A Cyber-Physical Systems Approach to Water Distribution System Monitoring

Water Distribution Systems (WDS) are critical infrastructures of national importance that supply water of desired quality and quantity to consumers. They are prone to damages and attacks such as leaks, breaks, and chemical contamination. Monitoring of WDS for prompt response to such events is of paramount importance. WDS monitoring has been typically performed using static sensors that are strategically placed. These solutions are costly and imprecise. Recently mobile sensors for WDS monitoring has attracted research interest to overcome the shortcomings of static sensors. However, most existing solutions are unrealistic, or disrupt the normal functioning of a WDS. They are also designed to be deployed on-demand, i.e., when the utility manager receives complaints or suspects the presence of a threat. We propose to solve the problem of WDS monitoring through a Cyber-Physical system (CPS) approach. We envision a Cyber-Physical Water Distribution System (CPWDS) with mobile sensors that are deployed in the CPWDS and move with the flow of water in pipes; mobile sensors communicate with static beacons placed outside the pipes and report sensed data; the flows in the pipes are controlled to ensure that the sensors continuously cover the main pipes of the WDS. We propose algorithms to efficiently monitor the WDS with limited number of devices, protocols to efficiently communicate among the devices, and mechanisms to control the flows in the WDS such that consumer demands are met while sensors continuously move around. We evaluate our algorithms, protocols, and design of communication, computation and control components of the CPWDS through a simulator developed specifically to model the movement of sensors through the pipes of the WDS. Our simulations indicate that investing on improving the sensing range of mobile sensors reduces the cost of monitoring significantly. Additionally, the placement of beacons, and the communication range impact the accuracy of localization and estimation of sensor locations. Our flow control system is observed to converge and improve the coverage over time

Technology for the Future: In-Space Technology Experiments Program, part 2

The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiments Program In-STEP 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/ university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part two of two parts and contains the critical technology presentations for the eight theme elements and a summary listing of critical space technology needs for each theme

Mobile Sensor Network Design and Optimization for Air Quality Monitoring

Air quality and personal pollutant exposure measurement are important for the health and productivity of individuals. Accurate measurement of personal exposure is challenging because of the spatially and temporally heterogeneous distribution of pollutant concentrations. We propose to use low-cost and miniature mobile sensor networks to provide real-time measurement of the environment directly surrounding the user. However, there are many challenges, including sensor drift, cross sensitivity, and noises, to be addressed before mobile sensor network can be deployed in large scale and real-world applications. My thesis aims to address those challenges by designing prototype sensor nodes of future generation mobile sensor networks, developing optimization techniques and systems, and evaluating the mobile sensor network in real-world deployments. My efforts can be divided into four categories: (1) we design the mobile sensor nodes and the mobile sensor network architecture that are capable of automatically collecting environment data and transferring them to a database; (2) we model the sensor drift based on measurement and develop techniques such as collaborative calibration and optimal human mobility-aware sensor placement to minimize the drift error of individual sensors; (3) we model the pollutant concentration in indoor environment considering inaccurate sensors and based on the model, we develop a hybrid sensor network synthesis technique to design accurate sensor networks under a cost constraint; and (4) we propose a Bayesian network based sensor noise reduction system that can correct abnormal sensor readings, re-calibrate the sensor functions, and identify the gas composition is the environment simultaneously. All the techniques are evaluated and validated using the data collected from real-world deployment. Experimental and simulation results show that our technique can reduce drift error significantly. For example, compared with the closest technique, our collaborative calibration technique can reduce sensor network error by 23.2%; our hybrid sensor network synthesis technique can improve the result by 35.8%; and our noise reduction technique can outperform the existing technique by 34.1%.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107188/1/xiangyun_1.pd

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Wireless sensor networks, actuation, and signal processing for apiculture

Recent United Nations reports have stressed the growing constraint of food supply for Earth's growing human population. Honey bees are a vital part of the food chain as the most important pollinator for a wide range of crops. Protecting the honey bee population worldwide, and enabling them to maximise productivity, are important concerns. This research proposes a framework for addressing these issues by considering an inter-disciplinary approach, combining recent developments in engineering and honey bee science. The primary motivation of the work outlined in this thesis was to use embedded systems technology to improve honey bee health by developing state of the art in-hive monitoring systems to classify the colony status and mechanisms to influence hive conditions. Specific objectives were identified as steps to achieve this goal: to use Wireless Sensor networks (WSN) technology to monitor a honey bee colony in the hive and collect key information; to use collected data and resulting insights to propose mechanisms to influence hive conditions; to use the collected data to inform the design of signal processing and machine learning techniques to characterise and classify the colony status; and to investigate the use of high volume data sensors in understanding specific conditions of the hive, and methods for integration of these sensors into the low-power and low-data rate WSN framework. It was found that automated, unobtrusive measurement of the in-hive conditions could provide valuable insight into the activities and conditions of honey bee colonies. A heterogeneous sensor network was deployed that monitored the conditions within hives. Data were collected periodically, showing changes in colony behaviour over time. The key parameters measured were: CO2, O2, temperature, relative humidity, and acceleration. Weather data (sunshine, rain, and temperature) were collected to provide an additional analysis dimension. Extensive energy improvements reduced the node’s current draw to 150 µA. Combined with an external solar panel, self-sustainable operation was achieved. 3,435 unique data sets were collected from five test-bed hives over 513 days during all four seasons. Temperature was identified as a vital parameter influencing the productivity and health of the colony. It was proposed to develop a method of maintaining the hive temperature in the ideal range through effective ventilation and airflow control which allow the bees involved in the activities above to engage in other tasks. An actuator was designed as part of the hive monitoring WSN to control the airflow within the hive. Using this mechanism, an effective Wireless Sensor and Actuator Network (WSAN) with Proportional Integral Derivative (PID) based temperature control was implemented. This system reached an effective set point temperature within 7 minutes of initialisation, and with steady state being reached by minute 18. There was negligible steady state error (0.0047%) and overshoot of <0.25 °C. It was proposed to develop and evaluate machine learning solutions to use the collected data to classify and describe the hive. The results of these classifications would be far more meaningful to the end user (beekeeper). Using a data set from a field deployed beehive, a biological analysis was undertaken to classify ten important hive states. This classification led to the development of a decision tree based classification algorithm which could describe the beehive using sensor network data with 95.38% accuracy. A correlation between meteorological conditions and beehive data was also observed. This led to the development of an algorithm for predicting short term rain (within 6 hours) based on the parameters within the hive (95.4% accuracy). A Random Forest based classifier was also developed using the entire collected in-hive dataset. This algorithm did not need access to data from outside the network, memory of previous measured data, and used only four inputs, while achieving an accuracy of 93.5%. Sound, weight, and visual inspection were identified as key methods of identifying the health and condition of the colony. Applications of advanced sensor methods in these areas for beekeeping were investigated. A low energy acoustic wake up sensor node for detecting the signs of an imminent swarming event was designed. Over 60 GB of sound data were collected from the test-bed hives, and analysed to provide a sound profile for development of a more advanced acoustic wake up and classification circuit. A weight measuring node was designed using a high precision (24-bit) analogue to digital converter with high sensitivity load cells to measure the weight of a hive to an accuracy of 10g over a 50 kg range. A preliminary investigation of applications for thermal and infrared imaging sensors in beekeeping was also undertaken

Internet of Things Applications - From Research and Innovation to Market Deployment

The book aims to provide a broad overview of various topics of Internet of Things from the research, innovation and development priorities to enabling technologies, nanoelectronics, cyber physical systems, architecture, interoperability and industrial applications. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from technology to international cooperation and the global "state of play".The book builds on the ideas put forward by the European research Cluster on the Internet of Things Strategic Research Agenda and presents global views and state of the art results on the challenges facing the research, development and deployment of IoT at the global level. Internet of Things is creating a revolutionary new paradigm, with opportunities in every industry from Health Care, Pharmaceuticals, Food and Beverage, Agriculture, Computer, Electronics Telecommunications, Automotive, Aeronautics, Transportation Energy and Retail to apply the massive potential of the IoT to achieving real-world solutions. The beneficiaries will include as well semiconductor companies, device and product companies, infrastructure software companies, application software companies, consulting companies, telecommunication and cloud service providers. IoT will create new revenues annually for these stakeholders, and potentially create substantial market share shakeups due to increased technology competition. The IoT will fuel technology innovation by creating the means for machines to communicate many different types of information with one another while contributing in the increased value of information created by the number of interconnections among things and the transformation of the processed information into knowledge shared into the Internet of Everything. The success of IoT depends strongly on enabling technology development, market acceptance and standardization, which provides interoperability, compatibility, reliability, and effective operations on a global scale. The connected devices are part of ecosystems connecting people, processes, data, and things which are communicating in the cloud using the increased storage and computing power and pushing for standardization of communication and metadata. In this context security, privacy, safety, trust have to be address by the product manufacturers through the life cycle of their products from design to the support processes. The IoT developments address the whole IoT spectrum - from devices at the edge to cloud and datacentres on the backend and everything in between, through ecosystems are created by industry, research and application stakeholders that enable real-world use cases to accelerate the Internet of Things and establish open interoperability standards and common architectures for IoT solutions. Enabling technologies such as nanoelectronics, sensors/actuators, cyber-physical systems, intelligent device management, smart gateways, telematics, smart network infrastructure, cloud computing and software technologies will create new products, new services, new interfaces by creating smart environments and smart spaces with applications ranging from Smart Cities, smart transport, buildings, energy, grid, to smart health and life. Technical topics discussed in the book include: • Introduction• Internet of Things Strategic Research and Innovation Agenda• Internet of Things in the industrial context: Time for deployment.• Integration of heterogeneous smart objects, applications and services• Evolution from device to semantic and business interoperability• Software define and virtualization of network resources• Innovation through interoperability and standardisation when everything is connected anytime at anyplace• Dynamic context-aware scalable and trust-based IoT Security, Privacy framework• Federated Cloud service management and the Internet of Things• Internet of Things Application

Strategic Monitoring of Networked Systems with Heterogeneous Security Levels

We consider a strategic network monitoring problem involving the operator of a networked system and an attacker. The operator aims to randomize the placement of multiple protected sensors to monitor and protect components that are vulnerable to attacks. We account for the heterogeneity in the components' security levels and formulate a large-scale maximin optimization problem. After analyzing its structure, we propose a three-step approach to approximately solve the problem. First, we solve a generalized covering set problem and run a combinatorial algorithm to compute an approximate solution. Then, we compute approximation bounds by solving a nonlinear set packing problem. To evaluate our solution approach, we implement two classical solution methods based on column generation and multiplicative weights updates, and test them on real-world water distribution and power systems. Our numerical analysis shows that our solution method outperforms the classical methods on large-scale networks, as it efficiently generates solutions that achieve a close to optimal performance and that are simple to implement in practice

Spacecraft Dormancy Autonomy Analysis for a Crewed Martian Mission

Current concepts of operations for human exploration of Mars center on the staged deployment of spacecraft, logistics, and crew. Though most studies focus on the needs for human occupation of the spacecraft and habitats, these resources will spend most of their lifetime unoccupied. As such, it is important to identify the operational state of the unoccupied spacecraft or habitat, as well as to design the systems to enable the appropriate level of autonomy. Key goals for this study include providing a realistic assessment of what "dormancy" entails for human spacecraft, exploring gaps in state-of-the-art for autonomy in human spacecraft design, providing recommendations for investments in autonomous systems technology development, and developing architectural requirements for spacecraft that must be autonomous during dormant operations. The mission that was chosen is based on a crewed mission to Mars. In particular, this study focuses on the time that the spacecraft that carried humans to Mars spends dormant in Martian orbit while the crew carries out a surface mission. Communications constraints are assumed to be severe, with limited bandwidth and limited ability to send commands and receive telemetry. The assumptions made as part of this mission have close parallels with mission scenarios envisioned for dormant cis-lunar habitats that are stepping-stones to Mars missions. As such, the data in this report is expected to be broadly applicable to all dormant deep space human spacecraft

Environmental Disasters Data Management Workshop Report

The Environmental Disasters Data Management (EDDM) project seeks to foster communication between collectors, managers, and users of data within the scientific research community, industry, NGOs, and government agencies, with a goal to identify and establish best practices for orderly collection, storage, and retrieval. The Coastal Response Research Center (CRRC) is assisting NOAA’s Office of Response and Restoration (ORR) with this effort. The objectives of the EDDM project are to: Engage the community of data users, data managers, and data collectors to foster a culture of applying consistent terms and concepts, data flow, and quality assurance and control; Provide oversight in the establishment and integration of foundational, baseline data collected prior to an environmental event, based on user requirements; Provide best‐practice guidance for data and metadata management; Suggest infrastructure design elements to facilitate quick and efficient search, discovery, and retrieval of data; Define the characteristics of a “gold standard” data management plan for appropriate data sampling, formatting, reliability, and retrievability; and Deliver workshop conclusions to end users in order to promote the use of the protocols, practices, or recommendations identified by participants