Raspberry Pi–Based Sensor Nodes

2020 ◽  
pp. 193-268
Author(s):  
Charles Bell
Keyword(s):  
Sensor Nodes ◽  
Raspberry Pi

scholarly journals Raspberry Pi as Visual Sensor Nodes in Precision Agriculture: A Study

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 45110-45122 ◽  
Author(s):  
Radhika Kamath ◽  
Mamatha Balachandra ◽  
Srikanth Prabhu
Keyword(s):  
Precision Agriculture ◽  
Sensor Nodes ◽  
Raspberry Pi ◽  
Visual Sensor

scholarly journals HVAC Control System Using Predicted Mean Vote Index for Energy Savings in Buildings

Buildings ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 38
Author(s):  
Daniel Fernando Espejel-Blanco ◽  
José Antonio Hoyo-Montaño ◽  
Jaime Arau ◽  
Guillermo Valencia-Palomo ◽  
Abel García-Barrientos ◽  
...  
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Temperature Control ◽  
Air Conditioning ◽  
Energy Savings ◽  
Sensor Nodes ◽  
Temperature Control System ◽  
Raspberry Pi ◽  
Predicted Mean Vote ◽  
The Coordinator

Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of these two factors, energy efficiency and comfort, is a considerable challenge for building operations. This paper introduces a design approach to control an HVAC, focused on an energy consumption reduction in the operation of the HVAC system of a building. The architecture was developed using a Raspberry Pi as a coordinator node and wireless connection with sensor nodes for environmental variables and electrical measurement nodes. The data received by the coordinator node is sent to the cloud for storage and further processing. The control system manages the setpoint of the HVAC equipment, as well as the turning on and off the HVAC compressor using an XBee-based solid state relay. The HVAC temperature control system is based on the Predicted Mean Vote (PMV) index calculation, which is used by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) to find the appropriate setpoint to meet the thermal comfort of 80% of users. This method combines the values of humidity and temperature to define comfort zones. The coordinator node makes the compressor control decisions depending on the value obtained in the PMV index. The proposed PMV-based temperature control system for the HVAC equipment achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment, when operating with the same setpoint of 26.5 grades centigrade.

scholarly journals A study of remotely booking slot for vehicle using Internet of Things

2020 ◽  
Vol 10 (5) ◽  
pp. 5392
Author(s):  
S. Metilda Florence ◽  
M. Uma ◽  
C. Fancy ◽  
G. Saranya
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Sensor Nodes ◽  
Raspberry Pi ◽  
Cost Information ◽  
System A ◽  
Parking System ◽  
Car Park ◽  
Smart Parking ◽  
The Cost

Internet Of Things (IoT) is a continually growing area which aids us to unite diverse objects. The proposed system exhibits the universal notion of utilizing cloud-based intellectual automotive car parking facilities in smart cities as a notable implementation of the IoT. Such services demonstrate to be a noteworthy part of the IoT and thus serving users in no small amount due to its pure commerce positioned qualities. Electromagnetic fields are being used by RFID to detect and track tags ascribed to objects automatically. The RFID technology is used in this system along with suitable IoT protocols to evade human interference, which reduces the cost. Information is bartered using readers and tags. RFID and IoT technologies are mainly used to automate the guide systems and make them strong and more accurate. Open Service Gateways can be effectively used for this module. This system established on the consequence of IoT and the purposes are solving the chaos, bewilderment, and extensive backlogs in parking spaces like malls and business parks that are customary as a consequence of the increased use of automobiles. The proposed work aims to solve these problems and offer car drivers a hassle-free and instantaneous car parking experience. While a number of nodes are positioned depends on topographical restrictions, positioning of prominent anchor sensor nodes in the smart parking is a primary factor against which the efficiency and cost of the parking system hang. A Raspberry Pi would act as a mini-computer in our system. A suitable smallest path methodology would be cast-off to obtain the shortest distance between the user and every car park in the system. Hence, the pausing time of the user is decreased. This work furthermore includes the practice of remotely booking of a slot with the collaboration of android application exercising smartphones for the communication between the Smart Parking system and the user.

scholarly journals IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction

2019 ◽  
Vol 9 (9) ◽  
pp. 1831 ◽  
Author(s):  
Xiaozheng Lai ◽  
Ting Yang ◽  
Zetao Wang ◽  
Peng Chen
Keyword(s):  
Kalman Filter ◽  
Air Quality ◽  
Low Cost ◽  
Moving Average ◽  
High Accuracy ◽  
Edge Computing ◽  
Sensor Nodes ◽  
Quality Monitoring ◽  
Raspberry Pi ◽  
Air Quality Monitoring

In order to obtain high-accuracy measurements, traditional air quality monitoring and prediction systems adopt high-accuracy sensors. However, high-accuracy sensors are accompanied with high cost, which cannot be widely promoted in Internet of Things (IoT) with many sensor nodes. In this paper, we propose a low-cost air quality monitoring and real-time prediction system based on IoT and edge computing, which reduces IoT applications dependence on cloud computing. Raspberry Pi with computing power, as an edge device, runs the Kalman Filter (KF) algorithm, which improves the accuracy of low-cost sensors by 27% on the edge side. Based on the KF algorithm, our proposed system achieves the immediate prediction of the concentration of six air pollutants such as SO2, NO2 and PM2.5 by combining the observations with errors. In the comparison experiments with three common predicted algorithms including Simple Moving Average, Exponentially Weighted Moving Average and Autoregressive Integrated Moving Average, the KF algorithm can obtain the optimal prediction results, and root-mean-square error decreases by 68.3% on average. Taken together, the results of the study indicate that our proposed system, combining edge computing and IoT, can be promoted in smart agriculture.

scholarly journals Proposition and Real-Time Implementation of an Energy-Aware Routing Protocol for a Software Defined Wireless Sensor Network

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2739 ◽  
Author(s):  
Muhammad Usman Younus ◽  
Saif ul Islam ◽  
Sung Won Kim
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Raspberry Pi ◽  
Delivery Ratio ◽  
Energy Aware

A wireless sensor network (WSN) has achieved significant importance in tracking different physical or environmental conditions using wireless sensor nodes. Such types of networks are used in various applications including smart cities, smart building, military target tracking and surveillance, natural disaster relief, and smart homes. However, the limited power capacity of sensor nodes is considered a major issue that hampers the performance of a WSN. A plethora of research has been conducted to reduce the energy consumption of sensor nodes in traditional WSN, however the limited functional capability of such networks is the main constraint in designing sophisticated and dynamic solutions. Given this, software defined networking (SDN) has revolutionized traditional networks by providing a programmable and flexible framework. Therefore, SDN concepts can be utilized in designing energy-efficient WSN solutions. In this paper, we exploit SDN capabilities to conserve energy consumption in a traditional WSN. To achieve this, an energy-aware multihop routing protocol (named EASDN) is proposed for software defined wireless sensor network (SDWSN). The proposed protocol is evaluated in a real environment. For this purpose, a test bed is developed using Raspberry Pi. The experimental results show that the proposed algorithm exhibits promising results in terms of network lifetime, average energy consumption, the packet delivery ratio, and average delay in comparison to an existing energy efficient routing protocol for SDWSN and a traditional source routing algorithm.

scholarly journals Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7774
Author(s):  
Laura Erhan ◽  
Mario Di Mauro ◽  
Ashiq Anjum ◽  
Ovidiu Bagdasar ◽  
Wei Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Sensor Nodes ◽  
Machine Learning Techniques ◽  
Raspberry Pi ◽  
Data Series ◽  
Data Imputation ◽  
Distribution Of Values ◽  
Data Collection Process ◽  
Recent Developments

Recent developments in cloud computing and the Internet of Things have enabled smart environments, in terms of both monitoring and actuation. Unfortunately, this often results in unsustainable cloud-based solutions, whereby, in the interest of simplicity, a wealth of raw (unprocessed) data are pushed from sensor nodes to the cloud. Herein, we advocate the use of machine learning at sensor nodes to perform essential data-cleaning operations, to avoid the transmission of corrupted (often unusable) data to the cloud. Starting from a public pollution dataset, we investigate how two machine learning techniques (kNN and missForest) may be embedded on Raspberry Pi to perform data imputation, without impacting the data collection process. Our experimental results demonstrate the accuracy and computational efficiency of edge-learning methods for filling in missing data values in corrupted data series. We find that kNN and missForest correctly impute up to 40% of randomly distributed missing values, with a density distribution of values that is indistinguishable from the benchmark. We also show a trade-off analysis for the case of bursty missing values, with recoverable blocks of up to 100 samples. Computation times are shorter than sampling periods, allowing for data imputation at the edge in a timely manner.

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