Designing an open source maintenance-free Environmental Monitoring Application for Wireless Sensor Networks
Introduction
Wireless Sensor Networks (WSN) are distributed networks of small autonomous devices known as motes. In these networks, usually at least one mote called sink, acts as a gateway to connect with other networks. Each mote contains power supply, processing unit, memory, sensors and wireless communications but with many constraints, such as limited energy, bandwidth, memory size and computational capabilities. The motes communicate using multi-hop routing protocols. One of the most important areas in WSN research is the reduction in energy consumption, to allow extended maintenance free operating periods of several months or even years. But the design and implementation of WSN applications remain a nontrivial task.
Our goal is to develop a platform-independent data gathering application for Environmental Monitoring (EM) using existing open source components to create an application completely distributed with the following features: (a) runs in a multihop low-cost network based on IEEE 802.15.4, (b) improved network reliability and lifetimes, (c) easy management and maintenance-free, (d) ported to different platforms and (e) allows different configurations and network topologies. In a typical data gathering network all motes send information from their sensors to the sink.
We follow a complete analysis and design process, from the selection of the embedded operating system to the different algorithms and strategies. The proposed Environmental Monitoring Application (EMA) follows the IEEE 802.15.4 standard (IEEE, 2006) and it is designed to reduce the energy consumption, but maintaining at the same time the reliability of data transmission from source motes to the data sink through a multihop network. EMA has been designed to gather temperature (T) and relative humidity (RH) data, following the rules of quality assurance for environmental measurements and allowing different configurations in the gathering process (different sending periods: 30 s, 10 or 60 min, denoted by EMA (30 s), EMA (10 min) and EMA (60 min) respectively). EMA application is based on TinyOS-2.x (Hill et al., 2000) components and has been tested in several real long term deployments (for several months), in both research and industrial situations. We have designed EMA for the popular TelosB motes (Polastre et al., 2005), but it can run on other platforms as described in the paper. During the tests, we have used different motes with appropriate sensors, antenna and housing. The code of EMA is available at the TinyOS repository (Delamo et al., 2010a).
The rest of the paper is structured as follows. Section 2 shows the related work. Section 3 defines the requirements for accurate data gathering in meteorological studies. In Section 4 the application algorithm is analyzed and in Section 5 the selection of the embedded operating system to implement EMA is considered. Section 6 describes the design process of the application and Section 7 details porting to different hardware platforms. Finally, in Section 8 the real deployments in which EMA has been tested are described, before presenting the conclusions in Section 9.
Section snippets
Related work
In the last decade WSNs have been used in many different applications, such as environment, habitat, precision horticulture, seismic, volcano hazard monitoring, coil mine monitoring etc. Most of the following references are analyzed in Strazdins et al. (2013), a recent survey based on real world deployments. Mainwaring et al. (2002) was a pioneering project in WSN for habitat monitoring, based on an outdoor single hop network. Macroscope (Tolle et al., 2005) was another interesting outdoor WSN
Analysis of the requirements for environmental monitoring
The first step in the analysis process is to determine the requirements for the application. Applications designed for environmental monitoring must collect raw data following a strict set of guidelines, such as those outlined in the US Environmental Protection Agency’s Quality Assurance Handbook for Air Pollution Measurement Systems (US Environmental Protection Agency, 2008).
The Centre for Mediterranean Environmental Studies (CMES), a research institute in Paterna (Spain) that collaborates
Analysis of the application algorithm
In EMA, the motes proceed with the sensor readings as seen in the flow diagram, Fig. 2, following the specifications given in previous section, sending data every 10 min (called EMA (10 min) configuration). As shown in Fig. 2, the motes after the startup launch the routing process (data collection process) and the timer. Next, raw data samples are taken from the T and RH sensors at 30 s intervals using the events from a periodic timer. If the T and RH samples are correct (no error was given by
Selection of the embedded operating system
We can find many embedded operating system (EOS) designed for WSN (such as TinyOS, Contiki, MantisOS, ERIKA Enterprise, Nano-RK, LiteOS, RIOT, Free RTOS, etc.) but few of them are still supported or have enough maturity. In our opinion, we must highlight only two of them, TinyOS (Hill et al., 2000) and Contiki (Dunkels et al., 2004), basically because they are the only ones that are maintained by important research groups and companies. Both EOS are open source, multi-platform and energy
Design of the application
Before going into detail, it is interesting to discuss the trade offs related to some aspects of EMA, such as synchronization, MAC and routing. These issues are directly related to the communications, which require great energy consumptions. In particular it should be noticed that for the TelosB mote, the radio module has the largest energy demand of any component. Thus, the communications are a key point for saving energy and are done by implementing a duty cycle on the motes, switching them
Porting to different hardware
Many different commercial motes exist, each with different features and capabilities. We have successfully implemented EMA on different supported platforms as shown in Table 3, although most of our tests have been done with TelosB motes (Polastre et al., 2005) due to their stability and good performance.
EMA was originally designed for the TelosB motes and programmed in TinyOS v2.x. It takes full advantage of the Synchronous LPL available for the TI CC2420 radio chip and it is completely
Deployments and results
EMA has been tested in various real outdoor deployments with successful results. Some deployments were made in urban areas and others in a wooded area. In reference Gallart et al. (2011), we show an urban WSN deployment of 15 motes for EM that has been working maintenance-free for over 12 months to measure the T and RH gradient in a street. In this reference, we present different issues related with energy (such as solar panels for the motes and 19,500 mAh batteries) and the proper motes
Conclusions and future work
We have created an EMA using open source components based on the TinyOS operating system, suitable for environmental monitoring with Wireless Sensor Networks. We compared TinyOS with other operating systems and showed more advantages. TinyOS is maintained by important research groups and companies, supports multiple radio chips and microcontroller families, and in particular for EMA shows lower energy consumption.
In EMA, several features have been included to increase the reliability of the
Acknowledgment
This Project has been funded by the Spanish Ministry of Industry, Tourism and Commerce, National Research Program, and the FEDER program with references VARIMOS2 (TSI 20110-2009-148) and VARIMOS (TSI 20100-2008-350) (Delamo et al., 2010b).
Manuel Delamo gained his technical degree in Telecommunications Engineering at the University of Valencia in 2010, specialising in Telematics. He is currently investigating Wireless Sensor Network applications at the University of Valencia. His interests include high level programming in various languages, TinyOS programming, and multihop routing protocols.
References (36)
- et al.
TASK: sensor network in a box
Proceeedings of the Second European Workshop on Wireless Sensor Networks, 2005
(2005) - et al.
Dozer: ultra-low power data gathering in sensor networks
6th International Symposium on Information Processing in Sensor Networks, 2007. IPSN 2007, IEEE
(2007) - et al.
A high-throughput path metric for multi-hop wireless routing
Wirel. Netw.
(2005) - Delamo, M., Felici-Castell, S., Foster, A., Pérez-Solano, J.J., Gallart, V., 2010a. Environmental Monitoring...
- Delamo, M., Felici-Castell, S., Foster, A., Pérez-Solano, J.J., Gallart, V., 2010b. Wireless Sensor Networks for...
- et al.
Gatherer: an Environmental Monitoring application based on IPv6 using Wireless Sensor Networks
IJAHUC
(2013) - et al.
Contiki – a lightweight and flexible operating system for tiny networked sensors
29th Annual IEEE International Conference on Local Computer Networks, 2004
(2004) - et al.
Wisemac: an ultra low power mac protocol for the downlink of infrastructure wireless sensor networks
Proceedings of the Ninth International Symposium on Computers and Communications 2004 Volume 2 (ISCC’04) - Volume 02
(2004) - et al.
Evaluation of a real, low cost, urban WSN deployment for accurate environmental monitoring
2011 IEEE 8th International Conference on Mobile Adhoc and Sensor Systems (MASS)
(2011) - et al.
Collection tree protocol
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
(2009)
FireWxNet: a multi-tiered portable wireless system for monitoring weather conditions in wildland fire environments
Proceedings of the 4th International Conference on Mobile Systems, Applications and Services
System architecture directions for networked sensors
SIGARCH Comput. Archit. News
System Architecture for Wireless Sensor Networks
SensorScope: application-specific sensor network for environmental monitoring
TOSN
Power consumption analysis of operating systems for wireless sensor networks
Sensors
Cited by (30)
Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes
2017, Future Generation Computer SystemsCitation Excerpt :However, many heterogeneous things compose the IoT. The most important are the Wireless Sensor Networks (WSN), which are the core of the Internet of Things [25]. A WSN interconnects sensors, in order to obtain data, with a server or special system to work and maybe, automate tasks in one place.
Improving time synchronization in Wireless Sensor Networks using Bayesian Inference
2017, Journal of Network and Computer ApplicationsCitation Excerpt :The constraints are given by the low-cost and quality hardware components of the motes, in particular their clocks and limited-energy. One of the most important areas on these WSN technologies is the energy reduction to allow extended maintenance free operating periods of several months or even years (Delamo et al., 2015) and to meet this goal synchronization is critical. Synchronization in WSN has been a hot topic of research over the last decade (Akyildiz et al., 2002; Diallo et al., 2012; Sivrikaya and Yener, 2004; Sundararaman et al., 2005), because the nodes within the WSN are usually required to perform distributed and collaborative tasks in a synchronized manner, under the mentioned constraints.
Improved multi-objective weighted clustering algorithm in Wireless Sensor Network
2017, Egyptian Informatics JournalCitation Excerpt :Wireless Sensor Networks (WSNs) have emerged as a promising research domain and have been used in a wide variety of applications [1]. They have been used in health field [2,3], Environmental field [4–6], and smart home-field [7]. By means of this recent technology, it becomes possible to interact with the surrounding environment through the use of multiple tiny sensors.
A scheduling scheme for stochastic event capture based on Bayes statistical method
2022, Journal of SupercomputingACTIONABLE FRAMEWORK for CITY DIGITAL TWIN-ENABLED PREDICTIVE MAINTENANCE and SECURITY MANAGEMENT SYSTEMS
2021, WIT Transactions on the Built Environment
Manuel Delamo gained his technical degree in Telecommunications Engineering at the University of Valencia in 2010, specialising in Telematics. He is currently investigating Wireless Sensor Network applications at the University of Valencia. His interests include high level programming in various languages, TinyOS programming, and multihop routing protocols.
Santiago Felici-Castell received the MSc and PhD degree in Telecommunication Engineering from Polytechnical University of Valencia in 1993 and 1998, respectively. He is currently Associate Professor at the University of Valencia at the School of Engineering (ETSE). His research has focused on various topics related to the general field of networking, communication systems and combination of multiresolution techniques for data transmission with quality of service over best effort networks. He is also a Cisco Systems certificated instructor. Dr. Felici is the author of more than 25 technical publications, including international journal and conference papers.
Juan J. Pérez-Solano received the MSc in Physics and PhD degree in Electrical Engineering from the University of Valencia in 1994 and 2002, respectively. In 1996 he joined the Computer Science Department of the University of Valencia, where he is currently Associate Professor. His research interests include multiresolution techniques and wavelet transforms applications, spread spectrum communications, wireless data transmission and wireless sensor networks.
Andrew Foster gained his Bachelors degree in Mechanical Engineering at Lancaster University in 1998, and his Masters degree at the University of Valencia in 2010. He is currently studying for a PhD in Technology of Information, Communication and Computational Mathematics at the University of Valencia. His main interest area is routing protocols for Wireless Sensor Networks.