Extending the Battery-Powered Operating Time of a Wireless Environmental Monitoring System

Advances in low-power microelectronics and sensor technologies have enabled the creation of sophisticated environmental monitoring systems capable of operating on battery power. Independence from a power outlet connection opens up many new potential applications, but limited battery life still imposes significant restrictions on a monitoring system’s capabilities and the number of systems that can be economically deployed and maintained. These restrictions have motivated much research into reducing monitoring system energy usage, increasing battery capacity, and harnessing alternative energy sources. While most of the research focuses on new system design, there is a need for techniques to extend the battery-powered operating time of existing environmental monitoring systems without compromising their sensor data quality. This thesis explores and develops methods for extending the operating time of an existing airquality monitoring system. The system contains seven environmental sensors that create a substantial energy demand and make long-term battery operation challenging. The resulting hardware and firmware modifications doubled the system’s battery-powered operating time without significantly reducing its environmental measurement data quality. The addition of an external battery sized to match the system’s form factor increased operating time well past the goal for the intended application. Although the modifications and results presented in this thesis are specific to one environmental monitoring system, the same techniques could be applied to other monitoring systems and to embedded systems in general

Demystifying Internet of Things Security

Break down the misconceptions of the Internet of Things by examining the different security building blocks available in Intel Architecture (IA) based IoT platforms. This open access book reviews the threat pyramid, secure boot, chain of trust, and the SW stack leading up to defense-in-depth. The IoT presents unique challenges in implementing security and Intel has both CPU and Isolated Security Engine capabilities to simplify it. This book explores the challenges to secure these devices to make them immune to different threats originating from within and outside the network. The requirements and robustness rules to protect the assets vary greatly and there is no single blanket solution approach to implement security. Demystifying Internet of Things Security provides clarity to industry professionals and provides and overview of different security solutions What You'll Learn Secure devices, immunizing them against different threats originating from inside and outside the network Gather an overview of the different security building blocks available in Intel Architecture (IA) based IoT platforms Understand the threat pyramid, secure boot, chain of trust, and the software stack leading up to defense-in-depth Who This Book Is For Strategists, developers, architects, and managers in the embedded and Internet of Things (IoT) space trying to understand and implement the security in the IoT devices/platforms

Embedded System Design

A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues

Reducing the computational demands of medical monitoring classifiers by examining less data

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 113-118).Instrumenting patients with small, wearable sensors will enable physicians to continuously monitor patients outside the hospital. These devices can be used for real-time classification of the data they collect. For practical purposes, such devices must be comfortable and thus be powered by small batteries. Since classification algorithms often perform energy-intensive signal analysis, power management techniques are needed to achieve reasonable battery lifetimes. In this thesis, we describe software-based methods that reduce the computation, and thus, energy consumption of real-time medical monitoring algorithms by examining less data. Though discarding data can degrade classification performance, we show that the degradation can be small. We describe and evaluate data reduction methods based on duty cycling, sensor selection, and combinations of the two. Random duty cycling was applied to an online algorithm that performs risk assessment of patients with a recent acute coronary syndrome (ACS). We modified an existing algorithm that estimates the risk of cardiovascular death following ACS. By randomly discarding roughly 40% of the data, we reduced energy consumption by 40%. The percentage of patients who had a change in their risk classification was 3%. A sensor selection method was used to modify an existing machine learning based algorithm for constructing multi-channel, patient-specific, delay-sensitive seizure onset detectors.(cont.) Using this method, we automatically generated detectors that used roughly 60% fewer channels than the original detector. The reduced channel detectors missed seven seizures out of 143 total seizures while the original detector missed four. The median detection latency increased slightly from 6.0 to 7.0 seconds, while the average false alarms per hour increased from 0.07 to 0.11. Finally, we investigated the impact of approaches that combine duty cycling with sensor selection on the energy consumption and detection performance of the seizure onset detection algorithm. In one approach, where we combined two reduced channel detectors to form a single detector, we reduced energy consumption by an additional 20% over the reduced channel detectors.by Eugene Inghaw Shih.Ph.D

Embedded System Design

A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues

Data collection system: Earth Resources Technology Satellite-1

Subjects covered at the meeting concerned results on the overall data collection system including sensors, interface hardware, power supplies, environmental enclosures, data transmission, processing and distribution, maintenance and integration in resources management systems

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Voyager capsule, preliminary design, phase B. Volume III - Surface laboratory system. Section I - Surface laboratory Final report

Surface Laboratory preliminary configuration for Voyager mission - space capsul

Early Abstraction of Inertial Sensor Data for Long-Term Deployments

Advances in microelectronics over the last decades have led to miniaturization of computing devices and sensors. A driving force to use these in various application scenarios is the desire to grasp physical phenomena from the environment, objects and living entities. We investigate sensing in two particularly challenging applications: one where small sensor modules are worn by people to detect their activities, and one where wirelessly networked sensors observe events over an area. This thesis takes a data-driven approach, focusing on human motion and vibrations caused by trains that are captured by accelerometer sensors as time series and shall be analyzed for characteristic patterns. For both, the acceleration sensor must be sampled at relatively high rates in order to capture the essence of the phenomena, and remain active for long stretches of time. The large amounts of gathered sensor data demand novel approaches that are able to swiftly process the data while guaranteeing accurate classification results. The following contributions are made in particular: * A data logger that would suit the requirements of long-term deployments is designed and evaluated. In a power profiling study both hardware components and firmware parameters are thoroughly tested, revealing that the sensor is able to log acceleration data at a sampling rate of 100 Hertz for up to 14 full days on a single battery charge. * A technique is proposed that swiftly and accurately abstracts an original signal with a set of linear segments, thus preserving its shape, while being twice as fast as a similar method. This allows for more efficient pattern matching, since for each pattern only a fraction of data points must be considered. A second study shows that this algorithm can perform data abstraction directly on a data logger with limited resources. * The railway monitoring scenario requires streaming vibration data to be analyzed for particular sparse and complex events directly on the sensor node, extracting relevant information such as train type or length from the shape of the vibration footprint. In a study conducted on real-world data, a set of efficient shape features is identified that facilitates train type prediction and length estimation with very high accuracies. * To achieve fast and accurate activity recognition for long-term bipolar patients monitoring scenarios, we present an approach that relies on the salience of motion patterns (motifs) that are characteristic for the target activity. These motifs are accumulated by using a symbolic abstraction that encodes the shape of the original signal. A large-scale study shows that a simple bag-of-words classifier trained with extracted motifs is on par with traditional approaches regarding the accuracy, while being much faster. * Some activities are hard to predict from acceleration data alone with the aforementioned approach. We argue that human-object interactions, captured as human motion and grasped objects through RFID, are an ideal supplement. A custom bracelet-like antenna to detect objects from up to 14 cm is proposed, along with a novel benchmark to evaluate such wearable setups. By aiming for wearable and wirelessly networked sensor systems, these contributions apply for particularly challenging applications that require long-term deployments of miniature sensors in general. They form the basis of a framework towards efficient event detection that relies heavily on early data abstraction and shape-based features for time series, while focusing less on the classification techniques