4,803 research outputs found
Predicting the Batteries' State of Health in Wireless Sensor Networks Applications
[EN] The lifetime of wireless sensor networks deployments
depends strongly on the nodes battery state of
health (SoH). It is important to detect promptly those motes
whose batteries are affected and degraded by ageing, environmental
conditions, failures, etc. There are several parameters
that can provide significant information of the battery
SoH, such as the number of charge/discharge cycles, the
internal resistance, voltage, drained current, temperature,
etc. The combination of these parameters can be used to
generate analytical models capable of predicting the battery
SoH. The generation of these models needs a previous
process to collect dense data traces with sampled values of
the battery parameters during a large number of discharge
cycles under different operating conditions. The collected
data allow the development of mathematical models that
can predict the battery SoH. These models are required to
be simple because they must be executed in motes with
low computational capabilities. The paper shows the complete
process of acquiring the training data, the models generation
and its experimental validation using rechargeable
batteries connected to Telosb motes. The obtained results
provide significant insight of the battery SoH at different
temperatures and charge/discharge cycles.This work was supported in part by the Spanish MINECO under Grant BIA2016-76957-C3-1-R and in part by the I+D+i Program of the Generalitat Valenciana, Spain, under Grant AICO/2016/046.Lajara Vizcaino, JR.; Perez Solano, JJ.; Pelegrà Sebastiá, J. (2018). Predicting the Batteries' State of Health in Wireless Sensor Networks Applications. IEEE Transactions on Industrial Electronics. 65(11):8936-8945. https://doi.org/10.1109/TIE.2018.2808925S89368945651
Model for Predicting Bluetooth Low Energy Micro-Location Beacon Coin Cell Battery Lifetime
Bluetooth Low Energy beacon devices, typically operating on coin cell batteries, have emerged as key components of micro-location wireless sensor networks. To design efficient and reliable networks, designers require tools for predicting battery and beacon lifetime, based on design parameters that are specific to micro-location applications. This design science research contributes to the implementation of an artifact functioning as a predictive tool for coin cell battery lifetime when powering Bluetooth Low Energy beacon devices. Building upon effective and corroborated components from other researchers, the Beacon Lifetime Model 1.0 was developed as a spreadsheet workbook, providing a user interface for designers to specify parameters, and providing a predictive engine to predict coin cell battery lifetime. Results showed that the measured and calculated predictions were consistent with those derived through other methodologies, while providing a uniquely extensible user interface which may accommodate future work on emerging components. Future work may include research on real world scenarios, as beacon devices are deployed for robust micro-location applications. Future work may also include improved battery models that capture increasingly accurate performance under micro-location workloads. Beacon Lifetime Model 1.x is designed to incorporate those emerging components, with Beacon Lifetime Model1.0 serving as the initial instantiation of this design science artifact
Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System
As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated
A Survey of Energy Harvesting Sources for IoT Device
Environmental Energy is an alternative energy for wireless devices. A Survey of Energy Harvesting Sources for IoT Device is proposed. This paper identifies the sources of energy harvesting, methods and power density of each technique. Many reassert have carried to extract energy from environment. The IoT and M2M are connected through internet or local area network and these devices come with batteries. The maintenance and charging of batteries becomes tedious due to thousands of device are connected. The concept of Energy harvesting gives the solution for powering IoT, M2M, Wireless nodes etc. The process of extracting energy from the surrounding environment is termed as energy harvesting and derived from windmill and water wheel, thermal, mechanical, solar
Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes
Time series forecasting is an important predictive methodology which can be
applied to a wide range of problems. Particularly, forecasting the indoor
temperature permits an improved utilization of the HVAC (Heating, Ventilating
and Air Conditioning) systems in a home and thus a better energy efficiency.
With such purpose the paper describes how to implement an Artificial Neural
Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous
intelligent wireless sensor network. The present paper uses a Wireless Sensor
Networks (WSN) to monitor and forecast the indoor temperature in a smart home,
based on low resources and cost microcontroller technology as the 8051MCU. An
on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs,
has been developed for real-time time series learning. It performs the model
training with every new data that arrive to the system, without saving enormous
quantities of data to create a historical database as usual, i.e., without
previous knowledge. Consequently to validate the approach a simulation study
through a Bayesian baseline model have been tested in order to compare with a
database of a real application aiming to see the performance and accuracy. The
core of the paper is a new algorithm, based on the BP one, which has been
described in detail, and the challenge was how to implement a computational
demanding algorithm in a simple architecture with very few hardware resources.Comment: 28 pages, Published 21 April 2015 at MDPI's journal "Sensors
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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