Citation: Piadyk, Y.; Steers, B.;
Mydlarz, C.; Salman, M.; Fuentes, M.;
Khan, J.; Jiang, H.; Ozbay, K.; Bello,
J.P.; Silva, C. REIP: A Reconfigurable
Environmental Intelligence Platform
and Software Framework for Fast
Sensor Network Prototyping. Sensors
2022, 22, 3809. https://doi.org/
10.3390/s22103809
Academic Editors: Hacene Fouchal
and Alvaro Araujo Pinto
Received: 7 April 2022
Accepted: 6 May 2022
Published: 17 May 2022
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Article
REIP: A Reconfigurable Environmental Intelligence Platform
and Software Framework for Fast Sensor Network Prototyping
Yurii Piadyk *, Bea Steers, Charlie Mydlarz , Mahin Salman, Magdalena Fuentes, Junaid Khan, Hong Jiang,
Kaan Ozbay, Juan Pablo Bello and Claudio Silva
Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA; bsteers@nyu.edu (B.S.);
cmydlarz@nyu.edu (C.M.); ms6617@nyu.edu (M.S.); mfuentes@nyu.edu (M.F.); khanj@wwu.edu (J.K.);
hj1274@nyu.edu (H.J.); kaan.ozbay@nyu.edu (K.O.); jpbello@nyu.edu (J.P.B.); csilva@nyu.edu (C.S.)
* Correspondence: ypiadyk@nyu.edu
Abstract:
Sensor networks have dynamically expanded our ability to monitor and study the world.
Their presence and need keep increasing, and new hardware configurations expand the range of
physical stimuli that can be accurately recorded. Sensors are also no longer simply recording the
data, they process it and transform into something useful before uploading to the cloud. However,
building sensor networks is costly and very time consuming. It is difficult to build upon other
people’s work and there are only a few open-source solutions for integrating different devices and
sensing modalities. We introduce REIP, a Reconfigurable Environmental Intelligence Platform for fast
sensor network prototyping. REIP’s first and most central tool, implemented in this work, is an open-
source software framework, an SDK, with a flexible modular API for data collection and analysis
using multiple sensing modalities. REIP is developed with the aim of being user-friendly, device-
agnostic, and easily extensible, allowing for fast prototyping of heterogeneous sensor networks.
Furthermore, our software framework is implemented in Python to reduce the entrance barrier for
future contributions. We demonstrate the potential and versatility of REIP in real world applications,
along with performance studies and benchmark REIP SDK against similar systems.
Keywords:
heterogeneous sensor networks; open-source; multi-modal; Internet of Things (IoT); SDK;
modular API; Python; multiprocessing
1. Introduction
Sensor networks have expanded our ability to monitor and study the world. They
have been used for a wide range of applications, such as monitoring air pollution [
1
], urban
noise [
2
] or energy management of smart buildings [
3
]. As their use cases expand, sensor
networks become more complex and powerful, enabling a new range of physical stimuli to
be accurately recorded, processed, ingested and analysed. However, implementing sensor
networks is an enormous endeavour with high cost in time and resources. Many decisions
have to be made, from which devices to use to which protocols to employ for connecting
them. In addition, the deployment and upkeep of sensor networks is critical and time
consuming, which requires sophisticated monitoring and alerting tools. Nowadays, it is
difficult to build on top of other people’s work as there are few accessible open-source
solutions suitable for integration into different devices, leading to countless hours of
engineering and software design invested every time.
Of note is the case of high throughput sensor applications that incorporate audio or
video data capture. Multithreading is typically needed to enable concurrent data capture,
processing and writing to disk. If not handled correctly multiple threads accessing hardware
devices or disk locations can lead to race conditions that can result in data corruption or
even hardware freezes. Race conditions and hardware lockups can be incredibly difficult
to identify and diagnose and are usually only addressed by more experienced developers
Sensors 2022, 22, 3809. https://doi.org/10.3390/s22103809 https://www.mdpi.com/journal/sensors
