An IoT approach for Wireless Sensor Networks ...

An IoT approach for Wireless Sensor Networks applied to e-health environmental monitoring J. Cabra, D. Castro, J. Colorado, D. Mendez, L. Trujillo Pontificia Universidad Javeriana, Bogota, Colombia Center of Excellence and Adoption in IoT (CEA-IoT) http://www.cea-iot.org Abstract—This paper presents an Internet of Things (IoT) approach for monitoring temperature and relative humidity applied to product maintenance in hospitals or pharmaceutical entities. Our goal is to integrate a low-cost and scalable network of smart sensors capable of mapping large areas in real-time. In this article, we provide a comprehensive insight into the technologies that compose the IoT architecture: (i) the node layer composed of Wireless Sensor Network (WSN), (ii) the local management layer of the WSN and (iii) the cloud-based layer for enabling remote monitoring. To the date, our IoT system has been working during (8) months in The Hospital Universitario San Ignacio, a 4th level university hospital located in Bogota, Colombia. Here, we present a field report of this work-in-progress system.

the local management layer of the WSN and (iii) the cloudbased layer for enabling remote monitoring. These layers work seamlessly integrated to provide 24/7 monitoring, generate alerts, storage log files and build automatic monthly reports of the entire system. This paper is organized as follows: Section II briefly introduces a literature review of related work, Section III describes the technologies behind our proposed IoT architecture, Section IV presents a field-report of our IoT system composed by 11 distributed nodes sensing the temperature and relative humidity, and Section V concludes the paper some remarks and upcoming work.

Keywords—Internet of Things - IoT, Wireless Sensor Networks - WSN, Hospital environmental monitoring.

I.

I NTRODUCTION

The Internet of Things (IoT) is a new technological paradigm that allows any object (or thing) to be connected to other things, services or people thought the internet. An IoT architecture is basically composed of a set of low processing sensing units called nodes, and a cloud-based layer that enables the user to monitor those nodes remotely and in real-time. The health-care industry is perhaps one of the most suited sectors to integrate IoT technologies. This paper presents a work-in-progress IoT approach for deploying a Wireless Sensor Network (WSN) applied to the environmental monitoring of temperature and relative humidity within hospitals or clinic laboratories. These facilities daily handle medicine and biological samples that demand constant monitoring. Commonly, it is the authorized personnel of the hospital in charge of manually keeping a record of the environmental conditions of those areas where these items are storage, i.e. laboratories, storage rooms, freezers. This manual supervision is typically done with a hygrometer installed in the corresponding area and the person that follow-up the hygrometer’s readings during several intervals per day. Here, we partner with the Hospital Universitario San Ignacio1 , a 4th level university hospital located in Bogota, Colombia, aimed at developing an IoT architecture capable of autonomously sensing the aforementioned environmental conditions (temperature and humidity) and providing to the user real-time remote monitoring. To this purpose, our IoT architecture is composed of three main layers: (i) the node layer composed by a Wireless Sensor Network (WSN), (ii) 1 http://www.husi.org.co

II.

R ELATED WORK

Since the IoT paradigm started gaining traction, the healthcare sector was one of the pioneer industries in identifying and integrating the potential behind delivering connected services (e-health). In [1], the authors present a comprehensive review of four key trends directly affected by IoT-based innovations. These four fields are: 1) 2) 3) 4)

Medical Equipment and Medication Control. Medical Information Management. Tele-medicine, remote patient monitoring. Health Management.

The key advantage of the IoT paradigm relies on the easy integration of services that seamlessly connect different devices that operate at different scales and layers; starting from the hardware/sensors deployed as a connected network, passing the sensed information to mobile applications (apps), condensing all the information up in the cloud, and processing huge amount of data by applying big data techniques. All these layers are embedded into one single IoT architecture in charge of handling those processes. One example of the aforementioned structure applied to health-care is presented by [2]. The authors propose a model to interact with multiples sources of information and administration of resources aimed at optimizing the Quality of Service (QoS). The proposed IoT system was in charge of integrating different hardware and software components from different sources to guarantee a minimum QoS. This was accomplished by using real-time sensing and expert systems to predict and re-arrange the network. The concept of Wireless Sensor Networks (WSN) has been widely adopted within the IoT structure [3]. A common hospital application that employs a WSN is monitoring the patient’s

vital signs [4]. Media Aminian et al. [5] propose a wireless monitoring of blood pressure, heart rate and other specific variables accordingly to the patient condition. The system is able to detect signal abnormalities and generate alarms. Also, their architecture support multi-patient monitoring with several replaying nodes to the base station. Nonetheless, Wireless Sensor Networks also have some adjacent problems mainly related to security issues in the data management [6]. The implementation of common security methods is highly limited due to power limitations, and low processing and memory resources. These kind of methods, such as symmetric and public key encryption, become a challenge to be implemented on a WSN. Nowadays, for WBSN as an alternative way of encryption, the key generation is bounded to the randomness of the signal, [7], [8]. In the area of infrastructure monitoring for the healthcare industry, one can find applications for supervising and monitoring medical equipment and medication. In [9], [10] was implemented an embedded IoT-enabled strain sensor. These sensors are typically bonded to a surface of a solid material to measure its infinitesimal dimensional changes when put into compression or tension. The deployed node contained the strain sensor plus wireless connectivity and a RFID antenna. This node is able to measure the vibrations within the process of DNA vaccination, a procedure of genetic modification for adding a DNA changed cell in a organism for generating an antigen for a immunologic response against an illness. This process requires a platform able of counteracting movement. An IoT solution of this class might enable not just to sense but to monitor these DNA vaccination platforms aimed at producing vaccines autonomously and in large-scales [11]. Other works [12], [13] has tackled the deployment of sensors for monitoring temperature and relative humidity in pharmacies. The problem with those solutions basically relies that in most cases, the nodes are complex and bulky telethermometers that depend on AC power connectivity and scaling the solution to hundreds of nodes is expensive. Herein, we propose a low-cost and low-power consumption WSN capable of working for about eight months by using classical AAA batteries (also work on AC power). Our nodes are small and can be easy deployed in any area. Also, the WSN is fully integrated with cloud services that enable real-time monitoring, log file managements, report generation and alarms. III.

I OT S YSTEM A RCHITECTURE

The proposed architecture is composed of three blocks (Figure 1): node layer (WSN), local management layer (PC) and cloud-based layer. The node layer composed by a WSN which is a set of embedded systems distributed across the area of interest aimed at sensing temperature and humidity, processing the data, and communicating the sensed data to the local PC. The WSN is also capable of reconfiguration in the case of one node discharged. It computes a routing algorithm for ensuring that the sensed information can be accurate concentrated into the local PC by employing a tree topology. Subsequently, the data is sent to a cloud platform. The following sections describe the layers shown in Figure 1 in more detail.

Fig. 1: IoT architecture with the three layers of interaction: the deployed sensor forming the WSN, a PC for collecting the sensed data and uploading the information to the cloud, and the cloud layer with a dashboard for enabling remote monitoring, visualization, and alarms.

A. Node layer (WSN) Each node composing the WSN is an embedded hardware of reference MTM-CM5000-MSP. The specifications of this board are found in Table I. This module can be programmed via the Contiki operating system 2 , an open Source OS for the Internet of Things. This module performs the reading of humidity and temperature, in turn, creates autonomously the network connection with the other nodes. The working board can be seen in Figure 2.

Fig. 2: MTM-CM5000-MSP module

Item Temperature and Humidity Sensor Range of temperature sensor Temperature sensor resolution Temperature sensor precision Range of humidity sensor Humidity sensor resolution Humidity sensor precision Frequency Band Power

Specification R SHT11 Sensirion

-40 ± ±

∼

123.8 ◦ C

0.01(typical)

0.4 ◦ C (typical)

0 ∼ 100% RH 0.05 (typical) ± 3 %RH (typical) 2.4GHz ∼ 2.485GHz 3V (2xAA Battery)

TABLE I: Specification list MTM-CM5000-MSP In the node layer, there are two kinds of roles: the sink module and the sensing module. The Sink module is in charge if receiving all the information sent by different sensing nodes and grouping all data to the local management layer (the local PC). The sensing module computes the readings from the temperature and humidity onboard sensor. The network is configured with RIME stack communication protocol with a tree-based topology which manages the discovery and network 2 http://www.contiki-os.org

establishment for each node. The steps computed by each module are listed as follows: •

Sink algorithm-steps 1) Start 2) Init the network 3) Configure node as a Sink 4) Wait until the network is settle 5) Wait until the periodic time is done 6) Send dummy data 7) Repeat step 5

•

Sensing algorithm-steps 1) Start 2) Init the network 3) Wait until the network is settle 4) Sensors activation 5) Wait until the periodic time is done 6) Get temperature from sensor 7) Get humidity value from sensor 8) Sensors deactivation 9) Build sensed data into one package 10) Send package to sink node 11) Repeat step 5

B. Local management layer A Local management layer is a software coded in Python programming language that runs on the host computer (Figure 1). The Local management layer consists basically of a software responsible for receiving information from the WSN system, i.e., the temperature and humidity data of the points described by the end user, then the recollected data is stored in the host computer and it is sent to the cloud layer. This program is composed of four blocks: sink data reading, frames discriminator, connection to cloud server powered by the company Ubidots and Local log report.

Fig. 3: Block diagram local management layer The first block of the program (Sink data reading) is responsible for receiving the information from the sink module through the serial port of the PC. This information comes in the form of data packets with the following information: the identifier (ID) of the node, humidity and temperature values. The next stage of operation (frames discriminator) extracts the information and then is sent to the cloud platform and remote access called Ubidots (more details of this IoT cloud provider in the forthcoming section). The last block (Local log report) generates a text file with the daily log of the measurement values points of temperature and humidity, tidy with the next items: file name, date and time of the file creation, temperature measured with its date and time of sample and humidity measure with its date and time of sample. All text files with

the information of each node are stored locally in the program folder located on the desktop of the host computer. Moreover, the Local log report has the functionality of generating a PDF report with the whole measures of the month for being presented to the regulatory authorities. This report is shown in Figure 4.

month node location

temp. range

Date

Time of the min. temperature

min. temperature

Time of the max. temperature

max. temperature

Fig. 4: Report automatically generated by the local PC management layer.

C. Cloud-based layer (Ubidots) Ubidots3 is a platform in charge of collecting the data generated by the installed nodes, which allows to the user store this information in the cloud and provides widgets that provide a visual interface data, which makes it suitable for use as a point of reference for the final customer. The consultation of the recorded data can be accessed via a URL link provided to the user, which can be reviewed by any electronic device using a web browser. Unlike other big players in IoT cloud services, such as the Microsoft IoT Azure suite, Ubidots is simple, with well-documented APIs to developed a dashboard control panel, reliable, and more important: affordable. This is key for a powerful, scalable low-cost system. The organization of the information of this platform is variable-based, i.e., every tool to use must be associated with a specific variable. From there the different features of Ubidots can be implemented . One is the live dashboard; a set of a different widget like a signal real time plot, gauge, maps, among others, each widget has the property of URL sharing for exporting to others visualization or for specific monitoring. Device libraries; they provide a set of libraries for different devices focus on IoT and a tool called Device Wizard to generate firmware code to the new hardware to use. Trigger events; when the value of some variables overpasses a personalized threshold, the final user can be notified by email or SMS. Math and Statistical engine for operating and interpreting data and comprehensive API for sending the data to Ubidots in different language programming. Per user and variable to use, the system generates a large string key to reference in the application code. 3 https://ubidots.com

In our implementation we use 20 variables and each hour is sent the data to Ubidots. A set of alarms is available when some value will be out of range. An URL is given to the user for web monitoring of the nodes. In figure 5, the real time information of three sensing nodes is detailed. Variables refer to the percentage of relative humidity in the area where the network is deployed sensor. The dashboard can be accessed at: https://app.ubidots.com/ubi/public/getdashboard/ page/Tc4cDjxe2iH9YVn4iH96PJ_EgS0/#/

module (another node) is required to be connected to the local PC due to it receives the data collected by the network via 802.15.4. In addition, each node location presented one or more scenarios that made the nodes susceptible to dust gathering, falling or even getting damaged by outside elements. So, we designed a housing (see Figure 6-b) with vents to facilitate the head exchange, a clear environment-sensor interface and the CEA-IoT marking on the side. Two of these nodes (node 2 and 3) where placed inside a refrigerator. With the node location selected, the connection guaranteed and the hardware well protected, an alpha-test was implemented to assed the behaviour of the network during a 5 week trial of normal condition operation. B. First approach: Alpha Test

Fig. 5: Cloud dashboard powered by Ubidots.

IV.

F IELD REPORT

After the definition of the main architecture of the system, the next step is to setup the distribution, physical assessment of each sensing location and an alpha-test to evaluate the power consumption and trend of a node in the proper environmental and sensing rate conditions. Afterward, we carried out power and disconnection/interruption analytics over the 8 month trial that the system has been on beta-test with special emphasis on the impact of the system on the health professionals in charge of temperature and humidity control.

The first steps for setting up the sera were:

2)

The results of the Alpha test are shown in figure 7. The nodes 7 and 8 were offline during this trial period. The result showed us that each node should last 1.5 months at the 2 time per hour sample rate, this could differ depending on the node environment and battery charge. This was deduced by the average time that all the nodes would get to a critical batery voltage level during the trial. After the successful Alpha test, we implemented a beta-test designed to last from 9 to 12 months of continuous operation, which still undergoing. C. 7 month period: beta-test

A. Setup

1)

The Alpha test was carried out within a 5 week period were the system was under constant monitoring, gathering information on the power consumption of the nodes with a ample rate of one hour. The main goal of this trial was to generate a schedule of battery exchange without compromising the stability or reliability of the system.

Assess the total number of nodes required to establish a stable network with the distributed nodes on the assigned locations. Create a proper housing to the electronics on board each embed system, accordingly to the environmental conditions of each assigned location.

To asses the number of nodes, we establish which locations needed a sensing node and where the sink module will be located. After this analysis we created a connection test protocol to evaluate the sink-sensing module connection at each location. The WSN was configured to ensure Point-toPoint (P2P) communication. In figure 6(a), one can see the 11 locations. The Sink module is located on the local PC, connected through a USB cable and a serial port adapter embedded on the node. The further nodes (node 11 and node 2), had a stable connection and was not necessary auxiliary node to guarantee P2P connection between any node and the sink node. It is important to mention that the WSN form their own wireless network following the IEEE 802.15.4 communication protocol. This is why a sink

The beta-test started on March 16th of 2016, with 8 of the 10 sensing nodes and a sink node; the remaining nodes came on-line a month and a half after the start of the beta-test. During this time, we focused on evaluating the system performance in terms of the reception by the health professionals, the impact on the day-to-day activities of those in charge of the monitoring of the variables and the system escalation to an implementation on a hospital-level integration (hundreds nodes). The results obtained summarized within three main categories: •

Power consumption.

•

Reliability.

•

Impact and remarks.

1) Power consumption: The main goal of the first test was to determinate an approximated time to battery exchange, so we did not needed to constantly measure the battery status during a 2 month period of time, but rather to measure for 2 weeks after the initial estimation. During this time we found that the actual time frame was of 55 to 65 days in cold

Fig. 6: Setup scenario. a) WSN spatial distribution, b) Hardware node and housing.

Fig. 7: Power consumption results after the 5 week trial.

environments nodes (node 2,3 and 4), and of 65 to 75 days for the rest. Additionally, a daily power consumption analysis was made, as can be seen in figure 8, to accurately predict the battery exchange chronograph. In this graph can be seen a 16 to 44% higher consumption for colder environments compared to the rest of the nodes. 2) Reliability: A second metric we decided to analyzed during the beta.test, was the error percentage. This metric was done by analyzing the missing messages from both local and remote platforms. For this metric we had an expected result of missing messages accordingly to the network stability, battery status and possible inconveniences it might present the system, especially during the first months of the trial. This metric was calculated for each location independently. Figure 9 shows the

Fig. 8: Daily power consumption results after 7 months per location.

mean error across all the locations per month compared to the expected mean error for that month (a-posteriori error vs a-priori error). During the first trimester of the implementation, we expected a larger error, as the adaptation process would lead to multiples disconnections of the system and the computer, and the people in charge must repair the connection, often leading to multiple day without connection. As time progressed, this would become a habit, and the disconnections would eventually descrease. Figure 10 shows the mean error of the whole 7 months sorted by location. Larger errors are presented by nodes 7 and 8. This is an expected result since those nodes came on-line after a month and a half from the start of the test. Still all nodes maintain a

Department of Science, Technology and Innovation (Departamento Administrativo de Ciencia, Tecnología e Innovación Colciencias) through the Fondo Nacional de Financiamiento para la Ciencia, la Tecnología y la Innovación Francisco José de Caldas (Project ID: FP44842-502-2015). R EFERENCES [1]

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Fig. 9: Mean error percentage vs the expected error per month.

[3]

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Fig. 10: Mean error percentage by location.

mean error below 10% , which mean less that 140 messages per month were lost during this implementation. V.

R EMARKS

After 8 months of the system deployment, we have received a positive feedback from the health personnel of the hospital. The personnel that was previously in-charge of manually monitoring the variables felt a relive in their amount of work with a positive impact in their daily activities. This system frees 20-40 minute frame each day of the personnel whom needed to move from their workplaces to annotate the measurements. The remote visualization of the variables made the process to be more accurate and fast, and the local storage made the report making process a more condensed and reliable one. The results of this project opened the possibility to expand the network to the whole Hospital as a new stage. ACKNOWLEDGEMENT The authors would like acknowledge the cooperation of all partners within the Centro de Excelencia y Apropiación en Internet de las Cosas (CEA-IoT) project. The authors would also like to thank all the institutions that supported this work: the Colombian Ministry for the Information and Communications Technology (Ministerio de Tecnologías de la Información y las Comunicaciones - MinTIC) and the Colombian Administrative

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