with accommodating food safety standards. Custom Model Predictive Control (MPC) schemes and a Machine Learning algorithm (Reinforcement Learning) are researched to realize an appropriate scheduling methodology which is implemented through COTS IoT hardware. Benefits afforded by the proposed schemes are investigated through experimental trials which show that the co-ordinated operation of domestic refrigerators can 1) reduce the peak power consumption as seen from the perspective of the electrical power grid (i.e. peak power shaving), 2) can adaptively control the temperature hysteresis band of individual refrigerators to increase operational efficiency, and 3) contribute to a widely distributed aggregated load shed for Demand Side Response purposes in order to aid grid stability. Comparative studies of measurements from experimental trials show that the co-ordinated scheduling of refrigerators allows energy savings of between 19% and 29% compared to their traditional isolated (non-co-operative) operation. Moreover, by adaptively changing the hysteresis bands of individual fridges in response to changes in thermal behaviour, a further 20% of savings in energy are possible at local refrigerator level, thereby providing benefits to both network supplier and individual consumer