Open-WiSe: A Solar Powered Wireless Sensor Network Platform

González, Apolinar; Aquino, Raúl; Mata, Walter; Ochoa, Alberto; Saldaña, Pedro; Edwards, Arthur

doi:10.3390/s120608204

Cited by 25 publications

(16 citation statements)

References 15 publications

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“…Each of the values used in the simulation are based on the WiSe mote [38], which was previously developed in previous works, and it is described as follows: the systems master module is the processor, which is built around a 32-bit ARM7TDMI-S CPU with real-time emulation and embedded trace support. The actual microcontroller used is the LPC2148, which, with its 512 K of high-speed flash memory and 32 K of SRAM data memory, is capable of running a POSIX real-time kernel and many kinds of different routing algorithms.…”

Section: Evaluation Results and Discussionmentioning

confidence: 99%

“…The worst-case execution time (WCET) can be measured by task, from the time that a message is read until it is sent, estimating the worst case that can be chosen. In our previous works [38][39][40], a description has also been made of the hardware and software implemented, and a complete calculation of the computing time has been made, taking into account the aspects mentioned above, and used in the same research works, which allows us to estimate the computation time using the WiSe mote as [38] in the worst-case, as 0.03 s by task and 0.08 s in the smart agent node, similarly by task.…”

Section: Worst-case End-to-end Delay Flow Control Signals Analysismentioning

confidence: 99%

“…This also leads us to simplifying the calculation of Equation (8), allowing one to know the delay impact (Tm k,i ) of the unslotted CSMA/CA algorithm, taking into account their maximum values of parameters that produce the worst-case transmission message between task k,i and, therefore, Tm k,i = Tm wc , where Tm wc is the worst-case transit delay for all messages sent from task k to task i. Various researches have described how to calculate the effective and actual data rates of an IEEE 802.15.4 wireless network, permitting the calculation of the transmission time between neighbors, giving a very rough estimate for Tm wc [33][34][35], which is approximated to Tm wc = 385.308 ms between two neighboring nodes that employ the WiSe mote [38] for wireless microcontrollers, allowing us to reduce the calculation for each flow of the control loop to:…”

Section: Worst-case End-to-end Delay Flow Control Signals Analysismentioning

confidence: 99%

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Smart Control of Multiple Evaporator Systems with Wireless Sensor and Actuator Networks

et al. 2016

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This paper describes the complete integration of a fuzzy control of multiple evaporator systems with the IEEE 802.15.4 standard, in which we study several important aspects for this kind of system, like a detailed analysis of the end-to-end real-time flows over wireless sensor and actuator networks (WSAN), a real-time kernel with an earliest deadline first (EDF) scheduler, periodic and aperiodic tasking models for the nodes, lightweight and flexible compensation-based control algorithms for WSAN that exhibit packet dropouts, an event-triggered sampling scheme and design methodologies. We address the control problem of the multi-evaporators with the presence of uncertainties, which was tackled through a wireless fuzzy control approach, showing the advantages of this concept where it can easily perform the optimization for a set of multiple evaporators controlled by the same smart controller, which should have an intelligent and flexible architecture based on multi-agent systems (MAS) that allows one to add or remove new evaporators online, without the need for reconfiguring, while maintaining temporal and functional restrictions in the system. We show clearly how we can get a greater scalability, the self-configuration of the network and the least overhead with a non-beacon or unslotted mode of the IEEE 802.15.4 protocol, as well as wireless communications and distributed architectures, which could be extremely helpful in the development process of networked control systems in large spatially-distributed plants, which involve many sensors and actuators. For this purpose, a fuzzy scheme is used to control a set of parallel evaporator air-conditioning systems, with temperature and relative humidity control as a multi-input and multi-output closed loop system; in addition, a general architecture is presented, which implements multiple control loops closed over a communication network, integrating the analysis and validation method for multi-loop control networks designed for multi-evaporator systems.

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Section: Evaluation Results and Discussionmentioning

confidence: 99%

Section: Worst-case End-to-end Delay Flow Control Signals Analysismentioning

confidence: 99%

Section: Worst-case End-to-end Delay Flow Control Signals Analysismentioning

confidence: 99%

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Smart Control of Multiple Evaporator Systems with Wireless Sensor and Actuator Networks

et al. 2016

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“…The network used for the detection and transmission of data was developed using WiSe nodes; therefore, the nodes have a low energy consumption rate [19]. Low energy consumption is a very important consideration because the network life, integrity and security for health applications requires exceptional reliability [17].…”

Section: Proposal Architecturementioning

confidence: 99%

WiSPH: A Wireless Sensor Network-Based Home Care Monitoring System

Magaña-Espinoza

Aquino-Santos

Cárdenas-Benítez³

et al. 2014

Sensors

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This paper presents a system based on WSN technology capable of monitoring heart rate and the rate of motion of seniors within their homes. The system is capable of remotely alerting specialists, caretakers or family members via a smartphone of rapid physiological changes due to falls, tachycardia or bradycardia. This work was carried out using our workgroup's WiSe platform, which we previously developed for use in WSNs. The proposed WSN architecture is flexible, allowing for greater scalability to better allow event-based monitoring. The architecture also provides security mechanisms to assure that the monitored and/or stored data can only be accessed by authorized individuals or devices. The aforementioned characteristics provide the network versatility and solidity required for use in health applications.

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“…Therefore, a general wireless sensor platform, which features flexible signal input channels and power supplies to different low-power and non-low-power sensors, is desirable.Compared with conventional wired sensors, wireless sensors still suffer some common limitations, such as a limited power supply capacity of the batteries, a limited bandwidth and unreliable radio communication, and a high-noise floor. The energy limitation can be mitigated by adopting power-efficient techniques [13] and energy harvesting techniques, such as the vibration [14,15], the solar [16], and the wind energies [17]. The limited bandwidth issue can be addressed by the distributed computation [18,19], which eliminates the need to collect the raw data.…”

mentioning

confidence: 99%

A low-noise, real-time, wireless data acquisition system for structural monitoring applications

Chen

Casciati

2013

Struct. Control Health Monit.

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Structural health monitoring systems are conceived to automatically monitor the structural health state in real‐time. The high‐cost and the labor intensive installation of wired monitoring system suggest that structural monitoring systems be realized of a wireless nature. This study explores the development of a general and high‐performance wireless data acquisition system (WDAQS) specifically designed for sensors commonly adopted in structural health monitoring applications.When compared with wired sensor technology, wireless sensor technology suffers limited energy supply, long data collection delay, big noise floor, and data loss. Addressing these issues, in the design of the WDAQS, several features are pursued, including flexible sensor interfaces, high power efficiency, low‐noise data acquisition, and real‐time and lossless data transmissions. The design of the system is presented in detail in terms of hardware, firmware, and software. Several experiments are carried out to validate and evaluate the system. The results show that the WDAQS is able to acquire high‐quality data. Copyright © 2013 John Wiley & Sons, Ltd.

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Open-WiSe: A Solar Powered Wireless Sensor Network Platform

Cited by 25 publications

References 15 publications

Smart Control of Multiple Evaporator Systems with Wireless Sensor and Actuator Networks

Smart Control of Multiple Evaporator Systems with Wireless Sensor and Actuator Networks

WiSPH: A Wireless Sensor Network-Based Home Care Monitoring System

A low-noise, real-time, wireless data acquisition system for structural monitoring applications

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