From missions to systems : generating transparently distributable programs for sensor-oriented systems

Early Wireless Sensor Networks aimed simply to collect as much data as possible for as long as possible. While this remains true in selected cases, the majority of future sensor network applications will demand much more intelligent use of their resources as networks increase in scale and support multiple applications and users. Specifically, we argue that a computational model is needed in which the ways that data flows through networks, and the ways in which decisions are made based on that data, is transparently distributable and relocatable as requirements evolve. In this paper we present an approach to achieving this using high-level mission specifications from which we can automatically derive transparently distributable programs.Postprin

IoT-Enabled Environmental Monitoring Technologies

Environmental monitoring is essential to protect human life and the environment due to issues such as global warming, pollution, and weather phenomena like hurricanes. To this end this thesis presents a prototype IoT (Internet of Things)-enabled environmental monitoring network. The network will include the ability to collect data from various locations that will be uploaded to a cloud so that the data can be accessed from any device with a network connection allowing for greater environmental awareness and a database that can be used for environmental analysis. The network will be flexible enough that every data collection point will either connect directly to the internet, send data through a gateway with an internet connection, or upload data separately. For communication between devices, a mesh network will be used to allow communication from end nodes to router nodes and finally to a central or coordinator node which will upload all the data from the mesh network to the cloud. This work will outline all the hardware, software, and setup required to prototype the proposed monitoring network and will conclude with some potential future improvements

Personal Food Computer: A new device for controlled-environment agriculture

Due to their interdisciplinary nature, devices for controlled-environment agriculture have the possibility to turn into ideal tools not only to conduct research on plant phenology but also to create curricula in a wide range of disciplines. Controlled-environment devices are increasing their functionalities as well as improving their accessibility. Traditionally, building one of these devices from scratch implies knowledge in fields such as mechanical engineering, digital electronics, programming, and energy management. However, the requirements of an effective controlled environment device for personal use brings new constraints and challenges. This paper presents the OpenAg Personal Food Computer (PFC); a low cost desktop size platform, which not only targets plant phenology researchers but also hobbyists, makers, and teachers from elementary to high-school levels (K-12). The PFC is completely open-source and it is intended to become a tool that can be used for collective data sharing and plant growth analysis. Thanks to its modular design, the PFC can be used in a large spectrum of activities.Comment: 9 pages, 11 figures, Accepted at the 2017 Future Technologies Conference (FTC

A State-Machine Model for Reliability Eliciting over Wireless Sensor and Actuator Networks

AbstractAdvances in communications and embedded systems have led to the proliferation of wireless sensor and actuator networks (WSANs) in a wide variety of application domains. One important key of many such WSAN applications is the needed to meet non-functional requirements (e.g., lifetime, reliability, time guarantees) as well as functional ones (e.g. monitoring, actuation). Some application domains even require that sensor nodes be deployed in harsh environments (e.g., refineries), where they can fail due to communication interference, power problems or other issues. Unfortunately, the node failures can be catastrophic for critical or safety related systems. State machines can offer a promising approach to separate the two concerns – functional and non-functional – bringing forth reliability exception conditions handling, by means of fault handling states. We develop an approach that allows users to define and program typical applications using their platform language, but also adds state machine logic to design, view and handle explicitly other concerns such as reliability. The experimental section shows a working deployment of this concept in an industrial refinery settin

Predictive maintenance of rotational machinery using deep learning

This paper describes an implementation of a deep learning-based predictive maintenance (PdM) system for industrial rotational machinery, built upon the foundation of a long short-term memory (LSTM) autoencoder and regression analysis. The autoencoder identifies anomalous patterns, while the latter, based on the autoencoder’s output, estimates the machine’s remaining useful life (RUL). Unlike prior PdM systems dependent on labelled historical data, the developed system doesn’t require it as it’s based on an unsupervised deep learning model, enhancing its adaptability. The paper also explores a robust condition monitoring system that collects machine operational data, including vibration and current parameters, and transmits them to a database via a Bluetooth low energy (BLE) network. Additionally, the study demonstrates the integration of this PdM system within a web-based framework, promoting its adoption across various industrial settings. Tests confirm the system's ability to accurately identify faults, highlighting its potential to reduce unexpected downtime and enhance machinery reliability